Mandatory Labeling and Aversion to GM foods
Experimental evidence from India
Sangeeta Bansal
Centre for International Trade & Development
School of International Studies
Jawaharlal Nehru University
New Delhi 110067, India
sangeeta@mail.jnu.ac.in
Sujoy Chakravarty
Department of Humanities and Social Sciences
Indian Institute of Technology
Hauz Khas, New Delhi-110016
sujoy@hss.iitd.ac.in
Bharat Ramaswami
Planning Unit
Indian Statistical Institute
7, S.J.S. Sansanwal Marg
New Delhi 110016, India
bharat@isid.ac.in
April 2008
The authors gratefully acknowledge research funding from the South Asia Biosafety
Program, an IFPRI managed program funded by the United States Agency for
International Development.
Mandatory Labeling and Aversion to GM foods
Experimental evidence from India
Abstract
A recent recommendation from the Ministry of Health of the Government of India has
proposed mandatory labeling of all GM food in the country. Internationally, countries
have pursued different approaches to the labeling of genetically modified foods. The
great divide has been between the policies in the European Union, that have favored
mandatory labeling and the United States, which has chosen not to impose such
requirements. The advocates of mandatory labeling argue that consumers need this
information to make informed choices. The groups opposing mandatory labels think that
if the demand for GM food is sufficiently high, the market on its own would provide this
information through voluntary labeling. While implementation of mandatory labeling
policy involves significant costs, the benefits of such a policy depend on consumer
attitudes towards GM foods. Some theoretical papers argue that voluntary labeling
policies achieve the same outcome as mandatory labeling, thus making the latter
redundant. The two policies have a different impact only if a significant proportion of
population is weakly GM averse. These are consumers who do not react to information
unless it comes in the form of a label. The paper has two-fold objectives; first - to elicit
willingness-to-pay for similar food products that differ only in their content of GMOs.
More importantly, this paper attempts to test presence of weakly GM averse consumers.
We use experimental auctions for our purpose and our subjects are from India. Our work
is the first attempt to capture weak GM aversion among consumers.
Key words: Experimental methods, genetically modified foods, willingness to pay, label
sensitive consumers
1
1. Introduction
Policies towards labeling of genetically modified or GM foods have varied
between countries. The great divide has been between the policies in the European Union
that has favored mandatory labeling and the United States, which has chosen not to
impose such requirements. Developing countries have also been confronted with this
issue. While Brazil and China have adopted mandatory labeling laws, Philippines and
South Africa have pursued approaches based on voluntary labeling. In India, a recent
recommendation from the Ministry of Health has proposed mandatory labeling of all GM
foods.
The trans-Atlantic divide over labeling policy is matched by corresponding
differences in other areas of policy as well as consumer acceptability of GM products.
Since 1999, the EU has followed a moratorium on growing GM crops. The EU
opposition to GM crops is strongly supported by lobbying efforts, including the Green
Party, Greenpeace, Friends of the Earth, and organic farmers (Schmitz 2004). Consumer
resistance to GM foods is also much greater in Europe and Japan than it is in the United
States. This was confirmed by the study of Lusk et. al (2006) in an experimental setting
where they showed that the level of compensation required to induce consumers to accept
GM food was much higher for European compared to US consumers. Whether as a result
of mandatory labeling or consumer resistance, most EU retailers have stopped selling
GM food altogether (Gruere, 2006; Lusk et.al, 2006).
A conventional analysis of consumer preferences towards GM foods is, however,
difficult because of unavailability of market data. As GM foods are not commonly sold
in Europe, consumer demands cannot be estimated. In the US, where GM foods are
2
available, market data cannot be used because the GM content is not labelled on the
foods. An alternative is to elicit valuations via hypothetical surveys. However, it is
questionable to what extent such hypothetical valuations match observed purchase
behaviour.
To simulate real world purchase decisions, some researchers have designed
experiments where subjects can bid for foods with money. In a typical experiment study,
valuations are elicited for a GM and a non-GM food. As it is not possible by visual
inspection to ascertain whether a product is GM, the foods used in the study are
appropriately labelled. Huffman, et. al (2003, 2004), Lusk et. al (2006) and Noussair
et.al (2002, 2004) are some of the studies that have utilized such experimental data to
analyse consumer demand for GM food. European and US consumers are the subject of
these studies. To our knowledge, there is no study that investigates consumer preferences
towards GM foods in a developing country context.
This paper is a contribution to this small and growing literature on consumer
preferences and perceptions of GM foods. Like the literature cited above, we too use
experimental methods to study attitudes towards GM foods. Our paper is, however, a
departure from the literature in two important ways. First, we use subjects from New
Delhi, India outside the usual developed country context. Second, while our study throws
light on aversion to GM foods (like the literature), our experiment is also designed to
answer questions about the impacts of mandatory labelling policies on GM foods.
Below, we elaborate on both of these feature.
The Indian context for GM products is different from both the US and the EU
scenarios. While there has been a vocal debate about GM products in India, food safety
3
has been secondary to issues about impacts on growers and the environment. NGOs have
typically opposed GM plants because of fears that it would damage biodiversity and
because they believe that it would facilitate corporate control of the seed sector. 1 Indian
policy towards plant biotechnology has neither been fully accepting or fully rejecting.
