An analysis of the territorial factors affecting milk purchase in Italy

Food Quality and Preference 27 (2013) 35–43

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An analysis of the territorial factors affecting milk purchase in Italy T. Tempesta, D. Vecchiato ⇑ LEAF, Department of Land, Environment, Agriculture and Forestry, University of Padova, Campus di Agripolis, Viale dell’Università 16, 35020 Legnaro (PD), Italy

a r t i c l e

i n f o

Article history: Received 1 June 2011 Received in revised form 2 May 2012 Accepted 13 June 2012 Available online 22 June 2012 Keywords: Milk Consumer preferences Choice experiment Latent classes Italy Marketing

a b s t r a c t Numerous international studies have pointed out that consumers are usually willing to pay a premium price for domestic and local food. This paper describes the use of a choice experiment to investigate the willingness to pay (WTP) a premium price for milk while considering three attributes: origin (south Italy, north-centre Italy, or other European Union countries), area of production (mountain or plains), and rearing method (cows grazed or kept in a barn). The data collected were analysed by means of three approaches. First, we used a multinomial logit model to estimate the average premium price for the three attributes considered. Second, an interaction term between price and quantity of milk purchased weekly was introduced into the model. This made it possible to verify whether the quantity purchased can modify the WTP, and the magnitude and significance of the effect. Finally, the latent class method was used to explore the heterogeneity of the preferences. The results suggest that the choice experiment, despite providing important qualitative measures, should be utilised with caution regarding premium price estimates in the analysis of food demand. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Milk is widely consumed in Italy. About 86% of people drink milk, and the yearly average consumption per capita is about 54 l. In 2008, 54% of the milk sold in Italy was Ultra High Temperature processed (UHT) while 46% consisted mainly of pasteurised milk or other kinds. The dairy sector in Italy produces only 59% of the milk consumed, with 41% being imported, most from Germany and to a lesser extent, from other EU countries. In 2008, Italy imported 1.94 million tonnes of milk, which was mainly used for the production of dairy products, especially yoghourt and cheese. Domestic milk, however, is mostly used in manufacturing high value-added products, such as Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) labelled cheese. For example, in the Veneto region, 40% of the milk produced is processed into PDO or PGI cheese, 35% into other cheese or yoghourt and only 25% is drunk by consumers. The high dependence on imported milk may to some extent explain why both farmers and consumers in Italy have long requested the traceability to be ensured, not only of fresh and organic milk (as currently required by the Italian legislation) but also of UHT milk. Note, however, that this is not peculiar to Italian consumers. The dairy farmers request is probably motivated by the higher production costs in Italy with respect to the foreign competitors. Considering the possibility that consumers are willing to pay a premium price for domestic

⇑ Corresponding author. Tel.:/fax: +39 049 827 2762. E-mail address: [email protected] (D. Vecchiato). 0950-3293/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodqual.2012.06.005

products, milk traceability will improve the competitiveness of the Italian production. In this respect it has to be considered that numerous international studies have pointed out that consumers are usually willing to pay a premium price for domestic and local food (Cicia & Colantuoni, 2010; Ehmke, 2006; Lusk et al., 2006; Roosen, Lusk, & Fox, 2003; Verlegh & Steenkamp, 1999). To quantify the premium price, scholars have generally used approaches belonging to three main groups: stated preferences (contingent valuation, choice experiments), auctions and experimental markets. Other methodologies are rarely considered (Lusk & Anderson, 2004; Rude, Iqbal, & Brewin, 2006). With reference to the food sector, researchers have considered numerous product differentiating strategies e.g., the production methods adopted by farmers (organic, animal welfare, animal diet), product characteristics (chemical residues free, GMO free, Bovine Spongiform Encephalopathy – BSE free, etc.) and place of origin (farm, geographical area, country of origin, region of origin, etc.) (McCluskey & Loureiro, 2003). Many past studies have analysed the effect of country of origin labelling (COOL). Some studies have looked at the ranking or relative importance of preferences (Ares, Giménez, & Deliza, 2010; Dekhili, Sirieix, & Cohen, 2011; Jaeger & Rose, 2008; Lockshin, Jarvis, d’Hauteville, & Perrouty, 2006; Schnettler, Ruiz, Sepúlveda, & Sepúlveda, 2008). Others have derived the premium price measures for the country of origin (Alfnes, 2004; Alfnes & Rickertsen, 2003; Barreiro-Hurlé, Colombo, & Cantos-Villar, 2008; Bolliger & Réviron, 2008; Carpio & Isengildina-Massa, 2009; Chung, Boyer, & Han, 2009; Dickinson & Bailey, 2005; Kallas, Lambarraa, & Gil,

