klomp de haan-jes_2010_inflation_and_cbi

doi: 10.1111/j.1467-6419.2009.00597.x

INFLATION AND CENTRAL BANKINDEPENDENCE: A META-REGRESSION

ANALYSISJeroen Klomp

University of Groningen, The Netherlands

Jakob de Haan

University of Groningen, The NetherlandsCESifo, Munich, Germany

Abstract. Using 59 studies, we perform a meta-regression analysis of studiesexamining the relationship between inflation and central bank independence(CBI). The studies considered are very different with respect to the CBI indicatorused, the sample of countries and time periods covered, model specification,estimators used and publication outlet. We conclude that there is a significantpublication bias. However, we also find a significant genuine effect of CBIon inflation. Differences between studies are not caused by differences in CBIindicators used.

Keywords. Central bank independence; Inflation; Meta-analysis

No wonder politicians often find the Fed a hindrance. Their better selvesmay want to focus on America’s long-term prosperity, but they are far moresubject to constituents’ immediate demands. That’s inevitably reflected in theireconomic policy preferences. If the economy is expanding, they want it to expandfaster; 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 authoritiesgreater independence. It is widely believed that central banks otherwise willgive in to pressure from politicians who may be motivated by short-runelectoral considerations or may value short-run economic expansions highly whilediscounting the longer-run inflationary consequences of expansionary policies(Walsh, 2005).1 If the ability of politicians to distort monetary policy results inexcessive inflation, countries with an independent central bank should experiencelower rates of inflation. Indeed, beginning with Bade and Parkin (1988), animportant line of empirical research focusing on the relationship between centralbank independence (CBI) and inflation suggests that average inflation is negatively

Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 MainStreet, Malden, MA 02148, USA.

594 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 beencriticized by various authors, claiming that the results are sensitive with respect tothe measure of CBI used (see, for instance, Forder, 1996), the specification of themodel (see, for instance, Posen, 1995; Campillo and Miron, 1997) or the inclusionof high-inflation observations (see, for instance, De Haan and Kooi, 2000).

Using meta-regression analysis (MRA), this paper addresses two issues. (1) Towhat extent has the literature confirmed that there is a negative association betweenCBI and inflation? (2) Can we explain the pattern in the results of empirical researchon the relationship between CBI and inflation? Using 59 studies we find that, onaverage, there exists a significant relation between CBI and inflation. We also findthat 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 focuson OECD countries (especially when studies control for outliers) and include the1970s. Furthermore, we find that when a bivariate regression is used or if the modelincludes the labour market the significance of the CBI indicator increases. We donot find significant differences between studies based on a cross-country settingand those that use panel models. Differences between studies are also not causedby differences in CBI indicators used.

The remainder of the paper is organized as follows. Section 2 reviews the mainissues in the empirical research on the relationship between CBI and inflation.Section 3 outlines the methodology of the MRA and the studies used in ouranalysis, 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 needsan indicator of the extent to which the monetary authorities are independent frompoliticians. Most empirical studies use either an indicator based on central banklaws 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) andCukierman et al. (1992),2 although alternative measures have been developed byAlesina (1988) and Grilli et al. (1991) among others (see Arnone et al. (2006) for anextensive comparison of the various CBI indicators). Even though these indicatorsare supposed to measure the same phenomenon and are all based on interpretationsof 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 thecentral bank board rather than by the government, is not subject to dismissal, andhas a long term of office. Second, the level of independence is higher the greaterthe extent to which policy decisions are made without government involvement.Third, a central bank is more independent if its charter states that price stabilityis the sole or primary goal of monetary policy. Fourth, independence is greater ifthere are limitations on the government’s ability to borrow from the central bank.Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd

INFLATION AND CENTRAL BANK INDEPENDENCE 595

Legal measures of CBI may not reflect the true relationship between the centralbank and the government. Especially in countries where the rule of law is lessstrongly embedded in the political culture, there can be wide gaps between theformal, 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 governormay therefore be a better proxy for CBI for these countries than measures based oncentral bank laws. The TOR is based on the presumption that, at least above somethreshold, a higher turnover of central bank governors indicates a lower level ofindependence.3 According to Cukierman’s data, TOR values range from a minimumof 0.03 (which corresponds to an average term in office for the governor of some33 years) to a maximum of 0.93 (which corresponds to an average term in officeof just 13 months). Cukierman’s data suggest that TORs in developing countriescover a much broader range of values than in OECD countries, where values areall below 0.20 turnovers per year.4

The next step is to employ these indicators in a particular model forinflation 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 bivariateregressions for particular periods, reported that CBI was negatively correlated withaverage inflation (see, for instance, Alesina and Summers, 1993). The estimatedeffect of independence on inflation turned out to be significant – in both astatistical and economic sense – especially during periods with flexible exchangerates.

While researchers found that legal CBI indicators were negatively associatedwith inflation among industrial countries, this was not the case for developingcountries. However, initial findings suggested that in these economies the TOR ofcentral bank governors is positively correlated with inflation, therefore also lendingsupport to the hypothesis that CBI and inflation are negatively related. Countriesthat experienced rapid turnover among their central bank heads (i.e. countries witha low level of CBI) also tended to experience high rates of inflation (see, forinstance, Cukierman, 1992). This is a case, however, in which causality is difficultto evaluate: Is inflation high because of political interference that leads to rapidturnover of central bank officials? Or are central bank officials tossed out becausethey cannot keep inflation down? (Walsh, 2005).

