Volume 9 2011 Article 6 Journal of Agricultural & Food Industrial Organization Willingness to Pay for Fair Trade Coffee: A Conjoint Analysis Experiment with Italian Consumers Lucia Rotaris, Università di Trieste Romeo Danielis, Università di Trieste Recommended Citation: Rotaris, Lucia and Danielis, Romeo (2011) "Willingness to Pay for Fair Trade Coffee: A Conjoint Analysis Experiment with Italian Consumers," Journal of Agricultural & Food Industrial Organization: Vol. 9: Iss. 1, Article 6. DOI: 10.2202/1542-0485.1305 Brought to you by | Georgia State University Authenticated | 131.96.12.74 Download Date | 9/24/13 2:54 PM
Transcript
Volume 9 2011 Article 6
Journal of Agricultural & FoodIndustrial Organization
Willingness to Pay for Fair Trade Coffee: AConjoint Analysis Experiment with Italian
Consumers
Lucia Rotaris, Università di TriesteRomeo Danielis, Università di Trieste
Recommended Citation:Rotaris, Lucia and Danielis, Romeo (2011) "Willingness to Pay for Fair Trade Coffee: AConjoint Analysis Experiment with Italian Consumers," Journal of Agricultural & FoodIndustrial Organization: Vol. 9: Iss. 1, Article 6.
DOI: 10.2202/1542-0485.1305
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Willingness to Pay for Fair Trade Coffee: AConjoint Analysis Experiment with Italian
ConsumersLucia Rotaris and Romeo Danielis
Abstract
Coffee can be distributed via the conventional supply chain or via the alternative fair tradesupply chain. The implications of this choice on the distribution of the value added among theactors of the chain are relevant. Fair trade coffee rewards relatively more the producers located inthe developing countries. A survey applying stated preference data collection methods to Italianhouseholds has demonstrated that they are willing to pay a premium price for the certified fairtrade coffee of about 2.2 euros for a 250 gram coffee packet. This premium price, however, canvary significantly according to age, gender, income, and purchasing habits of the consumers. Themethodology used implemented state-of-the-art survey design techniques and advanced modelsspecifications to capture preference heterogeneity.
Author Notes: We are grateful to one anonymous referee for detailed comments that substantiallyimproved earlier drafts of the paper.
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1. Introduction
Coffee is a widespread consumption product characterized by remarkable growth rates. In 2008 almost 130 million coffee bags1 were consumed and the average annual increase in consumption has been equal to 2.4% since the year 2000. In 2008 the largest importers of green coffee were the European countries with 70 million bags, while the United States and Japan imported 25 and 7 million bags respectively. Coffee production takes place in Latin America (Brazil, Colombia, Peru, Honduras and Guatemala) and in Asia (mostly in Vietnam, Indonesia and India), involving, altogether, almost 25 million small growers. For most of these countries coffee is the most important exporting sector.
The coffee supply chain is very complex and involves many actors. Almost 70% of the coffee produced worldwide is sold by thousands of very small farms (with less than 5 hectares) to few international traders and coffee roasters. The latter have recently undergone a process of horizontal and vertical integration, increasing the market share of the first three trading groups (Volcafe, Ecom and Neumann) up to 45%, and of the first two roasting groups (Nestlè and Kraft) up to 57%. As a result, the market power distribution among farmers, traders and roasters has become highly asymmetrical. In order to redistribute the value added among them and to protect local producers, beginning in the 1970s, especially in northern Europe, fair trade supply chains have been implemented (Fehr e Schmidt, 1999; Adriani e Becchetti, 2002; Becchetti e Solferino, 2003). It is estimated that, while conventional supply chains distribute to the farmers 8% of the price paid by the final consumers, the fair trade supply chain awards the farmers 18% of such value. Conversely, traders and coffee roasters get 83% of the shelf price within the conventional supply chain, and 73% within the fair trade one (FairTrade Italia, http://www.fairtradeitalia.it/).
Since the market share of the fair trade channel ultimately depends on the consumers’ preferences for the characteristics of the product and on the premium price they are willing to pay for the fair trade label, it is necessary to analyze the consumers’ choices in order to estimate the market potential of the fair trade coffee. Indeed, this paper focuses on households’ choice between labeled and unlabelled coffee for domestic consumption with the aim of answering to the following research questions. What is the relative importance of price, brand, and the fair trade label when choosing coffee for domestic consumption? Which segments of the market are more sensitive to the fair trade label? Does habit play a role in discouraging consumers from purchasing fair-trade coffee? What is the willingness to pay (WTP) for fair trade coffee? Is the premium price and the number of the potential consumers of fair trade coffee high enough to induce
1 a coffee bag is equivalent to 60 kg.
