- Email: [email protected]
Who targets inflation explicitly?
European Economic Review 43 (1999) 1257}1277
Who targets in#ation explicitly? Stefan Gerlach * Bank for International Settlements, CH-4002 Basel, Switzerland WWZ, Universita( t Basel, CH-4053 Basel, Switzerland Centre for Economic Policy Research, London, UK
Abstract Several countries have recently adopted explicit in#ation targeting (EIT). This paper applies probit analysis on data for 22 countries to show that the degree of central bank independence is positively correlated with the probability that in#ation targeting is adopted. There is also evidence (depending on whether Iceland and Norway are included in the sample) that the probability that EIT is introduced is in#uenced by membership in the European Union, the commodity concentration and composition of exports (which are correlated with measures of the importance of supply and external shocks), and the degree of openness. 1999 Elsevier Science B.V. All rights reserved. JEL classixcation: E52; E58 Keywords: In#ation targeting; Monetary policy framework
1. Introduction The adoption of explicit in#ation targeting (EIT hereafter) as a framework for monetary policy in a number of countries constitutes arguably the most important change in the way in which central banks conduct policy since the introduction of generalised #oating exchange rates in the early 1970s. EIT has in the last
* Correspondence address: Bank for International Settlements, CH-4002 Basel, Switzerland. Tel.: 41 61 280 9250; fax: 41 61 280 9100; e-mail: [email protected]. For overviews, see Haldane (1995) and Leiderman and Svensson (1995). 0014-2921/99/$ } see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 4 - 2 9 2 1 ( 9 8 ) 0 0 1 2 5 - 1
1258
S. Gerlach / European Economic Review 43 (1999) 1257}1277
decade been adopted in Australia, Canada, Finland, New Zealand, Spain, Sweden and the United Kingdom. Re#ecting this fact, a large literature has developed addressing a number of aspects of EIT. Somewhat surprisingly, however, this literature has not yet systematically addressed the question of which factors may have in#uenced countries' choice of this policy strategy. Svensson (1997/1998, p. 5), in summarising recent research on in#ation targeting, captures the state of the literature in writing that: ** 2the reason [ for the switch to EI¹] was the unsatisfactory performance under previous regimes. New Zealand, Canada, Australia, and Spain all introduced in-ation targets under persistently high in-ation; the ;nited Kingdom, Sweden, and Finland did so after having abandoned ,xed exchange rates, which had failed to achieve low and stable in-ation2++. While the empirical work below suggests that the past history of in#ation predicts the adoption of EIT, this "nding is incomplete in that both the monetary policy framework and the in#ation record are endogenous variables that are determined by deeper structural features of the economy. Thus, the observation that countries may have adopted EIT in reaction to high past in#ation merely raises the question what factors caused them to have high in#ation. Furthermore, focusing solely on in#ation as triggering the switch to EIT fails to explain why Canada, Finland and Sweden introduced EIT, while Denmark and Ireland did not, despite their similar in#ation record. In this paper we attempt to identify some structural di!erences between countries with and without EIT regimes. There are at least two reasons why this exercise is of interest. From the perspective of positive economics it is of interest to get a better understanding of the factors that determine central banks' choice of policy framework. Moreover, and perhaps more importantly, for those central banks that are considering adopting an EIT framework, it may be desirable to a have a good idea of what factors have led other central banks to choose this policy framework. Before proceeding, three points should be emphasised. First, it should be clear from the outset that the emphasis is on exploring the data in order to establish hypotheses for future work rather than on formally testing alternative theories of the choice of monetary policy framework. Second, a "nding that economies in which EIT is used to conduct monetary policy share some important structural feature does not necessarily imply that countries without that characteristic should not adopt this targeting framework. For instance, if we "nd that economies with EIT tend to be relatively closed, then this does not imply that more open economies should not adopt an EIT framework. Third, while EIT is
However and as shown below, past in#ation is not strongly correlated with the choice of EIT.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1259
a relatively recent phenomenon, the structural characteristics have most likely not changed substantially in the recent period. The analysis is thus not able to explain why EIT was "rst introduced in the 1990s. The goal is rather to explore whether there are di!erences between countries with and without EIT. The paper is structured as follows. Section 2 discusses some preliminary issues (including the choice of estimation strategy, and the choice and de"nition of the regressors), reviews possible problems that may a!ect the empirical work, and provides some simple descriptive statistics of the data. Section 3 reports estimates of probit models, in which the dependent variable is a dummy that takes the value of unity for the countries that have adopted EIT. Since it is possible that the results may hinge on the classi"cation of the monetary policy regime in the di!erent countries, Section 4 contains a sensitivity analysis. The paper ends with conclusions in Section 5.
2. Preliminaries In this section we use data for a group of 22 countries } Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and the United States } to explore whether there are structural di!erences between countries where monetary policy is pursued using EIT and those that have adopted other policy frameworks. To do so, we estimate probit models in which the dependent variable is a dummy that takes the value of unity for those countries that targeted in#ation in 1997. In selecting the regressors we approach the choice central banks face as mainly being between adopting EIT, which requires the exchange rate to be free to move to o!set in#ationary pressures, or adopting an intermediate exchange rate objective. This led to the following considerations. First, since there is much agreement in the literature that it is di$cult to maintain a "xed exchange rate if the domestic economy is subject to large (asymmetric) shocks, the "rst set of variables are measures that are meant to capture the size of external shocks. Second, because unanticipated monetary expansion leads to real exchange rate depreciation, which is more harmful the more open the economy is, policy makers in highly open economies have greater disincentives to in#ate (Romer, 1993). One would therefore expect the advantages of EIT to be smaller in very open economies, as measured by the export/GDP ratio. Third, since there is a considerable literature that suggests that average in#ation rates are lower in countries with more independent central
Of course, central banks can adopt monetary targeting or conduct policy without any intermediate target. However, few central banks do so. See also the discussion in Section 2.3.
1260
S. Gerlach / European Economic Review 43 (1999) 1257}1277
banks, we also use a measure of central bank independence in the analysis. Fourth, since "xed exchange rate regimes are common in Europe, we include a dummy variable for European countries and a dummy for countries belonging to the European Union in 1992, that is, at the time some European countries switched to EIT. Before turning to the econometric analysis, we brie#y review the approach taken, the data used, and some potential shortcomings of the analysis. 2.1. Probit analysis Since we are asking what factors in#uence the probability that a country choose to adopt an EIT approach, probit (or logit) analysis is the appropriate econometric technique to use. With at least four di!erent monetary policy frameworks in the countries we study } IT, exchange rate targeting, monetary targeting and &eclectic' (or &pragmatic') monetary policy } a multinomial probit approach is technically called for. However, with only 22 observations in the data set, our approach must necessarily be more modest and instead we use standard (binomial) probit regressions in which the dependent variable is a dummy that takes the value of unity for the countries that targeted in#ation in 1997 } Australia, Canada, Finland, New Zealand, Spain, Sweden, and the United Kingdom } and zero for the others. 2.2. Variables used In the analysis we use a range of variables to explore what factors a!ect the probability that EIT is adopted. The variables, and the motivation for including them, are as follows: Past in-ation. In order to explore whether high past in#ation has in#uenced the adoption of EIT, we computed the average annual in#ation rate over the period 1980}1992, using the CPI index (INF). Computing in#ation using the GDP de#ator led to similar results, which, for brevity, are not presented below. Since in#ation is an endogenous variable, it is not used in the probit regressions below (except in the sensitivity analysis). ¹rade-related measures. Three indicators of trade patterns were used to measure the degree to which an economy is exposed to external shocks. Since exporting a broad range of goods is likely to provide some diversi"cation bene"ts, an index of the commodity concentration of trade (CONC) published by UNCTAD (1994, Table 4.5, p. 241) was used. Countries that export a narrow group of goods have a high value of this index. Since they may experience relatively large external shocks, we hypothesise that it may be di$cult for them to maintain a "xed exchange rate. We therefore expect this variable to increase the probability that they adopt an EIT regime, and thus to have a positive parameter in the probit regressions. We also use an index of the diversi"cation
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1261
of trade (DIVER) published by the same source. This index essentially measures to what extent a country's exports di!er from that of the average country. A country exporting few goods will have a high value of this index, so that it is also expected to enter the probit regressions with a positive parameter. We show below that these variables are strongly correlated, and are best seen as capturing the same feature of the economy. The third structural variable we use is a measure of commodity composition of exports (COMM), which is de"ned as the fraction of exports that are related to the exploitation of natural resources. The reason for employing this variable is that many resource-based goods } de"ned here as metals, "sh, forest products and fuels } experience large price swings in response to international business cycle developments. One would therefore expect that economies in which such goods play an important role will tend to experience large external disturbances and be more inclined to operate with an EIT regime rather than with "xed exchange rates. External shocks. Two measures of the importance of external shocks were used in the empirical analysis: the variance of year-to-year changes of export revenue in terms of import prices (RER) and the variance of year-to-year changes of the terms-of-trade (TOT). We expect these variables to enter with a positive sign in the probit regressions. Since many countries adopted in#ation targeting in the early 1990s, we end the estimation period in 1992 to avoid having the results a!ected by reverse causality. We start the estimation in 1980 to avoid the period of oil-price shocks in the 1970s. Openness. The next variable we introduce into the analysis is the export/GDP ratio (OPEN) which proxies for the degree of openness of the economy. The intuition for including this variable is straightforward. Since unanticipated monetary expansions lead to real exchange rate depreciation, which is more harmful the more open the economy is, policy makers in open economies have greater disincentives to in#ate. One would therefore expect the advantages of adopting EIT to be smaller the more open the economy is. Central bank independence. There are two reasons for including a measure of central bank independence (CBI) in the regressions. First, since there is a large literature "nding a positive relationship between central bank independence and average in#ation, one would expect central bank independence also to be signi"cant in the probit regressions estimated below. Second and more
This variable is given by the ratio of exports of metals (including ferrous and non-ferrous metallic ores, concentrates and scraps, such as iron ores, bauxite and alumina, copper, zinc, gold, lead and uranium), energy (including hydrocarbon solids, liquids and gases), roundwood, and "sh to GDP. The variable is constructed using the tables entitled &National products and accounts' and &Extractive Industries' in Encyclopvdia Britannica (1994, pp. 791}799 and 816-821). See Romer (1993), who demonstrates that openness is negatively correlated with average in#ation in a cross-section of countries.
