Institutions, Arrangements and Preferences for
Inflation Stability: Evidence and Lessons from a Panel
Data Analysis
Stefan Krause and Fabio Méndez∗
October 2006
Abstract
We study how monetary, exchange-rate and other institutional arrangements
are associated with policy makers’ preferences for inflation stability. We argue that
focusing on policy intentions, represented by these preferences, constitutes a better
way of evaluating policy behavior, instead of looking at inflation outcomes that may
be unavoidable at times.
Using a panel of 34 countries over a period of 24 years we find that a high degree
of preference for inflation stability is significantly correlated only with central bank independence and membership to the European Economic and Monetary Union for low
inflation countries, whereas for high inflation countries only strict inflation targeting
and, to some extent, central bank independence, are relevant for inflation stabilizing
policies. Finally, we find no robust evidence suggesting that either adopting an exchange rate anchor or employing fiscal policy are associated with an inflation averse
behavior.
JEL classification: E31, E52, E58, F33
Keywords: Policy makers’ preferences, central bank independence, inflation targeting, monetary union, fixed exchange rate
∗
Department of Economics, Emory University, and Department of Economics, University of Arkansas.
We thank Bob Chirinko, Richard Luger, Francis T. Lui, Elena Pesavento, Mahmut Yasar, and participants
at the Missouri Economics Conference and the Southern Economic Association Meetings for their comments;
and Berrak Buyukkarabacak for excellent research assistance. Please send comments to skrause@emory.edu.
1
Introduction
The last ten to fifteen years have been characterized by the presence of lower inflation
in industrialized, transition and developing economies alike. Looking at a cross-section of 34
countries for which we have gathered data,1 we observe that median inflation has dropped
from 8.42% in 1980:I-1989:IV, to 3.28% in 1990:I-1999:IV, and further stabilized to 2.51%
in 2000:I-2003:IV.
At the same time, there exists widespread agreement that institutions and policy
arrangements are (at least partially) responsible for this anti-inflationary tendency. Several
authors have reported a link between the degree of central bank independence and both
the level and variability of inflation.2 Others have reported similar findings regarding the
effects of specific elements within the monetary policy framework. These elements include
the role of central bank transparency and accountability, the impact of inflation targeting
regimes,3 the legal and political environment, the adoption of specific exchange rate regimes,
the effects of joining a monetary union, and others.
The typical empirical study of inflation and institutional arrangements estimates a
cross-country equation that relates the average level of inflation for a given period to a set
of relevant variables. All these studies use the level of inflation as the dependent variable,
but the independent variables analyzed change. Thusly, the literature up to date can be
classified into three distinct categories according to the focus of the analysis:
• Studies on Central Bank Independence (CBI). The main differences across the studies
rest on the methods used to measure CBI, the sample of countries and the periods
considered. Even though there are some studies that question the robustness of the
results (Posen, 1995; de Haan and van‘t Hag, 1995; and Fuhrer, 1997), there appears to
1
These countries are: Australia, Austria, Barbados, Belgium, Canada, Chile, Colombia Costa Rica, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea Republic, Malaysia,
Mexico, Netherlands, New Zealand, Norway, Peru, Portugal, Spain, Sweden, Switzerland, Trinidad & Tobago, Turkey, the United Kingdom, the United States, and Uruguay.
2
See Berger, de Haan and Eijffinger (2001) for an exhaustive survey on central bank independence theoretical and empirical literature.
3
Truman (2003) provides an extensive literature review.
1
be general agreement that a higher level of CBI results in a lower level and variance of
inflation (see Alesina, 1988; Grilli, Masciandaro and Tabellini, 1991; Cukierman, 1992;
Alesina and Summers, 1993; and Eijffinger, van Rooij and Schaling, 1996, for evidence
in industrialized countries; and Loungani and Sheets, 1997; de Haan and Kooi, 2000;
and Sturm and de Haan, 2001, for the case of transition and developing economies).
• Studies on Inflation Targeting (IT). More recently, attention has shifted to analyzing
how countries can benefit from adopting an explicit inflation target regime. Bernanke
and Mishkin (1997) argue that an IT framework makes price stability the main goal of
policy, but not necessarily the only one, given that the central bank may make room
for stabilizing output and, perhaps, exchange rates as well. Still, focusing on inflation
does take precedence over other policy objectives. The empirical evidence shows that
most IT experiences have been successful in reducing inflation (Bernanke, Laubach,
Mishkin and Posen, 1999; Corbo and Schmidt-Hebbel, 2001; Neuman and von Hagen,
2002; and Mishkin and Schmidt-Hebbel, 2002), although there exist contrasting views
as to the extent of the costs of implementing an IT regime, and how this policy may
affect the sacrifice ratio.
• Studies on the contracts between the government and the monetary policy authority.
Some theoretical and empirical papers have examined the role of contracts between
the government and the central bank (in particular, central bank transparency and
accountability) and its effect on inflation (see Walsh, 1995; Fry, Julius, Mahadeva,
Rogers, and Sterne, 2000; Faust and Svensson 2001; Jensen, 2002; Chortareas, Stasavage and Sterne, 2002; Geraats, 2002; Posen, 2002; and Tallman, 2003). However, even
though more transparent policies and a higher degree of central bank accountability
are desirable qualities, neither the theory nor the evidence are conclusive as to whether
these features actually contribute towards lowering inflation.
Finally, aside from the national monetary authorities, there are other policy and
2
institutional arrangements that can have an impact on macroeconomic outcomes. These
include policy measures such as using the exchange rate as an anchor, currency boards,
joining a monetary union and employing fiscal policy. Other arrangements, which are not
under the direct control of policy makers (at least not in the short run), include the depth
of financial development and institutions (Posen, 1995), the legal environment (Cecchetti,
1999) and political stability (Aisen and Veiga, 2006), among others.
Our paper departs from the current empirical literature in at least two aspects. The
first difference is that we use a measure of policy preferences instead of inflation as the
dependent variable. The average level of inflation cannot measure the impact of the specific
institutional arrangements directly. As stated by Cukierman, Webb and Neyapti (1992):
“Institutions cannot absolutely prevent an undesirable outcome, nor ensure a desirable one,
but the way that they allocate decision-making authority within the public sector makes
some policy outcomes more probable and others less likely.” (p. 1)
Such undesirable outcomes (for instance, a higher than expected or targeted inflation
rate) usually stem from short-run adverse shocks to the economy. Therefore, a country can
experience a temporary higher rate of inflation, even in spite of the fact that policy makers
have done everything possible to avoid that result. The desirability of certain institutional
arrangements, thus, should be judged by their impact on the decisions taken by policy makers
and not by the success that these policy makers have had in attaining their goals.
The second difference refers to the econometric specification used in most of the literature. Clearly, outcome variables, such as average inflation and its volatility, are correlated
with other characteristics of the economy that are normally excluded from the analysis. Even
though Cukierman (1992); Posen (1995); Eijffinger, van Rooij and Schaling (1996); de Haan
and Kooi (2000); Chortareas, Stasavage and Sterne (2002); and Aisen and Veiga (2006) do
control for other variables in their respective empirical analyses, to the best of our knowledge,
only Fuhrer (1997) and Muscatelli and Trecroci (2000) consider central bank independence,
inflation targeting and other policies and institutions simultaneously as explanatory factors.
3
In this paper we concentrate directly on policy makers’ behavior, and use a dynamic
panel of 34 countries to estimate the likely affect of monetary, exchange rate and fiscal
arrangements. More specifically, our objective is to employ estimates of the relative weighs
that policy makers assign to deviations of output and inflation from their respective targets
at the time they conduct their policy, and analyze the impact that different policy and
institutional arrangements have on the policy makers’ intentions.
A study by van Lelyveld (1999) employs data from the Eurobarometer survey no. 5
to construct an ordinal measure of inflation aversion (relative to unemployment), in order
to study whether a rising income will lead to more concern towards inflation, while more
leftist political leaning will be associated with less concern for inflation. There are two main
differences between van Lelyveld’s (1999) work and ours (aside from the time frame and
number of countries): First, instead of employing individual survey data to construct the
preference variable, as we explain in more detail below, we employ measures of revealed
preferences obtained from macroeconomic data; second, while van Lelyveld (1999) addresses
the question of which individual determinants are relevant for explaining differences in these
preferences, in this paper we focus instead on how policy institutions and arrangements are
associated with inflation aversion.
Other studies that have analyzed how monetary policy is related to policy preferences
or intentions include Cecchetti and Ehrmann (2001), who examine how inflation targeting
affects output volatility in 23 countries; and Granato, Lo and Wong (2006), who employ
measures of inflation variability and persistence as a proxy for policy intentions in order to
study the relationship between economic openness and inflation performance. These studies
do not overlap with ours, since our goal is to determine how different institutions and policies
are associated with an anti-inflationary behavior of policy makers.
The remainder of the paper is organized as follows: Section 2 describes our data set. In
the first part of this section we discuss how preferences for inflation stability are measured
and further elaborate on the advantages of looking at preferences instead of the inflation
4
data to study the impact of policy arrangements and institutions. The second part of this
section describes the relevant policy variables and other institutions and how we go about
measuring them. Here, we introduce a new index for CBI based on the probability of a
change in the central bank governor as a result of a new government taking office.
