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AGRICULTURAL PRODUCER RESPONSES TO CHANGES IN

Ph

CONSUMER DEMAND FOR PRODUCTION PROCESS

ATTRIBUTES

presented by

Nicole J. Olynk

has been accepted towards fulﬁllment
of the requirements for the

degree In Agricultural, Food, and
Resource Economics

 

 

 

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AGRICULTURAL PRODUCER RESPONSES TO CHANGES IN CONSUMER
DEMAND FOR PRODUCTION PROCESS ATTRIBUTES
By

Nicole J. Olynk

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Agricultural, Food, and Resource Economics

2009

ABSTRACT
AGRICULTURAL PRODUCER RESPONSES TO CHANGES IN CONSUMER
DEMAND FOR PRODUCTION PROCESS ATTRIBUTES
By

Nicole J. Olynk

Livestock producers can respond to increasing consumer demand for production
process attributes by providing veriﬁable information on the practices employed.
Consumer willingness to pay data was used to inform producer decision making
regarding choosing veriﬁcation entities for production processes attributes. A
conceptual model was developed to aid producers in using consumer demand data to
make decisions regarding provision of veriﬁed attributes. Analysis was conducted to
determine whether evidence of social desirability bias was found when comparing
estimates of consumer value for veriﬁed attributes across direct and indirect
questioning. Statistically signiﬁcant evidence of social desirability bias was found.
Indirect questioning may yield more accurate representations of consumer value than
direct questioning when estimating value for production process attributes for livestock
products. Producer willingness to change was assessed for rbST-free milk production.
Welfare impacts were calculated for dairy producers when conventional milk
production systems, which allow the use of rbST, are eliminated from the producer
choice set. Statistically signiﬁcant welfare impacts were found for both dairy producers
who did and did not use rbST prior to the elimination of the conventional system from

the producer choice set.

ACKNOWLEDGEMENTS

I would like to thank my committee, namely Dr. Christopher Wolf, Dr. Glynn
Tonsor, Dr. Roy Black and Dr. Janice Swanson for their guidance in this research. The
entire committee provided feedback and insight that led to higher quality output. I am
appreciative of Dr. Tonsor’s contributions to this work, his willingness to go above and
beyond to help me succeed, and his friendship. Special thanks go to Dr. Wolf, my
research advisor and major professor. His encouragement, advice, and friendship have
been invaluable throughout my entire experience at Michigan State.

I have made wonderful friends in my time at Michigan State. Speciﬁcally, I
thank Nicky Mason a friend and colleague on whom I could always rely, regardless of
the task or time of day. I would like to thank Joleen Hadrich who has been a valuable
collaborator and friend throughout my graduate experience. Special thanks related to
this work especially go to Tim Komarek for his knowledge on this subject area and
willingness to discuss and debate in the name of improved research quality. Thank you
to Tara Oeschger for her friendship, willingness to listen, and editorial contributions.
Thanks very much to Greg and Susanna Sabin for their friendship and willingness to
open up The Whole Apple Farm to me and provide unlimited animal visitation.

Finally, I would like to thank my family for their unending support. A special
thanks goes to my ﬁance, Dave, for his constant companionship, support, and ability to
put things into perspective. I am forever grateful for his support and willingness to help

in any way he can.

iii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................ v
CHAPTER 1: GENERAL INTRODUCTION ................................................................. 1
CHAPTER 2: CONSUMER WILLINGNESS TO PAY FOR LIVESTOCK
PRODUCTION PROCESS VERIFICATION ................................................................. 4
2.1 Introduction ............................................................................................................. 4
2.2 Survey Instrument and Choice Experiment ............................................................ 8
2.3 Research Methods ................................................................................................. 13
2.4 Results and Discussion ......................................................................................... 19
2.5 Conclusions ........................................................................................................... 29

CHAPTER 3: CREDENCE ATTRIBUTE VERIFICATION IN L IVESTOCK

PRODUCTION ............................................................................................................... 32
3.1 Introduction ........................................................................................................... 32
3.2 Research Design .................................................................................................... 36
3.3 Results and Discussion ......................................................................................... 44
3.4 Conclusions ........................................................................................................... 52

CHAPTER 4: SURVEY OF MILK PRODUCTION PRACTICES REGARDING THE

USE OF RBST IN MICHIGAN ..................................................................................... 56
4.1 Introduction ........................................................................................................... 56
4.2 Survey Instrument and Choice Experiment .......................................................... 58
4.3 Research Methods ................................................................................................. 64
4.4 Results and Discussion ......................................................................................... 70
4.5 Conclusions ........................................................................................................... 77

CHAPTER 5: SUMMARY ............................................................................................. 80

APPENDICES ................................................................................................................ 82
APPENDIX 1: SURVEY INSTRUMENT ................................................................. 83
APPENDIX 2: CHOICE EXPERIMENT DEFINITIONS ....................................... 104
APPENDIX 3: EXAMPLE CHOICE SET SCENARIO FOR PORK CHOPS ....... 105
APPENDIX 4: INFORMATION SCHOCKS .......................................................... 106
APPENDIX 5: PRODUCER SURVEY INSTRUMENT ........................................ 109

REFERENCES ............................................................................................................. 118

iv

LIST OF TABLES

Table 2 a. Pork chop and milk attributes and attribute levels evaluated in choice
experiments .................. _ ........................................................................................... 1 2

Table 2 b. Parameters (standard errors) for pork chops from random parameters logit 22
Table 2 c. Parameters (standard errors) for milk from random parameters logit ........... 23
Table 2 d. Willingness-to-pay for direct and indirect questioning for pork chops ......... 25
Table 2 e. Willingness-to-pay for direct and indirect questioning for milk .................... 26
Table 3 a. Product attributes and certiﬁcation entities .................................................... 38

Table 3 b. Consumer willingness to pay (WTP) and share of WTP to livestock producer
................................................................................................................................. 41

Table 3 c. Percent of consumers with willingness to pay (WTP) greater than speciﬁc
increments for veriﬁed pork chop attributes with statistical evidence of positive
consumer demand ................................................................................................... 49

Table 3 (1. Percent of consumers with willingness to pay (WTP) greater than speciﬁc
increments for veriﬁed milk attributes with statistical evidence of positive

consumer demand ................................................................................................... 51
Table 4 a. Selected dairy producer demographics and operation characteristics .......... 60
Table 4 b. Estimated coefﬁcients for random parameters logit model .......................... 71
Table 4 c. Dairy producer willingness to change (WTC) .............................................. 73
Table 4 d. Welfare effects on dairy producers ............................................................... 74

CHAPTER 1: GENERAL INTRODUCTION

American consumers today are interested in the production practices employed
in food production and have paid mounting attention to processes like irradiation,
antibiotic use, hormone use, and pesticide use (Naygo, 1996). It is becoming routine for
consumers to incorporate concerns such as those for animal welfare, animal handling
techniques, potential environmental impacts from use of substances such as pesticides
or herbicides, and perceived social impacts into decisions regarding food purchases.1

Focusing on livestock products, including eggs, meat, and milk, consumers are
increasingly interested in the living conditions, housing of, and handling techniques
used for the animals.2 Analyses regarding the animal welfare implications of
production systems are abundant, such as the use of cages for laying hens (Matheny and
Leahy, 2007), gestation crates used in pork production (Matheny and Leahy, 2007),
systems used for milk production with dairy cattle (Morrow, 2002), and production
systems used for rearing veal calves (Matheny and Leahy, 2007). It seems likely that
consumers will continue to include information regarding animal handling techniques
and animal welfare-related information in their purchasing decisions as information
regarding these topics becomes increasingly abundant and easily accessed.

Consumer demand for production process attributes in livestock products has led
to a need to inform consumers regarding the production practices employed on farms.

Producers, marketers, and retailers can deveIOp and implement labeling schemes to

 

‘ Perceived social impacts would include locally grown food, whether food was produced by a family
farm, whether laborers and employees throughout the supply chain were paid a livable wage, whether it
was produced in the United States, etc.

2 For the purposes of this analysis livestock products which are focused upon include food products only.
Fiber production from livestock is not included in this analysis due to critical differences in consumer
sentiment towards and production methods associated with food production versus ﬁber production.

provide additional information on product or production process attributes. Providing
information to the consumer via labeling, however, is only valuable if consumers trust
and use the information. Veriﬁcation is only valuable if it increases consumer
conﬁdence in the validity of the information provided. Livestock producers are left to
decide which avenue to pursue in order to gain consumer conﬁdence that the claims
they are making are indeed true.

Producers can maximize proﬁt through their selection of veriﬁed production
process attributes to provide to the market. Alternatively, if a speciﬁc production
process attribute is already present in the systems used on an operation, producers can
maximize proﬁt by selecting from alternative veriﬁcation methods used to communicate
that attribute to the consumer. The decision facing producers regarding how to verify
their production processes is not a simple case of selecting the veriﬁcation process with
the lowest cost to the producer. It is conceivable that the veriﬁcation method in which
consumers place the highest value (e.g., a federal government system) is too costly for
producers to pursue, while a relatively lower valued program or veriﬁcation party (e.g.,
a private third-party) in the eyes of consumers provides higher net returns for producers.
Producers are facing decisions in the market regarding how to communicate about their
products to consumers. As veriﬁcation of production process attributes continues to
grow in response to consumer demand for increased information, livestock producers
will be faced with decisions regarding how to verify their processes and communicate
with consumers in a way that the consumer trusts.

Producers are facing decisions beyond how to verify their products or how to

label products to provide information that consumers want. In some cases producers are

forced to respond to changes in the market due to legislative or market pressures.
Looking at market pressures for change, as consumers place pressure upon major
retailers to provide products with speciﬁc attributes, those retailers in turn place
pressure on their suppliers. In many cases related to food product attributes, those
suppliers are livestock producers. Livestock producers face pressure from retailers to
adopt (disadopt) practices and processes which consumers like (dislike) or have
expressed preference for (against). If a practice is prohibited for use by regulation, or is
essentially prohibited for use through market pressures, meaning that retailers will not
accept the product from producers if a certain practice was used in its production, then
producers face welfare impacts of having their choice set of practices reduced.

This dissertation proceeds as follows, beginning with estimation of consumer
demand in Chapter 11. Chapter II seeks to provide insight into the consumer valuation
for veriﬁcation of common production process attribute claims, providing an in-depth
look at willingness to pay for veriﬁed attributes provided through different verifying
entities. Chapter III presents an analysis of the producers’ decisions on which
veriﬁcation source to employ when verifying the same claims investigated in Chapter
11. Chapter III provides analysis in which producers’ optimal veriﬁcation decision is
solved for under different sets of circumstances. Chapter IV uses survey data from
Michigan dairy producers to provide in-depth information regarding producer
preferences for milk production without rbST. Welfare impacts on Michigan dairy
farmers of having rbST use in milk production removed from their choice set are

analyzed.

CHAPTER 2: CONSUMER WILLINGNESS TO PAY FOR LIVESTOCK
PRODUCTION PROCESS VERIFICATION

2.1 Introduction
Consumers are increasingly sensitive to the processes employed in food production.
Livestock products, in particular, evoke consumer sentiment regarding the handling and
rearing of livestock animals. Speciﬁcally, consumers are concerned about the treatment
of livestock or welfare of animals used to produce meat and milk products (Frewer et al.,
2005). Beyond animal welfare concerns, consumers are considering other production
process attributes, such as environmental impacts, food safety implications, and social
implications of production methods, when selecting food products for purchase.

Consumers will select the bundle of food products which provides them with the
largest utility, as long as he or she is able to accurately determine the quality attributes of
the food products (Caswell, 1998). Since the quantity and quality of the information
available regarding the attributes of a food product will aid in making purchasing decisions,
the conﬁdence a consumer has that the information regarding a food product attribute is
correct will affect consumer preferences for food products. Consumer conﬁdence in the
information available may depend on several factors including the speciﬁc livestock
product, which attribute is veriﬁed, and the source of veriﬁcation information.

Caswell and Mojduszka (1996) categorize food product attributes as either search,
experience, or credence attributes. Caswell and Mojduszka (1996) classify an attribute as a
search attribute if consumers are able to identify quality before purchase through either
inspection or research, while an experience attribute is one in which consumers are able to

determine the quality after the product is purchased and consumed, but not prior to

purchase. In contrast a credence attribute is one in which quality cannot be assessed even
aﬁer the product is purchased and consumed (Caswell and Mojduszka, 1996). Speciﬁc to
livestock rearing, claims surrounding animal rearing, handling, and housing practices all
encompass credence attributes of the production processes used. Livestock production has
many challenges associated with veriﬁcation of production process attributes due to the
multi-stage rearing process in many species, transfer of ownership throughout an animal’s
life, and complicated production systems which may differ with the stage of life of the
animal. Communicating animal welfare aspects of production systems to consumers is
complicated by lack of ability to verify claims through livestock product testing. To
maintain consumer conﬁdence the burden of proof lies with the producers to ensure that
consumers believe producer claims.

A number of recent studies have assessed consumer willingness to pay (WTP) for
animal welfare attributes in meat products (Carlsson, Frykblom, and Lagerkvist, 2007a-b;
Lijenstolpe, 2008; Lusk, Norwood, and Pruitt, 2006; Tonsor, Olynk, and Wolf, 2009).
Lusk and Norwood (2009) used direct questioning versus indirect questioning. Fisher
(1993) determined that indirect questioning reduced social desirability bias for variables
which were subject to social inﬂuence. Recently, Lusk and Norwood (2009) sought to
determine if indirect questioning could reduce social desirability bias, or consumers
incentive to self-report answers on a socially sensitive topic, speciﬁcally farm animal
welfare, in a way that conforms to social norms.

Beyond animal welfare attributes, questions remain regarding consumer preferences
and WTP to verify claims regarding production process attributes. The WTP for the actual

production process attribute itself has been estimated in previous studies, however the WTP

for veriﬁcation must also be considered. The WTP for veriﬁcation can be assessed solely
by veriﬁcation source, however it is more complete to assess the WTP by veriﬁcation
source for speciﬁc attributes. For example, the WTP for veriﬁcation of no antibiotic use
claims by a consumer group versus by the USDA. This approach assessing both the
verifying entity and the veriﬁed attribute allows ﬂexibility in which consumers may place
different values on claims where those values may also differ based on verifying entity.

Consumer demand for production process attributes has led to a need to inform
consumers regarding food production practices. Producers, marketers, and retailers can
develop and implement labeling schemes to provide additional information on product or
production process attributes. Labeling of attributes for which consumers have some
positive WTP will only prove valuable if consumers trust the information being provided.
Veriﬁcation is only valuable in this sense if it increases consumer conﬁdence in the validity
of the information provided. Given that consumer purchase decisions are affected by
information available on a particular product and perceived quality of that information,
veriﬁcation of claims regarding production process attributes may enable increased
consumer conﬁdence.

The objective of this study is to estimate consumer WTP for veriﬁcation of
production process attribute claims by different verifying parties and over different
livestock products. Four different production process attributes are included in this
analysis, namely individual crates/stalls, pasture access, antibiotic use, and certiﬁed
trucking/transport. Veriﬁcation by a private third-party group, a consumer group, the

USDA in their Process Veriﬁed Program (PVP),3 and the producer his or herself (self

 

3 The Grain Inspectors, Packers and Stockyards Administration (GIPSA) PVP has official procedures in
place for veriﬁcation of products assigned to GIPSA and services associated with marketing these

veriﬁcation) is investigated for two livestock products, namely pork chops and milk. This
analysis also makes a contribution by analyzing the potential social desirability bias in how
consumers report their own values for animal welfare attributes. Identiﬁcation of consumer
WTP for veriﬁcation by various parties can inform producer and marketer choice regarding
verifying programs. Analysis regarding potential social desirability bias in self-reported
values for animal welfare production process attributes aids in guiding future data
collection techniques for consumer WTP assessments and in informing producers and

marketers as to potential biases which may exist in consumer valuation estimates.

Background and Prior Research

Several studies have analyzed changing consumer preferences for food product attributes,
and the WTP for certain attributes. Several studies have sought to determine what
consumers are willing to pay for or to avoid certain attributes (McCluskey et al., 2003;
Grannis and Thilmany, 2002; Roosen, Lusk, and Fox, 2003; Burton et al., 2001; Lusk,
Roosen, and Fox, 2003; Roosen, 2003; Alfnes, 2004; Tonsor et al., 2005). Several studies
have also sought to investigate products which incorporated animal friendly attributes, such
as Lusk, Nilsson, and Foster (2007) and Nilsson, Foster, and Lusk (2006). Analyses have
included focuses on how consumers value antibiotic use in pork production (Lusk,
Norwood, and Pruitt, 2006), the impacts of generic advertising on pork demand (Capps and
Park, 2002), and consumer preferences regarding the use of gestation crates in pork

production (Tonsor, Olynk, and Wolf, 2009). In analyzing consumer valuation for

 

products (USDA, 2007). Veriﬁcation services through GIPSA are voluntary and provided to producers,
marketers, processors, and other associated service providers of agricultural products for a fee (USDA,
2007).

 

attributes in milk production, Bernard and Bernard (2009) analyzed consumer WTP for
organic, rbST-free, no antibiotics used, and conventional milk.

Producers can choose to verify claims themselves (self veriﬁcation) via videotaping
processes or documenting production processes. Producers could also seek third-party
veriﬁcation by consumer groups, such as the Certiﬁed Humane Raised and Handled®
program by the Humane Farm Animal Care Program, a private ﬁrm, or through the USDA
PVP. Prior analyses have urged movement towards veriﬁcation of production process
claims in livestock products. For example, Bernard and Bernard (2009) suggested
movement towards veriﬁcation of rbST—free and no antibiotic use claims in milk in a
schematic such as that used in the National Organic Program. This analysis seeks to
determine WTP by consumers for veriﬁcation of various livestock production process
claims by various verifying parties across two different livestock products. The authors are
unaware of a previous analysis which examined WTP by consumers for alternative
veriﬁcation entities across multiple production process attributes (or in investigating a

single production process attribute).

2.2 Survey Instrument and Choice Experiment

To collect information about consumer valuation of veriﬁcation by various parties,
consumer perceptions regarding animal welfare, animal handling systems, and production
processes used in livestock rearing, an online consumer survey of representative consumers
from throughout the United States was conducted.4 A total of 1,334 respondents completed
the survey; 669 respondents completed the survey with a choice experiment for pork chops

and 665 respondents completed the survey with a choice experiment for milk. The survey

 

4 The survey instrument, with the pork chop choice experiment, is presented in Appendix I.

instrument was designed to elicit consumer preferences regarding valuation for and
preferences surrounding veriﬁcation of claims regarding livestock rearing techniques. In
addition to questions surrounding preferences for animal rearing techniques and veriﬁcation
of claims regarding production processes employed, socio-demographic information about
each respondent was collected.

The surveys were conducted through Decipher, Inc., a marketing research services
provider which specializes in online survey programming, data collection, data processing
and reporting, and custom technology development. Decipher, Inc. targeted one adult per
household who was familiar with the food consumption patterns of the household. The
average age of survey respondents was 52.7 and 52.6 years for respondents who completed
the pork chops and milk choice experiments, respectively. Seventy percent of pork chop
experiment respondents were female while 72% of milk respondents were female. The
average household size for both pork chop and milk respondents was 2.03 adults and 0.46

kids.

Choice Experiment

Choice experiments allow researchers to estimate tradeoffs among alternatives by
replicating realistic purchasing situations and allowing multiple attributes to be evaluated
(Lusk, Roosen, and Fox, 2003). Consumer WTP was estimated for veriﬁcation by various
parties for four different attributes over two different livestock products. Appendix 2 shows
the deﬁnitions of the attributes and verifying entities provided to survey respondents in

preparation for the choice experiment. Each consumer surveyed was randomly allocated to

participate in a choice experiment for only one of the livestock products, in order to reduce
potential survey fatigue.

Social desirability bias reﬂects the fact that people often have incentives to
provide answers to self-reported questions about happiness, well-being, health, and
attitudes that deviate from true answers in order to comply with what is socially
acceptable (Lusk and Norwood, 2009). Given that animal welfare and the treatment of
livestock animals used in food production can be an emotional issue, it is conceivable to

think that consumers might exhibit some bias when answering questions related to

 

animal welfare attributes. Each survey respondent considered 14 total choice
experiment scenarios. Seven scenarios asked the survey respondent to select their
preferred alternative from two livestock products and a no purchase option (direct
questioning) and seven scenarios repeated the same choices but asked participants to
select what they believe the average American would choose (indirect questioning).
Survey participants were randomly selected for the ordering of direct and indirect
choice experiment scenarios, resulting in half of respondents completing direct
questioning before indirect questioning and half of respondents completing indirect
questioning ﬁrst. Direct questioning, or asking people what they would select, and
indirect questioning, or asking what people think others would select, allows testing for
social desirability bias (Lusk and Norwood, 2009).

Fisher (1993) compared direct and indirect questioning in an effort to determine
the ability of indirect questioning to reduce social desirability bias and found that
indirect questioning reduced social desirability bias on those variables which were

subject to social inﬂuence (and had no signiﬁcant effect on socially neutral variables).

10

Lusk and Norwood (2009) tested indirect questioning as a method to mitigate social
desirability bias, on the topic of farm animal well-being. They assessed that the main
assumption behind the use of indirect questioning is that while people want to answer in
ways that are seen as socially acceptable, they are relatively unconcerned with making
other people look good. We investigated social desirability bias through the use of
direct and indirect questioning in consumer choice experiments. Testing for statistically
signiﬁcant evidence of social desirability bias across different livestock products and
attributes allows us to not only identify bias existence, but to begin to understand
whether the bias may be different across products or attributes.