The empirical work attributing low inflation to CBI has been criticized alongvarious dimensions. First, some studies suggest that the results for the relationshipbetween CBI and inflation may differ across estimation periods. However, onewould expect a different impact of CBI on inflation under fixed and under floatingexchange rate regimes. Under the Bretton Woods system of fixed exchange rates,countries were committed to an exchange rate target and had little room to conductan autonomous domestic monetary policy. Thus, the relation between CBI andinflation is likely to be much less straightforward before 1973. Indeed, Jonsson(1995) concludes that CBI has the strongest impact on inflation under floatingexchange rates.Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


Second, studies on the relationship between CBI and inflation often fail tocontrol adequately for other factors that might account for cross-country differencesin inflation. Countries with independent central banks may differ in ways thatare systematically related to average inflation. A good example of this line ofcritique is the work by Posen (1993, 1995) who argues that both low inflationand CBI reflect the presence of a strong financial sector constituency for lowinflation. Average inflation and the degree of CBI are jointly determined bythe strength of political constituencies opposed to inflation. Posen argues thatonce these constituencies are taken into account, the coefficient of CBI is nolonger significant in models explaining cross-country inflation differentials.5 AlsoCampillo and Miron (1997) claim little role for legal CBI when control variablesrelating to the degree of openness, political instability and a country’s inflation anddebt history are introduced. However, this result has been criticized as Campillo andMiron’s sample includes many developing countries for which legal CBI indicatorsmay not be appropriate. Sturm and De Haan (2001) use TORs in a similar modelas Campillo and Miron and conclude that the coefficient of this CBI indicator issignificant in a multivariate model.6

A recent strand of literature argues that the effects of CBI should not be analysedindependently of labour market institutions. Trade unions may, for instance, beinflation averse. The reason usually given is consistency: unions encompass mostof society, which in its majority is inflation averse, at least according to the standardRogoff (1985) model of monetary policy. Inflation-averse unions will make realvariables in equilibrium a function of the institutional set-up like the degree ofcentral bank conservatism given a certain degree of CBI. The more conservativethe central bank, the lower output will be and the higher the level of unemploymentin equilibrium. In that sense, monetary policy has real effects in these models. Alsothe effects of CBI on inflation will be different in this setting compared to thestandard 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 findthat the inflation reducing impact of CBI is stronger at intermediate levels of unioncentralization.

Finally, a few studies have sounded a warning that conclusions on the relationshipbetween CBI and inflation are highly sensitive to influential observations. Forinstance, Temple (1998) finds that if high-inflation countries are added to his sampleof OECD and developing countries, the effect of CBI (proxied by Cukierman’s(1992) legal index) on inflation disappears, while De Haan and Kooi (2000) andSturm and De Haan (2001) report that the TOR indicator only becomes significantif high-inflation countries are included in the sample.

3. Studies on CBI and Inflation

MRA has become an increasingly popular instrument in economics to examineparticular fields of research, especially if there are many alternative specificationsleading to diverging conclusions.7 As Stanley and Jarrell (1989, p. 300) put it:Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


Figure 1. Studies Included in Our MRA.

MRA not only recognizes the specification problem but also attempts to estimateits effects by modelling variations in selected econometric specifications. MRAprovides us with the means to analyze, estimate, and discount, when appropriate,the influence of alternative model specification and specification searches. In thisway, we can more accurately estimate the empirical magnitudes of the underlyingeconomic phenomena and enhance our understanding of why they vary acrossthe published literature.

The issue of CBI lends itself perfectly for such an analysis. However, to the bestof our knowledge, such an analysis has not been done so far. We have gathered 59studies that come up with empirical estimates of the effect of CBI on inflation ina cross-country and/or panel setting, using some proxy for CBI. That means thatcountry-specific studies are excluded from the analysis. We started our search forstudies with the surveys of Eijffinger and De Haan (1996a) and the update thereofin Berger et al. (2001). To find more recent (published and unpublished) studieswe used Google and JSTOR. Table A1 in the Appendix contains all the studies weidentified. We stopped searching at 31 December 2006. Table A1 also shows foreach of these studies the percentage of regressions in which there is a significantnegative relationship between inflation and CBI. We have coded all studies includedin our analysis independently; whenever we coded studies differently initially, thesedifferences 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 workingpapers. It follows from Figure 1 that the average number of studies per year isaround three in the 1990s and increases to five at the end of our sample period.Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


The average sample size is about 91 observations, while on average about 28countries are included. Most studies examine the effect of CBI on inflation byestimating (variants of) a single-equation model without extensively testing for therobustness of the results. Although most studies report a negative relation betweenCBI 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 theclassical regression model. As we are primarily interested in the relevance of CBIin explaining inflation, we focus on the estimated t-statistic of the coefficient ofthe CBI indicator. This also forgoes the problem that the coefficients of the variousindicators are not comparable, as their scaling differs. If the TOR is used as a CBIindicator, we multiply the reported t-statistic by −1 so that it becomes comparablewith studies using legal CBI indicators.

Table 1 shows the distribution of the t-statistics across time period, countrysample, and indicator used. The average t-statistic of the CBI indicator of allregressions in our sample is −1.78 if we take all estimates independently. Whenwe account for study differences, the average t-statistic increases to −1.85. Bothaverages indicate that the relation between inflation and CBI is significantlynegative at the 5% significance level.

We can draw some stylized facts from Table 1. First, in OECD countries theaverage 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 notdifferentiate between CBI indicators used, these results suggest that the relationbetween inflation and CBI is significant in OECD countries and insignificant indeveloping countries.

Second, in the period 1970–1979 the t-statistic of the CBI indicator becomesmost negative (i.e. significant). This is probably due to the breakup of the BrettonWoods 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 dueto the fact that central bank laws have converged over time and have thereforebecome less capable of explaining inflation differentials.9

Finally, the significance level varies not only across time periods and the sampleof countries considered, but also across indicators. The Alesina indicator is highlysignificant in OECD countries in all periods, while for the same country groupthe TOR is never significant; the Bade–Parkin indicator is only significant inthe period 1970–1979. For developing countries, the TOR indicates a significantnegative 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 significanceof the CBI indicator does not depend only on the time period or country sample,Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


Tabl

e1.

Det

aile

dD

istr

ibut

ion

ofth

et-

Stat

istic

s.

CB

Iin

dica

tor

used

Cuk

ierm

anT

urno

ver

rate

Gri

lliet

al.

(199

1)A

lesi

naB

ade

and

Park

in

OE

CD

LD

Cs

OE

CD

LD

Cs

Tra

nsiti

onO

EC

DL

DC

sT

rans

ition

OE

CD

OE

CD

Peri

od19

60–1

969

−0.7

341.

052

0.73

6−1

.897

−0.5

23−1

.345

0.26

40.

638

−2.2

61−0

.529

Peri

od19

70–1

979

−2.3

91−0

.606

−0.9

21−3

.554

−2.1

81−3

.002

−1.3

93−1

.019

−3.9

18−2

.186

Peri

od19

80–1

989

−1.7

670.