In Section 3 we present our main results for the 34 countries of interest. Analyzing the
entire sample, we find that a high level of preference for inflation stability is significantly correlated only with our CBI measures, membership to the European Economic and Monetary
Union (EMU), and adopting a flexible inflation target. Surprisingly, we do not observe that
a strict inflation targeting regime significantly contributes towards aversion to inflation, and
we also find no robust evidence suggesting that anchoring the exchange rate is associated
with a higher preference towards inflation stability. Finally, we do not find any conclusive
evidence regarding the relationship between fiscal deficit or government consumption and
inflation aversion.
Once we separate the panel into low inflation and high inflation countries we observe
that, for the former, only CBI and membership to the EMU are significantly correlated with
inflation-averse behavior, whereas for high inflation countries, a strict IT regime and, to a
certain extent, central bank independence, are the only relevant variables. These findings
lead us to conclude that, if the main objective of a policy maker is to reduce the level and
variance of inflation, low inflation countries are better off by either choosing an independent
central bank or joining a monetary union, instead of adopting an explicit inflation targeting
scheme, while high inflation countries may benefit more from an inflation targeting framework than from having an independent central bank or pegging their exchange rates. It
is worthwhile to note that we are not making any claims as to whether changes in policy
intentions result from changes in institutions or conversely; instead, our interest relies on
figuring out what types of institutions and arrangements are most likely to be consistent
with inflation-averse policies.
In Section 4 we provide several robustness exercises and discuss possible policy impli-
5
cations of our results. We also further elaborate on two of our key findings, namely that
multiple institutions and arrangements can simultaneously contribute towards explaining
outcomes and policy choices; and, furthermore, that the choice of institutions that result in
an inflation-averse behavior for low-inflation countries is not necessarily the same that can
accomplish a successful task of stabilization in high-inflation countries. Finally, in Section 5
we conclude and discuss some extensions.
2
Data Description
We collected data for 34 countries over a period of 24 years. As our dependent variable we
use the annual estimates of the relative preference for inflation stability provided by Krause
and Méndez (KM, 2005), which serve as a proxy measure for policy makers’ intentions.4 Such
a measure requires reliable quarterly data on interest rates, exchange rates, CPI inflation,
and GDP, for a relatively long time series (at least 40 quarters); therefore, the number of
countries included in our panel is limited to those economies for which the aforementioned
data is readily available. While this could generate a sample selection bias, this problem is
likely to be less severe when analyzing low inflation countries separately from moderate-high
inflation economies, as we do in Section 4.
2.1
Policy Maker’s Preferences
We begin by providing a brief description of the method employed by KM (2005) to obtain
measures of preferences towards inflation stability. They 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. They abstract from other policy goals,
such as stabilizing exchange rates and interest rates, as well as achieving more equity in
4
See description in Data Appendix.
6
income distribution, arguing that these serve rather as intermediate goals towards achieving
domestic macroeconomic performance.
Consistent with most contemporary analyses of government policy (see Persson and
Tabellini, 1999; and Clarida, Galí and Gertler, 1999), the policy maker’s objective can be
summarized through the following standard quadratic loss function:5
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, respectively; and λ is the relative weight
given to squared deviations of inflation relative to deviations of output from their respective
desired levels. Note that we have redefined the loss function to allow for λ to be bounded
between zero and one. A closer value to unity will be associated with more inflation-averse
policies.
To derive values for λ, KM (2005) minimize the loss function in equation (1) subject
to a stylized model that represents the structure of the economy, and a linear policy rule.
This results in the following representation of λ as a function of the structural parameters:
λ=
(ω − φb)
,
(ω − φb) + γ(1 + γφb)
(2)
where ω represents the reaction of aggregate demand to a change in inflation; φ is the effect
of changes in the interest rate on aggregate demand; γ represents the reaction of aggregate
supply resulting from a change in inflation; and b is the response of the interest rate to supply
shocks.6
5
The quadratic loss function restricts expansionary and contractionary policy preferences to be symmetric. See Ruge-Murcia (2003) for the derivation and estimation of a game-theoretic model with asymmetric
preferences around inflation and unemployment targets.
6
Krause (2006) shows how the aggregate demand - aggregate supply model and the one-period loss
function used to derive equation (2) is equivalent to the forward-looking New Keynesian model used by
Clarida, Galí, and Gertler (1999) and others, under general conditions.
7
KM (2005) obtain estimates for all parameters in equation (2) by performing a restricted
second-order vector autoregression, which represents the dynamics of inflation and output.
They also 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 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.
As a result, the key advantage of focusing on λ instead of the inflation rate is that
certain factors, other than macroeconomic policy, are likely to affect a particular inflation
outcome, but should not change policy behavior. For example, a negative and unexpected
shock to the economy can result in an inevitable increase in prices, even despite the fact that
authorities could be pursuing a contractive policy at the time. If we believe that a policy
maker’s performance should be assessed by looking at her intentions (which are under her
control) rather than her results (which sometimes may not), then it becomes more sensible
to focus on estimated preferences instead.7
The following two experiences provide some empirical support for our argument. 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).
Our 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
7
The extent to which inflation outcomes differ from policy intentions can be observed
by graphically comparing the annual estimates of lambda (which can be downloaded from
http://userwww.service.emory.edu/~skrause/pdf/lambdas.xls) with annual inflation rates. These figures are
available upon request from the authors.
8
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.
The above cases lead us to conclude that, at best, focusing on inflation gives us an
inaccurate measure of policy actions and intentions, since policy changes may only reflect
outcome changes with a substantial lag (as in the case of Germany), and, at worst, the
resulting inflation rate may not echo a policy intention or action at all (as in the case of
Malaysia). Therefore, if we’re interested in a time series analysis of the impact of institutions
and arrangements on policies, analyzing preferences for inflation stability gives us a more
accurate characterization of policy makers’ behavior and its changes.
2.2
Institutions and Policy Arrangements
In this section we analyze in more detail the institutions and arrangements that may
contribute to explaining inflation-averse policies. We also detail how we obtain operational
measures for these variables, so that we can later examine how they are related with the
policy makers’ preference for inflation stability, λ.
We divide the relevant variables into four distinct categories:
1. monetary policy and central bank structure;
2. exchange rate policy;
3. fiscal policy and the electoral cycle;
4. financial structure, legal environment and political stability.
9
2.2.1
Central Bank Independence
As we mentioned in the introduction, there exists compelling evidence suggesting that
a more independent central bank is associated with a lower level and, possibly, a smaller
variance of inflation. The main theoretical argument is that CBI contributes to reducing (and
even eliminating) the inflation-bias problem (Rogoff, 1985; Cukierman, 1992 and 1994; and
Alesina and Gatti, 1995), as long as the appointed central bank governor has a particularly
high aversion to inflation. It is therefore logical to infer that a higher level of CBI should be
associated with a higher preference for inflation stability.
Switching to the applied analysis, over the past 20 years several authors have constructed different measures of CBI. For the purpose of a brief examination, we can divide
them into four main types:
• Indices that consider legal and institutional characteristics of a central bank: Bade
and Parkin (1982), Alesina (1988), Grilli, Masciandaro and Tabellini (GMT, 1991),
and Cukierman (1992).
• Indices constructed using an estimated generalized country-specific effects variable:
Eijffinger, van Rooij and Schaling (1996).
• Indices that consider the institutional framework of a central bank based on surveys:
Cukierman (1992), Cukierman, Webb and Neyapti (CWN, 1992), Fry, Julius, Mahadeva, Rogers, and Sterne (FJMRS, 2000).
• Indices that use the average tenure of the central bank governor as a proxy for CBI:
Cukierman (1992), CWN (1992), de Haan and Kooi (dHK, 2000), and Sturm and de
Haan (SdH, 2001).
All these different indices tend to tell a similar story: a higher level of CBI is negatively
correlated with the level of inflation. Unfortunately, the first two types have only been
computed for industrialized countries, while these two and the survey indices have only been
10
estimated once. The turn-over ratio of the central bank governor (TOR) has the advantage
that it can be computed for a larger set of countries and for different periods, so it becomes
technically possible to use it to construct a time-varying measure of CBI.
For our empirical exercise, we construct a new measure of CBI: the probability that
there will be a turnover in the central bank governor following a government change. CWN
(1992) state that if central bank governors’ turnovers occur either concurrently or briefly after
a change in the government, this would be an indication of a lower CBI. Putting this concept
into practice, Cukierman and Webb (1995) develop a measure of political vulnerability of the
CB by constructing an index that looks at the frequency of cases in which the CB governor is
replaced within a short period after a political transition. This vulnerability index is closer
to an index of political independence than to legal independence. In the spirit of Cukierman
and Webb (1995), we compute a CBI index Prob by dividing the number of times that a
central bank governor has changed within six months after an election by the total number of
elections, for a determined period. In the first two columns of Table 1 we report the variable
Prob for two periods: 1975:I-1994:IV and 1985:I-2004:IV.
