Consumer preferences for alternative formats of nutrition information by pmm93834

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									 Consumer preferences for alternative formats of nutrition information on grocery
                                store shelf labels

                                        Joshua P. Berning
                                        Doctoral Candidate
                                   School of Economic Sciences
                                   Washington State University
                                       Pullman, WA 99164
                                      phone: (509) 335-8600
                                     email: jberning@wsu.edu

                                        Hayley Chouinard
                                        Assistant Professor
                                   School of Economic Sciences
                                   Washington State University
                                       Pullman, WA 99164
                                      phone: (509) 335-8739
                                    email: chouinard@wsu.edu

                                        Jill J. McCluskey
                             Professor and Chair of Graduate Studies
                                  School of Economic Sciences
                                  Washington State University
                                       Pullman, WA 99164
                                     phone: (509) 335-2835
                                   email: mccluskey@wsu.edu

Abstract: Alternative formats for providing nutrition information are being provided by
manufacturers as well as grocery stores. While the effect of nutrition labels has been well
studied, few papers examine the effect of different label formats. We use choice
experiments to examine preferences for two different formats of nutrition information
provided on grocery store shelf labels: detailed and easy-to-use information. We find
larger mean preferences for detailed nutrition information but also a greater dispersion of
preferences. Given the costs of obtaining information, the easy-to-use format may benefit
more shoppers. In the case of nutrition labels, detailed labels may provide too much
information.




We are grateful for funding from the USDA, National Research Initiative. We thank Ken Manning and
David Sprott for their input and direction. We want to recognize Rosangela Bando, Sophia Villas-Boas,
Grant Chen, Melissa Hidrobo, Kristin Kiesel, Quoc Luong, and Bruno Romani for their help conducting the
store surveys; Steve Flaming for his help facilitating store participation; and Sophia Villas-Boas for her
work organizing the survey events. We also thank Ying Hu for her research assistance and Washington
State University Graduate Research seminar for outstanding feedback on this paper.
Introduction

It is prohibitively expensive for shoppers to identify by themselves the nutritional content

of food items. Nutrition labels benefit shoppers by effectively turning nutritional content,

a credence characteristic, into a search characteristic (Caswell and Majduszka 1996).

Nutrition labels thereby enhance economic efficiency by directing expenditures towards

products that are most wanted (Golan et al 2001).

       Nutrition facts panels on product packaging are the standard source of nutrition

information; however many different nutrition labels have also been created by

manufacturers and grocery stores that vary in terms of both the information quality and

method of providing the information. These formats are generally easier to use and also

easier to locate for shoppers but offer less information than nutrition facts panels. While

there is an extensive literature examining the effect of nutrition information on shopper

behavior (Drichoutis et al 2006), few papers have discussed how shoppers perceive

different formats of nutrition information.

       In this article, we examine shopper preferences for different formats of nutrition

information, specifically focusing on nutrition information provided on grocery store

shelf labels. To this end, we develop two label formats to provide on grocery store shelf

labels: a format that provides more information defined as detailed and a format that

provides less information defined as easy-to-use. We then use choice experiments to

elicit preferences for the different label formats. We find larger mean preference for

detailed nutrition information. At the same time, however, the dispersion of preferences

is larger for the detailed information. Shoppers who face greater costs to obtaining

nutrition information may be less likely to use the detailed information than the easy-to-




                                              1
use information. Stores looking to provide shoppers with their own nutrition label should

consider both the presentation and the amount of nutrition information they provide.

The provision of nutrition information

Nutrition information is provided to shoppers in many different formats beside the

standard nutrition facts panels. Several major food companies have started providing their

own nutrition information using proprietary labels on their packaging to indicate healthier

choices (two examples are Pepsi’s Smart Choice and Kraft Food’s Sensible Solution).