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traders and coffee roasters to switch from conventional to fair trade supply chains?
The paper is structured as follows. The Literature review section describes the most recent studies aimed at estimating the consumer preferences for fair trade coffee. The Survey section illustrates the characteristics of the questionnaire we have used to collect the preferences for fair trade coffee, the sequential Bayesian procedure we have followed to generate the design of the stated preference experiment, and the results we have obtained. Finally, the Conclusions section presents some final remarks, discusses the methodology used and proposes research improvements.
2. Literature review
Despite the increased popularity of socially responsible consumption, with a global turnover in 2008 of 3 billion euros (Maietta, 2009), and despite the fact that coffee represents the most important item sold within the fair trade system, there have only been a few previous studies analyzing the consumer demand for this product, as illustrated in Table 1.
price; type; fair trade label/ price elasticity of fair trade coffee 0.42; of conventional coffee 1.56§
knowledge of fair trade system
Laureiro, Lotade (2005)/ USA
CV(SP)/ 284 customers of supermarkets
fair trade label/ 0.10 euros
female, high income level, higher education
Maietta (2005)/ Italy
HP/ 3,678 Italian observed purchases of coffee (1998-2002)
price; type; fair trade label/ 0.64 euros
Cicia et al. (2010)/ Italy
L/ 224 customers of Alternative Trade Organizations
16 attributes, including fair trade label/ average level for fair trade coffee by frequent consumers 5.36 euro; by occasional consumers 3.59 euro
young, female, higher education
Cranfield et al. (2010)/ Canada
CA(SP)/ 400 customers of shopping mall, cafes and supermarkets
price; organic; roast; bean; origin; fair trade label/ fair trade label is the second most important attribute
no single variable explains systematic higher preference for fair trade coffee
Callieba and Casteran (2008)/ France
CA(RP)/ 5,668 customers of supermarkets
Price; characteristics of the product purchased; quantity/ 3.54 euros
Income and culture
Acronyms: CA conjoint analysis; SP stated preferences; RP revealed preferences; CV contingent valuation; HP hedonic price; L Likert qualitative 7 levels scale; n.s.s. statistically insignificant. Note: * all monetary values are in euros 2008 and are referred to a 250 gram pack of coffee, except from Basu and Hicks and De Pelsmacker et al. whose reference quantity is not mentioned, and Arnot et al. who referred to a cup of brewed coffee.
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The review shows that the sensitivity for fair trade coffee, expressed as WTP for a 250 gram pack of fair trade coffee, is quite heterogeneous, with average values ranging from 0.10 euros to 2.38 euros and varying according to: (a) the geographical context of the study (Italy, Belgium, USA, Canada); (b) the sample type (customers of conventional supermarket, customers of Alternative Trade Organizations, university students, university staff); (c) the methodology used both to collect (CA/SP, CA/RP, VC/SP, RP) and to analyze (regression models, multinomial logit or ordered probit models) the data; (d) the item type (ground coffee for domestic use only, cup of brewed coffee); (e) the number and type of coffee attributes used for the data collection (price, fair trade label, quality, brand, type, aroma, origin, packaging, organic, environmental sustainability); (f) the scale and scope of the fair trade impact on the farmers involved in the system; (g) the certifying institution, and; (h) idiosyncratic preferences for ethical issues such as poverty and inequality. According to our review the most sensitive market segment is represented by young women with a higher education level, as they typically have higher WTP than the rest of the sample. Some researchers, however, demonstrate that the sensitivity and/or the WTP for fair trade can also be affected by: income level (medium-high), better knowledge of the principles governing fair trade, and consumption choice processes which are not substantially based on habit.
Despite the fact that the fair trade system is not as widespread in Italy as in other European countries (for example UK, or Switzerland), three out of nine of the quoted studies were conducted in Italy. Most of the mentioned research was conducted using CA and SP (or RP) data. Indeed CA shows several advantages if compared with CV because its estimates are less affected by hypothetical bias, starting point bias and social desirability bias, and because it allows us to estimate the sensitivity for each attribute characterizing the hypothetical (coffee) items used in the SP experiment (Stevens et al., 2000; King e Bruner, 2000).
