Consumer Attitudes toward GM Food and Pesticide Residues in India

Review of Agricultural Economics—Volume 30, Number 2—Pages 233–251

Consumer Attitudes toward GMFood and Pesticide Residuesin India

Vijesh V. Krishna and Matin Qaim

Bacillus thuringiensis (Bt) vegetables will likely be commercialized in India soon. The tech-nology could reduce pesticide residues in foods. Yet it is unclear whether consumers willappreciate this health advantage, or whether potential GM crop risks will dominate theirattitudes. Using contingent valuation methods and a sample of urban households, we findthat almost 60% of consumers would purchase Bt vegetables at current conventional veg-etable prices, indicating a high acceptance level. The rest would purchase at a certain pricediscount. Strikingly, the required discount increases for people particularly concernedabout pesticide residues, demonstrating that risk-averse consumers do not easily offsettechnology benefits against perceived risks.

Since the commercialization of the first genetically modified (GM) crops in themid 1990s, biotechnology has remained one of the most controversial topics in

agricultural sciences. There is a consensus among researchers that biotechnologyapplications can contribute to higher crop yields and improved food quality withenvironmentally friendly agronomic practices. Indeed, significant productivityincreases and pesticide reductions have already been demonstrated in severalcountries (Fernandez-Cornejo and Caswell; Qaim). At the same time, critics re-main suspicious about the wider impacts of agricultural biotechnology. GM foodcrops are argued to be associated with health risks for consumers, as newly in-troduced proteins could act as allergens or toxins and alter the metabolism ofthe crop (Altieri and Rosset). In addition, there are environmental, social, andethical concerns (Ervin et al.; Welsh and Ervin; Greenpeace). Consumer concernsand risk perceptions have emerged as important driving forces of biotechnol-ogy policies in developed countries (Moon and Balasubramanian; Foreman). For

� Vijesh V. Krishna is a postdoctoral fellow in the Department of Agricultural andResource Economics, University of California at Berkeley.� Matin Qaim is a professor in the Department of Agricultural Economics and RuralDevelopment, Georg-August-University of Gottingen, Germany.

DOI:10.1111/j.1467-9353.2008.00402.x

234 Review of Agricultural Economics

developing countries, political pressures and uncertainty regarding consumeracceptance were important reasons for the initial slow spread of GM crops (Paarl-berg; Lalitha). Issues of GM crop acceptance have been analyzed and debatedin the literature, but most available studies focus on developed economies, no-tably the European Union and the United States (Lusk et al.). Knowledge aboutconsumer attitudes in developing countries is much more limited, and the fewavailable studies suggest that insights from developed countries cannot simply betransferred (Curtis, McCluskey, and Wahl). As consumer attitudes can crucially in-fluence the welfare effects of GM crops (cf., Bredahl; Fulton and Giannakas), moreresearch on public acceptance is required with particular emphasis on developingcountries. Here, we analyze consumer attitudes toward Bacillus thuringiensis (Bt)vegetables in urban India.

So far, Bt cotton is the only GM crop technology that has been commercial-ized in India (Qaim et al.). But different GM food crops are near the end of theresearch pipeline. In particular, several insect-resistant vegetables are being de-veloped for use in Indian agriculture (Sharma, Charak, and Ramanaiah). An in-ternational public-private sector consortium is currently developing Bt crucifercrops, especially cabbage and cauliflower, with resistance to the diamondbackmoth. Furthermore, Bt eggplant technology, with resistance to the eggplant fruitand shoot borer, is being developed by the Maharashtra Hybrid Seed Company(MAHYCO). Bt eggplant has already been field-tested and is likely to become thefirst GM food crop to be commercialized in India in the near future (Krishna andQaim 2007).1

India is the second largest producer of vegetables in the world, and vegetablesplay an important role in the often vegetarian diets of Indian consumers. Yet, yieldlevels and productivity growth in the Indian vegetable sector are low, partly dueto considerable problems with insect pests. For pest management, farmers heavilyrely on chemical pesticides, even in smallholder production systems. The inten-sive pesticide use drives up production costs, but also entails serious pesticideresidue problems and associated health risks for consumers. Periodic monitoringof vegetable samples in different states of India showed that 40–60% were con-taminated with significant residues of toxic chemicals, many of which are bannedin developed country agriculture (Chahal et al.; Kole, Banerjee, and Bhattacharya;Bhanti and Taneja). The commercial experience with Bt cotton in India (Bennettet al.; Qaim et al.) and results of field trials with Bt eggplant (Krishna and Qaim2008) suggest that the introduction of GM vegetables could substantially reducepesticide application rates and residue problems.

Across the globe, consumer concerns about pesticide residues in foods arewidespread, which is reflected in a significant willingness to pay (WTP) for avoid-ing related risks (Florax, Travisi, and Nijkamp). On the other hand, GM cropsmight potentially be associated with new environmental and health risks. Hence,it is unclear whether consumers will appreciate the lower pesticide residues inBt vegetables, or whether their attitude will be influenced primarily by the factthat the vegetables are genetically modified. We will examine these aspects witha particular emphasis on the analysis of relative risk preferences toward pesticidecontamination and genetic modification. The study uses contingent valuation(CV) methods and builds on a comprehensive survey of urban households car-ried out in 2006 in different states of India.

