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Too Many Cooks? Committees in Monetary Policy Helge Berger and Volker Nitsch

Too Many Cooks? Committees in Monetary Policy Helge Berger and Volker Nitsch. Discussion by Christopher Crowe, IMF Research Department* for Conference on Central Bank Communication, Decision-Making and Governance , Waterloo University April 28-29 2009.

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Too Many Cooks? Committees in Monetary Policy Helge Berger and Volker Nitsch

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  1. Too Many Cooks? Committees in Monetary PolicyHelge Berger and Volker Nitsch Discussion by Christopher Crowe, IMF Research Department* for Conference on Central Bank Communication, Decision-Making and Governance, Waterloo University April 28-29 2009 * Does not necessarily reflect the views of the IMF, its management or Executive Board.

  2. Summary of the paper • Paper’s key contribution is a new dataset covering >30 countries for up to 40 years • De facto as well as de jure committee size for CB MPCs • Composition of MPC (industrial, regional, institutional background) • Principal Use of the Data: is there an optimal committee size for inflation control?

  3. Main Results • U-shaped relationship for inflation: minimized at 8-10 committee members. • Similar U-shaped relationship for inflation volatility. • Inverse-U for output growth. • Higher MPC member turnover associated with lower inflation (opposite of result with governor turnover). • Composition of MPC (e.g. government representation) has little impact.

  4. My comments • Very interesting paper on an important topic. • The dataset could be used to shed light on several interesting questions relating to committees, decision-making and central bank governance… • …but I’m not wholly convinced that the paper’s main question is the right one to ask with this data. • Plus a few other comments.

  5. Are the main results convincing? • Why should committee size affect the level of inflation? • Committee too large: cannot communicate easily; subject to factionalism or defense of own interests. • Committee too small: cannot benefit from lower volatility of mean decision. • But why higher inflation rather than more volatile inflation or inflation that’s harder to predict? • Any plausible model of the inflation bias with committee size as argument? Or of output growth? • Omitted variable bias likely large compared to any genuine effect. • Easy to think of omitted variables: • “Institutions” (effectiveness of government sector) • Heterogeneity (has macro implications, might require large committee) • “Culture” (shapes views on decision-making and individual responsibility, also macro policy and outcomes). • Regressions have only a minimum set of reasonable controls.

  6. What could be driving the results? • Inflation bias can be caused by inappropriate output gap target • E.g. because government is going for short run growth • CBI as remedy: devolve to conservative CB’er or one with appropriate objectives • If a government wanted to weaken CBI: • Have a small committee (less political cover for tough decisions; easier to replace with more compliant candidates) • Have a large committee (ineffective talking shop; agenda-setting by compliant governor/chairman). • i.e. committee of reasonable size more independent. • But committee size not ultimate causal factor.

  7. What other questions are there? • Inflation and output volatility • (paper already looks at variance of inflation) • Inflation and output predictability (look at variance of forecast errors) • Combine with voting behavior (more limited sample): • Do larger committees vote differently (e.g. agenda-setting by chair?) • Do different types of MPC member vote differently? (e.g. Besley, Meads and Surico, 2007; Meade and Sheets, 2005). • MPC member turnover (some interesting results in the paper, I would give these greater prominence).

  8. Some other comments • The baseline results could be made more convincing: • Cluster SE’s by country to capture country-specific patterns of serial correlation • Additional controls: • measures of governance • Political turnover measures • De jure CBI • Output gap rather than growth minus avg. growth • Use inflation tax transform π/(1+ π) to reduce impact of outliers • It would also be useful to have summary statistics for the main variables.

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