1. Measuring Multidimensional Preferences in Non- consumer Choice: Results of a Conjoint Analysis with Farmers Christian D. Schade, Wei-Shiun Chang, Christine Lauritzen The Institute for Entrepreneurial Studies and Innovation Management Humboldt-Universität zu Berlin Ravello (Italy): June 18 - 21, 2013 1

2. Outline Introduction o Motivation Theoretical background o Previous Literature o Methodology Experiment Design 2 Findings o Preferences of farmers o Cluster analysis on farmers’ preferences o Prediction of cluster memberships Conclusion and Limitations

3. Motivation "It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest.” ~ Adam Smith (1776) in The Wealth of Nations Elicitation of farmers‘ preferences of farming as producers from two general objectives ( the example of strawberry and cumcumber) o Short-term monetary self-regard aspect o Long-term other regardings Explore preference heterogeneity among farmers 3

4. Previous Literature Measuring individual preferences o Behavioral economics: Güth, 1982; Bolton and Ockenfels, 2000; Kahneman et al, 1986; Rabin, 1993; Fehr and Schmidt, 1999 o Consumer research (marketing): Green & Rao, 1971; Green, P. E., Srinivasan, 1978; 1990. o Psychological and sociological literature: Austin et al. 1996; Beus and Dunlap, 1990; Petzelka et al., 1996; Maybery et al, 2005 o Environmental research: Alriksson and Öberg, 2008; Columbo et al., 2006; Maybery et al, 2005 4

5. Methodology 5

6. Overview of attributes and corresponding levels Net Income  €20,000  €60,000 Income Volatility  Income ~UU(NI-10%, NI+10%)  Income ~UU(NI-30%, NI+30%) Degree of external effects of the farm onto the ecosystem  low concern of the farmer  high concern of the farmer Maintaining the fertility of the land  low concern of the farmer  high concern of the farmer 16 (2 x 2 x 2 x 2) possibilities. We use 8 sets based on orthogonal design. 6

7. 8 Scenarios of farming situations to rank 1 being the most preferred, 8 the least A:I 60,000, V 10, E H, F L B:I 20,000, V 30, E H, F L C:I 20,000, V 10, E L, F L D:I 20,000, V 30, E L, F H E:I 20,000, V 10, E H, F H F:I 60,000, V 30, E H, F H G:I 60,000, V 30, E L, F L H:I 60,000, V 10, E L, F H I: net income; V: volatility; E: negative externality; F: Fertility 7

8. Experimental Design Participants went through 4 steps in the experiment Holt and Laury Lottery ( to measure risk propensity) Description of Attributes leading to different agricultural situations Subjects were asked to rank 8 different screnarios Questionnaire: (demographics, training/ experinecne for farming, farming history in family (students), farming related questions for farmers (farm size, years of possesssing, etc.) 8

9. Data 14 Students recruited from HU Agricultural Department, Sessions run in Humboldt Decision Sciences Laboratory (HUDSciLab), November 2012 19 Farmers from Uelzen, Germany, Session run with 20-station mobile lab in Uelzen, December 2012 35 Students from Georg August Universität, sessions run with mobile lab in Göttingen, January 2013 9

10. General statistics of participants StudentFarmerPooled Age24.06 (3.886)30.68 (7.853)25.91 (6.029) Female ratio0.2860.1560.250 Parents in farming0.592 (0.497) Farming as career0.939 (0.242) Organic-favored0.305 Farm ownership ratio 0.842 Years of possession (farm) 5.684 (7.111) # of employee 4.579 (2.341) Farm size (hectares) 272.6 (220.0) Risk aversion (turning point)6.182 (1.646) a 6.000 (1.732)6.140 (1.652) # of obs491968 Parentheses are standard deviation. a : sample size=44 for students, 13 for farmers, 57 for pooled 10

11. Average ranking of situation SituationDescriptionAve. rankingStd. dev. AI 60,000, V 10, E H, F L 3.176(1.050) BI 20,000, V 30, E H, F L 6.750(1.164) CI 20,000, V 10, E L, F L 7.265(0.971) DI 20,000, V 30, E L, F H 5.750(1.250) EI 20,000, V 10, E H, F H 1.676(1.112) FI 60,000, V 30, E H, F H 4.117(1.399) GI 60,000, V 30, E L, F L 5.162(1.532) HI 60,000, V 10, E L, F H 2.103(1.161) I: net income; V: volatility; E: negative externality; F: Fertility 11

12. Data analyses 12

13. Findings 1 (conjoint analysis): Fertility is most important factor, followed by volatility, income and externality. 13 Part-worth utilities for all subjects AttributeAve. relative importance (%)Levels Utility (part-worth) Students (49)Farmers (19)Pooled Income26.29222.56325.250 60000E.860 20000E-.860 Volatility27.67026.81427.431 30%-.945 10%.945 Externality16.61616.27516.520 Low-.570 High.570 Fertility29.42234.34830.798 Low-1.088 High1.088

14. Finding 2 (Custer analysis): 3 clusters are identified; cluster 1 weighs heavily on externality, cluster 2 focuses on financial incentives, cluster 3 on fertility. ClusterANOVA 123F Sig. Income12.5032.0513.9665.929.000 Volatility18.7532.7918.0123.519.000 Externality44.3511.0718.7248.349.000 Fertility24.4024.0849.3261.797.000 # of farmers (% of farmers)1 (5.3%)12 (63.2%)6 (31.6%) # of Agri Students (% of st)6 (12.2%)31 (63.3%)12 (24.5%) Farmer ratio in cluster14.3%27.9%33.3% * : parentheses are numbers for student and farmer respectively. 14

15. Finding 3 (probit regression): Only farm type inclination is significant to predict cluster membership in cluster 1 for students (Coefficients are 1.38 and 1.35 with P values of 0.022 and 0.029 in the models without or with risk propensity respectively). 15

16. Conclusion Preserving fertility of the land is the most important element among the four attributes we considered critical to farming decisions, followed by risk, income, and externality. There is heterogeneity across farmers; three clusters are identified. The majority of farmers are classified into the financially driven cluster; however, externality and fertility are also quite important to this cluster. Some farmers fall in to groups with large responsibility toward environmental externality and fertility Classification of various preferences is not easily detected; though agricultural students with high concern for environmental externality would prefer to work in organic farming. 16

17. Limitations The findings are exclusively associated with German farming preferences for two reasons: o We ran the experiment with German farmers/agri students, the preferences on financial incentives and environmental concerns are subject to this specific role and region. o The findings are exclusively associated with the specific parameters (levels) of attributes to reflect German agricultural business. 17