Consumer Attitudes toward GM Food and Pesticide Residues in India 235

The remainder of this article is structured as follows. The next section brieflydiscusses the conceptual framework and CV estimation procedures. Then thesurvey sampling design and household data are described, followed by detailedanalyses of consumer WTP, first for residue-free vegetables and subsequently forBt vegetables. The last section concludes.

Methodology

Conceptual FrameworkThere is a growing body of literature on consumer acceptance and public

attitudes toward GM foods (e.g., Han; Anand et al.; Moon and Balasubrama-nian; Mucci, Hough, and Ziliani; Brown and Ping; Li et al.; Bredahl). Most ofthese studies point out the importance of perceptions in determining consumers’willingness to purchase GM products. Purchase intentions are generally deter-mined by attitudes toward the good in question, which in turn are influencedby the beliefs about the good’s attributes. Beliefs are formed by a set of dif-ferent factors, but ultimately they constitute the basis for consumer decisionmaking. The multi-attribute model, developed by Fishbein and Ajzen, was em-ployed to study consumer attitudes toward GM foods by Bredahl, Moon andBalasubramanian, and Han. We extend their approach by explicitly adding in-terrelations between GM crop attitudes and risk perceptions toward pesticideresidues. The values consumers attach to attributes of interest, viz. low pesti-cide residues and genetic modification, are assumed to reflect their relative riskperceptions.

A common approach to determine the values consumers attach to particu-lar attributes in the absence of observable market data is to elicit their WTPfor goods containing these attributes. In a setting where pesticide residue prob-lems are widespread, the WTP for foods with low or no residues is expected tobe positive; that is, consumers are likely to be willing to pay a premium. Forthe GM attribute, on the other hand, consumers might demand a discount, re-sulting in a negative WTP. For both attributes, WTP will depend on a numberof interrelated factors, including socioeconomic and demographic characteris-tics, risk perceptions, awareness, and trust in the food safety and regulatoryauthorities. Trust is as an important element in multi-attribute models, as neg-ative perceptions about a new technology may reflect the lack of public trust inthe government’s ability to effectively manage potential risks (Moon and Bal-asubramanian; Thompson). Apart from risks, however, consumers’ willingnessto purchase GM foods may also be influenced by general attitudes toward GM,possibly involving ethical, religious, and cultural considerations. This setup isshown in figure 1. Whether or not strong concerns about the risks associatedwith pesticide residues lead to a higher acceptance of Bt foods, which are GMbut contain lower chemical residues than conventionally produced vegetables,is unclear. Therefore the linkage is indicated with a dotted line in the figure.We will test the relationship by separately estimating the WTP for residue-freevegetables (WTPRF) and for Bt vegetables (WTPBt) and then analyzing the cor-relation between both variables. The hypothesis is that a positive correlationexists.


Figure 1. Conceptual framework for consumer attitudes toward Btvegetables and pesticide residues

Willingness to pay for residue-free vegetables (WTPRF)

Risk perceptions Trust

Awareness Sociodemographics

General attitude towards GM food

Willingness to pay for Bt vegetables (WTPBt)

CV Approach and Estimation ProceduresWe estimate WTPRF and WTPBt using CV techniques. CV approaches have been

criticized, because stated preference data are often associated with a bias (e.g.,Diamond and Hausman). Nonetheless, they can at least give general insights intoconsumer attitudes, and the bias is usually much lower when survey respon-dents make choices on marketable goods, as in our case, than when they areconfronted with hypothetical, nonmarket goods. Alternatively, an experimentalauction could have been used to estimate WTP. The experimental auction ap-proach can help reduce some potential biases, and it is a good way to test theinfluence of different information regimes (e.g., Huffman et al.). However, thesample is usually relatively small and often confined to population subgroupsin one or few locations. Since our intention is to get a representative picture ofattitudes of urban consumers in overall India based on current information levels,the CV approach appears more appropriate in our context.

For the CV questions in the survey, a double-bounded dichotomous choiceapproach was employed, which was shown to be statistically more efficient


than a single-bounded approach (Hanemann, Loomis, and Kanninen). In sep-arate sections of the questionnaire—one for pesticide-residue-free and one for Btvegetables—respondents were asked whether they would be willing to purchasethe specified good at a certain price bid. Price bids, expressed in terms of percent-age changes with respect to current vegetable prices, were randomly assigned tothe questionnaires. Depending on the answer to the first bid, a second randombid was given, which was higher than the first bid for a “yes” response, and lowerfor a “no” response. We denote the first bid with P∗, and the second bid with PH

if it was higher, and with PL if it was lower than P∗. Accordingly, there are fourpossible response groups: (G1) respondents who said “yes” to both bids, so thatWTP ≥ PH; (G2) those who said “yes” to the first, but “no” to the second bid, sothat P∗ ≤ WTP < PH; (G3) those who said “no” to the first, but “yes” to the secondbid, so that PL ≤ WTP < P∗; and (G4) those who said “no” to both bids, so thatWTP < PL.