Five aspects of animal rearing were incorporated into the choice experiments.
Consumers were given information regarding whether individual crates/stalls were
permitted or not permitted, pasture access was required or not required, antibiotic use was
permitted or not permitted, certiﬁed trucking/transport was required or not required, and
whether the certiﬁcation entity was the USDA-PVP, self certiﬁcation, private third party, or
a consumer group. In addition to the ﬁve aspects of animal rearing and veriﬁcation,
consumers were presented with three different price levels for each livestock product.
Boneless pork chops were offered at three different price levels ($3.24/1b, $3.99/lb, or
$4.7 9/lb) which were selected to be consistent and comparable with prices that consumers
were facing for these products in retail stores at the time the survey was administered. An
example scenario from the pork chop attributes choice experiment is presented in Appendix
3. In choice sets presented identically to those above, milk was offered at $2.99/gallon,
$3.99/gallon, and $4.99/ gallon. The attributes and attribute levels evaluated in the choice

experiments for both pork chops and milk are summarized in Table 2a.

11

Table 2 a. Pork chop and milk attributes and attribute levels evaluated in choice
experiments

 

Milk Pork Chop

Product Attrrbute Attribute Levels Attribute Levels

 

 

Price $2.99/ gallon $3.24/lb
$3.99/gallon $3.99/lb
$4.99/ gallon $4.79/lb

 

 

Individual Crates/ Stalls Not permitted
Permitted

 

Antibiotic Use Not required
Required

 

Certiﬁed Trucking/Transport Not required
Required

 

 

Certiﬁcation Entity Self Certiﬁcation

Consumer Group

Private, 3rd Party
USDA-PVP

 

 

 

Each consumer was randomly presented with one of four types of information: a) Base
Information, b) Consumer Group Information, 0) Industry Information, or (1) Industry and
Consumer Group Information. Appendix 2 contains copies of each of these. information
treatments for both the pork and milk surveys.

The choice experiments were simulated shopping experiences which did not
include the exchange of real livestock products or money, although the instructions
included stated “T he experience from previous similar surveys is that people often state
a higher willingness to pay than what one actually is willing to pay for the good. It is
important that you make your selections like you would if you were actually facing these
choices in your retail purchase decisions, noting that allocation of funds to these
products means you will have less money available for other purchases. ” This

information was provided to survey participants as part of a strategy of “cheap-talk”

12

aimed to reduce hypothetical bias in the choice experiment simply by informing

participants of this bias prior to their participation (Lusk, 2003).

2.3 Research Methods

Random Utility Theory

Choice experiments involve asking individuals to choose from alternative bundles of
attributes, as opposed to rating or ranking them (Adamowicz et al., 1998). Random
utility theory, which frequently underlies analysis utilizing choice experiments assumes
economic agents seek to maximize their expected utility subject to the choice set that
they are given. However, the individual’s utility is considered a random variable

because the researcher has incomplete information (Manski, 1977). Let utility be the

sum of observable and unobservable components U jt = V jt + 81-, , where U jt is the
latent, unobservable utility for the j-th alternative in choice scenario t, V ft is the

observable, systematic portion of utility determined by the attributes, and gjt is the

random component of utility, independently and identically distributed over all
alternatives and choice scenarios. The probability that alternative j will be selected is
the probability that the added utility from this selection is greater than (or equivalent to)
choosing another alternative presented in the choice experiment. The utilities
associated with each alternative are not directly observable because they include an

unobserved component, therefore researchers have determined the probability of

selecting altemativej as P(j) = P(Vj + ej 2 vk + ck; j ¢ k Vj e N), where N is the

total set of alternatives available to the participant (Boxall and Adamowicz, 2002;

Adamowicz et al., 1998). The resulting probability that alternative j is selected can be

13

ell/BX]
expressed as P( j) = where p is a scale parameter which is inversely

ZeﬂﬂXk
keN

 

related to the variance of the error term (Lusk, Roosen, and Fox, 2003) and ,6 is a
vector of parameters (Boxall and Adamowicz, 2002; Adamowicz et al., 1998).5

Assuming the systematic component of the total utility U ﬂ is linear in parameters
the speciﬁcation of the general model is, V j = ,6] x jl + ,82sz + + ﬂnxjn where xjn is

the n-th attribute for alternative j and ,6 n is a vector of parameters associated with the n-th

attribute of the j-th alternative. The )8 s are utility parameters to be estimated in the model.

Multinomial logit models assume that homogeneous preferences exist for the product

attributes.

Random Parameters Logit

Recent literature and research suggests that consumers possess heterogeneous
preferences, therefore employing a model that allows heterogeneous preferences is
appropriate (Lusk, Roosen, and Fox, 2003; Alfnes, 2004; and Tonsor et al., 2005). Two
common methods which allow preference heterogeneity are latent class models and random
parameters logit (RPL) (also called mixed logit). The RPL model allows for random taste
variation within the surveyed group of consumers. The RPL is free of the independence of
irrelevant alternatives assumption and allows correlation in unobserved factors over time

(Revelt and Train, 1998; Train, 2003; Tonsor et al., 2005). Through the use of the RPL we

 

5 The scale parameter p is assumed to equal one for the remainder of this analysis following similar
analyses because it is unidentiﬁable in any given data set (Lusk, Roosen, and Fox, 2003).

14

 

are able to directly estimate the heterogeneity in consumer preferences across the evaluated
attributes. The random utility of the consumer (U) is underlying the RPL model.
Following Tonsor et a1. (2005), the utility of attribute j for individual i in choice set I in the

RPL model is generally presented as follows, U ijt = Vijt + luij + gift J, where Vijt is the
systematic portion of the utility function, u ij is an error term which is distributed normally
over consumers and alternatives (but not choice sets), and gift is the stochastic error,

independently and identically distributed over all consumers, attributes and choice sets. A
panel set of data is described here, in which the cross-sectional element is the consumer, i,
and the time series component is the choice scenario I (Alfnes, 2004; Tonsor et al., 2005).6
In the random parameter logit model described here, the probability that consumer i

chooses alternative j in choice set t is P (U ijt 2 U ikt) , over all possible k attributes. Under
the assumption that Vijt is linear in parameters the utility function can be expressed as,
vi, = ﬂilxlit + ﬂi2x2it + + [Bijxijt where xijt is the j-th attribute for choice set I and ,6 j

is a vector of preference parameters associated with the j-th attribute of the t-th alternative
of the i—th consumer (Alfnes, 2004; Tonsor et al., 2005).

The RPL model estimated in this analysis necessarily included the interaction terms
between attributes and verifying entities because a production practice attribute was never
presented to choice experiment participants without a certifying party also being presented.
In order to address the objective of determining WTP values for consumers across not only

different veriﬁcation parties, but also across different attributes being veriﬁed by different

 

6 Model estimation was done in NLOGIT 4.0 using the program’s panel data capabilities.

15

 

 

veriﬁcation parties, interaction terms were included. The model estimated speciﬁed the
systematic portion of utility as:

v]- : ,61(Cons)+ ,62 (Price) + ﬂ3 (Private) + B4 (Consumer) + ﬂ5 (USDA) +

£6 (Self _ Pasture) + ,67 (Private _ Pasture) + ,63 (Consumer _ Pasture) + [39 (USDA __ Pasture) +
£1 0 (Self _ Indiv) + ,6] 1 (Private _ Indiv) + ,6] 2 (Consumer _ Indiv) + ,3] 3 (USDA __ Indiv) +

ﬂl 4 (Self _ Anti) + ,6] 5 (Private _ Anti) + m 6 (Consumer __ Anti) + ,6} 7 (USDA _ Anti) +

H] 3 (Self _ C T ruck) + ,6] 9 (Private _ C Truck) + ,320 (Consumer _ C Truck) + ,621 (USDA _ C Truck)

where, Cons is a constant included to capture the disutility associated with not having the

good (milk or pork chops) in the consumer’s choice set (Cons =1 if option C is selected,

 

C ons=0 otherwise) and Price is the price of the good in the choice set. Private, Consumer,
and USDA are effects coded dummy variables for veriﬁcation by a private third-party,
consumer group, and USDA relative to self veriﬁcation. Self_Pasture, Private_Pasture,

C onsumer_Pasture, and USDA_Pasture are effects coded interaction terms between the
veriﬁcation entity and pasture access. Self_Indiv, Private_Indiv, Consumer_lndiv, and
USDA_Indiv are effects coded interaction terms between the veriﬁcation entity and
individual housing in crates or stalls. Self_Anti, Private_Anti, Consumer_Anti, and
USDA_Anti are effects coded interaction terms between the veriﬁcation entity and the use
of antibiotics. Self_CTruck, Private_ C T ruck, Consumer_ C T ruck, and USDA_ C Truck are
effects coded interaction terms between the veriﬁcation entity and the use of certiﬁed
trucking. Interaction terms are identifying the veriﬁed attributes in the choice experiment.
An example of the interpretation of these interaction terms is that the WTP associated with
Consumer_Anti can be interpreted as the WTP for consumer group veriﬁed antibiotic-free

production, as opposed to not having the antibiotic-free production certiﬁed by a consumer

16

group. Effects coding was used to avoid confounding effects of attribute levels with the
opting out option presented to consumers.7

The ,6 coefﬁcients on all of the explanatory variables except for Cons and Price are

speciﬁed to vary normally across consumers. In this analysis, given the scenarios being
assessed, it is possible that consumers may have both positive and negative WTP values for
veriﬁed animal welfare attributes. The random parameters are assumed to be drawn from a
normal distribution which allows the ﬂexibility for WTP estimates to be either positive or

negative (Tonsor et al., 2005; Lusk, Roosen, and Fox, 2003). The random parameter for

alternative j is given by B j = E j + a j + pi]- where [—3]- is the mean estimate across all
consumers, 0' j is a diagonal matrix of coefﬁcient standard errors, and llij is a vector of

independent normal decisions for each individual consumer (Lusk, Roosen, and Fox, 2003;
Tonsor et al., 2005).

The coefﬁcients estimated from a random utility model have little interpretive value
themselves. Mean WTP estimates for the RPL models are generally calculated by taking
the negative ratio of the estimated coefﬁcient on the veriﬁed attribute to the price
coefﬁcient. The coefﬁcient on the veriﬁed attribute k is multiplied by two in the WTP ratio

in this analysis due to the effects coding (Lusk, Roosen, and Fox, 2003). The WTP for

*
veriﬁed attribute k in this analysis was calculated as, WT Pk = lZ—ﬂ—ﬂi] where ,Bk is the

C

coefﬁcient on a veriﬁed attribute8 and ,Bc is the coefﬁcient on price. If the standard

 

7 In effects coding, rather than the typical 0,1 dummy variable coding, the attributes take on a value of I
when applicable, a value of -1 when the base category applies, and zero otherwise (Tonsor, Olynk, and
Wolf, 2009).

8 In this context, 16k would be, more speciﬁcally, the coefﬁcient on the interaction term between an
attribute and a veriﬁcation entity (a veriﬁed attribute as deﬁned in the text) or just the veriﬁcation entity

17

deviations of the attribute constants are not statistically different ﬁom zero, the estimated
mean WTP can be interpreted as being representative for the entire surveyed consumer
group. In this case the RPL interpretation reverts to that of the standard multinomial logit,
as it is not revealing signiﬁcant heterogeneity in the group. Evidence of preference
heterogeneity exists if the standard deviations are statistically signiﬁcant, in which case the
mean WTP estimates calculated cannot be interpreted as being representative for the entire
sample.

In order to consider statistical variability in estimates of WTP a 95% conﬁdence
interval for mean WTP values was calculated using the delta method. A variety of methods
exist to determine conﬁdence intervals on the WTP estimates, including the delta, Fieller,
Krinsky Robb, and bootstrap methods, although the four methods have been found to be
reasonably accurate and to yield similar results to one another (Hole, 2007). The delta
method estimates the variance of a non-linear function of two or more random variables by
taking a ﬁrst-order Taylor series expansion around the mean value of the variable and
calculating the variance on that newly created random variable (Greene, 2003). Following

Hole (2007), the delta estimate of the variance of a WTP estimate is given by:

(WTPﬂk )2 var(pk)+ (WT‘PﬂC )2 var(,3‘c)

var(Wka) = . . ~ ~
+ 2 * WTPﬂk * WTPﬂc * covwk ﬂc)

where, WTP'gk and WTP’BC are

the partial derivates of the estimated WTP for veriﬁed attribute k with respect to [3k and

 

alone. By construction, the attributes themselves are not included in the model without being interacted
with a veriﬁcation entity because for the purposes of this analysis it is the veriﬁcation of the attributes
that is focused upon, as opposed to a WTP by consumers for the attributes themselves. Moreover,
veriﬁcation is necessarily included because by the construction of the choice experiment, or by design, it
was impossible to have an attribute present without a veriﬁcation entity verifying that the production
process attribute being claimed was actually present. This matches "the real world" as it is unlikely a
product could be marketed with the discussed animal welfare claims and not be participating in a
veriﬁcation or certiﬁcation system as well.

18

 

,Bc respectively. Once the variance of the WTP estimate is calculated, conﬁdence intervals

can be calculated in the standard way.

2.4 Results and Discussion
The model was estimated under each of the information treatments (see Appendix 4),
for each product, and separately for direct and indirect questioning treatments.

Subsequently a log likelihood ratio test was used to determine if the data from the four

 

information treatments can be pooled.9 Consumer responses were found to be (i,
insensitive to the information treatment they received as we failed to reject the null
hypothesis that we can pool observations across consumers receiving the four different
information treatments. This ﬁnding is similar to that of Lusk, Norwood, and Pruitt
(2006) and Tonsor, Olynk, and Wolf (2009). As a result the models presented
throughout this analysis result from pooled models of observations across the four
information treatments.

To assess whether observations from indirect and direct questioning could be
pooled for either livestock product, a log likelihood ratio test was employed. We
rejected the null hypothesis of pooling of data across direct and indirect questioning for
both pork chops and milk. Therefore, throughout this analysis indirect questioning and
direct questioning are treated separately and estimates are presented separately for direct

questioning versus indirect questioning. Additional log likelihood tests were employed

 

9 The log likelihood ratio test, which follows a 2'2 distribution with K(M-l) degrees of freedom
(Wooldridge, 2002) is — 2(LLj - 2 LL,‘) , where LL J is the unrestricted pooled sample log

likelihood value and LL,’ are the log likelihood values for each of the information treatment groups. K
is the number of parameters in the model and M is the number of information treatments.

19

to determine if data from survey respondents receiving indirect and direct questioning in
differing orders could be pooled. All consumers completing the survey were asked to
participate in the choice experiment through direct and indirect questioning, although
half of the respondents completed indirect questioning ﬁrst and direct questioning
second while the other half completed direct questioning ﬁrst and indirect questioning
second. We rejected the null hypothesis of pooling across orders of questioning through
log likelihood testing for the direct questioning data for milk. For indirect questioning
in milk and pork chops as well as direct questioning in pork chops we failed to reject
the null hypothesis that we can pool across ordering of questioning (whether indirect
questioning ﬁrst and direct questioning second, or Vice versa). Throughout the analysis
observations are pooled across the order of questioning for indirect questioning in pork
chops and milk and for direct questioning in pork chops. As a result of pooling across
orders of questioning being rejected for direct questioning in milk, the direct
questioning coefﬁcients and WTP estimates for milk are presented for those
respondents who saw direct questioning ﬁrst and indirect questioning second.
Observations from respondents who completed indirect questioning ﬁrst were
eliminated from the data set used to analyze direct questioning in milk.

Table 2b displays the parameters estimated in the RPL model for pork chops.
Table 2c presents the indirect questioning results for all those survey respondents who
completed the survey with respect to milk and the direct questioning results for those
survey respondents who completed the direct questioning portion of the choice

experiment ﬁrst.'0 A large number of the estimated means for random pork chop and

 

1° It was concluded through likelihood ratio testing that direct questioning for milk production process
attributes was sensitive to the order in which direct and indirect questioning choice experiments were

20

milk attribute parameters were statistically signiﬁcant in these models. In random
utility models the interpretation of individual coefﬁcients is generally discouraged
(Scarpa and Del Giudice, 2004). The coefﬁcients were used to compute estimates of

consumer WTP, which are discussed below.

 

presented to the consumer. Direct questioning coefﬁcients for milk presented throughout this analysis are
for those consumers who answered direct questioning choice sets before indirect questioning choice sets.
Direct questioning data for those milk survey respondents who completed direct questioning choice sets
second were omitted from these results.

21

Table 2 b. Parameters (standard errors) for pork chops from random parameters logit

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable Direct Direct Indirect Indirect
Questioning Questioning Questioning Questioning
Coefﬁcient Standard Coefficient Standard
Estimates Deviation Estimates Deviation
(n=669) Estimates (n=669) Estimates
-1.5876 -1.6068
Opt out (1.1046) (1.0270)
-0.4960* -0.4028*
Price (0.1638) (0.1648)
0.7030 0.0533 1.5409* 0.0831
Private (0.4447) (0. 1416) (0.4498) (0.1424)
0.3469 0.0635 0.2524 0.0846
Consumer (0.2128) (0. 1434) (0.2066) (0.1 162)
-0.8371* 0.8124* -1.4937* 1.0390*
USDA (0.2916) (0.0980) (0.3020) (0.0926)
Self veriﬁed pasture 0.3016* 0.3895* 0.0823 0.3135*
access (0.0930) (0.0673) (0.0903) (0.0697)
Private party veriﬁed -0.3208 1.3943* -0.6642* 1.2235*
pasture access (0.2083) (0.0720) (0.2049) (0.0720)
Consumer group 03298“ 0.6725* 0.3509* 0.6650*
veriﬁed pasture access (0.1436) (0.0667) (0.1422) (0.0571)
USDA veriﬁed pasture 0.9514* 0.4935* 1.2687* 0.5134*
access (0.1263) (0.0593) (0.1342) (0.0530)
Self veriﬁed individual 0.2310* 0.3895* 05355" 0.3135*
crates/stalls (0. 1207) (0.0673) (0.123 5) (0.0697)
Private party veriﬁed 0.0737 1.3943* 0.2481 1.2235*
individual crates/stalls (0.21 14) (0.0720) (0.2043) (0.0720)
Consumer group
veriﬁed individual 0.2715* 0.6725* -0.0074 0.6650*
crates/stalls (0.1583) (0.0667) (0.1575) (0.0571)
USDA veriﬁed 0.4313* 0.4935* 0.5203* 0.5134*
individual crates/stalls (0.0853) (0.0593) (0.0898) (0.0530)
Self veriﬁed antibiotic -0.0757 0.3895* 0.0798 0.3135*
use (0.1494) (0.0673) (0.1524) (0.0697)
Private party veriﬁed -0.3303 1.3943* -0.6912* 1.2235*
antibiotic use (0.2470) (0.0720) (0.2446) (0.0720)
Consumer group -0.0194 0.6725* 0.0576 0.6650*
veriﬁed antibiotic use (0.1224) (0.0667) (0.1145) (0.0571)
USDA veriﬁed 0.7228* 0.4935* 0.8596* 0.5134*
antibiotic use (0.1107) (0.0593) (0.1140) (0.0530)
Self veriﬁed certiﬁed 0.1830* 0.3895* -0.0500 0.3135*
trucking/transport (0.0961) (0.0673) (0.0956) (0.0697)
Private party certiﬁed -0.9791* 1.3943* -l.41 14* 1.2235*
trucking/transport (0.3308) (0.0720) (0.3235) (0.0720)
Consumer group
certiﬁed -0.3845* 0.6725* -0.2937* 0.6650*
trucking/transport (0.1383) (0.0667) (0.1354) (0.0571)
USDA veriﬁed certiﬁed -0.0610 0.4935* -O.2628* 0.5134*
trucking/transport (0.1 195) (0.0593) (0.1247) (0.0530)

 

Notes: Presented models (log likelihoods of -3,858 and -3,911, respectively) were estimated using
NLOGIT 4.0, with Halton draws, and 500 replications for simulated probability. Standard errors are

presented in parentheses. Asterisk (*) indicates statistical signiﬁcance at the 0.05 level.

22

 

 

Table 2 c. Parameters (standard errors) for milk from random parameters logit

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable Direct Direct Indirect Indirect
Questioning Questioning Questioning Questioning
Coefﬁcient Standard Coefﬁcient Standard
Estimates Deviation Estimates Deviation
(n=332) Estimates (n=665) Estimates
-2.5293* -2.4644*
Opt out (1.1994) (0.9063)
-0.2537 -0.9584*
Price (0.1658) (0.1303)
0.8250 0.0819 1.7760* 0.2330
Private (0.6004) (0. 1580) (0.4745) Q. 1569)
0.0988 0.0143 -0.5370* 0.1578
Consumer (0.3137) (0.2014) (0.2231) (0.1761)
-0.5879 0.6659* -1.1811* 0.8700“
USDA (0.3942) (0.1361) (0.3062) (0.0939)
Self veriﬁed pasture 0.51 14* 0.3433* 0.0973 0.2474*
access (0.1270) (9.1 173) (0.0871) (0.0978)
Private party veriﬁed 0.1579 1.1915* -0.7788* 1.4425*
pasture access (0.2768) (0.0921) (0.2362) (0.0775)
Consumer group 0.7760* 0.6828* 0.5608* 0.6172*
veriﬁed pasture access (0.2213) @0813) (0.1453) (0.0625)
USDA veriﬁed pasture 1.3089* 0.4993* 1.0268* 0.5495*
access (0.1822) (0.0746) (0.1399) (0.0543)
Self veriﬁed individual 0.4369* 0.3433* 0.2392“ 0.2474*
crates/stalls (0.1663) (0.1 173) (0.1244) (0.0978)
Private party veriﬁed -0.0438 1.1915* -0.2073 1.4425*
individual crates/stalls (0.2746) (0.092 1) (0.2460) (0.0775)
Consumer group
veriﬁed individual 0.0371 0.6828* -0.0373 0.6172*
crates/stalls (0.2234) (0.0813) (0.1619) (0.0625)
USDA veriﬁed 0.3432* 0.4993* 0.4891 * 0.5495*
individual crates/stalls (0.1 160) (0.0746) (0.0920) (0.0543)
Self veriﬁed antibiotic 0.2815 0.3433* -0.0118 0.2474*
use (0.2021) (0.1 173) (0.1517) (0.0978)
Private party veriﬁed -0.2908 1.1915* -1.0151* 1.4425*
antibiotic use (0.3192) (0.0921) (0.2689) (0.0775)
Consumer group 0.2262 0.6828* 0.2520* 0.6172*
veriﬁed antibiotic use (0.1795) (0.0813) (0.1232) (0.0625)
USDA veriﬁed 04089" 0.4993* 0.5167* 0.5495*
antibiotic use (0.1559) (0.0746) (0.1168) (0.0543)
Self veriﬁed certiﬁed 0.1260 0.3433* 0.0846 0.2474*
trucking/transport (0.1299) (0.1 173) (0.0950) (0.0978)
Private party certiﬁed -0.4645 1.1915* -1.5351* 1.4425*
trucking/transport (0.4223) (0.0921) (0.3454) (0.0775)
Consumer group
certiﬁed -0.2338 0.6828* -0.2010 0.6172*
trucking/transport 40.1927) (0.0813) (0.1428) (0.0625)
USDA veriﬁed certiﬁed 0.0733 0.4993* -0.0244 0.5495*
trucking/transport (0.1720) (0.0746) (0.1314) (0.0543)

 

Notes: Presented models (log likelihoods of -2,3 1 3 and -3,764, respectively) were estimated using
NLOGIT 4.0, with Halton draws, and 500 replications for simulated probability. Standard errors are

presented in parentheses. Asterisk (*) indicates statistical signiﬁcance at the 0.05 level.