For these reasons, in order to plan our experiment, we took as reference points the methodological settings already adopted in the CA/SP studies we have reviewed. Our research, however, is remarkably different in terms of the procedure we used both to select the sample and to generate the design of the hypothetical choice experiments to be administered to the interviewees. For what concerns the sample selection, we avoided interviewing only students, because previous empirical evidence on socially responsible consumption demonstrated that younger individuals with a higher education generally show higher sensitivity for ethical consumption. Moreover, since our goal was to estimate the preferences of a representative Italian consumer for fair trade coffee, we selected a sample of customers of supermarkets matching the actual market shares of the most widespread coffee brands in Italy. For what concerns the generation of the SP design, we selected primarily efficient rather than the orthogonal design, since our
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priority was to obtain highly statistically significant estimates even for experiments involving small samples and few repeated observations for each respondent. Moreover, in order to further reduce the risk of obtaining statistically insignificant estimates of all the parameters we studied, in particular of the fair trade one, we reviewed the design three times before choosing the definitive one.
3. The survey
3.1 The questionnaire
In order to collect the data we administered a two-part questionnaire. The first part was aimed at collecting information about: (a) the socio-economic characteristics of the sample; (b) the sensitivity for fair trade and for environmental sustainability issues; (c) the importance of: flavor and aroma; habit; price; discounts; brand; mixture type (percentage of Arabica/Robusta); caffeine percentage; fair trade labeling when choosing coffee; (d) the price, the brand and the labeling (fair trade or not) of the coffee item generally bought by the respondents.
The second part included 10 to 12 hypothetical choice tasks. Each task required the interviewee to choose between two hypothetical 250 gram coffee packages differing in terms of price, brand type and fair trade labeling and a third item representing his typical coffee choice. The item representing the status quowas included to enhance the realism of the exercise and to control for choice processes based on habit rather than on conjoint comparisons of the coffee attributes. We set the price levels equal to the average prices recorded in the supermarkets we visited during our survey. Similarly, we chose the coffee brands according to the highest percentages of shelf space assigned to each brand and to the largest market shares reported in a recent study published by Markup (www.mark-up.it), the only exception being the brand Alce Nero which was included because it is one of the few brands distributing exclusively fair trade coffee. The hypothetical coffee items were selected on the bases of the experimental designs described in the following subsection.
3.2 Experimental designs of choice tasks
When we started to plan our research we were aware that most of the accuracy of our results would depend on the properties of the design we would use and on the number of interviews we would be able to collect. Moreover, we knew that for most of the respondents the trade-off between price, habit and brand on the one hand, and fair trade labeling on the other hand, would be unusual, potentially generating inconsistent choices and statistically insignificant estimates. For these
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reasons we decided to use an efficient rather than orthogonal design2 and to update it in order to minimize the standard error and the covariance of the parameters we wanted to estimate3. In order to do so we used Ngene which is a software produced by ChoiceMetrics (2009) specifically aimed at generating efficient CA designs.
3.2.1First experimental design
We generated the first pivot design4 including two widespread Italian brands, that is Lavazza and Segafredo, and the fair trade brand Alce Nero. We selected the following levels for price: €1, €2, €3, €4 and €5. We defined the third alternative (from now on we will refer to it with the expression status quo) as a coffee item characterized by the brand type generally bought by the interviewee, with a price of 2 euros and without the fair trade label. Finally we hypothesized the following set of a-priori on the parameters to be estimated: (a) the price parameter can take any value uniformly distributed between -10 and 0; (b) the Segafredo versus Lavazza parameter is -0.2; (c) the AlceNero versus Lavazza parameter is -0.5; (d) the fair trade parameter can take any value uniformly distributed between 0 and 55. Figure 1 illustrates one of the ten choice tasks generated with the first design.
