Peer Effects in Central Banking

Abstract

We provide a new explanation for why central banks have become transparent over the last three decades. We apply recently developed social interaction panel regression models for the observational data, which allow the identification of peer effects. The identification is based on variations in the past monetary policy regime exogenously determined with respect to transparency. Previous literature has argued that domestic factors such as macroeconomic stability were behind the trend toward greater transparency. In contrast, our results indicate that transparency primarily increased because of a favorable global environment and, importantly, because of the peer effects among central bankers. Central bankers thus learned from each other’s experiences regarding transparency.

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Fig. 1

Notes

  1. 1.

    Manski (1993) also notes that “These effects may, depending on the context, be called “social norms,” “peer influences,” “neighborhood effects,” “conformity,” “imitation,” “contagion,” “epidemics,” “bandwagons,” “herd behavior,” “social interactions,” or “interdependent preferences”. Therefore, Manski (1993) states that the interpretation of peer effects depends on the context. We suppose that, in the context of central banks, peer effects largely coincide with learning, in the sense of both voluntary learning from experience of other central banks and peer pressure. We support this claim with the extensive anecdotal evidence provided in the following section.

  2. 2.

    These models have been typically applied to analyze individual behavior. Decisions made at central banks are typically collective (Reis 2013) and are made by a handful of central bank officials, although they are sometimes strongly influenced by the governor (Blinder et al. 2009). On a global scale, the average number of monetary policy committee members is approximately 5–7, which is slightly higher than the typical household. The peer effects among households in terms of consumption are examined by Maurer and Meier (2008) and Krishnan and Patnam (2014).

  3. 3.

    Leitemo and Roisland (2002) study the choice of monetary policy regimes and also note that this choice is motivated to achieve the target of stable inflation, exchange rate, or growth.

  4. 4.

    Instead of actual monetary policy regimes, we also randomly generate the regimes in one of our robustness checks. In this case, as expected, peer effects disappear.

  5. 5.

    Some of our robustness checks also consider membership in economic unions and in IMF regional departments as the measure of interaction.

  6. 6.

    Gibbons and Overman (2012) discuss the importance of identification for applied spatial econometric model exercises and argue that, without proper identification, spatial econometrics is pointless. Volden et al. (2008) formally show that the diffusion of policy experiments due to learning from each others’ experiences is often indistinguishable from the independent adoption of policy experiments; therefore, an identification strategy to distinguish between these two effects is critical.

  7. 7.

    In one of our robustness checks, we also use data on how central banks are transparent about their financial stability assessment from Horvath and Vasko (2016).

  8. 8.

    The European Central Bank data are used to assess monetary policy transparency in the euro area, and the explanatory variables are averaged across the member countries in this case (unless they are readily available at the euro area aggregate level). Financial stability transparency is assessed at the country level. Noteworthy is that our results remain largely the same if we exclude the euro area countries from our sample. The list of countries is as follows: Albania, Argentina, Armenia, Aruba, Australia, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belize, Bermuda, Bhutan, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cuba, Czech Republic, Denmark, Egypt, El Salvador, Estonia, Ethiopia, Euro Area countries, Fiji, Georgia, Ghana, Guatemala, Guyana, Hong Kong, Hungary, Iceland, India, Indonesia, Iraq, Israel, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Korea, Kuwait, Kyrgyzstan, Latvia, Lesotho, Libya, Lithuania, Malawi, Malaysia, Malta, Mauritius, Mexico, Mongolia, Namibia, New Zealand, Nigeria, Norway, Oman, Pakistan, Papua New Guinea, Peru, the Philippines, Poland, Qatar Republic, Moldova, Romania, Russian Federation, Rwanda, Saudi Arabia, Sierra Leone, Singapore, Slovak Republic, Slovenia, Solomon Islands, South Africa, Sri Lanka, Sudan, Sweden, Switzerland, Tajikistan, Thailand, Trinidad and Tobago, Tunisia, Turkey, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, United States, Vanuatu, Yemen, and Zambia.

