Consumers React to Biotech Food Information 

Biotech food labeling has become a contentious issue in the United States and between

the United States and some of its trading partners. The Economic Research Service has

released a technical bulletin, The Effects of Information on Consumer Demand for

Biotech Foods: Evidence from Experimental Auctions, that provides new evidence on the

power of science-based information to affect consumer response to agricultural biotechnology. Agricultural biotechnology is a collection of scientific techniques, including conventional hybridization that are used to modify or improve plants, animals, and microorganisms. Recently, the term “biotechnology” has been used to refer more specifically to

products that have been genetically engineered (biochemical manipulation of genes or

DNA). This is the meaning of the terms “biotech” and “genetically engineered” used in

this report.

What Is the Issue? Proponents of mandatory biotech-food labeling argue that consumers

have a right to know whether their food has been produced using genetic engineering. A

number of countries, including Australia, China, Japan, and the members of the European

Union have adopted mandatory biotech-food labeling provisions.

Opponents of mandatory labeling argue that such labeling will confuse, and in many

cases, unnecessarily alarm consumers. In the United States, only biotech foods that differ substantially from their conventional counterparts require special labeling (FDA,

2001). Thus far, no biotech foods on the market have required labeling. Numerous producers in the United States are voluntarily producing and marketing non-biotech foods.

In January 2001, the U.S. Food and Drug Administration issued voluntary guidelines for

non-biotech labeling (FDA, 2001). 

How Was the Study Conducted? Market data – data on price and quantities actually purchased – would provide the best indication of consumer attitudes towards labeled biotech

foods. Unfortunately, such data do not exist because, to our knowledge, no biotech labeled

foods are currently marketed in the United States. In the absence of market data, analysts

must rely on other sources of information to gauge consumer preferences, such as surveys

of consumer attitudes or experimental market studies. 

Experimental economics can generate consistent high quality data for testing hypotheses

when other options for data collection are unavailable. Although a relatively young field,

experimental economics helps us to understand consumer choices by allowing economists to test alternative hypotheses in a systematic manner. In the experiment reported

United States

Department of

Agriculture

Economic 

Research

Service

The Effects of Information on Consumer Demand for 

Biotech Foods: Evidence from Experimental Auctions

ERS

esearch riefs B

Technical Bulletin Number 1903 March 2003

Abebayehu Tegene, Wallace E. Huffman, Matthew Rousu, and Jason F. Shogren

www.ers.usda.gov

Full report follows

Research Brief.
here, real consumers facing real choices create data on economic choices and incentives. As in

other fields, experimental economics has limitations in its assumptions and hypotheses. A key

challenge is designing an experimental environment where the researcher can be certain that

the results mean what they think they mean.

In 2001, an experimental auction was conducted to elicit consumers’ willingness to pay for

food items with and without biotech labels. A total of 172 consumers participated in 12 separate auctions conducted in two Midwestern U.S. cities. Participants were given the opportunity

to bid for and purchase three different food products—a 5-pound bag of potatoes, a 32-ounce

bottle of vegetable oil, and a 1-pound bag of corn tortilla chips—with and without biotech

labels. None of the food products had biotech-enhanced consumer attributes (such as better

taste or nutrition) or biotech traits that could be detected without sophisticated testing technologies, if at all. 

Prior to the bidding, each participant received one of six information packets containing statements about biotechnology gathered from a variety of sources. Pro-biotech statements were provided by a group of leading biotech companies. Greenpeace provided anti-biotech statements.

Science-based verifiable statements were provided by a group of individuals knowledgeable

about biotechnology, including scientists, professionals, religious leaders, and academics, none

of whom had a financial stake in agricultural biotechnology. The six information packets varied

by the type of statements they contained. One packet contained only pro-biotech statements; one

contained only anti-biotech statements; one contained pro- and anti-biotech statements; one contained pro-biotech and scientific statements; one contained anti-biotech and scientific statements;

and one contained scientific and pro- and anti-biotech statements. The source of each statement

was identified. 

What Did the Study Find? Participants’ bids, or the amount they were willing to pay, for

biotech-labeled and plain-labeled foods were affected by the information packets they

received. Participants who received only pro-biotech information bid slightly more on the

biotech-labeled food for two of the three products. Participants who received only anti-biotech

information bid less for the biotech-labeled foods by an average of 35 percent. Those who

received both pro- and anti-biotech information bid less for the biotech-labeled foods by an

average of 16, 24 and 29 percent, depending on the food product. These results are consistent

with other studies that show individuals place a greater weight on negative information than

on positive information. 

Interestingly, scientific information had a greater affect on consumers’ behavior than probiotech provided by leading biotech companies. Participants who received information packets

including anti-biotech from Greenpeace and pro-biotech from biotech companies discounted

the biotech-labeled foods on average between 16 to 29 percent, depending on the food product. When participants received science-based information, in addition to both pro- and antibiotech information, the average price discount dropped to between 0 and 11 percent.

Science-based pro-biotech information strongly offset anti-biotech information.

This study provides new evidence on the powerful role of information in shaping consumer

response to agricultural biotechnology—and reveals that consumers react not just to the content of information, but also to the source. Scientific, verifiable information had a larger moderating effect on consumers’ reaction to anti-biotech statements than pro-biotech statements

from biotech companies. The results also highlight the erratic effect of biotech labeling in the

absence of unbiased scientific information. Without scientific information, the bid-price for

biotech-labeled foods varied from slightly above that of plain labeled foods to 35 percent

below. With scientific information and pro- and anti-biotech information, the price consumers

bid for biotech-labeled foods was only slightly below that for plain-labeled foods. 
Abstract

Consumers’ willingness to pay for food products decreases when the food label indicates

that a food product is produced with the aid of modern biotechnology. This bulletin presents empirical evidence on consumers’ willingness to pay for biotech foods based on the

presence or absence of labels advising that the food was prepared with the aid of biotechnology. The authors designed and conducted an experimental auction to elicit consumers’

willingness to pay for “genetically modified” (GM)-labeled and standard-labeled foods

under different information regimes. The evidence gathered for vegetable oil, tortilla

chips, and potatoes shows that labels matter. In particular, under all information treatments, consumers discounted food items labeled “GM” by an average of 14 percent.

While gender, income, and other demographic characteristics appeared to have only a

slight impact on consumers’ willingness to pay for biotech foods, information from interested parties and third-party (independent) sources was found to have a strong impact. 

Keywords: Biotech, bioengineering, biotechnology, food labels, auctions, experimental

economics, random nth price, willingness to pay.

Acknowledgments

The authors have benefited a great deal from the comments and suggestions of Julie

Caswell, Mark Denbaly, Elise Golan, Rebecca Fecitt, Fred Kuchler, Dominic Mancini,

Clark Nardinell, Robin Shoemaker, Bobby Richey, and Keith Wiebe. Also, we thank

Wayne Fuller and Phil Dixon in the Department of Statistics, Iowa State University, for

assistance with the statistical experimental design. Responsibility for the final draft rests

with the authors. Finally, the authors would like to thank Shashunga Clayton for excellent editorial assistance, and Melody Mathis-Pace for her help with the charts.

United States

Department

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

March 2003

Technical

Bulletin

Number 1903

The Effects of Information 

on Consumer Demand for 

Biotech Foods

Evidence from Experimental Auctions

Abebayehu Tegene, Wallace E. Huffman, 

Matthew Rousu, and Jason F. Shogren
Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Issues Related to Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Benefits of Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Costs of Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Divergent Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Random nth-price Auction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

The Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

The Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

The Effect of Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Econometric Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

ii ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA
Summary

This bulletin presents empirical evidence on consumers’ willingness to pay for

foods with and without biotech labels. The findings were derived from laboratory

auction experiments for three food items: vegetable oil, tortilla chips, and potatoes, about which consumers were given pro-biotechnology, anti-biotechnology,

and independent, third-party information. The results indicated that consumers’

willingness to pay for food products decreases when the food label indicates that a

food product is produced with the aid of modern biotechnology (biotech foods).

(According to the Food and Drug Administration (FDA) and U.S. Department of

Agriculture (USDA), “bioengineered” or “biotech” are preferable terms to

describe foods produced with the aid of modern biotechnology. However, “genetically modified (GM)” and “genetically engineered” are also commonly used to

describe biotech foods in academic publications. Since we used the terms GM and

“non-GM” in our experimental auctions, we continue to use these terms in this

bulletin whenever we refer to the labels in the experimental auctions.) Under all

information treatments, consumers discounted food items labeled “GM” by an

average of 14 percent. In addition, gender, income, and other demographic characteristics appear to have only a slight impact on consumers’ willingness to pay for

biotech foods. However, information from interested parties and third-party information do influence consumers’ willingness to pay for biotech and (perceived)

non-biotech foods. It was found that consumers who received only negative information about agricultural biotechnology paid 35-38 percent less for food products

labeled “GM,” depending on the product. When the negative information was

coupled with independent, third-party information, consumers were willing to pay

17-22 percent less for “GM”-labeled food. Likewise, when given only positive

information about agricultural biotechnology, consumers bid higher for “GM” than

for plain-labeled food in two of the three food items. However, when they are

provided with both positive information about biotechnology and the independent,

third-party information, consumers bid higher for plain-labeled food in all three

cases. An independent, third-party source that provides verifiable information on

biotechnology has a significant impact on consumers’ demand for biotech foods.

Third-party information had its greatest impact on consumers who received negative information, prompting them to view biotech foods more favorably. Also, the

use of the term “GM” (rather than biotech or bioengineered) might have influenced the results.

Consumers’ desires to make informed decisions about their food purchases have

made the biotech food labeling issue an important public policy debate. The debate

revolves around the benefits and risks of agricultural biotechnology. Agribusiness

companies like Monsanto support agricultural biotechnology and say that biotech

foods will help protect the environment, increase nutrition, and end world hunger.

