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Information Content of Russian Services Surveys

Author

Listed:
  • Liudmila Kitrar

    (Institute for Statistical Studies and Economics of Knowledge, Centre for Business Tendency Studies)

  • Tamara Lipkind

    (Institute for Statistical Studies and Economics of Knowledge, Centre for Business Tendency Studies)

  • Georgy Ostapkovich

    (Institute for Statistical Studies and Economics of Knowledge, Centre for Business Tendency Studies)

Abstract

This paper explores the information content of a business tendency survey on the service sector in Russia from 2012 to 2018. Two alternative survey-based indices were developed: a traditional confidence indicator as a simple arithmetic mean of the balance statistics of three respondents’ replies and a business climate indicator (BCI) as the first component of five balances. The latter indicator is expected to perform better in terms of tracking a reference quantitative variable (GDP growth). The survey results reveal a six-year negative trend in two composite indicators and the other balances of the respondents’ opinions. Despite a slight improvement in 2017 and 2018, the business climate in the service sector remains unfavourable. The dynamics of confidence and business climate indicators have a lagging nature relative to GDP growth; however, BCI performs slightly better in terms of a synchronous correlation. The shock in BCI in the framework of the bivariate VAR model produces a statistically significant positive response of GDP, which reaches a peak in the second quarter after the shock. The insufficient time series of survey results limits the cyclical analysis of BCI; however, BCI can be used as a main composite indicator for analysing the development of the service sector.

Suggested Citation

  • Liudmila Kitrar & Tamara Lipkind & Georgy Ostapkovich, 2020. "Information Content of Russian Services Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(1), pages 59-74, April.
    • Handle: RePEc:spr:jbuscr:v:16:y:2020:i:1:d:10.1007_s41549-020-00040-4
    DOI: 10.1007/s41549-020-00040-4
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    References listed on IDEAS

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    1. 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.
    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. Harald Blau, 2007. "Business Survey in Services," Chapters, in: Georg Goldrian (ed.), Handbook of Survey-Based Business Cycle Analysis, chapter 3, Edward Elgar Publishing.
    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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    Cited by:

    1. 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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    More about this item

    Keywords

    Service sector; Business tendency surveys; Confidence indicator; Business climate indicator; Russia;
    All these keywords.

    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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