019

−0.2

97−2

.930

−1.5

56−2

.378

−0.7

69−0

.395

−3.2

94−1

.562

Peri

od19

90–1

999

−1.5

520.

234

−0.0

82−2

.715

−1.3

41−2

.163

−0.5

54−0

.180

−3.0

78−1

.347

The

num

bers

inbo

ldre

pres

ent

asi

gnif

ican

tre

latio

nbe

twee

nin

flat

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and

CB

Iat

a10

%si

gnif

ican

cele

vel.

Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


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 anegative relation between CBI and inflation, we first have to analyse whether thereis a so-called ‘publication bias’ (i.e. journals only publish papers with particularresults).

4. Meta-regression Analysis: Approach

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

effecti = β1 + β0SEi +K∑

k=1

αk Z jk + ei (2)

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-independentvariables reflecting differences across studies, αk is the meta-regression coefficientwhich reflects the effect of particular study characteristics and ei denotes the meta-regression disturbance term. Without publication bias, the observed effects shouldvary randomly around the ‘true’ value, β1, independently of the standard error. Theterm β0SEi allows for the very common tendency of researchers and reviewers toprefer statistically significant results and for researchers therefore to rerun theiranalysis until they find such significance (Doucouliagos and Stanley, 2009). This isespecially the case for studies with only a small number of observations. To reporta significant relationship, these studies have to find a sufficiently large estimatedeffect, which compensates for the large standard errors associated with the smallnumber of observations. If the number of observations increases indefinitely, thestandard error will approach zero and the reported effects will approach β1, the‘true’ effect (Stanley, 2008; Doucouliagos and Stanley, 2009).

Studies that try to explain the same relationship usually use different sample sizesand 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 ofthese meta-regression errors, gives the weighted least squares version of equation(2):

ti = β0 + β1

(1

SEi

)+

K∑k=1

αkZ jk

SEi+ ei (3)

where ti represent the reported t-values. The conventional t-test of the intercept ofequation (3), β0, is a test for publication bias.

As follows from Section 2, the variation among the empirical results may beexplained by various study characteristics or model specifications, reflected in Zjk.



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. aredifferences in results related to the indicators and samples used?

2. the specification of the regression model, i.e. does the inclusion of controlvariables have an effect on the reported significance of the CBI indicator, andif so, which control variables matter?

3. characteristics of the publication, i.e. does the study focus on the relationshipbetween CBI and inflation? Does the publication form (journal, book orworking paper), outlet (does the journal in which the study is published havea social science citation impact (SSCI) score?) or publication year have anyrelationship with the reported results?

4. the estimation method, i.e. is there any systematic difference between cross-country versus panel studies and does it make a difference if a study controlsfor 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 forthe sensitivity of the choice of a particular CBI indicator. Since the observationsare not independent, ordinary least squares would lead to biased estimates. Thisis corrected by using a hierarchical linear model, which is a particular regressiontechnique 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∑k=1

αkZi jk

SEi j+

K∑k=1

γkV0 jk

SEi j+ ωi + u j (4)

The meta-independent variable is split up in two parts. One part explains thedifferences between studies and estimates Zijk, while the other part explains studydifferences V0j. 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 iscontributed to the variance on study level. This implies that there is dependencewithin a study and that a multilevel model is the appropriate model to use. Column 1of Table 2 shows the estimation results of the so-called funnel graph asymmetrytest (Doucouliagos and Stanley, 2009). The parameter of the inverse standard errorsis significant, which indicates that the effect of CBI on inflation is significantlynegative. However, the constant term is also significant at a 5% level, meaningthat the effect found in the CBI–inflation literature is subject to a publicationbias.

To sum up our first results, we find evidence for a genuine effect of CBI oninflation. At the same time, we find evidence that the literature on CBI and inflationsuffers from a publication bias.Journal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


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

t-statistic CBI coefficient

Coefficient z-value

Fixed parametersConstant −1.651∗∗ −5.67Inverse standard errors −0.073∗∗ −2.02

Random parametersVariance estimate level p-value 0.000Variance study level p-value 0.000Intra-class correlation 0.583

Diagnostic statisticsNumber of observations 356Number of studies 58Maximum likelihood ratio p-value 0.000

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

Next, we include variables to control for different specifications used in thevarious studies examined in the MRA. As in any regression model, the estimatedcoefficients in the MRA model can be biased when important explanatory variablesare omitted. Table 3 presents the definition of the control variables used in theMRA. 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 countrysample (OECD, LDCs, TRANS, MIXED) and time periods (1960, 1970, 1980,1990).

In bivariate regressions of inflation and CBI, the impact of omitted variableson inflation is attributed to the CBI indicator. Multivariate studies will thereforeprobably report lower absolute t-statistics of the CBI indicator (BIVARIATE). Inorder to examine which control variables reduce the impact of CBI on inflation,we have constructed dummy variables for a number of commonly used controlvariables.

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 thereforeconstruct a dummy variable reflecting whether a regression takes this controlvariable into account (OPEN).

As pointed out by Franzese (1999), also labour market institutions may changethe real effects of monetary policy and the anti-inflationary impact of CBI,depending on the extent to which a particular wage bargaining system internalizesthe costs associated with excessive wage settlements. We therefore include a dummyvariable that is one if some indicator for the labour market is taken up in a regressionand zero otherwise (LABMARKT). According to various studies, the interactionJournal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


Table 3. Variables Used.