23

 

In all four of the models estimated all of the explanatory variables except for
Cons and Price are speciﬁed to vary normally across consumers. All of the random
parameters except for Private and Consumer were estimated to have statistically
signiﬁcant standard deviation parameters in all four of the models estimated, as is
indicated in Tables 2b and 2c. The statistically signiﬁcant standard deviation
parameters are evidence of preference heterogeneity in which case the mean WTP

estimates calculated cannot be interpreted as being representative for the entire sample.

Willingness-to-Pay

Mean estimates of consumer WTP were calculated for direct and indirect questioning
for pork chops and milk and are presented in Table 2d and Table 2e, respectively. With
regards to direct estimates for WTP for pork, values are signiﬁcantly different than zero
for the majority of estimates. When looking at WTP for veriﬁcation of pasture access,
consumers had a higher WTP for veriﬁcation of this attribute by the USDA than for
veriﬁcation by self, private third-party, or a consumer group. The same relationship
exists for veriﬁcation of individual stalls/crates for livestock housing and for
veriﬁcation of no antibiotic use, in which consumers had a higher WTP for veriﬁcation
through USDA than for the other verifying entities. Clearly, USDA veriﬁcation of the
attributes included in the analysis (all attributes except for certiﬁed trucking or
transport) had the highest value to the consumer when compared to verifying those

same attributes by a different veriﬁcation entity.

24

Table 2 d. Willingness-to-pay for direct and indirect questioning for pork chops

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable WTP
Direct Indirect Evidence of Social
Questioning Questioning Desirability Bias
(n=669) (n=669)
Private $2.84* $7.65* No
Consumer $1.40* $1 .25* No
USDA $(3.38)* $(7.42)* No
Self veriﬁed pasture
access $1 .22* $0.41 No
Private party veriﬁed
pasture access $(1.29)* $(3.30)* No
Consumer group
veriﬁed pasture access $1.33* $1 .74* No
USDA veriﬁed
pasture access $3.84* $6.30* No
Self veriﬁed
individual crates/stalls $0.93* $2.66* No
Private party veriﬁed
individual crates/stalls $0.30 $1 .23* No
Consumer group
veriﬁed individual
crates/stalls $ 1 .09* $(0.04) No
USDA veriﬁed
individual crates/stalls $1 .74* $2.58* No
Self veriﬁed antibiotic
use $(0.3 1) $0.40 No
Private party veriﬁed
antibiotic use $(l.33)* $(3.43)* No
Consumer group
veriﬁed antibiotic use $(0.08) $0.29 No
USDA veriﬁed
antibiotic use $2.91 * $4.27* No
Self veriﬁed certiﬁed
trucking/transport $0.74* $(0.25) Yes
Private party certiﬁed
trucking/transport $(3.95)* $(7.01)* No
Consumer group
certiﬁed
trucking/transport $(1.55)* $(1.46)* No
USDA veriﬁed
certiﬁed
trucking/transport $(0.25) $(1.30)* No

 

 

Asterisk (*) indicates statistical signiﬁcance at the 0.05 level. It was determined that there was evidence
of social desirability bias if 95% conﬁdence intervals surrounding direct questioning and indirect
questioning estimates did not overlap and the absolute value of the direct willingness to pay estimate was
greater than the absolute value of the indirect willingness to pay estimate.

25

Table 2 e. Willingness-to-pay for direct and indirect questioning for milk

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable WTP
Direct Indirect Evidence of Social
Questioning Questioning Desirability Bias
(n=332) gi=665)
Private $6.50 $3.71 * No
Consumer $0.78 $(1.12)* No
USDA $(4.63) $(2.46)* No
Self veriﬁed pasture
access $4.03* $0.20* Yes
Private party veriﬁed
pasture access $1.24 $(1.63)* No
Consumer group
veriﬁed pasture access $6.12* $1.17* Yes
USDA veriﬁed
pasture access $10.32* $2. 14* Yes
Self veriﬁed
individual crates/stalls $3.44* $0.50* No
Private party veriﬁed
individual crates/stalls $Q.35) $(0.43) No
Consumer group
veriﬁed individual
crates/stalls $0.29 $(0.08) No
USDA veriﬁed
individual crates/ stalls $2.71* $1 .02* No
Self veriﬁed antibiotic
use $2.22 $(0.02) No
Private party veriﬁed
antibiotic use $(2.29) $(2.12)* No
Consumer group
veriﬁed antibiotic use $1.78 $0.53* No
USDA veriﬁed
antibiotic use $3.22* $1 .08* No
Self veriﬁed certiﬁed
trucking/transport $0.99 $0.1 8 No
Private party certiﬁed
trucking/transport $(3.66) $(3.20)* No
Consumer group
certiﬁed
trucking/transport $(l .84) $(0.42)* No
USDA veriﬁed
certiﬁed
trucking/transport $0.58 $(0.05) No

 

 

 

 

 

Asterisk (*) indicates statistical signiﬁcance at the 0.05 level. It was determined that there was evidence
of social desirability bias if 95% conﬁdence intervals surrounding direct questioning and indirect
questioning estimates did not overlap and the absolute value of the direct willingness to pay estimate was
greater than the absolute value of the indirect willingness to pay estimate.

As was true in pork, the WTP estimates for veriﬁcation by the USDA were

higher than those for self, private third-party, or consumer groups for veriﬁcation of

26

pasture access and antibiotic use. Veriﬁcation of the use of certiﬁed trucking did not
follow the pattern of the other four attributes. Looking at direct questioning, for
certiﬁed trucking in pork the mean WTP for self veriﬁcation was $0.74/lb. Veriﬁcation
of certiﬁed trucking by USDA had a WTP that was not statistically different than zero,
while veriﬁcation by private party and consumer group both had negative WTP values
for direct questioning. Direct questioning in milk revealed WTP for veriﬁcation by
each of the four entities which was not statistically different from zero.

Comparing the point estimates in milk and pork chops provided support for the
notion that consumer WTP for veriﬁcation of production process attribute claims was
different depending on the livestock product in question. Further, looking at WTP
estimates, it was apparent that the WTP for veriﬁcation varied depending on the speciﬁc

attribute.

Social Desirability Bias

Given the emotionally charged issue of animal welfare and the increasing consumer
concern for how animals raised for food are treated and how their food is produced
overall, it is conceivable that consumers exhibit social desirability bias when asked
questions regarding animal welfare attributes. Incorporating both direct and indirect
questioning in our choice experiment allowed testing of statistically signiﬁcant evidence
of social desirability bias across livestock products. The ﬁrst step in looking for social
desirability bias was to determine if the absolute value of the WTP estimated from
direct questioning exceeded the WTP estimated from indirect questioning. Evidence of

signiﬁcant social desirability bias was determined by comparing the 95% conﬁdence

27

intervals on the direct and indirect WTP estimates for each veriﬁed attribute to
determine if the conﬁdence intervals were overlapping. If the 95% conﬁdence intervals
surrounding the indirect and direct WTP estimates for a given veriﬁed attribute did not
overlap and the absolute value of the WTP from direct questioning exceeded that of
indirect questioning, there was evidence of social desirability bias. ”

For both pork and milk the Sign on the WTP for each attribute was consistent
across direct and indirect questioning for WTP point estimates which were statistically
different than zero (Tables 2d and 2e). In the case of pork, statistically signiﬁcant
evidence of social desirability bias was found for only one attribute, self veriﬁed use of
certiﬁed trucking or transport. In the case of milk, however, three different attributes
were found to have signiﬁcant evidence of social desirability bias. Interestingly,
evidence of statistically signiﬁcant social desirability bias was found to a greater degree
in milk than it was in pork, indicating that the degree that the bias is present may be
product-speciﬁc. While the degree of bias present when looking at individual attributes
differed by product, there was evidence against pooling of observations from direct and
indirect questioning for both livestock products through likelihood ratio testing,
indicating that differences exist in the data between indirect and direct questioning.

A number of factors are likely determining the degree to which bias is present
for speciﬁc products. In this speciﬁc case of pork chops versus milk production it can
be hypothesized that factors such as consumers association with cows versus pigs or the

distinction between milk as a livestock product versus pork chops as a meat product

 

” Examination of overlapping 95% conﬁdence intervals is intuitive and allows comparison by visual
inspection when conﬁdence intervals are presented. It is acknowledged that comparing of 95%
conﬁdence intervals and examining overlap is more conservative than the standard method of
signiﬁcance testing when the null hypothesis is true and falsely fails to reject the null hypothesis more
frequently than the standard method when the null hypothesis is false (Schenker and Gentleman, 2001).

28

may play a role in the evidence of bias which was found. There is potential that the
amount of exposure to the livestock species that consumers have or consumer sentiment
for a speciﬁc species will inﬂuence social desirability bias surrounding products from
certain species. Widespread consumer exposure to campaigns for milk and dairy
products, for example, could result in closer consumer association with or sentiment for
dairy cows. Potential other factors include the increased likelihood of consumers seeing
cows on pasture versus pigs on pasture, increased perceived familiarity with milk
production versus swine production, or different emotional attachment to animals which
produce food products versus those which are raised primarily to slaughter for meat. In
light of the results found through this analysis, it is possible that consumers, through
exposure to campaigns or simply increased exposure to the species versus other
livestock species have increased emotional attachment to cows.

Analysis of the WTP estimates for milk from the direct and indirect questioning
provides support to the notion that when questioned directly people have a tendency to
represent themselves in a way that is socially acceptable. Consistent with Lusk and
Norwood (2009), it is likely that the indirect WTP estimates, in the case in which
evidence of social desirability bias can be found, are a more accurate reﬂection of

people’s actual WTP than the direct questioning WTP estimates.

2.5 Conclusions
Consumers are increasingly interested in how their food is produced and in learning
about the production practices used in modern food production. Consumers are

particularly interested in attributes of the production practices associated with the use of

29

livestock. A growing body of literature has reviewed consumer preferences for certain
production practice attributes and welfare attributes associated with livestock used for
food production. This analysis assessed consumer value for or WTP for the veriﬁcation
of those production practices. Estimates of consumer WTP for veriﬁcation of four
different livestock production process claims across two livestock products were
obtained. Consumer WTP estimates differed across verifying entity as well as attributes
veriﬁed. For example, veriﬁcation of access to pasture by the USDA had the highest
WTP for both pork chops and milk of any verifying entity verifying access to pasture.

Weak evidence of social desirability bias was found in this analysis. Direct and
indirect questioning was used to aid in determining if statistically signiﬁcant evidence
of such a bias could be detected. Comparing WTP conﬁdence intervals for direct and
indirect questioning in milk revealed evidence of social desirability bias. Given the
tendency for people to overstate their own values in order to conform to social norms,
and the resulting inﬂation of WTP estimates that could occur in such a situation, the
indirect estimates are likely more indicative of consumers’ actual WTP. Potential for
social desirability bias to be different, or present to a varying degree, across different
livestock products, and therefore livestock species, was found.

Future work may consider veriﬁcation by speciﬁc parties, rather than across the
broad and general categories of private third-party and consumer groups, for example.
Additional livestock products should be evaluated to further analyze the differences in
consumer WTP and social desirability bias across livestock species and products. The

incorporation of different production process attributes may yield additional insights

3O

 

into the attribute types for which consumers have higher WTP for veriﬁcation through a
given party.

Future work related to assessing social desirability bias may include analysis of
increased numbers of livestock products. Evidence of social desirability bias was found
in this analysis which used online survey techniques for data collection, but further
work is necessary regarding the degree of bias which is present depending on the data
collection method employed. It is suspected that the degree of social desirability bias
present would be higher for phone interviews and higher still for in-person interviews.
The impact of the data collection method employed on the evidence of social

desirability bias found should be evaluated in future analyses.

31

CHAPTER 3: CREDENCE ATTRIBUTE VERIFICATION IN LIVESTOCK
PRODUCTION

3.1 Introduction
Today’s consumers are concerned about the treatment of livestock and welfare of animals
used to produce meat and milk products (Frewer et al., 2005). Food product safety and the
characteristics of the processes employed in the production of food products are
increasingly important in the operation of food systems (Caswell, 1998). Livestock
producers can respond to consumer concerns by providing veriﬁable information regarding
production process attributes. Many of the claims regarding process attributes are credence
attributes of the production processes. Caswell and Mojduszka (1996) deﬁne a credence
attribute as an attribute in which quality cannot be assessed even after the product is
purchased and consumed (Caswell and Mojduszka, 1996). Producers cannot provide
veriﬁcation of credence attributes through traditional testing methods. This informational
asymmetry begs questions surrounding how producers will convey information to
consumers.

Information on production processes used must be conveyed by the producer to the
consumer through an avenue that consumers trust.12 Speciﬁc to livestock rearing, claims
surrounding handling and housing practices are credence attributes of the production
processes used. For example, at no point before, after, or during consumption of a pork

chop is the consumer able to determine the housing system used to raise the hog. Along the

 

'2 In reality, the entire supply chain must be convinced of the value of the veriﬁcation for these
production process attributes. On-farm production practices must be veriﬁed by (or beginning with) the
livestock producer. Consumer willingness to pay was assessed in this analysis because it is ultimately the
consumers’ preferences and consumer demand which drives what will be produced, or the attributes of
what will be produced. It is, however, worth noting that while production practices must be conveyed by
the producer to the consumer in a way in which consumers trust, the actual conveying of this information
is likely to be through the supply chain, which may include multiple steps, depending on the speciﬁc
livestock product.

32

same lines, at no point before, during, or after consumption could a consumer of milk
determine if the cows which produced that milk had access to pasture.

Producers can seek to maximize proﬁt through their selection of veriﬁed production
process attributes to provide to the market. Producers will not decide to supply these
production process attributes unless 1) they are required to do so or 2) they ﬁnd it proﬁtable
to supply (Caswell and Mojhuszka, 1996). If a speciﬁc production process attribute is
already present in the systems used on an operation, producers can seek to maximize proﬁt
by selecting from alternative veriﬁcation methods used to communicate that attribute to the
consumer. Tonsor, Olynk, and Wolf (2009) describe ballot initiatives which have passed in
several states, which would phase out the use of gestation crates in pork production. In
these cases, the individual livestock producer would not seek to determine whether it is
economically advantageous to produce without individual crates or stalls. Instead, a
producer operating under a ban on the use of gestation crates would maximize expected
proﬁt by choosing the optimal verifying entity. Even in the case where the production
practice used is predetermined, the proﬁt maximizing choice of verifying entity may not be
the lowest cost entity, but will be the verifying party which yields the highest net return to
the livestock producer. It is conceivable that the veriﬁcation method in which consumers
place the highest value (e.g., a federal government system) is simply too costly for
producers to pursue, while a relatively lower valued program or veriﬁcation party (e.g., a
private third-party) in the eyes of consumers provides higher net returns for producers.

As noted by Hudson and Lusk (2004) willingness to pay (WTP) is usually discussed
in the context of consumer utility maximization, although the concept can also be applied to

producers. Recently, several studies have assessed producer willingness to change

33

 

operational practices. Schulz and Tonsor (2010) identiﬁed preferences of US. cow-calf
producers for traceability systems and found heterogeneity among producers not only in
their preferences, but also in the welfare effects of mandating traceability. Norwood et al.
(2006) provided information from Oklahoma cattle producers regarding preferences of the
design of voluntary checkoff programs. Roe, Sporleder, and Belleville (2004) examined
hog producer preferences for hog marketing contract attributes and found producers value
window contract ceiling and ﬂoor prices differently. Davis and Gillespie (2007) found that
hog producers differ in their valuations of autonomy and risk acceptance in selecting from
alternative business arrangements. Norwood et a1. (2005) conducted a survey to measure
crop producers’ willingness to pay for manure from livestock operations. These examples
demonstrate increasing evaluations of producer preferences and willingness to change with
applications being applied to agricultural producers of all levels, from individual farm-level
producers to agribusinesses and marketing ﬁrms.

It is imperative for producers to understand the preferences of consumers and to
consider how animal rearing methods are taken into account in food purchasing decisions.
The major focus of much consumer WTP work has been on theoretical issues,
methodological questions surrounding estimating WTP, or policy issues rather than on
making adoption or pricing decisions of producers (Hudson and Lusk, 2004). Estimates of
consumer WTP can be beneﬁcial in decision making for agribusinesses as they move
towards serving a more consumer demand driven market (Lusk and Hudson, 2004). Lusk
and Hudson (2004) explicitly sought to provide insight into the beneﬁts and challenges of
the use of consumer WTP data for decision making in agribusinesses. Estimates of

consumer demand could be particularly useful when agribusinesses or agricultural

34

producers are assessing provision of new products or services. For example, in this analysis
it is shown that livestock producers can use estimates of consumer WTP to assess the
potential net beneﬁts associated with providing certain veriﬁed attributes.

Incorporating both the value and cost of veriﬁcation programs is essential to
selecting the optimal veriﬁcation method for livestock producers. This approach includes
both demand side and supply side cost impacts and this analysis develops and applies a
conceptual model for this situation. In particular, we focus on two livestock products (pork
chops and milk) and four production process attributes (individual crates or stalls, pasture
access, antibiotic use, and certiﬁed trucking or transport). The incorporation of consumer
demand data, or estimates of WTP, into the decision making of livestock producers is
demonstrated. To better match the choices of farmers, this analysis allows producers to
select betWeen four potential verifying methods, including self, consumer group, private
party, or USDA Producer Veriﬁed Program (PVP)l3 veriﬁcation. Two different decisions
can be informed for producers through this analysis. Producers may wish to determine
which attributes to adopt concurrent with the decision of how to verify those attributes, or if
a producer is already using a production process with certain attributes, they may wish to
investigate how to verify those processes. Examples provided throughout this analysis are
focused on decision support for the scenario in which a producer is already providing a
certain attribute but is seeking how to verify that process attribute. The conceptual
application of using WTP estimates to support farm-level decision making is much more

widely applicable than the examples provided in this analysis.

 

’3 The Grain Inspectors, Packers and Stockyards Administration (GIPSA) PVP has official procedures in
place for veriﬁcation of products assigned to GIPSA and services associated with marketing these
products (USDA, 2007). Veriﬁcation services through GIPSA are voluntary and provided to producers,
marketers, processors, and other associated service providers of agricultural products for a fee (USDA,
2007)

35

3.2 Research Design

Estimates of consumer value, or consumer WTP, were included in this analysis in order to
calculate estimates of the potential producer beneﬁts of providing a veriﬁed attribute.
Critical points were sought in order to identify the ranges of costs over which veriﬁcation
by certain entities was optimal for livestock producers. Critical points were ﬁrst assessed
using mean WTP estimates. Then, assuming that livestock producers face heterogeneous
cost structures associated with providing veriﬁed attributes, or switching processes to
provide speciﬁc veriﬁed attributes, implications of adjustment costs are discussed. For
discussion throughout this analysis it is assumed that livestock producers can be broken into
two distinct groups, namely low or high adjustment cost groups. '4 Differences in costs to
provide a veriﬁed attribute could be due to a number of reasons including, but not limited
to, economics of scale, economies of scope, or ease of veriﬁcation due to other farm-
speciﬁc factors. The two cost groups are used to illustrate the potential impacts of varying

relative costs of providing veriﬁed attributes on the decisions of livestock producers.

Producer Decision Support Model Speciﬁcation and Data Utilized

Estimates of consumer WTP for veriﬁed production process attributes were obtained for
this analysis from Olynk, Tonsor, and Wolf (2009). Four livestock production process
attributes, four verifying entities, and two livestock products were included in this analysis.
The four livestock production process attributes investigated in this analysis included,

whether individual crates/stalls were permitted or not permitted, pasture access was

 

’4 The low adjustment cost group is expected to have relatively lower adjustment costs associated with
veriﬁcation and adoption of different production processes, while the high adjustment cost group has a
relatively higher cost of adjustment and veriﬁcation.