2 Traditionally to perform CA/SP surveys only orthogonal fractional factorial designs were used. They are characterized by the fact that the attributes are statistically independent (Kuhfeld, 1997). Efficient designs, instead, are not necessarily orthogonal, but capture the maximum amount of information by minimizing the asymptotic joint confidence sphere surrounding the parameter estimates (Rose e Bliemer, 2004, Sandor and Wedel, 2002). An experimental design is called efficient if it yields data that enables estimation of the parameters with as low as possible standard errors. These standard errors can be predicted by determining the asymptotic variance covariance (AVC) matrix based on the underlying experiment and some prior information about the parameter estimates. There are several efficiency measures based on the AVC matrix (Scarpa and Rose, 2008), the most widely used is called the D-error and it is equal to the determinant of the AVC matrix (Rose e Bliemer, 2005). 3 Ferrini and Scarpa (2007) and Scarpa et al. (2007) are among the most recent papers reporting on sequential (or adaptive) Bayesian design proven to be able to increase the efficiency of the original design by about 30%. A sequential procedure aimed at achieving balanced rather than efficient designs with respect to a single metric attribute was originally introduced by Kanninen (2002). 4 A pivot design constructs the hypothetical alternatives on the bases of the choices currently made by the respondents (Hess and Rose, 2009). 5 The generation process of the first, the second and the fourth designs were based on a multinomial logit model specification, while for the third design a random parameter logit model with repeated observations for each respondent specification was used. Refer to Ben-Akiva Lerman (1985), Louviere et al. (2000), Train, (2003), and Hensher et al. (2005), Marcucci (2005) for a detailed description of the random utility model, of the CA methodology, of the techniques to be used to collect the data and of the statistical and econometric tools to analyze them.
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Fig. 1 – One choice task generated with the first design. If you could choose among the following coffee items, which one would you buy?
Coffee Item A Coffee Item B Statu quo Price €1 €1 €2 Brand Segafredo Alce Nero Usual brand Fair trade label No Yes No
We administered the choice tasks generated with this design to 10 customers of a supermarket located in the city of Trieste6. The analysis of the preferences stated during this first round of interviews suggested us to add three more entries to the list of brands: Illy, Coop and Hag. In order to improve our a-priori on the βkj we estimated a multinomial logit model using the date we had collected. We obtained an adjusted Rho2 equal to 0.3, the price parameter had the correct negative sign, but was statistically insignificant, also the Lavazza parameter was statistically insignificant, while the Alce Nero parameter was statistically significant and was equal to -1.93, in line with our a-priori, and the fair trade parameter was both statistically significant and in line with our expectations since it was positive and equal to 2.8.
3.2.2 Second experimental design
The inclusion of Illy, Coop and Hag in the coffee brand list and the refinement of our a-priori on the bases of the estimates reported in the previous section allowed us to generate a new design. We administered the 10 choice tasks generated with this second design to a sample of 10 customers of another supermarket, collecting 100 more observations. The multinomial logit model we estimated using these observations had an adjusted Rho2 equal to 0.36 with the loglikelihood function reaching its maximum at -67.57 (starting from -109.86). The price parameter was highly significant and was equal to -0.42. All the brand parameters, specified in the model as dummy variables compared to Illy, were statistically significant, except for Lavazza, and were in line with our a-priori, that is they were negative, and varied between -4.18 and -2.16, and they were bigger, in absolute value, than the price parameter. The fair trade parameter, instead, was statistically insignificant.
6 All the interviews were carried out during July and August 2009.
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3.2.3 Third experimental design
In order to further increase the efficiency level of the design we reviewed our a-priori according to the estimates previously described, we redefined the price attribute adding two more levels, €6 and €7, and we hypothesized that the fair trade parameter had not the same value for all the interviewees, but was randomly distributed between 0 and 4 like a uniform probability function. We administered 12 choice tasks generated with this third design to 15 customers of another supermarket. The analysis of the preferences stated by the respondents suggested us to add two more entries to the brand list: Splendid and Kimbo. The mixed logit model we estimated using the 180 observations we collected during the third round of interviews showed a remarkably higher adjusted Rho2, now equal to 0.39, with the loglikelihood function reaching its maximum at -117.87 (and starting from -197.75). The price parameter was statistically significant and it was equal to -0.48. All the brand parameters were statistically significant, except for Lavazza, Kimbo and Splendid, and their parameters varied between -2.89 and -2.16. The fair trade parameter, however, was statistically insignificant.
3.2.4 Fourth experimental design
On the basis of the last estimates we refined for the fourth and last time the experimental design. The attributes and the levels of the attributes we have finally used for the fourth design are listed in table 2.
