Klomp de haan-jes_2010_inflation_and_cbi

doi: 10.1111/j.1467-6419.2009.00597.x

INFLATION AND CENTRAL BANK
INDEPENDENCE: A META-REGRESSION
ANALYSIS
Jeroen Klomp
University of Groningen, The Netherlands
Jakob de Haan
University of Groningen, The Netherlands
CESifo, Munich, Germany
Abstract. Using 59 studies, we perform a meta-regression analysis of studies
examining the relationship between inflation and central bank independence
(CBI). The studies considered are very different with respect to the CBI indicator
used, the sample of countries and time periods covered, model specification,
estimators used and publication outlet. We conclude that there is a significant
publication bias. However, we also find a significant genuine effect of CBI
on inflation. Differences between studies are not caused by differences in CBI
indicators used.
Keywords. Central bank independence; Inflation; Meta-analysis

No wonder politicians often find the Fed a hindrance. Their better selves
may want to focus on America’s long-term prosperity, but they are far more
subject to constituents’ immediate demands. That’s inevitably reflected in their
economic policy preferences. If the economy is expanding, they want it to expand
faster; if they see an interest rate, they want it to be lower. (Greenspan, 2007,
pp. 110–111)
1. Introduction

During the last two decades, many countries granted their monetary authorities
greater independence. It is widely believed that central banks otherwise will
give in to pressure from politicians who may be motivated by short-run
electoral considerations or may value short-run economic expansions highly while
discounting the longer-run inflationary consequences of expansionary policies
(Walsh, 2005).1 If the ability of politicians to distort monetary policy results in
excessive inflation, countries with an independent central bank should experience
lower rates of inflation. Indeed, beginning with Bade and Parkin (1988), an
important line of empirical research focusing on the relationship between central
bank independence (CBI) and inflation suggests that average inflation is negatively
Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621
C 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main
Street, Malden, MA 02148, USA.

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KLOMP AND DE HAAN

related to measures of CBI (see Eijffinger and De Haan, 1996a; Berger et al.,
2001; Crowe and Meade, 2007, for summaries). However, this evidence has been
criticized by various authors, claiming that the results are sensitive with respect to
the measure of CBI used (see, for instance, Forder, 1996), the specification of the
model (see, for instance, Posen, 1995; Campillo and Miron, 1997) or the inclusion
of high-inflation observations (see, for instance, De Haan and Kooi, 2000).
Using meta-regression analysis (MRA), this paper addresses two issues. (1) To
what extent has the literature confirmed that there is a negative association between
CBI and inflation? (2) Can we explain the pattern in the results of empirical research
on the relationship between CBI and inflation? Using 59 studies we find that, on
average, there exists a significant relation between CBI and inflation. We also find
that the results reported in the studies in our sample suffer from a publication bias.
Studies report the strongest relationship between CBI and inflation if they focus
on OECD countries (especially when studies control for outliers) and include the
1970s. Furthermore, we find that when a bivariate regression is used or if the model
includes the labour market the significance of the CBI indicator increases. We do
not find significant differences between studies based on a cross-country setting
and those that use panel models. Differences between studies are also not caused
by differences in CBI indicators used.
The remainder of the paper is organized as follows. Section 2 reviews the main
issues in the empirical research on the relationship between CBI and inflation.
Section 3 outlines the methodology of the MRA and the studies used in our
analysis, while Section 4 contains the MRA. Section 5 offers our conclusions.
2. Measuring Central Bank Independence

To examine whether there is any relationship between CBI and inflation, one needs
an indicator of the extent to which the monetary authorities are independent from
politicians. Most empirical studies use either an indicator based on central bank
laws in place, or the so-called turnover rate of central bank governors (TOR).
The most widely employed legal index of CBI is from Cukierman (1992) and
Cukierman et al. (1992),2 although alternative measures have been developed by
Alesina (1988) and Grilli et al. (1991) among others (see Arnone et al. (2006) for an
extensive comparison of the various CBI indicators). Even though these indicators
are supposed to measure the same phenomenon and are all based on interpretations
of the central bank laws in place, their correlations are sometimes remarkably low
(Eijffinger and De Haan, 1996a).
The Cukierman index is based on four characteristics of the central bank’s charter.
First, a bank is viewed as more independent if the governor is appointed by the
central bank board rather than by the government, is not subject to dismissal, and
has a long term of office. Second, the level of independence is higher the greater
the extent to which policy decisions are made without government involvement.
Third, a central bank is more independent if its charter states that price stability
is the sole or primary goal of monetary policy. Fourth, independence is greater if
there are limitations on the government’s ability to borrow from the central bank.
C 2009 Blackwell Publishing Ltd

INFLATION AND CENTRAL BANK INDEPENDENCE

595

Legal measures of CBI may not reflect the true relationship between the central
bank and the government. Especially in countries where the rule of law is less
strongly embedded in the political culture, there can be wide gaps between the
formal, legal institutional arrangements and their practical impact (Walsh, 2005).
This is particularly likely to be the case in many developing economies. Cukierman
(1992) argues that the actual average term in office of the central bank governor
may therefore be a better proxy for CBI for these countries than measures based on
central bank laws. The TOR is based on the presumption that, at least above some
threshold, a higher turnover of central bank governors indicates a lower level of
independence.3 According to Cukierman’s data, TOR values range from a minimum
of 0.03 (which corresponds to an average term in office for the governor of some
33 years) to a maximum of 0.93 (which corresponds to an average term in office
of just 13 months). Cukierman’s data suggest that TORs in developing countries
cover a much broader range of values than in OECD countries, where values are
all below 0.20 turnovers per year.4
The next step is to employ these indicators in a particular model for
inflation and estimate it for a specific group of countries and a sample period.
Initially, the research focused on industrial countries using legal CBI indicators.
Most of the older studies, which generally used simple cross-country bivariate
regressions for particular periods, reported that CBI was negatively correlated with
average inflation (see, for instance, Alesina and Summers, 1993). The estimated
effect of independence on inflation turned out to be significant – in both a
statistical and economic sense – especially during periods with flexible exchange
rates.
While researchers found that legal CBI indicators were negatively associated
with inflation among industrial countries, this was not the case for developing
countries. However, initial findings suggested that in these economies the TOR of
central bank governors is positively correlated with inflation, therefore also lending
support to the hypothesis that CBI and inflation are negatively related. Countries
that experienced rapid turnover among their central bank heads (i.e. countries with
a low level of CBI) also tended to experience high rates of inflation (see, for
instance, Cukierman, 1992). This is a case, however, in which causality is difficult
to evaluate: Is inflation high because of political interference that leads to rapid
turnover of central bank officials? Or are central bank officials tossed out because
they cannot keep inflation down? (Walsh, 2005).
The empirical work attributing low inflation to CBI has been criticized along
various dimensions. First, some studies suggest that the results for the relationship
between CBI and inflation may differ across estimation periods. However, one
would expect a different impact of CBI on inflation under fixed and under floating
exchange rate regimes. Under the Bretton Woods system of fixed exchange rates,
countries were committed to an exchange rate target and had little room to conduct
an autonomous domestic monetary policy. Thus, the relation between CBI and
inflation is likely to be much less straightforward before 1973. Indeed, Jonsson
(1995) concludes that CBI has the strongest impact on inflation under floating
exchange rates.

