1 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

2 PERCEIVED BENEFITSPERCEIVED RISKS The GM food debate 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 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’

3 Common recommendationLabelling What is the cost of labelling? Is there a viable market for GM-free products? What is the threshold for GM certification? May resolve trade disputes Retains consumer sovereignty Yet, it is a complex issue

4 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

5 Choice Set Design:

6 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 Elements of choice experiment/conjoint analysis:

7 Estimated  parameters used to calculate welfare measure, market shares, predicted probabilities etc. Random Utility Model:

8 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.

9 A Structural model of latent segmentation and choice (adapted from McFadden, 1986) Attitudinal perceptual and motivational indicators Attitudes, perceptions and motives Membership likelihood Latent Class selection Latent Class Institutional setting and constraints Preferences Socio-demographic characteristics Objective product attributes Perceptions of product attributes Choice Behaviour Decision Tool

10 Mixed-logit model: Multinomial choice function Segment membership function

11 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

12 Factor 1: Ethical resistance Factor 2: Mistrust and disbelief Factor 3: Environment concerns Factor 4: Cost and bargain concerns Factor 5: Food safety concerns Perceptual and attitudinal latent variables:

13 Choosing optimal number of segments:

14 Results from three segment model

15 Interpretation of Segments: Segment 1: food optimists (53.5%) Segment 2: food cautious (38.8%) Segment 3: ethical opponents (7.7%)

16 Implicit ranking of attributes across segments:

17 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)

18 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