Tab. 2 – Attributes and levels of the attributes used for the fourth design. ATTRIBUTES LEVELS
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The main difference between the fourth round of interviews and the previous ones is that with the fourth design we defined ex ante all the attributes of the possible status quos, including the brand, as illustrated in table 3. This procedure allowed us to administer to each group of respondents referring to one of the six status quos a unique set of choice tasks tailored to their usual purchasing behavior. In fact, we generated a specific “sub” design for each possible status quo, and we optimized the efficiency of the general design including all six “sub” designs.
Tab. 3 – Status quos used in order to generate the fourth design. Price €3.5 €1.5 €2.5 €2.5 €1.5 €5.5 Brand CremCaffè AlceNero Lavazza Hag-
Splendid Coop Illy
Fair trade label
no yes no no no no
In order to minimize the D-error of the general design we had to set some a-priori on the market share of each brand characterizing the six status quos. For this purpose we collected 43 more interviews specifically aimed at revealing the brand type generally purchased by coffee consumers. The results we obtained from analyzing these and the previous interviews are described in table 4.
Tab. 4 – A-priori on the market share of each status quo. Brand Estimated market
In order to test if there was a scale parameter difference among the data collected with the four designs we estimated an error component model (Scarpa et al., 2005). Since the standard deviation of the four error components entering the model were statistically insignificant we pooled all the data together in order to perform the subsequent econometric analysis. The sample we interviewed with the four designs included 89 women and 46 men. 14% of the respondents were 18-30 years old, 33% were 31-45 years old, 9% were 46-50 years old, 25% were 51-65 years old and 19% were older. Most of the interviewees had a high school or a bachelor education, 44% and 24% of the sample respectively, while 21% had a lower secondary school education, 7% had a PhD and 4% had a primary grade education. The sample included 63 employees, 44 housewives or retired, 18 self-
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employed, 7 students and 3 unemployed. Few respondents, 20% of the sample, were single, while the majority lived with one, 30% of the sample, two, 25% of the sample, three, 20% of the sample, or more than three, 5% of the sample, family members.
Most of the interviewees stated that they were very (48% of the sample) or quite (46% of the sample) interested in ethical issues like fair trade and environmental sustainability. When asked to state which factor most affected their coffee choice they answered: habit, cost (price or discounts), and brand (generally associated with a specific flavor or aroma), as reported in table 5.
Tab. 5 – Number of respondents that described each attribute as important when deciding which coffee to buy.
Flavor/ aroma 91 Habit 40 Price 28 Discounts 26 Brand 21 100% Arabica 9 High percentage of caffeine 5 Fair trade 4 Low percentage of caffeine 3
3.4 The results
We analyzed the data collected with the four designs using different random utility model specifications as summarized in the following sections7.
3.4.1 Error component multinomial logit models
We started the econometric analysis estimating a multinomial logit model including all the explanatory variables already described in the previous section, plus a dummy variable representing the status quo, whose parameter represents if, and to what extent, the coffee purchase process is affected by habit. We also added an error component accounting for the variation related exclusively to the two hypothetical alternatives as opposed to the status quo. The model was specified in order to take into account the panel nature of the data. We obtained a good fitting model with an adjusted Rho2 equal to 0.35 and an AIC equal to 1.44 and with all the estimates statistically significant, including the standard deviation of the error component, showing sign and values in line with our a-priori. More specifically, brands had the larger estimates with a range varying between -1.79
7 We used Nlogit v.4.0 to estimate the all the models described in the paper.
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for Alce Nero to -0.62 for Lavazza. Habit proved to be very important too, since its estimate is equal to 1.24, followed by the fair trade labeling and price whose estimates are 0.78 and -0.42 respectively.
In order to check for preference heterogeneity we estimated “segment specific” models, that is different models for each subsample defined according to the respondents’ gender, age, education, job, frequency of fair trade purchases, price level generally paid, and coffee brand generally purchased. The parameters estimated for each subsample, however, are directly comparable only if they have the same scale parameter, for this reason we estimated an error component model with as many additional error components as the number of subsamples to be compared for each segmentation variable. None of these models could converge and could estimate statistically significant standard deviations of the error components, proving that the differences of the scale parameters among the subsamples were statistically insignificant. These results were in line with our expectation and had been confirmed by the estimates obtained with nested logit models characterized by as many branches as the number of subsamples identified via each segmentation variable8, since most of them could not converge or were characterized by parameters of the inclusive values of the branches whose difference was statistically insignificant9. Since the subsample proved to have the same scale parameter, we compared the estimates obtained with the “segment specific models” to detect how they differed with respect to price, brand, fair trade and habit. It turned out that the sensitivity for price is higher for younger customers, having a lower education level and generally paying a price lower than 3 euros. Older customers, housewives or the retired, generally paying a price lower than 3 euros and purchasing brands other than Illy are more habit driven than the others. Younger customers, with a higher education level and who frequently buy fair trade products, are more sensitive for the fair trade labeling and less habit driven.