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KLOMP AND DE HAAN

Second, studies on the relationship between CBI and inflation often fail to
control adequately for other factors that might account for cross-country differences
in inflation. Countries with independent central banks may differ in ways that
are systematically related to average inflation. A good example of this line of
critique is the work by Posen (1993, 1995) who argues that both low inflation
and CBI reflect the presence of a strong financial sector constituency for low
inflation. Average inflation and the degree of CBI are jointly determined by
the strength of political constituencies opposed to inflation. Posen argues that
once these constituencies are taken into account, the coefficient of CBI is no
longer significant in models explaining cross-country inflation differentials.5 Also
Campillo and Miron (1997) claim little role for legal CBI when control variables
relating to the degree of openness, political instability and a country’s inflation and
debt history are introduced. However, this result has been criticized as Campillo and
Miron’s sample includes many developing countries for which legal CBI indicators
may not be appropriate. Sturm and De Haan (2001) use TORs in a similar model
as Campillo and Miron and conclude that the coefficient of this CBI indicator is
significant in a multivariate model.6
A recent strand of literature argues that the effects of CBI should not be analysed
independently of labour market institutions. Trade unions may, for instance, be
inflation averse. The reason usually given is consistency: unions encompass most
of society, which in its majority is inflation averse, at least according to the standard
Rogoff (1985) model of monetary policy. Inflation-averse unions will make real
variables in equilibrium a function of the institutional set-up like the degree of
central bank conservatism given a certain degree of CBI. The more conservative
the central bank, the lower output will be and the higher the level of unemployment
in equilibrium. In that sense, monetary policy has real effects in these models. Also
the effects of CBI on inflation will be different in this setting compared to the
standard Rogoff-type of model (see Berger et al. (2001) for a further discussion).
A good example of this line of research is the study of Cukierman and Lippi
(1999). Using data for 19 OECD economies for the period 1980–1994, they find
that the inflation reducing impact of CBI is stronger at intermediate levels of union
centralization.
Finally, a few studies have sounded a warning that conclusions on the relationship
between CBI and inflation are highly sensitive to influential observations. For
instance, Temple (1998) finds that if high-inflation countries are added to his sample
of OECD and developing countries, the effect of CBI (proxied by Cukierman’s
(1992) legal index) on inflation disappears, while De Haan and Kooi (2000) and
Sturm and De Haan (2001) report that the TOR indicator only becomes significant
if high-inflation countries are included in the sample.
3. Studies on CBI and Inflation

MRA has become an increasingly popular instrument in economics to examine
particular fields of research, especially if there are many alternative specifications
leading to diverging conclusions.7 As Stanley and Jarrell (1989, p. 300) put it:


597

Figure 1. Studies Included in Our MRA.

MRA not only recognizes the specification problem but also attempts to estimate
its effects by modelling variations in selected econometric specifications. MRA
provides us with the means to analyze, estimate, and discount, when appropriate,
the influence of alternative model specification and specification searches. In this
way, we can more accurately estimate the empirical magnitudes of the underlying
economic phenomena and enhance our understanding of why they vary across
the published literature.
The issue of CBI lends itself perfectly for such an analysis. However, to the best
of our knowledge, such an analysis has not been done so far. We have gathered 59
studies that come up with empirical estimates of the effect of CBI on inflation in
a cross-country and/or panel setting, using some proxy for CBI. That means that
country-specific studies are excluded from the analysis. We started our search for
studies with the surveys of Eijffinger and De Haan (1996a) and the update thereof
in Berger et al. (2001). To find more recent (published and unpublished) studies
we used Google and JSTOR. Table A1 in the Appendix contains all the studies we
identified. We stopped searching at 31 December 2006. Table A1 also shows for
each of these studies the percentage of regressions in which there is a significant
negative relationship between inflation and CBI. We have coded all studies included
in our analysis independently; whenever we coded studies differently initially, these
differences were discussed until we both agreed about the proper coding.
Figure 1 summarizes the number of studies according to their year of publication,
differentiating between journal articles, (contributions in) books and working
papers. It follows from Figure 1 that the average number of studies per year is
around three in the 1990s and increases to five at the end of our sample period.