For the evaluation of WTPRF and WTPBt, we estimate two separate interval-censored models using maximum likelihood techniques (Cameron). It is assumedthat WTP is influenced by a vector of explanatory variables x. Hence, WTP = �x +ε, where � is a vector of coefficients, and ε is a normally distributed random errorwith mean zero and variance �2. The log-likelihood function for the WTP modelis

ln L =∑G1

ln[

1 − �

(P H − �x

�

)]+

∑G2

ln[�

(P H − �x

�

)− �

(P∗ − �x

�

)]

+∑G3

ln[�

(P∗ − �x

�

)− �

(P L − �x

�

)]+

∑G4

ln[�

(P L − �x

�

)]

(1)

where � is the standard normal cumulative distribution function. The coefficientestimates can be interpreted as the marginal effects of the explanatory variableson WTP. The mean WTP is obtained as E(WTP) = �x.

As is well known, the bid structure used for the CV questions can play an im-portant role for the estimated mean WTP (Haab and McConnell). Neglecting theactual WTP distribution and using unrealistic bids can easily lead to a startingpoint bias in iterative bidding formats (Boyle, Bishop, and Welsh). Hanemann,Loomis, and Kanninen have argued that such a starting point bias is relativelyunlikely in a double-bounded approach, as used here. Nonetheless, an appropri-ate bid structure is important for reliable estimates. We determined a realistic bidrange by carrying out payment card experiments with twenty-five households inthe city of Bangalore prior to the actual survey. Furthermore, the questionnaire wascarefully tested before bids from the determined range were randomly assigned.

Sampling Design and Household CharacteristicsPeople living in cities are often better educated and better informed about new

technologies and food risks than people residing in the countryside, so that con-sumer attitudes are usually firstly formed in urban areas. Our analysis thereforefocuses on urban households in India. An interview-based survey was carried


out in early 2006 in five urban locations: New Delhi, as the nation’s, capital in thenorth; Bangalore and Kolar in the state of Karnataka in the south; and Kolkataand Barddhaman in the eastern state of West Bengal. New Delhi, Bangalore, andKolkata are among the largest cities in India, whereas Kolar and Barddhamanare two typical district headquarters in close proximity to important vegetableproduction regions. These locations were selected purposely, as they provide agood representation of urban India. Within each city, a stratified random sam-pling method was employed: corporation wards and households were selectedrandomly after stratifying each city into zones. The number of selected wardswas adjusted to the total number of wards in a zone. In each of the three largecities fifteen wards were selected, while in each of the two district headquarterseight wards were selected. Overall, 645 households from sixty-one corporationwards were interviewed, whereby the respondents were the persons in chargeof household vegetable purchases. The number of interviews in each location isdisplayed in table 1.

In addition to the CV questions related to pesticide residues and Bt, the struc-tured questionnaire covered general household characteristics and consumerperceptions. Socioeconomic characteristics of sample households are shown intable 1. A large proportion of the respondents (62%) were female. In spite of themean education level of 10.4 years of formal schooling, around 10% of the re-spondents had no formal education at all, and another 11% only had attendedprimary school. Forty percent of the respondents had an occupation. The mean

Table 1. Sample characteristics

NewVariable Delhi Bangalore Kolar Kolkata Barddhaman Overall

Female (1: if respondentis female; 0: otherwise)

0.65 0.81 0.83 0.42 0.39 0.62

Age of respondent 38.50 42.69 38.00 46.19 40.50 41.68(in years) (12.46) (14.41) (12.75) (13.46) (14.13) (13.77)

Education status of 8.38 10.46 10.60 12.94 8.95 10.40respondent (years of (5.46) (4.57) (4.70) (3.42) (5.12) (4.94)schooling)

Occupation (1: ifrespondent has anoccupation; 0:otherwise)

0.41 0.25 0.29 0.54 0.54 0.40

Per capita annual income 31.12 29.19 28.04 33.68 22.98 29.91of household (thsd Rs.) (21.61) (16.37) (16.87) (22.75) (12.60) (19.46)

Children (1: householdwith children under 14;0: otherwise)

0.73 0.60 0.68 0.45 0.53 0.59

Exposure to mass media 1.64 2.54 2.71 2.52 2.95 2.38(0–10 index) (1.55) (1.92) (1.89) (1.61) (1.92) (1.80)

Number of observations 163 157 80 165 80 645

Note: Figures in parentheses show standard deviations.


per capita household income was around Rs. 30,000 per year (around U.S. $685);one-fifth of the households fell below the World Bank poverty line of U.S. $1 perday. As exposure to mass media is expected to have an influence on consumerattitudes, an index was created considering the use of five different types of me-dia, viz. television, radio, newspapers, magazines, and the Internet. Respondentswere asked how often they use these media to obtain information on food andagriculture related topics. Accordingly, values of 0, 1, and 2 were assigned to eachtype of media for nonuse, occasional use, and regular use, respectively. Summingup these values yields an “exposure to mass media” index ranging between 0 and10, with 0 indicating no exposure at all, and 10 meaning regular exposure to allfive media types.

