| dc.description.abstract | First of all, I would like to thank my supervisors at the Norwegian School of Economics (NHH),
Evelina Gavrilova-Zoutman and Floris Zoutman for excellent guidance throughout my PhD.
Following my many years outside academia, prior to my PhD journey, their effort was timely
and appreciated. I would also like to thank Jarle Møen for facilitating the admission to the PhD
program at the NHH. Thanks also to Lars Jonas Andersson at NHH for providing good
guidance on machine learning, and support for my notion on its relevance to this project.
This PhD could not have been realised without the funding and support from the Norwegian
Research Council and the Norwegian Tax Administration (NTA). But equally important, the
many brilliant colleagues in the latter institution. I would like to thank Marcus Zackrisson who
paved the way for this project at management level, and Terje Nordli and Monica Bredesen for
anchoring the interest among the colleagues engaged in the important work of preventing tax
related labour market crime. Research coordinator at the NTA, Torhild Henriksen, deserves a
special thanks for facilitating and maintaining research interaction between the NTA and the
NHH, and for giving professional advice during the whole period.
I am very thankful for the great talks and substantial contributions to machine learning
provided by Nils Gaute Voll. Øystein Olsen and Tore Sjøstedt helped a lot with specifying the
data extraction. I would like to thank Hanne Beate Næringsrud for the many comments on
specific issues, including interpretation, message, and narratives. Nina Serdarevic, Julia
Tropina Bakke, Knut Løyland, Inge Sandstad Skrondal, and Arnstein Øvrum have provided
useful comments on earlier drafts as well. I would also like to thank Kari Djupdal, Anders
Berset, Andreas Olden, Joakim Døving Dalen, and Terje Dalen for the many fruitful
discussions I have had over the years at the NTA. What a great knowledge pool you all are!
A special thanks goes to Anne May Melsom who was appointed my co-supervisor at the NTA
and co-authored two of the papers. Not only do I owe you for improving my Stata knowledge
to an adequate level, but also for your impeccable understanding of the data, your substantial
inquiries, and finally for the considerable effort to the very end. Your help has been invaluable.
I would also like to thank academic staff at other institutions, for their valuable insights and
comments. Those are Joel Slemrod at the University of Michigan, Ann Arbor, Steinar Strøm,
Andreas Kotsadam and Thor Olav Thoresen at the University of Oslo, and Hamed Saiedi at
the Norwegian Business School.
I am grateful for the many conversations and colloquial preparations with peer students at the
Stockholm Doctoral Course Program in Economics, Econometrics and Finance (SDPE) jointly
by Stockholm University and Stockholm School of Economics, where I undertook most of my
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course components. I am equally thankful for the talks and encounters at various conferences
with my peer PhD students at the NHH.
A particular gratitude goes to my dear friends at the NTA, namely Hanne Beate Næringsrud,
Julia Tropina Bakke, Øystein Olsen, Anders Berset, and Ivana Haakens for being there in
challenging times. Friends like You last a lifetime.
I am forever thankful to Rebecka Maria Norman for the continuous, but nevertheless (at least
for me) useful discussions on so many topics on statistical inference over the years. I hope our
kids, Felicia, Gabriel, and August, were not permanently damaged by nitty gritty talks on
standard error clustering or heteroscedasticity. Their patience has been remarkable.
Finally, my heartfelt gratitude to Ann-Kristin Midtskog for new perspectives on compliance,
text clarifications, strategic choices, and for your unconditional love and support (“…Og jeg
kan ikke miste det uansett hva som skjer”).
Oslo, December 2023
Thomas Lange | en_US |