Environmental groups like Greenpeace oppose agricultural biotechnology and say

that biotech foods cause allergic reaction, hurt the environment, and increase the

power of multinational companies. The average consumer (and farmer) may rely

on the information from these interested parties to make their decisions on biotech

products. Currently the United States does not have a mandatory labeling policy

for biotech foods, but several other countries do. Implementing a mandatory

labeling policy in the United States might have benefits, but also involves costs,

both variable and fixed. Moreover, further analysis of the data collected for this

study shows that the current U.S. voluntary labeling policy is more efficient than

mandatory labeling policy (Huffman et al., 2002). 

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● iii
The bulletin examines several issues. First, do biotech food labels have any significant effect on consumers’ willingness to pay for biotech and non-biotech foods?

Second, is consumers’ willingness to pay for biotech foods affected when they

receive information from interested parties? Third, would an independent, thirdparty group providing information (e.g., verifiable information) about biotechnology change consumers’ purchasing behavior? Finally, do socioeconomic factors

(e.g., gender, income, education) affect consumers’ willingness to pay for biotech

food items? 

The bulletin combines tools of survey design, statistical experimental design, and

the laboratory experimental auction mechanism to answer these questions. The

experiments used a randomly selected sample of 172 consumers from two

Midwestern cities: Des Moines, IA, and St. Paul, MN. The 172 auction participants were assigned to 1 of 12 experimental units, each consisting of 13-16 participants. Six information packets: (1) pro-biotechnology; (2) anti-biotechnology; (3)

both pro- and anti-biotechnology; (4) pro-biotechnology and third-party, verifiable

information; (5) anti-biotechnology and third-party, verifiable information; and (6)

pro-biotechnology, anti-biotechnology, and third-party, verifiable information were

randomized among all 12 experimental units, with each information packet going

to two experimental units. When a participant received both pro-biotechnology and

anti-biotechnology information, the order was randomized, so that some participants received the pro-biotechnology information first, and others received the

anti-biotechnology information first. Participants then bid (sealed) on the three

products separately, each product with and without a “GM” label.

Much future research on this topic remains. The food products in these auctions,

although bioengineered, were deemed substantially equivalent to the conventional

commodity. In some biotech foods, however, biotechnology may be used to

enhance the quality (e.g., protein, fat, sugar content, shelf life) of the product,

although not all of these products are in the market. Future research could examine

how consumers react to biotech foods that have specific benefits. Future research

could also examine if the language on the labels (e.g., “GM,” biotech, bioengineered, genetically engineered, etc.) would have significant impact on consumers’

willingness to pay for biotech foods. 

iv ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA
Introduction

Consumers’ desires to make informed decisions about

their food purchases have made the biotech food labeling

issue an important public policy debate. The debate

revolves around the benefits and risks of agricultural

biotechnology. Agribusiness companies like Monsanto

support agricultural biotechnology and say that biotech

foods will help protect the environment, increase nutrition, and end world hunger (Council for Biotechnology

Information, 2001). Environmental groups like

Greenpeace oppose agricultural biotechnology and say

that biotech foods cause allergic reaction, hurt the environment, and increase the power of multinational companies (Greenpeace, 2001). Consumer advocates and a

wide range of environmental and food safety groups

have mounted an active campaign against biotech foods.

The average consumer (and farmer) may rely on the

information from these interested parties, both pro and

con, to make their decisions on biotech products.

Huffman and Tegene (2002) have hypothesized that

independent, third-party information improves social

welfare in this environment. According to qualitative and

quantitative research conducted in the United States by

the International Food Information Council (IFIC), consumers accept food biotechnology when the benefits are

effectively communicated (Schmidt, 2002). 

In a recent study, Lin, Chambers, and Harwood (2000)

concluded that “lack of information about the magnitude

of premiums that consumers may be willing to pay for

non-biotech crops makes near-term decisions difficult for

elevators and farmers” (p. 54). This bulletin presents

empirical evidence on U.S. consumers’ willingness to

pay for foods with and without biotech labels using laboratory auction experiments for three food items. In an

experimental auction with divergent information about

risks and benefits, we examined whether consumers

value information provided in biotech labels. Following

Fox, Hayes, and Shogren (2002), we created the divergent information design by providing six combinations

of pro-biotechnology, anti-biotechnology, and third-party

perspectives, which provided their corresponding views

on the scientific impact, human impact, financial impact,

and environmental impact of biotech foods. Iowa State

University (ISU) Statistics Department randomly chose

actual consumers in two major Midwestern U.S. cities.

The consumers were paid to participate in experiments

on food and household products. 

This bulletin examines several issues. First, do biotech

labels have any significant effect on consumers’ willingness to pay for biotech and non-biotech foods? Second,

is consumers’ willingness to pay for biotech foods affected when they receive information from interested parties? Third, would an independent, third-party group providing information (e.g., verifiable information) on

biotech foods change consumers’ purchasing behavior?

Finally, do socioeconomic factors (e.g., gender, income,

education) impact the effect of biotech food labels on

consumers’ willingness to pay for food items? 

We applied laboratory experimental auction markets,

along with a statistical design, to answer these questions.

Combining the positive features of statistical experimental design and auction markets yields a strong experimental design. Experimental auctions have been used to

evaluate consumers’ willingness to pay for food quality

attributes (see, for example, Melton et al., 1996; and

Fox, Hayes, and Shogren, 2002) and food safety (e.g.,

Hayes et al., 1995). Fisher, Wheeler, and Zwick (1993)

surveyed the use of experimental economics in agricultural and resource economics. 

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 1

The Effects of Information on Consumer

Demand for Biotech Foods

Evidence from Experimental Auctions

Abebayehu Tegene, Wallace E. Huffman, 

Matthew Rousu, and Jason F. Shogren1

1 Abebayehu Tegene is a senior economist, Food and Rural

Economics Division, ERS, U.S. Department of Agriculture;

Wallace E. Huffman is C. F. Curtiss distinguished professor of

agriculture and professor of economics, Department of Economics,

Iowa State University; Matthew Rousu is a research economist,

RTI International; and Jason F. Shogren is a Stroock distinguished

professor of natural resource conservation and management,

Department of Economics and Finance, University of Wyoming. 
Labeling is often used to deliver information to consumers on product characteristics that consumers are

not able to evaluate by looking at the product. Whether

a product is produced with the use of biotechnology is

difficult or impossible for the consumer to determine

by looking at the product. However, labeling of biotech

has become one of the most controversial issues in

agriculture today. Currently, the United States does not

require biotech foods to be labeled, unless the new

food product is substantially different from the original

product (the U.S. Food and Drug Administration, FDA,

2001). If the product is substantially different from the

traditional product, the label must identify the difference, perhaps through a new common name, but the

label does not have to identify the new trait or characteristic as being derived through bioengineering. FDA

requires labeling when the use of biotechnology introduces a known allergen or when it substantially

changes the food’s nutritional content or its composition (Schmidt, 2002). 

In January 2001, the FDA issued proposed guidance,

not yet finalized, regarding the voluntary labeling of

food containing or not containing bioengineered ingredients. The guidance also clarifies the position of FDA

with regard to the required labeling of products with

new or unique traits, as explained above. However this

proposed guideline does not mention a standard for

what percentage of an ingredient in a food product can

be bioengineered for the product still to be labeled as

non-biotech (FDA, 2001). Two things are of note:

first, the guidance states that FDA considers the term

“genetically modified” and its variants inaccurate,

since all modern foods have been genetically modified

through traditional breeding techniques. According to

FDA and USDA, “bioengineered” or “biotech” are

preferable terms to describe foods produced with the

aid of modern biotechnology. However, the terms

“genetically modified (GM)” and “genetically engineered” are also commonly used to describe biotech

foods in academic publications (see, for example,

AgBio Forum). Since we used the terms “GM” and

“non-GM” in our experimental auctions, we continue

to use them in this bulletin whenever we refer to the

labels in the experimental auctions. Second, FDA considers misleading a voluntary statement such as “bioengineered free” on a label, since it may imply that the

labeled food is superior to foods that are not so

labeled. FDA also considers “bioengineered free”

labels to be inaccurate because it implies that a product is 100 percent free of biotech components, which

is nearly impossible to guarantee or verify. Examples

of voluntary labels acceptable to the guidance are:

“Genetically engineered,” “This product contains cornmeal that was produced using biotechnology,” or “This

oil is made from soybeans that were not genetically

engineered” (FDA, 2001). 

The U.S. policy contrasts with the policies of many

other countries, including Australia, China, Japan, and

the countries in the European Union (EU), which all

have mandatory labeling requirements for biotech foods

(Rousu and Huffman, 2001). These countries often have

different levels of bioengineering that they will tolerate

before a product must be labeled as biotech. For example, the EU and Australia allow up to 1 percent of any

ingredient in a food product to be bioengineered before

the product must be labeled as biotech. Japan allows 5

percent of any ingredient to be bioengineered before the

product must be labeled as such. 

Caswell (1998 and 2000) has shown that there are

many possible policies that could be implemented,

including mandatory labeling of biotech foods, voluntary labeling of biotech and non-biotech foods, bans on

all labeling to indicate whether or not a food is bioengineered, or voluntary labeling of non-biotech foods with

an accompanying disclaimer noting the government’s

judgment about any difference between biotech and

non-biotech foods. Runge and Jackson (2000) propose

the last option as a solution to the current controversy

over labeling of biotech foods. The policies that each

country chooses are likely to be determined by the

information demanded by consumers of each country.

An informed decision on whether or not to implement a

labeling policy on bioengineered foods can only be

done after a benefit/cost analysis. The balance of the

benefit and cost depends on the type of program adopted (e.g., voluntary, mandatory) and market conditions.

A related issue in the ultimate benefits and costs is how

effectively labels convey information, the certainty of

the labeling (e.g., “does contain” versus “may contain”),

and the ability of the supply chain to provide high levels

of integrity in the segregation process (Caswell, 2000). 

Using the auction results presented in this bulletin,

Huffman et al. (2002) have provided evidence that a

2 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Issues Related to Labeling
voluntary biotech labeling policy results in greater

societal welfare than regulated mandatory labeling policy of biotech foods. They argue that United States has

been prudent in fending off calls for a mandatory

biotech labeling policy. 