Consumer WTP for Residue-Free VegetablesTo study consumer responses to possible reductions of toxic pesticide residues,

we estimate WTP for vegetables that are completely free of pesticide residues.2

The WTPRF will primarily depend on two factors: first, consumer awareness of thepresence of pesticide residues in vegetables, and second, the supposed impact ofthese residues on human health. Information on the sample structure with respectto these factors is shown in table 2. In order to measure awareness, an awarenessindex (AI), ranging between 0 and 1, was constructed.3 Overall, awareness of pes-ticide residues among Indian consumers is relatively high, especially in Kolkataand Barddhaman in West Bengal, which is consistent with the higher pesticideapplication rates in vegetable production observed in that part of the country(Krishna and Qaim 2007). Sixty-two percent of all respondents feel that pesti-cide residues are associated with high or medium human health risks. Only 7% ofconsumers consider pesticide residues as risk-free. Yet, although many consumersknow about organic farming practices and the existence of organic markets, only2.5% of all households purchase their vegetables in such markets. A relativelythin nature of organic markets was reported even in developed countries likeFrance, Denmark, and the United States (Thompson), where organic products areformally certified and labeled. In India, in contrast, organic vegetables are seldomcertified, so that purchases are primarily based on a trust relationship with theseller.

For the CV questions during the interviews, random price bids were given aspercentage changes with respect to current market prices. The estimation resultsof the WTPRF model are shown in table 3 (see above for details of the estimationprocedure). To account for the fact that there is not one uniform market price forvegetables, we include the current average vegetable price paid by householdsas an explanatory variable. The individual price paid reflects quality preferencesand is influenced by consumer characteristics and other unobservables. Hence,this variable might be associated with an endogeneity problem. To avoid a po-tential bias, we use an instrumental variable approach, taking place of purchasedummies (e.g., street hawkers, traditional markets, supermarkets), which provedsignificant in a separate hedonic price analysis, as instruments. The resultingpredicted price variable (mean price is around Rs.10/kg) shows a positive butinsignificant coefficient in the WTP model. Unsurprisingly, households havingchildren below 14 years of age show a higher WTPRF, indicating greater risk


Table 2. Consumer awareness of pesticide residues in vegetablesand risk perceptions

NewParticulars Delhi Bangalore Kolar Kolkata Barddhaman Overall

Awareness of 0.579 0.384 0.486 0.691 0.861 0.584pesticide residues (0.467) (0.456) (0.483) (0.383) (0.328) (0.456)in purchasedvegetables(0–1 index)

Respondents’perceived impactof pesticideresidues on humanhealth (% ofrespondents)

(i) No idea 4.91 14.65 3.75 2.42 8.75 6.98(ii) No risk 1.84 17.83 16.25 0.61 1.25 7.13

(iii) Low risk 16.56 38.85 23.75 26.06 7.5 24.19(iv) Medium risk 36.20 20.38 42.50 29.70 51.25 33.33(v) High risk 40.49 8.28 13.75 41.21 31.25 28.37

Respondents whoknow aboutorganic farming(%)

47.86 42.67 47.5 69.09 52.5 52.56

Households thatpurchasevegetables fromorganic markets(%)

1.23 3.82 5.00 1.82 1.25 2.48

Note: Figures in parentheses show standard deviations.

aversion. Those households are willing to pay around 6.3% more for residue-freevegetables than households without children. Education and income status arealso significant, and the coefficients show the expected positive sign: each addi-tional year of schooling of the respondent increases WTPRF by 1.6%, while theeffect of a Rs.1,000 increase in per capita annual income is 1.2%. The exposure tomass media index is insignificant in this model. Although this index is positivelyassociated with education and income, a problem of multicollinearity does notexist.

Consumer perceptions on the effectiveness of public food safety authorities arecaptured in the form of dummy variables. Figure 2 demonstrates that over one-quarter of respondents consider the Indian food safety authorities ineffective.4

Nonetheless, except for the “no idea” variable, the other dummies are insignifi-cant. This might be an indication that consumers do not consider pesticide residueproblems to be a responsibility of food safety authorities, or they simply knowthat regulations on maximum residue levels are difficult to enforce in traditional


Table 3. Model on WTP for residue-free vegetables (WTPRF)

Variable Coefficient Std. Error

Price paid by household for vegetables, predicted (Rs./kg) 8.526 5.467Female (dummy) −0.845 5.726Age (years) −0.110 0.149Children (dummy) 6.337∗ 3.903Education status (years of schooling) 1.623∗∗∗ 0.492Exposure to mass media (0–10 index) 0.030 1.229Occupation (dummy) −0.544 4.614Per capita annual income (thsd Rs.) 1.139∗∗∗ 0.389Square of per capita annual income −0.005 0.004Awareness of pesticide residues (0–1 index) 8.878∗∗ 4.548Impact of pesticide residues (i) No idea 15.665 10.085

dummies (reference category: (ii) Low risk 21.205∗∗∗ 7.547no risk) (iii) Medium risk 28.489∗∗∗ 7.853

(iv) High risk 30.880∗∗∗ 8.247

Effectiveness of food safety (i) No idea 13.628∗∗ 6.872authorities dummies (reference (ii) Moderately effective 3.850 4.890category: ineffective) (iii) Highly effective 4.419 5.160

Location dummies (i) Bangalore 23.639∗∗∗ 6.767(reference category: New Delhi) (ii) Kolar 38.933∗∗∗ 12.146

(iii) Kolkata 65.559∗∗∗ 24.477(iv) Barddhaman 57.938∗ 31.472

Intercept −145.636∗∗∗ 64.938Sigma 37.623∗∗∗ 1.521Log likelihood −705.68Chi-squared value 215.95∗∗∗

Mean WTPRF (%) 56.57Median WTPRF (%) 58.86

∗,∗∗,∗∗∗ Statistically significant at the 0.10, 0.05, and 0.01 level, respectively.

and often informal markets. More important are the pesticide awareness indexand the dummy variables directly capturing the perceived health impacts of pes-ticide residues.5 Unsurprisingly, respondents who associate low, medium, or highhealth risks with residues have a higher WTPRF. Similar effects were shown byBoccaletti and Nardella for consumers in Italy and by Florax, Travisi, and Nijkampin an international meta-analysis.

