Measuring Multidimensional Preferences in Non-consumer Choice: Results of a Conjoint Analysis with Farmers

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

2. Outline • Introduction • Motivation • Theoretical background • Previous Literature • Methodology • Experiment Design • Findings • Preferences of farmers • Cluster analysis on farmers’ preferences • 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) • Short-term monetary self-regard aspect • Long-term other regardings • Explore preference heterogeneity among farmers

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

5. Methodology • Conjoint analysis with rank orders (Green &Srinivasan, 1978; Fishbein and Aizen, 1975) • Select important attributes with plausible levels • Generate scenarios with a bundle of potential attributes (reduced set, full profile) • Each individual holds a set of utility weights for attribute levels, and the individual assesses the overall utility for each scenario. • Conjoint analysis then calculates the individuals’ part worth utilities of the attributes from the preference ordering of stimuli. Where represents the importance (part worth utility) of the attribute i, and represents the desirability of level j of attribute i

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.

7. 8 Scenarios of farming situations to rank 1 being the most preferred, 8 the least • A:I60,000, V10, EH, FL • B:I20,000, V30, EH, FL • C: I20,000, V10, EL, FL • D: I20,000, V30, EL, FH • E: I20,000, V10, EH, FH • F: I60,000, V30, EH, FH • G: I60,000, V30, EL, FL • H: I60,000, V10, EL, FH I: net income; V: volatility; E: negative externality; F: Fertility

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.)

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

10. General statistics of participants

11. Average ranking of situation

12. Data analyses • Conjoint analysis to analyze relative importance of attributes • Cluster analysis to analyze preference heterogeneity among farmers • Probit regressions to predict cluster memberships Y • Dependent variables • Cluster 1 vs other clusters (Y=1 if cluster 1, otherwise =0) • Cluster 2 vs other clusters (Y=1 if cluster 2, otherwise =0) • Cluster 3 vs other clusters (Y=1 if cluster 3, otherwise =0) • Independent variables • For students: age, gender, risk propensity, career plan (to be a farmer), parents in agri-business, farm type inclination (organic vs conventional farm) • For farmers: age, gender, risk propensity, ownership, years of possession, # of employee in farm, farm size

13. Findings 1 (conjoint analysis): Fertility is most important factor, followed by volatility, income and externality.

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.

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).

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.

17. Limitations • The findings are exclusively associated with German farming preferences for two reasons: • 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. • The findings are exclusively associated with the specific parameters (levels) of attributes to reflect German agricultural business.