ALES A dummy variable equal to 1 if the CBI indicator of Alesina is used,0 otherwise

GMT A dummy variable equal to 1 if the CBI indicator of Grilli et al.(1991) is used, 0 otherwise

CUK A dummy variable equal to 1 if the CBI indicator of Cukierman isused, 0 otherwise

BP A dummy variable equal to 1 if the CBI indicator of Bade–Parkin isused, 0 otherwise

TOR A dummy variable equal to 1 if the TOR indicator is used, 0 otherwiseOTHER A dummy variable equal to 1 if another CBI indicator is used, 0

otherwise

OECD A dummy variable equal to 1 if the analysed countries are all OECDcountries, 0 otherwise

LDCs A dummy variable equal to 1 if the analysed countries are alldeveloping countries, 0 otherwise

TRANS A dummy variable equal to 1 if the analysed countries are alltransition countries, 0 otherwise

MIXED A dummy variable equal to 1 if the analysed countries are mixed, 0otherwise

1960 A dummy variable equal to 1 if data refer to the 1960s, 0 otherwise1970 A dummy variable equal to 1 if data refer to the 1970s, 0 otherwise1980 A dummy variable equal to 1 if data refer to the 1980s, 0 otherwise1990 A dummy variable equal to 1 if data refer to the 1990s, 0 otherwise

BIVARIATE A dummy variable equal to 1 if the inflation and CBI relation isexamined using bivariate regression, 0 otherwise

OPEN A dummy variable equal to 1 if openness of a country is taken intoaccount, 0 otherwise

LABMARKT A dummy variable equal to 1 if some labour market variable is takeninto account, 0 otherwise

ILABMARKT A dummy variable equal to 1 if an interaction of the CBI indicatorwith the labour market is taken into account, 0 otherwise

EXCHANGE A dummy variable equal to 1 if the exchange rate regime is taken intoaccount, 0 otherwise

DEBT A dummy variable equal to 1 if government debt is taken intoaccount, 0 otherwise

POLSTAB A dummy variable equal to 1 if political stability is taken intoaccount, 0 otherwise

GDP A dummy variable equal to 1 if income is taken into account, 0otherwise

INTER A dummy variable equal to 1 if an interaction of the CBI indicatorwith other variables is taken into account, 0 otherwise

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



Table 3. Continued.

OUTLIER A dummy variable equal to 1 if the author controls for outliers, 0otherwise

NUMOBS Number of observationsPRIMDATA A dummy variable equal to 1 if the author creates his own CBI data,

0 otherwiseSECDATA A dummy variable equal to 1 if the author modified existing CBI data

of others, 0 otherwisePANEL A dummy variable equal to 1 if the author uses panel data, 0

otherwiseFIXEDTIME 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 fixedcountry effects, 0 otherwise (if panel data are used)

OBJECT A dummy variable equal to 1 if the inflation and CBI regression ofthe study focuses on this issue, 0 otherwise

BOOK A dummy variable equal to 1 if the study is published in a book, 0otherwise

WORKING A dummy variable equal to 1 if the study is a working paper, 0otherwise

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 ofmonetary policymaking and that is why we include a dummy that is one in casethis interaction is included and zero otherwise (ILABMARKT).

Other control variables that various studies have included – generally followingCampillo 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 exchangerate can be considered as an alternative commitment device to counter theinflationary bias of monetary policymaking. A high debt-to-GDP ratio and a highlevel of political instability are determinants of the inflation bias and are thereforeoften argued to lead to higher inflation, while income is often reported to have anegative impact on inflation. We include dummies in our MRA reflecting whetherthese control variables are taken up in regressions. Finally, we take up a dummythat is one if a regression includes an interaction (INTER) of the CBI indicator anda control variable other than the labour market variable, and zero otherwise.

Next we add some variables referring to differences in estimation methods anddata differences. First, we add dummies for regressions using the logarithm ofinflation (LOGINFL) or that delete countries or time periods from the samplebecause they are considered to be outliers (OUTLIER).11 As some countries haveextremely high inflation rates, using inflation instead of the log of inflation causesJournal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


these high-inflation observations to become very influential. Similarly, correctingfor outliers may affect the significance of the CBI indicator. However, the effect ofcorrecting for outliers may differ across country groups. Temple (1998) finds that inhis sample of OECD countries the CBI indicator of Cukierman et al. (1992) is onlysignificant if high-inflation countries are dropped, while De Haan and Kooi (2000)and Sturm and De Haan (2001) report for their sample of developing countriesthat the inclusion of high-inflation countries renders the coefficient of the TORindicator of CBI significant. We therefore include the interaction of our outlierdummy and our dummies for country groupings.

The next variable we include in our MRA is the number of observationsas more observations are expected to lead to higher significance levels of theCBI indicator (NUMOBS). We also test whether the t-statistic of the CBIindicator is different if the author uses his own CBI measure (PRIMDATA) ormodifies an existing index as there may be bias if an author uses his own CBIindicator (SECDATA). We also distinguish between various estimation methods,differentiating between cross-country and panel models (PANEL). In the lattercategory, we have dummies reflecting whether the author controls for time orcountry fixed effects (FIXEDTIME, FIXEDCOUNT).

Finally, we control for publication and study differences. First, we ask whetherthere are any differences between studies that only estimate the relationship betweenCBI and inflation and those that have a broader perspective. We use dummiesreflecting 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 ajournal is often considered as a quality indicator (IMPACT). The final variable weinclude in the MRA is the publication year (PUBYEAR), allowing us to analysedifferences over time in reported t-statistics.

We showed in Table 1 that the significance of the t-value of the CBI coefficientvaries across time and place; therefore in the first regression of Table 4 we controlfor time periods and country sample. All control variables are divided by thestandard error of the CBI coefficient as given in equation (4).

When we include multiple variables, the inverse of the standard error no longerrepresents the genuine effect of CBI on inflation. Rather, it is the combinationof all the coefficients on the variables that reflect the corrected effect of CBI oninflation. We find that the CBI coefficient is insignificant in the 1960s, 1980sand 1990s, while it is significant in the 1970s. We confirm the hypothesis thatthe t-statistic of the CBI coefficient is significantly negative in studies includingonly OECD countries. In the next column we control for the CBI indicatorused. We do not find any significant difference between the results of studiesthat are caused by differences in the indicator used. So even though the variousindicators are constructed in a different way, the significance of the relationshipbetween CBI and inflation is not dependent on the selection of a particular CBIindicator.