After a long drawn out process challenged repeatedly by NGOs, the government
approved commercial release of three Bt cotton varieties in 2002. Since then, however,
the regulatory process has speeded up approvals. In the last 3 years, the government has
approved more than a hundred different varieties of Bt cotton. At the end of 2007, Bt
cotton was grown on 6.2 million hectares accounting for two-thirds of the area under
cotton. In some parts of India, cottonseed oil is used in cooking. There are no
restrictions on its sale or use because of GM origin.
Cotton is, however, the only GM plant that has been commercialised. No GM
food crop has yet been approved for planting. While the government has allowed genetic
engineering experiments on plants such as mustard, rice and eggplant, future regulatory
approval is uncertain. The GM foods in the Indian market (other than cottonseed oil)
come from imports. Because of Indian patterns of consumption, India is not a large
consumer of either GM corn or GM soybeans. India is a large importer of soya oil
(including from primarily GM soybean producing countries). 2
In sum, official policies towards GM foods are still evolving and there is as yet no
fully coherent policy towards this sector. 3 The proposal of mandatory labelling of GM
foods must be seen in this context. Is it then possible that consumer attitudes could
shape government policy towards one direction or the other?
1
This could possibly change if GM foods were introduced on a large scale.
SPS barriers restrict the import of soybeans.
3
See Ramaswami (2007) for an analysis of the political economy that underlies official policies.
2
4
The second aspect of this study is that it is motivated by the debate about the pros
and cons of mandatory labelling. Bansal and Ramaswami (2007), argue that in terms of
provision of useful information, the mandatory labelling of GM foods is equivalent to
voluntary labelling. Then why is labeling of GM products such a contentious issue
internationally? One possible answer could be the presence of weakly GM averse
consumers i.e., consumers who switch their preferences from GM to GM-free products
only on seeing a label. Bansal and Ramaswami show that if there are enough such
consumers, then mandatory labelling will adversely affect the market shares of GM food
suppliers in equilibrium. On the other hand, if consumers with such preferences do not
exist or not in large numbers, then mandatory labelling policies would have no impact.
The experiment in this study aims to estimate the incidence of weakly GM averse labelsensitive consumers.
Like the other studies, we too use an experimental approach because there is no
alternative. As mentioned earlier, domestically produced cottonseed oil and imported
soya oil are the primary examples of GM foods. However, GM content or origin is not
labelled and so market data, even if available, would not be of use.
The rest of the paper is organized as follows. Theoretical issues pertaining to
voluntary vs mandatory labeling are analyzed in the next section. Section 3 describes the
methodology of the experiment. The results are discussed in Section 4. Finally, Section 5
contains the conclusions.
5
2. Theoretical Issues
Bansal and Ramaswami (2007) argues that voluntary labeling renders mandatory
labeling redundant in the sense that mandatory labeling would not result in greater
information or product choice to consumers. There is, however, a special set of
circumstances where mandatory labeling can alter outcomes.
Most studies on consumer preferences assume these preferences to be stable.
What that means is that consumer preferences between GM and GM-free food does not
depend on the label. The label provides information and consumers make choices
according to their preferences. However, the label itself does not alter preferences. With
stable preferences, mandatory labeling is redundant.
But suppose this assumption were not true. Suppose there are consumers who are
indifferent between GM and GM-free food but who shift their preference to GM-free
food when they see a label on GM food possibly because they interpret the label as a
signal of low quality. These are the weakly GM averse consumers. In addition, suppose
that there are fixed costs (due to the infrastructure for segregation and identity
preservation) that are incurred in establishing a GM-free marketing channel. If fixed
costs are large enough, then it might happen that GM-free foods are not labeled
differently from GM foods under voluntary labeling but that such distinction does take
place under mandatory labeling.
Consider the following example to clarify the logic. An economy consists of
three types of consumers. When GM-free and GM products are priced identically, α
consumers purchase GM products, γ consumers purchase only GM-free food (`strongly
GM averse’) while β consumers are weakly GM averse consumers and consume GM
6
products as long as there is no labeling, but switch to GM-free products when there is
labeling. Note that it is not assumed that these consumers are ignorant about the products
they buy. In particular, consumers are fully aware that foods can be made with or
without GM ingredients and they have preferences over these foods.
Suppose also there is a single firm in the industry. Net of variable costs, the firm
receives a profit r per unit of quantity from the sale of food which is the same whether the
product is GM or GM-free. However, the provision of GM-free food requires a fixed
cost k . Consider first the case where there is no mandatory labeling requirement. The
firm has the choice of either supplying only GM food, or only GM free food or both types
of foods. If the firm were to supply GM free food, then it would be in the firm’s interest
to label the food as such. Unlabeled products would necessarily be GM products. It is
assumed that the consumers can make this inference correctly. If the firm were to supply
only unlabeled food (i.e., GM products), its profit is (α+β)r. The γ consumers who are
strongly GM averse decline to consume the product. If the firm decides to label its food
and supply GM-free food as well, then its profit becomes (α+β+γ)r – k. Clearly then, it
would not be profitable for the firm to supply GM-free food if k ≥ γr .
Now suppose mandatory labeling is in place that identifies GM food with a label.
Now it is the unlabeled food that is, by implication, GM free. Once again, profits from
supplying both GM and GM-free food are (α+β+γ)r – k. However, profits from
supplying only GM food fall to αr because β consumers switch to GM-free foods
because of labeling. Hence, the firm would supply both products as long as (β+γ)r > k.
Thus, for given distribution of consumers, if fixed costs are such that (β+γ)r > k ≥ γr ,
then GM-free foods would not be supplied without mandatory labeling.