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The average sample size is about 91 observations, while on average about 28
countries are included. Most studies examine the effect of CBI on inflation by
estimating (variants of) a single-equation model without extensively testing for the
robustness of the results. Although most studies report a negative relation between
CBI and inflation, various papers find a positive or no effect of CBI on inflation.
Drawing on Stanley and Jarrell (1989), we can explain our MRA as follows.
Most studies on CBI and inflation involve a standard regression model such as
π = Xβ + ε

(1)

where π is the (n × 1) dependent variable vector, i.e. some measure of inflation,
X is an (n × m) matrix of explanatory variables, including an indicator of CBI,8
and ε denotes some random error, which is typically assumed to conform to the
classical regression model. As we are primarily interested in the relevance of CBI
in explaining inflation, we focus on the estimated t-statistic of the coefficient of
the CBI indicator. This also forgoes the problem that the coefficients of the various
indicators are not comparable, as their scaling differs. If the TOR is used as a CBI
indicator, we multiply the reported t-statistic by −1 so that it becomes comparable
with studies using legal CBI indicators.
Table 1 shows the distribution of the t-statistics across time period, country
sample, and indicator used. The average t-statistic of the CBI indicator of all
regressions in our sample is −1.78 if we take all estimates independently. When
we account for study differences, the average t-statistic increases to −1.85. Both
averages indicate that the relation between inflation and CBI is significantly
negative at the 5% significance level.
We can draw some stylized facts from Table 1. First, in OECD countries the
average t-statistic of the CBI and inflation relation is lower (i.e. more significant)
compared to developing and transition countries. In most cases, if we do not
differentiate between CBI indicators used, these results suggest that the relation
between inflation and CBI is significant in OECD countries and insignificant in
developing countries.
Second, in the period 1970–1979 the t-statistic of the CBI indicator becomes
most negative (i.e. significant). This is probably due to the breakup of the Bretton
Woods system in 1973. Under the Bretton Woods system of fixed exchange rates,
monetary policy in most countries was determined by the fixed exchange rate target.
The t-values for most country groups also decline overtime. This is probably due
to the fact that central bank laws have converged over time and have therefore
become less capable of explaining inflation differentials.9
Finally, the significance level varies not only across time periods and the sample
of countries considered, but also across indicators. The Alesina indicator is highly
significant in OECD countries in all periods, while for the same country group
the TOR is never significant; the Bade–Parkin indicator is only significant in
the period 1970–1979. For developing countries, the TOR indicates a significant
negative relation in all periods, while the legal CBI indicators of Cukierman (1992)
and Grilli et al. (1991) are insignificant in all periods. To sum up, the significance
of the CBI indicator does not depend only on the time period or country sample,


1960–1969
1970–1979
1980–1989
1990–1999

1.052
−0.606
0.019
0.234

−0.734
−2.391
−1.767
−1.552
0.736
−0.921
−0.297
−0.082

OECD
−1.897
−3.554
−2.930
−2.715

LDCs
−0.523
−2.181
−1.556
−1.341

Transition

Turnover rate

−1.345
−3.002
−2.378
−2.163

OECD
0.264
−1.393
−0.769
−0.554

LDCs

0.638
−1.019
−0.395
−0.180

Transition

Grilli et al. (1991)

The numbers in bold represent a significant relation between inflation and CBI at a 10% significance level.

Period
Period
Period
Period

LDCs

OECD

Cukierman

CBI indicator used

Table 1. Detailed Distribution of the t-Statistics.

−2.261
−3.918
−3.294
−3.078

Alesina
OECD

−0.529
−2.186
−1.562
−1.347

Bade and Parkin
OECD

599

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KLOMP AND DE HAAN

but also on the indicator used. The significance levels differ much across indicators,
when we hold the time period and country group fixed.
However, before we come to any conclusion with regard to the existence of a
negative relation between CBI and inflation, we first have to analyse whether there
is a so-called ‘publication bias’ (i.e. journals only publish papers with particular
results).
4. Meta-regression Analysis: Approach

The key research issues are whether there is a publication bias in research on the
link between CBI and inflation, and whether a meaningful CBI effect remains after
a publication bias is filtered out. Drawing heavily on Doucouliagos and Stanley
(2009), we can explain a typical meta-regression model as follows:
K

effecti = β1 + β0 SEi +

αk Z jk + ei

(2)

k=1

where effecti is the focus of the analysis (in our case, the effect of CBI on inflation),
SEi is the standard error of the estimated effect, Zjk is a vector of meta-independent
variables reflecting differences across studies, α k is the meta-regression coefficient
which reflects the effect of particular study characteristics and ei denotes the metaregression disturbance term. Without publication bias, the observed effects should
vary randomly around the ‘true’ value, β 1 , independently of the standard error. The
term β 0 SEi allows for the very common tendency of researchers and reviewers to
prefer statistically significant results and for researchers therefore to rerun their
analysis until they find such significance (Doucouliagos and Stanley, 2009). This is
especially the case for studies with only a small number of observations. To report
a significant relationship, these studies have to find a sufficiently large estimated
effect, which compensates for the large standard errors associated with the small
number of observations. If the number of observations increases indefinitely, the
standard error will approach zero and the reported effects will approach β 1 , the
‘true’ effect (Stanley, 2008; Doucouliagos and Stanley, 2009).
Studies that try to explain the same relationship usually use different sample sizes
and model specifications. Hence, the random estimation errors ei in equation (2)
are likely to be heteroscedastic. As suggested by Doucouliagos and Stanley (2009),
dividing equation (2) by SEi , i.e. a sample estimate of the standard deviation of
these meta-regression errors, gives the weighted least squares version of equation
(2):
ti = β0 + β1

1
SEi

K

αk

+
k=1

Z jk
+ ei
SEi

(3)

where ti represent the reported t-values. The conventional t-test of the intercept of
equation (3), β 0 , is a test for publication bias.
As follows from Section 2, the variation among the empirical results may be
explained by various study characteristics or model specifications, reflected in Zjk .