In column 3 of Table 4 we include variables for commonly used control variablesin studies on CBI and inflation. We find that inclusion of a labour market indicatorJournal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


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


(1) (2) (3)

Coefficient z-value Coefficient z-value Coefficient z-value

Fixed parameterConstant −1.684∗∗ −6.55 −1.604∗∗ −6.11 −1.670∗∗ −6.58Inverse standard

errors0.012 0.30 0.031 0.62 −0.002 −1.35

OECDcountries

−0.517∗∗ −2.21

Less developedcountries(LDCs)

−0.008 −0.19

Transitioncountries

0.001 0.03

Period1960–1969

0.265 1.04 0.225 1.36

Period1970–1979

−0.293∗∗ −4.71 −0.315∗∗ −4.85

Period1980–1989

−0.004 −0.16 0.005 0.16

Period1990–1999

−0.006 −0.15 −0.038 −0.94

Cukiermanindicator ×OECDcountries

0.033 0.93

Cukiermanindicator ×LDCs

0.005 0.17

TOR × OECDcountries

0.061 1.51

TOR × LDCs −0.056 −0.90TOR ×

transitioncountries

−0.009 −0.01

Grilli et al.(1991)indicator ×OECDcountries

−0.002 −0.28

Grilli et al.(1991)indicator ×LDCs

0.006 0.19



Table 4. Continued.


(1) (2) (3)

Coefficient z-value Coefficient z-value Coefficient z-value

Grilli et al.(1991)indicator ×transitioncountries

0.008 0.49

Bade andParkinindicator

0.004 0.24

Alesinaindicator

−0.007 −0.94

Labour market −0.409∗∗ −2.23Openness −0.004 −0.34Income 0.002 0.16Political

instability−0.007 −0.70

Exchange rateregime

−0.002 −0.23

Debt −0.008 −0.68Labour market

interaction−0.292∗∗ −2.54

Otherinteractions

−0.201 −0.89

Random parametersVariance

estimatelevel p-value

0.000 0.000 0.000

Variance studylevel p-value

0.000 0.000 0.000

Intra-classcorrelation

0.501 0.513 0.509

Diagnostic statisticsNumber of

observations372 372 368

Number ofstudies

57 57 56

Maximumlikelihoodratio p-value

0.000 0.000 0.000

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



and an interaction term between the labour market indicator and the CBI indicatorinfluences the t-value of the CBI coefficient and makes the relationship betweeninflation and CBI (more) significant. We do not find that any other variable thatis suggested by Campillo and Miron (1997) influences the significance of theCBI coefficient. This finding therefore does not support Campillo and Miron’sconclusion that the omission of control variables in earlier studies is behind thefact that these older studies found a significant relationship between CBI andinflation.

In Table 5 we add variables to control for the method of estimation and dataissues. Correcting for outliers by deleting countries or time periods from the samplehas only a significant effect in OECD countries, meaning that correcting for outliersin OECD countries makes the relation between CBI and inflation more significant.That the sign of the interaction of outlier correction and country group differsbetween OECD and less developed countries is due to the different impact ofoutliers in these country groups mentioned earlier. The use of the logarithm ofinflation instead of actual inflation as dependent variable has no effect on thesignificance of the CBI coefficient. Not surprisingly, studies that estimate therelation between inflation and CBI using a bivariate regression report a higher levelof significance of the CBI coefficient than studies that take control variables intoaccount. 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 beenconstructed or collected by the author of the study, or in studies in which the authorhas modified existing data. The estimation results do not support this hypothesis.Also there is no significant difference when panel estimation (with period or countryfixed 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 relationshipbetween CBI and inflation and those that do not. Likewise, the publication outletdoes not influence differences across studies in a systematic way. There is also nodifference between papers published in journals with different SSCI scores.

Finally, to test the joint significance of the regressors, we performed alikelihood ratio test of a full model, which contains all independent variablesused (except for the fixed effects indicator and the impact factor score becausethese reduce our sample drastically) against a baseline model with only thesignificant variables (i.e. OECD, OECD∗OUTLIER, 1970, LABORMARKT,ILABORMARKT, BIVARIATE). The results indicate that the full model does notperform better than the model that only includes significant variables (p > 0.10).Also we tested the model that only includes significant variables against a modelwith only a constant and the inverse standard errors included. The results show thatthe model with the significant variables included outperforms the model with onlythe constant and the inverse standard error (p < 0.05).

Furthermore the results in Tables 4–6 have been confirmed by estimating theregression using the random sample method. This robust method replicatesthe regression 1000 times by estimating it with a changing sample of about 60% ofthe total sample. The purpose of this procedure is to examine whether the regressionJournal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


Tabl

e5.

MR

ASp

ecif

icat

ion

onD

ata

and

Est

imat

ion

Met

hodo

logy

.

t-st

atis

ticC

BI

coef

fici

ent

(1)

(2)

(3)

(4)

Coe

ffic

ient

z-va

lue

Coe

ffic

ient

z-va

lue

Coe

ffic

ient

z-va

lue

Coe

ffic

ient

z-va

lue

Fixe

dpa

ram

eter

sC

onst

ant

−1.6

40∗∗

−5.7

7−1

.694

∗∗−4

.17

−1.8

54∗∗

−7.2

8−1

.684

∗∗−4

.50

Inve

rse

stan

dard

erro

rs0.

011

0.85

−0.0

10−0

.30

7.93

71.

01−0

.009

−0.6

9B

ivar

iate

regr

essi

on−0

.429

∗∗−2

.73

Num

ber

ofco

untr

ies

−0.0

01−1

.56

Log

infl

atio

nis

depe

nden

tva

riab

le0.

040

0.94

Out

lier

corr

ectio

n×

OE

CD

−5.6

24∗∗

−4.5

2O

utlie

rco

rrec

tion

×L

DC

s0.

005

0.25

Out

lier

corr

ectio

n×

tran

sitio

nco

untr

ies

−0.0

58−0

.42

Dat

aso

urce

prim

ary

0.00

20.

15D

ata

sour

cese

cond

ary

−0.3

95−1

.36

Pane

lre

gres

sion

−0.0

02−0

.16



Tabl

e5.

Con

tinu

ed.

t-st

atis

ticC

BI

coef

fici

ent

(1)

(2)

(3)

(4)

Coe

ffic

ient

z-va

lue

Coe

ffic

ient

z-va

lue

Coe

ffic

ient

z-va

lue

Coe

ffic

ient

z-va

lue

Fixe

dtim

eef

fect

0.00

90.

27Fi

xed

coun

try

effe

ct−0

.012

−0.9

5O

bjec

tof

the

stud

y0.

022

0.70

Wor

king

pape

r0.