Some grocery store chains have also affixed nutrition information labels on store shelves

along with product price labels to provide shoppers with easier access to nutrition

information. For example, grocery stores in the Washington, D.C. area affixed shelf label

markers to identify foods with specific positive nutrition claims1. Analysis of sales data

found that the shelf label markers affected purchasing patterns by directing shoppers to

healthier choices (Levy et al 1985). A similar experiment was conducted in the northeast

using shelf label nutrition information2 in which the authors (Teisl et at 2001) found that

shelf label nutrition information helps consumers increase their total satisfaction from

food consumption while maintaining their overall health risks. More recently, a grocery

store chain has developed its own star-rating shelf label which identifies the nutritional

quality of food items based on the number of stars it receives (New York Times, Nov. 6,

2006). This store-specific star-rating method is designed using proprietary methods not

regulated by the Food and Drug Administration (FDA) and therefore, the quality of the

star-rating system is not explicitly known. Reports by the store suggest that the star-rating



1
 These claims included low or reduced in sodium, calories, cholesterol and fat.
2
 Similar claims were made regarding products considered to be low or reduced in fat,
cholesterol, sodium or calories.


                                             2
labels have been successful at directing shoppers to healthier choices (New York Times,

Sep. 7, 2007).

       As more manufacturers and grocery stores begin to offer their own systems for

conveying nutrition information, it is relevant to examine how information quality affects

shopper behavior. Persons value informational attributes differently and therefore derive

different levels of utility from different information formats. As both opportunity cost

and preferences for information vary across consumers, their use of information will vary

as well (McCluskey and Swinnen 2004). Even if nutrition information is provided in a

costless manner some consumers may have other motivations and taste preferences that

preclude them from using nutrition information to make healthier diet choices (Teisl and

Roe 1998). The effectiveness of nutrition labels lies in providing the appropriate nutrition

label to specific consumer segments (Caswell and Padberg 1992). Additionally, nutrition

information is likely to be effective when it addresses specific informational needs and

can be processed and used by its target audience (Verbeke 2005). Labels that are

appealing and easily processed by shoppers may be more effective at influencing

consumer behavior.

The demand for nutrition information

People derive utility not just from the goods they consume, but also from the attributes of

the goods (Lancaster 1966). In this article we separate product attributes into three

groups: search, experience and credence characteristics. With search characteristics

(Nelson 1970), shoppers are able to make choices based solely on appearances. For

example, fruits and vegetables are often selected based on physical appearance. Other

goods may benefit from experience (Nelson 1974), that is, learned knowledge of their




                                             3
quality. For example, a shopper may develop preferences for wine after consuming it. In

the case of credence characteristics, however, shoppers are essentially unable to evaluate

which goods possess certain desired attributes (Darby and Karni 1973). Nutritional

content is an example of a credence characteristic since it is prohibitively expensive for a

person to identify nutritional content of an individual food item before or after

consumption. Shoppers must therefore use nutrition information to identify preferred

nutritional characteristics.

         A vector of goods (x) can be identified by its experience ( Qe ), credence ( Qc ) and

search ( Qs ) characteristics: x = x(Qe , Qc , Qs ) . A person who consumes a vector of goods

(x) receives utility:

         U = U [αQe , βQc (i ), δQs ]                                                (1)

The utility derived from experience ( Qe ), credence ( Qc (i ) ), and search ( Qs )

characteristics is weighted by the level of importance of each type of characteristic

using α , β and δ . An example is the preferences of a person who purchases spinach

because it is high in vitamins and low in fat versus a person who purchases spinach

because of its color and texture. The utility derived from credence characteristics is

unique in that it is a function of the product information (i) that a person has describing

each products credence characteristics. In other words, a person gets utility from credence

characteristics if they have information which identifies the presence of the

characteristics3. In this way, nutrition information acts as a complement to the

consumption of products with unknown nutritional quality similar to the way Becker and


3
 In the rest of the article, we focus strictly of nutrition which is essentially a subset of credence
characteristics.


                                                        4
Murphy (1993) describe advertisements acting as complements to advertised goods. The

greater the consumption of nutritional quality, the greater the marginal utility and demand

for the nutrition information. For certain individuals who have strong preferences for

nutrition information (high β -types), the level of information acquisition will be high.

They will seek out more product information to enhance their consumption of x.

Alternatively, high α and δ types may derive more utility from experience and search

characteristics respectively.