Benefits of Labeling

Consumer groups and environmental groups have

argued that there are benefits for consumers from

mandatory labeling of biotech foods. Some groups

(e.g., Greenpeace, Friends of the Earth) advocate that

labels on biotech foods give consumers the right to

choose whether or not to consume biotech foods. 

Golan, Kuchler, and Mitchell (2000) have analyzed the

potential benefits of general labels on foods. One benefit is making it easy to find information, e.g., on

nutritional content of foods. Thus, labeling of foods

can lead to more informed food choices by consumers. 

Caswell and Padberg (1992) recommend a more comprehensive view of the benefits of any labels on food

products. The benefits can be above and beyond what

are normally considered the typical benefits from

labels. The benefits from food labels include increased

consumer information and more consumer confidence

in product quality. Also, labels can provide an option

value, even for consumers who do not currently read

food labels. This option value exists because if a food

is labeled, consumers always have the option to view

the label, either now or in the future. 

Runge and Jackson (2000) argue that negative labeling

(e.g., “This product was not genetically engineered”)

has advantages for both consumers and producers.

Drawing lessons from the Recombinant Bovine

Somatotropin (rBST) experience, they argue that adoption of negative labeling can create significant niche

markets for producers and that a carefully designed

negative label would avoid the potential information

biases of the positive label system.

Costs of Labeling

While there might be benefits to labeling, implementing a labeling policy involves some costs. The cost of

labeling involves far more than the paper and ink to

print the actual labels. Biotechnology firms oppose

mandatory labeling for all biotech foods because they

do not think foods should be specially labeled unless

the food is different from the conventional product

(Council of Biotechnology Information, 2001). The

labels on a biotech food could be taken as a warning

about health, whereas no significant health-related differences between biotech and non-biotech foods have

been detected. 

Golan, Kuchler, and Mitchell (2000) listed many costs

associated with implementing a general food labeling

policy. Some of the costs if a mandatory labeling policy is enacted include:

(1) Cost of identity preservation: Accurate labeling requires an extensive identity preservation

system from farmer to elevator to grain

processors to food manufacturers to retailers.

When separating biotech from non-biotech

foods, mistakes in delivery of the product are

always a possibility, as is accidental mingling

of non-biotech crops with their biotech counterparts. For example, in the United States,

biotech corn, known as Starlink, was not

approved for human consumption, but got into

U.S. food system. Under identity preservation,

farmers must ensure that non-biotech crops are

not mingled with biotech variety. Farmers

need to develop buffer zones to ensure no

cross-pollination of biotech and non-biotech

crops. Farmers also need to make sure planting and harvesting equipment are cleaned

between biotech and non-biotech varieties. All

of these items imply real costs if a labeling

policy is implemented.

(2) Higher consumer prices: These added labeling

and segregation costs would lead to higher

prices for consumers (and possibly lower

prices to producers). Higher prices could cause

a regressive tax for lower income households,

because low-income households spend a larger

share of their income for food than do highincome households. In addition to low-income

persons’ having to pay for labeled food, the

poor and less educated are less likely to benefit from food labels. This might lead to a

“reverse Robin Hood effect” of taking money

from the poor to benefit the rich (Golan,

Kuchler, and Mitchell, 2000). Furthermore, the

costs would be borne by all consumers regardless of the level of their own concerns.

(3) Costs to the agricultural industry: Some argue

that labels could impose costs on the food and

agricultural industry without providing compensating benefits to consumers. Labels could steer

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 3
consumers away from biotech products, which

would lead to reduced consumption and trade,

and investment in research and marketing in

biotechnology and their products may be significantly diminished (Runge and Jackson, 2000).

Also, Golan, Kuchler, and Mitchell (2000) suggest that labeling could change industry structure. With some fixed costs associated with

labeling, small firms may have higher per-unit

labeling costs than large firms. This would

mean increasing returns to scale, and an incentive for firms to grow, or close down. A labeling

policy that decreases the number of firms could

decrease competition and might increase prices

for consumers. Firms could also face reformulation costs. 

(4) Adverse effect on information: Adding more

information to food labels may dilute consumer awareness of other information on the

label (Golan, Kuchler, and Mitchell, 2000).

This concern seems most important when the

labeling policy being considered would inform

consumers of an attribute that may not impact

human health, e.g., bioengineering. 

(5) Cost of verification: Labeling without independent verification is not likely to be useful. Hence,

a new labeling policy would require resources

for government or third-party verification.

There are relatively few cost estimates due to labeling

of biotech foods. An ERS study, based on a survey of

84 elevators, estimates that segregation of non-biotech

crops could add about $0.22 per bushel and $0.54 per

bushel to marketing costs for corn and soybeans,

respectively (Lin, Chambers, and Harwood, 2000).

These estimates do not include the premium to producers. These cost estimates reflect average costs to the elevators only and do not take into account additional costs

associated with segregation at the farm level.

A study commissioned by the Canadian government

estimates that mandatory labeling of bioengineered

foods would increase food prices a minimum of 9 to 10

percent (Byrne, 2002). This means, for example, that a

tortilla costing $1.50 would increase to about $1.65. 

Australia and New Zealand commissioned KPMG, an

accounting and consulting firm, to examine the costs

of complying with a new labeling law. KPMG estimated that the costs of the labeling laws could mean an

increase in consumer prices from 0.5 to 15 percent,

and that firms could also face lower profits (Phillips

and Foster, 2000). Even though they commissioned the

study, the Australian and New Zealand Food Standard

Council did not accept these estimates because the

study assumed a much more elaborate system of private certification/testing and government oversight

than the Council envisioned (Caswell, 2000). Updated

estimates are being prepared. Phillips and Smyth

(2000) estimated that, in 1995-96, a voluntary identitypreserved production and marketing system in Canada

cost from 13 to 15 percent. One thing seems apparent;

implementing a labeling policy on biotech foods

would not be cost free, even if the exact magnitude of

the cost is unknown. 

Divergent Information

When the first biotech food product, a tomato engineered for longer shelf life, was released in the United

States, it was accompanied by information in the

media and at the markets, which increased familiarity

of this new technology. However, commodity crops,

such as corn and soybeans with altered agronomic

traits, have subsequently been released without much

media coverage (Shoemaker, 2001), although they

received the same amount of public notification as all

other regulations related to food safety. While there

may not be any cause for concern about the safety of

biotech foods, some consumers object to consuming

food produced with any technology that lacks a long,

established history of use. In addition, food labeling

has become an issue of consumers’ “right to know”

(Shoemaker, 2001). At the same time, consumer and

environmental groups on one side and agribusiness

and biotech firms on the other side provide consumers

with conflicting information about the benefits and

risks of agricultural biotechnology. Also, bioengineering is a complex process and most people do not know

the intricate details of this process (see, for example,

FDA’s report on focus groups, FDA, 2000). With different sources giving information to consumers, what

are consumers likely to do? Studies by the

International Food Information Council (IFIC) have

shown the importance of the source of biotechnology

information to consumers. “Who is educating consumers, not just the education itself, has emerged as a

crucial factor to acceptance [of biotech foods]”

(Schmidt, 2002).

Viscusi (1997) studied consumer reaction to environmental risks from air pollution emissions from chemical

factories. He showed that when consumers receive

divergent information on environmental risks, they tend

4 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA
to put a greater weight on the high-risk assessment. The

individual’s reaction was similar for information from

both government and industry sources of information. 

Fox, Hayes, and Shogren (2002) examined the effects

of positive and negative information on consumers’

willingness to pay for irradiated pork sandwiches.

They found that when consumers were presented with

both positive and negative information on food irradiation, the negative information dominated the consumers’ decision-making. This was despite the fact

that the negative information source was identified as a

consumer advocacy group, and the information was

written in a non-scientific manner. 

Recent research shows that there may be a need for

third-party, verifiable information on biotech foods, so

consumers do not have to rely on the information from

biotechnology companies and environmental groups

(Huffman and Tegene, 2000). Research on organic

foods reached a similar conclusion, in that there may

be benefits from an independent, third-party monitor

to help reduce false claims by interested parties

(McClusky, 2000). 

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 5
An experiment was designed to incorporate the privateinformation-revealing feature of experimental auction

markets and the rigorous randomized treatment effects

of statistical experimental design.2 The experimental

design consisted of six biotech information-labeling

treatments with two replications. The treatments were

randomly assigned to 12 experimental units, each consisting of 13-16 consumers drawn from the households

of two major urban areas. Consumers were paid to participate. At the suggestion of the statisticians, each participant participated in only two trials.3 Using randomly

chosen consumers from the population of an urban area,

rather than undergraduate college students at a university, is a major advantage when drawing inferences from

the experiments or generalizing to the Midwest or

whole U.S. population. Conducting experiments in two

urban areas rather than one enhances credibility of generalizations and shows that the experiments can be

replicated across urban areas. 

Consumers might react differently to biotech content

in different types of food, or they may have no

demand for some food products. Hence, using only

one food item seemed unlikely to reveal enough information, given the sizeable fixed cost of conducting the

experiments. Three food items were chosen: vegetable

oil (made from soybeans), tortilla chips (made from

yellow corn), and Russet potatoes. In the distilling and

refining process for vegetable oils, essentially all of

the proteins (which are the components of DNA and

the source of bioengineering) are removed, leaving

pure lipids. Hence, minimal human health concerns

should arise from biotech oil, but consumers may

worry that biotech soybeans affect the environment or

that they lack adequate information on the distilling

process. Tortilla chips are highly processed foods that

may be made from biotech or non-biotech corn, and

consumers might have human health and environmental concerns. Russet potatoes are purchased as a fresh

product and are generally baked or fried before eating.

Consumers might see both human health and environmental risks from eating biotech Russet potatoes. 