1262
S. Gerlach / European Economic Review 43 (1999) 1257}1277
importantly, EIT may in some sense be a substitute for independence. By adopting EIT and by giving the central bank a clear mandate to pursue low-in#ation policies, it is easier for the central bank to resist political pressures for more expansionary policies that would lead to in#ation above target. This gain would seem to be largest among central banks that have enjoyed a low level of independence. Thus, we expect CBI to enter the regressions with a negative coe$cient. Since measuring the degree of central bank independence is not obvious, in preliminary work we used several indices (constructed by Cukierman, 1992; Alesina, 1988, 1989; Grilli et al., 1991; Eij$nger and Schaling; see Eij$nger and De Haan, 1996, Table 2, p. 23) to guard against the possibility that the results are sensitive to precisely what measure is used. Since the Alesina index was somewhat more signi"cant than the other indices, for space reasons only results using this index are reported. Dummy for Europe. One consideration in deciding whether to adopt a "xed exchange rate regime is whether there is a &natural' choice of currency to peg to. Since economic developments in Germany have strongly in#uenced business cycle developments in neighbouring countries and since in#ation in Germany has been below that in most other economies, European countries may be more likely to adopt a "xed exchange rate regime than non-European countries. Including a dummy for the European countries (EUROPE) in the regressions provides a simple way to explore whether this is indeed the case. Dummy for E;. A second possible reason why "xed exchange rate regimes are so common in Europe is related to the process of European integration. Adopting a "xed exchange rate may be meant to signal that a country is committed to further integration. If this is the explanation for why adjustable peg regimes are so common in Europe, we would expect that a dummy variable (EU) taking the value unity for those countries that were members of the European Union in 1992 would have a higher explanatory power than the Europe dummy. 2.3. Caveats Before turning to the empirical work, we emphasise that there are at least four reasons why the empirical work reported below should be interpreted with caution. Size of data set. EIT regimes have, so far, been adopted largely by industrialised countries, and the data set used below is therefore necessarily limited. With only 22 observations, it is di$cult to estimate models with several
These authors (except Cukierman) do not provide a measure of the degree of central bank independence for all countries in the sample. The missing observations are set equal to the mean of the di!erent series.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1263
explanatory variables. Since it seems likely that many factors play a role in in#uencing the choice of policy framework, the small sample size suggests the empirical analysis can at best only identify the most important factors. ¹he classi,cation of policy regimes. The fact that countries adopt an EIT regime by a public announcement of a target (band or point) for the in#ation rate, etc., implies that there is no doubt about what central banks operate with explicit in#ation targets. However, Finland and Spain in 1997 each had an EIT and were members of the ERM, raising the issue of how they should be classi"ed. They are classi"ed below as having an EIT, under the presumption that the adoption of $15% broad ERM exchange rate bands in 1993 e!ectively meant that monetary policy was no longer directly geared to the exchange rate. However, this classi"cation could be disputed. A further problem is that relying on formal announcements in order to classify a country as having an EIT regime may be inappropriate. For instance, academic economists have argued that the Bundesbank gears monetary policy to the near-term in#ation outlook, and that there is little evidence that it responds to deviations of M3 from target, implicitly suggesting that the Bundesbank targets in#ation. The Federal Reserve has also been interpreted by some observers as conducting a policy of implicit in#ation targeting. Since both these central banks may feel that the public knows that they de facto gear policy to maintaining low in#ation, they may have little reason to announce this and will thus not be classi"ed as having an EIT. Suboptimal policy frameworks. Another problem arises from the fact that some central banks may conduct monetary policy using a framework that they believe to be suboptimal but are unable to change. A particular issue for central banks that would like to introduce EIT is that doing so entails moving from a "xed to a #oating exchange rate regime, which typically requires the consent of the government. This agreement may be di$cult to obtain, particularly in countries with large and politically powerful export industries where the government may shy away from the increase in real exchange rate volatility that may follow if EIT is adopted. Missing variables. Some of the variables in#uencing the choice of policy framework are di$cult to measure. For instance, political considerations may have in#uenced the choice of a "xed exchange rates regime among many European countries. While the dummy for members of the European Union may capture this e!ect, it would be desirable with a better measure of such political considerations. Furthermore, the dummy for European countries may
See, for instance, Bernanke and Mihov (1997), Clarida and Gertler (1997), Clarida et al. (1998). See Rudebusch and Svensson (1998). This argument suggests that suboptimal policy frameworks are really a manifestation of a missing variables problem: there is some relevant variable missing from the analysis which makes the choice of policy framework appear suboptimal. See also the next paragraph.
1264
S. Gerlach / European Economic Review 43 (1999) 1257}1277
not appropriately capture the fact that some countries have a &natural' foreign currency to peg to. All in all, these considerations suggest that the empirical results reported below can at most be suggestive, and should not be interpreted as a formal test of hypotheses concerning the factors that in#uence a country's decision to adopt EIT. 2.4. Descriptive statistics Before turning to the probit regressions, it is useful to review the data, which may be helpful in suggesting whether the econometric results are likely to be sensitive to the inclusion of one or a few observations, and some descriptive statistics. Data. Table 1 presents the data. Two features are notable. While most countries have experienced an annual in#ation rate below 10%, the average in#ation rates in Iceland, Greece and Portugal in 1980}1992 were much higher. Since neither of these countries use in#ation targets in conducting monetary policy, it seems unlikely that the average past in#ation rate is a good predictor of the adoption of EIT. Furthermore, Iceland and Norway appear to be outliers with respect to the concentration of exports and, in particular, the role of commodities in exports. Iceland is also an outlier in the case of export diversi"cation. Thus, if these variables are used in the probit analysis, the results may largely depend on these two countries. For this reason we explore whether the results reported below are sensitive to the inclusion of Iceland and Norway in the data. Means. Table 2 presents the means for the di!erent variables with (Panel A), and without (Panel B), Iceland and Norway. Panel A points to only three signi"cant di!erences between countries with and without EIT: countries with EIT have enjoyed relatively little central bank independence, tend to be less open, and are less likely to be members of the EU. Calculating the means without Iceland and Norway (Panel B), however, suggests that countries with EIT tend also to have a high degree of concentration of exports, and tend to export commodities. Correlations. Next we review the correlations among the variables discussed above. For the reasons discussed, we provide results for the complete sample (Panel A) and for a sample without Iceland and Norway (Panel B). Several aspects of the full-sample results in Table 3 are of interest. First, the EIT dummy is strongly correlated only with CBI, and is moderately correlated with the EU dummy. Somewhat surprisingly, it is not correlated with the average past rate of in#ation. With 20 observations, correlations higher than about 0.35 are signi"cant at the 10% level, 0.42 at the 5% level and 0.54 at the 1% level. However, if Iceland, Greece and Portugal are dropped from the sample, a (signi"cant) relationship is found.
in#ation targeting EI¹ EI¹ EI¹, ERM EI¹ EI¹, ERM EI¹ EI¹
ERM ERM ERM ERM Money ERM Exchange rate ERM ERM Eclectic ERM Exchange rate ERM Money Eclectic
Countries with explicit Australia Canada Finland New Zealand Spain Sweden United Kingdom
Other countries Austria Belgium Denmark France Germany Greece Iceland Ireland Italy Japan Netherlands Norway Portugal Switzerland United States 3.8 4.4 5.8 6.3 3.1 19.2 30.7 7.8 10.0 2.5 2.9 7.1 16.0 3.7 5.2
7.3 5.9 6.6 9.7 9.2 7.8 7.1
CPI
0.06 0.11 0.07 0.06 0.08 0.12 0.53 0.12 0.06 0.14 0.07 0.36 0.10 0.10 0.08
0.21 0.13 0.24 0.17 0.14 0.11 0.07
CONC
0.40 0.38 0.45 0.26 0.27 0.63 0.93 0.55 0.35 0.44 0.35 0.63 0.53 0.52 0.30
0.65 0.40 0.52 0.67 0.35 0.40 0.24
DIVER
3.36 1.44 2.69 0.54 0.68 0.60 87.03 2.33 0.23 0.09 3.09 18.40 3.16 0.64 0.46
2.18 5.81 6.83 3.68 0.44 4.96 1.43 2.0 2.2 2.7 4.3 4.3 3.4 2.9 3.1 4.1 10.0 1.2 6.1 4.3 4.4 4.1
6.0 3.0 3.2 5.0 6.8 2.6 1.8
COMM TOT
3.1 3.4 2.2 4.4 4.2 8.6 6.6 5.2 6.1 7.4 2.8 8.0 7.7 2.9 5.4
7.6 5.3 4.7 5.7 7.0 4.1 2.7
RER
38.4 66.3 35.0 22.3 29.1 19.6 34.8 56.2 19.2 12.3 54.5 39.6 30.2 35.7 8.8
16.4 26.8 27.9 29.4 19.0 31.9 25.4
OPEN
} 2 2 2 4 } } } 1.5 3 2 2 } 4 3
1 2 2 1 1 2 2
CBI
1 1 1 1 1 1 1 1 1 0 1 1 1 1 0
0 0 1 0 1 1 1
EUROPE
1 1 1 1 1 1 0 1 1 0 1 0 1 0 0
0 0 0 0 1 0 1
EU
0.07 0.00 0.10 0.06 0.00 0.07 1.00 0.00 0.41 0.01 0.00 1.00 0.31 0.00 0.02
1.00 1.00 1.00 0.99 0.90 0.93 0.12
Prob
Notes: Regime: EI¹ denotes explicit in#ation targeting; ERM denotes membership in the ERM; Money denotes monetary targeting; and Eclectic indicates that monetary policy is conducted without an explicit in#ation target and that no intermediate target is being used. Variables: CPI denotes average annual CPI in#ation between 1980}1992; CONC and DI
Regime
Country
Table 1
S. Gerlach / European Economic Review 43 (1999) 1257}1277 1265
INF
5.30 4.88 (64.9) [60.0]
5.20 5.30 (91.4) [89.9]
RER
4.05 3.85 (83.5) [81.4]
4.06 3.94 (90.0) [88.9]
TOT
25.3 32.9 (28.1) [13.1]
25.3 33.5 (21.4) 6.9]
OPEN
1.57 2.47 (1.5) [0.1]
1.57 2.42 (1.4) [0.1]
CBI
0.57 0.85 (19.5) [19.5]
0.57 0.87 (13.6) [15.3]
0.29 0.77 (3.6) [1.9]
0.29 0.67 (10.4) [6.9]
EUROPE EU
Note: &Standard' p-values in parenthesis, ( ), and robust p-values in brackets, [ ]. The p-values stem from regressing the di!erent variables on a constant and the in#ation targeting dummy and testing whether the dummy is zero.
