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Outline of the Class Basic elements of the discrete choice experiment (DCE) approach Theoretical foundation: the Random Utility Model Estimation using.

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Presentation on theme: "Outline of the Class Basic elements of the discrete choice experiment (DCE) approach Theoretical foundation: the Random Utility Model Estimation using."— Presentation transcript:

1 Outline of the Class Basic elements of the discrete choice experiment (DCE) approach Theoretical foundation: the Random Utility Model Estimation using the Multinomial Logit model Designing a choice experiment: an example from India Worked example: valuing sustainable salmon farming in Canada using DCE and analysing heterogeneity with Latent Class Analysis (LCA) 1

2 Discrete Choice Experiments (DCE) Like dichotomous choice CVM, based on random utility theory (RUM) and use survey data However, it’s a multi-attribute approach with attributes (ideally) identified by stakeholders One attribute serves as the payment vehicle (P) Choices presented as choice sets (cards) developed by varying each attribute’s level Data analyzed using multinomial logit model Can create a statistical tool to evaluate stakeholder group support for constructed scenarios (DSS) 2

3 The Random Utility Model (RUM) 3

4 Estimation using the Multinomial Logit Model 4

5 D. Knowler, S. Nathan, N. Philcox, W. Delamare and W. Haider Simon Fraser University, Canada Designing a Choice Experiment: An example using the shrimp-mangrove system of West Bengal, India 5

6 Rapid rural appraisal 6

7 Discrete Choice Experiment - Attribute List Mangrove area near villages 0% Levels: 0%,+5%,+10%,+15%,+20% n No. of improved shrimp farms n Levels: 1000,2000,3000,4000,5000 n Employment in fry collection n Levels: 20000,30000,40000,50000,60000 n Income generation/micro-credit n Levels: 0%,5%,10%,15%,20% n Household contribution to ‘Sundarbans Development Fund’ n Levels: 0,5,10,25,50,100 Rs/yr 7

8 Discrete Choice Experiment – Choice Card BLOCK 1 CARD 1 8

9 Training of enumerators 9

10 A few review questions … What is the difference between (i) an attribute, (ii) an attribute level and (iii) a choice set? Recall we discussed the use of the conventional logit/probit models under CVM. Why do we need to use a Multinomial Logit Model here? 10

11 Worked Example: Assessing Willingness to Pay for Sustainable Salmon Farming in British Columbia Winnie Yip, Duncan Knowler and Wolfgang Haider School of Resource and Environmental Management, Simon Fraser University, Burnaby BC 11

12 Canadian & BC Salmon Farming Industries Expansion of global Atlantic salmon production – Forecast production of 197,000 t by 2020 vs 109,000 t in 2009 – Canada is 4 th largest farmed salmon producer globally – BC produces 70% of Canadian farmed salmon; 85% exported Environmental concerns with conventional aquaculture – Threats to wild salmon stock – Nutrient loading and toxics 12

13 Alternatives to conventional salmon farming Closed Containment Aquaculture (CCA) DFO, 2010 Living Oceans Society, 2011 13

14 Another Option? 1. Fed Salmon 2. Shellfish (e.g. oysters, mussels) Consume the residual food & organic waste from the salmon cages 3. Seaweeds (e.g. kelp) Consume inorganic wastes from shellfish and invertebrates 4. Invertebrates (e.g. sea cucumbers) Consume the heavier food & organic waste from the salmon cages Integrated Multi-trophic Aquaculture Chopin et al., 2010 14

15 Previous Studies & Research Questions Several economic assessment studies (Ridler et al. 2007, various FOC studies); also consumer perceptions & WTP for IMTA (Barrington et al., 2008; Shuve et al., 2009; Kitchen et al., 2011) Research questions address the gaps.. 1.How do salmon consumers in the Pacific Northwest perceive IMTA and CCA as alternatives to conventional salmon farming? 2.What are these consumers willing to pay for salmon produced by more sustainable aquaculture technologies? 15