7
This example has been deliberately constructed to be simple. It can be
generalized in several respects. The critical assumptions are the existence of labelsensitive weakly GM averse consumers and the presence of fixed costs. Without either
of these features, mandatory labeling will not result in outcomes any different from
voluntary labeling. When mandatory labeling with weakly GM averse consumers results
in a different outcome (from that with without weak GM aversion), the outcomes with
and without labeling cannot be ranked in terms of conventional welfare criteria because
such criteria assume stable preferences. The outcomes can be ranked only in terms of the
government’s own objective function. If the government wishes to shift consumer
preferences and hence food purchases from GM to GM-free products, then it can justify
mandatory labeling. 4 But if it wishes labeling to be neutral between these products, then
mandatory labeling is not justified. Given the importance of weakly GM averse
consumers, we wish to test their presence through experimental methods.
3. Methodology
The experiment is designed to study the extent that consumers value the absence
of GMOs in food products by measuring changes in willingness to pay in response to
new information about GMO content. The protocol we use is similar in spirit to several
other experimental protocols in the literature that use Vickrey auction type techniques
like Noussair et al (2002, 2004). Below we describe our subject pool and design.
3.1 Subject Pool
4
Health warnings such as cigarettes or alcohol clearly fall in the category where it is clear that government
would like to shift consumer preferences through labeling.
8
We ran three separate experimental sessions. Two of these used students, Masters
and Bachelors degree students (from the Indian Statistical Institute (ISI) and the Indian
Institute of Technology (IIT) both in Delhi). The other session consisted of university
teachers from all parts of India (participants at a staff college refresher course at the
Jawaharlal Nehru University (JNU)). Of the total pool of 136 subjects, 86 were students
and the rest 50 were older university teachers (table 1). As a result, close to 65% of the
subject pool is less than the age of 25 (table 2). Most of the cohort of college teachers are
in the early stages of their career – only about 8% of the subject pool is 36 or greater.
About 32% of the subject pool is female (table 3). In terms of parental
background, most of the pool comes from family with high levels of educational
attainment (tables 4 and 5). Nearly 80% of the pool have fathers who studied beyond
high school. The corresponding % for mothers is 56%. Most annual family incomes
(68%) are in the range of Rs. 100,000 to Rs. 500,000 which spans the range of what is
known as the middle class in India (table 6). Note, however, that these incomes are well
above median incomes (or more accurately household expenditures) in India. Tables 7 to
9 summarize information about lifestyle characteristics of the subject pool.
By no means is our sample representative. In particular, compared to a
representative sample, our study sample is biased towards urban consumers with higher
than average family incomes and educational attainment. However, it can be argued that
even such a limited group is worthy of study because (a) their attitudes and lifestyles are
aspired to by other socio-economic groups and more importantly (b) they are the primary
consumers of packaged foods that will be subject to mandatory labeling laws.
9
3.2 Subject decisions and protocol
The experiments were conducted in large classrooms with the subjects seated
away from each other. They were trained in the bidding protocol using a quiz and were
not allowed to communicate during the session. In our experiment, subjects bid for real
consumer goods using the Becker-De-Groot -Marschak (BDM) mechanism (Becker et al,
1964). The subjects have an endowment of 200 units of lab currency (deemed Francs)
which convert to Indian Rupees at the rate of 4 Francs to a Rupee). In each round of the
four rounds of auctions, they provide in writing the price that they would be willing to
pay for a unit of both the products (the GM and the non-GM). After all the four rounds,
one round is randomly picked and a valuation for each of the two products is picked from
the uniform distribution [1, 100]. If a participant’s valuation is above this he or she
purchases a unit at the drawn price, otherwise he or she keeps her endowment to take
home in Rupees.
In the BDM, a type of auction, bidders have a dominant strategy in bidding an amount
equal to their true valuations for the good. There are several advantages to using
demand-revealing mechanisms to elicit willingness to pay information. Firstly, the use of
money as a metric allows for comparisons of intensity of preferences between subjects, as
well as goods. Secondly in an auction, the subject is committing himself to an actual
purchase, unlike in a poll where there is no commitment. Thirdly, in a demand-revealing
mechanism, there is a dominant strategy to indicate one’s true valuation. In principle this
allows willingness to pay be directly measured, rather than inferred. Fourthly, notice that
though we deem it an “auction” there is no strategic (in the standard game theoretic
sense) incentive as in a usual sealed bid auction as every participant whose valuation lies
10
above the drawn price wins a unit Note that when bidding for the products, we do not
make the bids public information at any time, so that privacy of the valuations is
safeguarded and subjects cannot use others’ bids to update their own valuations. The
time line for the procedures is given in table 10.
We auctioned two products, which we called A and B during the session. In the
session using students from ISI they were Tortilla chips, one of which contained GM soy
(imported product) and a domestic substitute that was GM free. For the sessions in IIT
and JNU, the products were chocolate chips cookies that are available in stores in Delhi.
The products were close substitutes; very similar in taste and appearance. The experiment
consisted of four rounds, as outlined in Table 10. At the beginning of the experiment,
subjects received a sample of both products without its packaging or labeling. Before
bidding in the first period, subjects were required to taste each product. Then they marked
down how much they liked the product on a scale where “I like it very much” and “I
don’t like it at all” were at the extremes of the rating scale. Then the auction for period 1
took place. The two products were auctioned simultaneously. Each of the following
periods consisted of the revelation of some information about some or all of the products,
followed by a simultaneous auction for both products. The sale price was not drawn for
any period until the end of period 4 and no information was given to participants about
other players’ bids.