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2011; Loureiro & Umberger, 2003, 2007; Umberger, Feuz, Calkins, & Sitz, 2003) and feeding methods (Conner & Oppenheim, 2008; Lagerkvist, Carlsson, & Viske, 2006). Few studies have analysed the effect of the place of production on consumers’ willingness to pay a premium price for milk. Burchardi, Schroder, and Thiele (2005) used the contingent valuation method and found that consumers from the German federal state of Hesse are willing to pay a premium price for locally produced milk. However, the demand is rather price elastic. Lefevre (2010) reports the results of a survey conducted in Senegal, where consumers declared that they were willing to pay a premium price for national fresh milk as opposed to imported milk powder. According to these studies, the traceability of milk—and of food in general—may become an important marketing tool for countries, such as Italy, where a large part of the food consumed is imported. An aspect neglected by previous researches is the estimation of the effect of the quantity purchased on the premium price declared. Researchers have estimated an average willingness to pay (WTP) (or premium price) for the characteristics of the good under investigation. But, as Lusk and Hudson (2004) have pointed out, such information could be useless for the producers. Lusk and Hudson (2004) stated that ‘‘To assist firms in product adoption decisions, future research might provide illustrations of inverse demand curves under several distributional assumptions when discrete choice methods are employed and report frequency distributions of WTP when a direct elicitation approach is employed’’ (p. 164). However, this suggestion may not be sufficient to assist producers in market policies design. The knowledge of the frequency distribution of the WTP among interviewees permits an estimate of the demand function only if the WTP is independent of the quantity purchased. If a negative relationship exists between the premium price and the quantity consumed, then the distribution of the frequency of the WTP would give an overestimation of the real demand function. If the marginal utility for a good (or for its characteristics) is decreasing, one would expect that the premium price would also decline. Another aspect that is valuable for firms’ production strategies is the segmentation of demand. Very often, premium price estimations or the analysis of consumer behaviour have been used to investigate the extent to which some socioeconomic characteristics could influence the WTP. Such an approach, which surely has some important merits, disregards that consumer preferences depend on some unobservable elements that are only partly related to the socioeconomic characteristics or opinions of the interviewees. Approaches like the latent class analysis could be useful to better understand the heterogeneity of the preferences and could provide firms with useful insight into consumer demand. In this paper a choice experiment is used to investigate the willingness to pay a premium price for milk while considering three attributes: origin (southern Italy, north-central Italy, or other European Union countries), area of production (mountain or plains), and rearing method (cows grazed or kept in a barn). To this end, a sample of people living in the Veneto region of Italy were interviewed in 2009. The data were analysed by means of three approaches. First, we used a multinomial logit model to estimate the average premium price for the three attributes considered. Second, an interaction term between price and quantity of milk purchased weekly was introduced into the model. This made it possible to verify whether the quantity purchased can modify the WTP and the magnitude and significance of the effect. Finally, the latent class method was used to explore the heterogeneity of the preferences. The results suggest that the stated preferences approaches should be utilised with caution in the analysis of the food demand.