  9. 9.

    The working paper version contains an illustrative theoretical model along the lines of Calvó-Armengol et al. (2009) and Davezies et al. (2009) and derives the empirical specification used in this article.

  10. 10.

    Bramoullé et al. (2009) estimate the peer effects model as in Eq. (1) to analyze the participation in recreational activities. The dependent variable in their model is an index of participation with values from 0 to 4. Therefore, the nature of their dependent variable is identical to our central bank transparency indexes.

  11. 11.

    As an alternative, we classify countries according to the most common monetary policy regime that they had in 2000–2011. In principle, the common monetary policy regime can be endogenous to transparency scores even though the degree of endogeneity is likely to be low. In this case, we have 34 countries with exchange rate anchoring, 16 with monetary targeting, 27 with inflation targeting, and 33 with another regime, including fund-supported or other monetary programs and IMF-supported or other monetary programs.

  12. 12.

    Despite commonly held beliefs, LeSage and Pace (2011) show that the statistical inference in spatial econometric models is not very sensitive to the particular specifications used for the spatial weight structure in these models. Our results presented in the following section support this finding.

  13. 13.

    It is worth noting that an alternative such as the trade intensity among countries could, in principle, work as well, but trade links are instrumented by geographical distance in most empirical research on international trade.

  14. 14.

    We use the following economic unions to generate our matrix W: 1. CARICOM Single Market and Economy - CSME, 2. European Union - EU, 3. Eurasian Economic Union - EAEU, 4. Southern Common Market - MERCOSUR, 5. Gulf Cooperation Council - GCC, 6. Central American Integration System - SICA, 7. The Association of Southeast Asian Nations - ASEAN, 8. Economic Community of West African States - ECOWAS, 9. Common Market for Eastern and Southern Africa - COMESA, 10. European Free Trade Association - EFTA, 11. Greater Arab Free Trade Area - GAFTA, 12. North American Free Trade Agreement - NAFTA, and 13. Other. The membership is as of the year 2000 (given that the regressions use data from 2001 to 2010).

  15. 15.

    The IMF’s regional departments are as follows: African Department, Asia and Pacific Department, European Department, Middle East and Central Asia Department, and Western Hemisphere Department.

  16. 16.

    Note that Davezies et al. (2009) show that the model in Lee (2007) is identifiable even if group members are not observed.

  17. 17.

    The main issue is how to recover structural parameters from the reduced-form model. To understand the mechanics of model identification, expressing our regression equation as the within-group transformation is useful to eliminate the unobserved effects (group-invariant correlated effects) and present it separately for each monetary policy regime (group, R). Therefore, we obtain the following equation for central banks in the monetary policy regime R:

    $$\begin{aligned} y_{R,i}-\tilde{y_{R}}=\frac{\beta _{1}-\frac{\beta _{2}}{m_{R}-1}}{1+\frac{\lambda }{m_{R}-1}}(x_{R,i}-\tilde{x_{R}})+\frac{1}{1+\frac{\lambda }{m_{R}-1}}+(\epsilon _{R,i}-\tilde{\epsilon _{R}}) \end{aligned}$$
    (2)

    where \(\tilde{y_{r}}\), \(\tilde{x_{r}}\) and \(\tilde{\epsilon _{r}}\) are calculated using all central banks in the single monetary policy regime. There are three parameters—\(\lambda\), \(\beta _{1}\), and \(\beta _{2}\)—to be estimated in the equation; therefore, we need at least three different group sizes, m, to recover the structural parameters.

  18. 18.

    Lee (2007) also finds that conditional maximum likelihood estimates are more efficient than those from two-stage least squares. Therefore, we do not estimate Eq. (1) using the latter technique.

  19. 19.

