Accounting for Preference Heterogeneity in Random Utility Models:
An Application of the Latent Market Segmentation Model to the demand for
GM foods
Dr. Andreas Kontoleon
Department of Economics
University College London
Fourth BIOECON Workshop on The Economics and Biodiversity Conservation
28th -29th August, 2003
Venice, Italy
1
The GM food debate
PERCEIVED BENEFITS
•
•
•
•
•
•
•
•
Improved nutritional content
Improved taste and variety
Increased yield/output
Reduced pesticide use
Reduced water use
Cheaper food for consumers
Benefits to the environment
Benefits to LDCs
PERCEIVED RISKS
• Food safety and health concerns:
For example:
new allergies.
resistance to antibiotics.
new viruses and mutations.
•Environmental concerns:
For example:
‘super-weeds’
reduce the levels of genetic diversity
• Ethical and religious concerns
• Food ‘quality’ concerns
For example
Spoil food taste
Food ‘uniformity’
2
Common recommendation
Labelling
• May resolve trade disputes
• Retains consumer sovereignty
Yet, it is a complex issue
• What is the cost of labelling?
• Is there a viable market for GM-free products?
• What is the threshold for GM certification?
3
Hence, it is highly policy relevant to assess:
- Distributional impacts
- Size and nature of niche markets
Need to assess preference heterogeneity in the demand for GM foods
Case study:
• Stated preference Vs. revealed preference demand analysis
• Contingent valuation Vs. Choice Experiment modelling
• Single food product Vs. set of food products
• Which food product?  Eggs
4
Choice Set Design:
Q 16
Option A
Option B
Option C
Cage
Free
Range
Cage
No Use
Use
Use
GM content
30%
30%
0%
Information
Yes
Yes
Yes
£0.38
£0.38
£0.78
1
2
3
Eggs
Eggs
Eggs
Living conditions
Pesticides
Price of 6 eggs
 one of
Option D
I would
buy my
usual
brand of
eggs
4
these
How many eggs
do you consume
weekly
5
Elements of choice experiment/conjoint analysis:
• Select attributes and levels
• Design choice sets
• Collect multiple responses form each individual
• Analyse data using multinomial choice model
• Econometric model framed as a Random Utility Model
6
Random Utility Model:
U ni  Vni(X ni )  εni
 in 
e
 ( X n i )
e
 ( X n j )
jC
Estimated  parameters used to calculate
welfare measure, market shares, predicted
probabilities etc.
7
Accounting for preference heterogeneity in RUM
• Problematic since individual characteristics do
not vary across choices.
Various approaches to overcome this obstacle:
• Interaction effects models
• Random parameter logit models
• Latent class models
Latent Segmentation Model
Simultaneously accounts for choice and
segment membership.
8
A Structural model of latent segmentation and choice (adapted from McFadden, 1986)
Attitudinal perceptual
and motivational indicators
Socio-demographic
characteristics
Attitudes, perceptions
and motives
Objective product
attributes
Perceptions of product
attributes
Membership
likelihood
Latent Class selection
Latent Class
Institutional
setting and constraints
Preferences
Decision Tool
Choice Behaviour
9
Mixed-logit model:

 

  s (  s X in )    ( as Z n ) 
e
e



Pisn   in / s   Wns  
 S

 s (  s X jn )  
 ( ak Z n ) 
e
e

 