The paper is organised as follows: section two focuses on the presentation of the CE methodology, experimental design, questionnaire design and data collection. Results are presented in section three. Section four is devoted to a discussion of the results and conclusions. 2. Material and methods 2.1. Choice experiments The choice experiment (CE) methodology has been used for some time in marketing, transportation and psychology (Batsell & Louviere, 1991; Hensher, 1994; Louviere, 1988a, 1988b, 1991; Louviere & Hensher, 1982). The behavioural foundations of CE include the following: 1) the Lancastrian consumer theory (Lancaster, 1966), which proposes that utilities for goods can be decomposed into separable utilities for their characteristics or attributes; 2) information processing in judgement and decision making in psychology (Anderson, 1970; Hammond, 1955; Slovic & Lichtenstein, 1971); and 3) random utility theory, which forms the basis of several models and theories of consumer judgement and decision making in psychology and economics (Manski, 1977; McFadden, 1974; Thurstone, 1927; Yellott, 1977). The central assumption of CE methodology is that utility is derived from properties/characteristics of goods rather than directly from the goods. Therefore, utility becomes a function of commodity characteristics. In CE, the goods valued are decomposed into their key attributes. The researcher associates an array of values to each attribute, and these can be qualitative or quantitative, depending on the nature of the attribute considered. The researcher proceeds in the experimental design by varying the attribute levels to build different choice sets. Among monetary valuation approaches, CE is quite appropriate for valuing market product characteristics because it allows an estimation not only of the value of the product as a whole but also of the implicit value of its attributes (Bateman et al., 2002; Hanley, Wright, & Adamowicz, 1998). Welfare measures are derived looking at the marginal rate of substitution between non-monetary attributes and the monetary attribute included in the indirect utility function (IUF). Therefore, the consumer surplus can be calculated within the context of discrete choice models as the relative Hicksian compensating variation that ‘‘measures a change in the level of provision of each attribute by weighting this change by the marginal utility of income’’ (Hoyos, 2010). When dealing with additive IUFs, the formula for calculating WTP becomes:

WTPj ¼ 

bj @U=@xj ¼ @U=@p bp

ð1Þ

where j is the jth attribute, U is the indirect utility function and p is the price attribute. We refer the reader to Hensher, Rose, and Greene (2005) and Hoyos (2010) for further details related to the CE mathematical formulations. Different models can be used in applied discrete choice studies. Logit models have been a great success and are widely used because the formula for the choice probabilities takes a closed form and is readily interpretable. Some of the firm assumptions1 of the logit models can be addressed using generalised extreme value (GEV) 1 In particular, the Independence from Irrelevant Alternatives (IIA) assumption, which is namely that the ratio of the probabilities of choosing one alternative over another (given that both alternatives have a non-zero probability of choice) is unaffected by any additional alternative in the choice set (Louviere, Hensher, & Swait, 2000).

T. Tempesta, D. Vecchiato / Food Quality and Preference 27 (2013) 35–43

models (such as nested logit, paired combinatorial logit and generalised nested logit), latent class models (LCM), mixed logit models (RPL), or probit models. In the multinomial logit model, the IIA assumption holds if the researcher takes into account respondents’ heterogeneity by introducing socio-economic variables as interaction terms into the estimated equation (Bennett, & Blamey, 2001). Among the models quoted above, latent class models (Boxall & Adamowicz, 2002; McFadden, 1986; Swait, 1994) are quite informative and interesting when studying preference heterogeneity in a sample. Latent class models locate different segments of consumers/respondents according to their choice data and therefore independently from their socioeconomic characteristics. It is then possible to verify ex post whether the members of the different classes or clusters have common denominators in terms of socioeconomic characteristics. The number of segments is defined by the researcher exogenously. The best model is then selected by comparing LL function, AIC and BIC for different numbers of classes.

