This paper develops a new approach that combines firm margins, market-level industry data and a static demand model to construct sets containing unbiased estimates of long-run price elasticities for storable good industries. It obviates the need to solve the consumer’s value function and can be completed within a policy-making timeframe. This methodology allows for the effect of contemporaneous and inter-temporal substitution on pricing incentives to be measured by dynamic diversion ratios. Together with the margins, these are key inputs into a new price pressure test for mergers in industries with dynamic demand. This framework is applied to the UK laundry detergent industry from 2002 to 2012. I conduct two policy experiments that show how estimated sets of bias-corrected price elasticities and diversion ratios can be used to avoid misguided policy-decisions. In both cases demonstrating the efficacy of set-valued policy tools.
High-dimensional inventories and consumer dynamics: demand estimation for fast moving consumer goods
New version coming soon
This paper develops a high-dimensional dynamic discrete-continuous demand model for storable fast moving consumer goods. Assumptions of existing models are relaxed while retaining computational tractability. As a result, the model captures rich inter- and intra-temporal substitution patterns, allows for a detailed understanding of dynamic consumer behaviour, and provides a framework with wide applicability. To estimate and solve the dynamic demand model, I use techniques from approximate dynamic programming, large-scale dynamic programming in economics, machine learning, and statistical computing. In this paper I apply the model to the UK laundry detergent sector using household level purchase data.
Work in Progress
Storability and alcohol taxation joint with Lars Nesheim
Industry structure and price dynamics in storable good industries
The effect of the financial crisis on bank lending to SMEs joint with Ben Hemingway