PRIVATE ENFORCEMENT: Quantification of harm
23K - views

PRIVATE ENFORCEMENT: Quantification of harm

Similar presentations


Download Presentation

PRIVATE ENFORCEMENT: Quantification of harm




Download Presentation - The PPT/PDF document "PRIVATE ENFORCEMENT: Quantification of h..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.



Presentation on theme: "PRIVATE ENFORCEMENT: Quantification of harm"— Presentation transcript:

Slide1

PRIVATE ENFORCEMENT: Quantification of harm

Prof. Dr. Dr. Doris Hildebrand

Professor

of Economics, University Brussels (VUB) & Managing Partner EE&MC - European Economic & Marketing Consultants GmbHBonn - Brussels - ViennaAdenauerallee 87, D- 53113 BonnTel.: 0049-228-9267760www.ee-mc.com

ST. MARTIN’S CONFERENCE 2016

Brno, November 9th-10th 2016

Slide2

EU-wide price cartel for commercial vehicles/trucks for

14 yearsAll companies acknowledged their involvement EU Commission’s fine: € 3 bn.A rough not-founded first guess on potential cartel damagesEstimated sales during cartel period: approx. 5,6 mio. trucksAverage costs of a vehicle: € 80.000 / rough average overcharge in academic studies (not related to trucks): 20% First, completely unfounded guess on possible cartel damages based on these too general assumptions: damage per vehicle approx. € 13.000Completely unfounded guess on potential cartel damages: € 72,8 bn.Percentage of the fine in relation to the cartel damages: 4%It is likely that a lot of companies are going to claim damages

November 2016

Page 2

A starter:

First guess on cartel damages

Slide3

November 2016

Page 3

Methods of overcharge calculations

Type of methodMethod of calculation „But For“ PriceYardstickPrice comparison with similar product market Price elsewhereSimple before-and-afterPrice comparison before and after the infringementPrice before, while and after infringement Price PredictionStatistical estimation of relationship between prices and demand and supply factorsCalculate (predicted) price based on past relationshipsCost-Based (margin)Estimation of competitive price based on past marginsCosts plus marginTheoretical modelling (simulation) of oligopoly Theoretical models to understand effects on prices and output, with econometric and other data being inputs into the modelTheoretical price, based on model‘s estimates

Slide4

November 2016

Page 4

Price Comparison over Time

Price Prediction Method: Hypothetical competition prices („but for“-prices) are estimated for the cartel period based on a regression analysis with post-cartel prices Comparison “but for”-prices with cartel prices Difference = Cartel damage

Two time periods are examined: the cartel period and the post-cartel period

Calculation Approaches

:

1. Simple before-and

-after

2. Price Prediction

Selection of econometric method:

Regression analysis

Multiple regression analysis

Slide5

November 2016

Page 5

Econometric Tool: Regression Analysis

0

Price

Quantity

Identify a relationship between prices and costs in the

post-cartel period

by a regression formula

Regression formula is like a

recipe

Use e.g. the input costs in the regression formula to predict the “

but-for”-prices

or hypothetical competition prices for the cartel period

Regression analysis:

a statistical process for estimating the relationships among variables

The focus is on the relationship between a dependent variable (

price

) and one or more independent variables (e.g.

costs

)

Slide6

November 2016

Page 6

Example 1: Ingredients washing powder

Washing powder price

Surface-active preparation

Rape, colza and mustard oil, fractionated, refined, not chemically modified

Chemical elements and chemical compounds

Input costs are used in the regression formula (“recipe”) to model hypothetical competition prices

*Prices are the public available producer price indices of industrial products;Data collection on a monthly basis between 2000-2013

Overview possible washing powder-variables*

Based on theoretical considerations, possible price-explanatory variables are collected

Organic surface-active agents, except soap

Energy

Disodium carbonate

Electricity and natural gas

Mixtures of odoriferous substances (including alcoholic solutions)

Rape, colza and mustard oil, not chemically modified

Slide7

November 2016

Page 7

This blue line is the result of the regression analysis

This blue dotted line is the result of the prediction

The cartel damage is the difference between the blue dotted line left and the orange line ( on average

€ 61

in this example); multiplied with the purchased volume during the cartel period = cartel damage per month

The overcharge in this cartel is 39%

Example 2: Result Regression Analysis

Slide8

November 2016

Page 8

Pro’s and Con’s of the Price Prediction Tool

Applicable for markets with easily accessible market informationStatistical correlations are displayed Pro:Quantifying the hypothetical competition price by means of real historical determinantsDynamic Approach: possible changes in the market during the cartel period can be taken into account Contra:Reliability depends on the quality and availability of the respective data

Price prediction is the leading methodologyEE&MC has applied this methodology in about 30 cartel cases already – experience counts

Slide9

November 2016

Page 9

Usual cartel disclaimer:

No economic effect of the cartel

Slide10

November 2016

 

Parkgasse

1

1030 Vienna

Austria Tel +43-1-7123310

Vienna

Brussels

Bonn

Offices of EE&MC

Adenauerallee 8753113 BonnGermany Tel +49-228-926776-0Fax +49-228-926776-11

Rond Point Schuman 61040 Brussels Belgium Tel +32-2-2013150

Page

10

Thank you for your attention !