2.2. Choice experiment design A choice experiment (CE) has been developed to analyse consumer preferences with regard to milk place of origin and production and cows rearing. The CE design has considered four core attributes: price, production area, product origin, and type of rearing. Table 1 illustrates the attributes and levels in detail. With regard to the place of production, we distinguished between the area of production (mountain or plains) and the geographical area of origin (north-centre Italy, south Italy, other European countries). Indeed, during the focus groups for questionnaire testing, people revealed that the area of production is of great importance. In fact milk produced in mountain areas is richer in polyunsaturated fats and isomers of linoleic acid (Bugaud, Buchin, Coulon, Hauwuy, & Dupont, 2001; Leiber, Kreuzer, Leuenberger, & Wettstein, 2006; Leiber et al., 2005). The concentration of these compounds increases with the availability of fresh grazing grass. Besides the area of production, another important aspect with regard to the nutritional qualities of milk is related to how cows are fed. Milk produced from alpine-grazing cows has different qualities from that of traditional barn rearing (Bugaud et al., 2001). For this reason, we have introduced a further discriminant attribute: cows rearing. This has two levels: free rearing and barn rearing. In fact, even if the area of production is ‘‘mountain’’, the rearing can still be the same as that on the plain. With regard to the geographical area of origin, we consider it important to distinguish not only between national (Italian) and other European countries’ production but also between north-centre and south Italy. Nonetheless, many people living in northern Italy might still like southern products either because they have connectios to that part of the country or because of the uniqueness of some regional products. Starting from a full factorial of 36 choice profiles, we reduced their number to 16 with an orthogonal fractional factorial design using SPSSÒ software (Hensher et al., 2005). The 16 profiles (Table 2) obtained were organised in 4 choice sets with 4 choice options

Table 1 Choice Experiment attributes and levels. Attribute

Levels

Price (€/l) Area of production Origin Cows rearing

1.0; 1.3; 1.7 Mountain; Plain North-centre Italy; South Italy; Europe Free (outdoor); stable (barn)

37

each plus the no choice option. Each choice option was presented graphically as a bottle of milk (Fig. 1) to facilitate the respondent. Data were collected by means of a questionnaire that was divided in four sections: introduction, information about milk purchase consumption and preferences, choice experiment and socio-economic questions. The questionnaire was pre-tested with focus groups to spot possible problems related to the understanding of questions and the structure and graphical representation of the choice experiment. Between autumn 2009 and spring 2010, two trained interviewers collected 400 completed questionnaires through in person interviews in the provinces of Padova and Verona (north-eastern Italy) in places where people usually buy milk (i.e., supermarkets, groceries). 3. Results 3.1. Sample characteristics and milk consumption preferences Looking at sample characteristics (Table 3), the mean age of respondents is 42. On average, respondents have a good educational level, live in urban areas, are employed in the services sector and are in families with 2.9 members. Eighty per cent of the family members drink milk. Weekly mean consumption coincides with the median that is 4 l per family. Average weekly consumption, per person, is 1.7 l. There is significant variability in the amount of milk purchased per week per family, ranging from 0.25 to 14 l. The average price spent per litre is 1 € with a minimum of 0.45 €/l and a maximum of 2 €/l. In terms of interviewees’ family preferences (Table 4), we included pasteurised milk (55.8%) and UHT milk (46.5%). Nevertheless, the consumption of fresh milk (33.5%) is quite common, while organic milk is purchased just by a minority (6.8%) of the sample. The factors that have the greatest impact on milk purchase (Table 5) are place of origin (3.53)2, trust in the seller or supply chain (3.38), and price (3.20). Instead, the factors that are considered less influential are advertising (1.46), other people’s suggestions (1.84), habits (2.51) and brand (2.60). Looking at what consumers consider to be more influential in terms of quality of the milk produced (Table 6), animal feedstuffs (hay in particular) play the most important role (4.58)2, followed by the environment in which the animal is reared (4.36), and whether the animal is kept in a barn or is free to graze (4.26). Other important considerations are the reputation of the processing firm (4.17) and hygienic conditions in the barn (4.01). Little attention is given to the breed of cow. 3.2. Economic analysis: choice experiment results Choice Experiment data were analysed with Nlogit 4.0 using dummy coding. Three models were developed to analyse choice experiment data. We introduced alternative specific constants (ASC) in the utility functions for each alternative in all models. In the first two analyses, we applied multinomial logit models (MNL); in the third analysis, data were treated with a Latent Class model to consider the possibility of segmentation of purchase preferences into different consumer groups. Despite having the same analytical technique, the first two models are differentiated by the utility function specification. 3.2.1. Model 1: MNL base model The utility function for each i-th option in the MNL base model is written as follows: 2 Values in parentheses are calculated as an average on a Likert scale from 1 (minimum) to 5 (maximum).