To later assist us in performing robustness exercises, we also construct an alternative
measure of CBI using the TOR in a similar way as computed by Cukierman (1992), dHK
(2000), and SdH (2001). The main difference is that, as with the Prob index, we take averages
of the TOR for periods of 20 years instead of 10. It is important to notice that the TOR
takes into account those scenarios in which the CB governor is replaced before the end of
his term (and not necessarily associated with a government change), in contrast to the Prob
measure. We estimate the TOR for our 34 countries subdividing the sample into the same
two periods as with the Prob index (1975:I-1994:IV and 1985:I-2004:IV). These estimates
are displayed in the last two columns of Table 1.
Clearly, neither the probability of a turn-over after an election nor the turn-over ratio
engulf all possible aspects that characterize an independent central bank. Therefore, it
becomes useful to compare the Prob and the TOR variables with other CBI indices. Table 2
11
reports the simple correlations between the different indices and our measures for CBI. Since
higher values of both Prob and TOR represent, by construction, lower levels of independence,
one should expect the correlations with other indices to be negative.
The results confirm our expectations for most of the indices. The correlation between
either Prob and TOR, and the CBI measure by Alesina (1988) is negative; however, neither
one is significant. Both our measures of CBI are negatively and significantly correlated
with GMT’s (1991) political independence index and their average of economic and political
independence. Finally, our measure of Prob is negatively and significantly correlated with
the two survey indices (CWN, 1992; and FJMRS, 2000), whereas the measure for TOR is
negatively but not significantly correlated with these two measures.
In sum, both our measures of CBI have the advantage of being time-varying and
allowing us to study a larger set of countries and they rate relatively well in comparison to
other indices of CBI, in particular with survey measures.
2.2.2
Inflation targeting and other monetary arrangements
Ever since New Zealand implemented explicit inflation targeting in 1990, there has been
an exhaustive research on the benefits and costs of this policy framework. Currently, over
20 countries around the world have adopted either a full or a flexible IT scheme (Truman,
2003). In a full or strict IT regime, which is currently in place for most IT countries, the
target for inflation is usually specified as a range. In countries employing flexible or partial
IT (such as Peru, Mexico, Chile until 1999, and Israel until 2001, to name a few), there
is an initial stage with a moderate disinflation process, which is followed eventually by the
implementation of a full IT policy. Other differences across countries in the practice of IT
are: the target range and horizon; whether or not the central bank publishes forecasts; the
degree of accountability to the parliament or the executive; possible escape clauses; and the
entity that sets the target (Mishkin and Schmidt-Hebbel, 2002).
Given that in an IT framework price stability becomes the overriding goal of monetary
12
policy, we should expect that full and flexible IT should be associated with more inflationaverse policies and, as a result, a higher level of preference for inflation stability. For our
empirical analysis, we construct a dummy variable for the full IT regime (that takes a value
of one starting the date that the IT policy is implemented and zero otherwise) and another
dummy variable for the flexible IT regime (with an analogous definition). For the information
on the dates that IT was introduced we employ the data from Mishkin and Schmidt-Hebbel
(2002) and include Hungary, whose starting date is October 2001.
Aside from explicit IT policies, there are other elements of a monetary framework that
we can expect to be correlated with an inflation averse behavior by policy makers. Some of
these elements are: implicit inflation targeting, credibility, accountability and transparency.
Implicit IT and credibility are certainly important variables for consideration; however, they
have the disadvantage that they are quite difficult to measure, since they are usually associated with an inflation outcome. For example, we can observe how certain central banks,
such as the Federal Reserve and the Bundesbank, have had a successful history of low and
stable inflation over the last 20 years or more, which would lead us to conclude that: 1)
their policies have become quite credible; and/or 2) they must be practicing some sort of
implicit IT. This makes it nearly impossible to establish whether we should treat credibility
and implicit IT as "policies" or as "outcomes"; as a result, we choose not to define either of
these variables and opt to exclude them from our analysis.
There are more objective ways of measuring accountability and transparency (FJMRS,
2000) than credibility and implicit IT. Still, we do not include accountability and transparency into our analysis for two main reasons: 1) most IT schemes are accompanied by
a higher level of transparency and, to some extent, accountability (Bernanke and Mishkin,
1997; and Mishkin and Schmidt-Hebbel, 2002), which implies that incorporating both variables in the estimation could cause some multicollinearity problems; and 2) even if we could
correct for multicollinearity issues, data on these two indices is only available in a crosssectional form, and has only been computed once, which makes these features indistinguish-
13
able from other country specific effects.
2.2.3
Exchange Rate Policy
During the 1970s and 1980s, there was an active discussion about the potential gains of a
fixed exchange rate serving as an inflation anchor, mostly in the case of developing economies.
Over the last 10 years, however, there has been less agreement about the benefits of anchoring
the exchange rate, and calls have been made for this policy arrangement to be substituted by
an inflation targeting framework (Quirk, 1994; Eichengreen, 2002; Corbo, 2002; among others). However, there are alternative exchange-rate policies that have proven more successful,
mainly currency boards and the creation of the EMU. Regardless of whether or not these
arrangements result in higher real volatility (as evidenced by Kwan, Lui, and Cheng, 2001;
and Cecchetti and Ehrmann, 2001), they have been quite effective in contributing towards
achieving lower inflation rates (Ghosh, Gulde and Wolf, 2000; Issing, 2002). Therefore, we
should expect these arrangements to be associated with a higher preference for inflation
stability.
None of the countries in our sample have (or have had) a currency board in place, but 16
have maintained either a fixed or quasi-fixed exchange rate over part of the period, including
12 who had been members at various times of the European Exchange Rate Mechanism
(ERM) of the European Monetary System (EMS).8 There are also 8 countries in our sample
who signed the Maastricht Treaty in 1992, and two others (Austria and Finland), who joined
in 1995.9 We make an explicit distinction between accession to the EMU and maintaining
a fixed or quasi-fixed currency peg. The reason for it is that for EMU members, on the
one hand, the Maastricht Treaty signaled that there would be joint objectives of policy
coordination, fiscal discipline and low inflation from that point onward (again, with the
8
We consider Austria as having a quasi-fixed fixed exchange rate, even though the schilling was not part
of ERM until January 1995, given the peg that the Austrian currency had with respect to the Deutschmark
since 1979.
9
Greece is not included since it did not join the EMU until 2001, so its convergence process was different
from the other ten countries. Including Greece as a member of the EMU does not affect our main results,
however.
14
exception of Austria and Finland, who needed to start abiding by those rules after 1995). A
currency peg, on the other hand, consists of a unilateral decision by a country that can be
(somewhat) easily reversed. Therefore, we measure the implementation of these two regimes
by defining two separate dummy variables: one for membership to the EMU, which takes
a value of one for all current members of the EMU starting 1992 (except for Austria and
Finland, whose starting point is 1995) and zero otherwise; and a second one for the period
(or periods) during which the country has had a fixed or quasi-fixed exchange rate regime
in place.
2.2.4
Fiscal Policy and the Electoral Cycle
The effects of government spending and the fiscal deficit on aggregate demand have been
extensively discussed and even form part of any intermediate macroeconomics textbook. The
evidence suggests that the relationship between fiscal discipline and inflation is present in
developing countries (Cottarelli, Griffiths and Moghadan, 1998), but not in industrialized
countries (Persson and Tabellini, 1999). As to the overall magnitude of government spending
and its effect on inflation, the theory and evidence are even less compelling. On the one hand,
if a higher level government spending is a direct result of expansionary policies, then one
should expect a direct relationship between spending and inflation; on the other hand, a
larger government can also have a wider grasp on economic activity and, if the policy maker
is particularly inflation-averse, then this larger control will translate into a higher preference
for inflation stability.
Given the above arguments, we employ fiscal policy variables as controls, to establish
whether our results are robust to incorporating the government budget as an explanatory
variable for the degree of preference for inflation stability. We measure overall fiscal spending
by the ratio of government consumption to Gross Domestic Product (GDP). Unfortunately,
for the case of the fiscal deficit, we were unable to obtain reliable data for all the countries
15
and all time periods in our sample.10 Noticiably, as we will explain in more detail in our
discussion of robustness exercises in Section 3, including the budget deficit as an explanatory
variable for a restricted sample of countries neither explains inflation aversion significantly,
nor does it alter our main findings.
Finally, we pay attention to potential short-term changes in government policy due
to electoral concerns. KM (2005) find empirical support to the argument that incumbent
governments tend to lower their relative preference for stabilizing inflation (and, therefore,
increase their preference towards output growth stability) during election year for over 60%
of the countries they analyze. This opportunistic behavior (Nordhaus, 1975) constitutes
evidence suggesting changes in the degree of inflation-aversion of policy makers prior to an
election. To incorporate this element, we construct a dummy variable that takes a value of
one for the electoral years and zero for all other years.11
2.2.5
Financial Structure, Legal Environment and Political Instability
We now turn to discussing institutions and arrangements that are not under the direct
control of policy makers. The three main ones the literature has discussed are political
instability, financial structure and legal environment. Posen (1995) argues that the financial
sector is in a good position to provide a support to the objective of price stability and
shows, by constructing an index of effective financial opposition to inflation (FOI), that the
FOI is inversely and significantly correlated to average inflation. Supporting the argument
of the financial sector’s relevance, the evidence presented by Cecchetti (1999) for EMU
countries suggests that differences in financial structure are likely to be a consequence of a
heterogeneous legal environment across countries. Finally, Aisen and Veiga (2006) use the
10
We were only able to obtain data on the budget balance for 26 out of the 34 countries until 1997, and
for 15 countries until 2000.