At the beginning of period 2, we distributed a handout containing background
information about GMOs. The information consisted of
a) What are genetically modified foods?
b) Why are they produced?
11
c) Why is there opposition to their consumption?
d) What is government policy regarding GM foods in India?
The information was an unbiased characterization so as not to affect consumer
preferences towards GMO. The information handout is given in Appendix.
At the beginning of third period, we revealed the information regarding the GM status of
the product. The products were still in the packaging done by us and the label prepared
by us. The label of product A read “Chocolate Chip Cookies,” and the label of product B
had an additional information “This product may have been subject to genetic
modification”. The label matched the proposed stipulation regarding GM labeling in
India. Thus we revealed it to the participants that product A is GM free and product B
could be subject to genetic modification. Finally in the last period, we revealed the brands
of two products.
3.3 The products used
The greatest challenge we faced was to identify two products that are similar in all
attributes such as appearance, taste etc., except that one is GM and the other non-GM.
As already mentioned, there are no domestically produced GM foods in India, therefore,
a domestically produced product had to be compared with an imported product. For the
imported product also there are no labels that say it is genetically modified. We checked
with various testing labs in India if they could test the GM product. But even these labs
were not well equipped to test GM content in highly processed food items. We tried to
find a product from the US, containing soya or corn ingredients. Since US has adopted
voluntary labeling policy for GM products, unlabelled soya or corn products from the US
12
are highly likely to be GM. We tried several products, potato chips, tortilla chips, cookies
etc. For most products, there was a vast difference in taste in the Indian product and its
imported counterpart. When there is a vast difference in taste, preferences for taste could
outweigh preferences for non-GM attribute. Finally we could identify two brands of
chocolate chip cookies, a domestic brand and an imported brand, that were close in
appearance and taste. Additionally, we used an imported (containing GM) and an Indian
(GM free) brand of tortilla chips for one of the experimental sessions.
3.4 Prior Information and weak GM aversion
As discussed in the introduction, one of the objectives of our study was to identify
subjects that exhibit weak GM aversion. We define these consumers as those who switch
preferences to GM-free products when they see a label.
Labels can affect demand for two reasons. First, consumers may be unaware about
the properties of unlabeled foods on offer. For instance, they may believe that a food has
a lower fat content or a different type of preservative than what they actually contain. In
this case, by providing accurate information and correcting mistakes, labels can affect
demand.
The other case is when consumers have accurate estimates of the properties of
unlabeled foods that enter their utility function. An example is that of preference for
vegetarian foods which is common in India. While in many cases what is vegetarian can
be visually ascertained, this is not always so. Consider for instance, fried foods. If eating
places do not advertise that they use only vegetable oils (and not animal fat) in cooking,
we assume that consumers will assume that the probability that animal fats are used is
13
high and accordingly determine demands. Consumers with strongly vegetarian
preferences may decline to consume such products. On the other hand, this will not be a
consideration for those who do not have a preference for vegetarian foods. In this case, a
mandatory label (on what oils are used) does not provide new information. This is the
sort of case considered in Bansal and Ramaswami (2007). In this scenario, any change
in demand due to labels must be due to change in preferences which we call weak GM
aversion.
In our experiment, the challenge is to design it in such a way as to reduce and
eliminate the information effect. This is particularly important in the Indian context
because the notion of GM foods is still very new and not many subjects would have
imagined that possibility. That’s the reason why we provide in round 2 (after the blind
tasting) ample hints to consumers in this regard. First, we provide a one page handout
containing background information about GM foods. After the subjects have read it, we
ask them to report their subjective probability that the products on offer are genetically
modified. With nothing more than taste and appearance to guide them, their subjective
probabilities are nothing but guesses. But we would expect that those who are strongly
GM averse will react to their subjective probabilities. On the other hand, those who are
weakly GM averse would not react to the possibilities implied by the information
distributed in round 2. It would take a label (shown in round 3) to affect their responses.
This is the identification strategy used in the experiment.
4. Results
4.1 Blind Tasting
14
In the blind tasting, subjects are asked to rank each of the products on a taste scale of 1 to
7 (higher is the number, greater is the liking) with 0.5 increments. Therefore, a choice is
made from 14 possible values. Figure 1 plots the empirical cumulative density function
of rankings for both these products. If one ignores, the crossing of the distributions at
low taste levels, rankings for the non-GM product dominate that of the GM product by
first order stochastic dominance (the figure excludes the ISI subjects as they were faced
with different products than the other subjects). The sample mean of the taste rankings of
product A is 4.96 and that of product B is 4.44. The Spearman’s rank correlation
between the two taste rankings is –0.1664 and the null that the rankings are independent
is not rejected at the 8% level of significance.
4.2 Prior Probabilities of likelihood of products being GM
In round 2, subjects were asked to evaluate the likelihood of either product being GM on
a scale of 1 to 5. Figure 2 plots the empirical cumulative density of this evaluation. As
can be seen, the proportion of consumers who regard product A (the non-GM product) as
GM is higher than the similar proportion for product B at any likelihood level between 1
to 5. Thus, the sample mean of the likelihood that product B is GM is higher than that of
product A (2.96 for B as against 2.63 for A).