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T. Tempesta, D. Vecchiato / Food Quality and Preference 27 (2013) 35–43

Table 2 Choice Experiment choice profiles. Choice set

Choice profile

Option

Area of production

Origin

Cows rearing

Price (€/l)

1

1 2 3 4

A B C D

Mountain Plain Plain Mountain

Europe South Italy Europe North-centre Italy

Free Stable Free Free

1.7 1.7 1.3 1.7

2

5 6 7 8

A B C D

Plain Plain Plain Mountain

North-centre Italy North-centre Italy North-centre Italy South Italy

Free Stable Free Stable

1.3 1 1 1.3

3

9 10 11 12

A B C D

Plain Mountain Mountain Plain

South Italy North-centre Italy North-centre Italy Europe

Free Stable Free Stable

1 1.3 1 1

4

13 14 15 16

A B C D

Mountain Plain Mountain Mountain

North-centre Italy North-centre Italy South Italy Europe

Stable Stable Free Stable

1 1.7 1 1

Fig. 1. Graphical representation of a choice set.

Uðxi ¼ ASC þ borigin þ bfree

rearing



south OSi

þ borigin

north ONi

FRi þ bprice PRICEi

þ bmountain PMi ð2Þ

where ASC = dummy for the ‘‘none of these/no choice’’ option; OS = dummy for origin south Italy; ON = dummy for origin northcentre Italy; PM = dummy for area of production mountain; FR = dummy for rearing - free; PRICE = price in €/l. MNL results for the base model3 are reported in Table 7. All coefficients are significant (p < 0.05) at a 95% confidence level. Coefficient signs are consistent with expectations. The pseudo r-squared is reasonably good for this kind of model4. All the independent variables 3 Results for the MNL base model are reported for the sake of completeness and for comparison purposes given that the IIA assumption is not satisfied. 4 According to Hensher, Rose, and Greene (2005), a good pseudo r-squared for this model ranges from 0.2 to 0.4. In particular, an R-squared of 0.4 is equivalent to an Rsquared of 0.8 in a normal regression.

but price increase consumers’ utility. The most important attribute is the geographical area of origin. People prefer milk produced in north-centre Italy, followed by milk produced in south Italy. The rearing techniques also increase the people’ utility, but the effect is lower than geographical area of origin. Finally people ascribe the lowest importance to the area of production. On average it seems that consumers have only a slight preference for the milk coming from cows reared in mountain areas. As stated in Eq. (1), it is possible to estimate the premium price or WTP by calculating the ratio between the coefficient of the independent variables and the coefficient of the price attribute. The mean premium price given to milk produced in north-centre Italy compared with that produced in other European countries is 1.43 €/l. The WTP is lower for milk produced in south Italy (0.68 €/l), for free-reared cows (0.62 €/l) for milk from mountain cows (0.17 €/l). These values correspond to an increase in the mean price paid per litre by the interviewees (1 €/l) of 143%, 68%, 62% and 17% respectively.

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T. Tempesta, D. Vecchiato / Food Quality and Preference 27 (2013) 35–43 Table 3 Interviewee characteristics. n.

%

Family position

Husband Wife Son/daughter Other

77 225 54 44

19.3 56.3 13.5 11.0

Age

Less than 30 years Between 30 and 50 years More than 50 years

93 205 102

23.2 51.2 25.5

Family members

1–2 3–4 5 and more

140 235 25

35.0 58.8 6.3

Educational level

Primary Lower secondary Secondary Graduate

20 99 199 81

5.0 24.8 49.8 20.3

Sector of activity

Agriculture Industry Services Retired or housewives

22 69 206 103

5.5 17.3 51.5 25.8

Place of residence

Urban area - centre Urban area - suburbs Rural area - village Rural area - scattered housing