601

The types of design elements that we include in Zjk are as follows:
1. the CBI indicator, sample of countries, and time period used, i.e. are
differences in results related to the indicators and samples used?
2. the specification of the regression model, i.e. does the inclusion of control
variables have an effect on the reported significance of the CBI indicator, and
if so, which control variables matter?
3. characteristics of the publication, i.e. does the study focus on the relationship
between CBI and inflation? Does the publication form (journal, book or
working paper), outlet (does the journal in which the study is published have
a social science citation impact (SSCI) score?) or publication year have any
relationship with the reported results?
4. the estimation method, i.e. is there any systematic difference between crosscountry versus panel studies and does it make a difference if a study controls
for outliers and/or high-inflation observations?
In our analysis, the unit of observation is not a study, but every regression reported.
Many studies contain more than one regression, for instance, when they test for
the sensitivity of the choice of a particular CBI indicator. Since the observations
are not independent, ordinary least squares would lead to biased estimates. This
is corrected by using a hierarchical linear model, which is a particular regression
technique that is designed to take into account the hierarchical structure of the data
(Raudenbusch and Bryk, 1986). Equation (3) can be rewritten as
ti j = β00 + β1 j

1
SEi j

K

Z i jk
+
αk
+
SEi j
k=1

K

γk
k=1

V0 jk
+ ωi + u j
SEi j

(4)

The meta-independent variable is split up in two parts. One part explains the
differences between studies and estimates Zijk , while the other part explains study
differences V 0j . The ωi and uj are the error terms on estimate and study level,
respectively.10
5. Meta-regression Analysis: Results

Table 2 gives our first estimation results. About 60% of the total variance is
contributed to the variance on study level. This implies that there is dependence
within a study and that a multilevel model is the appropriate model to use. Column 1
of Table 2 shows the estimation results of the so-called funnel graph asymmetry
test (Doucouliagos and Stanley, 2009). The parameter of the inverse standard errors
is significant, which indicates that the effect of CBI on inflation is significantly
negative. However, the constant term is also significant at a 5% level, meaning
that the effect found in the CBI–inflation literature is subject to a publication
bias.
To sum up our first results, we find evidence for a genuine effect of CBI on
inflation. At the same time, we find evidence that the literature on CBI and inflation
suffers from a publication bias.

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KLOMP AND DE HAAN

Table 2. MRA Tests for Publication Bias and Genuine Empirical Effect.

t-statistic CBI coefficient
Coefficient

z-value

−1.651∗∗
−0.073∗∗

−5.67
−2.02

Fixed parameters
Constant
Inverse standard errors
Random parameters
Variance estimate level p-value
Variance study level p-value
Intra-class correlation

0.000
0.000
0.583

Diagnostic statistics
Number of observations
Number of studies
Maximum likelihood ratio p-value

356
58
0.000

∗∗ ∗

, Indicates significance at 5% and 10% level, respectively.

Next, we include variables to control for different specifications used in the
various studies examined in the MRA. As in any regression model, the estimated
coefficients in the MRA model can be biased when important explanatory variables
are omitted. Table 3 presents the definition of the control variables used in the
MRA. The first set of variables refers to the CBI indicator used in the regression
(ALES, GMT, CUK, BP, TOR, OTHER). The next variables focus on country
sample (OECD, LDCs, TRANS, MIXED) and time periods (1960, 1970, 1980,
1990).
In bivariate regressions of inflation and CBI, the impact of omitted variables
on inflation is attributed to the CBI indicator. Multivariate studies will therefore
probably report lower absolute t-statistics of the CBI indicator (BIVARIATE). In
order to examine which control variables reduce the impact of CBI on inflation,
we have constructed dummy variables for a number of commonly used control
variables.
According to Romer (1993), inflation depends on the openness of an economy.
Since the real effects of monetary policy are lower in more open economies,
governments in these countries have fewer incentives to inflate. We therefore
construct a dummy variable reflecting whether a regression takes this control
variable into account (OPEN).
As pointed out by Franzese (1999), also labour market institutions may change
the real effects of monetary policy and the anti-inflationary impact of CBI,
depending on the extent to which a particular wage bargaining system internalizes
the costs associated with excessive wage settlements. We therefore include a dummy
variable that is one if some indicator for the labour market is taken up in a regression
and zero otherwise (LABMARKT). According to various studies, the interaction


603

Table 3. Variables Used.

ALES
GMT
CUK
BP
TOR
OTHER
OECD

A dummy variable equal to 1 if the CBI indicator of Alesina is used,
0 otherwise
A dummy variable equal to 1 if the CBI indicator of Grilli et al.
(1991) is used, 0 otherwise
A dummy variable equal to 1 if the CBI indicator of Cukierman is
used, 0 otherwise
A dummy variable equal to 1 if the CBI indicator of Bade–Parkin is
used, 0 otherwise
A dummy variable equal to 1 if the TOR indicator is used, 0 otherwise
A dummy variable equal to 1 if another CBI indicator is used, 0
otherwise
A dummy variable equal to 1 if the analysed
countries, 0 otherwise
developing countries, 0 otherwise
transition countries, 0 otherwise
otherwise

countries are all OECD

1960
1970
1980
1990

A
A
A
A

the
the
the
the

BIVARIATE

A dummy variable equal to 1 if the inflation and CBI relation is
examined using bivariate regression, 0 otherwise
A dummy variable equal to 1 if openness of a country is taken into
account, 0 otherwise
A dummy variable equal to 1 if some labour market variable is taken
into account, 0 otherwise
A dummy variable equal to 1 if an interaction of the CBI indicator
with the labour market is taken into account, 0 otherwise
A dummy variable equal to 1 if the exchange rate regime is taken into
A dummy variable equal to 1 if government debt is taken into
A dummy variable equal to 1 if political stability is taken into
A dummy variable equal to 1 if income is taken into account, 0
otherwise
A dummy variable equal to 1 if an interaction of the CBI indicator
with other variables is taken into account, 0 otherwise

LDCs
TRANS
MIXED

OPEN
LABMARKT
ILABMARKT
EXCHANGE
DEBT
POLSTAB
GDP
INTER
LOGINFL

dummy
dummy
dummy
dummy

variable
variable
variable
variable

equal
equal
equal
equal

to
to
to
to

1
1
1
1

if
if
if
if

data
data
data
data

refer
refer
refer
refer

to
to
to
to

countries are all
countries are all
countries are mixed, 0
1960s,
1970s,
1980s,
1990s,

0
0
0
0

otherwise
otherwise
otherwise
otherwise

A dummy variable equal to 1 if the log of inflation is used as the
dependent variable, 0 otherwise


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KLOMP AND DE HAAN

Table 3. Continued.