Random nth-price auction

Auctions have been a popular mechanism in laboratory

valuation experiments by economists. In particular,

Vickrey’s (1961) second-price auction has been used

frequently. The second-price auction induces individuals to reveal private information contained in their

preferences for new goods and services (Shogren et

al., 1994; Fox et al., 1998; and Shogren, List, and

Hayes, 2000). The popularity of the second-price auction is largely due to the mechanism’s being demand

revealing in theory and relatively simple to explain,

and it has an endogenous market-clearing price. In the

second-price auction, bids (sealed) for a good are

ranked from highest to lowest, and the winner pays the

second highest price. Participants have an incentive to

tell the truth about their valuation for a good because

the auction separates what they say from what they

pay. Sincere bidding is the weakly dominant strategy

(Shogren, List, and Hayes, 2000). Further, evidence

from induced value experiments suggests the auction

mechanism can produce efficient outcomes in the

aggregate (Kagel, 1995).

The second-price auction, however, has a problem: it

does not accurately reveal the complete demand curve

for a good by all participants. Individuals whose value

for a good is far below or above the market-clearing

price frequently bid insincerely. Bidders whose values

are far below or above the market-clearing price are

called off-the-margin bidders (Shogren et al., 2001).

These bidders have a low opportunity cost from an

insincere bid, making it difficult to accurately measure

the entire demand curve. A second-price auction might

not engage low-value bidders who think they will

never lose by insincere bidding (see Miller and Plott,

1985, and Franciosi et al., 1993). Insincere bidding

can be sustained if the behavior is undetected and

unpunished by the institutional structure of the auction

mechanism (see Cherry et al., 2000).

We chose the random nth price auction for our biotechfood experiment because it is designed to engage both

the on- and off-the-margin bidders (see Shogren et al.,

2001). The auction combines elements of two classic

demand-revealing mechanisms: the second-price auction and the Becker-DeGroot-Marschak (1964) random

pricing mechanism. The key characteristic of the ran6 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Experimental Design

2 Phil Dixon and Wayne Fuller, Department of Statistics, Iowa

State University, provided assistance with the statistical design part

of the project.

3 This is in contrast to the tradition in experimental economics of

having an individual participate in multiple trials. See Shogren

(forthcoming).
dom nth price auction is that it is a random but endogenously determined market-clearing price. Randomness

is used to give all participants a positive probability of

being a purchaser of the auctioned good; the endogenous price guarantees that the market-clearing price is

related to the bidder’s private values. 

The random nth price works as follows. Each of k bidders submits a bid for one unit of a good; then each of

the bids is rank ordered from highest to lowest. The

auction monitor then selects a random number—the n

in the nth-price auction—which is drawn from a uniform distribution between 2 and k; and the monitor

sells one unit of the good to each of the (n-1) highest

bidders at the nth price. For instance, if the monitor

randomly selects n = 4, the three highest bidders each

purchase one unit of the good priced at the fourthhighest bid. Ex ante, bidders who have low or moderate valuations now have a nontrivial chance to buy the

good because the price is determined randomly. This

auction increases the odds that insincere bidding will

lead to a loss (Shogren et al., 2001).

The Experiments

Auctions were planned and conducted at two

Midwestern U.S. cities: Des Moines, IA, and St. Paul,

MN. The Iowa State University (ISU) Statistics

Laboratory obtained 1,200-1,500 randomly selected

residence telephone numbers from each of the metropolitan areas. Employees of the ISU Statistics

Laboratory called these numbers to make sure that the

phone number was for a residence. The employees

then asked to speak to an adult (18 years or older) in

the household. They were told that Iowa State

University was looking for people who were willing to

participate in a group session in Des Moines (St. Paul)

that relates to how people select food and household

products. The ISU Statistics Laboratory contacted consumers in these two areas who agreed to participate in

the study. The sessions were held on two different

Saturdays (April 7 and 21) in 2001, and participants

were informed that the session would last about 90

minutes. Each participant was told that he or she

would receive $40 in cash for his or her time. The sessions were held at the Iowa State University Learning

Connection, 7th and Locust Street, Des Moines (lower

level of the Classroom Office Building, University of

Minnesota, St. Paul). Three different times were available each auction day—9 a.m., 11:30 a.m., and 2 p.m.

Willing participants chose a time that best fit their

schedule. Participation per household was limited to

two adults, and they were assigned to different

groups.4 The Statistics Laboratory sent participants a

letter containing more information, including a map, a

meeting time and location, and a telephone number to

contact for more information.

There were 12 experimental units—six in Des Moines

and six in Minneapolis. Ninety-nine people in Des

Moines agreed to participate out of 1,200 called. Of

the 99 who agreed to participate, 77 actually attended.

For the Minneapolis experiments, 118 people, out of

1,500 called, agreed to participate. Of those 118, 95

participated. The sample size totaled 172, which is

large compared to most experimental auctions.

Each auction had 10 steps, which are summarized in

figure 1.5 Upon arrival at the experiment, participants

signed a consent form agreeing to participate in the

auction. After they signed this form, the participants

were given $40 for participating and an ID number (to

preserve their anonymity). The participants then read a

brief set of instructions and filled out a questionnaire. 

In step 2, participants were given detailed instructions

on the random nth-price auction, including an example

written on the board. After the participants learned

about the auction, a short quiz was given to ensure that

everyone understood how the auction worked. 

Step 3 was the first practice round of bidding, during

which participants bid on a brand-name candy bar. The

participants were asked to examine the product and then

place a bid (sealed) on the candy bar. The bids were collected and the first round of practice bidding ended.

Throughout the auctions, as the participants bid on items

in a particular round, they had no indication of what

other items they might be bidding on in future rounds.

Step 4 was the second practice round of bidding. In

this round, the participants bid separately on three different items. The three products were the same brandname candy bar, a deck of playing cards, and a box of

pens. The consumers were asked to examine the three

products in practice round two and make bids on the

products. Then the bids were collected. Only one of

the two rounds was chosen as binding (valid), so that

participants would not take home more than one of

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 7

4 When two adults in a household participated, the Iowa State

Statistics Laboratory talked to both adults separately to obtain a commitment to participate; and the adults were told that they would be

assigned to different groups.

5 The complete set of information given to participants may be

requested from the authors.
8 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Figure 1

Steps in the experiment

Step 1 Step 2 Step 3

nth-price auction is

explained

Auction of a candy

bar, a deck of cards,

and a box of pens

Binding practice

round and binding nth

prices are revealed

Second round of bidding on food products

Post-auction questionnaire; winning

people purchase

goods

Binding food round

and binding nth

prices are revealed

First round of bidding

on food products

Pro-biotechnology

Anti-biotechnology

Both Pro- and Anti-biotechnology

Pro-biotechnology and thirdparty information

Both Pro- and Anti-biotechnology and third-party information

Anti-biotechnology and thirdparty information

Candy bar auction

Step 5

Step 4

Step 6

Step 7

Step 9

Step 8

Step 10

Fills consent form and

questionnaire; receives

$40 and I.D. number
any product. The reason was to eliminate price reduction due to the consumer’s buying a larger quantity

because of diminishing marginal utility of these products (i.e., lower prices due to a consumer’s negatively

sloped demand curve).6 Participants were informed

that only one of the two rounds would bind before step

3 and were reminded of this again before step 4. 

After the two practice auction rounds were completed,

the binding round and the binding nth prices were

revealed in step 5. All of the bids were written on the

board, and the nth prices were circled for each of the

three products. This way, participants could see what

items they had won (bought) immediately, and what

price they had to pay for the items. The participants

were notified that all purchases of goods would take

place after the experiment was over, so that all

exchanges of money for goods would take place at the

end of the session.

In step 6, participants received one of six potential

information packets about biotechnology. The information packets were produced as follows. Three packets of information contained:

(1) the industry perspective—a collection of

statements and information on biotechnology

provided by a group of leading biotechnology

companies, including Monsanto and

Syngenta; 

(2) the environmental group perspective—a collection of statements and information on

biotechnology from Greenpeace, a leading

environmental group; 

(3) the independent, third-party group perspective—a statement on biotechnology

approved by a third-party group, consisting

of a variety of individuals knowledgeable

about biotech foods, including scientists,

professionals, religious leaders, and academics, none of whom have a financial stake in

genetically modified foods. 

To assist the participants’ processing of these different

sources of information, the volume of information

released of each type was limited to one page. To ease

the information-processing load on participants, the

information was organized into five categories: general information, scientific impact, human impact, financial impact, and environmental impact. Figures 2-4

show the exact format and wording of the three information sources.

The information was randomized to create the six

information packets: (1) pro-biotechnology; (2) antibiotechnology; (3) both pro- and anti-biotechnology;

(4) pro-biotechnology and third-party verifiable information; (5) anti-biotechnology and third-party7 verifiable information; and (6) pro-biotechnology, antibiotechnology, and third-party verifiable information.

The 6 information packets were then randomized

among all 12 experimental units, with each information packet going to 2 experimental units. When a participant received both pro-biotechnology and antibiotechnology information, the order was randomized,

so that some participants received the pro-biotechnology information first, and others received the antibiotechnology information first.

Two auction rounds followed the distribution of information. The rounds were differentiated by the food label—

the food had either a standard label or a biotech label. In

one of the rounds, participants bid on the three food

products each with just a standard food label. In the

other round, participants would be bidding on the same

three food products with a biotech label, which differed

from the standard label by inclusion of only one extra

sentence: “This product is made using genetic modification (GM).” (As noted earlier, USDA and FDA prefer

the terms “biotech” or “bio-engineered” to “GM,”

although “GM” and non-“GM” are commonly used in

academic publications, e.g., AgBio Forum.) These labels

were made as plain as possible to avoid any influence on

the bids from the label design. The labels are presented

in figure 5. The sequencing of biotech labels was randomized across experimental units. Each information

packet was given to two experimental units (fig. 6). One

of these experimental units bid on food with the standard

label in round one, and the food with the label indicating

bioengineering in round two. The other experimental

unit bid on food with the label indicating bioengineering

in round one, and the standard label in round two (fig.