          Since the passage of the Nutrition Labeling Education Act (NLEA) in 1990,

nutrition facts panels which provide a large amount of information describing nutritional

content and ingredients have been required on almost all manufactured products. A

relevant concern, however, is that nutrition facts panels offer too much information and

are placed so that the product has to be removed from the shelf to be used. Given the time

and effort required, the nutrition facts panels may are not always being used by shoppers.

Nutrition information is being provided to shoppers using alternative formats as well

which may be easier to use and process.

          Different formats of nutrition information can be distinguished by the amount of

information they provide. For example, a star-rating label is easy to use and requires little

mental processing. Comparison between products with star-ratings can be reduced to a

single metric: the number of stars. The star-rating label, however, provides a small

amount of nutrition information. A detailed nutrition label, on the other hand, provides

more information regarding nutritional quality. At the same time, however, detailed

nutrition labels require a greater amount of time and processing and are therefore more

costly.




                                              5
        Consider a person who purchases a vector of consumptive goods (x) at prices (p)

and faces a budget constraint x ⋅ p = w ⋅ Tw while earning wages w working Tw hours. Each

person derives utility from the consumptive goods according to some function f(x). At the

same time, the person acquires information (i) which describes the nutritional quality of

the vector of consumptive goods. The amount of time available to obtain nutrition

information ( ci ), where c is measured as time per units of information, is limited as is

leisure time and work time such that 1 = Tw + TL + ci . A person chooses to consume goods

and information to maximize their utility subject to their budget and time constraints:

        max U [αQe , βQc (i ), δQs , f ( x )]            s.t.   x ⋅ p = w(1 − TL − ci )            (2)
          i, x



Without specifying a functional form, the first order conditions imply that the optimal

acquisition of nutrition information is then: i (β , c, TL , w, p, x* ) where x* is the optimal

vector of consumptive goods.

        In equation 2, nutrition information acts as a complement to consumptive goods.

Consumers derive utility from the provision and presentation of nutrition information

according to how it describes preferred nutritional attributes. Accordingly, the marginal

                                                                      ∂U x
utility of nutrition information is positive such that                            > 0 , where U x is the marginal
                                                                             ∂i

utility from consumption. A detailed nutrition label provides more information and

therefore has a larger benefit per unit of consumption than an easy-to-use label. At the

same time, there is a clear tradeoff between acquiring nutrition information, leisure and

work such that ∂i         , ∂i        , ∂i        < 0 . A nutrition label providing detailed nutrition
                    ∂TL          ∂w          ∂c

information may require more time to process than an easy-to-use label. A person facing




                                                                6
higher costs of time may consume less of the detailed nutrition information than the easy-

to-use information.

        The preferences for credence characteristics ( β ) includes preferences for

nutritional characteristics. Shoppers who are high β -types are more likely to benefit from

nutrition information and nutrition labels. Put another way, a shopper who has

preferences for low fat foods is more likely to prefer nutrition information that identifies

this nutritional characteristic.

        The purpose of this article is to estimate shopper preferences for the presentation

and the type of nutrition information being provided. Specifically, we focus on nutrition

information provided on grocery store shelf labels. We describe the vector of preferences

for nutrition information using a random utility model (RUM). The non-stochastic utility

is therefore a function of the attributes of the shelf label. While we are interested in

preferences for the provision of nutrition information on grocery store shelf labels, stores

also provide price information, and in most cases, unit price information. The price and

unit price information may impact the preferences for nutrition information. As such,

non-stochastic utility is specified as:

V = β1 price label + β 2unit price label + β 3nutrition label   (3)

The term price label refers to the display of price information, unit price label refers to

the display of unit price information, and nutrition label refers to the presentation of

nutrition information.