    Finally, it is of interest to evaluate whether peer effects are concentrated in some specific dimensions of monetary policy transparency. However, estimating these regressions due to insufficient variation in the subcomponents of the monetary policy index is not possible. The overall monetary policy index consists of five subcomponents: political, economic, procedural, policy, and operational transparency. As previously noted, the frequency of changes in the overall monetary policy transparency index is 24%. Because we have 5 different subcomponents, on average, the frequency of changes in the subcomponent is 24/5, which is approximately 5%. This result implies that, on average, the subcomponent changes once in 20 years. For this reason, virtually no panel data research exists on the determinants of the subcomponents of monetary policy transparency.

  20. 20.

    In this regard, Conley and Udry (2010) emphasize that the adoption of new technologies (or the adoption of policy experiments, as in our case) may be spatially and serially correlated, not necessarily because of learning but because of some other omitted variable. These authors stress that the proper identification of social learning requires detailed data to control for otherwise confounding factors. Conley and Udry (2010) note that “Spatial proximity is correlated with the presence of information links, but it is not their sole determinant. Information links occur over long as well as short distances”.

  21. 21.

    Note that the other monetary policy group includes countries, such as Albania or Armenia. These countries subsequently adopted inflation targeting, and they were labeled as the “other” group because they initially lacked an exchange rate anchor or explicit inflation target. The “other” group also includes countries such as Belarus or Kazakhstan, which progressed from the “other” regime to the exchange rate anchor. The “other” group often consists of countries with the general ambition to safeguard price stability but did not accompany it with any explicit exchange rate anchor or inflation (or monetary) target.

  22. 22.

    The number of observations in the regressions is slightly lower than 300 because of missing observations.

  23. 23.

    However, regardless of whether the error term has or does not have the spatial structure, the peer effects remain significant.

  24. 24.

    Clearly, the matrix W would have the values of 1 in all of its elements (outside the diagonal).

  25. 25.

    The procedure for the Monte Carlo simulation is as follows: (1) Model is estimated with actual data. (2) The coefficient estimates from step 1 are taken as the true parameters, and the peer effect coefficient is set to 0.05. (3) Errors are randomly generated, and the dependent variable is calculated. (4) The coefficients are estimated with the dependent variable from step 3, and the estimates are saved. (5) Steps 3 and 4 are repeated 1000 times. The peer effects parameter is then consecutively increased to 0.1, 0.15, ..., 0.95.

  26. 26.

    Intuitively, a direct effect shows that when country i increases the rule of law, what will be the average impact on the central bank’s transparency in country i? To obtain the average direct effect, one needs to average across all countries. The indirect effects show the impact of all other countries raising their rule of law on central bank transparency on an individual country, again averaged over all countries.

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Correspondence to Roman Horvath.

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We thank the Editor, Emine Boz, the two anonymous referees, Peter Claeys, Fabrizio Coricelli, Michal Franta, Martin Gregor, Christopher Hartwell, Klodiana Istrefi, Evzen Kocenda, Michele Lenza, Katarina Lucivjanska, Alexander Michaelides, Michael Moritz, Morten Ravn, Lucjan Orlowski, Jakub Seidler, Raju Singh, Cedric Tille, Borek Vasicek, Jan Zapal, and seminar participants at the Friedrich-Alexander-Universitat Erlangen-Nurnberg, Leibniz Institute for East and Southeast European Studies, Czech Economic Society conference, European Public Choice Society conference, ICMAIF conference, Society for the Study of Emerging Markets conference, European Association for Comparative Economic Studies conference, Networks, Complexity and Economic Development workshop, Swiss Society for Economics and Statistics annual conference, and Slovak Economic Association annual conference for their helpful comments. Pavla Brizova, Daniil Kashkarov, Tomas Krehlik, Boril Sopov, and Ivan Trestcov provided excellent research assistance. We acknowledge support from the Grant Agency of the Czech Republic 19-15650S. Online appendix is available at https://ies.fsv.cuni.cz/en/staff/horvath.

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Appendix

Appendix

Data Definitions and Sources

Monetary policy transparency index: An index of monetary policy transparency taking values between 0 and 15. Dincer and Eichengreen (2014).

Financial stability transparency index: An index of financial stability transparency taking values between 0 and 15. Horvath and Vasko (2016).