6). For each experimental unit, participants knew that

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 9

6 If one assumes that there is little or no income effect from the deck

of cards and box of pens, the two bids on the candy bars should be

the same. If the deck of cards and box of pens are neither complements nor substitutes for the candy bar, they should not impact the

bids on the candy bar.

7 Third-party information was always distributed after the other

information sources.
10 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Figure 2

Information given to participants, industry perspective (pro-biotechnology)

The following is a collection of statements and information on genetic modification provided by a group 

of leading biotechnology companies, including Monsanto and Syngenta.

General Information Genetically modified plants and animals have the potential to be one of the 

greatest discoveries in the history of farming. Improvements in crops so far 

relate to improved insect and disease resistance and weed control. These 

improvements using bioengineering/GM technology lead to reduced cost of 

food production. Future GM food products may have health benefits.

Scientific Impact Genetic modification is a technique that has been used to produce food 

products that are approved by the Food and Drug Administration (FDA).

Genetic engineering has brought new opportunities to farmers for pest control 

and in the future will provide consumers with nutrient enhanced foods. GM 

plants and animals have the potential to be the single greatest discovery in the 

history of agriculture. We have just seen the tip of the iceberg of future potential.

Human Impact The health benefits from genetic modification can be enormous. A special type 

of rice called “golden rice” has already been created which has higher levels of 

vitamin A. This could be very helpful because the disease Vitamin A Deficiency 

(VAD) is devastating in third-world countries. VAD causes irreversible blindness 

in over 500,000 children, and is also responsible for over one million deaths 

annually. Since rice is the staple food in the diets of millions of people in the 

third world, Golden Rice has the potential of improving millions of lives a year by 

reducing the cases of VAD.

The FDA has approved GM food for human consumption, and Americans have 

been consuming GM foods for years. While every food product may pose risks, 

there has never been a documented case of a person getting sick from GM food.

Financial Impact Genetically modified plants have reduced the cost of food production, which 

means lower food prices, and that can help feed the world. In America, lower 

food prices help decrease the number of hungry people and also lets consumers 

save a little more money on food. Worldwide the number of hungry people has 

been declining, but increased crop production using GM technology can also 

help further reduce world hunger.

Environmental Impact GM technology has produced new methods of insect control that reduce 

chemical insecticide application by 50 percent or more. This means less 

environmental damage. GM weed control is providing new methods to control 

weeds, which are a special problem in no-till farming. Genetic modification of 

plants has the potential to be one of the most environmentally helpful 

discoveries ever.
Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 11

Figure 3

Information given to participants, environmental group perspective (anti-biotechnology)

The following is a collection of statements and information on genetic modification from Greenpeace, a leading

environmental group.

General Information Genetic modification is one of the most dangerous things being done to your 

food sources today. There are many reasons that genetically modified foods 

should be banned, mainly because unknown adverse effects could be 

catastrophic! Inadequate safety testing of GM plants, animals, and food products 

has occurred, so humans are the ones testing whether or not GM foods are 

safe. Consumers should not have to test new food products to ensure that they 

are safe.

Scientific Impact The process of genetic modification takes genes from one organism and puts 

them into another. This process is very risky. The biggest potential hazard of 

genetically modified (GM) foods is the unknown. This is a relatively new 

technique, and no one can guarantee that consumers will not be harmed.

Recently, many governments in Europe assured consumers that there would be 

no harm to consumers over mad-cow disease, but unfortunately, their claims 

were wrong. We do not want consumers to be harmed by GM food.

Human Impact Genetically modified foods could pose major health problems. The potential 

exists for allergens to be transferred to a GM food product that no one would 

suspect. For example, if genes from a peanut were transferred into a tomato, 

and someone who is allergic to peanuts eats this new tomato, they could display 

a peanut allergy.

Another problem with genetically modified foods is a moral issue. These foods 

are taking genes from one living organism and transplanting them into another.

Many people think it is morally wrong to mess around with life forms on such a 

fundamental level.

Financial Impact GM foods are being pushed onto consumers by big businesses, which care only 

about their own profits and ignore possible negative side effects. These groups 

are actually patenting different life forms that they genetically modify, with plans 

to sell them in the future. Studies have also shown that GM crops may get lower 

yields than conventional crops.

Environmental Impact Genetically modified foods could pose major environmental hazards. Sparse 

testing of GM plants for environmental impacts has occurred. One potential 

hazard could be the impact of GM crops on wildlife. One study showed that one 

type of GM plant killed Monarch butterflies.

Another potential environmental hazard could come from pests that begin to 

resist GM plants that were engineered to reduce chemical pesticide application.

The harmful insects and other pests that get exposed to these crops could 

quickly develop tolerance and wipe out many of the potential advantages of GM 

pest resistance.
12 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Figure 4

Information given to participants, third-party perspective (independent, verifiable information) 

The following is a statement on genetic modification approved by a third-party group, consisting of a variety of 

individuals knowledgeable about genetically modified foods, including scientists, professionals, religious leaders,

and academics. These parties have no financial stake in genetically modified foods.

General Information Bioengineering is a type of genetic modification where genes are transferred 

across plants or animals, a process that would not otherwise occur (In common 

usage, genetic modification means bioengineering). With bioengineered pest 

resistance in plants, the process is somewhat similar to the process of how a flu 

shot works in the human body. Flu shots work by injecting a virus into the body 

to help make a human body more resistant to the flu. Bioengineered plant-pest 

resistance causes a plant to enhance its own pest resistance.

Scientific Impact The Food and Drug Administration standards for GM food products (chips, 

cereals, potatoes, etc.) is based on the principle that they have essentially the 

same ingredients, although they have been modified slightly from the original 

plant materials.

Oils made from bioengineered oil crops have been refined, and this process 

removed essentially all the GM proteins, making them like non-GM oils. So even 

if GM crops were deemed to be harmful for human consumption, it is doubtful 

that vegetable oils would cause harm.

Human Impact While many genetically modified foods are in the process of being put on your 

grocers’ shelf, there are currently no foods available in the U.S. where genetic 

modification has increased nutrient content.

All foods present a small risk of an allergic reaction to some people. No FDA 

approved GM food poses any known unique human health risks.

Financial Impact Genetically modified seeds and other organisms are produced by businesses 

that seek profits. For farmers to switch to GM crops, they must see benefits from 

the switch. However, genetic modification technology may lead to changes in the 

organization of the agri-business industry and farming. The introduction of GM 

foods has the potential to decrease the prices to consumers for groceries.

Environmental Impact The effects of genetic modification on the environment are largely unknown.

Bioengineered insect resistance has reduced farmers’ applications of environmentally hazardous insecticides. More studies are occurring to help assess the 

impact of bioengineered plants and organisms on the environment. A couple of 

studies reported harm to Monarch butterflies from GM crops, but other scientists 

were not able to recreate the results. The possibility of insects growing resistant 

to GM crops is a legitimate concern.
Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 13

Figure 5

Labels used for the three food items

Vegetable Oil

Net weight 32 fl. oz.

Tortilla Chips

Net weight 16 oz.

Fresh made Thursday April 5th

Russet Potatoes

Net weight 5 lb.

Tortilla Chips

Net weight 16 oz.

Fresh made Thursday April 5th

This product is made using

genetic modification (GM)

Vegetable Oil

Net weight 32 fl. oz.

This product is made using

genetically modified (GM) soybeans

Russet Potatoes

Net weight 5 lb.

This product is made using

genetic modification (GM)

Figure 6

Information and labeling given to experimental units one through twelve

Exp. unit Positive/negative Third-party Round with “GM” labels

1. Pro-biotech Yes 1

2. Anti-biotech Yes 1

3. Pro-biotech, anti-biotech Yes 1

4. Pro-biotech Yes 2

5. Anti-biotech Yes 2

6. Pro-biotech, anti-biotech Yes 2

7. Pro-biotech No 1

8. Anti-biotech No 1

9. Pro-biotech and anti-biotech No 1

10. Pro-biotech No 2

11. Anti-biotech No 2

12. Pro-biotech and anti-biotech No 2
only one of the two food rounds would be chosen as the

binding (valid) round.

In step 7, participants bid on three different food products: a bag of potatoes, a bottle of vegetable oil, and a

bag of tortilla chips. The participants were instructed

to examine the three products and then write down

their (sealed) bid for each of the three goods.

Participants bid on each good separately. Then the bids

were collected from the individuals, and the participants were informed that they were about to look at

another group of food items.

Step 8 had participants examine the same three food

products, but each with a different label from round 1.

Again the participants examined the products and bid

on the three products separately. The bids were then

collected from all of the participants. Once again, consumers were informed that only one of the two food

rounds would be binding.8

Step 9 selected the binding round and the binding nth

prices for the three goods. After the binding round and

binding nth prices were chosen, the winners were notified and all participants were asked to fill out a brief

post-auction questionnaire. In step 10, the participants

who did not win any products were informed that they

could leave; the participants who had won products

exchanged their goods for money, and were then free

to leave. 

The Data 

A summary of the demographic characteristics of the

172 auction participants is presented in table 1. Sixtytwo percent of the participants in the auctions were

female. The mean age of the participants was 49.5 years

(a person had to be at least 18 years old to participate).

Two-thirds of the auction participants were married. On

average, the participants were well educated, with the

mean education level being more than 2 years in college.

The participants had a mean total household income

(before taxes) of $57,000, with an average household

size of three. Ninety percent of the participants in the

experiments were white, and most people indicated that

they read labels before they buy a new food product.