Data

Choice experiments were conducted to examine shopper preferences for different formats

of nutrition information provided on shelf labels. For the choice experiments, we created



                                                7
grocery store shelf labels using varying displays of price and unit price, a written

description of nutrition information and a star-rating of nutritional quality. Price

information is varied on 2-levels (low and high prominence), unit price on 2-levels (low

and high prominence), written nutrition information on 3-levels (not present, low and

high prominence), and star-rating nutrition information on 2-levels (not present and

present). High prominence refers to bold font; low prominence refers to standard font. All

of the labels were produced as color images and displayed below a picture of a can of

tomato soup to provide shoppers with a context for examining the labels4. Three label

examples are provided (Figure 1). The written nutrition information describes the soup

using the terms Fat Free, Saturated Fat Free, and Cholesterol Free and represents

detailed nutrition information. These claims are based on NLEA standards for

characterizing levels of total fat, saturated fat, calories, cholesterol, sugar and sodium

however survey participants were not explicitly informed of the source of the nutrition

claims5. The star rating system is a fictitious 4-star scale and is intended to represent

easy-to-use information. The formulation of the star-rating is not provided; therefore

shoppers are not made explicitly aware of what information the star-rating is

representing. Selecting from 24 possible labels (full factorial design: 2x2x3x2), the final




4
  Tomato soup was selected to be innocuous. However, there were a handful of survey
participants who voiced distaste for tomato soup.
5
  Since survey participants were not explicitly informed of the source or reliability of the
nutrition claim, preferences may include individual levels of trust. These choice
experiments are designed to test preferences for nutrition information provided on shelf
label information, and not a specific source of information. If these labels cause survey
participants to be skeptical, then preferences may be biased downward.


                                              8
experimental design has 16 choice sets comprised of 4 labels per set6. Survey respondents

were shown each choice set and asked to select the most preferred label.

       The choice experiments were administered using surveys at a major retail chain in

November 2006 over a three-day period in the East San Francisco Bay area of

California7. A total of 600 surveys were administered. Upon completing the survey,

participants were given a $10 grocery store gift card. After eliminating incomplete

surveys, 410 surveys remained for a total of 6560 choice experiments8. Summary

statistics for the demographic variables are presented in Table 1. In addition to

demographic variables, each respondent’s level of nutrition consciousness is calculated

using the summed value of 9 seven-point Likert scale questions (see the Appendix). We

define nutrition consciousness in terms of how likely a person is to pursue a healthy diet9.

Methods

6
  Based on pre-study trials, 16 choice sets seemed short enough to avoid fatiguing
participants while still providing an adequate number of observations. The fourth choice
is the same in all of the choice sets: a label with high prominence price and unit price
information and no nutrition information. The constant choice label closely resembles the
current label of the store where the research was conducted and provides survey
participants the option of selecting no label change. An optimal choice experiment was
created by maximizing the D-efficiency which improves efficiency by minimizing
variance. Survey participants were asked to view each set of four labels and select the one
label they prefer most from each set.
7
  Interviewers took care to prevent participants from taking the survey more than once.
The full survey included more than just the choice experiments and therefore took about
15 minutes to complete. The time requirement also acted as a deterrent to shoppers from
taking multiple surveys. The choice experiment alone took less than 5 minutes.
8
  Only 13 choice experiments were not fully completed. However, far more surveys had
incomplete demographic information. These experiments were removed to avoid biasing
the results.
9
  If more than two nutrition questions were not answered, the nutrition score was not
included. If two or fewer questions were unanswered, the mean value of the answered
questions was used to fill in the blank responses. The Cronbach alpha score, which
identifies how well a set of questions identify a latent construct was 0.93 indicating that
the 9 questions offer a fairly consistent measure of the survey participants perceptions of
their own nutrition consciousness.


                                             9
The results of the choice experiments are analyzed using a random parameters logit

model (RPL)10. Similar to standard logit, RPL models identify non-stochastic utility of

person n for choice i at time t ( Vnit ) as being linear in the attributes of any choice xnit such

that Vnit = βxnit and ε as a random component of utility such that U nit = Vnit + ε nit . The

probability that consumer n chooses alternative j out of choice set Ci at time t,

where Ci = {A, B, C , D} is then Pr{ j} = Pr{ njt + ε njt ≥ Vnkt + ε nkt ; ∀k ∈ Ci }. Assuming that
                                            V

the errors are independently and identically distributed with a type 1 extreme value

distribution, then the probability that consumer n choose shelf label j is given by:

                           βx njt
         Pr{ j} =
                      e
                                        .                                            (4)
                    ∑ eβ
                    k ∈C
                                x nkt




In an RPL model the vector of preferences β are allowed to vary over some density

     (    )
 f β | Ω* where Ω* are the true parameters of the distribution. To estimate β , a

distribution of the parameters is designated. Based on this distributional assumption, two

estimates of preferences are produced: an estimate of the population mean b and an

estimate of stochastic differences in taste η n such that: β n = b + η n . Inserting β n into the

random utility function, the random parameters logit model can be usefully expressed as:

         U nit = bxnit + η n xnit + ε nit .                                          (5)

On the right hand side of equation (5) utility is composed of mean preferences,

differences in taste preferences and random error.



