Illusion of stability: An empirical analysis of inflation data manipulation by russia after 2022

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This paper explores the perceived resilience of russia’s economy under severe sanctions, investigating the potential falsification of economic data to demonstrate the growth. The hypothesis is that the relationship between the official inflation rate and the FMCG deflator index during 2019–2021 significantly differs from that of 2022–2024. Statistical methods, such as correlation analysis, Granger causality tests, and differences tests (e.g., t-tests and Wilcoxon tests), are used along with vector autoregressive (VAR) models and robust linear regressions. The study covers the pre-invasion period (2019–2021) and the post-invasion period (2022–2024), focusing on indicators like the official inflation rate, inflation expectations, CPI, and the FMCG deflator index. Findings reveal a shift from a direct to an inverse correlation between official inflation and the FMCG deflator post-2022, suggesting data manipulation. Pre-2022 models predict inflation 2-3 times higher than both post-2022 models and official statistics, raising concerns about the reliability of russia’s economic data. Further research should explore indirect metrics, such as electricity production and cargo shipments, for additional evidence of data falsification.

Acknowledgments
Alex Plastun gratefully acknowledges financial support from the New Europe College (NEC), the Center for Advanced Study, and Sumy State University.
Anna Vorontsova gratefully acknowledges financial support from Sumy State University.

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    • Figure 1. The dynamics of the official inflation rate and inflation expectations among the population (median observed and expected inflation values) in russia for the period 2019–2024
    • Figure 2. The dynamics of the Consumer Price Index (CPI) of russia from 2019 to 2024
    • Figure 3. Dynamics of russia’s FMCG Deflator for 2019–2024
    • Figure 4. Comparison of scatterplot of inflation rate versus FMCG deflator for 2019–2021 and 2022–2024 periods
    • Figure 5. Comparison of scatterplot of inflation rate versus predicted model No. 1 and predicted model No. 2 for 2019–2021 and 2022–2024 periods
    • Figure 6. Comparison of scatterplot of inflation rate versus predicted model No. 1 and predicted model No. 2 for 2019–2021 and 2022–2024 periods for the case of vector regression modeling
    • Table 1. Correlation analysis
    • Table 2. Granger causality Wald tests for infl – fmcg
    • Table 3. Testing the direction of the regression relationship
    • Table 4. Testing differences between forecast models
    • Table A1. russian statistical data closed after February 24, 2024
    • Table B1. The list of indirect economic indicators for the case of russia
    • Conceptualization
      Alex Plastun, Liudmyla Huliaieva, Victor Sukhonos, Ruslan Bilokin
    • Funding acquisition
      Alex Plastun
    • Investigation
      Alex Plastun, Olha Yatsenko, Victor Sukhonos, Ruslan Bilokin
    • Project administration
      Alex Plastun
    • Resources
      Alex Plastun, Liudmyla Huliaieva
    • Supervision
      Alex Plastun
    • Writing – original draft
      Alex Plastun, Anna Vorontsova, Yaroslava Slyvka, Olha Yatsenko, Liudmyla Huliaieva
    • Writing – review & editing
      Alex Plastun, Liudmyla Huliaieva, Victor Sukhonos, Ruslan Bilokin
    • Data curation
      Anna Vorontsova, Olha Yatsenko
    • Formal Analysis
      Anna Vorontsova, Yaroslava Slyvka
    • Methodology
      Anna Vorontsova, Liudmyla Huliaieva
    • Software
      Anna Vorontsova, Olha Yatsenko
    • Visualization
      Anna Vorontsova, Yaroslava Slyvka
    • Validation
      Yaroslava Slyvka, Victor Sukhonos, Ruslan Bilokin