014

1.35

Boo

kpu

blic

atio

n−0

.076

−1.2

5Pu

blic

atio

nye

ar−0

.004

−1.0

1Im

pact

scor

e0.

003

0.12

Ran

dom

para

met

ers

Var

ianc

ees

timat

ele

vel

p-va

lue

0.00

00.

000

0.00

00.

000

Var

ianc

est

udy

leve

lp-

valu

e0.

000

0.00

00.

000

0.00

0In

tra-

clas

sco

rrel

atio

n0.

520

0.58

00.

500

0.59

0

Dia

gnos

ticst

atis

tics

Num

ber

ofob

serv

atio

ns36

311

336

315

8N

umbe

rof

stud

ies

5522

5527

Max

imum

likel

ihoo

dra

tiop-

valu

e0.

000

0.00

00.

000

0.00

0

∗∗,

∗ Ind

icat

essi

gnif

ican

ceat

5%an

d10

%le

vel,

resp

ectiv

ely.

All

cont

rol

vari

able

sar

edi

vide

dby

the

stan

dard

erro

rs.



Table 6. MRA General to Specific Approach.


(1)

Coefficient z-value

Fixed parametersConstant −1.412∗∗ −6.51Inverse standard errors −0.043 −1.34Outlier correction × OECD −0.651∗ −1.85OECD −0.401∗∗ −2.76Period 1970–79 −0.351∗∗ −4.65Bivariate −0.287∗∗ −2.01Labour market −0.025∗∗ −2.09Labour market interaction −0.176∗∗ −1.99

Random parametersVariance estimate level p-value 0.000Variance study level p-value 0.000Intra-class correlation 0.512

Diagnostic statisticsNumber of observations 363Number of studies 55Maximum likelihood ratio p-value 0.000

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

for the total sample is similar to those for only a part of the sample (results areavailable on request).

Finally, we performed a general-to-specific approach on the variables includedin this study. Stepwise we deleted the variable with the highest p-value, untilall variables were significant at a 10% significance level. The results as shown inTable 6 confirm our previous findings. Together, the variables included have a strongeffect, as evidenced by the p-value of the likelihood ratio. So there is a genuineeffect of CBI on inflation. The results in Table 6 offer a clear interpretation of thepresence of this result. There is a negative significant effect of CBI on inflationin OECD countries. This effect is even stronger if the researcher corrects thesample for outliers and includes a labour market indicator and the interaction ofthe labour market indicator and the CBI indicator. Inclusion of the 1970s in thesample strengthens this negative CBI effect further.

6. Conclusions

There are various surveys on the rationale for and the consequences of delegatingmonetary policy to an independent central bank; the most recent ones are fromJournal of Economic Surveys (2010) Vol. 24, No. 4, pp. 593–621C© 2009 Blackwell Publishing Ltd


Arnone et al. (2006) and Crow and Meade (2007). However, to the best of ourknowledge, this paper is the first to apply MRA on the vast amount of empiricalstudies examining the impact of CBI on inflation.13 MRA is an effective means toanalyse the influence of, among others, alternative indicators, model specificationand 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 conventionalview by finding a significant ‘true effect’ of CBI on inflation, once we controlfor a significant publication bias. The effect is strongest when a study focuses onOECD countries, the period 1970–1979, considers the labour market, and when therelation is estimated using a bivariate regression. We also find that the literatureon CBI and inflation suffers from a publication bias, i.e. the reported results aresubject to a selection effect. We do not find any significant difference betweenthe results of studies that are caused by differences in the indicator used. Soalthough the CBI indicators are constructed in a different way, the relationshipbetween CBI and inflation is not dependent on the selection of the CBI indicator.Furthermore, we conclude that there is no significant difference between studiesusing regressions in a cross-country setting and those using panel estimation withfixed time and/or country effects. Also there are no significant differences betweenpublications in scientific journals, chapters in books or working papers. Focusingon journal articles, there is no significant difference between high- and low-rankedjournals 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 Colloquiumfor Meta-Analysis in Economics (27–30 September 2007, Sønderborg, Denmark) and twoanonymous referees for their comments on a previous version of the paper.

Notes

1. One theory underlying this view is the time inconsistency approach to monetarypolicymaking. The basic message of this theory is that government suffers from aninflationary bias and that, as a result, inflation is sub-optimal. Rogoff (1985) hasshown that when monetary policy is delegated to an independent and ‘conservative’central banker, this inflationary bias will be reduced. Conservative means that thecentral 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 Cukiermanet al. (1992) is the procedure employed to aggregate the various dimensions of CBIinto one measure.

3. Still, this indicator is less than perfect, as it suffers from the limitation that centralbank governors can hold office for quite some time simply by being subservient topolitical leaders (Brumm, 2000).

4. Dreher et al. (2008) have extended the sample of countries and the time period forwhich TORs are available.



5. The empirical evidence that the financial sector is inherently inflation averse isnot compelling. Although Posen (1995) presents supportive evidence, other studiesfind 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-inflationcountries in the sample; excluding those countries makes the coefficients of theCBI indicator insignificant. Also De Haan and Kooi (2000) point to the role ofhigh-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 latentand mixed models command with the Newton–Raphson algorithm.

11. Studies focusing on OECD countries often include a dummy for Iceland as thiscountry had a very high rate of inflation in the 1980s and 1990s, but also anindependent central bank.

12. However, as one referee pointed out, this finding could also reflect the fact thatbivariate regressions are older and hence focus on older time periods. In otherwords, there could be a multicollinearity problem between our ‘multivariate’ variableand the sample period dummies. To check for this we calculated the correlationcoefficients between the period dummies and the multivariate indicator. We do notfind any evidence that the period dummies are related to the bivariate regressionindicator. The correlations range between 0.05 and 0.11.

13. Although there is a possibility of reverse causality, most papers have not examinedthis issue. An exception is the study by Dreher et al. (2008) who find that thelikelihood that a central bank governor will be replaced increases with high pastinflation, suggesting that the TOR is indeed endogenous.

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Tabl

eA

1.St

udie

son

Infl

atio

nan

dC

BI

Use

din

the

MR

A.

Num

ber

of%

sign

if.