OUTLIER
NUMOBS
PRIMDATA
SECDATA
PANEL
FIXEDTIME

A dummy variable equal to 1 if the author controls for outliers, 0
otherwise
A dummy variable equal to 1 if the author creates his own CBI data,
0 otherwise
A dummy variable equal to 1 if the author modified existing CBI data
of others, 0 otherwise
A dummy variable equal to 1 if the author uses panel data, 0
otherwise
A dummy variable equal to 1 if the author uses panel data with fixed
time effects, 0 otherwise (if panel data are used)

FIXEDCOUNT A dummy variable equal to 1 if the author uses panel data with fixed
country effects, 0 otherwise (if panel data are used)
OBJECT
A dummy variable equal to 1 if the inflation and CBI regression of
the study focuses on this issue, 0 otherwise
BOOK
A dummy variable equal to 1 if the study is published in a book, 0
otherwise
WORKING
A dummy variable equal to 1 if the study is a working paper, 0
otherwise
PUBYEAR
Publication year (1991 = 1, . . . , 2006 = 15)
IMPACT
SSCI score of a journal

of labour market institutions and CBI affects both the real and nominal effects of
monetary policymaking and that is why we include a dummy that is one in case
this interaction is included and zero otherwise (ILABMARKT).
Other control variables that various studies have included – generally following
Campillo and Miron (1997) – are the exchange rate regime (EXCHANGE),
government debt (DEBT), political instability (POLSTAB) and income (GDP).
Stable exchange rate regimes are often argued to reduce inflation; a fixed exchange
rate can be considered as an alternative commitment device to counter the
inflationary bias of monetary policymaking. A high debt-to-GDP ratio and a high
level of political instability are determinants of the inflation bias and are therefore
often argued to lead to higher inflation, while income is often reported to have a
negative impact on inflation. We include dummies in our MRA reflecting whether
these control variables are taken up in regressions. Finally, we take up a dummy
that is one if a regression includes an interaction (INTER) of the CBI indicator and
a control variable other than the labour market variable, and zero otherwise.
Next we add some variables referring to differences in estimation methods and
data differences. First, we add dummies for regressions using the logarithm of
inflation (LOGINFL) or that delete countries or time periods from the sample
because they are considered to be outliers (OUTLIER).11 As some countries have
extremely high inflation rates, using inflation instead of the log of inflation causes


605

these high-inflation observations to become very influential. Similarly, correcting
for outliers may affect the significance of the CBI indicator. However, the effect of
correcting for outliers may differ across country groups. Temple (1998) finds that in
his sample of OECD countries the CBI indicator of Cukierman et al. (1992) is only
significant if high-inflation countries are dropped, while De Haan and Kooi (2000)
and Sturm and De Haan (2001) report for their sample of developing countries
that the inclusion of high-inflation countries renders the coefficient of the TOR
indicator of CBI significant. We therefore include the interaction of our outlier
dummy and our dummies for country groupings.
The next variable we include in our MRA is the number of observations
as more observations are expected to lead to higher significance levels of the
CBI indicator (NUMOBS). We also test whether the t-statistic of the CBI
indicator is different if the author uses his own CBI measure (PRIMDATA) or
modifies an existing index as there may be bias if an author uses his own CBI
indicator (SECDATA). We also distinguish between various estimation methods,
differentiating between cross-country and panel models (PANEL). In the latter
category, we have dummies reflecting whether the author controls for time or
country fixed effects (FIXEDTIME, FIXEDCOUNT).
Finally, we control for publication and study differences. First, we ask whether
there are any differences between studies that only estimate the relationship between
CBI and inflation and those that have a broader perspective. We use dummies
reflecting that a study is published in a book (BOOK) or as a working paper
(WORKING) (at the time we did this research) instead of in a journal, respectively.
We also test for the effect of the SSCI score of journals as the citation impact of a
journal is often considered as a quality indicator (IMPACT). The final variable we
include in the MRA is the publication year (PUBYEAR), allowing us to analyse
differences over time in reported t-statistics.
We showed in Table 1 that the significance of the t-value of the CBI coefficient
varies across time and place; therefore in the first regression of Table 4 we control
for time periods and country sample. All control variables are divided by the
standard error of the CBI coefficient as given in equation (4).
When we include multiple variables, the inverse of the standard error no longer
represents the genuine effect of CBI on inflation. Rather, it is the combination
of all the coefficients on the variables that reflect the corrected effect of CBI on
inflation. We find that the CBI coefficient is insignificant in the 1960s, 1980s
and 1990s, while it is significant in the 1970s. We confirm the hypothesis that
the t-statistic of the CBI coefficient is significantly negative in studies including
only OECD countries. In the next column we control for the CBI indicator
used. We do not find any significant difference between the results of studies
that are caused by differences in the indicator used. So even though the various
indicators are constructed in a different way, the significance of the relationship
between CBI and inflation is not dependent on the selection of a particular CBI
indicator.
In column 3 of Table 4 we include variables for commonly used control variables
in studies on CBI and inflation. We find that inclusion of a labour market indicator

606

KLOMP AND DE HAAN

Table 4. MRA Specification on Time, Country Sample and Control Variables.

(1)

(2)

(3)

Coefficient
Fixed parameter
Constant
Inverse standard
errors
OECD
countries
Less developed
countries
(LDCs)
Transition
countries
Period
1960–1969
Period
1970–1979
Period
1980–1989
Period
1990–1999
Cukierman
indicator ×
OECD
countries
Cukierman
indicator ×
LDCs
TOR × OECD
countries
TOR × LDCs
TOR ×
transition
countries
Grilli et al.
(1991)
indicator ×
OECD
countries
Grilli et al.
(1991)
indicator ×
LDCs

z-value

Coefficient

z-value

−1.684∗∗
0.012

−6.55
0.30

−1.604∗∗
0.031

−6.11
0.62

−0.517∗∗

−2.21

−0.008

−0.19

0.001

0.03

0.265

1.04

0.225

1.36

−0.293∗∗

−4.71

−0.315∗∗

−0.004

−0.16

0.005

0.16

−0.006

−0.15

−0.038

−0.94

0.033

0.93

0.005

0.17

0.061

1.51

−0.056
−0.009

−0.90
−0.01

−0.002

−0.28

0.006

0.19


−4.85

Coefficient z-value
−1.670∗∗
−0.002

−6.58
−1.35


607

Table 4. Continued.

