The Journal of Tax Administration (JOTA) is a peer-reviewed, open access journal concerned with all aspects of tax administration. The journal provides an interdisciplinary forum for scholars and practitioners to share knowledge and research on issues of concern to tax administrations, governments, the tax practitioner community and wider society.

In recent years, empirical research on tax compliance and administration has greatly expanded, thanks to increased access to data and improved reliability of data. Both quantitative and qualitative researchers are increasingly relying on new techniques to exploit this improved data, such as through the use of machine learning or Bayesian process tracing.

This special issue of JOTA aims to showcase these new approaches and methodologies. We also welcome papers that may be critical of using such methods. Possible topics include:

•    Papers showcasing the use of machine learning
•    Papers using administrative data, especially “big data”.
•    Papers from tax professionals and administrators employing “big data” and machine learning”.
•    Papers demonstrating the use of new qualitative methods, such Bayesian process tracing.
•    The use of mixed-methods approaches.
•    The limitations of “big data”.

We invite submission of papers to be included in the special issue. Papers should conform to Journal style and be submitted by June 1, 2020. Papers will be refereed and it is anticipated that the special issue will be published in the summer of 2021. Submissions should be sent to jota@exeter.ac.uk.

To view previously published issues of the Journal and submission guidelines, see www.jota.website