36

required or not required, antibiotic use was permitted or not permitted, and whether
certiﬁed trucking/transport was required or not required. The two livestock products
analyzed were pork chops and milk. Throughout the analysis, in order to provide veriﬁed
attributes to consumers, livestock producers could choose to verify claims themselves (self
veriﬁcation), to use a private third-party, to use a consumer group, or to use the USDA PVP
(Olynk, Tonsor, and Wolf, 2009). Self veriﬁcation can be accomplished through
documenting production processes in various ways using video, photographs, or even
detailed written records of production practices. Alternatively, consumer groups may have
programs in place which may be applicable to livestock producers wishing to provide
veriﬁed attributes. An example of veriﬁcation through a consumer group is the Certiﬁed
Humane Raised and Handled® program by the Humane Farm Animal Care Program.
Veriﬁcation by a private third-party could include products being marketed under a speciﬁc
brand since many private brands make claims regarding the practices used to produce their
product. A livestock producer may choose to sell their meat or milk to be marketed under a
speciﬁc brand name which makes claims regarding and veriﬁes the production practices
used. As another alternative available to livestock producers, the USDA PVP provides
veriﬁcation of program approved claims (USDA, 2007). Table 3a summarizes the product

attributes and veriﬁcation entities included in this analysis.

37

Table 3 a. Product attributes and certiﬁcation entities

 

 

 

 

 

 

 

Product Attributes Attribute Levels
Individual Crates/Stalls Not permitted
Permrtted
Pasture Access NOI required
Requrred
Antibiotic Use NOI required
Requrred
' ' Not required
Certiﬁed Truckrng/Transport Required
Self Certiﬁcation
° ° - ' Consumer Group
Certiﬁcation Entities Private 3rd party
USDA PVP

 

 

 

The livestock producer must choose the veriﬁcation entity that maximizes proﬁt by
taking into account the expected revenue and costs associated with each veriﬁcation

method. The producer’s maximization problem is Max [a 'X ] — [,6 'X j,

where a = [(11, a2 ,a3, a4] is the per unit revenue for the producer for participating in
veriﬁcation method i=1, 2, 3, or 4, X = [x1,x2 ,x3 ,x4] is the choice of veriﬁcation method
i, and ,6 = [,61 , ,62 , ﬂ3 , ,84] is the per unit cost to the producer of veriﬁcation method i.

Taking the ﬁrst order condition of the producer’s maximization problem with respect to the

choice variable, X, yields the optimal condition that a' = ,B' , or that the marginal revenue

must equal the marginal cost for the producer.”'6 Solving the producer’s maximization

problem yields a decision rule of the form X * (01,5).

 

'5 Note that this maximization problem is solved considering each combination of livestock production
process attributes (pasture access, individual crates/stalls, antibiotic use, and certiﬁed trucking or
transport) and verifying entity (self veriﬁcation, private third-party veriﬁcation, consumer group, and
USDA PVP). If the problem is constrained to making a decision regarding verifying a speciﬁc attribute,
such as the use of crates in pork production in our example application, the maximization problem is
reduced to selecting the optimal verifying party from the four potential veriﬁers.

38

Consumer WTP estimates for veriﬁcation of key production process attributes by
speciﬁc verifying entity were taken from Olynk, Tonsor, and Wolf (2009). The values
fora , which were obtained from random utility models, identify the WTP of consumers for
veriﬁed attributes in pork chops or milk (Olynk, Tonsor, and Wolf, 2009). A total of 1,334
respondents completed the survey: 669 respondents completed the-survey with a choice
experiment for pork chops and 665 respondents completed the survey with a choice
experiment for milk (Olynk, Tonsor, and Wolf, 2009). The information and deﬁnitions
provided to consmners participating in the survey with regards to the production process
attributes and verifying entities are presented in Appendix 2.

The consumer WTP values were adjusted according to the portion of retail value
expected to be conveyed through the supply chain to the livestock producer to obtain a ,
the per unit revenue associated with providing a veriﬁed attribute. Estimates of the
farmers’ share of the retail value of the livestock products must be incorporated in this
analysis because the consumer WTP estimates are providing estimates of the total value to
consumers, while the livestock producer receives only a portion of this amount. The
estimate of the farmers’ share of the retail pork and milk (using whole milk sold in gallons)
value used in this analysis was 28.1% and 53%, respectively (USDA, 2009). It is assumed
throughout this analysis that the farmers’ share of the retail value of the veriﬁcation (the
increase in value due to veriﬁcation) will be the same as the share of the retail value which

the producer receives on the product overall.l7 For example, using the data from pork

 

'6 This simpliﬁed framework assumes that the volume impacted is not sufﬁcient to inﬂuence prices and
that all cross-price impacts (both within a ﬁrm’s product line and in aggregate markets) are zero.

‘7 This estimate of the farmers’ share of the retail value is likely conservative because it is likely that
those producers seeking voluntary veriﬁcation of production process attributes would also be seeking
other ways to obtain a larger share of the retail value. In other words, while the averages for farmers’
share of retail value are used in this analysis, the farmers engaging in veriﬁcation of process attributes are
likely obtaining a higher than average share of the retail value due to the increased likelihood to

39

chops, in the case of consumer group veriﬁed pasture access the estimate of consumer WTP
was $1.74 per lb, while the portion that the livestock producer is expected to receive is only

$0.49 per lb, as can be seen in Table 3b.18

 

participate in other activities (beyond this veriﬁcation) which increase their share of the retail value. The
average value is used throughout this analysis as a conservative estimate of the farmers‘ share of the retail
value.

'8 In Table 3b the farmers’ share of the retail value is presented as the maximum cost that farmers could
absorb to rationally provide a veriﬁed attribute. Conceptually, the total farmers’ share of the retail value
is the most that a farmer could spend to provide the attribute (without incurring a loss to do so).

40

Table 3 b. Consumer willingness to pay (WTP) and share of WTP to livestock

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

producer

Livestock Product Pork Chops Milk

Veriﬁed Attribute Consumer Breakeven Consumer Breakeven
Value Producer Value Producer

from Indirect Cost To Provide from Indirect Cost To Provide
Questioning Veriﬁed Questioning Veriﬁed
Attribute Attribute

Self veriﬁed pasture .

access $0.41 $0.00 $0.20* $0.1 1

Private party veriﬁed

pasture access $(3.30)* $0.00 $(l .63)* $0.00

Consumer group ‘

veriﬁed pasture access $1 .74* $0.49 $1 . l 7* $0.62

USDA veriﬁed pasture

access $6.30* $1.77 $2.14* $1.14

Self veriﬁed individual

crates/stalls $2.66* $0.75 $0.50* $0.26

Private party veriﬁed

individual crates/stalls $1 .23* $0.3 5 $(0.43) $0.00

Consumer group

veriﬁed individual

crates/stalls $(0.04) $0.00 $(0.08) $0.00

USDA veriﬁed

individual crates/stalls $2.58* $0.73 $1 .02* $0.54

Self veriﬁed antibiotic

use $0.40 $0.00 $(0.02) $0.00

Private party veriﬁed

antibiotic use $(3.43)* $0.00 $(2.12)* $0.00

Consumer group

veriﬁed antibiotic use $0.29 $0.00 $0.53* $0.28

USDA veriﬁed

antibiotic use $4.27* $1.20 $1.08* $0.57

Self veriﬁed certiﬁed

trucking/transport $(0.25) $0.00 $0.18 $0.00

Private party certiﬁed

truckirg/trangiort $(7.01)* $0.00 $Q.20)* $0.00

Consumer group

certiﬁed

truckirgtransport $(1.46)* $0.00 $(0.42)* $0.00

USDA veriﬁed

certiﬁed

truckingtransport $(1.30)* $0.00 $(0.05) $0.00

 

 

 

 

 

Indirect estimates of consumer value (WTP) are taken from Olynk, Tonsor, and Wolf (2009). Asterisk (*)
indicates statistical signiﬁcance at the 0.05 level. The farm shares of the retail value used for pork chops
and milk throughout this analysis were 28.1% and 53%, respectively (USDA, 2009). Breakeven producer

costs for the veriﬁed attributes are provided for those attributes for which there was positive and statistically

signiﬁcant mean WTP identiﬁed, otherwise the veriﬁed attribute is dropped from the analysis as the cost of
providing the attribute would always exceed the value of provision to the livestock producer.

41

 

It is conceivable to think that consumers might exhibit some bias when answering
questions related to animal welfare attributes because animal welfare can be a socially
charged issue. Social desirability bias reﬂects the fact that people often have incentives to
provide answers to self-reported questions about happiness, well-being, health, and
attitudes that deviate from true answers in order to comply with what is socially
acceptable (Lusk and Norwood, 2009). Due to the tendency for people to overstate their
own values in order to conform to social norms, and the resulting inﬂation of WTP
estimates that could occur in such a situation, the indirect estimates of WTP are likely to
be more indicative of consumers’ actual WTP than direct estimates.19 Olynk, Tonsor, and
Wolf (2009) found evidence of social desirability bias. Estimates of WTP obtained
through indirect questioning were used throughout this analysis due to the recognition
that direct questioning may lead to inﬂated values of consruner WTP due to the presence
of social desirability bias and in an effort to provide conservative estimates of consumer
demand to support producer decision making. Point estimates of consumer WTP
obtained through indirect questioning for pork chops and milk can be seen in Table 3b.

Reliable cost estimates for providing veriﬁed attributes were unavailable. Given
the wide range of producer costs for verifying the production process attributes through
the four different potential verifying entities, identiﬁcation of critical values at which the
optimal verifying entity changes can aid in supporting decisions across a wide range of
producers. To support producer decision making, critical points for relative costs to the

livestock producer between veriﬁcation methods (verifying through different entities) that

 

'9 Fisher (1993) compared direct and indirect questioning in an effort to determine the ability of
indirect questioning to reduce social desirability bias and found that indirect questioning reduced social
desirability bias on those variables which were subject to social inﬂuence (and had no signiﬁcant effect
on socially neutral variables). Speciﬁcally focusing on the topic of farm animal well-being, Lusk and
Norwood (2009) have tested indirect questioning as a method to mitigate social desirability bias.

42

change the optimal producer veriﬁcation method choice were identiﬁed. Through this
analysis, decision rules regarding the verifying entity which would be optimal for ranges
of relative costs will be identiﬁed for each of the livestock products and production
process attributes included. Such rules will enable producers to incorporate their own
information to determine the optimal veriﬁcation program and ensure that the results are

applicable over a wide range of producer cost structures.

Percent of consumers willing to pay at different levels

Point estimates of consumer WTP values and the variance of those WTP estimates were
used to identify the distribution of consumer WTP, under the assumption that WTP
estimates are normally distributed (Alfnes, 2004). This analysis allows the percentage of
consumers that have a higher WTP than some critical level to be estimated. Producer
decision making can be more cOmpletely informed by analyzing the distribution of
consumer WTP values, rather than relying on a single WTP estimate. Analyzing the
percentage of consumers that have a WTP higher than some predetermined level can aid
in determining the share of the market that producers can seek to serve. Producers can
utilize their own cost estimates to determine the segment of the consumer population that
has a WTP high enough to provide a return to providing the veriﬁed attribute. Producers
must, however, also recognize that as the WTP for veriﬁed attributes increases, so does

the number of producers who are willing and able to provide that attribute.

43

 

3.3 Results and Discussion

Estimates of consumer WTP for veriﬁcation of various livestock production process
attributes were used to establish the potential beneﬁts to livestock producers associated
with providing veriﬁed attributes. Consumer WTP for veriﬁed attributes differed across
both livestock species and attributes. As a result the critical points in producer veriﬁcation
costs at which a producer should switch verifying entities also differed by both the
livestock product and the attribute in question.

The producer decision support mechanism described in this analysis can be used
in two different manners. Producers can use such a mechanism to determine which
veriﬁed attributes to adopt or to determine how to verify production process attributes
which already exist on their operation. Results presented here assume that producers are
already providing the production process attribute in question and are seeking to
determine the proﬁt maximizing veriﬁcation method to verify the speciﬁc attribute. This
use of the mechanism for decision support described above is rather limiting, as it only
applies to those producers who are providing the attribute in question. However there are
several groups of producers who fall into this category of needing to select the optimal
verifying entity for a predetermined attribute, whether the attribute is legislatively
determined, determined by retailers providing market access, or producers have simply
already chosen a certain production system for other reasons. The presented model could
easily be applied to producers evaluating the value in changing on-farm production

practices following similar logic.

44

Critical Points Identiﬁed

The simplest criteria for whether or not producers may want to verify a speciﬁc
production process attribute is whether or not the mean estimate for consumer WTP is
positive. Table 3b highlights mean WTP estimates for pork chops and milk and the farm
share of the retail value of the WTP. In the case of pork chops, mean estimates of WTP
were negative and signiﬁcant at the 0.05 level for private veriﬁcation of pasture access
and antibiotic use, as well as for veriﬁcation of certiﬁed trucking or transport by private
third-party, consumer group, and USDA PVP. Clearly if the mean WTP was negative, a
producer would not rationally voluntarily spend money to provide the veriﬁed attribute.
Self veriﬁed pasture access, consumer veriﬁed individual stall or crates, self veriﬁed
antibiotic use, consumer group veriﬁed antibiotic use, and self veriﬁed certiﬁed trucking
or transport had mean WTP estimates for pork chOps which were not different from zero
at the. 0.05 signiﬁcance level. For those attributes for which the mean WTP estimate was
not different from zero, the maximum amount that the producer could spend on
veriﬁcation was also assumed to be zero, again because a rational producer would not
spend a positive amount to provide an attribute for which consumers did not have a
positive value.

When assessing mean estimates of WTP for milk, negative WTP estimates which
were signiﬁcant at the 0.05 level were observed for private party veriﬁcation of pasture
access, antibiotic use, or certiﬁed trucking or transport, and consumer group veriﬁcation
of certiﬁed trucking or transport. Mean estimates of WTP which were not statistically

different from zero at the 0.05 level for milk production were private party or consumer

45

group veriﬁcation of individual crates or stalls, self veriﬁed antibiotic use, and self or
USDA PVP veriﬁed certiﬁed trucking or transport.

Operating under the assumption that swine producers in question already have
access to pasture for their pigs, the question remains as to which verifying entity is the
best choice for a producer. In order to obtain critical points, or the points at which
producers should switch veriﬁcation entities in order to obtain optimal returns, the
ordering of costs of veriﬁcation must be known. It was assumed that self veriﬁcation was
the least costly, consumer group and private third-party are the next most costly
veriﬁcation entities, and that USDA PVP is the most costly veriﬁcation entity. Using the
mechanism described above, and assuming that the cost for consumer group veriﬁcation
of pasture access was $0.01 as a starting point”, the optimal decision for the producer
would be to switch to USDA PVP veriﬁcation if it could be obtained for less than
$1 .29/lb. Regardless of other veriﬁcation options presented as competing options, the
producer should not incur costs of over $0.49/lb for providing consumer group
veriﬁcation of pasture access. Alternatively, assuming consumer group veriﬁcation costs
$0.15/lb, participation in an USDA PVP is optimal if feasible for less than $1.43/lb,
otherwise consumer group veriﬁcation is the optimal choice.

Veriﬁcation of individual crates or stalls for the production of pork chops presents
an interesting case because even if the cost of veriﬁcation through self, private party, and
USDA PVP were $0.01/lb, the optimal veriﬁcation method would be self veriﬁcation,

which would return $0.74/lb in proﬁt to verifying, compared to $0.72/lb for USDA PVP,

 

2° Note that while the cost of self veriﬁcation relative to other veriﬁcation entities was assumed to be
the lowest, in the case of self veriﬁed pasture access for swine production, consumer WTP estimates
were not different than zero. Therefore, the analysis for pasture access for swine begins with consumer
group veriﬁcation of pasture access.

46

and $0.34/lb for private party veriﬁcation. In this case, self veriﬁcation is optimal under
the assumption that self veriﬁcation is the lowest cost option.

Veriﬁcation of antibiotic use in the production of pork chops presents a case in
which the only statistically signiﬁcant evidence of positive consumer demand is for
veriﬁcation by USDA PVP. If USDA PVP veriﬁcation can be obtained for less than
$1.20/lb, USDA PVP veriﬁcation becomes the optimal decision. If USDA PVP
veriﬁcation cannot be obtained for less than $1.20/1b veriﬁcation through any of the four
potential entities included here is not optimal.

Looking at veriﬁcation of production process attributes in milk production, USDA
PVP veriﬁcation has the highest value to consumers for each attribute. In the case of
verifying pasture access, if self veriﬁcation costs $0.01/ gallon, the producer should switch
to consumer group veriﬁcation if it can be obtained for less than $0.52/ gallon or USDA
PVP veriﬁcation if it can be obtained for less than $1.04/gallon. For veriﬁcation of
individual crates or stalls, assuming self veriﬁcation costs $0.01/gallon, the producer
should switch to USDA PVP veriﬁcation if it can be obtained for less than $0.29/gallon.
In the case of verifying the use of antibiotics for milk production, the producer’s decision
is between consumer group veriﬁcation and USDA PVP veriﬁcation, as these are the two
veriﬁcation entities with positive mean WTP values. In this case, if it is assumed that
consumer group veriﬁcation costs $0.01/gallon, the producer should switch to USDA
PVP veriﬁcation if it can be obtained for less than $0.30/gallon. As it is unlikely that
consumer group veriﬁcation could be obtained for $0.01, the starting value for consumer

group veriﬁcation cost was updated to $0.10 for comparison. If veriﬁcation by consumer

47

group costs $0.10, then the producer should switch to USDA PVP veriﬁcation if it can be
obtained for less than $0.39.

Statistical evidence of positive consumer demand for veriﬁcation of certiﬁed
trucking or transport was not found for any of the four verifying entities included in this
analysis for either pork chops or milk. This suggests a rational producer would not pay
any positive amount to provide this veriﬁed attribute given the lack of positive consumer

demand.

Incorporating distribution of consumer WT P values

Using the distributions obtained surrounding these mean consumer WTP values, the
percent of consumers that would be willing to pay more than a speciﬁed amount for
veriﬁcation of a given attribute can be determined. As heterogeneity is expected in the
cost structures of livestock producers, it is illustrative to think about the high cost and low
cost producers outlined earlier. Table 3c shows the percent of consumers with total WTP
greater than speciﬁed values for veriﬁed pork chop attributes in $0.50/lb increments of
consmner WTP. For interpretation from the livestock producers’ perspective, it is the

farm share of the consumer WTP that is enlightening.

48

Table 3 c. Percent of consumers with willingness to pay (WTP) greater than speciﬁc
increments for veriﬁed pork chop attributes with statistical evidence of positive
consumer demand

 

Consumer
WTP $0.00 $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00

 

Farm Share
of Retail $0.00 $0.14 $0.28 $0.42 $0.56 $0.70 $0.84 $0.98 $1.12
WTP Value
Veriﬁed Percent (%) of Consumers Willing to Pay More than the Above-Stated WTP
Attribute
Consumer
group
veriﬁed 100.00 100.00 99.77 82.31 16.20 0.19 0.00 0.00 0.00
pasture
access
USDA
veriﬁed
pasture
access

Self veriﬁed
individual 100.00 99.99 99.73 97.41 86.57 60.52 28.35 7.90 1.22
gates/stalls
Private party
veriﬁed
individual
crates/stalls

 

 

 

 

99.74 99.50 99.07 98.35 97.19 95.43 92.86 89.32 84.65

 

 

99.92 96.95 72.36 24.64 2.47 0.06 0.00 0.00 0.00

 

 

 

Veriﬁed 100.00 100.00 99.99 99.40 91.20 57.68 16.71 1.68 0.05
1nd1v1dual

crates/stalls

SDA

veriﬁed 100.00 99.98 99.91 99.58 98.46 95.38 88.64 76.77 60.08

antibiotic use
Only those veriﬁed attributes for which statistically signiﬁcant evidence of positive mean WTP was

found are included here for further analysis regarding the distribution of consumer WTP values. The
farm share of the retail value used for pork chops throughout this analysis was 28.1% (USDA, 2009).

 

 

 

 

 

 

 

 

 

 

 

 

Using Table 3c, for example, 92.86%, 16.71%, and 88.64% of consumers have a
WTP greater than $3.00/lb for USDA PVP veriﬁed pasture access, individual
crates/stalls, and antibiotic use, respectively. A more intuitive way for producers to
interpret these numbers is to assess the percent of consumers which are willing to pay
more than the cost of the veriﬁcation. This allows producers to assess the farm share of a

given WTP value and determine if their cost of providing the attribute is less than that

49

farm share of the WTP. For example, a high cost producer with a cost of providing self
veriﬁed individual crates or stalls approaching $0.98/lb can take note that only 7.90% of
consumers have the WTP of $3.50/lb which is needed to provide $0.98/lb payment at the
farm level. A low cost producer, however, may be able to provide self veriﬁed individual
crates or stalls for nearly $0.28/lb, at which point 99.73% of consumers possess a WTP
greater than the $1.00/1b necessary to provide $0.28/lb income at the farm level.
Complicating the decisions facing these producers is the fact that likely far fewer
producers can provide the attribute at $0.28/lb than can provide the attribute at $0.98/lb.
Not only is the segment of the consumer market which is willing to pay the $3.50/1b far
smaller than the portion willing to pay $1 .00/lb, but the number of producers seeking to
provide the attribute at this higher price is also likely much larger.

Table 3d shows the percent of consumers with total WTP greater than speciﬁed
values for veriﬁed milk attributes in $0.20 or $0.10 increments of consumer WTP.2|
Farm share of retail WTP are shown in Table 3d to allow comparison similar to that

presented for pork chops.

 

2' Increments of $0.20 or $0.10 were used for the veriﬁed attributes in milk rather than the $0.50
increments in pork chops due to the different WTP distributions observed in the milk analysis.