(1)
Coefficient

(2)
z-value

Random parameters
Variance
estimate
level p-value
Variance study
level p-value
Intra-class
correlation
Number of
observations
Number of
studies
Maximum
likelihood
ratio p-value

z-value

−0.23

−0.008
−0.292∗∗

−0.68
−2.54

−0.201

−0.89

0.24

−0.007

−2.23
−0.34
0.16
−0.70

0.49

0.004

z-value

−0.002

0.008

Coefficient

−0.409∗∗
−0.004
0.002
−0.007

Grilli et al.
(1991)
indicator ×
transition
countries
Bade and
Parkin
indicator
Alesina
indicator
Labour market
Openness
Income
Political
instability
Exchange rate
regime
Debt
Labour market
interaction
Other
interactions

Coefficient

(3)

−0.94

0.000

0.000

0.000

0.000

0.000

0.000

0.501

0.513

0.509

372

372

368

57

57

56

0.000

0.000

0.000

∗∗ ∗
, Indicates significance at 5% and 10% level, respectively. All control variables are divided by the
standard errors.


608

KLOMP AND DE HAAN

and an interaction term between the labour market indicator and the CBI indicator
influences the t-value of the CBI coefficient and makes the relationship between
inflation and CBI (more) significant. We do not find that any other variable that
is suggested by Campillo and Miron (1997) influences the significance of the
CBI coefficient. This finding therefore does not support Campillo and Miron’s
conclusion that the omission of control variables in earlier studies is behind the
fact that these older studies found a significant relationship between CBI and
inflation.
In Table 5 we add variables to control for the method of estimation and data
issues. Correcting for outliers by deleting countries or time periods from the sample
has only a significant effect in OECD countries, meaning that correcting for outliers
in OECD countries makes the relation between CBI and inflation more significant.
That the sign of the interaction of outlier correction and country group differs
between OECD and less developed countries is due to the different impact of
outliers in these country groups mentioned earlier. The use of the logarithm of
inflation instead of actual inflation as dependent variable has no effect on the
significance of the CBI coefficient. Not surprisingly, studies that estimate the
relation between inflation and CBI using a bivariate regression report a higher level
of significance of the CBI coefficient than studies that take control variables into
account. This suggests that bivariate regressions have an omitted variable bias.12
Next we check whether there exists a bias in studies using data that have been
constructed or collected by the author of the study, or in studies in which the author
has modified existing data. The estimation results do not support this hypothesis.
Also there is no significant difference when panel estimation (with period or country
fixed effects) is used instead of a cross-country estimation.
The final two columns in Table 5 show the results for various publication effects.
There is no systematic difference between studies that focus on the relationship
between CBI and inflation and those that do not. Likewise, the publication outlet
does not influence differences across studies in a systematic way. There is also no
difference between papers published in journals with different SSCI scores.
Finally, to test the joint significance of the regressors, we performed a
likelihood ratio test of a full model, which contains all independent variables
used (except for the fixed effects indicator and the impact factor score because
these reduce our sample drastically) against a baseline model with only the
significant variables (i.e. OECD, OECD∗OUTLIER, 1970, LABORMARKT,
ILABORMARKT, BIVARIATE). The results indicate that the full model does not
perform better than the model that only includes significant variables (p > 0.10).
Also we tested the model that only includes significant variables against a model
with only a constant and the inverse standard errors included. The results show that
the model with the significant variables included outperforms the model with only
the constant and the inverse standard error (p < 0.05).
Furthermore the results in Tables 4–6 have been confirmed by estimating the
regression using the random sample method. This robust method replicates
the regression 1000 times by estimating it with a changing sample of about 60% of
the total sample. The purpose of this procedure is to examine whether the regression

Fixed parameters
Constant
Bivariate regression
Number of countries
Log inflation is dependent variable
Outlier correction × OECD
Outlier correction × LDCs
Outlier correction × transition countries
Data source primary
Data source secondary
Panel regression
−1.640∗∗
0.011
−0.429∗∗
−0.001
0.040
−5.624∗∗
0.005
−0.058
0.002
−0.395
−0.002
−5.77
0.85
−2.73
−1.56
0.94
−4.52
0.25
−0.42
0.15
−1.36
−0.16

−1.694∗∗
−0.010

Coefficient

Coefficient

z-value

(2)

(1)

−4.17
−0.30

z-value
−1.854∗∗
7.937

Coefficient

(3)


Table 5. MRA Specification on Data and Estimation Methodology.

−7.28
1.01

z-value

−1.684∗∗
−0.009

Coefficient

(4)

−4.50
−0.69

z-value



609

0.000
0.000
0.520

Fixed time effect
Fixed country effect
Object of the study
Working paper
Book publication
Publication year
Impact score


Number of studies

113
22
0.000

0.000
0.000
0.580

0.27
−0.95

z-value

363
55
0.000

0.000
0.000
0.500

0.022
0.014
−0.076
−0.004

Coefficient

(3)

, Indicates significance at 5% and 10% level, respectively. All control variables are divided by the standard errors.

∗∗ ∗

363
55
0.000

Random parameters

0.009
−0.012

Coefficient

Coefficient

z-value

(2)

(1)


Table 5. Continued.

0.70
1.35
−1.25
−1.01

z-value

158
27
0.000

0.000
0.000
0.590

0.003

Coefficient

(4)

0.12

z-value

610
KLOMP AND DE HAAN


611

Table 6. MRA General to Specific Approach.