3.62 1.49 (1.6) [1.7]
COMM
Panel B. 20 observations (without Iceland and Norway) In#ation targeters 7.66 0.15 0.46 Others 6.98 0.09 0.42 p-values (74.0) (0.4) (47.3) [64.3] [0.5] [47.6]
DIVER
3.62 8.32 (58.8) [40.2]
0.15 0.14 (77.5) [69.7]
CONC
0.46 0.46 (97.3) [96.9]
Panel A. 22 observations ( full sample) In#ation targeters 7.66 Others 8.57 p-values (76.5) [65.0]
Table 2 Descriptive statistics
1266 S. Gerlach / European Economic Review 43 (1999) 1257}1277
1.00 0.67 0.46 0.45 0.37 !0.17 !0.34 !0.40 !0.60
Panel B (without Iceland and Norway) EIT 1.00 INF 0.08 1.00 CONC 0.61 0.15 DIVER 0.17 0.47 COMM 0.53 !0.01 RER 0.11 0.65 TOT 0.05 !0.01 OPEN !0.25 !0.24 CBI !0.53 !0.38 EUROPE !0.30 0.15 EU !0.47 0.22
CONC
1.00 0.81 0.87 0.45 0.17 0.04 !0.16 !0.02 !0.54
INF
1.00 0.64 0.70 0.76 0.50 !0.10 !0.10 !0.24 0.19 !0.05
Panel A ( full sample) EIT 1.00 INF !0.07 CONC 0.06 DIVER !0.01 COMM !0.12 RER 0.02 TOT 0.03 OPEN !0.28 CBI !0.51 EUROPE !0.33 EU !0.36
EIT
Table 3 Correlation coe$cients
1.00 0.28 0.47 0.20 0.03 !0.28 !0.27 !0.35
1.00 0.67 0.50 0.13 0.10 !0.22 !0.09 !0.46
DIVER
1.00 !0.19 !0.41 0.24 !0.28 !0.06 !0.36
1.00 0.20 !0.12 0.11 !0.04 0.13 !0.32
COMM
1.00 0.64 !0.55 !0.28 !0.39 !0.16
1.00 0.63 !0.45 !0.27 !0.30 !0.26
RER
1.00 !0.59 0.04 !0.50 !0.36
1.00 !0.54 0.03 !0.46 !0.36
TOT
1.00 0.02 0.46 0.37
1.00 0.01 0.47 0.30
OPEN
1.00 0.11 !0.10
1.00 0.10 !0.08
CBI
1.00 0.71
1.00 0.59
EUROPE
1.00
1.00
EU S. Gerlach / European Economic Review 43 (1999) 1257}1277 1267
1268
S. Gerlach / European Economic Review 43 (1999) 1257}1277
Second, average in#ation is strongly correlated with CONC, DIVER, COMM (which we henceforth refer to as the &export measures'), and RER. Third, the export measures are all strongly mutually correlated. Moreover, CONC and DIVER are strongly correlated with RER but, somewhat surprisingly, not with TOT. Moreover, COMM does not display much correlation with RER and TOT. Fourth, TOT and RER are strongly correlated, but are negatively correlated with openness. Fifth, the EU dummy is strongly negatively correlated with CONC, DIVER, COMM, TOT and (but less so) RER. Before attempting to interpret these results, it is useful to consider the correlation matrix in Panel B which drops Iceland and Norway from the sample. Focusing on the di!erences with Panel A, note that the correlation between the EIT dummy with both CBI and, on particular, the EU dummy have risen. More importantly, however, when Iceland and Norway are dropped from the sample the EIT dummy is strongly positively correlated with the three export measures (CONC, DIVER and COMM). Furthermore, the correlation between in#ation and export variables is much lower. In particular, the CONC and COMM, which are signi"cantly correlated with the EIT dummy, are not correlated with average past in#ation. Before interpreting these "ndings, two features of the data deserve being highlighted. The fact that the three export measures, REV and TOT are strongly correlated suggests that countries that export a relatively narrow range of goods also tend to export a relatively large amount of commodities, and tend to experience above-average terms-of-trade and export revenue instability. These variables may thus all capture one underlying structural feature of the economies in the sample. Furthermore, the interpretation of the EU dummy is problematic. While the variable is intended to capture political considerations that might make EU members more willing to adopt "xed exchange rates (and thus less likely to adopt EIT), the evidence suggest that EU countries are structurally di!erent from the other countries in that they seem to export a broad range of goods, commodities play little role in exports, and their terms-of-trade and real export earnings appear relatively stable. The EU dummy may therefore be better interpreted as a proxy for an economic structure that makes it easier to maintain a "xed exchange rate, rather than as a political variable. With these observations in mind, one can interpret the data as suggesting that EIT has been adopted by countries that in the past have enjoyed a limited degree of central bank independence and by countries that do not experience large external shocks or, alternatively, that are not members of the EU. Furthermore, the results suggest that it is of interest also to present results for a sample excluding Iceland and Norway. Moreover, given the high correlation between the EU dummy and the export measures, RER and TOT, it seems appropriate to exercise care when including these variables in the probit models.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1269
3. Multivariate probit regressions Since the results from probit regressions with a single regressor are similar to the results in Table 2, we turn directly to the results from the multivariate probit regressions. In Table 4 we report estimates for the full sample. For each regression we report the sign of the parameters (since it is di$cult to interpret their size), and two p-values for tests of the hypothesis that the individual parameters are zero. The "rst stems from the estimates of the standard errors of the parameters from the probit regression, and the second from quasi-maximum-likelihood estimates of the standard errors. Full sample. Since the correlation matrix indicated that the measure of CBI and the EU dummy were strongly correlated with the EIT dummy, these two variables were "rst included in the regression. As can be seen, both are strongly signi"cant and enter with negative signs. While this suggests that having enjoyed a relatively low degree of central bank independence and not being a member of the EU raises the probability that a country will adopt EIT, the fact that the EU dummy is so strongly correlated with the export measures, TOT and REV renders this interpretation hazardous. We return to this issue further below. It is notable that this simple speci"cation correctly predicts 19 of the 22 observations (using a cuto! of 50%). The three prediction errors are the United Kingdom, for which the model implies only a 10% probability of EIT, and Norway and Italy, for which the estimated probabilities that EIT is adopted are 63% and 51%, respectively. It is also notable that model attaches a 47% probability of EIT for Iceland. The table also contains the results when a third variable is added to the probit regression. As can be seen, while the number of correct predictions rises to 20, the results are discouraging in that the export measures and REV and TOT all enter with a negative sign, rather than with a positive sign as hypothesised above. The explanation for these "ndings are as follows. The probit model in which with EU and CBI were used as regressors attached (incorrectly) high probabilities to Iceland and Norway adopting EIT. Any variable for which Iceland and Norway have an unusually high or low value will essentially act as a dummy variable for these countries. Thus, the improvement in "t that can be seen as a third regressor is added in Table 4 arises essentially because Norway is correctly classi"ed, and the prediction error for Iceland falls. This suggests that it is sensible to re-estimate the models without Iceland and Norway in the sample. The latter are robust to some misspeci"cations of the likelihood function that are not su$ciently severe to render the parameter estimates inconsistent. The estimates come from applying the Estrella and Rodrigues (1997) estimator without the serial correlation adjustment. These estimates are asymptotically equivalent to the quasi-maximum-likelihood estimates available as an option in EViews 3.0.
!1.63 (4.6) [4.9]
!3.65 (4.0) [0.1]
!10.01 (51.2) [8.6]
!3.25 (2.0) [0.3]
!6.99 (10.0) [0.0]
!3.55 (9.6) [0.6]
!1.77 (7.2) [7.6]
!1.46 (9.4) [10.9]
!2.66 (5.2) [0.0]
!3.33 (1.5) [0.0]
!8.23 (49.5) [11.5]
!3.22 (1.7) [0.1]
!10.0 (8.4) [0.0]
!8.30 (17.1) [1.8]
!2.68 (3.5) [0.3]
!2.98 (3.7) [0.0]
!10.90 (17.3) [1.1]
CONC
!31.40 (56.2) [15.4]
DIVER
!0.17 (17.8) [1.3]
COMM
!1.69 (10.3) [0.0]
RER
!1.30 (27.6) [4.8]
TOT
!0.05 (37.8) [17.7]
OPEN
!1.17 (45.9) [14.1]
EUROPE
Note: &Standard' p-values in parenthesis, ( ); robust p-values in brackets, [ ]. Constants have been dropped from the table.
EU
CBI
Table 4 Probit regressions (full sample). 22 observations
!6.47 20
!6.21 20
!4.63 20
!2.65 20
!4.88 20
!2.91 20
!4.87 20
!6.78 19
Log-likelihood cases correct
1270 S. Gerlach / European Economic Review 43 (1999) 1257}1277
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1271
Sample without Iceland and Norway. The correlation coe$cients in Table 3 indicate that the dummy for EIT is strongly correlated with CONC, DIVER and COMM (and, in particular, more so than with the EU dummy) if Iceland and Norway are dropped from the sample. For this reason we start by including one of these variables together with the CBI variable, and then explore whether further variables should be included. Since the EU dummy is so strongly correlated with the export measures, REV and TOT that there are doubts as to whether it should be seen as a proxy for these variables or as a measure of political considerations, we do not include the dummy at this step of the analysis. Rather, our strategy is to "rst explore if we can "nd a set of structural measures that are signi"cant, and then see whether the results are sensitive to the inclusion of the EU dummy. Since preliminary work indicated that COMM was generally more signi"cant than DIVER and CONC in the probit equations excluding Iceland and Norway, for brevity Table 5 only reports results using COMM regressors. The "rst row shows the results when CBI and COMM are used. As can be seen, in this case both variables are signi"cant and have the expected signs. Moreover, this simple speci"cation correctly predicts the policy framework in 19 of 20 countries. The only country the model predicts incorrectly is the United Kingdom, which may be due to the fact that the United Kingdom exports fewer commodities than other countries with EIT in#ation targets. The results suggest that while CBI and COMM typically are signi"cant, the third regressor is not. However, the "t of the probit regression improves su$ciently when OPEN is included in the regression for likelihood ratio test to suggest that this variable should be included in the regression (p"10.3%). In what follows, we will think of this as the &preferred' model. Thus, the results suggest that CBI, COMM and OPEN all signi"cantly in#uence the probability that countries will adopt EIT: a relatively low degree of CBI, a relatively closed economy and a relatively large weight of commodities in exports increase the likelihood that EIT will be adopted as a policy framework. Estimated probabilities. In assessing the predictive ability of this regression, it is of interest to consider the estimated probabilities that EIT is adopted which are tabulated in the last column of Table 1. In general, the equation discriminates very well between countries that do and do not target in#ation. As can be seen, the only country the model predicts incorrectly is the United Kingdom, for which the estimated probability that EIT is adopted is 12%. Furthermore, the model attaches a high probability that Italy would adopt in#ation targeting.
The fact that the parameters are insigni"cant by the &standard' p-values suggests that multicollinearity may be present. (Indeed, the correlation between the estimated parameters for COMM and OPEN is !0.83.) Furthermore, in light of the fact that the model without OPEN predicts 19 of 20 observations correctly suggests that the contribution of an additional regressors is likely to be small.
1272
S. Gerlach / European Economic Review 43 (1999) 1257}1277
Table 5 Probit regressions (without Iceland and Norway). 20 observations CBI
COMM CONC
!3.63 (5.1) [0.8]
0.64 (3.9) [3.4]
!3.33 (11.5) [0.7]
0.56 (13.7) [2.9]
!4.76 (10.0) [0.0]
0.71 (3.9) [1.2]
!5.12 (11.6) [0.2]
0.69 (4.0) [1.9]
!2.51 (9.2) [0.0]
1.03 (15.8) [0.5]
!3.43 (6.9) [1.1]
0.57 (7.7) [5.4]
RER
TOT
OPEN
EUROPE
Log-likelihood cases correct !4.82 19
6.21 (13.7) [51.0]
!4.76 19
!0.28 (51.9) [29.8]
!4.52 19
!0.38 (53.6) [46.1]
!4.59 18
!0.15 (29.1) [0.6]
!3.49 19
!2.09 (79.2) [5.1]
!4.71 19
Notes: &Standard' p-values in parenthesis, ( ); robust p-values in brackets, [ ]. Constants have been dropped from the table.
It is notable that the "tted out-of-sample probabilities for Iceland and Norway are both unity. There are two possible reactions to this "nding. One is that the regressors do not adequately capture the factors that in#uence the probability that EIT is adopted. The alternative interpretation is that the policy regimes in these countries may be incorrectly classi"ed or may not be optimal. We therefore review the monetary frameworks in the two countries. Policy frameworks in Iceland and Norway. The monetary policy framework in Iceland has undergone sharp changes in the last decade (e.g., Gerlach, 1997). While annual in#ation was above 20% for most of the 1980s, by the early 1990s in#ation had fallen to the low single digits. This disin#ation was associated with a gradual hardening of the exchange rate policy, and from 1989 onwards maintaining an unchanged exchange rate became a key component of the monetary policy strategy. Following the establishment of an interbank market for foreign exchange in May 1993, an exchange rate band of $2.25% was
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1273
introduced. In September 1995, however, this target zone was broadened to $6% to permit policy to be geared more directly to the near-term in#ation outlook without provoking a con#ict with the exchange rate parity. Thus, following the successful disin#ation during which increased exchange rate stability played a decisive role, policy in Iceland is increasingly geared directly to the in#ation outlook. Monetary policy in Norway, where the determination of the monetary framework is in the prerogative of the government, has historically been geared to the exchange rate. However, a public debate has recently taken place as to whether EIT would not be a preferable monetary policy framework. Indeed, Norges Bank commissioned in the spring of 1997 a number of studies to explore monetary policy options for Norway (see Christiansen and Qvigstad, 1997). Several authors contributing to the study, including Haldane (1997), Isachsen (1997) and Svensson (1997), argued EIT framework would be preferable. These considerations suggest that it may not be surprising that the model attaches a high probability to Iceland and Norway adopting EIT.
