Does clarity of central bank communication affect credibility? Evidences considering governor-specific effects

ABSTRACT

Central banks have made great efforts to increase transparency and accountability to the public. Since then, studies seek empirical evidences about the effects of monetary policy communication over agent’s expectations. The recent literature on central bank communication draws attention to the importance of clarity of central bank communication. However, researches on this theme are still scarce, and there are few empirical studies with conclusive findings. Our study seeks empirical evidences on the relation between clarity of central bank communication and credibility of monetary policy. Estimates through different methods aim to identify whether clarity of central bank communication improves credibility. The study is the first to provide empirical evidence that a clearer communication can improve credibility. We also consider the differences between the two governors who ruled the Central Bank of Brazil in the period under analysis. The results indicate that a clear communication can improve credibility, but it depends on the commitment of the central banker with the goal of inflation control. Furthermore, estimates based on quantile regression indicate that the benefit brought by the clarity to the credibility depends on the commitment of the monetary authority with the goal guiding inflation expectations.

KEYWORDS:

• Credibility
• clarity
• communication
• central bank

JEL CLASSIFICATION:

• E44
• E58
• E37

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Blinder (2000) mailed a questionnaire to the heads of 127 central banks – the entire membership of the Bank for International Settlements (BIS) – soliciting their opinions on a variety of issues related to central bank credibility.

² Blinder (2000) highlights that ‘recent trends in central banking seem to be moving strongly in the direction of greater transparency, however, with such institutions at the Bank of England and the Reserve Bank of New Zealand in the vanguard. Both of these central banks explicitly adopted inflation targeting and a high degree of transparency as ways to create credibility from scratch. Brazil is embarking on that course now’ (1429).

³ Bernanke et al. (1999, 23) see the inflation targeting framework as serving two important functions: (1) improving communication between policymakers and the public, and, (2) providing discipline and accountability in the making of monetary policy.

⁴ Our intention was to analyse the specific effects of all CBB governors during the regime of inflation targeting but, due to data availability on expectations and due to the fact that prior to 2003 there was no standard for the period of time that the minutes of the Monetary Policy Committee (COPOM) meetings were published after each meeting, the analysis is for the period between May 2003 and March 2015.

⁵ Although different indexes of credibility have been proposed – as summarized in the works of de Mendonça and de Guimarães E Souza (2009) and Nahon and Meurer (2009) – and therefore there is a variety of indexes of credibility capable of being used in empirical analyses, this study does not seek to analyse the effect of clarity on each credibility index. The option for using the index proposed by de Mendonça (2007) is due to the following arguments: (i) the index is widely used in several applied studies and it is recognized by international literature; (ii) simplicity of understanding and preparation; (iii) the index captures the changes and fluctuations in credibility in a way compatible with the regime of inflation targeting adopted in Brazil, that is, the index uses predetermined tolerance bands, and not ad hoc tolerance bands as proposed by other indices; and (iv) the index has a better performance than other indices and it is rigorous enough and punishes appropriately deviations of inflation expectations in relation to the inflation target.

⁶ The studies of Montes (2012), Montes and Scarpari (2015), Montes and Nicolay (2015), Taborda (2015) and Montes et al. (2016) find evidence that central bank communication (based on the minutes of the COPOM meetings) affects the expectations of financial markets participants in Brazil.

⁷ According to Taborda (2015), ‘The empirical literature on IT implementation and central bank transparency regards the release of the minutes as a step toward IT success. No matter its contents, a release itself demonstrates purpose and enhances transparency. However, the mere release of the minutes (whether or not they record the deliberation process verbatim, present voting records, or scrutinize a Board member’s view) might not achieve two leading IT goals: establishing a clear communication with the public (Mishkin 2000); and attaining a coherent management of their expectations (Blinder et al. 2008). If the minutes (and their timely release) are a core element of procedural transparency, their clarity or the lack of it, their proper or improper wording, their verbose or succinct treatment of specific events, may have desirable or undesirable effects upon policy outcomes’.

⁸ Prior to this date, there was no standard for the period of time that the minutes of the COPOM meetings were published after each meeting.

⁹ Regarding OLS estimates, we estimate OLS with the Newey–West covariance matrix. Furthermore, Ramsey’s RESET test is presented for all OLS estimates. RESET is a general test for the following types of specification errors: omitted variables; incorrect functional form; and correlation between regressors and the error term, which may be caused, among other things, by measurement error in regressors or simultaneity (endogeneity) (see Ramsey 1969 and Wooldridge 2009). As one can see, the outcomes of the Ramsey’s RESET test in all OLS estimates indicate that the estimates do not present problems of model specification (such as simultaneity).

¹⁰ Regarding all GMM estimations, we present the Durbin–Wu–Hausman test of the endogeneity of regressors (Durbin 1954; Wu 1973; Hausman 1978).

¹¹ Instrumental variables GMM:Equation (3): CI(−1 to −5), INFD(−1), IR(−2 to −6) d_ER(−1 to −4), d_FKI(−2 to −3), d_PS(−1 to −4).Equation (4): CI(−1 to −5), INFD(−1), IR(−2 to −5), d_ER(−1 to −4) d_FI(−2 to −3) d_PS(−1 to −4).Instrumental variables GMM 2-STEP: Equation (3): CI(−1 to −5), INFD(−1), IR(−2 to −7), d_ER(−1 to −3), d_FKI(−2 to −6) d_PS(−1 to −6), DEBT(−1 to −5). Equation (4): CI(−1 to −5), INFD(−1), IR(−2 to −7), d_ER(−1 to −3), d_FI(−2 to −5) d_PS(−1 to −4) DEBT(0 to −6).

¹² We run TOBIT model with robust Huber–White covariance approach.