Both sample means indicate that the average probability that either product is GM
is greater than 0.5. Out of the 114 subjects who report these prior probabilities, 94 of
them have a prior probability of at least 0.5 on either or both products. Thus, with such
high subjective probabilities, it is expected that that it will affect the price bids of those
who are GM averse. If both probabilities are high, then we would expect the average bid
15
across these two products to fall for those who are GM averse. However, if consumer
perceptions are such that they put much greater probability on one product, then the
average bid may not fall.
Figure 3 plots the scatter between the consumer perceptions that either product is
GM. The scatter suggests that there is not much of a relation between the perceptions of
the two products. However, the Spearman rank correlation is 0.22 and is significant at
the 2% level. Thus, there is a small, positive and significant correlation between the
perceptions of both products.
4.3 Normalized Prices Across Rounds
Figure 4 plots the average price bids (normalized with respect to round 1 bids) across the
four rounds. As can be seen, the average bids of both products increases over the rounds.
It is surprising that the average bid for the GM product increases (especially in the early
rounds) although the bids for the GM free product increase at a faster rate than the bid for
the GM product. Consistent with GM aversion, the relative price of the GM free product
(the ratio of the price bid for product A to the price of product B) rises throughout (also in
figure 4). It is particularly interesting to note the increase in the relative price of the GM
free product from round 1 to round 2. In round 2, the label has not yet been revealed but
because there is greater suspicion that product B is GM, the rise in relative price of
product A (the GM free product) is consistent with GM food aversion.
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4.4 The Determinants of Round 2 Bids and GM free price premiums
Subjects place their bids in round 2 after reading the GM background information sheet
and after evaluating the probability that either product is GM. Table 11 presents the
results of a regression of round two bids on the variables that constitute the information
set at that time – round one bids, taste rankings of the two products and the prior
probabilities that either product is GM.
As might be expected, the second round bids are highly (and positively)
correlated with first round bids. Furthermore, the prior probabilities are significant in the
regression. The probability that product A is GM reduces the expected bid for product A
in round 2 and its impact on the expected bid for product B is negligible and
insignificant. For instance, if the prior probability is 0.5 (i.e., 2.5 on the likelihood scale),
the price of product A drops on average by nearly Rs. 11. Similarly, the expected bid for
product B is negatively affected by the perception that it could be GM. At a prior
probability of 0.5, the negative impact is Rs. 8. Both of these results are evidence of GM
aversion on average. From these results, we can calculate the average discounts for GM
product by evaluating the impact on price bids when the prior probability is 1. The
results are summarized in Table 12.
They indicate high discounts for GM food. This result is surprising because of
the following. Out of the 114 subjects, 36 did not alter their price bids from round 1 to
round 2. Of the subjects who altered their price bids, some of them did so by small
amounts. It is possible that faced with a second round, these subjects may have thought
that the “correct” response was to alter the price bid. Hence, suppose we consider the
subjects who did not either alter the price bids or they did so by Rs. 5 or less on both the
17
products. Let’s call them “information inert” subjects. Then we find that there are 56 of
them – almost half of the sample. Therefore the negative relation between second round
bids and the prior probability must come from only one half of the sample.
In Table 13, we tabulate the averages of the prior probabilities of informationinert (ii) and non-ii subjects. There is no statistical difference in the prior probability of
product A. There is a greater difference in the prior probability of product B but even
this is not significant at the 10% level. It seems therefore that the difference in price bids
between the ii and non-ii subjects comes not so much from differences in processing of
prior information but from preferences. Whether this conclusion is warranted needs more
investigation because the last two rows of the table indicate that the differences in prior
probabilities is mirrored in differences in price bids between the ii and non-ii subjects.
4.5 Identifying GM Averse Subjects
The identification of GM averse subjects comes from comparing the price bids in round 1
(blind tasting) with the price bids in round 3 when the labels are revealed. We define the
label-inert (li) subjects as those for whom the price bids on both products in round three
is either identical to the bids in round one or is within Rs. 5 of the round one bids (on
both products).
Table 14 shows that out of the 56 information-inert subjects, 33 or 29% of the
sample continue to be label-inert. Neither the information nor the label makes a
difference to their bids. GM aversion must therefore be sought in the remainder of the
sample. 23 of the information-inert subjects react to the label while almost all of the
subjects who are not information inert are also not label-inert.
18
Excluding the label-inert subjects, we have 78 subjects. We divide this pool into
6 cells as in Table 15. We define a subject is GM averse if either (1) product A price bid
in round three is greater than the price bid in round one and the product B price bid is
either unchanged or lower in round three compared to round 1 or (2) product A price bid
is the same as in round one and product B price bid is lower in round three. From the
table, these subjects (marked in yellow) number 35 which is about 30% of the sample.
By a similar logic, a subject is GM loving if either (1) product B price bid in
round three is greater than the price bid in round one and the product A price bid is either
unchanged or lower in round three compared to round 1 or (2) product B price bid is the
same as in round one and product A price bid is lower in round three. There are 16 such
subjects. These are marked in green in table 15. The diagonal terms cannot be classified
into either of these categories – these are subjects who increase the bids for both products
or decrease the bids for both products.