50

Table 3 d. Percent of consumers with willingness to pay (WTP) greater than speciﬁc
increments for veriﬁed milk attributes with statistical evidence of positive consumer

demand

 

Consumer
WTP
($0.20
increments)

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

$1.60

$1.80

 

Farm Share
of Retail
WTP Value

$0.00

$0.11

 

 

$0.21

 

$0.32

$0.42

 

 

$0.53

 

$0.64

 

$0.74

 

$0.85

 

$0.95

 

Veriﬁed
Attribute

Percent (%) of Consumers Willing to Pay More than the Above-Stated WTP

 

USDA
veriﬁed
individual
crates/stalls

100.00

100.00

100.00

100.00

100.00

85.58

0.00

0.00

0.00

0.00

 

Consumer
group
veriﬁed
pasture
access

100.00

100.00

100.00

100.00

100.00

100.00

16.06

0.00

0.00

0.00

 

USDA
veriﬁed
antibiotic
use

100.00

100.00

100.00

100.00

100.00

99.72

0.00

0.00

0.00

0.00

 

USDA
veriﬁed
pasture

Consumer
WTP
($0.10
increments)

100.00

$0.00

100.00

$0.10

100.00

$0.20

100.00

$0.30

100.00

$0.40

100.00

$0.50

100.00

$0.60

100.00

$0.70

100.00

$0.80

100.00

access

$0.90

 

Farm Share
of Retail
WTP Value

$0.00

$0.05

 

 

$0.11

 

$0.16

$0.21

 

 

$0.27

 

$0.32

 

$0.37

 

$0.42

 

$0.48

 

Veriﬁed
Attribute

Percent (%) of Consumers Willing to Pay More than the Above-Stated WTP

 

Self veriﬁed
individual
crates/stalls

100.00

100.00

100.00

100.00

100.00

48.57

0.00

0.00

0.00

0.00

 

Self veriﬁed
pasture
access

100.00

100.00

63.73

0.00

0.00

0.00

0.00

0.00

0.00

0.00

 

Consumer
group
veriﬁed
antibiotic
use

 

 

100.00

100.00

 

 

100.00

 

100.00

92.80

 

 

0.00

 

0.00

 

0.00

 

0.00

 

0.00

 

Only those veriﬁed attributes for which statistically signiﬁcant evidence of positive mean WTP was
found are included here for further analysis regarding the distribution of consumer WTP values. The
farm share of the retail value used for milk throughout this analysis was 53% (USDA, 2009).

51

 

Interestingly, 100% of consumers have a WTP high enough (WTP of greater than
$1.00/gallon) to have a farm share of the retail WTP of $0.53/gallon for consumer group
veriﬁed pasture access, although only 16.06% are willing to pay the $1.20/ gallon required
to provide a farm share of WTP of $0.64/gallon. This large drop in the segment of the
consumer population which is willing to pay this additional $0.20/ gallon for this veriﬁed
attribute can provide valuable information to producers who are making decisions
regarding which veriﬁed attributes to provide on their operation. If a dairy producer is
unable to provide consumer group veriﬁcation for less than $0.53/gallon, the segment of
consumers with WTP enough to justify the cost to the producer is shrinking quickly
beyond this price point. Additionally, the portion of producers that can produce this
attribute is increasing as the price increases, resulting in more competition to serve this

shrinking consumer segment.

3.4 Conclusions

The use of consumer WTP estimates in farm-level decision making regarding the
provision of veriﬁed attributes was demonstrated in this analysis. A key contribution of
this work is to demonstrate the link between consumer demand assessments and livestock
producer decision making. Critical points in veriﬁcation program costs at which the
optimal program for a producer changes were illustrated. Distributions of consumer WTP
were used to determine the percent of consumers with WTP greater than speciﬁc cutoff
points. Acknowledging the heterogeneous cost structures across farms associated with

providing these veriﬁed attributes, it was demonstrated that the size of consumer

52

segments with WTP at various levels can be identiﬁed. Producer decisions can be
informed regarding whether or not to provide veriﬁed attributes if it is known that only
2% of consumers have a WTP sufﬁciently high enough to support the provision of the
veriﬁed attribute versus 98% having a WTP high enough. Producers from all different
cost structures can beneﬁt from this analysis. Low cost producers are clearly more likely
to engage in the provision of veriﬁed attributes and to have larger consumer segments
with WTP high enough to support the provision of that veriﬁed attribute. High cost
producers can also beneﬁt by observing that, depending on their costs of provision and
the percent of consumers with a WTP sufﬁciently high enough to cover the costs of
providing that veriﬁed attribute, that they should not adopt unproﬁtable veriﬁcation
strategies.

The producer decision support tool described in this analysis can be used in two
distinct ways: 1) to inform producer decision making regarding which production process
attributes to adopt and how to verify those attributes; and 2) to aid producers who are
already employing certain production processes which are desirable to consumers in
deciding which verifying method is optimal. In both cases the consumer value or
consumer WTP must be incorporated into the decision. The data regarding costs,
however, is quite different for the two uses described. In order to determine adoption of
and veriﬁcation of production process attributes, accurate cost data must be obtained for
not only verifying the attribute to the consumer, but also for the actual costs associated
with altering the production processes. More data is necessary regarding the actual
production-related costs associated with these production processes. These costs are

expected to vary widely across farms, although cost estimates for production with access

53

to pasture, for example, would aid in creating baseline assumptions for analyses regarding
the adoption and veriﬁcation of such production systems. In addition to veriﬁcation costs
varying across farms, it is expected that the costs of veriﬁcation will vary even within the

entities as they have been deﬁned in this analysis. Costs for veriﬁcation by private party,

for example, are expected to differ depending on the speciﬁc party.

F our speciﬁc production process attributes for two food products were assessed in
this analysis. Continued research should include analysis of increased numbers of
attributes across a wider range of livestock products. Even across the four production
process attributes included in this analysis there exist substantial differences in the
optimal producer decision regarding veriﬁcation. In the case of verifying pasture access
for swine the producer must compare costs of verifying through a consumer group versus
USDA PVP to select the optimal verifying entity. In the case of certiﬁed trucking for
both milk and pork chops, however, the producer’s optimal decision is to not provide the
veriﬁed attribute because no statistical evidence of positive consumer demand was found.

While it is demonstrated that producer decision making can be informed by
estimates of consumer WTP for speciﬁc veriﬁed attributes in pork chops and milk, it
should also be acknowledged that actual on-farm decision making regarding marketing or
provision of value-added veriﬁed attributes are made on a wider scope than a single
product, such as pork chops or ﬂuid milk. Pork producers, for example, would want to
consider consumer demand for various pork products, rather than WTP for attributes of a
pork chop solely. In general, consumer demand analyses focus on assessments of demand
surrounding individual products, but for the purposes of on-farm decision making the

marketing of an entire hog (or carcass) is considered rather than marketing of individual

54

pork products. Clearly the single-product analysis may be an oversimpliﬁcation of
reality, although the degree to which this is true is likely dependent on the speciﬁc
livestock product and species. Future research could incorporate analyses of consumer
WTP across a number of pork products, rather than just pork chops. Potential analysis
might include assessments of consumer WTP across a number of the higher end cuts of
pork to determine if the consumer value placed upon veriﬁcation of these cuts is
sufﬁcient to elicit producers to verify production processes used to raise their hogs when
equivalent premiums on lower valued products may not exist.

Potential extensions of this model could include increased ﬂexibility to assess
multiple attribute decisions jointly, rather than assessing veriﬁcation decisions for
individual production process attributes. Considerations such as economies of scale and
scope to verify multiple attributes may become increasingly important in this case. The
potential for multiple veriﬁers also becomes an issue when assessing veriﬁcation of
multiple process attributes concurrently, either due to consumer preferences for certain
veriﬁers to verify speciﬁc attributes but not other attributes, or due to other on—farm or
veriﬁer-speciﬁc cost considerations. As veriﬁcation of production process attributes
becomes increasingly common across all products, not just livestock products, and the
market for veriﬁed attributes becomes more developed, model extensions such as those

highlighted above should be investigated.

55

CHAPTER 4: SURVEY OF MILK PRODUCTION PRACTICES REGARDING
THE USE OF RBST IN MICHIGAN
4.1 Introduction
Dairy producers in many parts of the U.S. have recently assessed the adjustments they
were willing to make to satisfy changing retailer, and ultimately consumer,
preferences. Consmners are increasingly interested in the production processes used
in food production. As retailers react to changes in consumer tastes and preferences
dairy farmers may be forced to alter their practices in order to continue to serve
markets (Cook-Mowery, Olynk, and Wolf, 2009). Changes in production practices
used on farms in production systems can largely be attributed to market pressures
rather than changes in legislation as the ability of consumer groups to inﬂuence ﬁrms
on issues relating to animal handling is increasing (Hudson and Lusk, 2004).

A particular case in which market pressures led to wide-scale changes in the
production processes employed is the case of recombinant bovine somatotropin
(rbST) use and milk production. The Food and Drug Administration approved rbST,
also called recombinant bovine growth hormone (rbGH), in November 1993 for
commercial use to increase milk production in dairy cattle. In February 1994 rbST
was ﬁrst commercially available in the U.S. Controversy continues today over the use
of rbST in milk production. Several major U.S. retailing chains have moved towards
procurement of milk supplies from cows not treated with rbST. Looking speciﬁcally
at Michigan, a major retailer decision which sparked change for cooperatives and
producers throughout the state was the Kroger Company’s announcement via press

release on August 1, 2007 that it would transition to selling rbST-free ﬂuid milk by

56

February 2008 (Press Release, The Kroger Company, 2007). This decision led to a
chain of events occurring in the Michigan milk market as cooperatives and individual
producers adjusted to meet changing demands. These changes in the Michigan milk
market offered'a chance to elicit producer sentiments and beliefs (via a survey
mechanism) in the face of major industry adjustments.

Recent literature has investigated methods of assessing individuals’ preferences
surrounding purchasing decisions, adoption of or participation in industry programs, or to
determine values associated with situation-speciﬁc attributes, such as preferences for
contract or program design. Consumer utility maximization frequently underlies
willingness to pay (WTP) analyses, although the concept can also be applied to producers
(Hudson and Lusk, 2004). Recently, several studies have assessed producer willingness
to change (WTC), which encompasses both the concepts of WTP and willingness to
accept (WTA) for operational practices in agriculture (Schulz and Tonsor, 2010).
Norwood et a1. (2005) conducted a survey to measure crop producers’ WTP for manure
from livestock operations. Roe, Sporleder, and Belleville (2004) examined hog producer
preferences for hog marketing contract attributes and found producers value window
contract ceiling and ﬂoor prices differently. Norwood et a1. (2006) provided information
from Oklahoma cattle producers regarding preferences of the design of voluntary check-
off prOgrams. Davis and Gillespie (2007) found that hog producers differ in their
valuations of autonomy and risk acceptance in selecting from alternative business
arrangements. Beyond assessment of producer WTC, a recent study by Schulz and
Tonsor (2010) sought to determine the producer welfare implications of changes in

options or choices available to producers. Schulz and Tonsor (2010) identiﬁed

57

preferences of U.S. cow-calf producers for traceability systems and found heterogeneity
among producers not only in their preferences, but also in the welfare effects of
mandating traceability. These examples demonstrate increasing evaluations of producer
preferences and WTC with applications to agricultural producers.

Much debate has followed regarding 'why consumers are expressing demand
for rbST-free milk. Regardless of the reasons that retailers and consumers are
demanding milk produced without the use of rbST, producers must respond to
changes in consumer demand. This article presents analyses of the situation of
Michigan dairy producers who faced major change to meet demand for rbST-free
milk. Dairy producer preferences surrounding selling milk rbST-free and herd size
growth were two key factors in the choice experiment used in this analysis. The
distribution of welfare effects due to the movement away from rbST use in milk
production is assessed. A key component of this analysis is to determine the welfare

effects of the disadoption of rbST on producers with varying farm characteristics.

4.2 Survey Instrument and Choice Experiment

A survey was developed to obtain information from Michigan dairy producers
regarding preferences for changes in milk production practices, speciﬁcally regarding
a movement towards rbST-free milk. The survey was mailed to 1,200 dairy farms in
Michigan in December of 2007, with reminder postcards sent approximately two
weeks later. The survey instrument used is presented in Appendix 5. In the survey
introduction, dairy farmers were told, “Recently, there has been a movement towards

milk labeled “rbST-free” for beverage consumption. Several Michigan retailers have

58

requested milk from cows not supplemented with rbST beginning February 1, 2008.
With this in mind, please answer the following questions.” Many questions on the
survey incorporated preferences for or against rbST-free milk production. The timing
of the survey was such that these questions were posed not only hypothetically on the
survey, but were also being faced by producers in the Michigan dairy industry.

Dairy farms were selected randomly to receive surveys from all those
permitted to sell milk in Michigan. A total of 258 surveys were returned, resulting in
a 21.5% response rate. Only those respondents who were actively operating dairy
farms in 2007 and chose to participate in the choice experiment portion of the survey
were included in this analysis, resulting in 183 usable responses. The survey
respondents were mostly male, averaged approximately 49 years old, and had, on
average, 229 cows on their operation. Distribution of dairy farm sizes of survey
respondents roughly approximated the distribution of dairy farm sizes in Michigan in
2007 (NASS, 2009). Summary statistics of selected dairy producer and farm
characteristics of the 183 dairy producers included in the analysis are provided in
Table 4a. Speciﬁc to the use of rbST on Michigan dairy farms, 67% of respondents
indicated that they had tried rbST on milk cows at some point. In comparison, only
37% of respondents indicated that they had Used rbST in 2007. Therefore, several
respondents indicated that while they had tried rbST in the past, they had disadopted

prior to 2007.

59

Table 4 a. Selected dairy producer demographics and operation characteristics

 

 

 

 

Variable Description Summary
Statistics

Gender Gender of respondents 95% male

Age Average age of respondents 49.2

 

 

2007 Herd Size Number of milking and dry cows in the herd at the time
of the survey 229 cows

 

 

Production Cost Average cost of production ($/cwt) for the past year

 

 

 

 

($/cwt) (dairy herd only, not including calves, heifers, or crops) $12.75/cwt
2007 rbST use Percent of herds havingusing rbST at any time in 2007 37 %
rbST use ever Percent of herds which have ever tried rbST on milk

cows 67%

 

 

Next generation Percent of respondents that indicated that the next
generation was coming back to the farm 53%

 

 

 

 

 

 

Choice Experiment
Choice experiments involve asking individuals to choose from alternative bundles of
attributes, as opposed to rating or ranking them (Adamowicz et al., 1998). Choice
experiments simulate real-life situations and permit multiple attributes to be
evaluated, thereby allowing estimation of tradeoffs among different alternatives
(Lusk, Roosen, and Fox, 2003). Each dairy producer completed a choice experiment
designed to simulate decisions regarding milk production in the upcoming years using
a given milk price, corn price (to proxy feed cost), production practice, and on-farm
milk production trend.

In response to each situation producers were asked to respond with which
production option they would select given that they expected the situation presented

to be an accurate prediction of the dairy industry environment in January 2013. In

60

each of the presented situations, producers were asked to select either one of two
production options or to stop milking cows under the given conditions. In this
particular choice experiment dairy producers were presented with a set of ﬁve or six
dairy industry scenarios, each of which involved selecting a preferred alternative from
two possible situations and an option to stop milking cows altogether.22 Scenarios
presented to producers in the choice experiment included information on future milk
prices, corn prices, production practices, and milk production trends. Producers were
faced with either a high, medium, or low milk price, which were characterized as
$18.00 per cwt, $15.00 per cwt, or $12.00 per cwt, respectively. Producers were
shown either a high corn price of $3.50 per bushel or a low corn price of $2.00 per
bushel. Two possible production practices were included, conventional, which was
presented in the survey as, “Conventional (rbST ok) means the milk was produced
using practices typical for the industry potentially including the use of rbST/rbGH on
a portion of the herd,” or rbST-free, which was identiﬁed in the survey instrument as,
“rbST-free is the same as conventional except you voluntarily choose not to use
rbST.” Three possible milk production trends included increasing milk production at
1.5% per year (typical status quo trend increase in milk per cow with constant herd
size), increasing herd milk production 25% per year (minor expansion), and

increasing herd milk production 100% per year (major expansion).

 

22 Following Tonsor et al. (2005), the choice experiment was divided into blocks to ease respondent
fatigue. Two versions of the survey were mailed to producers, one version had ﬁve scenarios and the
other had six scenarios.

61

Income Over Feed Cost Calculation

Most choice experiment research involves WTP and WTA calculations which
determine the change in cost that keeps utility unchanged given a change in an
attribute level. In other words, such calculations ﬁnd the amount of a given attribute
that equates the marginal beneﬁt and the marginal cost of a change. Generally price
or some money metric is included in choice experiments to allow for interpretation of
the tradeoffs of the utility associated with an amount of money and an attribute level.
The choice experiment used in this analysis included both an input price (corn price
($/bu)) and an output price (milk price ($/cwt)).

Unlike consumer WTP studies where a single purchase price can be assigned,
in this analysis both input and output prices are expected to affect producer
preferences. Dairy producer proﬁtability depends on the relationship between input
and output prices. The income over feed cost (IOFC) was used in the analysis of the
choice experiment because it incorporates both output and input price changes. In
order to incorporate both the input and output costs into a single money metric that
could be included in the analysis of the choice experiment IOF C was calculated for
each scenario. The IOFC was calculated as the price received for one hundred pounds
of milk minus the feed cost to produce one hundred pounds of milk. The milk price
($/cwt) was given in each choice experiment scenario. The feed cost to produce one
cwt of milk was calculated using the information provided to producers in the choice
experiment and average Michigan feed component prices.

The cost for 100 pounds of feed (as fed) was calculated using the average

price for hay, and incorporating corn price and estimated resulting soybean price

62

presented to producers in the choice experiment for a representative dairy ration was

calculated as23 :

 

 

 

[(Corn Price ($/bu)) ,, 51 + (Soybean Price ($/bu)) .. 8 + (Hay Price ($/ton)) .. 41]
56 60 2000

Then, in order to determine the cost of 100 pounds of dry matter, each of the feeds
included in the ration above was divided by percent of dry matter from the Nutrient
Requirements of Dairy Cattle: Seventh Revised Edition (2001). The percent of dry
matter used for corn, soybeans, and hay was 88%, 90%, and 88%, respectively. The
average hay price for Michigan in 2007 was $124/ton (USDA, 2009). Soybean price
($/bu) was assumed to be 2.4 times the corn price presented in the scenario (Wolf,
2008). The production of milk from dry matter intake of the cow, called dairy or feed
efﬁciency, is deﬁned as the yield of milk per unit of dietary dry matter consumed
(Britt et al., 2003). A feed efﬁciency value of 1.4 was used throughout this analysis
(Britt etal., 2003), which can be interpreted as a pound of dietary dry matter
consumed yields 1.4 pounds of milk. In our analysis this value means that it takes
0.7143 pounds of dry matter fed to produce a pound of milk. Incorporating the dry
matter conversions and the feed efﬁciency parameter, as well as the average hay price
($/ton) for 2007 and estimated soybean price ($/bu), the IOFC calculated for each

scenario was:

 

23 It is unlikely that any farm will actually have this exact feed price, but corn, soybeans, and hay are
common sources of energy, protein, and forage in dairy rations (Wolf, 2007).

63

 

I (Corn Price ($/bu)) * 51]
56
0.88

(((Corn Price ‘24) ($/bu)) * 8]

 

+

 

Milk Price ($/cwt) - 60 + * 0.7143

0.90
[($124/ton)) * 41]
2000
0.88

 

 

 

 

4.3 Research Methods

Random utility theory frequently underlies analyses utilizing choice experiments.
Random utility theory is built upon the assumption that economic agents seek to
maximize their expected utility subject to the choice set they are presented. The
individual’s utility is considered a random variable because the researcher has

incomplete information (Manski, 1977). Let utility be the sum of observable and

unobservable components U ft = Vj, + 81-, , where U jt is the latent, unobservable
utility for the j-th alternative in choice scenario t, V -, is the systematic, observable

portion of utility determined by the attributes and their values, and 31-, is the random,

unobservable component of utility, independently and identically distributed over all
alternatives and choice scenarios. The probability that alternative j will be selected is
the probability that the added utility from this selection is greater than (or equivalent

to) choosing another alternative presented in the choice experiment. The utilities

64

associated with each alternative are not directly observable because they include an
unobserved component, therefore researchers have determined the probability of

selecting altemativej as P(j) = P(vJ- + 5] 2 vk + 3k; j ¢ k ‘v’j e N), where N is

the total set of alternatives available (Boxall and Adamowicz, 2002 and Adamowicz

et al., 1998). The probability that alternative j is selected by the producer can be

eﬂﬂXf . . . .
expressed as P( j ) = where ,u 18 a scale parameter whrch lS rnversely

Zeﬂﬂxk
keN

 

related to the variance of the error term (Lusk, Roosen, and Fox, 2003) and ,6 is a

vector of parameters (Boxall and Adamowicz, 2002; Adamowicz et al., 1998).24

Assuming the systematic component of the total utility U jt is linear in parameters
the speciﬁcation of the general model is Vj = :51le + ,Bzsz + ...+ .anjn where x jn is

the n-th attribute for alternative j and ﬁn is a vector of parameters associated with the n-

th attribute of the j-th alternative. The ,8 s are utility parameters to be estimated in the

model.

Random Parameters Logit

The (random parameters logit) RPL model allows for random taste variation
within the surveyed group of producers. Through the use of the RPL we are able to
directly estimate the heterogeneity in dairy producer preferences across those attributes

included in the choice experiment. The utility of attribute j for individual i in choice set t

 

2‘ The scale parameter p is typically set equal to one in such analyses because it is unidentiﬁable in

any given data set (Lusk, Roosen, and Fox, 2003). The remainder of this analysis assumes the scale
parameter, p ,is equal one.