4. Sensitivity analysis Several considerations } including the low number of observations and the risk of missing variables } suggest that the results in Table 5 may not be robust. We therefore explore whether the results are sensitive to the introduction of additional variables, the classi"cation of Finland and Spain as in#ation targeters, and the classi"cation of Germany and the United States as not pursuing in#ation targeting. We also attempt to determine if the results are sensitive to the exclusion of individual observations. Additional variables. Since the EU dummy was strongly signi"cant in the full-sample regressions reported in Table 4, the issue arises whether adding this variable will improve the "t of the preferred probit model in Table 5 (that is, the model incorporating CBI, OPEN and COMM as regressors). The results in the second row of Table 6 indicate that the EU-dummy is insigni"cant. Furthermore, a likelihood ratio test for omitted variables indicates that the EU dummy is not signi"cant (p"78.3%). Note, however, that while CBI remains signi"cant, the signi"cance of COMM and OPEN fall sharply, which may be due to
It is worth noting that the broadening of the band was undertaken without the currency being under pressure in the foreign exchange market. The Central Bank of Iceland has repeatedly argued that if changes in the real exchange rate are necessary it may be preferable to let the nominal exchange rate move to avoid #uctuations in the in#ation rate. See, for instance, the Autumn Statement in 1994 and more recently Central Bank of Iceland, 1994. See also Murray (1997).
1274
S. Gerlach / European Economic Review 43 (1999) 1257}1277
Table 6 Probit regressions. Sensitivity analysis. 20 observations (without Iceland and Norway) Explanation
CBI
OPEN
COMM EU
Original probit
!2.51 (9.2) [0.0]
!0.15 (29.1) [4.9]
1.03 (15.8) [0.5]
Including EU dummy
!2.61 (8.5) [0.0]
!0.10 (60.2) [13.2]
0.77 (47.5) [6.7]
Including in#ation
!3.47 (18.8) [0.0]
!0.25 (34.4) [0.1]
1.58 (37.6) [0.4]
Classifying Finland and Spain as not targeting in#ation
!1.22
!0.44
0.36
(15.1) [2.6]
(42.0) [7.6]
(6.4) [14.1]
0.12
!0.11
0.60
(78.0) [83.6]
(6.5) [0.6]
(7.6) [0.7]
Classifying Germany and United States as targeting in#ation
INF
Log-likelihood cases correct p-value for omitted variable !3.49 19 NA
!0.89 (77.5) [53.4]
!3.46 19 (78.6) !0.24 (47.1) [0.5]
!2.88 19 (26.7) !6.97
16 NA !8.63
15 NA
Notes: &Standard' p-values in parenthesis, ( ); robust p-values in brackets, [ ]. Constants have been dropped from the table. NA denotes not applicable.
multicollinearity. The "nding that the EU dummy is not signi"cant in this regression throws some doubts on the interpretation of the signi"cance of EU in the full-sample regressions as indicating that political factors make EU members more likely to adopt "xed exchange rates. Since one would expect that the average past rate of in#ation is correlated with the choice of monetary policy framework (both being endogenous variables), we next introduced INF in the model to see if this changes the results. The
Note that Table 3, Panel B indicates that the while the correlation between EU and CBI is essentially zero, the correlations between EU and COMM and EU and OPEN are much larger and statistically signi"cant.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1275
the standards of the robust p-value, a likelihood ratio test for omitted variables fails to reject (p "26.7%). Finland and Spain. Both the Bank of Spain and the Bank of Finland (in 1994 and 1996, respectively) have adopted in#ation targets, but were also members of the ERM in 1997. This raises the question whether the classi"cation of Finland and Spain as having EIT in#uences the results. To explore this issue we reclassi"ed both countries as not having EIT and reestimated the model. The results in the fourth row are clearly worse than those for the reference equation in the "rst row in that the number of prediction errors rises from one to four. The errors are as follows: the United Kingdom and Sweden are predicted not to have, while Finland and Spain are predicted to have, EIT. It is in this context notable that both the Bank of Finland and the Bank of Spain have suggested that the in#ation target is the overriding policy objective, as assumed by our initial classi"cation. Germany and the ;nited States. As noted above, it has been argued that monetary policy makers in Germany and the United States conduct policy &as if ' they had an in#ation target. We therefore reclassi"ed these countries as having EIT, and reestimated the model. The results in row "ve indicate that both OPEN and COMM remain signi"cant at the percent level, but that CBI is no longer signi"cant. Moreover, the model makes "ve prediction errors: it predicts that Japan and Portugal will adopt an EIT, but that Germany, Spain and the United Kingdom will not do so. These results do suggest that the Germany and the United States are di!erent from the countries that target in#ation, most likely because the latter have had less independent central banks. Dropping individual observations. As an additional test of the sensitivity of the results in Table 4, we reestimated the probit equation dropping one observations at a time, and compared the predicted probabilities with those obtained if all the observations are used. More formally, let PK denote the estimated G probability that EIT is adopted by country i, estimated on the basis of all data. Furthermore, let PK * denote the same probability, estimated without using the G ith observation. By comparing the di!erence PK !PK *, we can obtain a simple G G measure of the extent to which the prediction for country i depends on it being in
Spain remained a member of the ERM through, and after, the exchange market turbulence in Europe in 1992}1993. By contrast, Finland abandoned its unilateral ECU peg in 1992 and let the markka #oat until joining the ERM in 1996. See Bank of Finland (1996, p. 10). Bank of Spain (1996) contains a general discussion of monetary policy in Spain following the introduction of EIT, and indicates how the in#ation objective took precedence in the early spring of 1995 when then peseta was under temporary pressure in the foreign exchange markets. Except, of course, the observations for Iceland and Norway (since these observations were not used in the equations reported in Table 4).
1276
S. Gerlach / European Economic Review 43 (1999) 1257}1277
the sample. The largest di!erence was observed for Greece (7.4%) and Sweden (6.2%). These results suggest that the results do not seem to hinge on the inclusion of a single observation.
5. Conclusions This paper has attempted to identify structural factors that in#uence the probability that a country will chose to conduct monetary policy under IT. While the empirical results are subject to interpretation, the following conclusions appear warranted. A low degree of central bank independence increases the probability that EIT is introduced. This suggests that EIT may have been adopted by countries to signal a clear break with past in#ationary experiences. Second, less open economies are more likely to conduct monetary policy using EIT. This conclusion is compatible with the "nding in Romer (1993) that average in#ation rates are negatively correlated with the degree of openness. Third, EIT tends to be adopted in countries which tend to export a relatively low number of goods and commodities. Such trade patterns likely make these countries more exposed to external shocks that move the equilibrium real exchange rate over time and thus make it di$cult to maintain a "xed exchange rate. Finally, there is also some evidence that EIT is less likely to be employed by countries that are members of the EU. However, why this is the case is less clear. One interpretation is that this re#ects a desire for nominal exchange rate stability, perhaps because this would contribute to deeper integration in the EU area more generally. An alternative interpretation is that most EU countries tend to export few commodities and tend to have relatively diversi"ed export patterns, making them less subject to external shocks and facilitating the maintenance of "xed nominal exchange rates.
Acknowledgements The paper was prepared for the International Seminar on Macroeconomics, hosted by the Bank of Portugal in Lisbon, 14}15 June, 1998. I am grateful to the discussants, Nouriel Roubini and Vitor Gaspar, two anonymous referees, and the editor, Harald Uhlig, for helpful suggestions; to Palle Andersen, Joe Bisignano, Mar Gudmundsson, Craig Fur"ne, Srichander Ramaswamy, "isten R+island, Frank Smets, Lars Svensson and Kostas Tsatsaronis for discussions; to Arturo Estrella for RATS code and helpful discussions; and to Gert Schnabel for help with the data. The views expressed in this paper are solely my own and not necessarily those of the BIS.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1277
References Alesina, A., 1988. Macroeconomics and politics. In: Fischer, S. (Ed.), NBER Macroeconomics Annual. MIT Press, Cambridge, MA. Alesina, A., 1989. Politics and business cycles in industrial democracies. Economic Policy 8, 343}389. Bank of Finland, 1996. Bulletin. October. Bank of Spain, 1996. Annual Report 1995. Bernanke, B.S., Mihov, I., 1997. What does the Bundesbank target? European Economic Review 41, 1025}1053. Central Bank of Iceland, 1994. Autumn Statement. Christiansen, A.B., Qvigstad, J.F., 1997 (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Clarida, R., Gertler, M., 1997. How the Bundesbank conducts monetary policy. In: Romer, C.D., Romer, D.H. (Eds.), Reducing In#ation: Motivation and Strategy. The University of Chicago Press, Chicago, IL. Clarida, R., GalmH , J., Gertler, M., 1998. Monetary policy rules in practice: Some international evidence. European Economic Review 42, 1033}1067. Cukierman, A., 1992. Central Bank Strategy, Credibility and Independence. MIT Press, Cambridge, MA. Eij$nger, S.C.W., De Haan, J., 1996. The political economy of central-bank independence. Special papers in International Economics, No. 19, Princeton University, Princeton, NJ. Encyclopvdia Britannica, 1994. Britannica Book of the Year 1994. Chicago, IL. Estrella, A., Rodrigues, A.P., 1997. Consistent covariance matrix estimation in probit models with autocorrelated errors. Unpublished working paper, Federal Reserve Bank of New York. Gerlach, S., 1997. Monetary policy issues in the Nordic countries since 1992. Monetary policy in the Nordic countries: Experiences since 1992. BIS Policy papers No. 3, October. Grilli, V., Masciandaro, D., Tabellini, G., 1991. Political and monetary institutions and public "nancial policies in the industrial countries. Economic Policy 13, 341}392. Haldane, A.G., 1995. (Ed.), Targeting In#ation, Bank of England, London. Haldane, A.G., 1997. The monetary framework in Norway. In: Christiansen, A.B., Qvigstad, J.F. (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Isachsen, A.J., 1997. Choosing a monetary policy target, Other aspects II. In: Christiansen, A.B., Qvigstad, J.F. (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Leiderman, L., Svensson, L.E.O., 1995 (Eds.), In#ation Targets. CEPR, London. Murray, J., 1997. Comments on &Monetary policy in Norway * Experience since 1992'. Monetary policy in the Nordic countries: Experiences since 1992. BIS Policy papers No. 2. Romer, D., 1993. Openness and in#ation: Theory and evidence. Quarterly Journal of Economics 108, 869}903. Rudebusch, G.D., Svensson, L.E.O., 1998. Policy rules for in#ation targeting. Unpublished manuscript. Svensson, L.E.O., 1997. Exchange rate target or in#ation target for Norway. In: Christiansen, A.B., Qvigstad, J.F. (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Svensson, L.E.O., 1997/1998. Monetary policy and in#ation targeting. NBER Reporter, 5}8. UNCTAD, 1994. Handbook of International Trade and Development Statistics. United Nations, New York and Geneva.
