Assessing the relationship between non-cash payments and various economic indicators

  • Received December 22, 2021;
    Accepted January 26, 2022;
    Published February 10, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/bbs.17(1).2022.06
  • Article Info
    Volume 17 2022, Issue #1, pp. 67-79
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This work is licensed under a Creative Commons Attribution 4.0 International License

This study is aimed at evaluating the correlation between determinants of non-cash payments (ATMs, number of bank branches, and number of mobile phone users) and various economic indicators (broad money, inflation, consumer prices) as well as further studying which of the factors and to what extent influence each other in different periods. Non-cash payments are provided by ATMs. The sample considers panel data on nine developing countries. The data for calculation were taken from The World Bank, for Kazakhstan – from the Bureau of National Statistics of the Republic of Kazakhstan. The data collected during the study were analyzed using the SPSS software. Spearman’s correlation analysis was used. The results obtained in the empirical study briefly showed that the alternative hypothesis is confirmed for the period 2004–2009 (that the existing relationships are significant), at the same time, the null hypothesis was confirmed in terms of the level of significance for the period 2019–2020. Accordingly, this study showed that modern developments differ from those provided earlier and financial technology transformation is still in the process. The results of this study also indicated the need for further studies of non-traditional measures of financial development, which can lead to sustainable economic growth in the post-crisis period.

Acknowledgments
The study was carried out within the framework of program targeted IRN OR11465433 funding by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan “Development of the concept and mechanisms of balanced territorial development of the economy and society of Kazakhstan”.

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    • Figure 1. Dynamics of Broad Money (% of GDP)
    • Figure 2. Normal distribution P-Plots diagrams
    • Table 1. Variables and their measurements used in the study
    • Table 2. Dynamics of the count of ATMs (per 100,000 adults)
    • Table 3. Commercial bank branches (per 100,000 adults)
    • Table 4. Results according to Spearman’s rho
    • Table A1. Significance coefficients (2-tailed)
    • Conceptualization
      Anna Kredina, Saule Nurymova, Azimkhan Satybaldin, Anel Kireyeva
    • Investigation
      Anna Kredina, Anel Kireyeva
    • Methodology
      Anna Kredina
    • Software
      Anna Kredina, Anel Kireyeva
    • Supervision
      Anna Kredina, Azimkhan Satybaldin
    • Writing – original draft
      Anna Kredina, Anel Kireyeva
    • Data curation
      Saule Nurymova
    • Project administration
      Saule Nurymova
    • Resources
      Saule Nurymova
    • Formal Analysis
      Azimkhan Satybaldin
    • Funding acquisition
      Azimkhan Satybaldin