The mean WTPRF among urban consumers in India is a premium of 56.6%. Thisis higher than results from previous studies focusing on consumers in developedcountries (e.g., Roosen et al.; Boccaletti and Nardella). There are two possibleexplanations for this phenomenon. First, as Florax, Travisi, and Nijkamp found,the WTP for pesticide risk reduction is systematically lower if it relates to justone vegetable rather than vegetables in general, as it does in our study. Second,it is quite plausible that the WTPRF expressed in percentage terms is higher indeveloping countries, because food regulations are less strict than in developedcountries, so that the pesticides used are often more toxic and the residue levelsmuch higher. Nonetheless, consumers in developing countries might face income


Figure 2. Consumer perceptions about the effectiveness of foodsafety authorities

constraints, so that a reality check is certainly expedient. According to our sam-ple, the average monthly per capita expenditure for vegetables is around Rs. 120(U.S. $2.74), accounting for about 4.8% of total per capita income. A 56.6% pricepremium for residue-free vegetables would therefore imply an additional Rs. 68(U.S. $1.55) per month, which is high but not completely unrealistic. In any case,the results indicate that a more formal market for organic products would havesubstantial profit potentials and could lead to significant structural shifts in theIndian vegetable sector. This is especially so against the background of rapideconomic growth and rising per capita incomes in urban areas.

Consumer Attitudes toward Bt VegetablesIn the household survey, after finishing the section on pesticide residues, a

new questionnaire section focused on consumer attitudes towards Bt vegetables.However, even in developed countries, many people have limited knowledgeabout the details of biotechnology and GM foods (Hoban), so that the level ofinformation available to consumers in India was expected to be still lower. Thiswas confirmed in a small pilot study carried out prior to the actual survey. Hence,during the survey, basic information was given to those respondents who hadnever heard about genetic modification before (83% of all respondents). To min-imize a possible interviewer bias, a formal information script was prepared (seeAppendix A), which was translated into different local languages and read to therespondents.6 Chong had employed a similar approach in his study on farmers’reactions to Bt eggplant risks and benefits.

Subsequently, respondents were asked about their general attitude toward theintroduction of Bt vegetables in India. A five-point scale ranging from “stronglysupporting” to “strongly opposing” was used. A sixth category termed “can’ttell” was added to allow for the option when respondents were unable to form


Table 4. Consumer attitudes toward the introduction of Btvegetables

% of Households inAttitude towardBt Vegetables New Delhi Bangalore Kolar Kolkata Barddhaman Overall

Strongly supporting 13.50 11.46 18.75 49.70 10.00 22.48Mildly supporting 47.85 57.96 51.25 26.06 53.75 45.89Indifferent 13.50 8.28 7.50 12.12 13.75 11.16Mildly opposing 11.66 9.55 17.50 3.64 18.75 10.70Strongly opposing 9.20 5.73 2.50 7.27 2.50 6.20Can’t tell 4.29 7.01 2.50 1.21 1.25 3.57

Overall 100.00 100.00 100.00 100.00 100.00 100.00

a clear opinion based either on prior knowledge or the information providedduring the survey. Table 4 shows the distribution of respondents across thesesix categories in different locations. Overall, 68% support the introduction of Btvegetables, while 17% mildly or strongly oppose the new technology. With fewexceptions, the variation in attitudes across the different locations is relativelylow. The rather positive perception of the technology in India is consistent withother studies, which showed that the attitude toward agricultural biotechnologyin developing countries is generally less negative than in developed countries,and often even positive (Li et al.; Pachico and Wolf; Anand et al.; Qiu and Huang).

WTP for Bt VegetablesSimilar to the valuation of the residue-free attribute discussed above, a double-

bounded dichotomous choice approach was used to estimate the WTPBt. Again,random price bids were given as percentage changes with respect to current mar-ket prices. The explanatory variables included in estimation are the same as abovefor the WTPRF model.7 The results are shown in table 5. While many of the coef-ficients are statistically insignificant, education and the exposure to mass mediaindex are significant and negative. The media effect is not surprising. Over the lastfew years, Bt cotton featured prominently in the Indian mass media, and reportswere often biased toward reporting potential health, environmental, and socialrisks of the technology (Naik et al.). The education effect is probably also relatedto risk considerations. Educated people better understand potential risks of GMcrops. While they might also better understand the benefits, risks that cannotbe controlled individually are usually valued higher than benefits in consumerdecision making (e.g., Huffman et al.). Each additional year of formal educationdecreases WTPBt by 1.3% on average. Women are often more risk averse thanmen, which explains the negative coefficient for the female dummy.

