Amit Gandhi | Searcharxiv

arXiv2026

Micro and Macro Perspectives on Production-Based Markups

We review the "production approach" to estimating markups, the ratio of price to marginal cost. The approach is uniquely scalable: it requires no model of consumer demand or market structure and applies broadly across firms, industries, and time. Our organizing insight is that the production-based markup is a residual. Like the Solow residual, it is clean in theory but potentially contaminated by misspecification and mismeasurement. This framing helps explain why small differences in implementation can produce starkly different results from the same data. In some cases, markups have risen sharply. In others, they have not. Despite the disagreements in the literature, the importance of understanding and measuring market power cannot be overstated. We provide conceptual rationales for this disagreement, offer practical guidance on data and estimation, and call for greater transparency about how much of the variation attributed to markups may instead reflect technology.

econ.GN↗

arXiv2023

From Augmentation to Decomposition: A New Look at CUPED in 2023

Ten years ago, CUPED (Controlled Experiments Utilizing Pre-Experiment Data) mainstreamed the idea of variance reduction leveraging pre-experiment covariates. Since its introduction, it has been implemented, extended, and modernized by major online experimentation platforms. Many researchers and practitioners often interpret CUPED as a regression adjustment. In this article, we clarify its similarities and differences to regression adjustment and present CUPED as a more general augmentation framework which is closer to the spirit of the 2013 paper. We show that the augmentation view naturally leads to cleaner developments of variance reduction beyond simple average metrics, including ratio metrics and percentile metrics. Moreover, the augmentation view can go beyond using pre-experiment data and leverage in-experiment data, leading to significantly larger variance reduction. We further introduce metric decomposition using approximate null augmentation (ANA) as a mental model for in-experiment variance reduction. We study it under both a Bayesian framework and a frequentist optimal proxy metric framework. Metric decomposition arises naturally in conversion funnels, so this work has broad applicability.

stat.AP↗