16 Research Methods Household survey (1631 respondents) – sampled households in San Francisco, Seattle and Portland – administered online using market research firm – Screened for main grocery shopper & ate salmon at home in last 12 months; final sample:  67% females & 33% males; mostly over 25 years old  have Bachelor’s degree & household income of > US$50,000 Analysis – willingness to pay  Discrete Choice Experiment – respondent heterogeneity  Latent Class Analysis 16

17 Discrete Choice Experiment (DCE) Designed to consider both a “true” shopping decision environment and a broader social perspective Attributes used: – Species [Atlantic, Sockeye or King] – Production method [conventional, CCA, IMTA or wild sockeye] – Product origin [Canada, USA, Norway, Chile] – Whether eco-certified [yes or no] – Price [various levels, by species] 17

18 Sample Choice Set 18

19 Information Treatments Assumption: respondents do not know about IMTA and/or CCA  education needed Problem: possible biases – Sequence: IMTA first or CCA first? – Type of description: Favorable or Balanced? Solution: split sample with alternating sequence and extra information on negative aspects of each technology: “IMTA does not address escapes by farmed salmon and may not significantly reduce the infestation of wild salmon by sea lice.” “CCA requires a significant amount of energy and could face issues related to land use and waste disposal.” 19

20 Dealing with Heterogeneity: Latent Classes Preference heterogeneity was addressed using Latent Class Analysis (LCA), which is an expanded, mixed logit form of the MNL model (Train, 2009) Assumes a heterogeneous sample made up of a number of relatively homogenous classes Assumes homogeneous preferences within and heterogeneous preferences between classes LCA defines the number of classes endogenously 20

21 Attitudes towards Aquaculture Alternatives Respondent perceptions of: IMTACCA - 59% felt positive- 40% felt positive - 11% felt negative- 29% felt negative 63% agree more sustainable method should be adopted 39% will buy more farmed salmon if IMTA or CCA exist Favorable description > balanced description When directly compared: 44% prefer IMTA > 16% prefer CCA (IMTA more natural, sustainable & uses a mix of spp, whereas CCA better separates farmed spp) 21

22 Results for DCE and LCA Coefficients for all DCE attributes significant at 5% level using a linear model; interaction effects not significant Latent Class Analysis indicated 4 & 5 class models were unstable; based on BIC & AIC statistics the 3 class model preferred Classes were described as: – “Wild salmon lovers” (45%) – “Price-sensitive consumers” (29%) – “Sustainably farmed salmon supporters” (26%) 22

23 Part-worth Utility Results by Latent Class (I) 23

24 Part-worth Utility Results by Latent Class (II) 24

25 Mean WTP for Atlantic Salmon from IMTA and CCA vs. Conventional Salmon Farming, by Latent Class All segments 3-class model Wild salmon lovers Price- sensitive consumers Sustainably farmed salmon supporters IMTA vs. Conventional IMTA - -$4.48$0.96$2.00 Conventional farming- -$9.05$0.46$1.62 Difference (Marginal WTP)$1.07$4.58$0.50$0.38 CCA vs. Conventional CCA- -$8.90$0.69$1.50 Conventional farming- -$9.05$0.46$1.62 Difference (Marginal WTP)$0.43$0.15$0.23 -$0.11 * Note: All prices expressed in USD dollar per lb of salmon; (*) Confidence interval is -0.68 to 0.46

26 Conclusions Consumers want adoption of sustainable aquaculture – stronger preference for IMTA over CCA (44% vs. 16%) – potential increase in overall demand for farmed salmon – LCA produces plausible interpretation of heterogeneity WTP for IMTA > WTP for CCA (9.8% vs. 3.9% premium) Education is necessary – 7% awareness of IMTA & 20% awareness of CCA – Information on technology limitations seems not to affect WTP but further analysis is needed (G-MNL modeling ??) 26

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