11
An electoral year is defined as the same year of the election, if the voting takes place between March and
December of that year, and as the year prior to the election, when voting takes place on either January of
February. For countries for which the head of state is the president, we only consider presidential elections;
whereas for countries where the head of state is the Prime Minister, we consider parliamentary or congress
elections exclusively.
16
data on political institutions compiled by Beck, Clarke, Groff, Keefer and Walsh (BCGKW,
2001) and the Cross National Time Series Database, to show that a higher degree of political
instability is associated with both higher inflation levels and volatility when analyzing a
sample of over 100 countries.
Following Aisen and Veiga (2006), we employ two indicators of political instability
as control variables in our analysis. In the baseline regressions, we include the number of
cabinet changes. As a robustness check, consistent with Aisen and Veiga (2006), we also
consider the number of government crises. Both variables come from the Cross National
Time Series Database (2004).
With respect to the financial and legal environment, we have opted not to include
them in our panel data analysis. Our rationale for this exclusion is that these institutions
are likely to be country-specific and not change much over time. Hence, their influence on
the preference for inflation stability is most likely going to be captured as a general country
specific effect. Still, it would be interesting to extend the analysis to incorporate these
variables, and we hope to do so in the future.
3
3.1
Results
Effect of institutions and arrangements on average inflation
As we mentioned in the introduction, most of the empirical literature that analyzes
the effect of arrangements and institutions on the growth and variance of the general price
level focuses exclusively on studying the correlation of one particular framework variable
with inflation. Therefore, we consider useful, as a preliminary step, to examine how the
combination of different arrangements is associated with the level of inflation, before focusing
our analysis on policy makers’ preferences.
Consistent with the studies by Cukierman (1992), dHK (2000), and SdH (2001) we
employ a transformed rate of inflation (T RI = π/[1 + π]) as our dependent variable, to avoid
17
extreme observation issues associated with having high inflation countries in our sample. We
compute an average T RI for our 34 countries and the decades of the 1980’s and 1990’s using
quarterly data of CPI inflation. The first estimation can be represented by:
T RIi,τ = δ 0 + δ 1 P robi,τ + δ 2 F STi,τ + δ 3 F F Ti,τ + δ 4 EMUi,τ + δ 5 Fi,τ + φAIi,τ + εi,τ , (3)
where: i = 1, ..., 34 stands for the individual country; τ ∈ {0, 1} represents the decade; P rob
is our measure of central bank independence as defined in Section 2.2; F ST represents the
fraction of the entire decade that a strict inflation targeting scheme was implemented; F F T
represents the fraction of years in which the country employed flexible or partial inflation
targeting; EMU is an indicator variable that denotes membership to the European Monetary
Union; F is a dummy variable representing whether a fixed or quasi-fixed exchange rate
regime was in place for most of that decade; and AI represents the average level of the index
of political instability for each particular decade and country.
Resulting from our description of the variables in Section 2, we can make the following
conjectures for the framework and institutional variables:
• δ 1 > 0; a larger value for Prob (i.e., lower CBI) is correlated with higher inflation;
• δ 2 , δ 3 < 0; adopting a strict or flexible IT scheme is followed by a reduction in average
inflation;
• δ 4 , δ 5 < 0; joining the EMU or using an exchange rate anchor contributes towards
price stability.
With respect to the political instability indices, a larger number of either cabinet changes
or government crises is associated with a higher inflation rate (φ > 0).
The first column of Table 3 displays the simple regression results for equation (3) for
the entire sample. At the 10% significance level, most of our hypotheses are corroborated by
the data: A higher probability of a central bank governor leaving office after an election is
18
positively correlated with inflation; while implementing a strict IT framework and a currency
peg have a significant and negative correlation with inflation. Adopting flexible IT framework
does not appear as statistically relevant variable; being a member of the EMU does indicate
lower inflation, but controlling for government crises, the coefficient is no longer significant
at the 10% level. As for the instability indices, the coefficient on the number of government
crises is positive and significant, while cabinet changes is not significantly correlated with
inflation.12
Given the differences in institutions and arrangements between high and low inflation
countries, it is interesting to establish whether we observe any empirical support for the
hypotheses once we split the sample using this criterion. We define medium-to-high inflation
countries as all those who experienced an average annualized quarterly inflation rate of
nine percent or more over the period 1980:I-1999:IV. A total of 12 countries fall under this
category: Chile, Colombia, Costa Rica, Greece, Hungary, Israel, Mexico, Peru, Portugal,
Trinidad & Tobago, Turkey, and Uruguay. The remaining 22 countries experienced average
inflation rates ranging from 1.9% (Japan) to 7.7% (Italy) over the same period, so we classify
them as low inflation countries. The primary reason we use a nine percent threshold for
inflation in order to perform a sample split is to obtain a good enough balance of countries
in each subsample. To verify the robustness of these results we alternatively set the threshold
for inflation at 25%, following Fisher, Sahay and Végh (2002). Although this split just leaves
us with five high inflation countries, the results are basically unchanged.13
The regressions results for low inflation countries are reported in the second column of
Table 3. Since none of these countries practiced flexible IT during that period, we omit that
variable from our analysis. For this sub-sample, monetary and exchange rate arrangements
(except fixing the exchange rate) seem to matter for achieving lower inflation: at the 10%
level, CBI, IT, and belonging to the EMU have the expected sign; while, out of the political
12
The results are mainly unchanged when the measures of political instability are used in levels, instead
of first differences.
13
The results from this alternative sample split are available from the authors upon request.
19
instability measures, only the coefficient on cabinet changes is positive and significant.
Finally, for high inflation countries only the coefficient on Prob is significant at the 10%
level, regardless of which political instability index is used.14 Adopting a fixed exchange rate
is only significantly associated with inflation when cabinet changes are included; if instead,
we introduce government crises (which, itself, has a positive and significant coefficient),
the absolute value of all other coefficients is substantially reduced. Finally, we note how
implementing a strict IT regime appears to have a sizable correlation with the transformed
inflation variable (very likely driven by the successful inflation stabilization process in Chile
towards the end of the 1990s); however, it is not statistically different from zero.
In sum, the results from this simple regression analysis seem to be in sync with
the findings of the literature regarding the relevance of central bank independence, EMU
membership and, to some degree, inflation targeting, in accomplishing low levels of inflation.
Still, as we argued in the Introduction and in Section 2, we believe that it is more relevant
to focus on the impact that arrangements and institutions have on the relative preference
for inflation stability. This is the task at hand for the remainder of the paper.
3.2
Preferences for Inflation Stability: Panel Data Analysis
Rather than simply replacing inflation with the measure of policy makers’ inflation-
aversion coefficient and estimating equation (3), we opt for analyzing the impact of the
policy framework variables on preferences by employing a panel data approach. The key
advantage of this procedure is that it enables us to use more information and exploit both
the cross-sectional and time-series features of the data set we have been able to construct.
We now proceed to analyze the behavior of policy makers’ preferences for inflation as
a function of monetary and exchange rate policies, controlling for the government size and
14
Out of these 12 countries only Portugal is a member of the EMU (since we are excluding Greece), so we
opted to eliminate this variable from the estimation.
20
the election variable. The estimation can be represented by the following equation:
λi,t = αi +β 1 P robi,t +β 2 STi,t +β 3 F Ti,t +β 4 EMUi,t +β 5 Fi,t +γ 1 Gi,t +γ 2 Ei,t +γ 3 Ii,t +η i,t , (4)
where: t = 1, ...24 represents the time period; λ is the relative preference for inflation
stability; αi is the country-specific effect; ST and F T are indicator variables which take a
value of one each year a strict (respectively, flexible) inflation targeting scheme is in place
and zero otherwise; G represents government consumption as a percentage of GDP; E is the
election dummy variable; and I is the first difference in the measure of political instability.
All other variables are as defined above.
Analogously as before, here are our hypotheses for the coefficients of the framework
and institutional variables:
• β 1 < 0 (a larger value for Prob is associated with lower preference for inflation stability);
• β 2 , β 3 > 0 (strict or flexible IT is associated with a higher preference towards stabilizing
inflation);
• β 4 , β 5 > 0 (joining the EMU or using an exchange rate anchor increases the preference
for inflation stability).
Regarding our control variables, we do not have any a priori conjecture with respect to
the correlation between the government size and policy preferences; however, we do expect a
negative impact of an election year and political instability (γ 2 < 0, and γ 3 < 0) on aversion
to inflation.
Before we present our results from estimating equation (4), it is important to point out
that our specification tests give us strong evidence to prefer the individual country effects to
the pooled regression, since the Breusch and Pagan (1980) Lagrangian Multiplier test yields
a chi-squared value of 1077.16 (p-value = 0.00). Also, the Hausman (1978) test rejects the
hypothesis that individual country effects are uncorrelated with other regressors (chi-squared
21
value = 18.57; p-value = 0.01), which leads us to conclude that the fixed effects model is the
best choice.