GDP p.c.: GDP per capita in current USD. International Monetary Fund.

Past inflation: % change in the consumer price index. International Monetary Fund.

Openness: Exports of goods and services as a percentage of GDP. World Bank.

Financial depth: Private credit as a percentage of GDP. World Bank.

Rule of Law: Captures perceptions of the extent to which agents have confidence in and abide by the rules of society and, in particular, the quality of contract enforcement, property rights, the police, and the courts as well as the likelihood of crime and violence. Ranges from -2.5 (the lowest possible score) to 2.5 (the highest possible score). The Worldwide Governance Indicators - World Bank.

Voice and Accountability: Captures perceptions of the extent to which a country’s citizens are able to participate in selecting their government as well as freedom of expression, freedom of association, and a free media. Ranges from -2.5 (the lowest possible score) to 2.5 (the highest possible score). The Worldwide Governance Indicators - World Bank.

Government efficiency: Captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Ranges from -2.5 (the lowest possible score) to 2.5 (the highest possible score) The Worldwide Governance Indicators - World Bank.

Political stability and the absence of violence: Measures perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically motivated violence and terrorism. Ranges from -2.5 (the lowest possible score) to 2.5 (the highest possible score). The Worldwide Governance Indicators - World Bank.

Democracy: Ordinal variable taking values from 0 to 10 that measures the level of democracy in the country by deliberating three main elements: 1. ”presence of institutions and procedures through which citizens can express effective preferences about alternative policies and leaders”, 2. ”the existence of institutionalized constraints on the exercise of power by the executive”, and 3. ”the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation.” Polity IV.

Autocracy: Ordinal variable taking values from 0 to 10 measuring the level of autocracy in the country, taking into account the essential attributes: “chief executives are chosen in a regularized process of selection within the political elite, and once in office, they exercise power with few institutional constraints.” Polity IV.

Overall polity score: The difference between the democratic score and the autocratic score. Ranges from \(+\) 10 (for the most democratic countries) to − 10 (for the most autocratic countries). Polity IV (Figs. 2, 3, 4 and Tables 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19).

Table 7 Determinants of monetary policy transparency: common monetary policy regime
Table 8 Determinants of monetary policy transparency: lagged regressors
Table 9 Determinants of monetary policy transparency: time effects
Table 10 Determinants of monetary policy transparency: membership in economic unions
Table 11 Determinants of financial stability framework transparency: geographical distance
Table 12 Determinants of financial stability framework transparency: common monetary policy regime, controlling for supervisory structure
Table 13 Determinants of financial stability framework transparency: geographical distance, controlling for supervisory structure
Table 14 Determinants of monetary policy transparency: no change in monetary policy regime
Table 15 Determinants of monetary policy transparency: only exchange rate targeters included
Table 16 Determinants of monetary policy transparency: only monetary targeters included
Table 17 Determinants of monetary policy transparency: only inflation targeters included
Table 18 Determinants of monetary policy transparency: only central banks with “other” policy regime
Table 19 Determinants of monetary policy transparency: W randomly generated
Fig. 2
figure2

Evolution of monetary policy transparency: different regimes. Notes the figure presents the evolution of monetary policy transparency indexes for the different monetary policy regimes

Fig. 3
figure3

Reversals in monetary policy transparency. Notes: the figure presents the number of reversals of monetary policy transparency indexes over time. The reversal denotes the situation in which the value of the transparency index decreases with respect to the previous year

Fig. 4
figure4

Monte Carlo simulations: true versus simulated values. Note: the figures compare the simulated to the true values of the peer effect coefficient. The diagonal line pictures the true value; the dots represent the corresponding simulated values for the peer effect coefficient. The simulated values closer to the diagonal line suggest a smaller bias of our estimator

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Horvath, R. Peer Effects in Central Banking. IMF Econ Rev 68, 764–814 (2020). https://doi.org/10.1057/s41308-020-00121-5

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JEL Classificaion

  • C31
  • D83
  • E58