Finally, for each segmentation variable, we estimated an error component multinomial logit model with interaction effects. We chose the interaction effects according to the parameters that differed the most among the subsamples. All the interaction models we report in the remainder of this section are characterized by
8 Refer to Hensher et al. (2005) p. 580 for further details on how to use nested models in order to detect different scale parameters among different data sets (RP/SP, different segments of the same sample). 9 To the best of our knowledge none of the research reviewed in this paper used nested, latent class or mixed logit models to analyze preferences heterogeneity, the only exceptions being De Pelsmacker et al. (2005) performing a cluster analysis of the individual relative importance of the attributes and identifying five segments of the sample, and Arnot Boxall (2006), adding to the multinomial logit specification two socio-economic variables: gender and knowledge of the fair trade system.
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goodness of fit indicators (adjusted Rho2 and AIC) slightly better than those refereed to the model without interaction terms.
According to our estimates and in line with previous results reported in the literature (Cicia et al., 2010; De Pelsmacker et al.,2005; Laureiro and Lotade, 2005), women are more sensitive than men to the fair trade issues since the only interaction term that proved to be statistically significant with respect to gender identified those women that during the choice exercise selected coffee items characterized by the brand Alce Nero10.
The specification of interaction terms based on the attributes and the age of the respondents (codified in 5 categories) proved that younger customers are more sensitive to price and fair trade issues, similarly to the results obtained by Cicia et al. (2010), De Pelsmacker et al.(2005), and Marcucci and Gatta (2006), while older respondent are more habit driven, quite in line with our expectation11.
The model comprising interaction terms based on the educational level of the respondents had goodness of fit indicators almost identical to those obtained for the model without interactions. It produced, however, results in line with our expectation and with the results already reported in the literature (Cicia et al., 2010; De Pelsmacker et al.,2005; Laureiro and Lotade, 2005), that is higher educated respondents are more sensitive to the fair trade labeling (and, according to the educational level specific models, also to the brand type).
According to the estimations of the interaction model based on occupation type, students are more sensitive than the other segments to the fair trade labeling, while housewives and the retired are more affected than the others by habit (the job type specific models identified the self-employed s the most sensitive to the brand type).
The interaction model based on how frequently the respondents buy fair trade products proved that those who normally buy fair trade products are more sensitive than the other segment to the fair trade label and are less influenced by habit (the other respondents, according the segment specific models, are more sensitive to the brand type).
The model comprising interaction terms based on the price level normally paid when buying coffee, “low” if lower than 3 euros, “high” otherwise, allowed us to conclude that the segment generally paying less than 3 euros are more sensitive to price, in line with our expectations, to the fair trade labeling and to habit (the other respondents, according the segment specific models, are more sensitive to the brand type).
Finally, the specification of interaction terms based on the brand type generally bought; Illy rather than one of the other brands, proved that the
10According to the gender specific models, men are more brand sensitive that women. 11 According to the age specific models, the respondents within the segment 46-50 years old were more brand sensitive.
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respondent typically buying Illy are less sensitive to price and to the fair trade labeling (and, according to the segment specific models, are more sensitive to the brand type).