Who targets in#ation explicitly? Stefan Gerlach * Bank for International Settlements, CH-4002 Basel, Switzerland WWZ, Universita( t Basel, CH-4053 Basel, Switzerland Centre for Economic Policy Research, London, UK
Abstract Several countries have recently adopted explicit in#ation targeting (EIT). This paper applies probit analysis on data for 22 countries to show that the degree of central bank independence is positively correlated with the probability that in#ation targeting is adopted. There is also evidence (depending on whether Iceland and Norway are included in the sample) that the probability that EIT is introduced is in#uenced by membership in the European Union, the commodity concentration and composition of exports (which are correlated with measures of the importance of supply and external shocks), and the degree of openness. 1999 Elsevier Science B.V. All rights reserved. JEL classixcation: E52; E58 Keywords: In#ation targeting; Monetary policy framework
1. Introduction The adoption of explicit in#ation targeting (EIT hereafter) as a framework for monetary policy in a number of countries constitutes arguably the most important change in the way in which central banks conduct policy since the introduction of generalised #oating exchange rates in the early 1970s. EIT has in the last
* Correspondence address: Bank for International Settlements, CH-4002 Basel, Switzerland. Tel.: 41 61 280 9250; fax: 41 61 280 9100; e-mail: [email protected]. For overviews, see Haldane (1995) and Leiderman and Svensson (1995). 0014-2921/99/$ } see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 4 - 2 9 2 1 ( 9 8 ) 0 0 1 2 5 - 1
1258
S. Gerlach / European Economic Review 43 (1999) 1257}1277
decade been adopted in Australia, Canada, Finland, New Zealand, Spain, Sweden and the United Kingdom. Re#ecting this fact, a large literature has developed addressing a number of aspects of EIT. Somewhat surprisingly, however, this literature has not yet systematically addressed the question of which factors may have in#uenced countries' choice of this policy strategy. Svensson (1997/1998, p. 5), in summarising recent research on in#ation targeting, captures the state of the literature in writing that: ** 2the reason [ for the switch to EI¹] was the unsatisfactory performance under previous regimes. New Zealand, Canada, Australia, and Spain all introduced in-ation targets under persistently high in-ation; the ;nited Kingdom, Sweden, and Finland did so after having abandoned ,xed exchange rates, which had failed to achieve low and stable in-ation2++. While the empirical work below suggests that the past history of in#ation predicts the adoption of EIT, this "nding is incomplete in that both the monetary policy framework and the in#ation record are endogenous variables that are determined by deeper structural features of the economy. Thus, the observation that countries may have adopted EIT in reaction to high past in#ation merely raises the question what factors caused them to have high in#ation. Furthermore, focusing solely on in#ation as triggering the switch to EIT fails to explain why Canada, Finland and Sweden introduced EIT, while Denmark and Ireland did not, despite their similar in#ation record. In this paper we attempt to identify some structural di!erences between countries with and without EIT regimes. There are at least two reasons why this exercise is of interest. From the perspective of positive economics it is of interest to get a better understanding of the factors that determine central banks' choice of policy framework. Moreover, and perhaps more importantly, for those central banks that are considering adopting an EIT framework, it may be desirable to a have a good idea of what factors have led other central banks to choose this policy framework. Before proceeding, three points should be emphasised. First, it should be clear from the outset that the emphasis is on exploring the data in order to establish hypotheses for future work rather than on formally testing alternative theories of the choice of monetary policy framework. Second, a "nding that economies in which EIT is used to conduct monetary policy share some important structural feature does not necessarily imply that countries without that characteristic should not adopt this targeting framework. For instance, if we "nd that economies with EIT tend to be relatively closed, then this does not imply that more open economies should not adopt an EIT framework. Third, while EIT is
However and as shown below, past in#ation is not strongly correlated with the choice of EIT.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1259
a relatively recent phenomenon, the structural characteristics have most likely not changed substantially in the recent period. The analysis is thus not able to explain why EIT was "rst introduced in the 1990s. The goal is rather to explore whether there are di!erences between countries with and without EIT. The paper is structured as follows. Section 2 discusses some preliminary issues (including the choice of estimation strategy, and the choice and de"nition of the regressors), reviews possible problems that may a!ect the empirical work, and provides some simple descriptive statistics of the data. Section 3 reports estimates of probit models, in which the dependent variable is a dummy that takes the value of unity for the countries that have adopted EIT. Since it is possible that the results may hinge on the classi"cation of the monetary policy regime in the di!erent countries, Section 4 contains a sensitivity analysis. The paper ends with conclusions in Section 5.
2. Preliminaries In this section we use data for a group of 22 countries } Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and the United States } to explore whether there are structural di!erences between countries where monetary policy is pursued using EIT and those that have adopted other policy frameworks. To do so, we estimate probit models in which the dependent variable is a dummy that takes the value of unity for those countries that targeted in#ation in 1997. In selecting the regressors we approach the choice central banks face as mainly being between adopting EIT, which requires the exchange rate to be free to move to o!set in#ationary pressures, or adopting an intermediate exchange rate objective. This led to the following considerations. First, since there is much agreement in the literature that it is di$cult to maintain a "xed exchange rate if the domestic economy is subject to large (asymmetric) shocks, the "rst set of variables are measures that are meant to capture the size of external shocks. Second, because unanticipated monetary expansion leads to real exchange rate depreciation, which is more harmful the more open the economy is, policy makers in highly open economies have greater disincentives to in#ate (Romer, 1993). One would therefore expect the advantages of EIT to be smaller in very open economies, as measured by the export/GDP ratio. Third, since there is a considerable literature that suggests that average in#ation rates are lower in countries with more independent central
Of course, central banks can adopt monetary targeting or conduct policy without any intermediate target. However, few central banks do so. See also the discussion in Section 2.3.
1260
S. Gerlach / European Economic Review 43 (1999) 1257}1277
banks, we also use a measure of central bank independence in the analysis. Fourth, since "xed exchange rate regimes are common in Europe, we include a dummy variable for European countries and a dummy for countries belonging to the European Union in 1992, that is, at the time some European countries switched to EIT. Before turning to the econometric analysis, we brie#y review the approach taken, the data used, and some potential shortcomings of the analysis. 2.1. Probit analysis Since we are asking what factors in#uence the probability that a country choose to adopt an EIT approach, probit (or logit) analysis is the appropriate econometric technique to use. With at least four di!erent monetary policy frameworks in the countries we study } IT, exchange rate targeting, monetary targeting and &eclectic' (or &pragmatic') monetary policy } a multinomial probit approach is technically called for. However, with only 22 observations in the data set, our approach must necessarily be more modest and instead we use standard (binomial) probit regressions in which the dependent variable is a dummy that takes the value of unity for the countries that targeted in#ation in 1997 } Australia, Canada, Finland, New Zealand, Spain, Sweden, and the United Kingdom } and zero for the others. 2.2. Variables used In the analysis we use a range of variables to explore what factors a!ect the probability that EIT is adopted. The variables, and the motivation for including them, are as follows: Past in-ation. In order to explore whether high past in#ation has in#uenced the adoption of EIT, we computed the average annual in#ation rate over the period 1980}1992, using the CPI index (INF). Computing in#ation using the GDP de#ator led to similar results, which, for brevity, are not presented below. Since in#ation is an endogenous variable, it is not used in the probit regressions below (except in the sensitivity analysis). ¹rade-related measures. Three indicators of trade patterns were used to measure the degree to which an economy is exposed to external shocks. Since exporting a broad range of goods is likely to provide some diversi"cation bene"ts, an index of the commodity concentration of trade (CONC) published by UNCTAD (1994, Table 4.5, p. 241) was used. Countries that export a narrow group of goods have a high value of this index. Since they may experience relatively large external shocks, we hypothesise that it may be di$cult for them to maintain a "xed exchange rate. We therefore expect this variable to increase the probability that they adopt an EIT regime, and thus to have a positive parameter in the probit regressions. We also use an index of the diversi"cation
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1261
of trade (DIVER) published by the same source. This index essentially measures to what extent a country's exports di!er from that of the average country. A country exporting few goods will have a high value of this index, so that it is also expected to enter the probit regressions with a positive parameter. We show below that these variables are strongly correlated, and are best seen as capturing the same feature of the economy. The third structural variable we use is a measure of commodity composition of exports (COMM), which is de"ned as the fraction of exports that are related to the exploitation of natural resources. The reason for employing this variable is that many resource-based goods } de"ned here as metals, "sh, forest products and fuels } experience large price swings in response to international business cycle developments. One would therefore expect that economies in which such goods play an important role will tend to experience large external disturbances and be more inclined to operate with an EIT regime rather than with "xed exchange rates. External shocks. Two measures of the importance of external shocks were used in the empirical analysis: the variance of year-to-year changes of export revenue in terms of import prices (RER) and the variance of year-to-year changes of the terms-of-trade (TOT). We expect these variables to enter with a positive sign in the probit regressions. Since many countries adopted in#ation targeting in the early 1990s, we end the estimation period in 1992 to avoid having the results a!ected by reverse causality. We start the estimation in 1980 to avoid the period of oil-price shocks in the 1970s. Openness. The next variable we introduce into the analysis is the export/GDP ratio (OPEN) which proxies for the degree of openness of the economy. The intuition for including this variable is straightforward. Since unanticipated monetary expansions lead to real exchange rate depreciation, which is more harmful the more open the economy is, policy makers in open economies have greater disincentives to in#ate. One would therefore expect the advantages of adopting EIT to be smaller the more open the economy is. Central bank independence. There are two reasons for including a measure of central bank independence (CBI) in the regressions. First, since there is a large literature "nding a positive relationship between central bank independence and average in#ation, one would expect central bank independence also to be signi"cant in the probit regressions estimated below. Second and more
This variable is given by the ratio of exports of metals (including ferrous and non-ferrous metallic ores, concentrates and scraps, such as iron ores, bauxite and alumina, copper, zinc, gold, lead and uranium), energy (including hydrocarbon solids, liquids and gases), roundwood, and "sh to GDP. The variable is constructed using the tables entitled &National products and accounts' and &Extractive Industries' in Encyclopvdia Britannica (1994, pp. 791}799 and 816-821). See Romer (1993), who demonstrates that openness is negatively correlated with average in#ation in a cross-section of countries.