We define the weakly GM averse subjects as those who are GM averse and who
are information-inert. Table 16 divides the information-inert subjects into the 6 cells as
in Table 15. From here it can be seen that 13 subjects satisfy the weak GM averse
condition. Unlike the other GM averse subjects, these individuals react only to the label
and not to the background information. Table 17 summarizes the findings from this
discussion. Tables 14-17 exclude data from the ISI experiment where different products
were used. The corresponding tables for the entire sample are in tables 18-21. Note the
percentages of different categories is quite robust to whether we use the entire sample or
whether we exclude the ISI sub-sample.
19
Finally table 22 reports the correlates of GM aversion (for the entire sample). The
probability of being GM averse is negatively and significantly correlated with being a
student and with those who report frequent exercise. With students, higher is the
evaluation of the taste for the GM- free product, lower is the probability of being GM
averse. Surprisingly, this relation does not hold for non-students. Some of the other
variables (smoker dummy, frequency of snacking junk food and mother’s education) also
have the “expected” signs but are not significant at the 5% level. These results reassure
that our identification of the GM averse subject pool is not off the mark.
5. Conclusions:
This paper aims at studying consumer attitudes towards GM foods in the context
of a developing country, India. The motivation came from the proposed law in India to
have mandatory labeling of all GM products in India. The data set is obtained by
assigning labeling and information treatments to subjects who participated in lab
experiments of food items that might be genetically modified.
We obtain a number of interesting results. First, we find that, on average,
consumers are willing to pay a high price premium for GM free products. Alternatively,
they are willing to buy GM products at a discount. The individual as well as relative price
of the two products is negatively related to consumers’ perception of the products being
GM. Further, the relative price of the GM free product rises consistently in each round.
These results are evidence of GM food aversion and are consistent with the earlier
literature on experimental evidence of consumer attitudes towards GM foods.
20
Second, the GM averse subjects correlated with variables in an expected way.
Students are less likely to be GM averse. For students, GM aversion is negatively
correlated with taste ranking. Similarly higher is the frequency of exercise, higher is GM
aversion.
The aversion to GM foods comes, however, from only 30% of the sample. In
fact, about 50% of the sample do not alter their bids after receiving background
information, and about 30% of the sample do not revise their bids even after the label is
revealed. These latter subjects do not care about GM status of the product. We also find
that about 13% of the subjects are GM lovers.
Most interestingly, we identify consumers who do not react to the background
information but react negatively to the GM label. We term these as weakly GM averse.
Recall that in Section 2, we argued that existence of such label sensitive weakly GM
averse consumers can alter market outcomes with mandatory labeling. In our experiment
we find that there is a significant number (about 11% ) of such subjects. This is a robust
result. Presence of such consumers means that mandatory labeling can change market
shares of GM foods in equilibrium.
21
References
Bansal, S. and Ramaswami, B. (2007), “The economics of GM food labels: An
evaluation of mandatory labeling proposals in India, IFPRI Discussion Paper 00704
Becker, G., DeGroot, M. and Marschak, J. (1964), “Measuring utility by a singleresponse sequential method,” Behavioral Science, vol 9 (July), 226-32.
Caswell, Julie A. 200. “Labeling policy for GMOs: To each his own?” AgbioForum 3:
53-57.
Gruere, G “A Preliminary Comparison of the Retail Level Effects of Genetically
Modified Labelling Policies in Canada and France”, Food Policy, 31, 148-161.
Gruere, G. and S. R. Rao. 2007. A review of international labeling policies of genetically
modified food to evaluate India’s proposed rule. AgBioForum, 10 (1): 51-64.
Huffman, W.E., J.F. Shogren, M.C. Rousu, and A. Tegene (2003). “Consumer
Willingness to Pay for Genetically Modified Food Labels in a Market with Diverse
Information: Evidence from Experimental Auctions.” Journal of Agricultural and
Resource Economics. 28(2003):481–502.
Huffman, W.E., J.F. Shogren, M.C. Rousu, and A. Tegene (2004). “Consumer’s
Resistance to Genetically Modified Foods: The Role of Information in an Uncertain
Environment”, Journal of Agricultural and Food Industrial Organization, 2, Article 8.
Kalaitzandonakes, Nicholas, (2004), “Regulating Biotechnology: GM Food Labels”, in
Biotechnology: Science & Society at a Crossroad, National Agricultural Biotechnology
Consortium Proceedings, Cornell University.
Jayson L. Lusk, W. B.Traill, L. O. House, C. Valli, S. R. Jaeger, M. Moore and B.
Morrow (2006) “Comparative Advantage in Demand: Experimental Evidence of
Preferences for Genetically Modified Food in the United States and European Union,”
Journal of Agricultural Economics, Vol. 57, No. 1, 2006, 1–21
Noussair, C., Robin, S. and B. Ruffieux (2004), “Do consumers really refuse to buy
genetically modified food?” The Economic Journal, 114, 102-120.
Noussair, C., Robin, S. and Ruffieux, B. (2002), “Do consumers not care about biotech
foods or do they just not read the labels?” Economic letters, 75, 47-53.
Rousu, M., Huffman, W., Shogren, J., Tegene, A., (2003) “Should the United States
regulate mandatory labeling for genetically modified foods?” Evidence from
experimental auctions. Working paper
Schmitz A., (2004), “Controversies over the Adoption of Genetically Modified
Organisms”, Journal of Agricultural and Food Industrial Organization, 2, Article 1.
22
Vickrey, William (1961), “Counterspeculation, Auctions, and Competitive Sealed
Tenders,” Journal of Finance, 16: 8-37.