3.4.2 Mixed logit models with error components
Since our results revealed the existence of preference heterogeneity within the sample, we decided to use a mixed logit model able to trace this heterogeneity via the estimation of random rather than fixed parameters, focusing the analyses on price, fair trade label sensitivity and importance of habit. In order to perform the estimates we hypothesized three distributional forms for the random parameters: normal, uniform and triangular. The results we obtained, however, both in terms of goodness of fit of the model, of the statistical significance of the parameters, and of the meaningfulness of the estimates according to the economic theory and to our a-priori, suggested that we limit our analysis to the triangular distributional form. The adjusted Rho2 of the mixed logit model reported in table 6 is equal to 0.36, the AIC is 1.42 and the ranking of the parameters is very similar to the one estimated via the error component multinomial logit model described in the previous section. Since only price and fair trade parameters proved to have statistically significant standard deviations we specified the model with a fixed parameter for habit, that is the status quo parameter. Although we constrained the price parameter to be negative, merely for interpretability issues, the constraint did not alter the estimate of the mean of the parameter. In order to describe the distribution of the price and fair trade parameters we followed the procedure suggested by Hensher et al. (2006), and described in Hensher et al. (2005, p. 682) and in Green (2007, N17.8.2), that is, we derived the mean and the standard deviation of the distribution of the random parameters on the bases of the common-choice-specific conditional parameter estimates. More specifically the mean and the standard deviation of the conditional distribution of the price parameter are respectively -0.69 and 0.29, while the mean and the standard deviation of the conditional distribution of the fair trade parameter are 1.13 and 1.18 respectively.
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Tab. 6 – Parameters estimated via a mixed logit model with a random component for the hypothetical alternatives and random parameters for price and fair trade characterized by a triangular probability distribution function.
Parameter estimates
Wald statistic
Price* -0.691 -12.000 Spread of Price 0.691 12.000 Fair trade * 1.181 6.598 Spread of Fair Trade 2.906 6.099 CremCaffè -1.932 -9.977 Alce Nero -2.523 -10.576 Lavazza -0.912 -6.529 Hag-Splendid -2.420 -12.069 Coop -1.882 -9.806 Segafredo -2.08 -6.056 Status quo 1.32 7.121 SD error component of the hypothetical alternatives
1.681 10.171
N. Oss. 1380LL -970.2326Pseudo adjusted R2 0.36AIC 1.42
Note: * mean of parameter estimates
In order to detect which segments of the sample are more sensitive to price and fair trade we specified the model with interaction terms based on the segmentation variables described in the previous section. Only few interaction terms proved to be statistically significant, in particular younger respondent and individuals already accustomed to fair trade products proved to be more sensitive to the fair trade labeling. None of the segmentation variables previously described produced statistically significant estimates when interacted with price. The results we obtained are reported in table 7.
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Tab. 7 – Parameters estimated via a mixed logit model with a random component for the hypothetical alternatives, random parameters for price and fair trade characterized by a triangular probability distribution function and two interaction terms
Parameter estimates
Wald statistic
Price* -0.685 -11.846 Spread of Price 0.685 11.846 Fair trade * 0.93 4.269 Spread of Fair Trade 2.70 5.760 Fair trade : Age 1.074 2.310 Fair Trade : Generally buying fair trade products
0.594 1.582
CremCaffè -1.913 -9.717 Alce Nero -2.502 -10.494 Lavazza -0.895 -6.431 Hag-Splendid -2.393 -11.854 Coop -1.858 -9.752 Segafredo -2.033 -5.902 Status quo 1.273 6.366 SD error component of the hypothetical alternatives
1.665 9.803
N. Oss. 1380LL -966.7825Pseudo adjusted R2 0.36AIC 1.42
Note: * means of parameter estimates
3.4.3 WTP for fair trade coffee and purchase probability scenarios
Since one on the aims of our research was to estimate the WTP for fair trade coffee, we had to calculate it on the basis of the ratio of the fair trade and the price parameters estimated with the mixed model described in table 7. Since these two parameters are randomly distributed, the WTP is not represented by a single value but it is necessary to derive its distribution on the bases of the common-choice-specific conditional parameters estimates as described in Hensher et al. (2005, p. 682) and in Greene (2007, N17.8.2). The mean of the distribution of the WTP we have obtained following this procedure is equal to 2.20 euros, while the standard deviation is equal to 3.72.
Focusing our analysis on the segment of the sample proved to be more sensitive to the fair trade labeling, that is younger customers already accustomed
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to fair trade products, we estimated how much their purchase choices would change if one of the brands included in the survey, except Alce Nero, would switch, ceteris paribus, to the fair trade system. According to our estimates the probability of purchasing Coop would rise from 3% to 33%, that of Lavazza would rise from 5% to 39%, while that of Illy would rise from 1% to 16%. If all these three brands were to switch to the fair trade system simultaneously they would still gain a remarkable purchasing probability, although it would be lower than in the previous case, specifically Coop would rise up to 21%, Lavazza would rise up to 28%, while Illy would rise up to 9%. It should be noticed that if the same brands were to use the price lever instead of the fair trade one as a marketing strategy, specifically if Lavazza and Illy were to lower their price by 1 euros and Coop by 0.5, given the price strategy of all the other competitors, the purchasing probability increase referable to this specific segment of the market would be substantially lower, that is Coop would get 5% of the segment, while Lavazza and Illy would get, respectively, 9% and 3%.