Unlike the WTPRF model, the variables capturing the perceived effectiveness ofpublic food safety authorities significantly influence WTPBt. Especially in situa-tions where consumers find it hard to assess the safety of innovations themselves,


Table 5. Model on WTP for Bt vegetables (WTPBt)

Variable Coefficient Std. Error

Price paid by household for vegetables, predicted (Rs./kg) −0.107 4.421Female (dummy) −8.351∗ 4.669Age (years) −0.079 0.120Children (dummy) −3.244 3.223Education status (years of schooling) −1.255∗∗∗ 0.430Exposure to mass media (0–10 index) −1.953∗ 1.026Occupation (dummy) −0.500 3.754Per capita annual income (thsd Rs.) −0.234 0.332Square of per capita annual income 0.003 0.003Awareness of pesticide residues (0–1 index) 4.289 3.728Impact of pesticide residues dummies (i) No idea 2.896 8.665

(reference category: no risk) (ii) Low risk −1.809 6.554(iii) Medium risk −7.328 6.768(iv) High risk −3.042 7.140

Effectiveness of food safety (i) No idea 9.957∗ 5.741authorities dummies (ii) Moderately effective 9.261∗∗ 3.943(reference category: ineffective) (iii) Highly effective 19.552∗∗∗ 4.268

Heard about GM foods (dummy) 3.127 4.831

Location dummies (i) Bangalore −3.060 5.402(reference category: New Delhi) (ii) Kolar 3.617 9.896

(iii) Kolkata −0.665 19.994(iv) Barddhaman 3.731 25.529

Intercept 24.347 51.993Sigma 30.201∗∗∗ 1.768Log likelihood −656.88Chi-squared value 59.20∗∗∗

Mean WTPBt (%) 1.46Median WTPBt (%) 1.44

∗,∗∗,∗∗∗ Statistically significant at the 0.10, 0.05, and 0.01 level, respectively.

technology acceptance often hinges on their trust in the regulatory bodies (Echols).A sense that risk managers are not caring or are incompetent may result in theadoption of overly restrictive remedial actions by the public (Qiu and Huang),which is reflected in the marginal effects on WTPBt. Respondents who considerthe authorities moderately and highly effective are willing to pay 9.3% and 19.6%more for Bt vegetables, respectively. Also respondents who cannot assess the ef-fectiveness have a higher WTPBt than their counterparts who consider the author-ities ineffective. On the other hand, the variables capturing awareness of pesticideresidues and related risks are not statistically significant. This is already a first in-dication that lower pesticide residues in Bt vegetables will not be the key factorfor public technology acceptance.

In order to test for the possibility that the information provided in the survey ledto an upward or downward bias in the WTP estimates, we also included a dummyvariable for those respondents who had heard about GM foods before. The result-ing coefficient is statistically insignificant, so we conclude that the information


Figure 3. Estimated percentage of consumers willing to purchase Btvegetables at different price levels

0

10

20

30

40

50

60

70

80

90

100

-30 -20 -10 0 10 20 30% price change with respect to current market price

% o

f co

nsum

ers

will

ing

to p

urch

ase

Bt

vege

tabl

es Supporting

Opposing

Overall

provided during the survey did not bias the results. Omitting this dummy fromthe model somewhat changes the coefficients of education and other variables,because of existing correlation. However, the signs of all coefficients remain un-changed, and the mean WTPBt is unaffected.

The estimated mean WTPBt is 1.5; that is, on average consumers are willing topay a 1.5% premium for Bt vegetables.8 This is consistent with our finding of a gen-erally positive consumer attitude toward the introduction of the technology. Also,the results are in line with other studies on acceptance of GM food in developingcountries, including a recent study in India: Anand et al. found that—withoutprovision of additional information—consumers in the cities of New Delhi andPatna were willing to pay a premium of 7% for GM wheat. Figure 3 shows that55% of respondents in our sample would purchase Bt vegetables at current marketprices of conventional vegetables. More than 80% would purchase Bt vegetablesat a price discount of 10%. Figure 3 also shows separate acceptance curves forthe subsamples of those who generally oppose and those who support the intro-duction of Bt vegetables. The subsamples were generated by pooling the stronglyand mildly opposing and supporting categories, respectively (see table 4). Unsur-prisingly, respondents who generally oppose the technology would require largerprice discounts in order to purchase Bt vegetables. Still, over 30% of them wouldbe willing to purchase also without any discounts, indicating that the oppositionis not very pronounced.

After the introduction of Bt technology in the vegetable sector, consumers willhardly be able to differentiate GM from non-GM products. In India, there is nolabeling system in place. In fact, mandatory labeling of GM products would bevery difficult to monitor and enforce, especially for fresh vegetables traded in


traditional markets. As mentioned above, productivity increases caused by Bttechnology might reduce market prices, but without market segregation suchprice decreases will apply to both GM and non-GM products. Therefore, the notionof price discounts used here is somewhat artificial and should not be overinter-preted in terms of a marketing and pricing strategy for Bt vegetables. Nonetheless,the findings suggest that—given current attitudes and perceptions—GM foodcrops would be widely accepted in India.