Some participants chose to bid zero in both trials, i.e.,

for both the “GM”-labeled and the plain-labeled variety of a particular food product. These participants

provide no information about their taste for biotech

foods; they were willing to pay zero for one unit, a

small demand. Table 2 presents the mean bids for participants, segregated by information treatment, but

does not include bids for consumers who bid zero for

both the “GM”-labeled and plain-labeled varieties of a

product.9 In table 2, the number of participants who

bid a positive amount for a product is different for

each of the three goods. This occurs because more

consumers bid zero for the “GM”-labeled and plainlabeled vegetable oils than for the “GM”-labeled and

plain-labeled bags of tortilla chips. “GM”-labeled and

plain-labeled bags of potatoes had the largest number

of non-zero bids. Many consumers who bid zero for

both the biotech and non-biotech varieties of one product bid a positive amount for the other products.10

The Effect of Information

Part A of table 2 addresses the question, “Do labels

have any significant effect on consumers’ willingness

to pay for biotech and non-biotech foods?” Part A of

table 2 shows the mean bid prices for all participants

for each of the three products: the 5-pound bag of

potatoes, the 32-ounce bottle of vegetable oil, and the

1-pound bag of tortilla chips. Under all information

treatments and all auctions, consumers, on average,

discounted food items labeled “GM” by 14 percent.11

Thus, “GM” labels affect consumers’ willingness to

pay for “GM” and plain-labeled food products. It is

possible that participants might have perceived the

14 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

8 These experiments were set up to minimize endowment effects;

i.e., participants were endowed at the beginning of the experiment

with $40 but not GM-food. See Shogren (forthcoming) for evidence on endowment effects.

9 The percentage discount of foods is similar to the percentage

when all bids are included.

10 Only 7 of the 172 participants bid zero for all six products.

11 In another similar experiment (Huffman et al., 2002), 142 consumers in Des Moines and Minnesota participated in auctions for the

same 3 food products. There were 2 labeling regimes: (1) 86 consumers participated in plain-labeled and “GM”-labeled products; and

(2) 56 consumers participated in plain-labeled and non-“GM”-labeled

products. We examined the difference in bids for the “perceived” GM

product to the “perceived” non-GM product. In (1), the GM-labeled

was the perceived GM product while the plain-labeled was assumed

to be the perceived non-GM product. In (2), the plain-labeled food

was assumed the perceived GM product while the non-GM product

was the perceived non-GM product. Statistical tests showed that consumers’ bid for the perceived non-GM products in the auctions

(labeling regimes 1 and 2) are not different. That is, consumers discounted perceived GM foods similarly in the two auctions. In this

experiment, all participants received positive and negative information while some participants received third-party information.
plain-labeled products as non-biotech when comparing

the “GM”-labeled and plain-labeled food products. 

Table 2, parts B-G, shows how consumers’ willingness

to pay for “GM”-labeled and plain-labeled foods is

affected by divergent information about the benefits

and risk associated with biotechnology. Part B shows

that participants who received only positive information actually put a premium on the “GM”-labeled food

for two of the three products. This was despite the fact

that biotechnology was only used to enhance the agronomic traits and did not give the foods any enhanced

attributes. Part C shows that when consumers received

only negative information, they discounted the “GM”-

labeled foods by an average of 35 percent. Part D

shows that consumers who received both positive and

negative information discounted the “GM”-labeled

foods by an average of 16-29 percent, depending on

the food product.

Third-party information has an impact on the willingness

to pay for “GM”-labeled foods. Part E shows that consumers who received positive and third-party information discounted “GM”-labeled foods slightly. This is in

contrast to the consumers who received only positive

information and valued the “GM”-labeled foods more

than their plain-labeled counterpart on average. Part F

shows that participants who received negative and thirdparty information still discounted the “GM”-labeled

foods, but by a smaller amount than the participants who

received only negative information. Participants who

received negative and third-party information discounted

the “GM”-labeled foods by an average of 17-22 percent,

depending on the product. Part G shows that participants

who received positive, negative, and third-party information were more accepting of the “GM”-labeled foods

than those who received only positive and negative information. The participants who received positive, negative,

and third-party information discounted the “GM”-

labeled food by an average of 0-11 percent, depending

on the product. Figures 7-9 show the average bids for

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 15

Table 1—Characteristics of the auction participants

Variable Definition Mean Standard deviation

Gender 1 if female 0.62 0.49

Age The participant’s age 49.5 17.5

Married 1 if the individual is married 0.67 0.47

Education Years of schooling 14.54 2.25

Household Number of people in participant’s household 2.78 1.65

Income The household’s income level (in thousands) 57.0 32.6

White 1 if participant is white 0.90 0.30

Read 1 if never read labels before a new food purchase 0.01 0.11

1 if rarely read labels before a new food purchase 0.11 0.31

1 if sometimes read labels before a new food purchase 0.31 0.46

1 if often read labels before a new food purchase 0.37 0.48

1 if always read labels before a new food purchase 0.20 0.40

Informed 1 if an individual considered themselves at least

somewhat informed regarding genetically modified foods 0.42 0.49

Labels 1 1 if the participant bid on foods with GM labels in round 1 0.52 0.50

Figure 7

Oil: Mean bids for GM-labeled and 

standard-labeled foods

Dollars

Note: A = only positive information; B = only negative information; 

C = both positive and negative information; D = both positive 

and third-party information; E = both negative and third-party 

information; and F = positive, negative, and third-party information. 

0

0.4

0.8

1.2

1.6

2.0

GM Non-GM

AB CDE F
16 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Table 2—Mean bids for participants receiving positive, negative, and third-party information 

excluding double-zero bids

A. Mean bids – all participants

n mean bid std dev Median Minimum Maximum

Biotech oil 146 1.07 0.81 0.99 0 3.99

Oil 146 1.24 0.78 1.00 0 3.79

Biotech chips 155 1.03 0.85 0.99 0 3.99

Chips 155 1.20 0.81 1.00 0.05 4.99

Biotech potatoes 159 0.84 0.66 0.75 0 3

Potatoes 159 0.98 0.65 0.89 0 3.89

B. Mean bids when participants received only positive information

n mean bid std dev Median Minimum Maximum

Biotech oil 26 1.56 0.73 1.50 0 2.99

Oil 26 1.54 0.79 1.55 0 3.50

Biotech chips 30 1.31 0.72 1.13 0 2.99

Chips 30 1.36 0.72 1.18 0.05 2.99

Biotech potatoes 27 1.30 0.71 1.25 0 2.50

Potatoes 27 1.26 0.67 1.25 0 2.00

C. Mean bids when participants received only negative information

n mean bid std dev Median Minimum Maximum

Biotech oil 26 0.79 0.82 0.50 0 3.25

Oil 26 1.22 0.65 1.00 0.25 2.49

Biotech chips 29 0.81 0.94 0.50 0 3.99

Chips 29 1.25 1.02 1.00 0.05 4.99

Biotech potatoes 29 0.61 0.68 0.50 0 2.75

Potatoes 29 0.98 0.88 0.75 0.05 3.89

D. Mean bids when participants received both positive and negative information

n mean bid std dev Median Minimum Maximum

Biotech oil 24 0.68 0.55 0.50 0 1.79

Oil 24 0.90 0.72 0.85 0 3.00

Biotech chips 23 0.68 0.74 0.35 0 2.25

Chips 23 0.81 0.79 0.49 0.05 2.75

Biotech potatoes 26 0.50 0.39 0.50 0 1.50

Potatoes 26 0.70 0.43 0.50 0.05 1.60

Continued--
Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 17

Table 2—Mean bids for participants receiving positive, negative, and third-party information 

excluding double-zero bids--Continued

E. Mean bids when participants received both positive and third-party information

n mean bid std dev Median Minimum Maximum

Biotech oil 26 1.12 0.62 1.00 0 2.39

Oil 26 1.14 0.57 1.00 0.10 2.39

Biotech chips 25 1.24 0.77 1.19 0 2.79

Chips 25 1.33 0.73 1.16 0.20 2.89

Biotech potatoes 26 0.92 0.45 0.99 0 1.85

Potatoes 26 0.93 0.39 0.99 0.25 1.90

F. Mean bids when participants received both negative and third-party information

n mean bid std dev Median Minimum Maximum

Biotech oil 21 1.33 1.05 1.25 0 3.99

Oil 21 1.60 0.97 1.50 0.49 3.79

Biotech chips 25 1.12 0.97 0.99 0 3.50

Chips 25 1.38 0.77 1.01 0.49 3.00

Biotech potatoes 27 0.89 0.77 0.89 0 3.00

Potatoes 27 1.14 0.67 0.99 0.50 3.00

G. Mean bids when participants received positive, negative, and third-party information

n mean bid std dev Median Minimum Maximum

Biotech oil 23 0.94 0.77 0.95 0 2.75

Oil 23 1.06 0.82 1.00 0.05 3.29

Biotech chips 23 0.95 0.81 0.85 0 3.25

Chips 23 0.95 0.66 0.99 0.1 2.89

Biotech potatoes 24 0.82 0.61 1.00 0 1.99

Potatoes 24 0.84 0.55 0.84 0.01 2.00
“GM”-labeled and plain-labeled foods when participants

are given divergent information. 

Our results as well as Viscusi’s (1997) indicated that

individuals placed a slightly greater weight on negative

information than on positive information. In our auction, those who received only positive information did

not discount the “GM”-labeled food, while those who

received only negative information discounted the

“GM”-labeled food by 35 percent (oil and chips) and

38 percent (potatoes). Those who received both positive and negative information put slightly more weight

on the negative information, discounting the “GM”-

labeled foods by 16-29 percent, depending on the

product. In their experiments, Fox, Hayes, and

Shogren (2002) found that negative information dominated positive information. They argued that one reason could be due to a “status quo bias” (or endowment

effect). They argued that losses associated with switching to an alternative are given greater weight than corresponding gains; that is, there is a bias in favor of status quo. In their experiment, participants were originally endowed with a regular pork sandwich (the product under investigation) and could bid to upgrade to an

irradiated pork sandwich, where irradiation reduces the

risk of parasitic illness. Our auction had participants

bid on items in separate rounds (trials), thereby eliminating the possibility of a “status quo bias.”

18 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Figure 8

Chips: Mean bids for GM-labeled and 

standard-labeled foods

Dollars

Note: A = only positive information; B = only negative information; 

C = both positive and negative information; D = both positive 

and third-party information; E = both negative and third-party 

information; and F = positive, negative, and third-party information. 