140 148 27 85

35.0 37.0 6.8 21.3

3.2.2. Model 2: MNL quantity-cost (MNL-QC) interaction In the second MNL model (we will refer to this model as MNLQC), we introduced the interaction term quantity multiplied by price in the indirect utility function. This function allows us to understand in what terms the mean WTP is influenced by the weekly quantity of milk purchased (in litres). This additional information (with respect to model 1) is useful in order to better understand consumers’ behaviour for the design of effective market strategies. Furthermore, the introduction of an interaction term accomplishes the IIA assumption for this kind of model as explained in Section 2.1. The utility function considered is written as follows:

Uðxi ¼ ASC þ borigin þ bfree



rearing

south OSi

þ borigin

north OMi

þ bmountain PMi

FRi þ þbprice-quant PRICE QUANTi þ bprice PRICEi 

ð3Þ where PRICE_QUANTi is the interaction term PRICE ⁄ QUANTITY of milk purchased weekly by each interviewee family. In this case, the formula to calculate the WTP in the Eq. (1) becomes:

WTPj ¼  ¼

@U=@xj @U=@p bj ; for each j-th attribute: bprice þ QUANTITY  bpricequantity ð4Þ

Table 4 Characteristics of the milk consumed.

Fresh milk Pasteurised milk UHT Whole Semi-skimmed Skimmed Organic

n.

%

134 223 186 169 244 25 27

33.5 55.8 46.5 42.3 61.0 6.3 6.8

Table 5 Factors considered when buying milk, with average scores from a five-point scale (5 indicating strong agreement).

Price Brand Habit Origin Trust in the seller or supply chain Advertising Advice of other people

Mean

Std. Deviation

3.205 2.605 2.515 3.530 3.388 1.468 1.843

1.052 1.099 1.182 1.205 1.096 0.718 0.845

Table 6 Factors affecting milk quality, with average scores from a five-point scale (5 indicating strong agreement).

Environment in which the animal lives Hygiene in the stable Rearing method (in the barn or outdoors) Type of feed (hay or concentrates) Seriousness of the company that processes and bottles milk Breed of the animal

Mean

Std. Deviation

4.365 4.013 4.265 4.583 4.175

0.757 0.964 0.769 0.624 0.756

2.610

1.054

where bprice-quantity is the coefficient for each specific interaction term created by multiplying the attribute price by the weekly quantity of milk purchased. The coefficients of model 2 (Table 8) are quite similar to those of model 1. There is an improvement in the LL function and in the adjusted R-squared. According to both the Likelihood-ratio test and Wald-test (Table 9) the introduction of the price-quantity interaction term in model 2 is significant at a 90% confidence level (p < 0.1). All estimated coefficients are statistically significant at a 95% confidence level (p < 0.05), with the exception of the interaction term price-quantity that is significant at a 90% confidence level (p < 0.1). This level of significance can be considered acceptable if we take into account the imprecision of interviewees in providing average consumption data for goods and services on a weekly and monthly basis. By means of the MNL-QC model it is possible to analyse the effect of the quantity purchased on the premium price. The sign of the interaction term is negative meaning that the WTP decreases as consumption increases. This finding is coherent with the consumer theory. In the last three columns of Table 8, we report the WTP estimates for 3 levels of milk consumed weekly per family: minimum consumption (0.25 l/week), mean consumption (4 l/week) and maximum consumption (14 l/week). The premium price for 1 l of milk from north-centre Italy drops by 36% from the minimum consumer (0.25 l/week – WTP 1.65 €/l) to the maximum consumer (14 l/week – WTP 1.05 €/l). A similar behaviour can be noticed for the other attributes: for milk produced by free-reared cows, the WTP ranges from 0.71 €/l to 0.46 €/l, and for mountain-produced milk, the WTP varies from 0.19 to 0.12 €/l. If the estimates for the attribute production of north-centre Italy in model 1 are compared with those of model 2 for the families with the highest weekly milk consumption, the effect of quantity is a 26% reduction of WTP. Thus, if we would like to design market policies to preserve market quotas for milk produced in north-centre Italy, we could raise the price by 1.05 €/l according to the model 2 results instead of 1.43 €/l as suggested by model 1.

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T. Tempesta, D. Vecchiato / Food Quality and Preference 27 (2013) 35–43

Table 7 Base model results.