10
  Random parameter’s logit are a common method of estimation and we do not perform
any modifications to the basic simulation methods. As such, a thorough description of
RPL is not included here. Interested readers are referred to Revelt and Train (2000) for a
detailed description.


                                                 10
        In our analyses, we first specify a normal distribution for all parameter estimates

and then a triangular distribution. The normal distribution is a useful and common

distribution and the triangular distribution is use to identify maximum and minimum

values. To investigate possible correlation among preferences for attributes we

specify β to be correlated between parameter estimates. Accounting for any correlation

should improve the estimates and provide additional insight into shopper preferences.

Individual differences may also affect preferences for the provision of the nutrition

information as well. To identify differences in preferences according to demographic

differences, a model is estimated that includes interaction terms. In this article we only

focus on the interaction of demographic variables with the preferences for the nutrition

labels and not price and unit price labels.

Estimation

We first estimate a base model with no demographic interaction terms included11. The

estimation results of the model (Table 2, columns 1 and 2) provide evidence that the


11
  For this article, we rely on Limdep’s NLOGIT 3.0 to estimate our models. Alternative
specific constants (ASC’s) are designated for each of the four choices in a set to capture
the average effect on utility of factors not explicitly included in the model and to ensure
that the model error, ε njt , has a mean of zero. Since only differences in utility matter in a
random utility framework, the three ASC’s are calculated relative to one constant choice,
in this case, the fourth label. The estimated coefficients of the three ASC’s represent the
average affect of utility of a given choice relative to the fourth label.
Systematic difference in the unexplained utility between the locations can be accounted
for using a scaling parameter. The scaling parameter is estimated relative to a base
location (Swait and Louviere) by setting the scaling parameter for the base location to 1
and estimating the remaining scaling parameters relative to the base group using an
artificial nested logit (Adamowicz 1998). Location 1 is designated as the base location
and has a scaling parameter of 1. Location 2 has a scaling parameter of .99, suggesting
that the unexplained variance in location 1 and 2 are fairly similar. Location 3, the largest
sample population, had more unexplained variance than location 1 as indicated by the
scaling parameter of .84. The scaling parameters are used to scale the data from each
location before all of the data are pooled together.


                                               11
display of the label information affects shopper preferences. Low prominence price, unit

price and nutrition information all have a negative affect on utility. Alternatively, high

prominence nutrition information is significantly positive. Based on a normal

distribution, the estimates of the mean and the standard error of the preferences for the

high prominence nutrition information suggest that roughly 75 percent of survey

respondents have a positive preference for the label. The estimates of the mean and the

standard error of the preferences for the star-rating information suggest that roughly 64

percent of survey respondents have a positive preference for the label. As such, we may

expect that there is a larger acceptance of the high prominence nutrition information.

        Using the mean, standard deviation and distributional assumptions we calculate

an arc elasticity of demand for the two different formats of shelf label information. While

the mean value of the high prominence nutrition label (1.82) is not a true willingness to

pay estimate, it does represent the perceived value of the label, which incorporates

individual opportunity cost. With a normal distribution, 50 percent of the survey

respondents have higher valuation of the high prominence nutrition label. At a one

percent increase in the valuation of the label, 49.7 percent of the survey respondents have

a higher valuation of the label. That represents an arc elasticity of 0.52. By itself, this

elasticity is vague because it represents a change in demand in response to a change in

personal opportunity costs as opposed to actual prices. It may be useful, however to

compare to the measure of elasticity for the star-rating label. At a one percent increase in

the valuation of the star-rating label, 49.9 percent of the respondents have a higher

valuation, resulting in an arc elasticity of 0.29. Comparing the two elasticities suggests

that given an overall increase in the opportunity cost of time, we might expect that greater




                                              12
numbers would value the high prominence (i.e. detailed information) less. That is, with

an increase in the cost to acquire the information, more shoppers will continue have

positive preferences for the easy-to-use star-rating label. Intuitively, this makes sense

since the more detailed high prominence nutrition label takes more effort to process the

information. Although the mean valuation of the high prominence nutrition label is

higher than the star-rating label, shoppers may be quicker to move away from using more

detailed nutrition information if search becomes more costly.