4. Conclusions
In this paper we have reported the results of a research aimed at detecting which factors mostly affect Italian consumers’ preferences for coffee and at testing the potentialities of the fair trade label as a marketing strategy alternative to price discounts.
Our analysis shows that Italian consumers are primarily influenced by brand, generally representing a specific flavor/aroma, followed by habit and price. The sensitivity for these factors, however, substantially depends on gender, age, job, and purchasing habits of the consumers. More specifically the sensitivity for price is higher for younger customers, having a lower educational level and generally paying a price lower than 3 euros. Older customers, housewives or the retired, generally paying a price lower than 3 euros and purchasing brands other than Illy are more habit driven than the others. The fair trade label, however, plays a significant role too, especially if the customers are young, with a higher educational level and generally buying fair trade products, their sensitivity for the fair trade labeling, in fact, is higher than the other segments of the market.
Habit, instead, seems to discourage the purchase of fair trade coffee, since the market segment most sensitive to the fair trade labeling is significantly less habit driven than the rest of the sample.
The average WTP for a fair trade certified 250 gram coffee packet is about 2.2 euros. This premium price, however, can vary significantly according to gender, age, job, and purchasing habits of the consumers.
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With regard to the possibility of using the fair trade label as a marketing lever, we have estimated that the Italian coffee roasters and distributors would have a higher probability of increasing their market share, especially within fair trade oriented customers, if they join the fair trade system rather than if they drastically lower their price.
The interviews we collected, however, show that there is an information issue: on average consumers know very little about the fair trade system (its goals, the actors involved, its impact on the developing countries producing and exporting coffee). Indeed, many times we had to briefly describe the main features of the fair trade channel before administering the choice experiments to the interviewees. Moreover, we noted that the share of shelf space for fair trade products is quite small and randomly located within the supermarkets. At times all fair trade products are displayed in a dedicated multiproduct shelf, other times they are displayed alongside the conventional products by product category. It is likely that marketing campaigns aimed at describing the main features of the fair trade channel and an adequate shelf display would substantially raise the awareness of these products and their market shares, transforming marketing opportunities to actual gains.
A caveat of our research is in order with reference to the transferability of our results. It is most likely limited because all interviews were collected in Trieste, an Italian city historically and economically deeply linked to the coffee industry and hosting one of the most popular Italian coffee brands, Illy, and because revealed preferences data, needed to calibrate discrete choice models on the basis of the actual market shares of the brands, were not available.
From the methodological point of view, the research documented in this paper applied a recently proposed Bayesian approach to extract the experimental designs for the choice tasks which proved to be extremely useful for three reasons. Firstly, with a limited number of interviews, it allowed us to obtain highly statistically significant estimates for attributes like fair trade labeling which are complex and difficult to compare and to be traded-off with attributes like price or brand type. Secondly, it proved helpful in the interpretation of the results because, in order to generate the designs, we had to update our a-priori on the parameters we wanted to estimate on the basis of the data we collected during each round of interviews. Finally, the technique of updating the designs allowed us to overcome the risk of administering an incorrectly specified questionnaire relative to the specific characteristics of the issue to be analyzed or of the sample to be interviewed, a typical research issue when ex-ante little is known about the phenomenon under study.
Further lines of research for the near future include the refinement of the questionnaire to better trace for education, income, and socially responsible consumption awareness; and the integration of an SP data set with RP data in
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order to, at least partially, overcome the attitude-behavior issue stating that attitude alone is a generally poor prediction of buyer’s behavior when not contextualized in real choice situations. Moreover, it would be worth investigating whether and how much the WTP for fair trade coffee varies according to: the credibility of the fair trade labeling institution; the level and quality of knowledge of the fair trade channel; the frequency of fair trade purchases; the quantity and type of fair trade products offered by the supermarkets and the percentage and location of their shelf space. Finally, one could test whether the fair trade labeling can generate some reputation effects, that is if the labeling of one product can increase also the market share of the entire product line.
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