ASC Cows rearing – free Origin – South Italy Origin – North-centre Italy Area of production – Mountain Price LL AIC BIC

Estimate

Std. Error

T-value

p-value

WTP(€/l)

2.149 1.269 1.406 2.939 0.343 2.061 1477.6 1.855 1.875

0.343 0.079 0.164 0.153 0.103 0.190

6.269 16.122 8.554 19.150 3.342 10.871

0.000 0.000 0.000 0.000 0.001 0.000 R2 R2-adjusted

0.62 0.68 1.43 0.17 0.426 0.425

Table 8 The model with the interaction term price-quantity.

ASC Cows rearing – free Origin – South Italy Origin – North-centre Italy Area of production – Mountain Price Price-quantity LL AIC BIC

Estimate

Std. Error

T-value

p-value

2.182 1.269 1.405 2.938 0.346 1.761 0.073 1476.1 1.854 1.877

0.345 0.079 0.164 0.153 0.103 0.253 0.041

6.315 16.126 8.550 19.144 3.365 6.951 1.783

0.000 0.000 0.000 0.000 0.001 0.000 0.075

Table 9 Likelihood ratio test and Wald test of Model 2 compared to Model 1. Likelihood ratio test

#Df

LogLik

MNL1 MNL2 Wald test MNL1 MNL2

6 7

1477.6 1476.1 Res.Df 1594 1593

Df

Chisq

WTP for a given quantity (€/l) 0.25 (l/week)

4 (l/week)

14 (l/week)

0.71 0.79 1.65 0.19

0.62 0.68 1.43 0.17

0.46 0.5 1.05 0.12

R2 R2-adjusted

0.427 0.426

Table 10 Statistical indicators for models comparison. p-value

1 Df

2.9925 Chisq

0.084 p-value

1

3.1802

0.074

3.2.3. Model 3: latent class model For the estimation of the Latent class model, we applied the utility function with the price-quantity interaction term described by Eq. (3). The identification of the number of classes in a Latent class model is an exogenous process. We tried to estimate the model with two and three classes. Considering the LL function, AIC, BIC, HQIC and the pseudo R-squared indicators (Table 10), the model with three classes has a better performance. Results for the 3 classes model are reported in Table 11, where 54% of respondents belong to class 1, 18% to class 2 and the remaining 28% to class 3. Estimates for the 3 classes are quite different. We will now analyse the results obtained for each class. All coefficients for people belonging to class 1 are significant at a 95% confidence level (p < 0.05). Comparing the class 1 model results with the MNL and MNL-QC models, we detect an inversion of the sign of the coefficient for mountain production, which becomes negative contrary to expectations. Consumers belonging to class 1 do not seem to care much about the area of production, but appear to place more importance on the geographical area of origin and the way cows are reared. The preference for milk produced in north-centre Italy is much higher than that for milk produced abroad and in south Italy. The impact of the interaction term on the premium price in this model is higher than in the MNL-QC one. Looking at class 2, we notice that all coefficients are statistically significant at a 95% confidence level apart from the interaction term quantity-price that is significant at an 82% confidence level.

LL AIC BIC HQIC McFadden pseudo R2

MNL

MNL-QC

LCM-2

LCM-3

1477.6 1.8545 1.8747 1.8620 0.425

1476.1 1.8539 1.8774 1.8626 0.426

1380.427 1.7443 1.7947 1.7630 0.464

1352.727 1.7197 1.7970 1.7484 0.475

People belonging to this class have only a generic preference for Italian milk, independently of the north or the south location of the livestock farm. On the contrary, they seem to attribute more importance to milk produced in mountain areas. Note however that only a small fraction (18%) of the sample belongs to this class. The coefficients for class 3 are all significant at a 95% confidence level (p < 0.05) with the exception of the price coefficient. People belonging to this class consider the country of origin very important. They prefer milk produced in Italy (better if north Italy). At a lower level they also seem concerned about rearing methods and area of production. Given the insignificance of the price coefficient, it does not make sense to estimate the WTP for this class of people. Nevertheless, it is still possible to look at the WTP5 for the members of class 1 and 2. For the members of class 1, the WTP for the milk produced in north-centre Italy is lower than that estimated with MNL models and ranges from 1.17 €/l for families with the minimum weekly milk consumption to 0.68 €/l for those with the maximum one. The estimated WTP for the mean consumption (4 l/week) is 68.5% of that estimated with model 1 and reported in Table 7. Similar considerations can be made for the WTP for milk produced by cows reared outdoors: WTP ranges from 0.15 to 0.12 €/l for families with the minimum to the maximum weekly milk consumption, respectively.