Titl

eA

utho

rs(y

ear

publ

ishe

d)re

gres

sion

sne

gativ

e

Polit

ical

and

mon

etar

yin

stitu

tions

and

publ

icfi

nanc

ial

polic

ies

inth

ein

dust

rial

coun

trie

sG

rilli

etal

.(1

991)

510

0.0

The

case

for

cent

ral

bank

inde

pend

ence

De

Haa

nan

dSt

urm

(199

2)12

58.3

Cen

tral

bank

stra

tegy

,cr

edib

ility

and

inde

pend

ence

:th

eory

and

evid

ence

(Cha

pter

20)

Cuk

ierm

an(1

992)

1010

0.0

Mea

suri

ngth

ein

depe

nden

ceof

cent

ral

bank

san

dits

effe

cton

polic

you

tcom

esC

ukie

rman

etal

.(1

992)

340

.0

Why

cent

ral

bank

inde

pend

ence

does

not

caus

elo

win

flat

ion:

ther

eis

noin

stitu

tiona

lfi

xfo

rpo

litic

sPo

sen

(199

3)2

66.7

How

inde

pend

ent

shou

lda

cent

ral

bank

be?

Deb

elle

and

Fisc

her

(199

4)4

66.7

Inst

itutio

nsan

dm

acro

econ

omic

outc

omes

–th

eem

piri

cal

evid

ence

Jons

son

(199

5)10

0.0

The

anti-

infl

atio

nary

infl

uenc

eof

corp

orat

ist

stru

ctur

esan

dce

ntra

lba

nkin

depe

nden

ce:

the

impo

rtan

ceof

the

hum

psh

aped

hypo

thes

isA

l-M

arhu

bian

dW

illet

t(1

995)

466

.7

Polit

ical

infl

uenc

eon

the

cent

ral

bank

:in

tern

atio

nal

evid

ence

Cuk

ierm

anan

dW

ebb

(199

5)4

80.0

Dec

lara

tions

are

not

enou

gh:

fina

ncia

lse

ctor

sour

ces

ofce

ntra

lba

nkin

depe

nden

cePo

sen

(199

5)10

0.0

Cen

tral

bank

inde

pend

ence

inan

othe

rel

even

coun

trie

sE

ijffi

nger

and

van

Keu

len

(199

5)4

25.0

Cen

tral

bank

inde

pend

ence

:cr

iteri

aan

din

dice

sE

ijffi

nger

and

Scha

ling

(199

5)12

100.

0T

hest

atis

tical

asso

ciat

ion

betw

een

cent

ral

bank

inde

pend

ence

and

infl

atio

nC

argi

ll(1

995)

950

.0

Cen

tral

bank

inde

pend

ence

inde

xes

inec

onom

ican

alys

is:

are

appr

aisa

lFu

jiki

(199

6)12

100.

0

Cen

tral

bank

inde

pend

ence

,w

age

barg

ain

stru

ctur

ean

dm

acro

econ

omic

perf

orm

ance

inO

EC

Dco

untr

ies

Ble

any

(199

6)7

17.2



Cen

tral

bank

inde

pend

ence

,in

flat

ion

and

polit

ical

inst

abili

tyD

eH

aan

and

Sier

man

n(1

996)

1830

.0C

entr

alba

nkin

depe

nden

cean

din

flat

ion

perf

orm

ance

:pa

nace

aor

plac

ebo

Jenk

ins

(199

6)6

100.

0

Cen

tral

bank

inde

pend

ence

:on

lypa

rtof

the

infl

atio

nst

ory

Hey

len

and

Van

Poec

k(1

996)

1063

.6C

omm

ent

on‘C

entr

alba

nkin

depe

nden

ce:

only

part

ofth

ein

flat

ion

stor

y’E

ijffi

nger

and

De

Haa

n(1

996b

)8

50.0

Cen

tral

bank

inde

pend

ence

,in

flat

ion

and

grow

thin

tran

sitio

nec

onom

ics

Lou

ngan

ian

dSh

eets

(199

7)3

0.0

Why

does

infl

atio

ndi

ffer

acro

ssco

untr

ies?

Cam

pillo

and

Mir

on(1

997)

29W

hat

real

lym

atte

rs:

cons

erva

tiven

ess

orin

depe

nden

ceD

eH

aan

and

Koo

i(1

997)

833

.3C

entr

alba

nkin

depe

nden

cean

din

flat

ion

targ

etin

g:m

onet

ary

polic

ypa

radi

gms

for

the

next

mill

enni

um?

Fuhr

er(1

997)

1275

.0

Mea

suri

ngce

ntra

lba

nkin

depe

nden

ce:

ata

leof

subj

ectiv

ityan

dof

itsco

nseq

uenc

eM

anga

no(1

998)

566

.7

Mix

edsi

gnal

s:ce

ntra

lba

nkin

depe

nden

ce,

coor

dina

ted

wag

e-ba

rgai

ning

and

Eur

opea

nM

onet

ary

Uni

onH

all

and

Fran

zese

(199

8)3

75.0

Rec

onsi

deri

ngth

epr

inci

pal

com

pone

nts

ofce

ntra

lba

nkin

depe

nden

ce:

the

mor

eth

em

erri

er?

Ban

aian

etal

.(1

998)

138

.9

Cen

tral

bank

inde

pend

ence

:a

sens

itivi

tyan

alys

isE

ijffi

nger

etal

.(1

998)

2410

0.0

Cen

tral

bank

inde

pend

ence

and

infl

atio

n:go

odne

ws

and

bad

new

sTe

mpl

e(1

998)

410

0.0

Part

ially

inde

pend

ent

cent

ral

bank

s,po

litic

ally

resp

onsi

vego

vern

men

tsan

din

flat

ion

Fran

zese

(199

9)3

100.

0

Cen

tral

bank

inde

pend

ence

and

infl

atio

n:co

rpor

atis

m,

part

isan

ship

,an

dal

tern

ativ

ein

dice

sof

cent

ral

bank

inde

pend

ence

Oat

ley

(199

9)4

60.0

The

polit

ical

econ

omy

ofin

flat

ion:

barg

aini

ngst

ruct

ure

orce

ntra

lba

nkin

depe

nden

ce?

Iver

sen

(199

9)8

75.0



Tabl

eA

1.C

onti

nued

.