Our discussion of the panel data analysis is organized as follows: First, we report
the results of a baseline model, which first considers the entire sample of 34 countries and
then divides them into low and high inflation. Later, in Section 4, we provide a series of
robustness checks, including stationarity tests, employing alternative measures of CBI and
fiscal policy, and redefining the sample split. Finally, we perform some comparative statics
exercises to help us understand the implications of our empirical findings.
We estimate a fixed effects model using an unbalanced panel of 34 countries from
1979 to 2002. For all our variables we have yearly data available, except for central bank
independence, for which we assume that the values of CBI for 1979-1990 are given by the
value of Prob for 1975-1994, while for 1991-2002 we use the Prob measures for 1985-2004.15
To allow for time-variation and given that the preference parameter λ is estimated using
rolling regressions of 20 quarters, we employ a dynamic panel data procedure with an AR(1)
disturbance.
The regression results for the entire sample are reported in the first column of Table 4.
The results are consistent with most of our hypothesis: the coefficients for CBI and EMUmembership have the expected sign and are significant at the 1% level. Adopting a strict
or flexible IT scheme and fixing the exchange rate are statistically indistinguishable from
zero. As for the control variables, the coefficient on government consumption is significantly
positive at the 5% level, while a reduction in inflation aversion is observable during election
years. The first difference of the number of cabinet does not have any significant association
with policy preferences.
Once more, we want to verify whether these relationships hold once we divide the
15
We include four (five) additional years in our measure since the term lengths of a central bank governor
are either four or five years for most of the countries in our sample. Therefore, for example, a governor
appointed in July of 1989 should technically still be in office until through either June of 1993 or June of
1994.
22
sample into low and high inflation countries.16 Since we are focusing on preferences towards
inflation stability, this sub-division is natural; nevertheless, later in the paper we consider
an alternative sample split (based on the GDP level) to verify the robustness of our results.
The second column of Table 4 describes the results for the 22 low-inflation countries.
Since none of these countries practiced flexible IT during that period, we exclude that variable from our analysis. We observe that in the first two specifications all monetary and
exchange rate arrangements (except strict IT) have a significant correlation with λ; however, only the coefficients on CBI and membership to the EMU have the expected sign and
remain significant at the 5% and 10% levels. Surprisingly, the correlation between λ and
strict IT becomes negative (but insignificant), and we find no evidence of any contribution
of an exchange rate anchor to a higher preference for inflation stability. Finally, the coefficients on government consumption, the election variable, and political instability index are
insignificant.
Switching our attention to the high-inflation countries in the fourth column of Table
4, we observe how adopting a strict IT scheme seems to be the main monetary arrangement
that can explain a high preference for inflation stability. The coefficient on CBI is negative
and significant at the 5% level, while a fixed exchange rate does not contribute in explaining
the variability in λ. The election dummy is negatively and significantly (at the 5% level)
correlated with preferences, suggesting the presence of an opportunistic behavior in high
inflation countries, while the coefficients on the other control variables are insignificant.
4
Sensitivity analysis and implications of our findings
In this section we verify the robustness of the model specification and the results. We
start by assessing the reliability of the results and then proceed to conduct comparative
static excercises.
16
Again, we perform an alternative sample split using 25% inflation as the threshold level. These results
are available from the authors upon request.
23
4.1
Assessing the reliability of the results
The first robustness exercise we perform is checking the stationarity of the data, specifically, the dependent variable λ, in order to make sure our results are not the outcome of
a spurious regression. Our explanatory variables are either indices or dummy variables, so
there is no reason to believe they are not stationary. By construction, λ is bounded between
zero and one; however, there is a chance that it may follow a near unit-root process for some
countries. To verify if we can dismiss this possibility, we employ a panel data unit root test
using the procedure developed by Levin, Lin and Chu (LLC, 2002). The null hypothesis of
the LLC test is that each series in the panel has a unit root, against the alternative that all
individual series are stationary. For the entire sample we find that the test-statistic is 21.53
(with a critical value of 13.00 at the 1% level). Alternatively, we split our data set again into
low and high inflation countries, and perform the LLC test for each subsample separately.
For the low inflation countries, the test statistic is -15.58 (with a critical value of -7.01 at the
1% level), while for high inflation countries the test statistic yields -8.16 (-3.58). This leads
us to conclude that there is no statistical evidence suggesting that the λ’s are not stationary.
Another important exercise consists in considering an alternative sample split, to verify
if our results are robust to redefining the sub-groups. Once more, we employ an objective
criterion, which is average GDP per capita for the period 1991-2000. 14 countries exhibit
a per capita income lower that US$12,000, which includes 11 out of the 12 high inflation
countries (all except Israel) and it also includes Barbados, Korea Republic, and Malaysia.
The other 20 countries have an average GDP per capita of over US$16,000 per year, and
this sub-sample includes all industrialized economies.
The third and fifth columns of Table 4 display the coefficients of the variables of
interest for this alternative sample split. The results reinforce the ones we obtain when we
divide the countries using the average inflation criterion. For high income countries, as is
the case for low inflation countries, CBI and membership to the EMU are the only robust,
relevant variables correlated with a higher preference for inflation stability at the 10% level.
24
For medium-to-low income economies, CBI and adopting a strict IT framework are the only
arrangements significantly related inflation-averse policies, parallel to the results for high
inflation countries.
The next task is to establish whether our alternative measure for CBI, namely the
average turn-over ratio of a central bank governor, yields similar results as the ones we
obtain using the Prob measure. Apart from the scaling issue resulting from using the TOR
index, the estimates reported in Table 5 mirror our results from the baseline model. For
low inflation economies a higher degree of CBI (i.e., a lower TOR) and joining the EMU
are significantly associated with more inflation-averse policies, while high inflation countries
exhibit a larger preference for inflation stability when implementing a strict IT scheme and
increased CBI. Once again, for low inflation countries we find no evidence that adopting an
IT framework helps in explaining a larger value for λ.
A further robustness exercise consists of replacing cabinet changes with government
crises as the proxy for political instability. The results are reported on Table 6. A direct
comparison with Table 4, which also uses Prob as the measure for CBI, reflects very similar
coefficients for the institutions and policy arrangement variables for the entire sample and all
the subsamples. The only difference is that the CBI coefficients for moderate/high inflation
countries (and medium-low income economies) is no longer significant at the 10% level.17
Finally, we also considered replacing the government consumption variable with a
measure of fiscal deficit. As we mentioned in Section 2, the problem of doing this is that we
could obtain the information on the budget balance only for a fraction of the countries in
our sample, and not for all years, which renders ineffective any comparison with the baseline
model. Still, we perform the analysis using the reduced sample, and find that the relevant
variables have the correct sign and are still significant (CBI and EMU for low inflation
countries, and SIT and CBI for high inflation countries), and also, that the fiscal deficit
has a negative but insignificant correlation with λ in most specifications.18
17
18
These findings are analogous to the ones we obtained when we substitute Prob with TOR.
The results for this analysis are not provided in the paper but are available upon request from the
25
The above tests and sensitivity analyses lead us to conclude that our results are quite
robust. Therefore, we feel comfortable about performing some comparative statics exercises
and discuss the implications of our findings, which we do next.
4.2
Comparative Statics Exercises
As we have seen from the above analysis, institutions differ on their impact depending
on whether the particular country exhibits high or low inflation. This distinction is of course
translated into quite different preferences for inflation stability. The average value for λ in
low inflation (high income) countries between 1981 and 2000 is approximately 0.79 (0.77). In
simple terms, this can be interpreted as policy makers in these countries assigning roughly
79% (77%) of their efforts to price stability and the remaining 21% (23%) to stabilizing
output growth. In contrast, our sample of high inflation (medium-to low income) economies
have an average value for λ equal to 0.45 (0.52) for the same time frame. Based on our
empirical findings, we are interested in determining how much closer can a policy maker
approach an inflation-averse behavior in a low (respectively, high) inflation country in lieu
of changes in institutions and policy framework.
Table 7.1 presents the comparative statics analysis for low inflation (high income)
economies. We employ the ranges for the coefficients of CBI (both Prob and TOR) and
EMU-membership to examine the potential final effect on λ. Using a starting value of 0.79
we find:
• A change in Prob from 0.2 to zero (which implies going from changing the central
bank governor once every five elections to not replacing her/him after an election) is
associated with an increase in λ to a value ranging between 0.86 and 0.88.
• A change in TOR from 0.2 (i.e., an average tenure of five years for the central bank
governor, which is the entire sample’s average for the 1980s and 1990s) to 0.1 is related
authors.
26
to an increase in λ to a value ranging between 0.87 and 0.92.
• Joining the EMU is be associated with a sizable increase in the value of λ to anywhere
between 0.93 and 0.94.
Turning our attention to high inflation (medium-to-low income) countries, Table 7.2
summarizes the likely effect of adopting a strict IT regime and the potential impact of
increased CBI. Using a starting value of 0.45 for λ we find:
• Adopting a strict IT framework may considerably increase λ to anywhere between 0.60
to 0.76.
