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Information Content Of The Russian Services Surveys

Author

Listed:
  • Liudmila Kitrar

    (National Research University Higher School of Economics)

  • Tamara Lipkind

    (National Research University Higher School of Economics)

  • Georgy Ostapkovich

    (National Research University Higher School of Economics)

Abstract

The paper explores the information content of surveys of the Russian services sector using the surveys’ results since 2012Q1 to 2018Q4. To summarise entrepreneurial opinions in a one-dimensional index the indicators of confidence and business climate are calculated. To examine the reaction of GDP to impulses in the business climate indicator and to forecast GDP growth by the end of 2019, the Vector Autoregression Model was used. The results of services surveys provide reliable information on the economic sentiment that is essential to measure recession and recovery development of the sector. Since 2013, the survey’s results demonstrate a stable five-year trend of ‘pessimism accumulation’ in the indicators dynamics. The slight increase in entrepreneurial optimism in 2016-2018 did not result in moving confidence to a positive zone. The business climate indicator (BCI) performs better than the traditional confidence indicator in terms of synchronous correlations with GDP growth. A longer observation period needs to draw conclusions about the BCI cyclic properties; however, it can be used now to analyze the development of the Russian services sector.

Suggested Citation

  • Liudmila Kitrar & Tamara Lipkind & Georgy Ostapkovich, 2019. "Information Content Of The Russian Services Surveys," HSE Working papers WP BRP 93/STI/2019, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:93sti2019
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    References listed on IDEAS

    as
    1. Harald Blau, 2007. "Business Survey in Services," Chapters, in: Georg Goldrian (ed.), Handbook of Survey-Based Business Cycle Analysis, chapter 3, Edward Elgar Publishing.
    2. Kajal Lahiri & Yongchen Zhao, 2016. "Determinants of Consumer Sentiment Over Business Cycles: Evidence from the US Surveys of Consumers," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 187-215, December.
    3. Layton, Allan P & Moore, Geoffrey H, 1989. "Leading Indicators for the Service Sector," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 379-386, July.
    4. Kajal Lahiri & Wenxiong Yao, 2012. "Should transportation output be included as part of the coincident indicators system?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2012(1), pages 1-24.
    5. Shyam Upadhyaya & Liudmila Kitrar & Georgy Vladimirovich Ostapkovich & Tamara Lipkind, 2016. "The Main Vectors of Cross-Border Development in the CIS Industrial and Economic Space: Convergence, Potential, Cross-Country Gaps," HSE Working papers WP BRP 60/STI/2016, National Research University Higher School of Economics.
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    2. Juan G Brida & Bibiana Lanzilotta & Lucia I Rosich, 2021. "On the empirical relations between producers expectations and economic growth," Economics Bulletin, AccessEcon, vol. 41(3), pages 1970-1982.

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    Keywords

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    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • L89 - Industrial Organization - - Industry Studies: Services - - - Other

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