23
Appendix I
Background information about GMOs
1. What are genetically modified foods?
Foods derived from plants that are genetically modified are called genetically modified
(GM) foods. A plant is genetically modified if it contains genes that have been inserted
using genetic engineering techniques.
2. How is genetic engineering different from traditional plant breeding?
Genetic engineering makes it possible to insert a gene from another organism (such as
another plant species, bacteria or animal) into the plant variety of interest. This is not
possible with the traditional techniques of producing improved plant varieties.
3. Why are GM foods produced?
GM foods are developed – and marketed – because there is some perceived advantage
either to the producer or consumer of these foods. The first generation of GM plants have
given more direct benefits to growers than to consumers although the latter have possibly
gained from lower prices.
4. What are examples of genetically modified plants?
The principal examples of genetically modified crops occur in soyabeans, maize (i.e.,
corn) and cotton. For instance, genes from a commonly found soil bacteria have been
used to produce soybeans, maize and cotton that are naturally resistant to certain pests.
5. Why are GM foods regulated?
There are two broad concerns with GM plants. First, because the foods are novel, the
must be tested for toxicity and possible allergenicity. The second issue is whether the
engineered gene can escape into wild populations and other unintended plants. For these
reasons, GM crops must be assessed for food and environmental safety before they can
be planted.
6. What is the status of GM foods in India?
In India, no GM food crop has been approved for planting yet. Therefore, foods
produced from domestically produced crops are not genetically modified. Foods that are
imported could contain ingredients that are genetically modified. As of now, India does
not have separate regulations for imports of GM food other than what applies to imported
foods generally.
7. Why do some people oppose GM foods?
24
Several NGOs and individuals claim that GM plants pose unacceptable risks to food
safety as well as environment safety. They argue that transferring genes between
organisms creates new risks for human health that cannot be fully comprehended by our
existing scientific knowledge. They would therefore recommend that GM foods should
be banned or severely curtailed until risk assessments are more comprehensive in testing
the adverse effects on human health.
This is disputed by biotechnology advocates who point out that GM crops are extensively
tested before they are approved. According to the World Health Organization (WHO),
"GM foods currently available on the international market have passed risk assessments
and are not likely to present risks for human health. In addition, no effects on human
health have been shown as a result of the consumption of such foods by the general
population in the countries where they have been approved."
25
Table 1: Distribution of Subject Pool
Institution # %
ISI 22 16
JNU 50 37
IIT 64 47
Table 2: Age Distribution
Age
Number Percentage
<=25
87
64.93
26-30
17
12.69
31-35
20
14.93
36-40
8
5.97
41-45
1
0.75
>=46
1
0.75
Table 3: Sex Distribution
Sex
Number Percentage
Female
43
31.62
Male
93
68.38
Total
136
100
Table 4: Father’s highest education
Father's education
Freq. Percent
High school or less
28 20.59
Vocational Diploma
2
1.47
Bachelor’s degree
57 41.91
Post-Graduate Degree
49 36.03
Total
136
100
Table 5: Mother’s highest education
Mother’s education
Freq. Percent
High school or less
60 44.12
Bachelor’s degree
49 36.03
Post-Graduate Degree
27 19.85
Total
136
100
26
Table 6: Family Income
Annual Income
Freq. Percent
< Rs, 100,00
12
9.02
Rs 100,000-250,000
43 32.33
Rs. 250,000-500,000
48 36.09
Rs. 500,000-750,000
9
6.77
Rs. 750,0009
6.77
1,000,000
> Rs. 1,000,000
12
9.02
133
100
Table 7: Lifestyle characteristics: Smoking
Do you smoke ? Freq. Percent
yes
20 14.71
no
116 85.29
Total
136
100
Table 8: Lifestyle characteristics: Exercise
How often do you exercise in a week? Freq. Percent
never
36 31.58
once or twice
41 35.96
more than twice
19 16.67
everyday
18 15.79
Total
114
100
Table 9: Lifestyle characteristics: Snacking
How often do you consumer read-to-eat snacks in a week? Freq. Percent
never
14 12.39
once or twice
50 44.25
more than twice
38 33.63
everyday
11
9.73
Total
113
100
27
Table 10
Sequence of Events in the Experiment Session
Period 1
- Information: blind tasting of two products
- Recording of hedonic rating of the two products
- Auction
Period 2
- Additional information: General information about GM products
- Recording of consumer perception about likelihood of each product
being GM
- Auction
Period 3
- Additional information: Product A is non-GM and product B may be
subject to genetic modification (Product Labeling)
- Auction
Period 4
- Additional information: Brand names of the two products
- Auction
Transactions
- Random draw of the auction that counts towards final allocations
- Random draw of sale price of two products
- Implementation of the transaction for the period that counts
28
Figure 1: Cumulative density function of taste rankings
120
100
80
tastea
tasteb
60
40
20
0
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
Figure 2: Cumulative density function of GM likelihood rankings
120
100
80
gmproba
60
gmprobb
40
20
0
1
1.5
2
2.5
3
29
3.5
4
4.5
5
1
2
gmprobB
3
4
5
Figure 3: Scatter of subject perceptions of likelihood of product A is GM vs likelihood of
product B is GM
1
2
3
gmprobA
30
4
5
Figure 4: Average bids for products across the 4 rounds (normalized by round 1 bid).