Num

ber

of%

sign

if.

Titl

eA

utho

rs(y

ear

publ

ishe

d)re

gres

sion

sne

gativ

e

Cen

tral

bank

inde

pend

ence

and

wag

eba

rgai

ning

stru

ctur

e–

empi

rica

lev

iden

ceK

ilpon

en(1

999)

310

0.0

The

case

for

anin

depe

nden

tE

urop

ean

cent

ral

bank

:a

com

men

tD

eH

aan

(199

9)2

0.0

Inst

itutio

nal

dim

ensi

ons

ofco

ordi

natin

gw

age-

barg

aini

ngan

dm

onet

ary

polic

yFr

anze

sean

dH

all

(200

0)1

83.3

Infl

atio

nan

dce

ntra

lba

nkin

depe

nden

ce:

conv

entio

nal

wis

dom

redu

xB

rum

m(2

000)

30.

0In

equa

lity,

infl

atio

nan

dce

ntra

lba

nkin

depe

nden

ceD

olm

aset

al.

(200

0)10

0.0

Doe

sce

ntra

lba

nkin

depe

nden

cere

ally

mat

ter?

De

Haa

nan

dK

ooi

(200

0)11

20.0

Dec

entr

aliz

atio

nan

din

flat

ion:

com

mitm

ent,

colle

ctiv

eac

tion

orco

ntin

uity

Tre

ism

an(2

000)

270

.0

Bur

eauc

ratic

dele

gatio

nan

dpo

litic

alin

stitu

tions

:w

hen

are

inde

pend

ent

cent

ral

bank

sir

rele

vant

?K

eefe

ran

dSt

asav

age

(200

0)6

0.0

Cen

tral

bank

inde

pend

ence

intr

ansi

tion

coun

trie

sM

alis

zew

ski

(200

0)10

33.3

Inst

itutio

nal

and

sect

oral

inte

ract

ions

inm

onet

ary

polic

yan

dw

age–

pric

eba

rgai

ning

Fran

zese

(200

1)1

0.0

Fisc

alde

cent

raliz

atio

n,ce

ntra

lba

nkin

depe

nden

ce,

and

infl

atio

nK

ing

and

Ma

(200

1)3

0.0

Infl

atio

nin

deve

lopi

ngco

untr

ies:

does

cent

ral

bank

inde

pend

ence

mat

ter?

New

evid

ence

base

don

ane

wda

tase

tSt

urm

and

De

Haa

n(2

001)

60.

0

The

impa

ctof

cent

ral

bank

inde

pend

ence

and

unio

nco

ncen

trat

ion

onm

acro

econ

omic

perf

orm

ance

inth

epr

esen

ceof

aggr

egat

esu

pply

shoc

ks:

evid

ence

from

10O

EC

Dco

untr

ies

(197

1–19

85)

Cho

u(2

001)

1010

0.0

Cen

tral

bank

inde

pend

ence

,ec

onom

icfr

eedo

man

din

flat

ion

rate

sB

anai

anan

dL

ukse

tich

(200

1)1

0.0

Polit

ical

syst

emtr

ansp

aren

cyan

dm

onet

ary

com

mitm

ent

regi

mes

Bro

z(2

002)

80.

0C

entr

alba

nkst

ruct

ure,

effi

cien

cypo

licy

and

mac

roec

onom

icpe

rfor

man

ceC

ecch

etti

and

Kra

use

(200

2)1

60.0



Mea

suri

ngce

ntra

lba

nkin

depe

nden

ce:

ala

tent

vari

able

sap

proa

chD

eH

aan

etal

.(2

003)

1275

.0C

apita

lism

,no

tgl

obal

ism

capi

tal

mob

ility

,ce

ntra

lba

nkin

depe

nden

ce,

and

the

polit

ical

cont

rol

ofth

eec

onom

yC

lark

(200

3)1

0.0

Infl

atio

npe

rfor

man

cean

dco

nstit

utio

nal

cent

ral

bank

inde

pend

ence

:ev

iden

cefr

omL

atin

Am

eric

aan

dth

eC

arib

bean

Gut

ierr

ez(2

003)

275

.0

The

limits

ofde

lega

tion:

veto

play

ers,

cent

ral

bank

inde

pend

ence

and

the

cred

ibili

tyof

mon

etar

ypo

licy

Stas

avag

ean

dK

eefe

r(2

003)

4

Soci

alde

moc

ratic

corp

orat

ism

,ce

ntra

lba

nkin

depe

nden

ce,

and

econ

omic

perf

orm

ance

:an

empi

rica

lan

alys

isof

17in

dust

rial

ized

econ

omie

s,19

61–1

998

Saka

mot

o(2

003)

166

.7

Mul

tiple

hand

son

the

whe

el:

empi

rica

lm

odel

ling

part

ial

dele

gatio

nan

dsh

ared

cont

rol

ofm

onet

ary

polic

yin

the

open

Fran

zese

(200

3)1

88.9

The

uses

ofau

tono

my:

cent

ral

bank

ers’

care

ers,

inst

itutio

nal

cont

ext

and

econ

omic

perf

orm

ance

Ado

lph

(200

4)2

16.7

Cen

tral

bank

inde

pend

ence

and

infl

atio

npe

rfor

man

cein

tran

sitio

nec

onom

ies:

new

evid

ence

from

apr

imar

yda

taap

proa

chIl

ieva

and

Gre

gori

ou(2

004)

110

0.0

Any

link

betw

een

cent

ral

bank

inde

pend

ence

and

infl

atio

n?E

vide

nce

from

Lat

inA

mer

ica

and

the

Car

ibbe

anJa

com

ean

dV

azqu

ez(2

008)

1890

.0

On

the

rela

tions

hip

betw

een

cent

ral

bank

inde

pend

ence

and

infl

atio

n:so

me

mor

eba

dne

ws

Bou

wm

anet

al.

(200

5)1

50.0

Infl

atio

n,ce

ntra

lba

nkin

depe

nden

cean

dth

ele

gal

syst

emH

ayo

and

Voi

gt(2

005)

610

0.0

Goa

l-in

depe

nden

tce

ntra

lba

nks:

why

polit

icia

nsde

cide

tode

lega

teC

row

e(2

008)

366

.7


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