1262
S. Gerlach / European Economic Review 43 (1999) 1257}1277
importantly, EIT may in some sense be a substitute for independence. By adopting EIT and by giving the central bank a clear mandate to pursue low-in#ation policies, it is easier for the central bank to resist political pressures for more expansionary policies that would lead to in#ation above target. This gain would seem to be largest among central banks that have enjoyed a low level of independence. Thus, we expect CBI to enter the regressions with a negative coe$cient. Since measuring the degree of central bank independence is not obvious, in preliminary work we used several indices (constructed by Cukierman, 1992; Alesina, 1988, 1989; Grilli et al., 1991; Eij$nger and Schaling; see Eij$nger and De Haan, 1996, Table 2, p. 23) to guard against the possibility that the results are sensitive to precisely what measure is used. Since the Alesina index was somewhat more signi"cant than the other indices, for space reasons only results using this index are reported. Dummy for Europe. One consideration in deciding whether to adopt a "xed exchange rate regime is whether there is a &natural' choice of currency to peg to. Since economic developments in Germany have strongly in#uenced business cycle developments in neighbouring countries and since in#ation in Germany has been below that in most other economies, European countries may be more likely to adopt a "xed exchange rate regime than non-European countries. Including a dummy for the European countries (EUROPE) in the regressions provides a simple way to explore whether this is indeed the case. Dummy for E;. A second possible reason why "xed exchange rate regimes are so common in Europe is related to the process of European integration. Adopting a "xed exchange rate may be meant to signal that a country is committed to further integration. If this is the explanation for why adjustable peg regimes are so common in Europe, we would expect that a dummy variable (EU) taking the value unity for those countries that were members of the European Union in 1992 would have a higher explanatory power than the Europe dummy. 2.3. Caveats Before turning to the empirical work, we emphasise that there are at least four reasons why the empirical work reported below should be interpreted with caution. Size of data set. EIT regimes have, so far, been adopted largely by industrialised countries, and the data set used below is therefore necessarily limited. With only 22 observations, it is di$cult to estimate models with several
These authors (except Cukierman) do not provide a measure of the degree of central bank independence for all countries in the sample. The missing observations are set equal to the mean of the di!erent series.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1263
explanatory variables. Since it seems likely that many factors play a role in in#uencing the choice of policy framework, the small sample size suggests the empirical analysis can at best only identify the most important factors. ¹he classi,cation of policy regimes. The fact that countries adopt an EIT regime by a public announcement of a target (band or point) for the in#ation rate, etc., implies that there is no doubt about what central banks operate with explicit in#ation targets. However, Finland and Spain in 1997 each had an EIT and were members of the ERM, raising the issue of how they should be classi"ed. They are classi"ed below as having an EIT, under the presumption that the adoption of $15% broad ERM exchange rate bands in 1993 e!ectively meant that monetary policy was no longer directly geared to the exchange rate. However, this classi"cation could be disputed. A further problem is that relying on formal announcements in order to classify a country as having an EIT regime may be inappropriate. For instance, academic economists have argued that the Bundesbank gears monetary policy to the near-term in#ation outlook, and that there is little evidence that it responds to deviations of M3 from target, implicitly suggesting that the Bundesbank targets in#ation. The Federal Reserve has also been interpreted by some observers as conducting a policy of implicit in#ation targeting. Since both these central banks may feel that the public knows that they de facto gear policy to maintaining low in#ation, they may have little reason to announce this and will thus not be classi"ed as having an EIT. Suboptimal policy frameworks. Another problem arises from the fact that some central banks may conduct monetary policy using a framework that they believe to be suboptimal but are unable to change. A particular issue for central banks that would like to introduce EIT is that doing so entails moving from a "xed to a #oating exchange rate regime, which typically requires the consent of the government. This agreement may be di$cult to obtain, particularly in countries with large and politically powerful export industries where the government may shy away from the increase in real exchange rate volatility that may follow if EIT is adopted. Missing variables. Some of the variables in#uencing the choice of policy framework are di$cult to measure. For instance, political considerations may have in#uenced the choice of a "xed exchange rates regime among many European countries. While the dummy for members of the European Union may capture this e!ect, it would be desirable with a better measure of such political considerations. Furthermore, the dummy for European countries may
See, for instance, Bernanke and Mihov (1997), Clarida and Gertler (1997), Clarida et al. (1998). See Rudebusch and Svensson (1998). This argument suggests that suboptimal policy frameworks are really a manifestation of a missing variables problem: there is some relevant variable missing from the analysis which makes the choice of policy framework appear suboptimal. See also the next paragraph.
1264
S. Gerlach / European Economic Review 43 (1999) 1257}1277
not appropriately capture the fact that some countries have a &natural' foreign currency to peg to. All in all, these considerations suggest that the empirical results reported below can at most be suggestive, and should not be interpreted as a formal test of hypotheses concerning the factors that in#uence a country's decision to adopt EIT. 2.4. Descriptive statistics Before turning to the probit regressions, it is useful to review the data, which may be helpful in suggesting whether the econometric results are likely to be sensitive to the inclusion of one or a few observations, and some descriptive statistics. Data. Table 1 presents the data. Two features are notable. While most countries have experienced an annual in#ation rate below 10%, the average in#ation rates in Iceland, Greece and Portugal in 1980}1992 were much higher. Since neither of these countries use in#ation targets in conducting monetary policy, it seems unlikely that the average past in#ation rate is a good predictor of the adoption of EIT. Furthermore, Iceland and Norway appear to be outliers with respect to the concentration of exports and, in particular, the role of commodities in exports. Iceland is also an outlier in the case of export diversi"cation. Thus, if these variables are used in the probit analysis, the results may largely depend on these two countries. For this reason we explore whether the results reported below are sensitive to the inclusion of Iceland and Norway in the data. Means. Table 2 presents the means for the di!erent variables with (Panel A), and without (Panel B), Iceland and Norway. Panel A points to only three signi"cant di!erences between countries with and without EIT: countries with EIT have enjoyed relatively little central bank independence, tend to be less open, and are less likely to be members of the EU. Calculating the means without Iceland and Norway (Panel B), however, suggests that countries with EIT tend also to have a high degree of concentration of exports, and tend to export commodities. Correlations. Next we review the correlations among the variables discussed above. For the reasons discussed, we provide results for the complete sample (Panel A) and for a sample without Iceland and Norway (Panel B). Several aspects of the full-sample results in Table 3 are of interest. First, the EIT dummy is strongly correlated only with CBI, and is moderately correlated with the EU dummy. Somewhat surprisingly, it is not correlated with the average past rate of in#ation. With 20 observations, correlations higher than about 0.35 are signi"cant at the 10% level, 0.42 at the 5% level and 0.54 at the 1% level. However, if Iceland, Greece and Portugal are dropped from the sample, a (signi"cant) relationship is found.
in#ation targeting EI¹ EI¹ EI¹, ERM EI¹ EI¹, ERM EI¹ EI¹
ERM ERM ERM ERM Money ERM Exchange rate ERM ERM Eclectic ERM Exchange rate ERM Money Eclectic
Countries with explicit Australia Canada Finland New Zealand Spain Sweden United Kingdom
Other countries Austria Belgium Denmark France Germany Greece Iceland Ireland Italy Japan Netherlands Norway Portugal Switzerland United States 3.8 4.4 5.8 6.3 3.1 19.2 30.7 7.8 10.0 2.5 2.9 7.1 16.0 3.7 5.2
7.3 5.9 6.6 9.7 9.2 7.8 7.1
CPI
0.06 0.11 0.07 0.06 0.08 0.12 0.53 0.12 0.06 0.14 0.07 0.36 0.10 0.10 0.08
0.21 0.13 0.24 0.17 0.14 0.11 0.07
CONC
0.40 0.38 0.45 0.26 0.27 0.63 0.93 0.55 0.35 0.44 0.35 0.63 0.53 0.52 0.30
0.65 0.40 0.52 0.67 0.35 0.40 0.24
DIVER
3.36 1.44 2.69 0.54 0.68 0.60 87.03 2.33 0.23 0.09 3.09 18.40 3.16 0.64 0.46
2.18 5.81 6.83 3.68 0.44 4.96 1.43 2.0 2.2 2.7 4.3 4.3 3.4 2.9 3.1 4.1 10.0 1.2 6.1 4.3 4.4 4.1
6.0 3.0 3.2 5.0 6.8 2.6 1.8
COMM TOT
3.1 3.4 2.2 4.4 4.2 8.6 6.6 5.2 6.1 7.4 2.8 8.0 7.7 2.9 5.4
7.6 5.3 4.7 5.7 7.0 4.1 2.7
RER
38.4 66.3 35.0 22.3 29.1 19.6 34.8 56.2 19.2 12.3 54.5 39.6 30.2 35.7 8.8
16.4 26.8 27.9 29.4 19.0 31.9 25.4
OPEN
} 2 2 2 4 } } } 1.5 3 2 2 } 4 3
1 2 2 1 1 2 2
CBI
1 1 1 1 1 1 1 1 1 0 1 1 1 1 0
0 0 1 0 1 1 1
EUROPE
1 1 1 1 1 1 0 1 1 0 1 0 1 0 0
0 0 0 0 1 0 1
EU
0.07 0.00 0.10 0.06 0.00 0.07 1.00 0.00 0.41 0.01 0.00 1.00 0.31 0.00 0.02
1.00 1.00 1.00 0.99 0.90 0.93 0.12
Prob
Notes: Regime: EI¹ denotes explicit in#ation targeting; ERM denotes membership in the ERM; Money denotes monetary targeting; and Eclectic indicates that monetary policy is conducted without an explicit in#ation target and that no intermediate target is being used. Variables: CPI denotes average annual CPI in#ation between 1980}1992; CONC and DI
Regime
Country
Table 1
S. Gerlach / European Economic Review 43 (1999) 1257}1277 1265
INF
5.30 4.88 (64.9) [60.0]
5.20 5.30 (91.4) [89.9]
RER
4.05 3.85 (83.5) [81.4]
4.06 3.94 (90.0) [88.9]
TOT
25.3 32.9 (28.1) [13.1]
25.3 33.5 (21.4) 6.9]
OPEN
1.57 2.47 (1.5) [0.1]
1.57 2.42 (1.4) [0.1]
CBI
0.57 0.85 (19.5) [19.5]
0.57 0.87 (13.6) [15.3]
0.29 0.77 (3.6) [1.9]
0.29 0.67 (10.4) [6.9]
EUROPE EU
Note: &Standard' p-values in parenthesis, ( ), and robust p-values in brackets, [ ]. The p-values stem from regressing the di!erent variables on a constant and the in#ation targeting dummy and testing whether the dummy is zero.