1.6
1.4
1.2
1
Non-GM
0.8
GM (labelled)
Relative price of non-GM
0.6
0.4
0.2
0
1
2
3
31
4
Table 11: The determinants of second round bids
(1)
(2)
Price bid Price bid
for
for
Product A Product B
First round price bid 0.8***
0.08
for Product A
(0.08)
(0.09)
First round price bid -0.001
0.8***
for Product B
(0.05)
(0.09)
Probability that
-4.4**
-0.3
Product A is GM
(1.8)
(2.0)
Probability that
1.2
-3.2*
Product A is GM
(1.8)
(1.9)
Taste Ranking of
-0.7
-3.3*
Product A
(1.6)
(1.9)
Taste Ranking of
-1.1
-1.5
Product B
(1.1)
(1.3)
Constant
30**
37**
(14)
(16)
Observations
101
101
R-squared
0.683
0.608
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
COEFFICIENT
Table 12: Price Discounts for GM product
Discount for Product A if it were
GM
Rupees
As % of Mean
price
Rs 22
37%
Discount for Product B if it were
GM
Rupees
As % of mean
bid
Rs. 16
32%
32
Table 13: Difference in Average Prior Probabilities and price bids between Inert and Non-Inert
Subjects
Inert
Non-Inert Difference p-value of test that difference = 0
Prob that A is GM 2.616071 2.640351 0.024279
0.9
Prob that B is GM 2.705357 3.206897 0.501539
0.014
Price bid for A
58.58929 59.26456 0.675276
0.9063
Price Bid for B
55.91071 42.26893 -13.6418
0.0201
Table 14: Cross-tabulation of information-inert and label-inert subjects
Not label-inert Label-inert Total
Not information-inert
55
3
58
Information-inert
23
33
56
Total
78
36
114
Table 15: Counting the GM Averse: # of subjects satisfying the following condition
Product A (GM-free)
Price bid is higher in round 3
than in round 1
Price bid is the same in round 3
as in round 1
Price bid is lower in round 3
than in round 1
Total
Product B (GM)
Price bid is higher in Price bid is the same in Price bid is lower in
round 3 than in round 1 round 3 as in round 1 round 3 than in round 1Total
14
5
21
40
9
0
9
18
7
30
0
5
13
43
20
78
Table 16: Counting the weakly GM Averse: # of information-inert subjects satisfying the
following condition
Product A (GM-free)
Price bid is higher in round 3
than in round 1
Price bid is the same in round 3
as in round 1
Price bid is lower in round 3
than in round 1
Total
Product B (GM)
Price bid is higher in Price bid is the same in Price bid is lower in
round 3 than in round 1 round 3 as in round 1 round 3 than in round 1Total
33
1
2
6
9
2
0
5
7
2
5
0
2
5
16
7
23
Table 17: Summary classification
GM Averse
Weakly GM
averse (subset of
GM averse)
GM Loving
Label-inert
Not classifiable
Total
35
13
16
36
27
114
% of sample
30 %
37 %(of GM
averse)
14 %
31%
24 %
Table 18: Cross-tabulation of information-inert and label-inert subjects: Entire sample
Not information-inert
Information-inert
Total
Not label-inert Label-inert Total
70
3 73
25
38 63
95
41 136
Table 19: Counting the GM Averse: # of subjects satisfying the following condition: Entire
sample
Product A (GM-free)
Price bid is higher in round 3
than in round 1
Price bid is the same in round 3
as in round 1
Price bid is lower in round 3
than in round 1
Total
Product B (GM)
Price bid is higher in Price bid is the same in Price bid is lower in
round 3 than in round 1 round 3 as in round 1 round 3 than in round 1Total
18
10
25
53
9
0
9
18
8
35
0
10
16
50
24
95
Table 20: Counting the weakly GM Averse: # of information-inert subjects satisfying the
following condition: Entire sample
Product A (GM-free)
Price bid is higher in round 3
than in round 1
Price bid is the same in round 3
as in round 1
Price bid is lower in round 3
than in round 1
Total
Product B (GM)
Price bid is higher in Price bid is the same in Price bid is lower in
round 3 than in round 1 round 3 as in round 1 round 3 than in round 1Total
34
1
3
7
11
2
0
5
7
2
5
0
3
5
17
7
25
Table21: Summary classification – Entire sample
GM Averse
Weakly GM
averse (subset of
GM averse)
GM Loving
Label-inert
Not classifiable
Total
% of sample
32 %
34 %(of GM
averse)
44
15
17
41
34
136
12.5 %
30 %
25 %
Table 22: Correlates of GM Aversion
COEFFICIENT
Student dummy
Taste ranking of GM-free product
Taste ranking of GM product
Interaction between student dummy and taste ranking of GM-free
product
Interaction between student dummy and taste ranking of GM-free
product
Dummy if exercise is more than twice a week
Dummy if subject consumes read to eat snacks more than twice a
week
Dummy for smoker
Dummy if Mother’s education > = Bachelors degree
Constant
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
35
(1)
(2)
Probit Marginal Effects
-1.919
-0.659**
(1.187)
(0.309)
-0.464***
-0.171***
(0.139)
(0.0515)
0.0958
0.0353
(0.134)
(0.0493)
0.498***
0.183***
(0.169)
-0.113
(0.0620)
-0.0416
(0.170)
0.710***
(0.254)
-0.403
(0.0624)
0.262***
(0.0908)
-0.149
(0.271)
-0.490
(0.331)
0.372
(0.275)
2.026*
(1.201)
134
.
(0.0989)
-0.181
(0.122)
0.135
(0.0973)
134
.