Pesticide Reduction versus Genetic ModificationIn the conceptual framework above we hypothesized that consumer concerns

about pesticide residues would lead to a higher acceptance of Bt vegetables, or,in other words, that there would be a positive correlation between WTPRF andWTPBt. The high-estimated mean WTPRF confirms that many consumers are in-deed worried about pesticide-related health risks, which could possibly explainthe rather positive overall attitude toward the introduction of Bt vegetables. How-ever, the insignificant pesticide-risk perception variables in the WTPBt model al-ready indicated that this might not be the main explanation. Strikingly, the analysisin table 6 shows that the correlation between WTPRF and WTPBt is not positive,but negative and highly significant. This negative relationship can also be seen infigure 4, where mean WTP values for both attributes are compared across quin-tiles, which were formed on the basis of the WTPRF distribution. Consumers inthe lowest quintile—that is, those least concerned about pesticide residues—arewilling to pay an average price premium of 11.5% for Bt vegetables, whereas con-sumers in the highest quintile require a discount of 2.8%. These findings clearlydisprove our a priori hypothesis.

Consumer decision making appears to be largely driven by general risk at-titudes. Consumers who do not pay a lot of attention to the risks of pesticideresidues are also not much concerned about potential GM food risks. Risk-averseconsumers, in contrast, are concerned about both types of risks, and for thema reduction in one risk cannot easily compensate for an increase in the other.While they would clearly value a reduction in pesticide residues, the dislikedGM attribute weighs heavier and seems to overshadow the benefits. As men-tioned above, in the Indian context education seems to be an important factor

Table 6. Correlation between WTP for residue-free and Btvegetables

Attitude toward Bt Mean WTPRF Mean WTPBt Correlation betweenVegetables N (%) (%) WTPRF and WTPBt

Supporting 441 57.77 2.81 −0.404∗∗∗

Indifferent and can’t tell 95 48.34 2.06 −0.532∗∗∗

Opposing 109 58.85 −4.55 −0.431∗∗∗

Overall 645 56.57 1.46 −0.421∗∗∗

∗∗∗ Statistically significant at the 0.01 level.


Figure 4. Mean WTPRF and mean WTPBt by WTPRF quintiles

15.83

42.74

58.58

72.02

93.65

11.53

2.38

-1.07 -2.70 -2.84

-20

0

20

40

60

80

100

lower 20% 20-40% 40-60% 60-80% upper 20%

WTPRF quintiles

WT

PR

F /

WT

PB

t(%

)

WTP-RF

WTP-Bt

determining risk attitudes, with better educated consumers being more recep-tive to potential food risks. The negative correlation between WTPRF and WTPBtindicates a preference for organically produced vegetables among risk-averseconsumers, since in that case the reduction in pesticide residues would not beassociated with a new type of risk. In this respect, it does not matter whetherGM foods are really associated with human health risks from a scientific point ofview; a subjective feeling suffices for risk-averse consumers to lower acceptanceconsiderably. This phenomenon is often exploited by antibiotech pressure groups.

ConclusionsBt vegetables, which are likely to be commercialized in India in the near fu-

ture, have the potential to reduce pesticide use considerably. The GM technologyentails not only clear agronomic advantages, but consumers could also benefit.Productivity increases will lead to a reduction in market prices for vegetables.In addition, lower pesticide residues could bring about advantages for humanhealth. In this article, we have analyzed consumer attitudes toward the intro-duction of Bt vegetables, paying particular attention to the question whetherawareness of and concerns about current pesticide residue problems influencetechnology acceptance.

We found that urban households in India are generally well aware of the healthrisks associated with pesticide residues in foods. Mean WTP for residue-free veg-etables is more than 50% above current market prices. This suggests that a re-duction in pesticide residues through Bt technology could lead to substantialwelfare gains for consumers. On the other hand, there are perceived risks andethical concerns associated with GM foods, which cannot be ignored when ana-lyzing public attitudes. Among the households surveyed, 68% showed a positive


attitude toward the introduction of Bt vegetables, while only 17% were mildlyor strongly opposed to the technology. Better education and greater exposure tomass media seem to reduce acceptance in the Indian context, partly reflectingbiased information flows. Furthermore, consumer trust in the public food safetyauthorities matters: those who consider the authorities ineffective are less willingto accept GM foods.

Nevertheless, the overall attitude toward GM foods in India is quite positive.The WTP analysis demonstrates that 55% of all consumers would purchase Btvegetables even if they were sold at current price levels for conventional vegeta-bles. At a price discount of 10%, more than 80% of households would purchase Btvegetables. This high acceptance level bodes well for the future of this particulartechnology and other GM crop innovations to come. Our results confirm those ofprevious studies showing that GM food acceptance is often higher in developingthan in developed countries. One plausible explanation is that people in develop-ing countries are generally poorer and sometimes food-insecure, so that they aremore dependent on productivity-increasing agricultural technologies than better-off consumers in developed countries. However, consumer acceptance and WTPstudies can only provide a snapshot, which is based on consumers’ informationlevels at a particular point in time. In most developing countries, including India,no GM food crops have been released so far, and related public knowledge is stillvery limited. Therefore, our results should be considered as preliminary. Oncesuch crops are introduced, information levels will increase, but the additionalinformation will not be objective in all cases. Depending on the success of differ-ent interest groups in spreading their subjective viewpoints, consumer attitudescould certainly change in either direction. The public media can and should playan important role in spreading unbiased information, a role that was not alwaysfulfilled in the past.

