IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v215y2022ics0165176522001239.html
   My bibliography  Save this article

Crypto-assets, corruption, and capital controls: Cross-country correlations

Author

Listed:
  • Alnasaa, Marwa
  • Gueorguiev, Nikolay
  • Honda, Jiro
  • Imamoglu, Eslem
  • Mauro, Paolo
  • Primus, Keyra
  • Rozhkov, Dmitriy

Abstract

Empirical investigation of the factors underlying the growing usage of crypto-assets is in its infancy, owing to data limitations. In this paper, we present a simple cross-country analysis drawing on recently released survey-based data. We explore the correlation of crypto-asset usage with indicators of corruption, capital controls, a history of high inflation, and other factors. We find that crypto-asset usage is significantly and positively associated with corruption and capital controls. Notwithstanding the data limitations, the results support the case for regulating crypto-assets, including know-your-customer approaches, as opposed to taking a laissez-faire stance.

Suggested Citation

  • Alnasaa, Marwa & Gueorguiev, Nikolay & Honda, Jiro & Imamoglu, Eslem & Mauro, Paolo & Primus, Keyra & Rozhkov, Dmitriy, 2022. "Crypto-assets, corruption, and capital controls: Cross-country correlations," Economics Letters, Elsevier, vol. 215(C).
  • Handle: RePEc:eee:ecolet:v:215:y:2022:i:c:s0165176522001239
    DOI: 10.1016/j.econlet.2022.110492
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176522001239
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2022.110492?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Auer, Raphael & Tercero-Lucas, David, 2022. "Distrust or speculation? The socioeconomic drivers of U.S. cryptocurrency investments," Journal of Financial Stability, Elsevier, vol. 62(C).
    2. Kevin D. Hoover & Stephen J. Perez, 2004. "Truth and Robustness in Cross‐country Growth Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 765-798, December.
    3. Graf von Luckner, Clemens & Reinhart, Carmen M. & Rogoff, Kenneth, 2023. "Decrypting new age international capital flows," Journal of Monetary Economics, Elsevier, vol. 138(C), pages 104-122.
    4. Gabriel Chodorow-Reich & Gita Gopinath & Prachi Mishra & Abhinav Narayanan, 2020. "Cash and the Economy: Evidence from India’s Demonetization," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(1), pages 57-103.
    5. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, Decembrie.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bao, Hong & Li, Jianjun & Peng, Yuchao & Qu, Qiang, 2022. "Can Bitcoin help money cross the border: International evidence," Finance Research Letters, Elsevier, vol. 49(C).
    2. Lennart Ante & Florian Fiedler & Fred Steinmetz & Ingo Fiedler, 2023. "Profiling Turkish Cryptocurrency Owners: Payment Users, Crypto Investors and Crypto Traders," JRFM, MDPI, vol. 16(4), pages 1-13, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Søren Johansen & David F. Hendry & Carlos Santos, 2007. "Selecting a Regression Saturated by Indicators," CREATES Research Papers 2007-36, Department of Economics and Business Economics, Aarhus University.
    2. Ulaşan, Bülent, 2012. "Cross-country growth empirics and model uncertainty: An overview," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-69.
    3. Kevin S. Nell & A.P. Thirlwall, 2017. "Perche' la produttivita' degli investimenti varia tra paesi? (Why does the productivity of investment vary across countries?)," Moneta e Credito, Economia civile, vol. 70(279), pages 197-231.
    4. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
    5. David F. Hendry & Hans‐Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
    6. Asad Zaman, 2017. "Lessons in Econometric Methodology: The Axiom of Correct Specification," International Econometric Review (IER), Econometric Research Association, vol. 9(2), pages 50-68, September.
    7. David F. Hendry & Hans-Martin Krolzig, 2003. "Sub-sample Model Selection Procedures in Gets Modelling," Economics Papers 2003-W17, Economics Group, Nuffield College, University of Oxford.
    8. Feyen,Erik H.B. & Kawashima,Yusaku & Mittal,Raunak, 2022. "Crypto-Assets Activity around the World : Evolution and Macro-Financial Drivers," Policy Research Working Paper Series 9962, The World Bank.
    9. Hendry, David F. & Mizon, Grayham E., 2001. "Reformulating empirical macro-econometric modelling," Discussion Paper Series In Economics And Econometrics 0104, Economics Division, School of Social Sciences, University of Southampton.
    10. Geert Bekaert & Campbell R. Harvey & Christian T. Lundblad & Stephan Siegel, 2011. "What Segments Equity Markets?," Review of Financial Studies, Society for Financial Studies, vol. 24(12), pages 3841-3890.
    11. Julia Korosteleva & Colin Lawson, 2010. "The Belarusian case of transition: whither financial repression?," Post-Communist Economies, Taylor & Francis Journals, vol. 22(1), pages 33-53.
    12. Lucas A. Mariani & Jose Renato Haas Ornelas & Bernardo Ricca, 2023. "Banks’ Physical Footprint and Financial Technology Adoption," Working Papers Series 576, Central Bank of Brazil, Research Department.
    13. Ghosh, Soumya Kanti & Nath, Hiranya K., 2023. "What determines private and household savings in India?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 639-651.
    14. Tsang, Shu-ki & Ma, Yue, 1997. "Simulating the impact of foreign capital in an open-economy macroeconomic model of China," Economic Modelling, Elsevier, vol. 14(3), pages 435-478, July.
    15. B. Bhaskara Rao, 2010. "Time-series econometrics of growth-models: a guide for applied economists," Applied Economics, Taylor & Francis Journals, vol. 42(1), pages 73-86.
    16. Beyer, Robert C.M. & Franco-Bedoya, Sebastian & Galdo, Virgilio, 2021. "Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity," World Development, Elsevier, vol. 140(C).
    17. Kevin S. Nell & A.P. Thirlwall, 2017. "Why does the productivity of investment vary across countries?," PSL Quarterly Review, Economia civile, vol. 70(282), pages 213-245.
    18. Martha Misas A. & Carlos Esteban Posada P & Diego Mauricio Vásquez E, 2003. "¿Está determinado el nivel de precios por las expectativas de dinero y producto en Colombia?," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 21(43), pages 8-31, June.
    19. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    20. Tadesse, Tasew, 2011. "Foreign aid and economic growth in Ethiopia," MPRA Paper 33953, University Library of Munich, Germany, revised 20 Sep 2011.

    More about this item

    Keywords

    Crypto-assets; Cryptocurrency; Corruption; Capital controls;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:215:y:2022:i:c:s0165176522001239. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.