       The parameter estimates from the triangular distribution (Table 2, columns 3 and

4) provide a picture of the maximum and minimum values of the preferences. While the

mean estimates of the high prominence nutrition label and the star-rating label do not

change drastically, it is clear from the estimates of the standard error that preferences are

more greatly dispersed for the high prominence nutrition label. While there may be a

greater preference for the detailed information, there is also more disparity amongst the

population of preferences.

       We next estimate models using the normal and triangular distribution in which we

specify the interaction of demographic variables with the preferences for the nutrition

labels (Table 3). Both models have similar performance in terms of their LRI score. The

main effect of the high prominence nutrition label is negative in both models, however,

adjusting for the nutrition consciousness score results in a positive effect suggesting that

those persons who identify themselves as being nutritionally conscious prefer to have the

shelf label nutrition information. The significance of the nutrition consciousness

interaction, however, may also indicate a social desirability bias. Survey respondents who




                                             13
identify themselves as being nutritionally conscious (whether they really are or not) may

also select the shelf label that they expect to be the right choice.

        While demographic characteristics should influence preferences for the label

attributes, in these models few of the other demographic interactions have a significant

effect or are inconsistent based on the distributional assumptions. Changes in sign

between different distributional assumptions are not uncommon with RPL models and

warrant caution in interpreting the results. The shape of the normal distribution relative to

the triangular may effect the signing of the interaction terms, calling into question which

distribution is most appropriate. If the preferences of the population tend to gather around

the mean then the normal distribution may be more appropriate, whereas a larger spread

of preferences may suggest the triangular distribution is a better choice.

        The correlation matrix is calculated for the model using the normal distribution.12

There is significant interaction between preferences for the different label formats (see

Table 4). Preferences for the high prominence and low prominence nutrition label are

positively correlated. The correlation of the high prominence nutrition label and the star

rating label is negative however, which suggests that too much information may be

overwhelming for shoppers. Providing the high prominence nutrition label and the star-

rating label detracts from the label presentation. The low prominence nutrition label and

the star rating label, however, are positively correlated. Again, this indicates that the

presentation of the nutrition labels impacts shopper preferences and possibly their usage

of the labels.

Conclusions

12
  The correlation matrix was also estimated for the triangular distribution, but the results
for the nutrition labels are similar so it is left out.


                                              14
The results of the choice experiments suggest that shoppers have positive preferences for

nutrition information provided on shelf labels, with the caveat that presentation also

appears to have an effect. The current nutrition facts panels may be greatly underused

because they offer too much information and therefore require too much time to use.

Other formats for providing nutrition information that require less effort may be more

effective. This result is clearly important for the prospect of nutrition labels in other

markets as well, such as the food away from home category.

       While there is an overall stronger mean response for the high prominence

nutrition label the star-rating may be all around more effective. The preferences for the

star-rating are less elastic suggesting that their use may be less sensitive to changes in

opportunity costs. Although this is not covered in this article, the star-rating may also

effectively act as a sorting mechanism for food products. That is, a shopper facing a large

shopping list or a large set of choices may prefer to use the easier to use star-rating label

to initially sort through potential purchases. More detailed nutrition facts panels may then

be used to select from a smaller subset of choices. This multi-step process, however,

needs to be further examined.

       Grocery stores can choose to meet shopper demand for nutrition information by

providing their own nutrition label format. Stores may gain some level of price control by

offering product information to differentiate their products. That is, nutrition labels

complement the product being described in such a way that there is perceived higher

quality. As such, grocery stores can maximize profits by selecting both the optimal price

and the level of information detail to provide describing nutritional quality. If a store

provides labels that offer a large amount of detailed nutrition information to shoppers this




                                              15
requires that the nutrition label be very explicit about nutritional content, identifying

specific details about nutritional quality similar to a nutrition facts panel. As a

consequence, only high quality products are identified with nutrition information since

negative nutrition information is generally not used. Put another way, the detailed

description only highlights high quality products. This results in a market defined by high

quality and low quality products.