k

1

j

C


Multinomial
choice function
Segment
membership
function
10
Estimation Strategy:
1. Use factor analysis to estimate attitudinal proxies
2. Parameterise the full mixture model (i.e. the vectors X, Z
above)
3. Run the full model for various segments (1, 2, 3, 4 …)
4. Stop when estimation ceases improving the LogL
5. Use a combination of criteria based on the AIC statistic to
choose the model with optimal number of segments
11
Perceptual and attitudinal latent variables:
Factor 1: Ethical resistance
Factor 2: Mistrust and disbelief
Factor 3: Environment concerns
Factor 4: Cost and bargain concerns
Factor 5: Food safety concerns
12
Choosing optimal number of segments:
Number of segments
1
2
3
4
5
1）
2）
3)
4）
Parameters Logarithm Likelihood
（P）
8
24
40
56
72
（LL）
-2084.45
-1737.29
-1653.58
-1620.35
-1587.87
ρbar2
AIC
BIC
0.139
0.275
0.303
0.310
0.315
4184.90
3522.57
3387.15
3352.71
3319.74
2106.37
1803.05
1763.19
1773.81
1785.18
N=240 individuals
AIC (Akaike Information Criterion) is -2（LL-P）.
ρbar2＝{1-AIC/2LL(0)}
BIC(Bayesian Information Criterion) is -LL+（P/2）*ln（N）.
13
Results from three
segment model
Variable
Coeff
Std. Error
t-Stat
P-Value
-0.315
1.616
1.728
0.686
-0.429
1.637
-1.219
0.189
0.131
0.151
0.200
0.148
0.147
0.207
0.243
0.055
-2.411
10.707
8.638
4.637
-2.917
7.890
-5.005
3.424
0.016
0.000
0.000
0.000
0.004
0.000
0.000
0.001
Food
Cautious
-4.387
4.294
1.294
-1.898
2.586
-0.766
3.870
-1.149
0.242
0.815
0.399
0.548
0.513
0.399
0.795
0.341
-18.151
5.272
3.241
-3.463
5.044
-1.923
4.866
-3.366
0.000
0.000
0.001
0.001
0.000
0.055
0.000
0.001
Ethical
Opponents
2.106
0.755
0.163
-0.141
-0.017
0.012
-1.464
0.231
0.056
0.049
0.136
0.005
0.047
0.186
9.115
13.442
3.353
-1.040
-3.699
0.254
-7.873
0.000
0.000
0.001
0.298
0.000
0.799
0.000
Food
Optimists
-5.611
8.105
3.241
2.054
-3.471
2.228
-5.718
0.164
0.273
0.426
0.682
0.752
0.257
0.490
-34.309
29.685
7.601
3.014
-4.618
8.658
-11.670
0.000
0.000
0.000
0.003
0.000
0.000
0.000
Food
Cautious
2.636
0.010
1.316
3.324
-2.312
0.788
-5.679
0.858
0.284
0.508
1.152
0.636
0.370
1.829
3.071
0.036
2.589
2.886
-3.636
2.129
-3.104
0.002
0.971
0.010
0.004
0.000
0.033
0.002
Ethical
Opponents
Segment 2: segment function coefficients
Constant2
Ethical resistance
Mistrust and disbelief
Environment concerns
Cost and bargain concerns
Food safety concerns
Dummy Education
Log Income
Segment 3: segment function coefficients
Constant2
Ethical resistance
Mistrust and disbelief
Environment concerns
Cost and bargain concerns
Food safety concerns
Dummy Education
Log Income
Segment 1: utility function coefficients
ASCS1
Living condition
Pesticides
NonGM
GMCont
Information
Price
Segment 2: utility function coefficients
ASCS2
Living condition
Pesticides
NonGM
GMCont
Information
Price
Segment 3: utility function coefficients
ASCS3
Living condition
Pesticides
NonGM
GMCont
Information
Price
Log of Likelihood
Number of Observations
-1653.5777
1753
14
Interpretation of Segments:
Segment 1: food optimists (53.5%)
Segment 2: food cautious (38.8%)
Segment 3: ethical opponents (7.7%)
15
Implicit ranking of attributes across segments:
Living conditionsa
Pesticidesb
NonGMc
GMContd
Informatione
MN logit model
Model without
Model with
individual
individual
Characteristics
Characteristics
0.89
0.65
(2)
(2)
0.34
0.32
(3)
(3)
1.04
0.81
(1)
(1)
-0.01
-0.03
(5)
(5)
0.11
0.09
(4)
(4)
Three Segment LS Model
Food Optimist
Food Cautious
Segment
Segment
0.516
0.111
-0.096
-0.012
0.008
(1)
(2)
(3)
(4)
(5)
1.417
0.567
0.359
-0.607
0.390
(1)
(3)
(5)
(2)
(4)
Ethical
Opponent
Segment
0.002 (5)
0.232 (3)
0.585 (1)
-0.407 (2)
0.139 (4)
Notes:
Bold numbers in parentheses denote implicit ranking of choice attributes.
a
Calculated as the marginal WTP to have free range eggs
Calculated as the marginal WTP to have organic eggs
c
Calculated as the marginal WTP for reducing GM from 1% to the 0% content level
d
Calculated as the marginal WTP for reducing GM from 30% to the 1% content level
e
Calculated as the marginal WTP to have information (labelling) on egg boxes
b
16
Concluding remarks:
GM debate on labelling can benefit from such quantitative studies that:
Explore preference heterogeneity at the segment level.
Assess the nature and extent of latent segments.
Simultaneously accounts for choice and segment membership.
Utilise an interdisciplinary approach that incorporates:
Economic information (observable choices).
Demographics information (observable individual characteristics)
Psychometric information (latent individual characteristics)
17
Accounting for Preference Heterogeneity in Random Utility Models:
An Application of the Latent Market Segmentation Model to the demand for
GM foods
Dr. Andreas Kontoleon
Department of Economics
University College London
Fourth BIOECON Workshop on The Economics and Biodiversity Conservation
28th -29th August, 2003
Venice, Italy
18