3.62 1.49 (1.6) [1.7]
COMM
Panel B. 20 observations (without Iceland and Norway) In#ation targeters 7.66 0.15 0.46 Others 6.98 0.09 0.42 p-values (74.0) (0.4) (47.3) [64.3] [0.5] [47.6]
DIVER
3.62 8.32 (58.8) [40.2]
0.15 0.14 (77.5) [69.7]
CONC
0.46 0.46 (97.3) [96.9]
Panel A. 22 observations ( full sample) In#ation targeters 7.66 Others 8.57 p-values (76.5) [65.0]
Table 2 Descriptive statistics
1266 S. Gerlach / European Economic Review 43 (1999) 1257}1277
1.00 0.67 0.46 0.45 0.37 !0.17 !0.34 !0.40 !0.60
Panel B (without Iceland and Norway) EIT 1.00 INF 0.08 1.00 CONC 0.61 0.15 DIVER 0.17 0.47 COMM 0.53 !0.01 RER 0.11 0.65 TOT 0.05 !0.01 OPEN !0.25 !0.24 CBI !0.53 !0.38 EUROPE !0.30 0.15 EU !0.47 0.22
CONC
1.00 0.81 0.87 0.45 0.17 0.04 !0.16 !0.02 !0.54
INF
1.00 0.64 0.70 0.76 0.50 !0.10 !0.10 !0.24 0.19 !0.05
Panel A ( full sample) EIT 1.00 INF !0.07 CONC 0.06 DIVER !0.01 COMM !0.12 RER 0.02 TOT 0.03 OPEN !0.28 CBI !0.51 EUROPE !0.33 EU !0.36
EIT
Table 3 Correlation coe$cients
1.00 0.28 0.47 0.20 0.03 !0.28 !0.27 !0.35
1.00 0.67 0.50 0.13 0.10 !0.22 !0.09 !0.46
DIVER
1.00 !0.19 !0.41 0.24 !0.28 !0.06 !0.36
1.00 0.20 !0.12 0.11 !0.04 0.13 !0.32
COMM
1.00 0.64 !0.55 !0.28 !0.39 !0.16
1.00 0.63 !0.45 !0.27 !0.30 !0.26
RER
1.00 !0.59 0.04 !0.50 !0.36
1.00 !0.54 0.03 !0.46 !0.36
TOT
1.00 0.02 0.46 0.37
1.00 0.01 0.47 0.30
OPEN
1.00 0.11 !0.10
1.00 0.10 !0.08
CBI
1.00 0.71
1.00 0.59
EUROPE
1.00
1.00
EU S. Gerlach / European Economic Review 43 (1999) 1257}1277 1267
1268
S. Gerlach / European Economic Review 43 (1999) 1257}1277
Second, average in#ation is strongly correlated with CONC, DIVER, COMM (which we henceforth refer to as the &export measures'), and RER. Third, the export measures are all strongly mutually correlated. Moreover, CONC and DIVER are strongly correlated with RER but, somewhat surprisingly, not with TOT. Moreover, COMM does not display much correlation with RER and TOT. Fourth, TOT and RER are strongly correlated, but are negatively correlated with openness. Fifth, the EU dummy is strongly negatively correlated with CONC, DIVER, COMM, TOT and (but less so) RER. Before attempting to interpret these results, it is useful to consider the correlation matrix in Panel B which drops Iceland and Norway from the sample. Focusing on the di!erences with Panel A, note that the correlation between the EIT dummy with both CBI and, on particular, the EU dummy have risen. More importantly, however, when Iceland and Norway are dropped from the sample the EIT dummy is strongly positively correlated with the three export measures (CONC, DIVER and COMM). Furthermore, the correlation between in#ation and export variables is much lower. In particular, the CONC and COMM, which are signi"cantly correlated with the EIT dummy, are not correlated with average past in#ation. Before interpreting these "ndings, two features of the data deserve being highlighted. The fact that the three export measures, REV and TOT are strongly correlated suggests that countries that export a relatively narrow range of goods also tend to export a relatively large amount of commodities, and tend to experience above-average terms-of-trade and export revenue instability. These variables may thus all capture one underlying structural feature of the economies in the sample. Furthermore, the interpretation of the EU dummy is problematic. While the variable is intended to capture political considerations that might make EU members more willing to adopt "xed exchange rates (and thus less likely to adopt EIT), the evidence suggest that EU countries are structurally di!erent from the other countries in that they seem to export a broad range of goods, commodities play little role in exports, and their terms-of-trade and real export earnings appear relatively stable. The EU dummy may therefore be better interpreted as a proxy for an economic structure that makes it easier to maintain a "xed exchange rate, rather than as a political variable. With these observations in mind, one can interpret the data as suggesting that EIT has been adopted by countries that in the past have enjoyed a limited degree of central bank independence and by countries that do not experience large external shocks or, alternatively, that are not members of the EU. Furthermore, the results suggest that it is of interest also to present results for a sample excluding Iceland and Norway. Moreover, given the high correlation between the EU dummy and the export measures, RER and TOT, it seems appropriate to exercise care when including these variables in the probit models.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1269
3. Multivariate probit regressions Since the results from probit regressions with a single regressor are similar to the results in Table 2, we turn directly to the results from the multivariate probit regressions. In Table 4 we report estimates for the full sample. For each regression we report the sign of the parameters (since it is di$cult to interpret their size), and two p-values for tests of the hypothesis that the individual parameters are zero. The "rst stems from the estimates of the standard errors of the parameters from the probit regression, and the second from quasi-maximum-likelihood estimates of the standard errors. Full sample. Since the correlation matrix indicated that the measure of CBI and the EU dummy were strongly correlated with the EIT dummy, these two variables were "rst included in the regression. As can be seen, both are strongly signi"cant and enter with negative signs. While this suggests that having enjoyed a relatively low degree of central bank independence and not being a member of the EU raises the probability that a country will adopt EIT, the fact that the EU dummy is so strongly correlated with the export measures, TOT and REV renders this interpretation hazardous. We return to this issue further below. It is notable that this simple speci"cation correctly predicts 19 of the 22 observations (using a cuto! of 50%). The three prediction errors are the United Kingdom, for which the model implies only a 10% probability of EIT, and Norway and Italy, for which the estimated probabilities that EIT is adopted are 63% and 51%, respectively. It is also notable that model attaches a 47% probability of EIT for Iceland. The table also contains the results when a third variable is added to the probit regression. As can be seen, while the number of correct predictions rises to 20, the results are discouraging in that the export measures and REV and TOT all enter with a negative sign, rather than with a positive sign as hypothesised above. The explanation for these "ndings are as follows. The probit model in which with EU and CBI were used as regressors attached (incorrectly) high probabilities to Iceland and Norway adopting EIT. Any variable for which Iceland and Norway have an unusually high or low value will essentially act as a dummy variable for these countries. Thus, the improvement in "t that can be seen as a third regressor is added in Table 4 arises essentially because Norway is correctly classi"ed, and the prediction error for Iceland falls. This suggests that it is sensible to re-estimate the models without Iceland and Norway in the sample. The latter are robust to some misspeci"cations of the likelihood function that are not su$ciently severe to render the parameter estimates inconsistent. The estimates come from applying the Estrella and Rodrigues (1997) estimator without the serial correlation adjustment. These estimates are asymptotically equivalent to the quasi-maximum-likelihood estimates available as an option in EViews 3.0.
!1.63 (4.6) [4.9]
!3.65 (4.0) [0.1]
!10.01 (51.2) [8.6]
!3.25 (2.0) [0.3]
!6.99 (10.0) [0.0]
!3.55 (9.6) [0.6]
!1.77 (7.2) [7.6]
!1.46 (9.4) [10.9]
!2.66 (5.2) [0.0]
!3.33 (1.5) [0.0]
!8.23 (49.5) [11.5]
!3.22 (1.7) [0.1]
!10.0 (8.4) [0.0]
!8.30 (17.1) [1.8]
!2.68 (3.5) [0.3]
!2.98 (3.7) [0.0]
!10.90 (17.3) [1.1]
CONC
!31.40 (56.2) [15.4]
DIVER
!0.17 (17.8) [1.3]
COMM
!1.69 (10.3) [0.0]
RER
!1.30 (27.6) [4.8]
TOT
!0.05 (37.8) [17.7]
OPEN
!1.17 (45.9) [14.1]
EUROPE
Note: &Standard' p-values in parenthesis, ( ); robust p-values in brackets, [ ]. Constants have been dropped from the table.
EU
CBI
Table 4 Probit regressions (full sample). 22 observations
!6.47 20
!6.21 20
!4.63 20
!2.65 20
!4.88 20
!2.91 20
!4.87 20
!6.78 19
Log-likelihood cases correct
1270 S. Gerlach / European Economic Review 43 (1999) 1257}1277
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1271
Sample without Iceland and Norway. The correlation coe$cients in Table 3 indicate that the dummy for EIT is strongly correlated with CONC, DIVER and COMM (and, in particular, more so than with the EU dummy) if Iceland and Norway are dropped from the sample. For this reason we start by including one of these variables together with the CBI variable, and then explore whether further variables should be included. Since the EU dummy is so strongly correlated with the export measures, REV and TOT that there are doubts as to whether it should be seen as a proxy for these variables or as a measure of political considerations, we do not include the dummy at this step of the analysis. Rather, our strategy is to "rst explore if we can "nd a set of structural measures that are signi"cant, and then see whether the results are sensitive to the inclusion of the EU dummy. Since preliminary work indicated that COMM was generally more signi"cant than DIVER and CONC in the probit equations excluding Iceland and Norway, for brevity Table 5 only reports results using COMM regressors. The "rst row shows the results when CBI and COMM are used. As can be seen, in this case both variables are signi"cant and have the expected signs. Moreover, this simple speci"cation correctly predicts the policy framework in 19 of 20 countries. The only country the model predicts incorrectly is the United Kingdom, which may be due to the fact that the United Kingdom exports fewer commodities than other countries with EIT in#ation targets. The results suggest that while CBI and COMM typically are signi"cant, the third regressor is not. However, the "t of the probit regression improves su$ciently when OPEN is included in the regression for likelihood ratio test to suggest that this variable should be included in the regression (p"10.3%). In what follows, we will think of this as the &preferred' model. Thus, the results suggest that CBI, COMM and OPEN all signi"cantly in#uence the probability that countries will adopt EIT: a relatively low degree of CBI, a relatively closed economy and a relatively large weight of commodities in exports increase the likelihood that EIT will be adopted as a policy framework. Estimated probabilities. In assessing the predictive ability of this regression, it is of interest to consider the estimated probabilities that EIT is adopted which are tabulated in the last column of Table 1. In general, the equation discriminates very well between countries that do and do not target in#ation. As can be seen, the only country the model predicts incorrectly is the United Kingdom, for which the estimated probability that EIT is adopted is 12%. Furthermore, the model attaches a high probability that Italy would adopt in#ation targeting.
The fact that the parameters are insigni"cant by the &standard' p-values suggests that multicollinearity may be present. (Indeed, the correlation between the estimated parameters for COMM and OPEN is !0.83.) Furthermore, in light of the fact that the model without OPEN predicts 19 of 20 observations correctly suggests that the contribution of an additional regressors is likely to be small.
1272
S. Gerlach / European Economic Review 43 (1999) 1257}1277
Table 5 Probit regressions (without Iceland and Norway). 20 observations CBI
COMM CONC
!3.63 (5.1) [0.8]
0.64 (3.9) [3.4]
!3.33 (11.5) [0.7]
0.56 (13.7) [2.9]
!4.76 (10.0) [0.0]
0.71 (3.9) [1.2]
!5.12 (11.6) [0.2]
0.69 (4.0) [1.9]
!2.51 (9.2) [0.0]
1.03 (15.8) [0.5]
!3.43 (6.9) [1.1]
0.57 (7.7) [5.4]
RER
TOT
OPEN
EUROPE
Log-likelihood cases correct !4.82 19
6.21 (13.7) [51.0]
!4.76 19
!0.28 (51.9) [29.8]
!4.52 19
!0.38 (53.6) [46.1]
!4.59 18
!0.15 (29.1) [0.6]
!3.49 19
!2.09 (79.2) [5.1]
!4.71 19
Notes: &Standard' p-values in parenthesis, ( ); robust p-values in brackets, [ ]. Constants have been dropped from the table.
It is notable that the "tted out-of-sample probabilities for Iceland and Norway are both unity. There are two possible reactions to this "nding. One is that the regressors do not adequately capture the factors that in#uence the probability that EIT is adopted. The alternative interpretation is that the policy regimes in these countries may be incorrectly classi"ed or may not be optimal. We therefore review the monetary frameworks in the two countries. Policy frameworks in Iceland and Norway. The monetary policy framework in Iceland has undergone sharp changes in the last decade (e.g., Gerlach, 1997). While annual in#ation was above 20% for most of the 1980s, by the early 1990s in#ation had fallen to the low single digits. This disin#ation was associated with a gradual hardening of the exchange rate policy, and from 1989 onwards maintaining an unchanged exchange rate became a key component of the monetary policy strategy. Following the establishment of an interbank market for foreign exchange in May 1993, an exchange rate band of $2.25% was
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1273
introduced. In September 1995, however, this target zone was broadened to $6% to permit policy to be geared more directly to the near-term in#ation outlook without provoking a con#ict with the exchange rate parity. Thus, following the successful disin#ation during which increased exchange rate stability played a decisive role, policy in Iceland is increasingly geared directly to the in#ation outlook. Monetary policy in Norway, where the determination of the monetary framework is in the prerogative of the government, has historically been geared to the exchange rate. However, a public debate has recently taken place as to whether EIT would not be a preferable monetary policy framework. Indeed, Norges Bank commissioned in the spring of 1997 a number of studies to explore monetary policy options for Norway (see Christiansen and Qvigstad, 1997). Several authors contributing to the study, including Haldane (1997), Isachsen (1997) and Svensson (1997), argued EIT framework would be preferable. These considerations suggest that it may not be surprising that the model attaches a high probability to Iceland and Norway adopting EIT.
