Reconsidering the principal components of central bank independence

Public Choice 97: 1–12, 1998. c 1998 Kluwer Academic Publishers. Printed in The Netherlands.

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Reconsidering the principal components of central bank independence: The more the merrier? KING BANAIAN1 , RICHARD C.K. BURDEKIN2 & THOMAS D. WILLETT3 1

Department of Economics, St. Cloud State University, St. Cloud, MN 56301-4498, U.S.A.; Department of Economics, Claremont McKenna College, Claremont, CA 91711-6420, U.S.A.; 3 Department of Economics, Claremont Graduate School and Claremont McKenna College, Claremont, CA 91711-6420, U.S.A. 2

Accepted 6 March 1996 Abstract. We use principal component analysis to reassess the link between different attributes of central bank independence and inflation performance. We suggest that coding problems may account for the fact that almost none of the attributes included in the Cukierman index has a systematic, plausible relationship with inflation. The multi-faceted Cukierman index also seems to be out-performed by a much narrower index focusing solely on policy independence. These findings point to the importance of using public choice analysis to isolate the real problem here: namely, finding specific central bank structures that effectively insulate central bankers from political pressures.

1. Introduction In recent years it has become much more widely recognized that political processes tend to generate inflationary biases in countries’ macroeconomic policymaking. This, in turn, draws attention to the importance of adopting institutional mechanisms to reduce or constrain such biases. A substantial negative correlation between central bank policy independence and average inflation rates among the industrial countries was noted by researchers over a decade ago (Parkin and Bade, 1978; Banaian, Laney, and Willett, 1983) and has been confirmed in more recent work (see, for example, Alesina and Summers, 1993; Havrilesky and Granato, 1993; Al-Marhubi and Willett, 1995; Banaian et al., 1995). While criticism remains, growing interest in central bank independence among economists has paralleled institutional reforms over the last several years in a wide variety of countries – including Chile,  The authors thank Richard MacDonald for helpful comments. Please address all corre-

spondence to: Richard C.K. Burdekin, Department of Economics, Claremont McKenna College, 850 N. Columbia Avenue, Claremont, California 91711-6420.

2 Egypt, France, Mexico and New Zealand as well as certain formerly centrally planned economies of Central and Eastern Europe. The picture presented so far, however, overlooks the considerable amount of disagreement that has developed as to the relative importance of the different institutional features that may, or may not, impart meaningful central bank independence. The gargantuan literature on political influences on the supposedly “independent” US Federal Reserve System (cf. Mayer, 1990) highlights the need for paying careful attention to the precise details of central bank institutional arrangements. Even more dramatically, the rampant inflation in Russia in the early 1990s was presided over by a central bank that was often described as being “independent”. In this case, the puzzle is easy to explain. While the Russian National Bank may have been nominally independent of the executive branch of government, its institutional structure made it highly responsive to parliament. And parliament was the very branch of government that had the greatest inflationary propensities! Thus, a careful analysis of the Russian case illustrates the importance of having a sound institutional structure – rather than implying that creating an institutionally independent central bank has no effect on performance (see Lewarne, 1995). What are the most important elements of a strongly anti-inflationary institutional structure for the central bank? Here, the recent literature on central bank independence does not offer a clear answer. Despite the unusually strong degree of conformity across studies in finding a significant negative correlation between degree of central bank independence and average inflation rates – at least for the industrial countries – there has actually been considerable variation in the way in which central bank independence is defined and classified. Accepting that central bank independence can aid inflation control is one thing. But for this to have any empirically useful content, we must determine which specific institutional frameworks are best suited to obtaining the desired outcome. A disturbing characteristic of the “new breed” of studies on central bank independence is the relatively atheoretic manner in which the various indices of independence are constructed. In recent work (Banaian, Burdekin, and Willett, 1995), we have argued that basic theoretical principles suggest that greater weight should be given to the formal ability of the central bank to set monetary policy free of government over-ride and less weight accorded to limitations on the central bank’s intervention in the market for government securities than in several recent studies – most prominently Grilli, Masciandaro, and Tabellini (1991) and Cukierman (1992). In this paper we take advantage of the most comprehensive data set currently available – as published by Cukierman (1992)1 – to investigate the relationships among 15 institutional categories of central bank arrangements and average inflation