65

in the RPL model is presented as follows, U ijt = Vijt + luij + Sijt J, where Vijt is the
systematic portion of the utility function, u g]- is an error term which is distributed
normally over producers and alternatives (but not choice sets), and 5ijt is the stochastic

error, independently and identically distributed over all producers, attributes and choice

sets (Tonsor et al., 2005). A panel data set is described here, in which the cross-sectional
element is the producer, i, and the time series component is the choice scenario I (Alfnes,
2004 and Tonsor et al., 2005).25 In the random parameter logit model the probability that

producer i chooses alternative j in choice set I is P (U {it 2 U ikt) , over all possible k
attributes. Under the assumption that Vijt is linear in parameters the utility function can
be expressed as Vijt = ﬂilxlit + 73123521? + + ﬂijxijt where xijtis the j-th attribute for
choice set t and ,6 j is a vector of preference parameters associated with the j—th attribute

of the t-th alternative of the i-th producer (Alfnes, 2004; Tonsor et al., 2005; and Schulz
and Tonsor, 2010).

The RPL model estimated in this analysis speciﬁed the systematic portion of
utility as:

v 1- = ﬂl(Cons) + ,62(IOF C ) + ,B3(Cons_Generation) + ,64 (Cons_2007Size) +
,8 (Cons_2007rbST) + ,6 (MilkProd) + ﬂ (rbSTFree) + B (rbST Age) +

5 6 7 8 _
ﬂ9(rbST_2007rbST) + ,Blo(rbST _2007Size)

where, rbSTFree is identifying the production practice, whether conventional or rbST
free, both as identiﬁed above. Interactions between operator age (Age), 200 7rbST, and
200 7Size with rbSTFree, namely rbSTFree_Age, rbSTFree_200 7rbST, and

rbSTFree_2007Size, were also included. The variable 2007rbST indicated whether the

 

25 Model estimation was done in NLOGIT 4.0 using the program’s panel data capabilities.

66

farm used rbST at any. point during 2007 and 200 7Size is the herd size in 2007. MilkProd
is the increase in herd milk production presented in the choice experiment which could be
an increase of 1.5% per year, an increase of 25% per year, or an increase of 100% per
year. Cons is a constant included to capture producer sentiment regarding stopping
milking cows (exiting the dairy industry or opting out) and Cons=l if option C is selected
(or Cons=0 otherwise), and [OF C is the income over feed cost that is calculated as
described above incorporating the corn price and milk price presented to the dairy
producers in the choice experiment. Three variables are interacted with Cons to produce
Cons_Generation, C ons_2007Size, and Cons_2007rbST, which are opting out interacted
with whether or not there is a next generation expected to return to the farm
(Generation=0 if next generation not expected, Generation=1 if next generation is
expected), the herd size in 2007 (2007Size), and whether or not the herd used rbST in
2007 (2007rbST=0 if rbST not used in 2007, 2007rbST=1 if rbST used in 2007),
respectively.

To examine preference heterogeneity the ,B coefﬁcients on Cons, rbSTFree, and

MilkProd are speciﬁed to vary normally across producers. The RPL model estimates
both the mean and standard deviation of the utility coefﬁcients assumed to vary across
producers. If the estimates of the standard deviation parameters are signiﬁcant, there is
evidence of preference heterogeneity for that attribute. Given the dairy industry scenarios
and industry attributes being analyzed, it is possible that producers may have both
positive and negative preferences for the dairy industry attributes. The random
parameters are assumed to be drawn from a normal distribution which allows the

ﬂexibility for value estimates to be either positive or negative (Tonsor et al., 2005; Lusk,

67

Roosen, and Fox, 2003). The preference for attribute j is given by B j = B J + 0' j + #ij
where B j is the mean estimate across all producers, 0' j is a diagonal matrix of coefﬁcient

standard errors, and pi]- is a vector of independent normal decisions for each individual

producer (Lusk, Roosen, and Fox, 2003; Tonsor et al., 2005).

Individual estimated coefﬁcients in a random utility model have little interpretive
value. Relative combinations of select coefﬁcients, however, can provide insights on
producer preferences. As in Schulz and Tonsor (2010), willingness to change (WTC) is
used to generally capture both WTA and WTP terms which are broadly used in the

literature. The WTC in this analysis is generally given by WTC = ——A£—U— , which is

M U IOFC

evaluated at the mean values of the independent variables (Schulz and Tonsor, 2010), and
where MU is the mean marginal utility of dairy farm and industry attributes and M UIOFC

is the marginal utility of income over feed cost.

Producer Welfare Analysis

The use of rbST has not been legislatively prohibited in Michigan, although
due to the decisions of key retailers in the state the use of rbST in ﬂuid milk
production in Michigan was rapidly disadopted in late 2007 and early 2008. Given
that rbST was essentially removed as a technology available for use in Michigan
through market forces, a key question to ask is how producers were impacted by no
longer having rbST available for use. Prior to the changes by retailers towards rbST-
free milk dairy producers could choose between conventional production (in which

rbST use was allowed), voluntary rbST-free production, or to decide to no longer milk

68

cows and exit the dairy industry. Given the options available to dairy producers, in
the realm of rbST use on farms, after the decisions by retailers to move away from
rbST use producers had two of the three options remaining available, rbST-free
production or to stop milking cows.

Welfare impacts were estimated for producers when conventional production,
in which the use of rbST was okay, was eliminated as an option for dairy producers in

Michigan. Small and Rosen (1978) showed that expected maximum utility from

making a choice from a particular choice set is given by CV = ln(Zer ) + C, where
C is Euler’s constant and V j is as previously deﬁned. The welfare impact when

moving from one situation denoted by CV0 to a situation denoted by CV1 is 1/(

MUIOFC) x [(CV1)-(CVO)]. This calculation represents the maximum per choice
occasion that producers would be willing to pay to have the choices in situation 1
versus situatiOn 0 (Lusk, Norwood, and Pruitt 2006). Throughout this analysis the
welfare impacts per choice occasion are converted to a per cow per year basis for ease
of interpretation. This welfare measure captures producer value of autonomy, thereby
including more than just the differences in perceived costs associated with disadoption
(Key, 2005). Welfare impacts for those producers which did not use rbST in 2007
were compared to the impacts for those producers who did use rbST in 2007. Welfare
impacts for producers with combinations of different herd sizes and rbST use in 2007
were also determined.

A combinational technique suggested by Poe, Giraud, and Loomis (2005) was

used to test if welfare impacts differed among dairy producer situations described above

69

(e.g., dairy producers using rbST in 2007 versus those not using rbST in 2007).
Following Krinsky and Robb (1986), 1,000 observations were drawn from a multivariate
normal distribution which was parameterized with the coefﬁcients estimated in the RPL
model and the variance-covariance matrix resulting from the same model. The difference
between two simulated welfare series was evaluated by calculating the difference for all
possible combinations in the two series. Poe, Giraud, and Loomis (2005) determined that
the complete combinatorial approach, which provides an exact measure of the difference
of two distributions, is the preferred method to estimating the signiﬁcance of the
difference between two distributions. Therefore, the complete combinatorial test is
employed to test the signiﬁcance of the difference among welfare impacts throughout this

analysis.

4.4 Results and Discussion
The estimated coefﬁcients of the RPL model used in this analysis are shown in

Table 4026

 

2° The use of a multinomial logit model was rejected in favor of the RPL model described in this
analysis through the likelihood ratio test.

70

Table 4 b. Estimated coefﬁcients for random parameters logit model

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable Coefﬁcient estimate Standard deviation estimate
Milk Production Growth -0.0133* 0.0271*
(0.0031) (0.0039)
rbST Free 2.7648* 1.1 163*
(0.8294) (0.2999)
Opt Out 6.8600* 2.2361 *
(0.6319) (0.3205)
Opt Out * Next Generation -2.6397*
- (0.5330)
Opt Out * 2007 Herd Size -0.0009
(0.0011)
Opt Out * 2007 rbST Use -1.4929*
(0.6014)
Income Over Feed Cost 0.6875*
(0.0499)
rbST Free * 2007 Herd Size 0.0007
(0.0005)
rbST Free * 2007 rbST Use -2.5802*
(0.4246)
rbST Free * Age -0.0326*
' (0.0152L

 

Notes: Presented model (log likelihood of -617.78) was estimated using NLOGIT 4.0, with Halton
draws, and 500 replications for simulated probability. Standard errors are presented in parentheses.
Asterisk (*) indicates statistical signiﬁcance at the 0.05 level.

All coefﬁcient estimates in the model were individually signiﬁcant at the 0.05
level, except for the coefﬁcients on the interaction between herd size and opting out of
milking cows and herd size and rbST-free prodirction. In random utility models the
interpretation of individual coefﬁcients is generally discouraged (Scarpa and Del
Giudice, 2004), although signs of coefﬁcients can be indicative of general directions
'of producer preferences. For instance the Sign on rbST-free production is positive,
indicating a preference for such production for the representative producer. However,
as expected, the sign on the interaction between rbST-free production and having used
rbST in 2007 is negative, indicating that if a producer was using rbST in 2007 they

hold a preference against rbST-free production (in favor of conventional production as

71

 

deﬁned in this analysis). The coefﬁcients were used to compute estimates of producer
WTC and welfare impacts, which are discussed below.

Evidence of preference heterogeneity exists if the standard deviations are
statistically signiﬁcant, in which case the mean WTC estimates calculated cannot be
interpreted as being representative for thegentire sample of producers. All three of the
variables speciﬁed to vary normally across producers had statistically signiﬁcant
standard deviation parameters (Table 4b). These statistically signiﬁcant standard
deviation parameters reveal signiﬁcant preference heterogeneity across producers in
these variables which is not captured by interaction terms or other explanatory
variables. Using the estimated coefﬁcients and standard deviations on milk
production growth and rbST-free prOduction, the percent of producers for which
utility was positive or negative can be determined, by evaluating independent
variables at their mean values (Train, 2003). Thirty-one percent of producers had
positive Utility associated with milk production growth. Preferences for rbST-free
prodUction offer an interesting comparison becaUse 37% of producers had a negative
preference for rbST-free production, while the remaining 63% had a positive
preference for rbST-free production. In other words, when controlling for rbST use in
2007, 2007 herd size, and producer age, 37% of producers had preferences for
conventional milk production. From Table 4a, it can be seen that 37% of producers
surveyed were using rbST in 2007. Therefore, it is not surprising that the percent of
producers with a preference against the rbST-free production is the same as the
percent of producers using rbST in 2007, prior to the market movement away from

rbST use. Additionally, while only 37% of producers surveyed used rbST in 2007,

72

67% of producers indicated they had used rbST on milk cows at some point in the
past. Therefore, many producers had already disadopted rbST prior to 2007 for other

reasons.

Willingness to Change

Estimates of WTC were calculated to determine producer preferences for change
related to milk production growth and rbST-free production. Table 4c displays WTC
values with 95% conﬁdence intervals, when independent variables are evaluated at

their means.

Table 4 c. Dairy producer willingness to change (WTC)

 

 

 

 

 

Producer Group Variable WTC
Characteristics
Milk Production ($0.02)
A” Pmduce’s Growth [(3003), ($0.01)]
rbST Free $0.54

All Producers

 

 

 

{($0.07}, $1.12]

 

Estimates are $/cwt of milk sold and simulated 95% conﬁdence
intervals (identiﬁed using 1,000 Krinsky-Robb simulations) are
presented in brackets.

The WTC estimates calculated indicate that the average dairy producer would require
additional payment of $0.02 per cwt to be indifferent to milk production growth. In
other words, a preference against milk production growth, although at a very small
value, is observed. The WTC on rbST estimated is not signiﬁcant at the 0.05
signiﬁcance level, indicating that the average prod-ucer'has a WTC which is not

different than zero.

73

Producer Welfare Impacts
Table 4d displays the welfare impacts for producers of going from a situation in
which conventional, rbST free, and exiting the industry were options to the producer

to a' scenario in which the option of conventional production is eliminated.

Table 4 d. Welfare effects on dairy producers

 

 

 

 

 

 

 

 

 

 

 

 

Producer Group $/cwt per choice Annual per cow welfare impacts for cow production level:
Characteristics scenario

190 cwt/year 210 cwt/year 230 cwt/year
All Producers -$0.23 -$43.70 -$48.30 $52.90
Producers using rbST in -$0.32 -$60.80 -$67.20 -$73.60
2007
Producers not using rbST -$0. 1 8 -$34.20 -$37.8 -$41.40
in 2007
Producers using rbST in
2007 (assuming herd size -$0.31 -$59.90 -$65.10 -$71.30
of 100 cows)
Producers using rbST in
2007 (assuming herd size -$0.33 $62.70 -$69.30 -$75.90
of 500 cows)
Producers not using rbST
in 2007 (assuming herd -$0. 1 8 ~$34.20 -$37.80 -$41.40
size of 100 cows)
Producers not using rbST
size of 500 cows)

 

 

 

 

 

 

Mean estimates of annual welfare impacts are for going from a situation in which conventional
production, rbST free production, and exiting the industry are options available to the dairy producer to
a situation in which conventional production is eliminated from those options.

Welfare impacts quantiﬁed here go beyond the costs of disadoption perceived by the
producer, because they also include producer valuation for autonomy and control over
decision making for their operation. The welfare calculation used here represents the
maximum that producers would be willing to pay to have the choices in one situation

versus another (Lusk, Norwood, and Pruitt 2006). Using survey data a maximum

74

WTP by producers of $0.23/cwt per choice occasion was estimated. Using a milk
price of $15.00/cwt, this $0.23/cwt is about 1.5% of the milk price received. In other
words, the average producer, with a production level of 210 cwt/year, would be
equally well-off to give up $48.30/cow/year or to have conventional milk production
option removed from their choice set.

Table 4d also shows the welfare impacts for producers with different '
characteristics. Assuming a farm with a production level of210 cwt/cow/year, a
producer using rbST in 2007 has a welfare loss of $67.20/cow/year to lose
conventional production from their choice set. A producer who was not using rbST in
2007 has a welfare loss of $3 7.80/cow/year to lose conventional production from their
choice set. The welfare impacts on both producer grotrps were statistically.
signiﬁcant. Therefore, even those producers who were not using rbST in 2007 were
willing to pay to keep conventional milk production, in which rbST use was accepted,
in their choice set for available production practices. Recall that these welfare values
capture not only the costs of disadoption, or perceived lost income from disadoption,
but also the producer valuation of autonomy. Although only 37% of producers were
using rbST in 2007 and they had higher mean welfare losses than those prOducers
who were not using rbST in 2007, all producers, whether using rbST in 2007 or not
have statistically signiﬁcant estimated welfare losses.

Herd size and rbST uSe in 2007 were investigated jointly as farm '
characteristics to determine differential welfare impacts of having conventional
production removed from the choice Set of dairy producers for a herd size of 100 cows

that was on rbST in 2007, a herd size of 100 cows that was not on rbST in 2007, a

75

herd size of 500 cows'that was on rbST in 2007, and a herd size of 500 cows that" was
not on rbST in 2007. For a producer with a herd of 500 cows that was using rbST in
2007 the welfare loss was $0.33/cwt/choice scenario or, for a cow producing 210
cwt/year in this situation, the welfare 1033 was $69.30/cow/year. Welfare loss
estimates are shown in Table 4d for each of the four herd size and rbST use scenarios
described above.

The complete combinatorial test prescribed by Poe, Giraud, and Loomis
(2005) was used to determine if the welfare impacts on producers with different
characteristics were statistically different from one another. In the case of producers
using rbST in 2007 versus those not using rbST in 2007, we reject the null hypothesis
(at the 0.05 signiﬁcance level) that the two groups have the same welfare impacts.
Recognition of the heterogeneity of welfare impacts across producers, depending on
whether or not a producer used rbST prior to the disadoption of rbST by the market, is
important for determining the impacts of such market change on the industry. In this
case, as expected, those producers forced to make adjustments had larger welfare
impacts than those not forced to make adjustments. Still, those not making
adjustments had statistically signiﬁcant welfare losses due to the loss of a Choice of
production system, even though it was a system that they had not used. in the year
preceding the ‘change.

Further teSts for heterogeneous welfare impacts were conducted to determine
if welfare impacts differed across other groups Of producers investigated in this
analysis, speciﬁcally groups differing by herd size and rbST use eonCUrrently. As

stated above, heterogeneous welfare impacts were found when lOoking at producers

76

on rbST versus not on rbST in 2007. The null hypothesis of homogenous impacts was
also rejected when distinguishing small producers (herd size of 100 cows) and large
producers (herdtsize of 500 cows) by whether or not they used rbST in 2007.
HeterogeneoUs welfare impacts were found across small producers when
distinguishing by rbST use in 2007 versus those not using rbST in 2007. Similarly,
heterogeneous welfare impacts were found across large producers when
distinguishing based upon rbST use in 2007. When comparing farm size and rbST
use we failed to reject the null hypothesis of homogenous welfare impacts when
comparing large producers on rbST in 2007 versus small producers on rbST in 2007,
large producers not on rbST in 2007 versus small producers not on rbST in 2007, and
large producers not on rbST in 2007 versus small producers on rbST. Heterogeneous
welfare impacts were found when comparing large producers on rbST in 2007 versus
small producers not on rbST in 2007. Overall, heterogeneous welfare impacts were
highlighted in all cases where producers were distinguished based on rbST use, except
when comparing large producers not on rbST in 2007 versus small producers on rbST

in .2007.

4.5 Conclusions.

Changes in consumer tastes and preferences led retailers throughout the U.S. to seek
to provide ﬂuid milk which is produced without the use Of rbST to their customers. In
order to supply this changing retailer demand milk cooperatives and dairy producers
adjusted the production practices used, namely whether rbST use is permitted.

Conventional production systems have allowed rbST use in milking cows. Now,

77

rbST free production claims which have been adopted by an increasing number of
cooperatives, and therefore individual dairy producers, have essentially eliminated
conventional production from the choice set of many dairy farms. While the use of
rbST has not been eliminated through legislative channels, the market has effectively
limited the choice set of producers.

This analysis sought to identify Michigan dairy producer preferences for rbST
free milk production. Random parameters logit models revealed heterogeneous
preferences for rbST free production. Willingness to change values for rbST free
production were not statistically different than zero for the representative producer.
Statistically Signiﬁcant welfare impacts, however, were found for the representative
producer. Welfare impacts of eliminating conventional production, in which rbST use
was accepted in the market, were calculated. Eliminating conventional production
from the choice set of producers had a negative welfare impact on the representative
producer of $48.30/cow/year (assuming 210 cwt/year production level). ‘

Welfare impacts were further investigated for producers who were using rbST
in 2007 versus those who were not using rbST in 2007. ‘ In addition to analysis for
producers using and not using rbST in 2007, herd size was incorporated to determine
the welfare impacts for producers with differing herd sizes and rbST use. Statistically
signiﬁcant welfare impacts were found in all of these described scenarios. Evidence
of heterogeneity in welfare impacts was identiﬁed across producers. The removal of
rbST from producers’ choice sets for production practices had different-welfare

effects for producers with differing rbST use characteristics.

78

Forced disadoption of practices, in the case of rbST in milk production, was
shown to have heterogeneous welfare effects. Similar analyses could be completed
for welfare analyses of producers facing potential disadoption of other practices, such
as tail docking or individual crates. While disadoption of practices is often discussed
as an adjustment issue for a given subset of producers, the signiﬁcant welfare loss for
all producers was highlighted in this analysis. Implications of heterogeneous welfare
effects must be recognized when production systems are eliminated from producers’

options, whether via legislative channels or through market changes.

79

CHAPTER 5: SUMMARY

Consumers are increasingly interested in how their food is produced and in
learning about the production practices used in modern food production. This
dissertation incorporated three different analyses related to producer response to
changing consumer tastes and preferences surrounding production process attributes
in livestock. The three components of this analysis were to assess consumer value for
WTP for the veriﬁcation of production practices, develop a conceptual framework for
incorporating consumer values and consumer WTP into producer decisions, and to
highlight the heterogeneous welfare impacts for producers when the set of potential
production practices facing producers shrinks.

Estimates of consumer WTP for veriﬁcation of four different livestock
production process claims across two livestock products were obtained. Weak
evidence of social desirability bias was found in this analysis. Given the tendency for
people to overstate their own values in. order to conform to social norms, and the
resulting inﬂatiOn of WTP estimates that could Occur in such a situation, the indirect
estimates are likely more indicative ‘of censumers’ actual WTP. Further analysis
regarding the data collection method, whether via mail survey, intemet survey, or in-
person interviews would provide valuable insight into the prevalence of social
desirability bias by data collection'me‘thOd.

The use of consumer WTP estimates in farm-level decision making regarding the
provision of veriﬁed attributes was demonstrated. A key contribution of this work is to

demonstrate the link between consumer demand assessments and livestock producer

80

decision making. Acknowledging the heterogeneous cost structures across farms
associated with providing these veriﬁed attributes, it was demonstrated that the size of
consumer segments with WTP at various levels can be identiﬁed. Producers from all
different cost structures can beneﬁt from this analysis. Other agricultural producers,
beyond livestock producers, can beneﬁt from this concept. Consumer demand data can
be incorporated into producer decision making, which could be particularly helpful when
producers are making decisions regarding provision of new attributes, goods, or services.
Heterogeneous welfare impacts were found across dairy producers when rbST
was eliminated from the producers’ choice set. Changes in consumer tastes and
preferences led retailers throughout the U.S. to seek to provide ﬂuid milk which is
produced without the use of rbST to their customers. Dairy producers adjusted the
production practices used, meaning whether rbST use is permitted, in order to meet
this'changing demand. Conventional production systems have allowed rbST use in
milking cows. Now, rbST free production claims which have been adopted by an
increasing number of cooperatives, and therefore individual dairy producers, have
essentially eliminated conventional production from the choice set of many dairy
farms. While the use of rbST has not been eliminated through legislative channels,
the market has effectively limited the choice set 0f producers. Statistically signiﬁcant
welfare impacts, however, were found for the representative producer. The removal
of rbST from producers’ choice sets for production practices had different welfare
effects for producers with differing rbST use characteristics. While'disadoption of
practices is often discussed as an adjustment issue for a given subset of prOducers, the

signiﬁcant welfare loss for all producers was highlighted in this analysis.

