The impact of readability of the Bank of Russia press releases on infl ation expectations: an experimental approach
https://doi.org/10.55959/MSU0130-0105-6-60-3-2
Abstract
Under inflation targeting, the success of monetary policy largely depends on the central bank’s ability to influence inflation expectations through effective communication. Particular importance is attached to interaction with non-specialists, the general public, whose expectations in Russia have historically been elevated and less anchored. This article examines the impact of readability of Bank of Russia press releases on public inflation expectations. Readability is a property of textual material that reflects how easily it can be perceived during reading. To test the hypothesis, the authors conducted a laboratory experiment with 274 participants divided into control and experimental groups. The participants received either original or simplified versions of press releases, with more readable versions generated using generative artificial intelligence. The experiment simulated both easing and tightening monetary policy conditions. The results showed that in the case of an interest rate hike, simplified texts significantly reduced inflation expectations—on average by 1.8–2 percentage points. In the case of a rate cut, the effect was statistically insignificant. Thus, improving the clarity of communication is especially effective in a tight monetary policy context. The findings highlight the need to adapt central bank communication materials for a broad audience. The study concludes that enhancing readability can increase public trust in monetary authorities and strengthen the transmission mechanism of monetary policy. The work relies on methods of experimental economics, textual analysis, and regression modeling, confirming the importance of communication quality in managing inflation expectations.
About the Authors
F. S. KartaevRussian Federation
Moscow
O. A. Klachkova
Russian Federation
Moscow
References
1. Evstigneeva, A. (2023). Kommunikatsiya kak instrument denezhno-kreditnoy politiki. Bank Rossii. Analiticheskie zapiski. URL: https://www.cbr.ru/StaticHtml/File/146496/research_policy_notes_b_4_1.pdf (data obrashcheniya: 09.10.2024).
2. Erokhin , A., & Klachkova, O. (2024). Infl uence of Readability and Tone of Bank of Russia Text on Infl ation Expectations. Russian Journal of Money and Finance, 83(4), 27–47.
3. Oborneva, I. V. (2006). Avtomatizirovannaya otsenka slozhnosti uchebnykh tekstov na osnove statisticheskikh parametrov. Kandidatskaya dissertatsiya. Moskva.
4. Angrist , J. D., & Pischke, J.-S. (2008) Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
5. Bholat, D., & Broughton, N., Ter Meer, J., & Walczak, E. (2019). Enhancing central bank communications using simple and relatable information. Journal of Monetary Economics, 108, 1–15. doi: 10.1016/j.jmoneco.2019.08.007
6. Carotta, G., Mello, M., & Ponce, J. (2023). Monetary policy communication and infl ation expectations: New evidence about tone and readability. Latin American Journal of Central Banking, 4, 3, Article 100088. doi: 10.1016/j.latcb.2023.100088
7. Coibion, O., Gorodnichenko, Y., & Kumar, S. (2018). How do fi rms form their expectations? New survey evidence. American Economic Review, 108, 9, 2671–2713. doi: 10.1257/aer.20151299
8. Coibion, O., Gorodnichenko Y., & Weber, M. (2022). Monetary Policy Communications and Their Eff ects on Household Infl ation Expectations. Journal of Political Economy, 130, 1537–1584. doi: 10.1086/718982
9. Evstigneeva, A., & Sidorovskiy, M. (2021). Assessment of clarity of bank of Russia monetary policy communication by neural network approach. Russian Journal of Money and Finance, 80, 3, 3–33. doi: 10.31477/rjmf.202103.03
10. Flesch, R. (1948). A New Readability Yardstick. Journal of Applied Psychology, 32(3), 221–233. doi: 10.1037/h0057532
11. Kincaid, J. P., Fishburne, R. P. Jr., Rogers, R. L., & Chissom, B. S. (1975). Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) For Navy Enlisted Personnel. Naval Technical Training Command: Research Branch Report, 8.
12. Hansen, A. L., & Kazinnik, S. (2023). Can ChatGPT Decipher Fedspeak? SSRN Electronic Journal. doi: 10.2139/ssrn.4399406
13. Mochhoury, S. (2023). Central bank communication and trust: an experimental study on the European Central Bank and the general public. ECB Working Paper, 2023/2824.
14. Smales, L. A. (2023). Classifi cation of RBA monetary policy announcements using ChatGPT. Finance Research Letters, 58.
15. Svensson, L. E. O. (2010). Infl ation Targeting. NBER Working Paper, w16654, Available at SSRN: https://ssrn.com/abstract=173292
16. Woodhouse, D., & Charlesworth, A. (2023). Can ChatGPT Predict Future Interest Rate Decisions? SSRN Electronic Journal. doi: 10.2139/ssrn.4572831
Review
For citations:
Kartaev F.S., Klachkova O.A. The impact of readability of the Bank of Russia press releases on infl ation expectations: an experimental approach. Moscow University Economics Bulletin. 2025;(3):20-31. (In Russ.) https://doi.org/10.55959/MSU0130-0105-6-60-3-2