3 rates. Unfortunately, our results turn out to be largely negative, suggesting that the Cukierman rankings, and especially his classification of independence in monetary policy formulation, should be used only with great care. 2. The Cukierman index vs. policy independence The Cukierman index weights its 16 components in such a way as to give restrictions on the central bank’s participation in the primary market for government securities more than three times as much importance as that attached to policy formulation and central bank freedom from government over-ride. Such a weighting scheme is hardly implied by the prior literature on the subject, and seems sorely in need of more justification than Cukierman, Webb, and Neyapti’s (1992: 361) statement that these weights were simply “considered most plausible”. Grilli, Masciandaro, and Tabellini (1991), meanwhile, settle for equal weights on their 15 categories. But again, because so many categories are included, relatively little importance is attributed to freedom from government over-ride in their overall index measures. The empirical work presented in Banaian, Burdekin, and Willett (1995) suggests that inflation over the industrial countries is better predicted by a simple index of policy independence based on freedom from government over-ride (Burdekin and Willett, 1991) than by the Cukierman index. Indeed, when included alongside our policy independence dummy, the coefficient on the Cukierman index is insignificant across a sample of 21 industrial countries for the 1971–1988 period (see column (1) of Table 1). Our simple policy independence dummy is set equal to one for Austria, Germany, Switzerland and the United States based on the absence of any legal provision for the government to directly over-ride the central bank, and is set equal to zero in all other cases. Given that Cukierman’s index is undeniably far more comprehensive, its apparent weaker relationship with inflation performance is surprising.2 One problem may be that many of the attributes – such as the central bank’s participation in the market for government securities – are surely of little import unless the central bank also has some degree of freedom from government over-ride. In other words, basic theory would suggest that the various dimensions of central bank independence are not all simply additive. In our view some degree of formal freedom to make monetary policy decisions is a necessary condition for effective central bank independence. Attributes such as mandated objectives, terms and conditions of appointment, and financial relationships with the government may well provide some indication of the strength of the pressures placed on monetary authorities and influence the central bank’s ability to withstand such pressures. But, where the government

4 Table 1. Industrial country regression results with the Cukierman index Variable

(1)

(2)

(3)

(4)

(5)

Deficit/GDP

0.199 (2.84) –0.029 (2.35) 3.789 (0.81)

0.269 (1.95) –0.023 (1.37)

0.241 (1.98) –0.023 (1.60)

0.284 (3.81) –0.024 (1.84)

0.223 (3.00) –0.028 (2.30)

–2.22 (0.77)

3.00 (0.89) –4.678 (1.61)

–2.628 (0.93) –2.738 (2.08) 0.58 2.10

Trade volume/GDP Cukierman index Cukierman’s policy formation grouping Cukierman’s CEO grouping Policy independence dummy Adjusted R2 Equation standard error

–4.317 (2.29) 0.57 2.11

0.32 2.64

–4.249 (2.30) 0.48 2.31

0.50 2.30

Dependent variable is mean inflation, 1971–1988; Sample size is 21 in columns (1), (4) and (5); 18 in columns (2) and (3); A constant term was also included in the regressions; t-ratios are in parentheses; Numbers in bold are significant at the 95% confidence level.

makes the basic policy decisions and the role of the central bank is limited to simply implementing the government’s instructions, the effects of these other attributes are likely to be severely compromised. Even if we were to accept the significant role attributed to the overall Cukierman index in Cukierman (1992) and Cukierman, Webb, and Neyapti (1992), the source of this explanatory power remains unclear. For example, none of the index “groupings” is found to be individually significant in Cukierman, Webb, and Neyapti’s (1992) industrial country regressions.3 Based on the 21-country sample from Banaian, Burdekin, and Willett (1995), we too find the key Cukierman groupings for policy formation and the powers of the central bank governor (CEO) to be insignificant (see Table 1, columns (2)–(5)). And this is true even if our own independence dummy is excluded from the regression. Cukierman, Webb, and Neyapti (1992: 370) argue that such insignificant results for the component parts of the index may simply reflect multicollinearity. But accepting this would still leave us with no information on the seemingly-critical issue of which particular central bank attributes are most important. In other words, we surely need to know the source of any explanatory power attributed to the overall Cukierman index measure. This is the focus of the empirical work conducted in the next section, where we use

