Measuring Consumer Detriment from Postal
Quality Price Misperceptions in France
Magali Ceccheta, Mette Damgaard*, Nicole Doisea, Julien Coulierb, Lionel Janinb, Patrice Muller*,
and Gregory P. Swinand*
*London Economics, aDGCIS-France, bARCEP
Presentation to
CCRP Workshop, Aston University,
Birmingham
by
Greg Swinand
London Economics
London Economics
www.londecon.co.uk
1
Birmingham
February 2011
Introduction and Agenda
Introduction to the problem
‰ Consumer detriment-short review
‰ Approach
‰ Data
‰ Results
‰ Conclusion and directions
‰
2
Birmingham
February 2011
Introduction to the problem
‰
‰
‰
USP’s world over subject to increasing pressures
Quality of service elements of USO such as J+1 a major cost
element
Is J+1 first class still needed?
• If not why relatively so little 2nd class mail volume in France?
‰
‰
Apparently consumers in France have poor knowledge of 2nd
class product, existence, price, relative speed
But what should be the policy response?
• How big is this problem?
‰
Consumer detriment a framework for measuring how big is this
problemÆ so estimate consumer detriment
3
Birmingham
February 2011
Introduction to the problem
Figure 5: Knowledge of second‐class mail – percentage of hosueholds and businesses who know and do not know the existence of second‐class mail
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
19%
36%
81%
64%
Households
Know
Businesses
Do not know
4
Birmingham
February 2011
Introduction to the problem
Figure 7: Knowledge of price of first and second class mail
5
Birmingham
February 2011
Consumer detriment-short review
‰
A number of key works on consumer detriment
• LE-OFT, EE-EC, etc.
• Related also seminal works on quality and information (Ackerlof 1970)
‰
Key concepts
• Counterfactual—what would have happened otherwise?
• Direct cost/losses
• Structural detriment (market failure)
− Information problems
− Deadweight loss-based measures – welfare loss
• Estimate a value based on
− direct costs and on
− assumptions/estimates about demand curve
6
Birmingham
February 2011
Approach
P
D
D’
P
P’
q’
q
7
q
Birmingham
February 2011
Approach
P
D
D’
P
P’
q’
q
8
q
Birmingham
February 2011
Approach
‰
A challenge is always to predict the counterfactual
• What would have happened had consumers ‘known’ about the
2nd class mail product?
• We use an econometric approach using the socio-demographical
and mail demand characteristics of the survey respondents
− We predict out of sample for those who did not know of the 2nd class letter
− Assumption is those who didn’t know the 2nd class letter have the same
demand ‘parameters’ as those who did and variation in the independent
variables drives differences
− Assumed proportional shift also done as a check
9
Birmingham
February 2011
Results
‰
Econometric model-dep. var. volume 2nd class mail
• A number of models tried; linear model chosen based on fit, judgement
− 406 observations; R-squared 26%
− ln(vol), and various including tobit model tried, but fit not as good and losing
observations
• Significant determinants of 2nd class mail demand (preferred model) {+
or - and sign expectation}
− Belief 2nd class mail is slow (J+4 and greater) { - expected}
− Self employed { + expected}
− Estimated price minus actual price {+ expected?} and squared term {expected}
− Volume first class mail
• Not significant independent variables
− Age class, occupation group, employment status,use of internet or sms
10
Birmingham
February 2011
Results
‰
Three predictions based on value of independent vars
• Proportional forecast
− (2nd class volume in same proportions)
• Perfect knowledge forecast
− Deviations from ‘truth’ set to zero
• Prediction at the sample means
‰
Roughly 2nd class mail volume predicted to almost double
Table 2: Volume predictions
Item
Proportional forecast
Volume first‐class (millions)
Volume
(millions)
Perfect knowledge
Sample means
1,727
1,690
1,697
759
796
789
360
397
390
second‐class
Change volume (millions)
11
Birmingham
February 2011
Results
‰
Detriment estimates observations
• The cash-transfer value is about 2.5x the DWL value
• 10.3% increase in values from proportional forecast to ‘perfect
knowledge’
• 2% increase in values from sample means to perfect knowledge
Table 3: Detriment estimates
Item
Cash Savings €m
DWL €m
Total Detriment €m
Proportional forecast
Perfect knowledge
Sample means
18.0
19.9
19.5
7.0
7.7
7.5
24.96
27.54
27.03
12
Birmingham
February 2011
Conclusions and directions
‰
Conclusions
• Detriment is “significant” but not “big”
− Non industrial mail spend is about €1.8bn (£1.5bn)
• Estimates of size of the problem useful in terms of suggesting range
of possible policy responses (proportionality)
‰
Directions for future
• Considering the net benefits including producers would be
interesting
• Addition of ‘other sources’ of detriment in mail market
− E.g., getting price wrong
• Allowing for different welfare ‘weights’ for different consumer groups
13
Birmingham
February 2011