GM Non-GM

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

AB CDE F

Figure 9

Potatoes: Mean bids for GM-labeled and 

standard-labeled foods

Dollars

Note: A = only positive information; B = only negative information; 

C = both positive and negative information; D = both positive 

and third-party information; E = both negative and third-party 

information; and F = positive, negative, and third-party information. 

GM Non-GM

AB CDE F

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4
Regression analysis provides the statistical framework

for examining consumer behavior and answering the

questions raised earlier in the bulletin. The dependent

variable in all of the regressions is the difference in bid

prices for a plain-labeled and “GM”-labeled product

for each participant. Thus, consumers’ tastes are held

constant for each of the three products. We can express

bid difference as the difference between inverse

demand functions for the two products. Let the inverse

demand equation for the “GM”-labeled food (called

labeled), and the plain or standard-labeled food (called

non-labeled) be:

(1) Pj

non-labeled=β1non-labeled+β2non-labeledXj2+μjnon-labeled

and 

(2) Pj

labeled=β1labeled+β2labeledXj2+μjlabeled

where Pj represents the price bid for a good by participant j; β1 is an intercept term; Xj2 is a vector of exogenous variables and β2 is the associated vector of coefficients. μj is the random error term for participant j. 

Subtracting equation (2) from equation (1), we obtain

an equation where the dependent variable is the difference in bid prices for the two trials, as shown in equation (3):

The coefficients and error terms can be condensed and

rewritten as shown in equation (3a):

The difference in bid prices is explained by an intercept term , a slope term that is multiplied by a

vector of exogenous characteristics Xj2, and a random

error term . Equation (3a) can now be estimated.

Equation (3a), however, is likely to be censored since

consumers were restricted to a minimum bid of zero

for a product. The censored bid prices are the zero

bids for the “GM”-labeled and/or the plain-labeled

product. This censored regression model has four

cases. In case (1), consumer j bids a positive amount

for both the “GM”-labeled and the plain-labeled product. The measured difference in bid prices is just the

difference between the two bid prices. Case (2) occurs

when consumer j bids zero for the “GM”-labeled product and a positive amount for the plain-labeled product. The true difference in bid prices with the censored

regression will be greater than the difference between

the two observed bid prices. This arises from the fact

that the bids on the “GM”-labeled product were censored at zero. Case (3) occurs when consumer j bids a

positive amount for the “GM”-labeled product and

zero for the plain-labeled product. Like case (2), the

true difference in bid prices for the censored regression is (absolutely) larger than the measured difference

between the two bid prices, because the participant’s

bid for the plain-labeled product is censored. Case (4)

occurs when consumer j bids zero for both products.

This does not give any information about their true

demand for biotech products. A summary of the four

cases is given in figure 10. 

By using the censored regression model, the zero bid

prices are correctly accounted for, and effects of bias

from the zero bids are minimized. Hence, we expect

the coefficient estimates for this regression method to

have better statistical properties than if we had ignored

the censoring. Statistical tests are conducted using the

likelihood ratio test statistic (Greene, 2000).12 The

large sample distribution of – 2 1n λ is chi-squared,

with degrees of freedom equal to the number of

restrictions imposed.

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 19

Econometric Model

( )

(3) 

 

j

non-labeled

j

labeled non-labeled labeled

non-labeled labeled

j

non-labeled labeled

P P −= −+

− +−

1 1

2 2

β β

β β X μ μ j j 2

(3a) j

non-labeled

j

labeled * * P P − =+ + 1 2 ββ μ Xj2 j

*

.

1

*

2 β * β

j

* μ

12 The likelihood ratio takes the maximum of the likelihood function of a regression that only has an intercept term (the restricted

equation) divided by the maximum of the likelihood function of

the regression that includes some explanatory variables (the unrestricted equation). This is shown in the following equation:

In this equation, represents the maximum of the likelihood function for the regression with only the intercept term, and represents the maximum of the likelihood function for the unrestricted

equation.

λ =

$

$ . L

L

R

U

L$

R

L$

U
Regression Results

The censored regression results are presented in tables

3-5.13 For all three goods, models were fitted using

five dummy variables to test for impacts of different

information types. Dummy variables are defined for

negative information; negative and positive information; positive and third-party information; negative and

third-party information; and positive, negative, and

third-party information. Positive information is the

omitted information type. Other regressors include

gender (dummy variable taking a value of 1 if a person

is female), income, “labels Round 1” (a dummy variable taking a value of 1 if a participant bid on foods

with “GM” labels in round 1 and plain labels in round

2), and “informed” (a dummy variable taking a value

of 1 if the participant perceived himself (herself) to be

well informed about biotech foods).14

Regression (1) in tables 3-5 reports on the test of the

difference in bid prices due to “GM” labels. The intercept term in regression (1) of tables 3-5 is positive and

statistically significant, implying that, on average, participants were willing to pay 19 cents less for a 32-

ounce bottle of vegetable oil labeled as “GM,” 21

cents less for a 1-pound bag of tortilla chips labeled as

“GM,” and 17 cents less for a 5-pound bag of Russet

potatoes labeled “GM.” Hence, “GM” labels have a

statistically significant effect on consumers’ willingness to pay for each food item. 

Regression (2) is used for testing the effect of information types on the willingness to pay for “GM”-labeled

and plain-labeled food products. The coefficients for

“Anti,” a dummy variable taking a value of 1 if a participant received only negative information, are large, positive, and statistically significant in all cases. The coefficients for “Pro and Anti,” a dummy variable taking a

value of 1 if an individual received positive and negative

information, are positive, and these coefficients are statistically significant for some of the products. These

results show that individuals who received only negative

or both positive and negative information behaved differently than individuals who received only positive

information when making purchasing decisions. The

coefficients for “Pro- and Third-party,” a dummy variable taking a value of 1 if an individual received positive and third-party information, are small, positive, and

not statistically significant. Hence, third-party information does not have a large impact on the difference in

bids between the plain-labeled and “GM” labeled foods

for participants who received only positive information.

The coefficients for “Anti- and Third-party,” a dummy

variable indicating that a participant received negative

and third-party information, are generally statistically

significant. The coefficients for “All information,” a

dummy variable indicating that a participant received all

three types of information, are not statistically different

from zero for any of the food products. Hence, the

impact is similar to the outcome when consumers

received only positive information. The coefficient for

the dummy variable “labels Round 1” is negative in all

cases, indicating that participants who bid on the “GM”-

labeled food in the first round paid a smaller premium

for plain-labeled food than the other participants.

However, the coefficient is statistically significant for

only one of the food items (potatoes). 

In regression (3), the difference in bid prices is regressed

on an intercept term, information types, and the variable

“labels round 1.” The coefficient of this dummy variable

is negative for all three products, indicating that participants who bid first on the “GM”-labeled food in the first

round discounted the “GM”-labeled food less than the

other participants. However, the coefficient of the “labels

round 1” was not statistically significant. 

20 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Figure 10

The four cases of the censored regression

Case Plain-labeled bid "GM"-labeled bid Censored regression difference

1. P non-labeled P labeled P non-labeled_ P labeled

2. P non-labeled 0 > P non-labeled

1

3. 0 Plabeled <−P labeled or >− P labeled

4. 0 0 •

Note: "• " represents a missing value, due to the zero bid.

13 Ordinary least square regressions were also fitted to 172 observations, and to the observations remaining after “double-zero” bids

were excluded. The results for these regressions are similar and

may be requested from the authors.

14 Several other models were fitted which included (as regressors)

the participant’s age, marital status, religious upbringing, and educational attainment. None of these variables, however, impacted

the difference in bid prices in a statistically significant way (at the

10-percent level).
Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 21

Table 3—Censored regression estimates explaining difference in bid prices between “GM”-labeled 

and plain-labeled tortilla chips

Dependent variable: Bid price non-labeled food–bid price GM-labeled food

Regressors (1) (2) (3) (4) (5) (6) (7)

Intercept 0.209** 0.060 0.093 0.106 0.008 0.039 -0.034

(.046) (0.099) (0.110) (0.119) (0.137) (0.119) (0.151)

Anti - 0.481 ** 0.473 ** 0.474 ** 0.481 ** 0.489 ** 0.494 **

(0.145) (0.145) (0.145) (0.144) (0.145) (0.144)

Pro and Anti - 0.132 0.124 0.128 0.138 0.136 0.147

(0.152) (0.153) (0.153) (0.152) (0.152) (0.153)

Pro and - 0.035 0.031 0.035 0.003 0.023 0.001

Third-party (0.147) (0.147) (0.147) (0.148) (0.146) (0.148)

Anti and - 0.245 * 0.241 0.246 ** 0.244 * 0.241 0.256 *

Third-party (0.148) (0.148) (0.149) (0.147) (0.147) (0.148)

All information - -0.027 -0.023 -0.019 0.003 -0.028 -0.009

(0.151) (0.151) (0.151) (0.151) (0.151) (0.151)

Labels-Round 1 - - -0.063 -0.063 -0.064 -0.045 -0.050

(0.089) (0.089) (0.089) (0.090) (0.090)

Gender - - - -0.025 - - -0.011

(0.091) (0.091)

Income - - - - 0.0017 - 0.0015

(0.0014) (0.0014)

Informed - - - - - 0.104 0.087

(0.092) (0.093)

Likelihood ratio - 15.92 ** 16.28 ** 16.49 ** 17.93 ** 17.70 ** 18.83 **

** indicates that a variable is significant at 5 percent.

* indicates that a variable is significant at 10 percent.