81

APPENDICES

82

APPENDIX 1: SURVEY INSTRUMENT

 

This is a survey designed to obtain information from U.S. consumers regarding food
consumption. Your participation in this survey is entirely voluntary and your
responses will be kept in strict confidence. If you have any questions, comments, or
concerns regarding this survey, please contact Dr. Glynn Tonsor by email
(gtonsor@msu.edu) or by phone (517-353-9848).

1.

2.

I am: Male Female
I am years old (ﬁll—in the blank or drop down).
Our annual pre-tax, household income is:

-- drop down menu with 10 ranges: 1) Less than $20,000; 2) $20,000 -
$39,999; etc. up to 10) $180,000 or more

The best description of my educational background is:

 

a. Did not graduate from high school

b. Graduated from high school, Did not attend college

c. Attended College, No Degree earned

(I. Attended College, Associates or Trade Degree earned

e. Attended College, Bachelor’s (BS. or BA.) Degree earned

f. Graduate or Advanced Degree (M.S., Ph.D., Law School)

g. Other (please explain):
There are __ adults and _ children living in my household (please ﬁll-in
the two blanks)
My state of residence is: (drop down menu of 50 states)

 

Which best describes your race?
White, Caucasian

Black, African American
Asian, Paciﬁc Islander
Mexican, Latino
American Indian

Other (please describe):

me .e-n 9‘1»

 

83

8. How much thought have you given to voting on upcoming public
ballot/referendums and /or elections?

a. Quite a lot
b. Some
c. Only a little
d. None
e. Don’t know
9. How often would you say you vote?
a. Always
b. Nearly always
c. Part of the time
d. Seldom
e. Never
f. Don’t know

10. Do you, yourself, plan to vote in upcoming public elections and
ballot/referendums on November 4th?
a. Yes
b. No
c. Don’t know

11. Most households consume an average of 21 meals (7 days, 3 meals per day) in
a typical week. How many of these 21 meals consumed by your household
normally include the following?

a. Beef meals
b. Pork meals
c. Poultry (chicken and turkey) meals
d. Other meats meals
e. Eggs meals
f. Meals with neither a meat dish/entree nor eggs meals

i. If 0 is the response on a, b, c, and (I ask the following two

questions:
1. Do you consider yourself a vegetarian?
a. Yes
b. No
2. Do you consider yourself a vegan?
a. Yes
b. No

84

12. How much beverage milk does your household consume in a typical week?

[:I None El < 0.5 gallons
[I 0.5 — 1.0 gallons Cl 1.1 — 2.0 gallons
[3 2.1 - 3.0 gallons E] > 3.0 gallons

13. How much would you estimate your household spends each week for total
food consumption including at home, in restaurants, take-outs, etc.?
$ /week (please provide your best estimate).

14. Over time U.S. consumers have consumed an increasing portion of food away
from the home (e.g., eating out at restaurants rather than preparing food to
consume at home). What portion of your household’s total food expenditures
would you estimate to occur away from the home?

a. Drop down of 10 ranges (0%-10%, 11%-20%,. . ., 91%-100%)

15. Consumers purchase food from many sources. Please allocate the proportion of
your household’s total food expenditures across each of the following parties:
Supermarket Retailers (e.g., Kroger, Safeway,

 

 

 

 

 

Wal-Mart) %
Targeted Retailers (e.g., Whole Foods, Foods for
Living) %
Convenience Stores (e.g., 7-Eleven)

%
Farmers Markets / Direct from farmers

%
Others (please describe):

%

(Sum to

100%)

 

16. When was the last time you visited a farm with animals/livestock being raised
for milk, meat, or egg production?

a. I have never visited such a farm
b. Over 10 years ago
c. 6-10 years ago
(1. 1-5 years ago
e. Within the last year
17. Please circle the number of each pet you currently have in your household:
a. Dogs O-l—2-3—4ormore
b. Cats 0—1—2-3—4ormore

c. Other (please describe):

 

85

18. How often do you read the information on meat, egg, or milk product
packaging in making your purchasing decisions?

i.

a. Always
b. Usually
c. Sometimes
(I. Rarely
6. Never

If the respondent did NOT choose never in the previous
question, follow-up with:
1. Please indicate all of the following pieces of
information that you assess in reviewing meat, egg, or
milk product packaging:

f":

g.

rte-99‘s»

Nutritional information
Price

Food Safety information

Animal welfare information

Other production practice information (besides
animal welfare)

Product expiration or “sell-by” date

Other (please describe)

 

19. Please indicate if you have reduced consumption over the past three years of
the following products because of concerns with the well-being and handling
of farm animals.

 

 

 

 

 

 

 

Pork YES NO
Poultry (chicken and turkey) YES NO
__E_gg YES NO
Beef YES NO
Milk YES NO

 

 

 

If yes to any of the 5, then follow up with (showing only the rows for which a Yes

was given):

 

estimate):

If yes, reduced by roughly what °/o (please give your best

 

Pork

 

Poultry (chicken and
turkey)

 

£99

 

Beef

 

 

Milk

 

 

 

86

20. Please rank the following animal industry segments and production stages in
order of animal welfare/handling concerns (where 1 indicates your highest
level of concern and 4 indicates your lowest level of concern):

a. Farmer / On-the farm production

b. Transportation / Hauling and moving of animals between farms or to
points of sale

c. Auction Markets / Locations where animals change ownership

(1. Processors / Locations of animal slaughter and meat processing

21. Please rate the following statements in terms of your agreement (by circling
one number for each statement):

“Low meat or milk prices are more important than the well-being of farm animals.”
“The average American thinks that low meat or milk prices are more important than
the well-being of farm animals.”
“I consider the well-being of farm animals when I make decisions about purchasing
meat or milk.”
“The average American considers the well-being of farm animals when I make
decisions about purchasing meat or milk.”
“It is important to me that animals on farms are well cared for.”
“It is important to the average American that animals on farms are well cared for.”
“I believe that farmers face a trade-off between on-farm productivity and animal
welfare efforts.”
“The average American believes that farmers face a trade-off between on-farm
productivity and animal welfare efforts.”
“I believe that factory farms are less likely to have sound animal welfare practices.”
“The average American believes factory farms are less likely to have sound animal
welfare practices.”

87

22. Below you will ﬁnd a list of statements. Please read each careﬁrlly and decide
if the statement describes you or not. If it describes you, check the word
“true;” if not, check the word “false.”
“I sometimes litter.”
“I always admit my mistakes openly and face the potential negative consequences.”
“In trafﬁc I am always polite and considerate of others.”
“I have tried illegal drugs (for example, marijuana, cocaine, etc.).”
“I always accept others’ opinions, even when they don’t agree with my own.”
“I take out my bad moods on others now and then.”
“There has been an occasion when I took advantage of someone else.”
“In conversations I always listen attentively and let others ﬁnish their sentences.”
“I never hesitate to help someone in case of emergency.”
“When I have made a promise, I keep it - no ifs, ands or buts.”
“I occasionally speak badly of others behind their back.”
“I would never live off other peOple.”
“I always stay friendly and courteous with other people, even when I am stressed
out.”
“During arguments I always stay objective and matter-of-fact.”
“There has been at least one occasion when I failed to return an item that I borrowed.”
“I always eat a healthy diet.”
“Sometimes I only help because I expect something in return.”

23. Listed below are various sources you may look to for information on animal
welfare. Please indicate (by circling the appropriate number from the scale
below) how accurate you consider the animal welfare information provided by
each of the following sources:

Very Very

SOURCE Inaccurate Accurate
Federal Governmental
Agencies 1 2 3 4 5 6 7
State Governmental Agencies 1 2 3 4 5 6 7
University
Scientists/Researchers 1 2 3 4 5 6
United Egg Producers (UEP) 1 2 3 4 5 6
U.S. Poultry & Egg
Association 1 2 3 4 5 6 7
National Pork Producers
Council (NPPC) 1 2 3 4 5 6 7
National Cattlemen's Beef
Association (NCBA) 1 2 3 4 5 6 7
National Milk Producers
Federation (NMPF) 1 2 3 4 5 6 7
The Humane Society of the
U.S. (HSUS) 1 2 3 4 5 6 7
People for the Ethical
Treatment of Animals (PETA) 1 2 3 4 5 6 7

88

24. Which one of these sources do you most ﬁequently use in obtaining
information on animal welfare?

Federal Governmental Agencies

State Governmental Agencies

University Scientists/Researchers

United Egg Producers (U EP)

U.S. Poultry & Egg Association

National Pork Producers Council (N PPC)

National Cattlemen’s Beef Association (N CBA)

National Milk Producers Federation (N MPF )

The Humane Society of the U.S. (HSUS)

People for the Ethical Treatment of Animals (PETA)

Other (please describe):

.7???“an 2'99 P—P 9‘s»

 

25. Which form of media do you most frequently use in obtaining information on
animal welfare?

Government web-sites

University web-sites

United Egg Producers (UEP) web-site

U.S. Poultry & Egg Association web-site

National Pork Producers Council (N PPC) web-site

National Cattlemen’s Beef Association (NCBA) web-site

National Milk Producers Federation (N MPF ) web-site

The Humane Society of the U.S. (HSUS) web-site

People for the Ethical Treatment of Animals (PETA) web-site

Television news

Newspapers/magazines

. Radio

m. Other (please describe):

“rt-we're r99 9.0 9‘9»

89

26. How much ability does each of the following parties have to inﬂuence and

assure proper animal welfare practices?

Farmer/Grower

Meat or Milk Processor
Retail Grocer

Food Service Restaurant

Consumer — Food Purchaser
Government
Inspectors/Regulators

The Humane Society of the
U.S. (HSUS)

People for the Ethical
Treatment of Animals (PETA)

Animal Industry
Representative Groups

Very
Low
Ability
1

AAA—L

NNNNN

N

90

030303030)

h-h-hb-b

A

0101010101

0'!

03010301050)

Very
High
Ability
7

\INNN

27. How much do you agree that the following practices seriously reduce the

welfare of farm animals?

Very
strongly
agree

Castration (neutering; loss of
sexual function) of male
hogs/pigs

Castration (neutering; loss of
sexual function) of male dairy
cattle

Castration (neutering; loss of
sexual function) of male beef
cattle

Docking (removal) tails of
hogs/pigs

Docking (removal) tails of
dairy cows/cattle

Docking (removal) tails of
beef cows/cattle

Housing calves in
cages/crates

Housing sows/gilts in
cages/crates

Housing laying hens in
cages/crates

Conﬁning hogs/pigs indoors
Conﬁning dairy cows/cattle
indoors

Conﬁning beef cows/cattle
indoors

Conﬁning hens/chickens
indoors

1

1

2

2

3
3

4

4

010101

01

5

5

01030303010100

Very
strongly
disagree

28. Please rank the following species in order of concern you have regarding
current animal welfare/handling practices (1 being most concerned and 5

being the least concerned):

{DP-PST!”

91

Beef cattle (livestock raised for beef)

Dairy cattle (livestock raised for milk)
Swine/hogs (livestock raised for pork)
Broilers (chicken/turkeys raised for poultry)
Laying hens (chickens producing eggs)

29. Please rank the following societal issues in order of importance to you (1
being most important and 7 being the least important):

Human Poverty

U.S. Health Care System

Food Safety

The Environment

Financial Well-Being of U.S. Farmers

Food Prices

Well-Being of Farm Animals

992-999-991»

92

While considering your answers to the remaining questions, please bear in
mind that there may be many issues that you feel strongly about and, in
principle, might be willing to pay something toward. However, your budget
is limited and payment toward one issue means that there is less money
available to you to contribute to other issues.

30. There has been a recent increase in media attention to handling of animals at
livestock auction markets (facilities where animals are transacted and change
ownership) as well as at processing plants (slaughter or packing plants where
animals are processed, generating products for human consumption). Suppose
the next time you go to vote, there is a related referendum on the ballot. If the
referendum passes, mandatory USDA (United States Department of
Agriculture) surveillance of animal handling at livestock auction markets and
processing facilities in the U.S. will increase by X%. Please answer as if you
were actually voting on a real referendum.

Would you vote (circle answer) FOR or AGAINST the referendum?

31. Suppose you were told that the referendum, if it passes, would result in a Y%
increase in YOUR federal income taxes. Would you then change your vote to
“AGAINST?”

i. Yes, I would change my vote to “AGAINST” the referendum
ii. No, I would maintain my vote “FOR” the referendum

 

INFORIMA T ION, OR INDUS T R Y/CONSUMER GROUP INF ORlllA T ION) IS

 

HERE ONE OF FOUR TOTAL INF ORlllA T ION T REA T MEN T S (BASE
INF ORlllA T I ON, IND US T R Y INF ORlllA T I ON, CONSUMER GROUP

INSER TED

 

 

 

32. Would you be in favor of mandatory labeling of all pork that was produced by
farms using gestation crates/stalls?
a. YES OR NO
b. If yes, would you still prefer the mandatory labeling if it resulted in an
X% increase in the price you paid for pork?

33. Do you believe the average American would be in favor of mandatory labeling
of all pork that was produced by farms using gestation crates/stalls?
a. YES OR NO
b. If yes, do you believe the average American would still prefer the
mandatory labeling if it resulted in an X% increase in the price they
paid for pork?

93

34.

35.

36.

37.

38.

Would you be in favor of mandatory labeling of all eggs that were produced
by farms using laying hen cages?
a. YES OR NO
b. If yes, would you still prefer the mandatory labeling if it resulted in an
Y% increase in the price you paid for egg?

Do you believe the average American would be in favor of mandatory labeling

 

of all eggs that were produced by farms using laying hen cages?
a. YES OR NO
b. If yes, do you believe the average American would still prefer the
mandatory labeling if it resulted in an Y% increase in the price they

paid for eggs?

Would you be in favor of mandatory access to grass pasture for all dairy cows
used to produce milk in the U.S.?
a. YES OR NO
b. If yes, would you still prefer mandatory access to grass pasture if it
resulted in an 2% increase in the price you paid for milk?

Do you believe the average American would be in favor of mandatory access
to grass pasture for all dairy cows used to produce milk in the U.S.?
a. YES OR NO
b. If yes, do you believe the average American would still prefer the
mandatory outdoor access to grass pasture if it resulted in an Z%
increase in the price themaid for milk?

Suppose the next time you go to vote, there is a referendum on the ballot. If
the referendum passes, law will require farmers nationally to conﬁne calves
raised for veal, egg-laying hens, and pregnant pigs only in ways that allow
these animals to lie down, stand up, fully extend their limbs, and turn around
freely. Please answer as if you were actually voting on a real referendum.
Would you vote (circle answer) FOR or AGAINST the referendum?

94

The ﬁnal portion of this survey presents you with multiple different sets of
hypothetical pairs of boneless pork chops that could be available for purchase in a
retail store where you typically shop. Besides the attributes listed below, each
boneless pork chop is produced in the U.S. and possesses the same characteristics
(e.g., similar color, freshness, packaging date, etc.). Prices vary for each product. For
each pair of boneless pork chops, please select the one you would purchase or neither,
if you would not purchase either boneless pork chop. For your information in
interpreting alternative boneless pork chops:

Individual Crates/Stalls refers to the use of practices individually conﬁning animals
where:
0 Not Permitted means the animal was raised on an operation certiﬁed to
not conﬁne animals in individual crates, stalls, or cages
o Permitted indicates that no claims regarding conﬁnement of animals in
individual crates, stalls, or cages are being made

Pasture Access refers to the ability of animals to access grass pasture (when weather
permits) and not be conﬁned solely to indoor production facilities:
0 Required means the animal was raised on an operation certiﬁed to provide
animals with access to grass pasture (when weather permits),
0 Not Required indicates that no claims regarding access to grass pasture
are being made

Antibiotic Use refers to the use of antibiotics on animals where:
0 Not Permitted means the animal was raised on an operation certiﬁed to
not administer antibiotics to animals,
0 Permitted indicates that no claims regarding use of antibiotics are being
made

Certiﬁed Trucking/'1‘ ransport refers to the use of certiﬁed trucking and
transportation methods that enhance the care and welfare of animals during transport:
0 Required means the animal was raised on an operation using certiﬁed
trucking and transportation methods,
0 Not Required indicates that no claims regarding trucking and
transportation methods are being made

Certiﬁcation Entity refers to the process used in verifying animal welfare and
handling claims made on the product label where:
o USDA-PVP means the label is backed by a producer’s participation in a
certiﬁcation and process veriﬁcation program (PVP) managed by the
United States Department of Agriculture (USDA),
0 Self Certiﬁcation mean the label is backed by a producer’s participation
in a certiﬁcation and veriﬁcation program managed by the industry itself,
0 Private, 3rd Party means the label is backed by a producer’s participation
in a certiﬁcation and veriﬁcation program managed by a private, third

95

party company that is neither associated with the livestock industry nor
any consumer groups,

Consumer Group means the label is backed by a producer’s participation
in a certiﬁcation and veriﬁcation program managed by a consumer group
interested in animal welfare and handling issues

96

 

The experience from previous similar surveys is that people often state a higher
willingness to pay than what one actually is willing to pay for the good. It is
important that you make your selections like you would if you were actually facing
these choices in your retail purchase decisions, noting that allocation of funds to these
products means you will have less money available for other purchases. Please place

an “X” in the “I choose” box, below the option that you would choose from each

of the following scenarios:

 

 

 

 

Choice Set1

Attribute Option A Option B Option C
Price ($IIb) $3.24 $3.99

Individual Crates/Stalls Not permitted Permitted

Pasture Access Not required Required 1,33,73,23: at;
Antibiotic Use Not permitted Permitted eﬁlth e r of
Certiﬁed these two
Trucking/Transport Required Not required ro d u of 3
Certiﬁcation Entity Self Certiﬁcation USDA-PVP p '

I choose ] L

Choice Set2

Attribute Option A Option 8 Option C
Price ($Ilb) $4.74 $4.74

Individual Crates/Stalls Permitted Permitted

Pasture Access Not required Required ltg’bzebsga’bgt
Antibiotic Use Not permitted Not permitted either of
Certiﬁed ' these two
Trucking/Transport Required Required products
Certiﬁcation Entity Consumer Group Self Certiﬁcation '

 

Ichoose."

 

 

97

 

 

Choice Set3

 

 

 

 

 

 

 

 

 

 

Attribute 0 tion A 0 tion 8 0 tion C
Price ($Ilb) $3.99 $4.74

Individual I choose
Crates/Stalls Not permitted Not permitted not to
Pasture Access Required Required urch a se
Antibiotic Use Permitted Not permitted ﬁfths, of
Certiﬁed

Trucking/Transport Not required Not required thrisdeugo
Certiﬁcation Entity Self Certiﬁcation Consumer Group p '
lchoose 1

Choice Set4

Attribute _thi_ou 2M 0 tion C
Price (Sllb) $4.74 $3.99

Individual

Crates/Stalls Not permitted Permitted I 21;???
Pasture Access Not required Not required urch a $9
Antibiotic Use Permitted Not permitted peither of
Certiﬁed these two
TruckingITransport Not required Not required roducts
Certification Entity USDA-PVP Self Certiﬁcation " '
lchoose [

Choice Set5

Attribute _Qp_tio_nl_\ 0 tion 8 0 tion C
Price ($Ilb) $3.99 $3.99

Individual

Crates/Stalls Permitted Not permitted I‘ll)???
Pasture Access Not required Required purchase
Antibiotic Use Not permitted Not permitted either of
Certiﬁed these two
Trucking/Transport Not required Required roducts
Certiﬁcation Entity Private, 3rd Party USDA-PVP p ‘

lchoose."

 

 

98

 

 

 

Choice Set6

 

 

 

 

 

 

Attribute thion A Option 8 Option C
Price ($Ilb) $3.24 $4.74

Individual Crates/Stalls Permitted Permitted I choose
Pasture Access Required Not required not to
Antibiotic Use Permitted Not permitted purchase
Certiﬁed either of
Trucking/Transport Not required Required these two
Certiﬁcation Entity Private, 3rd Party USDA-PVP products.
lchoose." [

Choice Set?

Attribute Option A Option 8 Option C
Price ($Ilb) $3.99 $3.99

Individual Crates/Stalls Not permitted Permitted I choose
Pasture Access Required Not required not to
Antiblotic Use Not permitted Permitted purchase
Certiﬁed either of
Trucking/Transport Not required Required these two
Certiﬁcation Entity Consumer Group Private, 3rd Party products.

lchoose."