5 principal component analysis to shed further light on which – if any – of Cukierman’s categories are significantly linked to average inflation performance. 3. Principal component analysis Principal component analysis has the key advantage of potentially allowing us to circumvent the multicollinearity problems arising from simply entering in an inflation regression all the attributes from the Cukierman index. In applying this procedure, we begin with the full array of attributes of central bank structure and extract a set of factors that are orthogonal. The first step is to derive a set of eigenvectors and their associated eigenvalues, restricting ourselves to those components that are associated with an eigenvalue greater than one (as suggested by, for example, Joliffe, 1986). We then regress this subset against average inflation in the 1980s for a set of 27 industrial and developing countries.4 Our analysis excludes the remaining 43 countries covered by Cukierman (1992) because each has missing data on at least one of the attributes of central bank independence that comprise the Cukierman index.5 We also exclude the last of the 16 attributes – on the central bank’s right to buy and sell government securities in the primary market – because it has a zero value for all 27 countries studied. This leaves us with 15 attributes that are incorporated in the empirical work below.6 Table 2 describes the factors generated by the principal component analysis. We find nine components associated with eigenvalues greater than one. These components are listed in descending order of their explanatory power in defining central bank structure. Factor 1 is highly correlated with many of the 15 attributes in the set, suggesting a common set of features in central bank structure. One can see in factors 2 through 5 that there are some independent features in central bank structure, but that these are correlated again with a set of fiscal independence attributes, particularly statutory limitations on the size, maturity and interest rate charged by central banks on government debt. The last four factor loadings are each correlated with only one attribute of central bank independence. In Table 3, the nine components identified in Table 2 plus an industrial country dummy (equaling one if the country is considered developed by the World Bank) are regressed against mean inflation rates in the 27 countries. The results show only two of the nine factors from Table 2 to be significant in explaining inflation. Most importantly, when the factors are unscrambled, many of the attributes have positive signs instead of the negative signs that would be consistent with an anti-inflationary effect of greater central bank independence. Note that this listing of the significance of the differ-

6 Table 2. Principal components analysis with factor loadings of 15 Cukierman central bank attributes for 27 countries Attribute

1

Term of office Who appoints CEO Provisions for dismissal Another office held Monetary policy formation Conflict resolution Active role in budget Central bank objectives Limit on advances to government Limits on securitized lending Who sets terms on lending to government Width of circle of borrowers Type of limit Maturity of loans Limit on interest rate

–0.03 0.15 –0.63 –0.50 –0.31 –0.17 –0.22 –0.15 0.14 –0.80 0.22 –0.58 0.46 0.91 0.34 –0.38 –0.23 –0.12 –1.25 –0.72 0.08 –0.03 –0.27 –0.38 0.20 –0.13 0.44 0.75 –1.33 0.67 –0.61 0.51 0.52 –0.30 0.11 0.16 –0.35 –0.24 0.28 –0.74 0.40 –0.32 0.47 –0.46 –0.49 –0.90 0.44 –0.37 –0.33

2

3

4

0.49 0.11 –0.84 0.23 0.30 –0.10 0.40 –0.06 –0.47 0.07 –0.17 –0.87

5

6

7

8

9

0.17 –0.03 –0.38 0.01 0.23 0.09 0.33 0.07 –0.01 –0.17 0.00 0.36 0.67 –0.22 0.11 0.12 0.11 0.11 0.83 0.07

–0.97 –0.03 –0.12 –0.03

0.29

–0.59

0.65 –0.30 –0.27 –0.23

0.11 –0.13 –0.16

0.94 –0.76 –1.37 –0.16

0.21 –0.02

0.50 –0.40

0.18

0.31 –0.57 0.18

0.12 –0.04

–0.44 –0.40 –0.93 –0.25 –0.88 0.72 –0.06 –0.28 –0.06 –1.05 –0.79 0.06 0.75 0.30 0.21 0.37 0.23 0.17 –0.60 –0.75 0.26 –0.05 –0.68 –0.38 –0.39 0.05 –0.31

Numbers in bold are significantly correlated with the factor at the 95% confidence level.

ent attributes in the lower part of Table 3 is not subject to multicollinearity due to the orthogonality of the different factors entered in the actual inflation regression. The insignificance of the majority of the attributes is consistent with the findings of Cukierman (1992) and Cukierman, Webb, and Neyapti (1992) – and our own Table 1 – but is more troubling here given that multicollinearity can no longer provide a plausible explanation for this result. Of the three attributes that are significant at the 95% confidence level, only “term of office” has the expected negative sign. “Another office held” and “maturity of loans” are significant with seemingly-perverse positive signs. The weak results for the different attributes may be influenced by the inclusion of insignificant factors in the inflation regression, however. Certainly, none of factors 6 through 9 is significant at any reasonable confidence level. Accordingly, Table 4 shows the results of re-estimating the equation using only the first five factors. In this regression we find that “type of lending limitation” has the predicted negative impact on mean inflation rates but that “another office held” and “maturity of loans” continue to be (perversely) pos-