• A reduction in Prob from 0.4 (average value for high inflation countries in the 1980s)
to 0.2 (average value in the 1990s) is associated with a more modest change in λ: its
final value would range between 0.49 and 0.51.
• Similarly, a reduction in TOR from 0.3 to 0.2 is associated with an increase in λ to a
value ranging between 0.53 and 0.56.
From these comparative statics exercises and, in general, our estimation results, we
can conclude the following:
• If policy makers in low inflation countries are interested in stabilizing inflation, they
should pay particular attention to achieving a higher degree of independence of their
central bank. In case this is no longer an option, another possibility is to join a
monetary union (the EMU, for example, or even form their own union, a possibility
that is currently being discussed among South American countries). Adopting an IT
framework, surprisingly, does not appear to have any positive correlation with more
inflation-averse policies, once you control for time effects.
• Authorities in high inflation countries should focus on adopting a strict IT scheme.
While, increasing central bank independence has a significant impact, the size of the
27
associated change in preferences is rather small. Finally, we find no conclusive evidence
suggesting that an exchange rate anchor contributes towards a higher preference for
inflation stability (for either low or high inflation countries), or that fiscal policy helps
or hurts in designing inflation-averse policies.
5
Conclusions
We study the relevance of different types of policy and institutional arrangements
in determining the relative preferences of policy makers for inflation stability. We argue
that focusing on policy intentions, represented by these preferences, constitutes a better
way of evaluating policy behavior, rather than looking at inflation outcomes that may be
unavoidable at times.
Our results suggest that policy makers in low inflation countries should focus their
efforts on strengthening the independence of their central banks or consider the possibility
of joining a monetary union. While these two arrangements have robust and sizable effects
for framing more inflation-averse policies, contrary to findings in the literature to date, we
do not encounter evidence suggesting that inflation targeting is associated with a higher
preference for inflation stability.
Alternatively, if policy makers in high inflation countries are interested in stabilizing
inflation, they can benefit a lot more from adopting a strict inflation targeting framework,
and, to a lesser extent, in attempting to reform their legislations to make their central banks
more independent.
Finally, neither for high-inflation, nor for low-inflation countries, do we find any evidence
suggesting that the use of fiscal policy, or employing the exchange rate as an anchor, are
associated with an inflation-averse behavior.
The above results point to two observations. First, several institutions and arrangements can be simultaneously relevant in explaining outcomes and policy choices. Second,
28
the election of monetary policies that lead to inflation stability in low-inflation countries
does not necessarily coincide with the institutional design that can be effective for highinflation countries. This implies that findings arising from "one-size-fits-all" econometric
specification, which consider either only inflation targeting or focus exclusively on central
bank independence, can be largely misleading.
One possible extension to our analysis is to incorporate additional institutional variables, such as the financial and legal structure, and political stability. Another interesting
question would be to determine whether maintaining a fixed exchange-rate can cause smaller
economies to "inherit" the preferences of the country with respect to which they are pegging
their currency. We hope to be able to address these issues in the near future.
29
6
Appendix: Data Sources
Data on policy makers’ preferences is from Krause and Méndez (2005) and is available for
download at: http://userwww.service.emory.edu/~skrause/pdf/Lambdas.xls (yearly data).
The information used to compute the turnover ratio of central bank governors (TOR) and
the probability that a turnover follows a change in the government (Prob) was obtained directly from individual Central Banks’ websites and direct inquiries to staff members. Data on
electoral dates was obtained mainly from the Database on Political Institutions in BCGKW
(2001), and also partly completed through direct inquiries to individual government sources.
For the information on the dates that inflation targeting was introduced, we employ the
data from Mishkin and Schmidt-Hebbel (2002) and include Hungary. CPI Inflation, GDP
data, and exchange rate regime information come from the International Financial Statistics CD_ROM (December 2004). Data on fiscal deficit and government consumption was
obtained from the World Bank’s World Development Indicators (2004). Finally, data on the
number of cabinet changes and the number of government crises is from the Cross National
Time Series Database (2004).
30
References
[1] Aisen, A., and F. J. Veiga (2006). "Does Political Instability Lead to Higher Inflation?
A Panel Data Analysis", Journal of Money, Credit, and Banking, 38(5), pp. 1379-90.
[2] Alesina, A. (1988). "Macroeconomics and Politics", in S. Fisher (ed.), NBER Macroeconomics Annual, MIT Press, Cambridge, MA, pp. 17-52.
[3] Alesina, A., and L. Summers (1993). "Central Bank Independence and Macroeconomic
Performance: Some Comparative Evidence", Journal of Money, Credit, and Banking,
25(2), pp. 151-62.
[4] Alesina, A., and R. Gatti (1995). "Independent Central Banks: Low Inflation at No
Cost?", American Economic Review, 85(2), pp. 196-200.
[5] Bade, R., and M. Parkin (1982). "Central Bank Laws and Monetary Policy", mimeo.
[6] Beck, T., G. Clarke, A. Groff, P. Keefer, and P. Walsh (2001). "New Tools and New
Tests in Comparative Political Economy: The Database of Political Institutions", World
Bank Economic Review , pp. 165-76.
[7] Bernanke, B. S., and F. Mishkin (1997). "Inflation Targeting: A New Framework for
Monetary Policy?", Journal of Economic Perspectives, 11(2), pp. 97-116.
[8] Bernanke, B. S., T. Laubach, F. S. Mishkin, and A. S. Posen (1999). Inflation Targeting:
Lessons from the International Experience, Princeton University Press.
[9] Berger, H., J. de Haan, and S. C. W. Eijffinger (2001). "Central Bank Independence:
An Update of Theory and Evidence", Journal of Economic Surveys, 51(1), pp. 3-40.
[10] Breusch, T., and A. Pagan (1980). "The LM Test and Its Application to Model Specification in Econometrics", Review of Economic Studies, 47, pp. 239-54.
31
[11] Cecchetti, S. G. (1999). "Legal Structure, Financial Structure and the Monetary Transmission Mechanism", in H. Hermann and R. Köning (eds.), The Monetary Transmission
Process, proceedings of the conference on ‘The Monetary Transmission Process: Recent
Developments and Lesson for Europe,’ sponsored by the Deutsche Bundesbank, Frankfurt, Germany, March 26, 1999.
[12] Cecchetti, S. G., and M. Ehrmann (2001). "Does Inflation Targeting Increase Output
Volatility? An International Comparison of Policymakers’ Preferences and Outcomes",
in K. Schmidt-Hebbel (ed.), Monetary Policy: Rules and Transmission Mechanisms,
Proceedings of the Third Annual Conference of the Central Bank of Chile, pp. 247-74.
[13] Chortareas, G., D. Stasavage, and G. Sterne (2002). "Does it Pay to be Transparent?
International Evidence from Central Banks", Economic Review of the Federal Reserve
Bank of St. Louis, 79, July/August, pp. 99-118.
[14] Clarida, R., J. Galí, and M. Gertler (1999). "The Science of Monetary Policy: A New
Keynesian Perspective", Journal of Economic Literature, 37, pp. 1661-707.
[15] Corbo, V. (2002). "Exchange Rate Regimes in the Americas: Is Dollarization the Solution?", Institute for Monetary & Economic Studies, Bank of Japan, 20(S-1), pp. 91-122.
[16] Corbo, V., and K. Schmidt-Hebbel (2001). "Inflation Targeting in Latin America",
Working Papers, Central Bank of Chile, No. 105.
[17] Cottarelli, C., M. Griffiths, and R. Moghadan (1998). “The Non-monetary Determinants
of Inflation: A Panel Data Study”, IMF Working Paper No. 23.
[18] Cukierman, A. (1992). Central Bank Strategy, Credibility, and Independence, MIT Press,
Cambridge, MA, Part IV.
[19] Cukierman, A. (1994). "Central Bank Independence and Monetary Control", The Economic Journal, Vol. 104, pp. 1437-48.
32
[20] Cukierman, A., S. B. Webb, and B. Neyapti (1992). "Measuring the Independence of
Central Banks and Its Effect on Policy Outcomes", World Bank Economic Review, 6,
pp. 353-98 (reprinted as Occasional Paper No. 58, International Center for Economic
Growth, pp. 1-62).
[21] Cukierman, A., and S. B. Webb (1995). "Political Influence on the Central Bank: International Evidence", World Bank Economic Review, 9, pp. 397-423.
[22] Eichengreen, B. (2002), "Can Emerging Markets Float? Should They Inflation Target?",
Working Paper Series, Central Bank of Chile.
[23] Eijffinger S. C. W., M. van Rooij, and E. Schaling (1996). "Central Bank Independence:
A Panel Data Approach", Public Choice, 89, pp. 163-82.
[24] Faust, J., and L. E. O. Svensson (2001). "Transparency and Credibility: Monetary
Policy with Unobservable Goals", International Economic Review, 2, pp. 369-97
[25] Fisher, S., R. Sahay and C. Végh. (2002). "Modern Hyper- and High Inflations", Journal
of Economic Literature XL(3), pp. 837-80.