Another interesting finding of our analysis is that WTP for residue-free andWTP for Bt vegetables in India are negatively correlated, that is, people who aremost concerned about pesticide residues are least willing to accept Bt food. Thiscan be explained by general risk attitudes: for risk-averse consumers, a reductionin one risk cannot easily compensate for the perceived increase in another. Moregenerally, consumers who are concerned about the potential risks of a new tech-nology may undervalue tangible benefits. This could also have implications forsecond-generation GM crops, which primarily focus on consumer traits such ashigher nutrient contents in foods or other health-improving ingredients. While itis generally believed that such second-generation traits might improve GM ac-ceptance among those who currently oppose the technology, our results suggestthat this might not always be the case. More research in this direction is certainlyneeded before conclusive statements can be made. A better understanding of howconsumers value different types of food risks and benefits can help improve theinnovation process.

Appendix A: Information Script Read to RespondentsWho Had Never Heard about GM Crops Before

There are a number of studies indicating harmful human health effects causedby pesticide residues in food crops. In India, many popular vegetables—especially


eggplant, cabbage, and cauliflower—are being produced with large quantitiesof toxic pesticides. Through advancements in biotechnology, scientists are cur-rently developing genetically modified (GM) vegetables, which are resistant tocertain insect pests. By introducing a gene from a bacterium, scientists have en-gineered what is called Bt eggplant, Bt cabbage, and Bt cauliflower, where higheryields can be obtained with lower amounts of pesticides. Such GM vegetableswill taste exactly like the common vegetables you currently consume. Bt cropsare being developed under the supervision of the regulatory bodies of the In-dian Government, where possible risks for human health and the environmentare tested. GM crops are common in the United States, Canada, and a num-ber of other countries. But European consumers are somewhat more skepticalabout possible negative long-term consequences. Bt cotton is the only GM cropcurrently being used in Indian agriculture, but this is not a food crop. Bt vegeta-bles could be the first GM food crops that might become available on the Indianmarket in the near future. Various organizations are voicing against the intro-duction of GM crops due to a number of reasons, including possible long-termadverse impacts on human health and the environment, which however have notoccurred so far in other countries where GM crops have been used for severalyears. Proponents of the technology, on the other hand, point out the potentialhealth and environmental benefits associated with pesticide use reduction in foodproduction.

AcknowledgmentsThe authors thank Marcus Mergenthaler, Katinka Weinberger, and Ramasamy Srinivasan for helpful

inputs and constructive comments. The financial support by the United States Agency for InternationalDevelopment (USAID) and the Eiselen Foundation, Ulm, is gratefully acknowledged.

Endnotes1While cotton seed oil is used for food purposes, the regulatory authorities in India do not consider

Bt cotton to be a GM food crop.2This does not imply that Bt vegetables will be completely free of pesticide residues. Field trials

with Bt eggplant indicate that pesticide quantities might be reduced by about 40% (Krishna and Qaim2008). However, here the focus is on consumers’ risk perceptions. A scenario of zero pesticide residuesis easier to imagine, and thus consumer responses are less prone to be biased.

3The awareness index was calculated for each individual household i as AIi = [∑

Ri j Ei j ]/[∑

Ei j ]where Rij = 1 if the respondent believes that vegetable species j as purchased in the market is subject tosignificant pesticide residues, and 0 otherwise. Eij is the monthly household expenditure for purchasesof vegetable j, that is, relative expenditures are used as weighting factors to aggregate over differentvegetable species. The vegetable species particularly considered are eggplant, cauliflower, cabbage,and an aggregate “other vegetables.” For all of them significant pesticide residues have frequentlybeen reported in India (Chahal et al.; Kole, Banerjee, and Bhattacharya; Bhanti and Taneja).

4In the survey, the question on the effectiveness of food safety authorities was asked in generalterms, not particularly referring to pesticide residue or GM crop issues.

5Especially the awareness index (AI) might potentially be endogenous. Unfortunately, all variablesthat could explain AI are also relevant to explain WTPRF directly, so that an instrumental variableapproach could not be used. However, since omitting AI from the model does not change the overallresults, we conclude that a potential bias is negligible.

6When preparing the information script, we had to decide how many specifics on GM crops toinclude. On the one hand, respondents should have sufficient background information to be able toreveal their preferences. On the other hand, too many details could also lead to confusion, especiallyamong consumers with relatively little formal education. Therefore, we decided to mention factsand arguments in a rather general way, without many specifics. This appears appropriate in the


given setting, where even those who knew GM crops before, only had relatively little knowledge onparticular details.

7Inclusion of variables capturing consumers’ perceptions about different GM crop attributes mighthave been interesting in general, but did not appear appropriate in our context, given that mostrespondents had no related knowledge prior to the information given during the interviews.

8A disaggregation of results by groups with different prior knowledge levels about GM cropsresulted in only small differences between group-specific mean WTPBt values. This is further proofthat the information provided in the survey did not lead to any systematic bias.

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Consumer Attitudes toward GM Food and Pesticide Residues in India

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