       If a store provides labels that offer low amounts of nutrition information to

shoppers the labels are less useful for discriminating between products. This is similar to

a store in which all food items are labeled with a star rating to indicate nutritional quality.

While the star rating may identify foods of higher overall quality, the rating does not

necessarily discriminate between, for example high fat, low sodium foods and high sugar,

low fat foods. As such, differences in product quality are less perceptible.

       The quality discrimination that occurs as a result of providing labels with a large

amount of nutrition information may be a less than desirable outcome. With only high

quality products having detailed nutrition labels, only shoppers with strong preferences

for nutrition information or low opportunity costs are using the nutrition information.

These are the same people, however, who are more likely to seek out nutritional

information to begin with. As such, the provision of the detailed in-store nutrition

information does not affect people who normally do not seek out nutritional information

to being with. This is similar to the current marketplace. Nutrition facts panels offer a

large amount of detailed information, but only those motivated shoppers are using this

information to seek out products with higher nutritional content.




                                              16
       Labels offering less information may direct more people to products with higher

nutrition quality. Less motivated shoppers may be more likely to use labels that require

less time to process and will therefore be more capable of identifying foods with better

nutritional quality. If stores provide labels with less information, however, shoppers who

prefer a large amount of nutrition information are worse off because they will be under-

provided nutrition information by the store. As such, the existence of nutrition facts

panels provides an important function for nutritionally conscious shoppers.

       In terms of public policy, although it often seems that more information is better,

too much information may neutralize the intended effect of the information. If the goal is

to direct shoppers to healthier purchases, then it is important to consider what format (or

amount) of information is likely to direct those shoppers if they are currently not making

healthy choices.

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                                           18
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                                           19
   Table 1. Characteristics of Survey Respondents
   demographic                      mean st. deviation
   age (years)                       42.4     16.6
   gender (female)                  62.0%       --
   household size                    3.26     1.56
   household shopping performed     69.6%     30%
   nutrition conciousness score      44.3     12.9

   educational                                educational
   level                           percentage level                               percentage
   grade school                       2.2% 2-year associate degree                   8.5%
   some high school                  5.6% 4-year bachelor degree                    13.2%
   graduated from high school        22.9% some graduate school                      5.1%
   some college                      28.8% graduate degree                          13.7%

   annual household                           annual household
   income (gross)                  percentage income (gross)                      percentage
   $0-5,000                           4.9% $50,001-60,000                            8.3%
   $5,001-10,000                      4.1% $61,001-70,000                            9.5%
   $10,001-15000                      4.6% $70,001-80,000                            8.8%
   $15,001-20,000                     3.2% $80,001-90,000                            5.6%
   $20,001-25,000                     5.9% $90,001-100,000                           3.7%
   $25,001-30,000                     6.6% $100,001-111,000                          4.6%
   $30,001-40,000                     7.1% $110,001-120,000                          2.4%
   $40,001-50,000                    11.2% over $120,000                             9.5%
Table 2. Base Model Parameter Estimates
                                                   Normal Distribution    Triangular Distribution
variable                                           mean standard error      mean standard error
price: low prominence                            -0.67**       1.21**     -0.40**     3.2**
unit price: low prominence                       -1.16**       1.60**     -1.25**      3.8**
low prominence nutrition label                   -0.62**       1.06**     -0.67**      2.4**
high prominence nutrition label                   1.82**       2.76**      1.84**     6.22**
star rating label                                0.51**        1.43**      0.58**     3.78**
number of surveys                                   410                     410
LRI                                                0.474                    0.48
Note: * and ** indicate significance at .05 and .01 levels respectively




