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Managing Director

Ph.D. in Economics, Northeastern University

B.A. in Economics, University of California at Los Angeles

Econ One, January 2018 – Present

UCLA Department of Sociology, 2015 – Presen

Independent Economic Consultant, 2015 – 2018

EY (Formerly Ernst & Young), Transfer Pricing, 2013 – 2014, Advisory Services, 2014 – 2015

UCLA for Int’l Science, Technology, and Cultural Policy, 2005 – 2017

Northeastern University, 2007 – 2009

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May 1, 2024

Consulting with a Major Airline on Tax Deferred Miles

In 2018, Econ One’s client, a prominent player in the airline sector, engaged our services to enable the client to accurately estimate the miles earned by its loyalty program members that were deferred for taxation purposes. As part of its loyalty program, the client airline offers various incentives, including earning and redeeming miles or points for flights, upgrades, and other rewards. However, accurately estimating the miles earned by members that are deferred for taxation purposes poses a significant challenge for airlines. To mitigate this challenge, Dr Amarita Natt, an expert in using sophisticated statistical and machine learning techniques to help inform business decisions and regulatory compliance, was engaged by the airline.

Dr Natt and her team proposed an approach based on LIFO (Last-In-First-Out) methodology that requires complex computations where miles issued or earned last are assumed to be redeemed first from a member’s account. This approach accounted for the reverse chronological order of mile redemption using client’s historical loyalty transaction data (including member profile, transactional details, miles earned, redeemed, and expired), enabling precise estimation of deferred miles for taxation purposes, enabling the client to fulfil regulatory requirements and optimize financial reporting. The team evaluated the performance of the computational approach through extensive testing and validation procedures. This included comparing the estimated deferred miles with actual data and conducting sensitivity analyses to assess the robustness of the computation under different scenarios and using alternate techniques. By accurately calculating deferred miles based on the LIFO methodology, the airline client enhanced regulatory compliance, optimized financial reporting, and minimized the risk of tax-related discrepancies. Moreover, the insights derived from these computations empowered the client to make informed decisions regarding taxation strategies and optimize the management of their loyalty program.

Industries: Airlines and Aviation

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