 

 

99

 

 

The previous set of questions asked you to indicate choices you would make. We are
now interested in what you believe the average American would select, if faced with
the same set of alternatives. Please place an “X” in the “Average American would
choose” box, below the option that you believe the Average American would
choose from each of the following scenarios:

 

 

 

 

 

 

 

 

Choice Set1
Attribute Option A Option B Option C
Price ($llb) $3.24 3.99 Average
Individual Crates/Stalls Not permitted Permitted American
Pasture Access Not required Required would
Antibiotic Use Not permitted Permitted choose
Certiﬁed not to
Trucking/1' ransport Required Not required purchase
Certiﬁcation Entity Self Certification USDA-PVP either of
these two
products.
Average American would
choose
Choice Set2
Attribute Option A Option B Option C
Price ($llb) $4.74 $4.74 Average
Individual Crates/Stalls Permitted Permitted American
Pasture Access Not required Required would
Antibiotic Use Not permitted Not permitted choose
Certiﬁed not to
Trucking/Transport Required Required purchase
Certification Entity Consumer Group Self Certiﬁcation either of
these two
products.
Average American would J
Llioose
Choice Set3
Attribute Option A Option B Option C
Price ($Ilb) $3.99 $4.74 Average
Individual Crates/Stalls Not permitted Not permitted American
Pasture Access Required Required would
Antibiotic Use Permitted Not permitted choose
Certiﬁed not to
Trucking/Transport Not required Not required purchase
Certification Entity Self Certiﬁcation Consumer Group either of
these two
products.
Average American would
choose

 

 

 

100

Choice Set4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Attribute Option A Option B Option C
Price ($IIb) $4.74 $3.99 Average
Individual Crates/Stalls Not permitted Permitted American
Pasture Access Not required Not required would
Antibiotic Use Permitted Not permitted choose
Certiﬁed not to
Trucking/Transport Not required Not required purchase
Certiﬁcation Entity USDA-PVP Self Certiﬁcation either of
these two
products.
Average American would
choose."
Choice Set5
Attribute Option A Option B W
Price ($Ilb) $3.99 $3.99 Average
Individual Crates/Stalls Permitted Not permitted American
Pasture Access Not required Required would
Antibiotic Use Not permitted Not permitted choose
Certiﬁed not to
Truckingrl’ransport Not required Required purchase
Certiﬁcation Entity Private, 3rd Party USDA-PVP either of
these two
products.
Average American would
choose."
Choice Set6
Attribute Option A Option 8 Option C
Price ($Ilb) $3.24 $4.74 Average
Individual Crates/Stalls Permitted Permitted American
Pasture Access Required Not required would
Antibiotic Use Permitted Not permitted choose
Certiﬁed not to
Truckinngransport Not required Required purchase
Certiﬁcation Entity Private, 3rd Party USDA-PVP either of
these two
products.

Average American would
choose."

 

 

 

 

 

101

 

 

 

Choice Set7

 

 

Attribute Option A Option 8 Option C
Price ($llb) $3.99 $3.99 Average
Individual Crates/Stalls Not permitted Permitted American
Pasture Access Required Not required would
Antibiotic Use Not permitted Permitted choose
Certiﬁed not to
Trucking/1' ransport Not required Required purchase
Certiﬁcation Entity Consumer Group Private, 3rd Party either of
these two
products.

Average American would
choose

 

 

 

 

 

102

 

39. What percentage of time do you think animals in the each industry are:

% of time
o- 21- 41- 61- 81-
20 40 60 80 100

Swine/hog industry
individually conﬁned in crates, stalls, or cages

provided access to grass pasture
transported by trucking and transportation methods certiﬁed to
enhance care and welfare of animals

administered antibiotics

Poultry industry
individually conﬁned in crates, stalls, or cages

provided access to grass pasture
transported by trucking and transportation methods certiﬁed to
enhance care and welfare of animals

administered antibiotics

Dairy industry
individually conﬁned in crates, stalls, or cages
provided access to grass pasture
transported by trucking and transportation methods certiﬁed to
enhance care and welfare of animals
administered antibiotics

 

 

% of time
0- 21- 41- 61- 81-
20 4O 60 80 100

 

% of time
0- 21- 41- 61- 81-
20 40 60 80 100

 

Thank you for your time in completing this survey. Your input will strengthen our
research and help us obtain more accurate conclusions. If you wish to add any

comments please feel free to do so here:

103

APPENDIX 2: CHOICE EXPERIMENT DEFINITIONS

 

Individual Crates/Stalls refers to the use of practices individually conﬁning animals
where:
0 Not Permitted means the animal was raised on an operation certiﬁed to
not conﬁne animals in individual crates, stalls, or cages
o Permitted indicates that no claims regarding conﬁnement of animals in
individual crates, stalls, or cages are being made

Pasture Access refers to the ability of animals to access grass pasture (when weather
permits) and not be conﬁned solely to indoor production facilities:
0 Required means the animal was raised on an operation certiﬁed to provide
animals with access to grass pasture (when weather permits),
0 Not Required indicates that no claims regarding access to grass pasture
are being made

Antibiotic Use refers to the use of antibiotics on animals where:
0 Not Permitted means the animal was raised on an operation certiﬁed to
not administer antibiotics to animals,
0 Permitted indicates that no claims regarding use of antibiotics are being
made

Certified Trucking/Transport refers to the use of certiﬁed trucking and
transportation methods that enhance the care and welfare of animals during transport:
0 Required means the animal was raised on an operation using certiﬁed
trucking and transportation methods,
0 Not Required indicates that no claims regarding trucking and
transportation methods are being made

Certiﬁcation Entity refers to the process used in verifying animal welfare and
handling claims made on the product label where:
o USDA-PVP means the label is backed by a producer’s participation in a
certiﬁcation and process veriﬁcation program (PVP) managed by the
United States Department of Agriculture (USDA),
0 Self Certification mean the label is backed by a producer’s participation
in a certiﬁcation and veriﬁcation program managed by the industry itself,
0 Private, 3rd Party means the label is backed by a producer’s participation
in a certiﬁcation and veriﬁcation program managed by a private, third
party company that is neither associated with the livestock industry nor
any consumer groups,
0 Consumer Group means the label is backed by a producer’s participation
in a certiﬁcation and veriﬁcation program managed by a consumer group
interested in animal welfare and handling issues

104

APPENDIX 3: EXAMPLE CHOICE SET SCENARIO FOR PORK CHOPS

 

 

 

 

 

Pork Chop Attribute Option A Option B Option C
Price (Sllb) $3.24 $3.99
Individual 1 choose
Crates/Stalls Not permitted Permitted
Pasture Access Not required Required nothto
Antibiotic Use Not permitted Permitted p are ase
. either of
Certified these two
Trucking/Transport Required Not required d t
Certiﬁcation Entity Self Certiﬁcation USDA-PVP 1”“ “C S'
I choose [ l I

 

105

APPENDIX 4: INFORMATION SCHOCKS

 

1) BASE INFO:

 

Use of Gestation Crates in Park Production

Gestation crates (also called gestation stalls) refer to metal crates (approximately 7
feet long and 2 feet wide) that house female breeding stock in individually conﬁned
areas during an animal's approximately four-month pregnancy.

Access to Grass Pasture in Milk Production

Dairy cow housing systems vary from conﬁnement operations with cows housed in
barns and fed at bunks to grazing dairies that do not conﬁne cows. The typical US
dairy has some of both systems with grazing occurring when climate is favorable and
housing available in inclement weather. Organic production requires access to pasture
when weather permits.

2) INDUSTRY INFO:

Use of Gestation Crates in Pork Production

Gestation crates (also called gestation stalls) refer to metal crates (approximately 7
feet long and 2 feet wide) that house female breeding stock in individually conﬁned
areas during an animal's approximately four-month pregnancy.

The American Veterinary Medical Association (AVMA) supports the use of sow
housing that “minimizes aggression and competition between sows; protects sows
from detrimental effects associated with environmental extremes, particularly
temperature extremes; reduces exposure to hazards that result in injuries; provides
every animal with daily access to appropriate food and water; and facilitates
observation by caretakers of individual sow appetites, respiratory rates, urination and
defecation and reproductive status.” The National Pork Producers Council (N PPC)
supports this position of AVMA.

Access to Grass Pasture in Milk Production

Dairy cow housing systems vary from conﬁnement operations with cows housed in
barns and fed at bunks to grazing dairies that do not conﬁne cows. The typical US
dairy has some of both systems with grazing occurring when climate is favorable and
housing available in inclement weather. Organic production requires access to pasture
when weather permits.

The National Milk Producer’s Federation supports access to pasture as part of the
National Organic Program provided it is “size neutral.”

 

106

3) CONSUMER GROUP INFO:

Use of Gestation Crates in Park Production

Gestation crates (also called gestation stalls) refer to metal crates (approximately 7
feet long and 2 feet wide) that house female breeding stock in individually conﬁned
areas during an animal's approximately four-month pregnancy.

 

The Humane Society of the U.S. (HSUS) states that gestation crates are “individual
metal enclosures so restrictive that the pigs cannot turn around.” Moreover, HSUS
states “Crated sows suffer a number of signiﬁcant welfare problems, including
elevated risk of urinary tract infections, weakened bones, overgrown hooves,
lameness, behavioral restriction, and stereotypes. Due to concerns for the welfare of
intensively conﬁned sows, legislative, industry, and corporate policies are
increasingly phasing out the use of gestation crates.”

 

Access to Grass Pasture in Milk Production L
Dairy cow housing systems vary from conﬁnement operations with cows housed in i
barns and fed at bunks to grazing dairies that do not conﬁne cows. The typical US

dairy has some of both systems with grazing occurring when climate is favorable and

housing available in inclement weather. Organic production requires access to pasture

when weather permits.

The Humane Society of the U.S. (HSUS) states that cows conﬁned to cement-ﬂoored
barns have a higher incidence of lameness. HSUS further states that cows prefer lying
and resting on soft surfaces like wood chips or straw rather than hard surfaces like
concrete or gravel.

4) INDUSTRY & CONSUMER GROUP INFO:

Use of Gestation Crates in Park Production

Gestation crates (also called gestation stalls) refer to metal crates (approximately 7
feet long and 2 feet wide) that house female breeding stock in individually conﬁned
areas during an animal's approximately four-month pregnancy.

 

The American Veterinary Medical Association (AVMA) supports the use of sow
housing that “minimizes aggression and competition between sows; protects sows
from detrimental effects associated with environmental extremes, particularly
temperature extremes; reduces exposure to hazards that result in injuries; provides
every animal with daily access to appropriate food and water; and facilitates
observation by caretakers of individual sow appetites, respiratory rates, urination and
defecation and reproductive status.” The National Pork Producers Council (N PPC)
supports this position of AVMA.

The Humane Society of the U.S. (HSUS) states that gestation crates are “individual
metal enclosures so restrictive that the pigs cannot turn around.” Moreover, HSUS
states “Crated sows suffer a number of signiﬁcant welfare problems, including
elevated risk of urinary tract infections, weakened bones, overgrown hooves,
lameness, behavioral restriction, and stereotypes. Due to concerns for the welfare of

107

 

intensively conﬁned sows, legislative, industry, and corporate policies are
increasingly phasing out the use of gestation crates.”

Access to Grass Pasture in Milk Production

Dairy cow housing systems vary from conﬁnement operations with cows housed in
barns and fed at bunks to grazing dairies that do not conﬁne cows. The typical US
dairy has some of both systems with grazing occurring when climate is favorable and
housing available in inclement weather. Organic production requires access to pasture
when weather permits.

The National Milk Producer’s Federation supports access to pasture as part of the
National Organic Program provided it is “size neutral.”

The Humane Society of the U.S. (HSUS) states that cows conﬁned to cement-ﬂoored
barns have a higher incidence of lameness. HSUS further states that cows prefer lying
and resting on soft surfaces like wood chips or straw rather than hard surfaces like
concrete or gravel.

 

 

108

APPENDIX 5: PRODUCER SURVEY INSTRUMENT

 

Conﬁdential Survey — For Research Purposes Only

This is a survey designed to obtain information from Michigan dairy producers
regarding possible changes in milk production practices. We are very interested in
your input. Your participation in this survey is entirely voluntary and your responses
will be kept in strict confidence. If you wish to comment on any questions please feel
free to use the space at the end of the survey. Although we would like you to answer
all of the questions (note there are questions on both sides of each page), you may
choose to skip any question. This will help us conduct the best research and draw
appropriate conclusions regarding Michigan dairy producers.

Recently, there has been a movement towards milk labeled “rbST-free” for beverage
consumption. Several Michigan retailers have requested milk from cows not
supplemented with rbST beginning February 1, 2008. With this in mind, please
answer the following questions.

1. How big is your milking herd (milking and dry cows) today? head
2. How big was your milking herd ﬁve years ago? head

3. How big do you expect your milking herd to be ﬁve years from today?
head

4. How many pounds of milk were sold by your operation in 2006?
pounds

5. On average, over the past year, how much did it cost you to produce a

hundredweight of milk? Include labor, feed, overhead (dairy herd cost only, not
calves, heifers or crops) $lcwt

6. What was your use of rbST (bovine somatotropin) in the past year (2007)?

El Not used at all C] Stopped using during 2007

[:l <25 % of eligible milk cows [3 25-50% of eligible milk cows

B 51-75% of eligible milk cows [:1 76-100% of eligible milk
cows

If you have used rbST at any time in the past year, please answer questions 7 - 9.
If you did not use rbST in 2007, please skip to question 10.

7. What is your herd’s average production response from rbST?
lbs/cow/day

109

 

 

 

 

 

8. What criteria are used to determine which cows are given rbST? (check all that
apply)
D Milk production level
[:I Cow age
E] Milk price
1:] Feed cost
1:] Other (specify)
9. If you discontinue use of rbST, how much do you think it would cost your
operation in annual foregone proﬁts? [:1 $0
[3 Less than $5,000 I] $30,000-$39,999
[:1 $5,000-$9,999 D $40,000-$49,999
El $10,000-$14,999 El $50,000—$74,999
1: $15,000-$19,999 CI $75,000-$99,999
|'_'I $20,000-$29,999 [:1 $100,000 or more
10. If you are NOT currently using rbST, have you ever tried rbST on milk cows?
D Yes D No
1 1. If you used rbST any time in the past, when did you stop? (year)
12. If you used rbST any time in the past, why did you stop? (check all that apply)
D Not large enough production response
[:1 Concerned about consumer reaction
I] Not proﬁtable El Mastitis concerns
[:1 Too difﬁcult to administer I] Other
(Specify)
13. There may be an array of reasons some consumers have expressed desire for milk

produced without the use of rbST. Please check the one reason from the following
options that you believe is most likely or prevalent:

1:] Quality perceptions: Belief that milk generated using rbST is of lower
quality.

1:] Food safety perceptions: Belief that there are potential food safety issues
using rbST.

D Animal welfare: Belief that cows should not be injected with rbST.

I:I Farm size issues: Belief that milk should be produced on small farms.

I: Other (please list/explain):

 

110

14. Please rate your concerns regarding the following issues surrounding the
movement away from rbST in beverage milk in Michigan:

a. Cost implications for M1 milk producers
Not concerned 1 — 2 — 3 — 4 — 5 Very concerned

b. Competition implications for M1 milk producers with milk producers
in other US states

Not concerned 1 — 2 — 3 - 4 — 5 Very concerned
c. Inadequate compensation for M1 milk producers to forego use of rbST
Not concerned 1 — 2 — 3 — 4 — 5 Very concerned
d. Milk price implications and consumer demand for ﬂuid milk
Not concerned 1 — 2 — 3 — 4 — 5 Very concerned
15. Please respond to the following statements (circle one answer for each statement):

a. The use of rbST detrimental to the welfare of cows.
Strongly disagree 1 -— 2 — 3 — 4 — 5 Strongly agree

b. All things considered, labeling “rbST-free” milk products is good for
U.S. consumers.
Strongly disagree 1 — 2 — 3 — 4 — 5 Strongly agree

c. Prior to this survey, how aware were you of the recent rbST labeling
issues?

Totally unaware 1 — 2 — 3 - 4 — 5 Very aware
16. Please rate your level of agreement with the following statements regarding
comparisons of operations using rbST to those that do not use rbST:

a. Feed efﬁciency is increased using rbST.
Strongly disagree 1 -— 2 — 3 — 4 — 5 Strongly agree

b. rbST is more proﬁtable on large herds.
Strongly disagree 1 - 2 — 3 — 4 — 5 Strongly agree

0. Herds with rbST produce less manure per hundredweight of milk.

Strongly disagree 1 — 2 — 3 — 4 — 5 Strongly agree

111

17. Different parties/entities may be actively supporting rbST-free milk. How much
do you think each of the following parties is the source of increasing pressure on
the dairy industry to adjust its use of rbST? Please allocate the proportion of
overall pressure generated by each of the following parties:

 

Retailers: Restaurants

 

Retailers: Grocery Stores

 

Consumer Lobby Groups

 

“Average” Consumers

 

Others (please describe (Sum to 100%)
below) .

 

 

 

 

 

18. If all beverage milk in Michigan is produced by cows not supplemented with
rbST, how do you think the resulting beneﬁts and costs will be distributed
throughout the milk supply chain? Please allocate the percentages each of the
following market participants captures:

Beneﬁts: Costs:
Consumers Consumers
Retailers Retailers
Processors Processors
Cooperatives Cooperatives
Farmers Farmers

We now have a few short questions to gather demographic and production practice
information from you. These questions are used to ensure our sample is
representative of Michigan milk producers. Please remember that your responses are
anonymous and will be held strictly conﬁdential.

19. I am: [I Male I: Female
20. I am years old.
21. How many years have you been milking cows?
I:I Less than 5 years 1:] 16-20 years
D 6—10 years C] 21-25 years
[I 11-15 years [I 25 years
22. How many more years do you expect your operation to be milking cows?
[:I Less than 5 years CI 16-20 years
1:] 6-10 years 1:] 21-25 years
[I 11-15 years [325 years

23. Do you expect the next generation to continue operation of your dairy farm?
[:1 Yes D No

112

24. Approximately, what is the average age of your operation’s facilities for milking
cows?

 

E] Less than 5 years 1:] 16-20 years
El 6-10 years 1:] 21-25 years
[:111-15 years 1:] 25 years
25. Please estimate your annual pro-tax household income:
1:] Less than $25,000 [:1 $75,000-$99,999
[:1 $25,000-$49,999 El $100,000-$124,999
El $50,000-$74,999 |'_‘| $125,000 or more
26. Approximately what portion of your household income is from off-farm sources?
Less than 20% E] coo/0-79%
[:1 zoo/0-39% [j 80% or more

C] 40%-59%

27. The best description of my educational background is:

Did not attend college

Attended College, No Bachelor’s (BS. or BA.) Degree
Bachelor’s (BS. or B.A.) College Degree

Graduate or Advanced Degree (M.S., Ph.D., Law School)
Other:

BEECH]

 

28. How likely do you think it is that consumers who have expressed support for milk
labeled “rbST-free” also support the following adjustments:

a. Ban the use of antibiotics
Very unlikely 1 — 2 — 3 - 4 — 5 Very likely

b. Ban all supplemental hormones (e. g., synchronization programs)
Very unlikely 1 — 2 -— 3 —— 4 - 5 Very likely

c. Ban conﬁnement operations
Very unlikely 1 — 2 — 3 — 4 — 5 Very likely

d. Require access to pasture for milk cows
Very unlikely 1 — 2 — 3 — 4 — 5 Very likely

29. What portion of your operation’s labor is supplied by non-family, paid employees?

E] lessthan 25% [I 51-75%
[:3 26-50% [:1 Over 75%

113

30. If you are planning to discontinue rbST use, please brieﬂy describe any actions to
replace the milk produced with rbST (e.g., add cows, go to 3X, extended photo
period, ration changes, etc):

 

 

114

The ﬁnal portion of this survey presents you with 5 different situations that might
represent future milk prices, corn prices, and corresponding production practices you
may adopt in the future. If you expected each these situations to be accurate predictions
of the dairy industry environment on January 2013 (5 years from now), which
production option would you select? That is, given these milk and corn prices, which
combination of production practice (Conventional or rbST-free) and milk production
(Trend Increase in milk per cow with a constant herd size, 25% Increase, or 100%
Increase) would you select? For each pair of situations please select the one you would
prefer, or neither if you stop milking cows under those conditions. For your
information in interpreting each situation:

Milk price is either high ($l8/cwt), medium ($15/cwt), or low ($12/cwt).
Corn Price is either high ($3.50/bu) or low ($2.00/bu).

Production Practice is the method used in producing the milk where:
0 Conventional (rbST ok) means the milk was produced using practices
typical for the industry potentially including the use of rbST/rbGH on a
portion of the herd,
- rbST-Free is the same as conventional except you voluntarily choose not to
use rbST.

Milk production on your operation can increase 1.5% per year (status quo trend
increase in milk per cow with constant herd size), increase herd milk production 25%
(minor expansion), or increase herd milk production 100% (major expansion).

115

 

It is important that you make your selections like you would if you were actually facing
these choices in your milk production decisions. Please place an “X” in the “I

choose” box, below the option that you would choose from each of the following 5
scenarios:

1

Scenario 1

 

 

Milk price ($lcwt)
Corn price ($lbu)

Production Practice
Herd Milk Production

lchoose.“

QM
$18.00

$2.00

Conventional
Trend

El

Option B
$18.00

$2.00

rbST-Free
1 00%

1:]

0 tion C

Stop milking
cows

D

 

Scenario 2

 

 

Milk price ($lcwt)
Corn price ($lbu)
Production Practice
I-lerd Milk Production

lchoose."

22am
$15.00

$2.00
Conventional
Trend

E1

Option B
$12.00

$2.00
Conventional
25%

D

QM

Stop milking
cows

1:1

 

Scenario 3

 

 

Mllk price ($lcwt)
Corn price ($lbu)
Production Practice
Herd Milk Production

lchoose."

Option A
$15.00

$2.00
rbST-Free
25%

[:1

some
$18.00

$3.50
rbST-Free
25%

Cl

O tion C

Stop milking
cows

[:1

 

116

 

 

 

Scenario 4

 

 

 

 

 

 

 

Option A Option 8 Option C
Milk price ($lcwt) $12.00 $15.00
Production Practice Conventional rbST-Free cows
Herd Milk Production Trend 25%
I choose D D D
Scenario 5

Option A Option 8 Option C
Milk price ($lcwt) $15.00 $12.00
Corn price ($lbu) $3.50 $3.50 Stop milking
Production Practice rbST—Free rbST-Free cows
Herd Milk Production Trend 100%
I choose 1:1 1:] D

 

Thank you for your time in completing this survey. Your input will strengthen our
research and help us obtain more accurate conclusions. After taking a quick moment to
double-check that you have answered each question, please mail us your completed
survey using the enclosed, postage-paid envelope. If you wish to add any comments
please feel free to do so.

117

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