(1)
Coefficient
Fixed parameters
Constant
Outlier correction × OECD
OECD
Period 1970–79
Bivariate
Labour market
Labour market interaction

z-value

−1.412∗∗
−0.043
−0.651∗
−0.401∗∗
−0.351∗∗
−0.287∗∗
−0.025∗∗
−0.176∗∗

−6.51
−1.34
−1.85
−2.76
−4.65
−2.01
−2.09
−1.99

Random parameters

0.000
0.000
0.512

Number of studies

363
55
0.000

∗∗ ∗
, Indicates significance at, respectively, 5% and 10% level. All control variables are divided by
the standard errors.

for the total sample is similar to those for only a part of the sample (results are
available on request).
Finally, we performed a general-to-specific approach on the variables included
in this study. Stepwise we deleted the variable with the highest p-value, until
all variables were significant at a 10% significance level. The results as shown in
Table 6 confirm our previous findings. Together, the variables included have a strong
effect, as evidenced by the p-value of the likelihood ratio. So there is a genuine
effect of CBI on inflation. The results in Table 6 offer a clear interpretation of the
presence of this result. There is a negative significant effect of CBI on inflation
in OECD countries. This effect is even stronger if the researcher corrects the
sample for outliers and includes a labour market indicator and the interaction of
the labour market indicator and the CBI indicator. Inclusion of the 1970s in the
sample strengthens this negative CBI effect further.
6. Conclusions

There are various surveys on the rationale for and the consequences of delegating
monetary policy to an independent central bank; the most recent ones are from

612

KLOMP AND DE HAAN

Arnone et al. (2006) and Crow and Meade (2007). However, to the best of our
knowledge, this paper is the first to apply MRA on the vast amount of empirical
studies examining the impact of CBI on inflation.13 MRA is an effective means to
analyse the influence of, among others, alternative indicators, model specification
and sample selection.
It is widely believed that countries with a more independent central bank will,
on average, have lower levels of inflation. Our MRA corroborates the conventional
view by finding a significant ‘true effect’ of CBI on inflation, once we control
for a significant publication bias. The effect is strongest when a study focuses on
OECD countries, the period 1970–1979, considers the labour market, and when the
relation is estimated using a bivariate regression. We also find that the literature
on CBI and inflation suffers from a publication bias, i.e. the reported results are
subject to a selection effect. We do not find any significant difference between
the results of studies that are caused by differences in the indicator used. So
although the CBI indicators are constructed in a different way, the relationship
between CBI and inflation is not dependent on the selection of the CBI indicator.
Furthermore, we conclude that there is no significant difference between studies
using regressions in a cross-country setting and those using panel estimation with
fixed time and/or country effects. Also there are no significant differences between
publications in scientific journals, chapters in books or working papers. Focusing
on journal articles, there is no significant difference between high- and low-ranked
journals in terms of their SSCI score.

Acknowledgements
We thank participants at the conference ‘Does Central Bank Independence Still Matter?’
(14–15 September 2007) at Bocconi University (Milan, Italy) and the Aarhus Colloquium
for Meta-Analysis in Economics (27–30 September 2007, Sønderborg, Denmark) and two
anonymous referees for their comments on a previous version of the paper.

Notes
1. One theory underlying this view is the time inconsistency approach to monetary
policymaking. The basic message of this theory is that government suffers from an
inflationary bias and that, as a result, inflation is sub-optimal. Rogoff (1985) has
shown that when monetary policy is delegated to an independent and ‘conservative’
central banker, this inflationary bias will be reduced. Conservative means that the
central banker is more averse to inflation than the government, in the sense that
(s)he places a greater weight on price stability than the government does.
2. The only difference between the indicators of Cukierman (1992) and Cukierman
et al. (1992) is the procedure employed to aggregate the various dimensions of CBI
into one measure.
3. Still, this indicator is less than perfect, as it suffers from the limitation that central
bank governors can hold office for quite some time simply by being subservient to
political leaders (Brumm, 2000).
4. Dreher et al. (2008) have extended the sample of countries and the time period for
which TORs are available.


613

5. The empirical evidence that the financial sector is inherently inflation averse is
not compelling. Although Posen (1995) presents supportive evidence, other studies
find less or no support (De Haan and van’t Hag, 1995; Campillo and Miron, 1997;
Temple, 1998).
6. However, they also find that this result is driven by the inclusion of high-inflation
countries in the sample; excluding those countries makes the coefficients of the
CBI indicator insignificant. Also De Haan and Kooi (2000) point to the role of
high-inflation observations.
7. Examples include Abreu et al. (2005), Doucouliagos (2005), Rose and Stanley
(2005) and Nijkamp and Poot (2005).
8. In various studies, especially the older ones, X consists only of some CBI indicator.
9. We thank Alex Cukierman for this observation.
10. All regressions have been estimated with STATA using the generalized linear latent
and mixed models command with the Newton–Raphson algorithm.
11. Studies focusing on OECD countries often include a dummy for Iceland as this
country had a very high rate of inflation in the 1980s and 1990s, but also an
independent central bank.
12. However, as one referee pointed out, this finding could also reflect the fact that
bivariate regressions are older and hence focus on older time periods. In other
words, there could be a multicollinearity problem between our ‘multivariate’ variable
and the sample period dummies. To check for this we calculated the correlation
coefficients between the period dummies and the multivariate indicator. We do not
find any evidence that the period dummies are related to the bivariate regression
indicator. The correlations range between 0.05 and 0.11.
13. Although there is a possibility of reverse causality, most papers have not examined
this issue. An exception is the study by Dreher et al. (2008) who find that the
likelihood that a central bank governor will be replaced increases with high past
inflation, suggesting that the TOR is indeed endogenous.