(n=172, standard errors are in parentheses).
22 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA

Table 4—Censored regression estimates explaining difference in bid prices between “GM”-labeled 

and plain-labeled vegetable oil

Dependent variable: Bid price non-labeled food–bid price GM-labeled food

Regressors (1) (2) (3) (4) (5) (6) (7)

Intercept .193** -0.032 0.053 0.117 -0.128 -0.015 -0.104

(.056) (0.126) (0.142) (0.150) (0.169) (0.152) (0.180)

Anti - 0.530 ** 0.496 ** 0.504 ** 0.505 ** 0.516 ** 0.529 **

(0.180) (0.181) (0.180) (0.179) (0.181) (0.178)

Pro and Anti - 0.259 0.231 0.262 0.255 0.250 0.295

(0.183) (0.183) (0.183) (0.181) (0.182) (0.181)

Pro and - 0.061 0.046 0.079 -0.012 0.032 0.014

Third-Party (0.179) (0.178) (0.179) (0.178) (0.177) (0.179)

Anti and - 0.335 * 0.301 0.338 * 0.288 0.312 * 0.333 *

Third-Party (0.190) (0.191) (0.192) (0.188) (0.190) (0.189)

All information - 0.186 0.181 0.204 0.208 0.170 0.218

(0.185) (0.184) (0.184) (0.182) (0.184) (0.182)

Labels-Round 1 - - -0.139 -0.136 -0.149 -0.115 -0.127

(0.109) (0.108) (0.107) (0.110) (0.108)

Gender - - - -0.140 - - -0.131

(0.111) (0.109)

Income - - - - 0.0032 * - 0.0029 *

(0.0017) 0.0017)

Informed - - - - - 0.142 0.112

(0.113) (0.112)

Likelihood ratio - 10.90 * 12.52 * 14.11 ** 16.21 ** 14.09 ** 18.63 **

** indicates that a variable is significant at 5 percent

* indicates that a variable is significant at 10 percent.

(n=172, standard errors are in parentheses).
Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 23

Table 5—Censored regression estimates explaining difference in bid prices between “GM”-labeled 

and plain-labeled potatoes

Dependent variable: Bid price non-labeled food–bid price GM-labeled food

Regressors (1) (2) (3) (4) (5) (6) (7)

Intercept 0.175** -0.039 0.064 0.076 -0.019 -0.029 -0.069

(.039 (0.088) (0.096) (0.103) (0.117) (0.101) (0.124)

Anti - 0.504 ** 0.470 ** 0.472 ** 0.478 ** 0.501 ** 0.507 **

(0.125) (0.122) (0.122) (0.122) (0.120) (0.120)

Pro and Anti - 0.262 ** 0.233 * 0.239 * 0.248 ** 0.258 ** 0.271 **

(0.126) (0.124) (0.125) (0.124) (0.122 (0.123)

Pro and - 0.057 0.041 0.046 0.018 0.026 0.015

Third-Party (0.125) (0.123) (0.123) (0.124) (0.120) (0.122)

Anti and - 0.339 ** 0.323 ** 0.329 ** 0.322 ** 0.332 ** 0.337 **

Third-Party (0.125) (0.123) (0.124) (0.122) (0.120) (0.122)

All information - 0.088 0.095 0.099 0.110 0.078 0.092

(0.128) (0.126) (0.126) (0.126) (0.124) (0.124)

Labels-Round 1 - - -0.174 ** -0.174 ** -0.177 ** -0.146 ** -0.150 **

(0.073) (0.073) (0.072) (0.072) (0.072)

Gender - - - -0.026 - - -0.022

(0.075) (0.074)

Income - - - - 0.0014 - 0.0009 

(0.0014) (0.0011)

Informed - - - - - 0.190 ** 0.179 **

(0.074) (0.075)

Likelihood ratio - 22.54 ** 28.17 ** 28.29 ** 29.69 ** 34.56 ** 35.36 **

** indicates that a variable is significant at 5 percent.

* indicates that a variable is significant at 10 percent.

(n=172, standard errors are in parentheses).
Regressions (4)-(6) are used to test the effect of gender,

income, and being “well informed about ‘GM’ foods”

on the willingness to pay. Few demographic variables

had an impact on the difference in bid prices. Women

discounted “GM”-labeled foods less than men for all

three products in the auctions. However, none of the

coefficients was statistically significant from zero.

Those consumers who had higher incomes discounted

all three “GM”-labeled food products more than those

who had lower incomes. The coefficient is statistically

significant for one of the three products—vegetable oil.

It is not surprising that higher income consumers discounted “GM”-labeled foods more heavily. This is consistent with “food quality” being a luxury good.

Consumers who considered themselves “well

informed about ‘GM’ foods” (as recorded in the preauction survey) discounted “GM”-labeled foods more

than other participants. This coefficient is statistically

significant (10-percent level) for one of the three

food products–Russet potatoes. Those who perceived

themselves to be informed bid far less for the “GM”-

labeled foods than other bidders. This result suggests

they had heard negative information on “GM” foods.

This result contrasts with the survey results obtained

by Boccaleti and Moro (2000) which indicates,

among people who rated their degree of awareness

about “GM” as good, over 60 percent were positive

toward “GM” foods. Regression (7) includes all the

variables in regressions (1)-(6). The results are similar to the regressions (1)-(6). In all of the censored

regression equations, we rejected the null hypothesis

that the explanatory variables included in the regression had no explanatory power (or all non-intercept

coefficients were jointly zero).

The results from our experiments are noteworthy.

Consumers discounted food products labeled “GM” by

14 percent, based on information they received. In

comparing “GM”-labeled with standard- or plainlabeled products, consumers might have perceived the

plain-labeled products as non-“GM.” The discounting

was similar across the three products, suggesting that

most consumers perceive net biotech effects similarly

for the three goods. If the term “GM” “taps anxieties

that precondition consumers to be wary of biotechnology” (Schmidt, 2002), then the 14-percent discount for

biotech foods could be considered as an upper bound.

The result that consumers discount “GM”-labeled

foods has notable implications given the ongoing global controversy over the issue of labels on biotech

foods. This debate has forced many countries around

the world to consider or to implement new food labeling policies. This trend has strong implications for

grain handlers, food manufacturers, and others in the

supply chain. For example, in January 2000, Best

Foods Inc. decided to end its use of biotech ingredients in manufactured foods destined for the EU, in

order to avoid the biotech labeling requirements; and

Frito-Lay Inc. announced that it would cease using

biotech corn in its snack foods manufacturing (Lin,

Chambers, and Harwood, 2000). 

Given that the participants in the study did reveal a

significant discount for foods labeled “GM,” a mandatory biotech labeling policy seems likely to reduce

demand for biotech products. Assuming that the participants in the experiment are representative of the U.S.

population, there appears to be a strong preference for

non-biotech. However, the demand for these nonbiotech (and biotech) products is influenced by the

type of information about biotechnology that consumers receive. FDA focus groups and IFIC studies

also indicate that consumers may react differently to

terms such as “GM,” “biotech,” “genetically engineered,” “bioengineered,” etc. According to IFIC, if

the benefits of biotechnology are effectively communicated, U.S. consumers will accept biotech foods

(Schmidt, 2002). 

24 ● The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 Economic Research Service/USDA
This study has shown that consumers’ willingness to

pay for food products decreases when the food label

indicates that a food product is produced using

biotechnology. Consumers discounted food items

labeled “GM” by 14 percent. In addition, gender,

income, and other demographic characteristics appear

to have only a slight impact on consumers’ willingness

to pay for biotech foods. However, information from

interested parties and third-party information do influence consumers’ willingness to pay for biotech and

(perceived) non-biotech foods. The use of “GM” rather

than “biotech” or “bioengineered” on the labels could

also have influenced the results. 

The data presented in table 2 show that consumers

who received only negative information about biotechnology paid 35-38 percent less for food products

labeled “GM,” depending on the product. When the

negative information is coupled with independent

third-party information, they were willing to pay only

17-22 percent less for “GM”-labeled food. Likewise,

when consumers were given only positive information

about biotechnology, consumers bid higher for “GM”-

labeled than plain-labeled food for two of the three

food items. However, when consumers were provided

with positive information from the industry perspective

and the independent third-party perspective, they bid

higher for plain-labeled food in all three cases.

The econometric results presented in tables 3-5 also

showed that consumers who received only positive

information behaved differently from those who

received only negative information and both negative

and third-party information. The third-party information does not appear to change the behavior of those

who receive positive information. Demographic variables such as gender and income have little impact on

the willingness to pay for “GM”-labeled and plainlabeled food products.

With bioengineering remaining controversial, information on biotech foods will have a major impact on consumer acceptance of foods with biotech labels. This

experimental auction has produced the following

results. First, people, on average, were willing to pay

17-21 cents per unit more to purchase plain-labeled

food than “GM”-labeled food. Consumers might have

perceived the plain-labeled products as non-biotech.

Nonetheless, the observation that such a large “premium” exists for food items that are perceived to be nonbiotech has strong implications for grain handlers,

food manufacturers, and others in the marketing system. Second, information about biotechnology from

interested parties has an impact on consumer demand.

This helps explain why groups such as Greenpeace

and Friends of the Earth have been disseminating massive amounts of negative information on biotech foods.

Likewise, it explains why biotechnology companies

have invested heavily to advertise the positive aspects

of biotechnology (Thrane, 2001).

Third, an independent, third-party source that provides

verifiable information about biotechnology has a significant impact on consumers’ demand for biotech

foods. Third-party information had its greatest impact

on consumers who received negative information,

prompting them to view biotech foods more favorably. 

Much future research on this topic remains. New

research is currently testing the impact of information

on the willingness to pay for foods with negative

biotech labels, that is, labels that say, “This product is

not made using biotechnology.” The food products in

these auctions were bioengineered and deemed substantially equivalent to the conventional commodity.

However, there are biotech foods being developed to

enhance the quality (e.g., protein, fat, sugar content,

and shelf life) of the product. None of these products

are currently on the market. Future research could

examine how consumers react to biotech foods that

have specific benefits. Finally, future research could

also examine if language on the label (e.g., “GM,”

biotech, bioengineered, genetically engineered, etc.)

would have an impact on consumers’ willingness to

pay for these biotech foods.

Economic Research Service/USDA The Effects of Information on Consumer Demand for Biotech Foods / TB-1903 ● 25

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