7 Table 3. Inflation equation estimated using principal components of Cukierman’s index Regression using the nine factors from Table 2 Variable

Coefficient

Standard error

t-ratio

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Factor 9 Industrial country dummy Constant

2.128 –74.526 46.606 13.773 87.805 41.503 57.403 –45.748 17.821 –62.901 61.862

24.138 31.689 33.258 40.113 35.829 55.322 54.609 58.057 62.158 41.894 16.367

0.09 –2.35 1.40 0.34 2.45 0.75 1.05 –0.79 0.29 –1.50 3.78

Rearranged to show effects of each attribute Attribute

Coefficient

Standard error

t-ratio

Term of office Who appoints CEO Provisions for dismissal Another office held Monetary policy formation Conflict resolution Active role in budget Central bank objectives Limit on advances to government Limits on securitized lending Who sets terms on lending to government Width of circle of borrowers Type of lending limitation Maturity of loans Limit on interest rate Industrial country dummy Constant

–44.322 24.065 22.554 89.361 50.232 –28.183 13.855 33.405 –29.973

22.186 33.485 35.242 42.290 46.098 29.025 18.072 35.993 45.567

–2.00 0.72 0.64 2.11 1.09 –0.97 0.77 0.93 –0.66

–5.474

38.304

–0.14

12.616

26.287

0.48

8.379 –22.225 63.711 –41.042 –62.901 –30.968

32.233 43.636 31.375 38.303 41.894 55.727

0.26 –0.51 2.03 –1.07 –1.50 –0.56

Dependent variable is mean inflation, 1980–1989 (mean value = 47.346); R2 = 0.567; Adjusted R2 = 0.278; Equation standard error = 67.340.

8 itive and significant. Meanwhile, “term of office” retains the expected negative sign but is now significant only at the 90% level. Perhaps most troubling of all, “monetary policy formation” is found in Table 4 to be positive and significant at the 95% level. This implies that the more the government is able to influence monetary policy the lower is the inflation rate! Such a finding is, of course, quite opposite to our expectations from theory and to the empirical results obtained using prior indices of central bank independence that explicitly focused on policy independence as the chief (or sole) criterion (see Burdekin and Willett, 1991; Banaian et al., 1995). 4. Are there problems with the coding of the Cukierman index? The principal components analysis seems to imply that the majority of the attributes included in the Cukierman index are either insignificant or of the wrong sign. This general finding is true regardless of whether or not we restrict the number of factors included in the inflation regression. Such poor performance of the individual index components likely lies behind the results for the overall index in the cross-country inflation regressions from Banaian et al. (1995). But we cannot now attribute the poor performance of the overall index simply to the rather large weight attached to central bank-government financial relationships. It is important to consider some of the implications that follow from this unexpected result. With the Cukierman index’s weighting scheme implicitly emphasizing the importance of financial relationships between the central bank and the administration, our initial goal in this study was simply to use the principal components analysis to uncover some empirical evidence as to which categories really seemed to be more important in practice. While we admit to being predisposed towards the greater importance of policy independence, this weighting issue remains a complex matter upon which reasonable people are likely to continue to disagree. Given that Cukierman’s categories seem to include the policy independence factors previously found to be significant in explaining inflation performance, we did anticipate that this component – at least – would perform strongly in the principal component analysis. Could its poor performance reflect problems with the codings that have been used to construct the Cukierman index? With our focus on the individual components of the Cukierman index having essentially freed us from any issues raised by the way in which the different components are weighted, we do, in fact, seem to be reduced to two unpleasant alternatives. One is that almost none of the attributes is relevant for inflation performance. The other is that at least some of the attributes are relevant but have been inappropriately coded in the Cukierman scheme.