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Political and monetary institutions and public financial policies in the
industrial countries
The case for central bank independence
Central bank strategy, credibility and independence: theory and
evidence (Chapter 20)
Measuring the independence of central banks and its effect on policy
outcomes
Why central bank independence does not cause low inflation: there
is no institutional fix for politics
How independent should a central bank be?
Institutions and macroeconomic outcomes – the empirical evidence
The anti-inflationary influence of corporatist structures and central
bank independence: the importance of the hump shaped hypothesis
Political influence on the central bank: international evidence
Declarations are not enough: financial sector sources of central bank
independence
Central bank independence in another eleven countries
Central bank independence: criteria and indices
The statistical association between central bank independence and
inflation
Central bank independence indexes in economic analysis: a
reappraisal
Central bank independence, wage bargain structure and
macroeconomic performance in OECD countries

Title

3
2
4
10
4
4
10
4
12
9
12
7

Posen (1993)
Debelle and Fischer (1994)
Jonsson (1995)
Al-Marhubi and Willett (1995)
Cukierman and Webb (1995)
Posen (1995)
Eijffinger and van Keulen (1995)
Eijffinger and Schaling (1995)
Cargill (1995)
Fujiki (1996)
Bleany (1996)

12
10

5

Number of
regressions

Cukierman et al. (1992)

De Haan and Sturm (1992)
Cukierman (1992)

Grilli et al. (1991)

Authors (year published)

Table A1. Studies on Inflation and CBI Used in the MRA.


17.2

100.0

25.0
100.0
50.0

80.0
0.0

66.7
0.0
66.7

66.7

40.0

58.3
100.0

100.0

% signif.
negative

618
KLOMP AND DE HAAN

Central bank independence, inflation and political instability
Central bank independence and inflation performance: panacea or
placebo
Central bank independence: only part of the inflation story
Comment on ‘Central bank independence: only part of the inflation
story’
Central bank independence, inflation and growth in transition
economics
Why does inflation differ across countries?
What really matters: conservativeness or independence
Central bank independence and inflation targeting: monetary policy
paradigms for the next millennium?
Measuring central bank independence: a tale of subjectivity and of
its consequence
Mixed signals: central bank independence, coordinated
wage-bargaining and European Monetary Union
Reconsidering the principal components of central bank
independence: the more the merrier?
Central bank independence: a sensitivity analysis
Central bank independence and inflation: good news and bad news
Partially independent central banks, politically responsive
governments and inflation
Central bank independence and inflation: corporatism, partisanship,
and alternative indices of central bank independence
The political economy of inflation: bargaining structure or central
bank independence?

18
6


8

24
4
3

Eijffinger et al. (1998)
Temple (1998)
Franzese (1999)

Iversen (1999)

1

Banaian et al. (1998)

4

3

Hall and Franzese (1998)

Oatley (1999)

5

29
8
12

Campillo and Miron (1997)
De Haan and Kooi (1997)
Fuhrer (1997)
Mangano (1998)

3

Heylen and Van Poeck (1996)
Eijffinger and De Haan (1996b)
Loungani and Sheets (1997)

10
8

De Haan and Siermann (1996)
Jenkins (1996)

75.0

60.0

100.0
100.0
100.0

38.9

75.0

66.7

33.3
75.0

0.0

63.6
50.0

30.0
100.0
619

Central bank independence and wage bargaining structure –
empirical evidence
The case for an independent European central bank: a comment
Institutional dimensions of coordinating wage-bargaining and
monetary policy
Inflation and central bank independence: conventional wisdom redux
Inequality, inflation and central bank independence
Does central bank independence really matter?
Decentralization and inflation: commitment, collective action or
continuity
Bureaucratic delegation and political institutions: when are
independent central banks irrelevant?
Central bank independence in transition countries
Institutional and sectoral interactions in monetary policy and
wage–price bargaining
Fiscal decentralization, central bank independence, and inflation
Inflation in developing countries: does central bank independence
matter? New evidence based on a new data set
The impact of central bank independence and union concentration on
macroeconomic performance in the presence of aggregate supply
shocks: evidence from 10 OECD countries (1971–1985)
Central bank independence, economic freedom and inflation rates
Political system transparency and monetary commitment regimes
Central bank structure, efficiency policy and macroeconomic
performance

Title
3
2
1
3
10
11
2
6
10
1
3
6
10
1
8
1

De Haan (1999)
Franzese and Hall (2000)
Brumm (2000)
Dolmas et al. (2000)
De Haan and Kooi (2000)
Treisman (2000)
Keefer and Stasavage (2000)
Maliszewski (2000)
Franzese (2001)
King and Ma (2001)
Sturm and De Haan (2001)
Chou (2001)
Banaian and Luksetich (2001)
Broz (2002)
Cecchetti and Krause (2002)

Number of
regressions

Kilponen (1999)

Authors (year published)

Table A1. Continued.


0.0
0.0
60.0

100.0

0.0
0.0

33.3
0.0

0.0

0.0
0.0
20.0
70.0

0.0
83.3

100.0

% signif.
negative

620
KLOMP AND DE HAAN

Measuring central bank independence: a latent variables approach
Capitalism, not globalism capital mobility, central bank
independence, and the political control of the economy
Inflation performance and constitutional central bank independence:
evidence from Latin America and the Caribbean
The limits of delegation: veto players, central bank independence
and the credibility of monetary policy
Social democratic corporatism, central bank independence, and
economic performance: an empirical analysis of 17 industrialized
economies, 1961–1998
Multiple hands on the wheel: empirical modelling partial delegation
and shared control of monetary policy in the open
The uses of autonomy: central bankers’ careers, institutional context
and economic performance
Central bank independence and inflation performance in transition
economies: new evidence from a primary data approach
Any link between central bank independence and inflation? Evidence
from Latin America and the Caribbean
On the relationship between central bank independence and inflation:
some more bad news
Inflation, central bank independence and the legal system
Goal-independent central banks: why politicians decide to delegate

12
1


2
1
18

Franzese (2003)
Adolph (2004)
Ilieva and Gregoriou (2004)
J´ come and V´ zquez (2008)
a
a

6
3

1

Sakamoto (2003)

Hayo and Voigt (2005)
Crowe (2008)

1

Stasavage and Keefer (2003)

1

4

Gutierrez (2003)

Bouwman et al. (2005)

2

De Haan et al. (2003)
Clark (2003)

100.0
66.7

50.0

90.0

100.0

16.7

88.9

66.7

75.0

75.0
0.0
621

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