Fideres explores the available economic methods used to estimate pass-on damages. We present two approaches that avoid common issues in measuring pass-on damages directly.
What Does “Passing-On” Mean?
If a firm experiences an increase in input costs, it may respond by increasing the price of its product. This is known as ‘passing-on’. If the cost increase occurred because of an anti-trust infringement by suppliers, the damages recoverable by the firm are limited by how much of the cost increase is not passed on to its customers. Parties lower down the supply chain, including the end consumers, may also be able to claim damages, even if they were not directly affected by the initial infringement.
To quantify pass-on damages, courts generally require estimates of:
a) the overcharge from the initial infringement; and
b) the ’pass-on rate’ at each stage of the supply chain – what proportion of the overcharge was passed downstream.
Quantifying Pass-On Damages
Academic literature and previous cases suggest three main ways to calculate pass-on damages:
- Economic theory can predict how firms operating under perfect or imperfect competition react to cost increases. These models can be used to infer the pass-on rate from data on product prices and input costs, but it is often necessary to simplify assumptions about demand conditions and firms’ marginal costs to implement such models.
- Pass-on can be estimated using multivariate regression analysis. This involves modelling how a product’s price changes in response to input cost increases. This approach is more empirical than theoretical methods, but requires data on product prices over time (before and after the infringement), which may not be available in litigation.
- Pass-on can also be estimated indirectly, by considering similar industries with better data available. These approaches have less stringent data requirements, but require the court accepting a ‘proxy’ pass-on rate for the relevant product.
Pass-On In Litigation: Two Examples
In 2013, the Delaware Supreme Court heard a class action brought by purchasers of cathode ray tubes (or CRTs), a common part in television sets. Plaintiffs alleged that several manufacturers fixed the price of CRTs between 1995 and 2005. The plaintiffs’ expert witness relied partly on economic theory predicting high levels of pass-on, as CRTs were used by most producers in the television supply chain. She also presented regressions using firm-level data, showing that prices increased substantially throughout the supply chain as a result of the initial infringement. The court ultimately accepted that the price increase was passed onto consumers.
In contrast, HUK-Coborg, a German motor insurance company, filed a claim against a cartel of car glass manufacturers in 2015. They claimed that the inflated price was passed onto their glass suppliers for insurance claims. A Dusseldorf court found that HUK-Coborg could not demonstrate a causal link between the cartel and the pricing of the replacement glass manufacturers. The damages claim was dismissed (but was on appeal at the time of writing).
Problems With Measuring Pass-On Directly – Alternative Approaches
In contrast with the CRT case, most litigants do not have access to firm-level pricing data. It is also common for firms to aggregate their pricing data across product categories, preventing direct pass-on estimation for a particular product. In these cases, alternative approaches must be used:
- Fideres has recently developed a model tracking how product categories in the consumer price index (or CPI) respond to changes in the price of their inputs, as shown in the producer price index (or PPI). This yields a more ‘general’ estimate of the pass-on rate for a given product market. Unfortunately, product categories in the CPI and PPI do not always match. Algorithmic matching techniques can mitigate this issue.
- Fideres has also modelled how prices in the CPI respond to general consumption tax increases. Academic studies have found that firms generally raise prices in response to VAT increases, rather than bearing the cost of the new tax themselves. Fideres has built a model linking changes in VAT and the CPI to arrive at a general estimate of pass-on for a given product market.
Max joined Fideres in 2016. He has led the development and implementation of economic models for a range of high-profile cases in the US and the UK, contributing to litigation on a variety of topics. He has contributed economic work to cases involving FX rate manipulation and LIBOR suppression financial antitrust and financial benchmark manipulation), the abuse of dominance by major US hospital systems (healthcare antitrust), and supplier cartels in agricultural industries (agricultural antitrust). Before joining Fideres, Max worked at the national laboratory in Los Alamos, New Mexico, as part of a team designing neural networks for applications in machine learning. Max holds an MSc in Economic History from the London School of Economics.