[26] Fry, M., D. A. Julius, L. Mahadeva, S. Rogers, and G. Sterne (2000). "Key Issues in the
Choice of Monetary Policy Framework", in L. Mahadeva and G. Sterne (eds.), Monetary
Policy Frameworks in a Global Context, Routledge, London, United Kingdom, pp. 1-216.
[27] Fuhrer, J. C. (1997). "Central Bank Independence and Inflation Targeting: Monetary
Policy Paradigms for the Next Millennium?", New England Economic Review, 1/2, pp.
19-36.
[28] Geraats, P. (2002). "Central Bank Transparency", The Economic Journal, 112, pp.
532-65.
[29] Ghosh, A. R., A. Gulde, and H. C. Wolf (2000). "Currency Boards: More than a Quick
Fix?", Economic Policy, 15(31), pp. 269-335.
33
[30] Granato, J., M. Lo and M. C. S. Wong (2006). "Testing Monetary Policy Intentions in
Open Economies", Southern Economic Journal, 72(3), pp.730-46.
[31] Grilli, V., D. Masciandaro, and G. Tabellini (1991). "Political and Monetary Institutions
and Public Financial Policies in the Industrial Countries", Economic Policy, 13, pp. 34192.
[32] de Haan, J., and van‘t Hag, G.J. (1995). "Variation in Central Bank Independence
Across Countries: Some Provisional Empirical Evidence", Public Choice, 85, pp. 33551.
[33] de Haan, J., and W. Kooi (2000). "Does Central Bank Independence Really Matter?
New Evidence for Developing Countries Using a New Indicator", Journal of Banking
and Finance, 24, pp. 643-64.
[34] Hausman, J. (1978). "Specification Tests in Econometrics", Econometrica, 46, pp. 125171.
[35] Issing, O. (2002). "The Euro and the ECB: Experiences and Perspectives", Proceedings
from the XLII EUROFINAS Annual Conference, (September).
[36] Jensen, H. (2002). "Optimal Degrees of Transparency in Monetary Policymaking", Scandinavian Journal of Economics, 104, pp. 399-422.
[37] Krause, S., and F. Méndez (2005). "Policy Makers’ Preferences, Party Ideology and the
Political Business Cycle", Southern Economic Journal, 71(4), pp.752-67.
[38] Krause, S. (2006). "Optimal Monetary Policy and the Equivalency between the OnePeriod AD-AS Model and the Forward-Looking New Keynesian Model", Applied Economics Letters, 13(9), pp.541-44.
[39] Kwan, Y-K., F. Lui, and L. K. Cheng (2001). "Credibility of Currency Boards Under
Attacks: The Role of Institutional Arrangements" Chapter 7, p. 233-59, in T. Ito and
34
A. O. Krueger (eds.), Regional and Global Capital Flows: Macroeconomic Causes and
Consequences (NBER- East Asia Seminar on Economics, vol. 10), Chicago: University
of Chicago Press.
[40] Levin, A., C. F. Lin, and C-S. Chung (2002). "Unit Root Test in Panel Data: Asymptotic
and Finite Sample Properties", Journal of Econometrics, 108(1), pp. 1-24.
[41] van Lelyveld, Iman, (1999). "Inflation or Unemployment? Who Cares?", European
Journal of Political Economy, 15(3), pp. 463-84.
[42] Loungani, K. P. and N. Sheets (1997). "Central Bank Independence, Inflation, and
Growth in Transition Economies", Journal of Money, Credit, and Banking, 29, pp.
381-99.
[43] Mishkin, F. S., and K. Schmidt-Hebbel (2002). "A Decade of Inflation Targeting in the
World: What Do We Know and What Do We Need to Know?", in N. Loayza and R.
Soto (eds.) Inflation Targeting: Design, Performance, Challenges, Santiago, Chile, pp.
171-219.
[44] Muscatelli, A., and C. Trecroci (2000). "Monetary Policy Rules, Policy Preferences,
and Uncertainty: Recent Empirical Evidence", Journal of Economic Surveys, 14(5),
pp. 597-627.
[45] Neuman, M., and J. von Hagen (2002). "Does Inflation Targeting Matter?", Economic
Review of the Federal Reserve Bank of St. Louis, 79, July/August, pp. 127-48.
[46] Nordhaus, W. D. (1975). "The Political Business Cycle", The Review of Economic
Studies, 42(2), pp. 169-90.
[47] Quirk, P. (1994). "Recent Experiences with Floating Exchange Rates in Developing
Countries," in R. C. Barth and C-H. Wong (eds.), Approaches to Exchange Rate Policy:
Choices for Developing and Transition Economies, International Monetary Fund.
35
[48] Persson, T., and G. Tabellini (1999). "Political Economics and Macroeconomic Policy",
in J. B. Taylor and M. Woodford (eds.), Handbook of Macroeconomics, Vol. 1, pp.
1397-482.
[49] Posen, A. S. (1995). "Declarations Are Not Enough: Financial Sector Sources of Central
Bank Independence", in B. Bernanke and J. Rotemberg (eds.), NBER Macroeconomics
Annual, MIT Press, Cambridge, MA, pp. 253-74.
[50] Posen, A. S. (2002). "Six Practical Views of Central Bank Transparency", Working
Paper, Institute for International Economics (May).
[51] Rogoff, K. (1985). "The Optimal Degree of Commitment to an Intermediate Monetary
Target", Quarterly Journal of Economics, 110, pp. 1169-90.
[52] Ruge-Murcia, F. J. (2003). "Inflation Targeting under Asymmetric Preferences", Journal of Money, Credit, and Banking, 35, pp. 763-85.
[53] Sturm, J-E., and J. de Haan (2001). "Inflation in Developing Countries: Does Central
Bank Independence Matter? New Evidence Based on a New Data Set", Ifo Studien,
No. 4, pp. 389-404.
[54] Tallman, E. (2003). "Monetary Policy and Learning: Some Implications for Policy and
Research", Federal Reserve Bank of Atlanta Economic Review, Third Quarter, pp. 1-9.
[55] Truman, E. M. (2003). Inflation Targeting in the World Economy, Institute for International Economics, Washington, DC.
[56] Walsh, C. (1995). "Recent Central-Bank Reforms and the Role of Price Stability as the
Sole Objective of Monetary Policy", in B. Bernanke and J. Rotemberg (eds.), NBER
Macroeconomics Annual, MIT Press, Cambridge, MA, pp. 237-52.
36
Table 1: Measures of Central Bank Independence (CBI)
Country
Australia
Austria
Barbados
Belgium
Canada
Chile
Colombia
Costa Rica
Denmark
Finland
France
Germany
Greece
Hungary
Ireland
Israel
Italy
Japan
Korea Republic
Malaysia
Mexico
Netherlands
New Zealand
Norway
Peru
Portugal
Spain
Sweden
Switzerland
Trinidad & Tobago
Turkey
United Kingdom
United States
Uruguay
Average
CBI (Prob.) /a
CBI (TOR) /b
1975-1994
1985-2004
1975-1994
1985-2004
0.000
0.167
0.200
0.200
0.200
0.500
0.400
0.800
0.000
0.250
0.200
0.167
0.400
0.400
0.333
0.200
0.167
0.143
1.000
0.000
0.500
0.000
0.143
0.200
1.000
0.286
0.000
0.429
0.000
0.250
0.250
0.250
0.000
1.000
0.295
0.000
0.167
0.200
0.167
0.250
0.250
0.000
0.600
0.000
0.000
0.200
0.200
0.167
0.200
0.200
0.000
0.000
0.000
0.750
0.200
0.000
0.000
0.000
0.200
0.250
0.167
0.167
0.000
0.000
0.250
0.200
0.000
0.000
0.750
0.163
0.15
0.15
0.10
0.15
0.10
0.55
0.20
0.55
0.05
0.15
0.20
0.20
0.25
0.20
0.20
0.20
0.15
0.20
0.40
0.15
0.20
0.05
0.20
0.10
0.45
0.35
0.20
0.20
0.10
0.15
0.45
0.10
0.15
0.25
0.21
0.10
0.25
0.15
0.10
0.10
0.30
0.10
0.35
0.05
0.15
0.15
0.20
0.20
0.25
0.15
0.15
0.05
0.20
0.35
0.20
0.05
0.05
0.10
0.15
0.35
0.25
0.10
0.10
0.20
0.20
0.30
0.10
0.05
0.35
0.17
a
CBI (Prob.): Central Bank Independence is measured using the probability of a change in the central bank
governor within 6 months after an election. b CBI (TOR): Central Bank Independence is measured using the
average turn-over ratio of the central bank governor.
