THE METHODOLOGY OF DIGITAL SHADOW ECONOMY ESTIMATION
Name and surname of author:
Ligita Gasparėnienė, Yuriy Bilan, Rita Remeikienė, Romualdas Ginevičius, Martin Čepel
Shadow economy, digital shadow economy, indicators of shadow economy, causal variables, MIMIC model
DOI (& full text):
The article introduces a new methodology of digital shadow economy estimation, which is based on the principles of the MIMIC method. This new methodology complements traditional methodologies of…more
The article introduces a new methodology of digital shadow economy estimation, which is based on the principles of the MIMIC method. This new methodology complements traditional methodologies of shadow economy estimation with such a component as digital shadow economy.
Our analysis of the most popular today methods of shadow economic estimation proves that, despite some of its drawbacks, the MIMIC model can be treated as the most comprehensive and appropriate method for such calculations since it takes into account both causal and indicators of shadow economy.
As the causal variables here, as applied to digital shadow economy, we use household access to the Internet and IT overall, the volume of non-cash payments and the use of most advanced ﬁnancial instruments. While as the indicators of the digital shadow economy spread we use: the volume of non-cash payments at online platforms, the frequency of cryptocurrency payments, and the cost of parcels to which customs duties have not been applied.
For further empirical veriﬁcation of the model proposed here, numerical values of both causal variables and indicators would be necessary. Unfortunately, ofﬁcial statistical sources are unable to provide such data in full volume, especially when it comes to cryptocurrencies and other informal payments. Thus, in our further research we plan to not only prove the practical applicability of the offered here model for estimations of digital shadow economy size as well as overall size of shadow economy on the examples of particular countries, but also to accumulate the necessary statistics for such calculations.