                                              20
        Table 3. Parameter Estimates of Model Including Interaction Terms
                  Full Model Parameter Estimates
                                                   Normal Distribution Triangular Distribution
        variable                                   mean standard error mean standard error
        price: low prominence                    -0.36**      1.42**       -0.47**    3.14**
        unit price: low prominence               -0.94**      1.60**       -1.04**    3.60**
        low prominence nutrition label              0.29      0.78**          0.36    2.30**
        high prominence nutrition label          -1.57**      2.14**       -1.72**    5.68**
        star rating label                          -0.42      1.39**        -0.86*    3.65**
        low prom nutr x shopping %               -0.62**         --         -0.46*       --
        high prom nutr x shopping %               0.77**         --           0.29       --
        star label x shopping %                     0.33         --           0.12       --
        low prom nutr x household size             0.04          --           0.01       --
        high prom nutr x household size            -0.03         --           0.07       --
        star label x household size                -0.01         --           0.06       --
        low prom nutr x expenditures per cap -0.005              --          -0.03       --
        high prom nutr x expenditures per cap 0.03               --         0.09**       --
        star label x expenditues per cap            0.01         --          0.04*       --
        low prom nutr x gender (female)             0.17         --           -0.1       --
        high prom nutr x gender (female)         -0.42**         --         0.57**       --
        star label x gender (female)              -0.32*         --          0.39*       --
        low prom nutr x age                       -0.001         --        -0.0005       --
        high prom nutr x age                       -0.01         --          -0.01       --
        star label x age                         -0.0005         --         -0.002       --
        low prom nutr x nutrition score          -0.01**         --         -0.01*       --
        high prom nutr x nutrition score          0.07**         --         0.04**       --
        star label x nutrition score              0.02**         --           0.01       --
        low prom nutr x education level             0.01         --           0.02       --
        high prom nutr x education level            0.05         --           0.03       --
        star label x education level                0.02         --           0.06       --
        number of surveys                                  410                     410
        LRI                                                0.48                    0.48
        Note: * and ** indicate significance at .05 and .01 levels respectively

Table 4. Correlation of Parameter Estimates (normal distribution)
1: price 2: unit price 3: low prominence nutrition   4: high prominence nutrition       5: star rating
    1        -0.05                 0.11                         -0.41**                     -0.37**
    2                              0.05                           -0.02                       0.08
    3                                                            0.59**                      0.12*
    4                                                                                      -0.012**
Note: * and ** indicate significance at .05 and .01 levels respectively




                                                    21
            Figure 1. Examples of shelf labels used in conjoint experiments
                                                                                                                                          Nutritional Value
       02      119 846
                                                    02      119 846                                                                       Rating
                                              75 39024654 0 6554 654                                                                           BEST
                                                                                                        Campbell’s Soup- Tomato
 75 39024654 0 6554 654

          Campbell’s Soup- Tomato                      Campbell’s Soup- Tomato              10.7 OZ




                                                                       99
                                             10.7 OZ




                          99
10.7 OZ

                               ¢ 329.25¢                                     ¢ Unit Price
                                                                                                                         99
                                                                                                                         ¢ 32PER OZ
                                              Fat Free                                                                         45345        BETTER
 Fat Free                            45345                                                                                   9.25¢
 Saturated Fat Free
 Cholesterol Free
                                   PER OZ
                                              Saturated Fat Free
                                              Cholesterol Free                 9.25¢               02      119 846
                                                                                  PER OZ    75 39024654 0 6554654
                                                                                                                     3496954]]9439 939]       GOOD



  High price prominence, low                   High price prominence, high                                 Low price prominence, low
  unit price prominence, high                  unit price prominence, high                                 unit price prominence, star
     nutrition information                        nutrition information                                            rating label
           prominence                                   prominence




            Appendix. Survey questions used to calculate nutrition consciousness scores

            (all questions bounded by strongly disagree and strongly agree)

            1. My diet is nutritionally balanced.
            2. I try to monitor the number of calories I consume daily.
            3. I try to consume a healthy amount of calories each day.
            4. I try to avoid high levels of fat in my diet.
            5. I try to avoid high levels of saturated fat in my diet.
            6. I try to avoid high levels of cholesterol in my diet.
            7. I try to avoid high levels of sodium in my diet.
            8. I try to avoid high levels of sugar in my diet.
            9. I am interested in nutritional information about the food I eat.




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