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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

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Outline

• Introductiono Motivation

• Theoretical background

o Previous Literature

o Methodology

• Experiment Design

• Findingso Preferences of farmers

o Cluster analysis on farmers’

preferences

o Prediction of cluster

memberships

• Conclusion and Limitations

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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

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Previous Literature

• Measuring individual preferenceso 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

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Methodology

• Conjoint analysis with rank orders (Green &Srinivasan, 1978;

Fishbein and Aizen, 1975)o Select important attributes with plausible levels

o Generate scenarios with a bundle of potential attributes (reduced set,

full profile)

o Each individual holds a set of utility weights for attribute levels, and the

individual assesses the overall utility for each scenario.

o 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

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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.

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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

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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.)

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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

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General statistics of participants

  Student Farmer Pooled

Age 24.06 (3.886) 30.68 (7.853) 25.91 (6.029)

Female ratio 0.286 0.156 0.250

Parents in farming 0.592 (0.497)    

Farming as career 0.939 (0.242)    

Organic-favored 0.305    

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 obs 49 19 68

Parentheses are standard deviation. a: sample size=44 for students, 13 for farmers, 57 for

pooled

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Average ranking of situation

Situation Description Ave. ranking Std. dev.

A I60,000, V10, EH, FL3.176 (1.050)

B I20,000, V30, EH, FL6.750 (1.164)

C I20,000, V10, EL, FL7.265 (0.971)

D I20,000, V30, EL, FH5.750 (1.250)

E I20,000, V10, EH, FH1.676 (1.112)

F I60,000, V30, EH, FH4.117 (1.399)

G I60,000, V30, EL, FL5.162 (1.532)

H I60,000, V10, EL, FH2.103 (1.161)

I: net income; V: volatility; E: negative externality; F: Fertility

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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 o 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)

o 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

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

Part-worth utilities for all subjects

Attribute Ave. relative importance (%) Levels Utility

(part-worth)

  Students (49) Farmers (19) Pooled    

Income 26.292 22.563 25.25060000E .860

20000E -.860

Volatility 27.670 26.814 27.43130% -.945

10% .945

Externality 16.616 16.275 16.520Low -.570

High .570

Fertility 29.422 34.348 30.798Low -1.088

High 1.088

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Finding 2 (Custer analysis): 3 clusters are identified; cluster 1 weighs heavily on externality, cluster 2 focuses on financial incentives, cluster 3 on fertility.

  Cluster ANOVA

1 2 3 F Sig.

Income 12.50 32.05 13.96 65.929 .000

Volatility 18.75 32.79 18.01 23.519 .000

Externality 44.35 11.07 18.72 48.349 .000

Fertility 24.40 24.08 49.32 61.797 .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 cluster 14.3% 27.9% 33.3%   

*: parentheses are numbers for student and farmer respectively.

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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).

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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.

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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.