4. Sensitivity analysis Several considerations } including the low number of observations and the risk of missing variables } suggest that the results in Table 5 may not be robust. We therefore explore whether the results are sensitive to the introduction of additional variables, the classi"cation of Finland and Spain as in#ation targeters, and the classi"cation of Germany and the United States as not pursuing in#ation targeting. We also attempt to determine if the results are sensitive to the exclusion of individual observations. Additional variables. Since the EU dummy was strongly signi"cant in the full-sample regressions reported in Table 4, the issue arises whether adding this variable will improve the "t of the preferred probit model in Table 5 (that is, the model incorporating CBI, OPEN and COMM as regressors). The results in the second row of Table 6 indicate that the EU-dummy is insigni"cant. Furthermore, a likelihood ratio test for omitted variables indicates that the EU dummy is not signi"cant (p"78.3%). Note, however, that while CBI remains signi"cant, the signi"cance of COMM and OPEN fall sharply, which may be due to
It is worth noting that the broadening of the band was undertaken without the currency being under pressure in the foreign exchange market. The Central Bank of Iceland has repeatedly argued that if changes in the real exchange rate are necessary it may be preferable to let the nominal exchange rate move to avoid #uctuations in the in#ation rate. See, for instance, the Autumn Statement in 1994 and more recently Central Bank of Iceland, 1994. See also Murray (1997).
1274
S. Gerlach / European Economic Review 43 (1999) 1257}1277
Table 6 Probit regressions. Sensitivity analysis. 20 observations (without Iceland and Norway) Explanation
CBI
OPEN
COMM EU
Original probit
!2.51 (9.2) [0.0]
!0.15 (29.1) [4.9]
1.03 (15.8) [0.5]
Including EU dummy
!2.61 (8.5) [0.0]
!0.10 (60.2) [13.2]
0.77 (47.5) [6.7]
Including in#ation
!3.47 (18.8) [0.0]
!0.25 (34.4) [0.1]
1.58 (37.6) [0.4]
Classifying Finland and Spain as not targeting in#ation
!1.22
!0.44
0.36
(15.1) [2.6]
(42.0) [7.6]
(6.4) [14.1]
0.12
!0.11
0.60
(78.0) [83.6]
(6.5) [0.6]
(7.6) [0.7]
Classifying Germany and United States as targeting in#ation
INF
Log-likelihood cases correct p-value for omitted variable !3.49 19 NA
!0.89 (77.5) [53.4]
!3.46 19 (78.6) !0.24 (47.1) [0.5]
!2.88 19 (26.7) !6.97
16 NA !8.63
15 NA
Notes: &Standard' p-values in parenthesis, ( ); robust p-values in brackets, [ ]. Constants have been dropped from the table. NA denotes not applicable.
multicollinearity. The "nding that the EU dummy is not signi"cant in this regression throws some doubts on the interpretation of the signi"cance of EU in the full-sample regressions as indicating that political factors make EU members more likely to adopt "xed exchange rates. Since one would expect that the average past rate of in#ation is correlated with the choice of monetary policy framework (both being endogenous variables), we next introduced INF in the model to see if this changes the results. The
Note that Table 3, Panel B indicates that the while the correlation between EU and CBI is essentially zero, the correlations between EU and COMM and EU and OPEN are much larger and statistically signi"cant.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1275
the standards of the robust p-value, a likelihood ratio test for omitted variables fails to reject (p "26.7%). Finland and Spain. Both the Bank of Spain and the Bank of Finland (in 1994 and 1996, respectively) have adopted in#ation targets, but were also members of the ERM in 1997. This raises the question whether the classi"cation of Finland and Spain as having EIT in#uences the results. To explore this issue we reclassi"ed both countries as not having EIT and reestimated the model. The results in the fourth row are clearly worse than those for the reference equation in the "rst row in that the number of prediction errors rises from one to four. The errors are as follows: the United Kingdom and Sweden are predicted not to have, while Finland and Spain are predicted to have, EIT. It is in this context notable that both the Bank of Finland and the Bank of Spain have suggested that the in#ation target is the overriding policy objective, as assumed by our initial classi"cation. Germany and the ;nited States. As noted above, it has been argued that monetary policy makers in Germany and the United States conduct policy &as if ' they had an in#ation target. We therefore reclassi"ed these countries as having EIT, and reestimated the model. The results in row "ve indicate that both OPEN and COMM remain signi"cant at the percent level, but that CBI is no longer signi"cant. Moreover, the model makes "ve prediction errors: it predicts that Japan and Portugal will adopt an EIT, but that Germany, Spain and the United Kingdom will not do so. These results do suggest that the Germany and the United States are di!erent from the countries that target in#ation, most likely because the latter have had less independent central banks. Dropping individual observations. As an additional test of the sensitivity of the results in Table 4, we reestimated the probit equation dropping one observations at a time, and compared the predicted probabilities with those obtained if all the observations are used. More formally, let PK denote the estimated G probability that EIT is adopted by country i, estimated on the basis of all data. Furthermore, let PK * denote the same probability, estimated without using the G ith observation. By comparing the di!erence PK !PK *, we can obtain a simple G G measure of the extent to which the prediction for country i depends on it being in
Spain remained a member of the ERM through, and after, the exchange market turbulence in Europe in 1992}1993. By contrast, Finland abandoned its unilateral ECU peg in 1992 and let the markka #oat until joining the ERM in 1996. See Bank of Finland (1996, p. 10). Bank of Spain (1996) contains a general discussion of monetary policy in Spain following the introduction of EIT, and indicates how the in#ation objective took precedence in the early spring of 1995 when then peseta was under temporary pressure in the foreign exchange markets. Except, of course, the observations for Iceland and Norway (since these observations were not used in the equations reported in Table 4).
1276
S. Gerlach / European Economic Review 43 (1999) 1257}1277
the sample. The largest di!erence was observed for Greece (7.4%) and Sweden (6.2%). These results suggest that the results do not seem to hinge on the inclusion of a single observation.
5. Conclusions This paper has attempted to identify structural factors that in#uence the probability that a country will chose to conduct monetary policy under IT. While the empirical results are subject to interpretation, the following conclusions appear warranted. A low degree of central bank independence increases the probability that EIT is introduced. This suggests that EIT may have been adopted by countries to signal a clear break with past in#ationary experiences. Second, less open economies are more likely to conduct monetary policy using EIT. This conclusion is compatible with the "nding in Romer (1993) that average in#ation rates are negatively correlated with the degree of openness. Third, EIT tends to be adopted in countries which tend to export a relatively low number of goods and commodities. Such trade patterns likely make these countries more exposed to external shocks that move the equilibrium real exchange rate over time and thus make it di$cult to maintain a "xed exchange rate. Finally, there is also some evidence that EIT is less likely to be employed by countries that are members of the EU. However, why this is the case is less clear. One interpretation is that this re#ects a desire for nominal exchange rate stability, perhaps because this would contribute to deeper integration in the EU area more generally. An alternative interpretation is that most EU countries tend to export few commodities and tend to have relatively diversi"ed export patterns, making them less subject to external shocks and facilitating the maintenance of "xed nominal exchange rates.
Acknowledgements The paper was prepared for the International Seminar on Macroeconomics, hosted by the Bank of Portugal in Lisbon, 14}15 June, 1998. I am grateful to the discussants, Nouriel Roubini and Vitor Gaspar, two anonymous referees, and the editor, Harald Uhlig, for helpful suggestions; to Palle Andersen, Joe Bisignano, Mar Gudmundsson, Craig Fur"ne, Srichander Ramaswamy, "isten R+island, Frank Smets, Lars Svensson and Kostas Tsatsaronis for discussions; to Arturo Estrella for RATS code and helpful discussions; and to Gert Schnabel for help with the data. The views expressed in this paper are solely my own and not necessarily those of the BIS.
S. Gerlach / European Economic Review 43 (1999) 1257}1277
1277
References Alesina, A., 1988. Macroeconomics and politics. In: Fischer, S. (Ed.), NBER Macroeconomics Annual. MIT Press, Cambridge, MA. Alesina, A., 1989. Politics and business cycles in industrial democracies. Economic Policy 8, 343}389. Bank of Finland, 1996. Bulletin. October. Bank of Spain, 1996. Annual Report 1995. Bernanke, B.S., Mihov, I., 1997. What does the Bundesbank target? European Economic Review 41, 1025}1053. Central Bank of Iceland, 1994. Autumn Statement. Christiansen, A.B., Qvigstad, J.F., 1997 (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Clarida, R., Gertler, M., 1997. How the Bundesbank conducts monetary policy. In: Romer, C.D., Romer, D.H. (Eds.), Reducing In#ation: Motivation and Strategy. The University of Chicago Press, Chicago, IL. Clarida, R., GalmH , J., Gertler, M., 1998. Monetary policy rules in practice: Some international evidence. European Economic Review 42, 1033}1067. Cukierman, A., 1992. Central Bank Strategy, Credibility and Independence. MIT Press, Cambridge, MA. Eij$nger, S.C.W., De Haan, J., 1996. The political economy of central-bank independence. Special papers in International Economics, No. 19, Princeton University, Princeton, NJ. Encyclopvdia Britannica, 1994. Britannica Book of the Year 1994. Chicago, IL. Estrella, A., Rodrigues, A.P., 1997. Consistent covariance matrix estimation in probit models with autocorrelated errors. Unpublished working paper, Federal Reserve Bank of New York. Gerlach, S., 1997. Monetary policy issues in the Nordic countries since 1992. Monetary policy in the Nordic countries: Experiences since 1992. BIS Policy papers No. 3, October. Grilli, V., Masciandaro, D., Tabellini, G., 1991. Political and monetary institutions and public "nancial policies in the industrial countries. Economic Policy 13, 341}392. Haldane, A.G., 1995. (Ed.), Targeting In#ation, Bank of England, London. Haldane, A.G., 1997. The monetary framework in Norway. In: Christiansen, A.B., Qvigstad, J.F. (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Isachsen, A.J., 1997. Choosing a monetary policy target, Other aspects II. In: Christiansen, A.B., Qvigstad, J.F. (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Leiderman, L., Svensson, L.E.O., 1995 (Eds.), In#ation Targets. CEPR, London. Murray, J., 1997. Comments on &Monetary policy in Norway * Experience since 1992'. Monetary policy in the Nordic countries: Experiences since 1992. BIS Policy papers No. 2. Romer, D., 1993. Openness and in#ation: Theory and evidence. Quarterly Journal of Economics 108, 869}903. Rudebusch, G.D., Svensson, L.E.O., 1998. Policy rules for in#ation targeting. Unpublished manuscript. Svensson, L.E.O., 1997. Exchange rate target or in#ation target for Norway. In: Christiansen, A.B., Qvigstad, J.F. (Eds.), Choosing a Monetary Policy Target. Scandinavian University Press (Universitetsforlaget), Oslo. Svensson, L.E.O., 1997/1998. Monetary policy and in#ation targeting. NBER Reporter, 5}8. UNCTAD, 1994. Handbook of International Trade and Development Statistics. United Nations, New York and Geneva.