9 Table 4. Inflation equation estimated using principal components with the first five factors only Regression using the first five factors from Table 2 Variable

Coefficient

Standard error

t-ratio

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Industrial country dummy Constant

13.280 –88.870 43.360 –17.630 –89.590 –58.870 55.008

23.660 30.840 32.390 35.020 35.280 35.300 14.980

0.56 –2.88 1.34 0.50 –2.53 –1.67 3.67

Rearranged to show effects of each attribute Attribute

Coefficient

Standard error

t-ratio

Term of office Who appoints CEO Provisions for dismissal Another office held Monetary policy formation Conflict resolution Active role in budget Central bank objectives Limit on advances to government Limits on securitized lending Who sets terms on lending to government Width of circle of borrowers Type of lending limitation Maturity of loans Limit on interest rate Industrial country dummy Constant

–29.188 16.303 11.616 92.589 39.173 –9.133 3.710 –9.024 –8.470

17.044 22.976 15.211 24.632 17.522 18.146 7.171 12.915 13.070

–1.71 0.71 0.76 3.76 2.24 –0.50 0.52 –0.70 –0.65

15.946

10.463

1.52

23.565

13.463

1.75

26.295 –55.458 45.834 1.867 –58.871 –33.397

27.910 23.966 20.039 17.812 35.303 46.081

0.94 –2.31 2.29 0.10 –1.67 –0.72

Dependent variable is mean inflation, 1980–1989 (mean value = 47.346); R2 = 0.504; Adjusted R2 = 0.356; Equation standard error = 66.331.

As so many prior studies have emphasized the importance of policy independence, the insignificance of Cukierman’s “monetary policy formation”

10 attribute seems to represent a particularly vivid red flag. Our assessment of the coding of this key element leaves us with little doubt as to why its empirical performance seems so out of line with the earlier literature. The scale used in compiling Cukierman’s “monetary policy formation” attribute has four points: complete control by the central bank; joint control; government control with central bank advice; and complete government control. One problem is that the joint control category could mean any number of things from government advice; to the government having one of many seats on the policy board; to the government and central bank having equal power. Worse, the categorization leaves only three countries – Romania, Nicaragua and Austria – where the central bank has complete control, in Cukierman’s coding. This ranking of the Romanian and Nicaraguan central banks as having more control over monetary policy formation than the German Bundesbank is, to say the least, in direct conflict with almost all of the prior literature.

5. Conclusions We use principal component analysis to examine the role played by 15 of the attributes of central bank independence that are included in the Cukierman index. We find that most appear to have an insignificant and/or a positive rather than negative relationship with mean inflation rates. As the principal components method automatically corrects for multicollinearity between the different attributes, these findings suggest caution in using the Cukierman index as a measure of central bank independence. Statistical analysis of the components of the Cukierman index certainly fails to yield insights into the aspects of institutional design that would be most important for effective central bank independence. Some of the problems with the Cukierman index may derive from what is, in our view, an inappropriate approach to classifying degrees of formal independence in monetary policy formation. We hope that the results of this study will encourage future researchers on central bank independence to focus much more explicitly on public choice analysis of the channels through which political pressures are brought to bear on monetary policymakers and how these pressures are influenced by alternative institutional arrangements.

Notes 1. This same index is also laid out by Cukierman, Webb, and Neyapti (1992). For convenience, however, we refer to the rankings as simply the “Cukierman index” in the discussion below.

11 2. Cukierman (1992) and Cukierman, Webb, and Neyapti (1992) report a significant negative role for the Cukierman index in explaining inflation performance. One factor accounting for the different results may be their use of a large sample that includes 49 developing countries. 3. The 16 categories are combined into eight groupings for the purposes of Cukierman, Webb, and Neyapti’s regression analysis (see also Cukierman, 1992). This allows empirical work to be performed for a total of 70 countries even though 43 of them are missing data on one or more of the individual components of the Cukierman index. (For the purposes of arriving at these averages, missing categories are simply subsumed in the calculations.) 4. These countries are Argentina, Austria, Bolivia, Canada, Colombia, Egypt, Germany, Ghana, Greece, Hungary, Indonesia, Israel, Malta, Netherlands, Nigeria, Peru, Philippines, Spain, Tanzania, Thailand, Turkey, United Kingdom, Venezuela, Western Samoa, Zaire, Zambia and Zimbabwe. The codings and data are drawn from Cukierman (1992: 373–376, 396–411). 5. Since our analysis focuses directly on the individual attributes that comprise the Cukierman index we have to exclude all countries with missing data. 6. In focusing only on the legal independence issue, we do not assess the additional “turnover” measure that Cukierman (1992) and Cukierman, Webb, and Neyapti (1992) find to be negative and significant for their developing country sub-sample. We also are unable to consider industrial and developing countries separately as this would leave insufficient degrees of freedom. Our use of a dummy variable may, or may not, adequately correct for different patterns of behavior in the two sets of countries. Nevertheless, the weak performance of the Cukierman index shown in Table 1 was based on a sample exclusively comprised of industrial countries – and unfortunately seems to be consistent with the similarly weak performance of the individual attributes in the mixed sample considered here.

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