37
Table 2: Correlations of Prob. and TOR with other measures of
Central Bank Independence (CBI)
Simple correlation
Prob.(75-94)
Prob.(85-04)
TOR(75-94)
A-BP (1988)
-0.045
(0.87)
-0.046
(0.87)
GPI (1991)
-0.420
(0.06)
-0.420
(0.04)
GAV (1991)
-0.472
(0.03)
-0.550
(0.01)
CWN (1992)
-0.479
(0.06)
-0.312
(0.25)
-0.382
(0.02)
FJMRS (2000)
TOR (85-04)
0.244
(0.16)
P-values are in parentheses. Prob.: Central Bank Independence (CBI) is measured using the probability of a
change in the central bank governor within 6 months after an election. TOR: CBI is measured using the
average turn-over ratio of the central bank governor. A-BP: Alesina’s (1988) measure based on an update
of Bade and Parkin’s (1982) legal CBI index for 16 industrialized economies. GPI: Grilli, Mascindaro and
Tabellini’s (GMT, 1991) measure of political CBI for 18 industrialized countries. GAV: GMT’s (1991)
average measure of political and economic CBI. CWN: Cukierman, Neyapti and Webb’s (1992) measure
of CBI based on a survey to 23 central banks. FJMRS: Fry, Julius, Mahadeva, Rogers, and Sterne’s
(2000) measure of CBI based on a survey to 94 central banks.
38
Table 3: Dependent variable: Transformed Rate of Inflation (TRI = π/[1+π])
1980-1989 / 1990-1999
All countries
Low inflation
Mod./High inflation
CBI (Prob.) /a
0.286
(0.02)
0.291
(0.01)
0.044
(0.00)
0.038
(0.01)
0.326
(0.10)
0.367
(0.00)
Strict IT
-0.082
(0.06)
-0.066
(0.08)
-0.017
(0.09)
-0.022
(0.03)
-4.596
(0.26)
-1.824
(0.62)
Flexible IT
0.246
(0.34)
0.202
(0.40)
0.371
(0.43)
0.151
(0.72)
Membership to the EMU
-0.029
(0.09)
-0.039
(0.22)
-0.020
(0.03)
-0.024
(0.00)
Fixed exchange rate
-0.068
(0.00)
-0.074
(0.00)
0.000
(0.99)
-0.002
(0.84)
-0.134
(0.02)
-0.040
(0.52)
Cabinet Changes /b
0.038
(0.39)
0.172
(0.05)
Government Crises /c
R-squared / No. of obs.
0.023
(0.08)
0.334
68
0.408
68
-0.031
(0.77)
0.017
(0.16)
0.337
44
0.325
44
0.587
(0.00)
0.281
24
0.558
24
P-values are in parentheses. a Prob. Index: Central Bank Independence (CBI) is measured using the
probability of a change in the central bank governor within 6 months after an election. b Cabinet Changes is
measured by the average number of cabinet changes for each decade. c Government Crises is measured by
the average number of government crises in each decade.
39
Table 4: Dependent variable: Relative preference for inflation stability (λ)
Central Bank Independence: Prob. Index /a
All
Countries
Low
Inflation
High
Income
Mod./High
Inflation
Med./Low
Income
CBI (Prob.) /a
-0.314
(0.00)
-0.396
(0.03)
-0.460
(0.02)
-0.280
(0.05)
-0.231
(0.10)
Strict IT
0.043
(0.32)
-0.007
(0.88)
0.014
(0.76)
0.307
(0.00)
0.173
(0.09)
Flexible IT
-0.013
(0.84)
0.063
(0.38)
0.060
(0.44)
Membership to the EMU
0.116
(0.01)
0.146
(0.00)
0.152
(0.00)
Fixed exchange rate
-0.002
(0.96)
0.012
(0.81)
0.010
(0.85)
0.004
(0.95)
0.003
(0.96)
Government consumption
0.013
(0.03)
0.008
(0.31)
0.020
(0.06)
0.013
(0.14)
0.010
(0.20)
Election
-0.022
(0.06)
-0.011
(0.41)
-0.013
(0.37)
-0.054
(0.02)
-0.040
(0.05)
Cabinet Changes /b
0.006
(0.54)
0.009
(0.42)
0.002
(0.87)
-0.008
(0.71)
0.011
(0.53)
F-statistic
3.29
(0.00)
2.65
(0.01)
3.17
(0.00)
3.01
(0.00)
1.90
(0.07)
667
34
450
22
400
20
217
12
267
14
No. of obs. / countries
P-values are in parentheses. a Prob. Index: Central Bank Independence (CBI) is measured using the
probability of a change in the central bank governor within 6 months after an election. b Cabinet Changes is
measured by first differencing the number of cabinet changes.
40
Table 5: Dependent variable: Relative preference for inflation stability (λ)
Central Bank Independence: TOR Index /a
All
Countries
Low
Inflation
High
Income
Mod./High
Inflation
Med./Low
Income
CBI (TOR) /a
-1.046
(0.00)
-0.873
(0.05)
-1.327
(0.00)
-1.049
(0.00)
-0.830
(0.02)
Strict IT
0.039
(0.37)
0.002
(0.96)
0.016
(0.72)
0.276
(0.01)
0.164
(0.11)
Flexible IT
-0.014
(0.81)
0.050
(0.48)
0.052
(0.49)
Membership to the EMU
0.104
(0.02)
0.136
(0.00)
0.135
(0.00)
Fixed exchange rate
-0.011
(0.78)
-0.004
(0.93)
-0.014
(0.80)
0.002
(0.97)
0.009
(0.88)
Government consumption
0.013
(0.02)
0.010
(0.21)
0.022
(0.04)
0.013
(0.14)
0.010
(0.19)
Election
-0.021
(0.07)
-0.011
(0.40)
-0.013
(0.36)
-0.050
(0.04)
-0.038
(0.07)
Cabinet Changes /b
0.007
(0.48)
0.010
(0.38)
0.003
(0.80)
-0.008
(0.72)
0.017
(0.36)
F-statistic
4.10
(0.00)
2.50
(0.02)
3.56
(0.00)
3.71
(0.00)
2.31
(0.03)
667
34
450
22
400
20
217
12
267
14
No. of obs. / countries
P-values are in parentheses. a TOR Index: CBI is measured using the average turn-over ratio of the central
bank governor. b Cabinet Changes is measured by first differencing the number of cabinet changes.
41
Table 6: Dependent variable: Relative preference for inflation stability (λ)
Central Bank Independence: Prob. Index /a
All
Countries
Low
Inflation
High
Income
Mod./High
Inflation
Med./Low
Income
CBI (Prob.) /a
-0.294
(0.01)
-0.392
(0.03)
-0.449
(0.02)
-0.207
(0.16)
-0.204
(0.15)
Strict IT
0.045
(0.31)
-0.007
(0.87)
0.014
(0.76)
0.310
(0.00)
0.163
(0.09)
Flexible IT
-0.008
(0.89)
0.075
(0.29)
0.004
(0.94)
Membership to the EMU
0.120
(0.01)
0.149
(0.00)
0.155
(0.00)
Fixed exchange rate
-0.013
(0.74)
0.002
(0.96)
-0.009
(0.87)
-0.006
(0.94)
0.011
(0.86)
Government consumption
0.013
(0.03)
0.008
(0.31)
0.021
(0.05)
0.014
(0.11)
0.009
(0.24)
Election
-0.021
(0.07)
-0.008
(0.53)
-0.012
(0.40)
-0.065
(0.00)
-0.041
(0.05)
Government Crises /b
-0.016
(0.04)
-0.008
(0.39)
-0.016
(0.08)
-0.035
(0.01)
-0.021
(0.35)
F-statistic
3.76
(0.00)
2.63
(0.01)
3.56
(0.00)
3.85
(0.00)
2.11
(0.04)
667
34
450
22
400
20
217
12
267
14
No. of obs. / countries
P-values are in parentheses. a Prob. Index: Central Bank Independence (CBI) is measured using the
probability of a change in the central bank governor within 6 months after an election. b Government Crises is
measured by first differencing the number of government crises.
42
Table 7.1: Effect of relevant policy institutions and arrangements on λ
(Low-inflation and high-income countries)
Coefficient (range)
Final value of λ /a
Change in Prob. /b from 0.2 to 0
[-0.460, -0.392]
[0.864, 0.878]
Change in TOR /c from 0.2 to 0.1
[-1.327, -0.845]
[0.871, 0.919]
Membership to the EMU
[+0.135, +0.155]
[0.931, 0.941]
Comparative Statics
a
: Final range of values for the preference parameter (λ), starting from a baseline value of 0.786 (average
value of λ for 22 low inflation countries, 1981-2000). b: Prob: Central Bank Independence (CBI) is measured
using the probability of a change in the central bank governor within 6 months after an election. c TOR: CBI is
measured using the average turn-over ratio of the central bank governor.
Table 7.2: Effect of relevant policy institutions and arrangements on λ
(High-inflation and medium/low income countries)
Coefficient (range)
Final value of λ /a
Change in Prob. /b from 0.4 to 0.2
[-0.280; -0.204]
[0.494, 0.509]
Change in TOR /c from 0.3 to 0.2
[-1.049, -0.790]
[0.532, 0.558]
Adopt Strict IT
[+0.144, +0.310]
[0.597, 0.763]
Comparative Statics
a
: Final range of values for the preference parameter (λ), starting from a baseline value of 0.453 (average
value of λ for 10 high inflation countries, 1981-2000). b: Prob: Central Bank Independence (CBI) is measured
using the probability of a change in the central bank governor within 6 months after an election. c TOR: CBI is
measured using the average turn-over ratio of the central bank governor.
43