5 Given the presence of the interaction term price-quantity, WTP has been estimated using Eq. (4).

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T. Tempesta, D. Vecchiato / Food Quality and Preference 27 (2013) 35–43 Table 11 Latent Class model results. Estimate

Std. Error

T-value

p-Value

Class1 ASC Cows rearing – free Origin – South Italy Origin – North-centre Italy Area of production – Mountain Price Price-quantity

6.215 3.391 1.287 5.485 1.994 4.644 0.242

0.701 0.394 0.354 0.537 0.372 0.599 0.078

8.87 8.604 3.633 10.207 5.364 7.75 3.096

0.000 0.000 0.000 0.000 0.000 0.000 0.002

Class2 ASC Cows rearing – free Origin – South Italy Origin – North-centre Italy Area of production – Mountain Price Price-quantity

5.555 2.093 3.970 3.183 2.406 5.227 0.150

1.450 0.227 0.458 0.391 0.210 1.007 0.109

3.831 9.219 8.676 8.141 11.434 5.191 1.369

0.000 0.000 0.000 0.000 0.000 0.000 0.171

Class3 ASC Cows rearing – free Origin – South Italy Origin – North-centre Italy Area of production – Mountain Price Price-quantity

2.060 0.346 2.153 3.763 0.884 0.095 0.095

0.464 0.101 0.378 0.351 0.127 0.290 0.047

4.444 3.423 5.69 10.715 6.978 0.329 2.038

0.000 0.001 0.000 0.000 0.000 0.742 0.042

Estimated latent class probabilities Class1 Class2 Class3

0.54 0.18 0.28

0.025 0.040 0.036

21.9 4.536 7.852

0.000 0.000 0.000

LL AIC BIC McFadden Pseudo R-squared

1352.7 1.720 1.797 0.475

For the members of class 2 we again have a lower WTP (looking at the mean consumption values, 4 l/week) than that reported in Table 7 for milk produced in north-centre Italy (0.55 vs. 1.43 €/l) and for milk produced by cows reared outdoors (0.36 vs 0.62 €/l). The WTP is exactly the same for milk produced in southern Italy (0.68 €/l) and is higher than that obtained with the MNL model for milk produced on the mountains (0.41 vs. 0.17 €/l). The WTP estimates obtained for the first two classes of the LCM model seem to be more reliable than those of the MNL models (MNL and MNL-QC) if we consider the average price per litre of milk actually paid (1 €/l) by the interviewees.

4. Discussion and conclusions The aim of this study was to investigate the preferences of milk consumers with reference to three attributes: origin (south Italy, north-centre Italy, or other European Union countries), area of production (mountain or plains), and rearing method (cows grazed or kept in a barn). We also tried to verify if the choice experiment can be useful in order to quantify the willingness to pay a premium price for these attributes. Using a latent class approach it was possible to highlight the presence of a not negligible degree of heterogeneity among the interviewees’ preferences at least for some attributes. On the one hand, people living in northern Italy tend to prefer milk produced in north-centre Italy (or in general in Italy) and coming from grazed cows. This finding is supported by the results of the LCM model applied to analyse the heterogeneity of the interviewees’ preferences.

WTP for a given quantity (€/l) 0.25 (l/week)

4 (l/week)

14 (l/week)

0.72 0.27 1.17 0.42

0.60 0.23 0.98 0.36

0.42 0.16 0.68 0.25

0.40 0.75 0.60 0.46

0.36 0.68 0.55 0.41

0.29 0.54 0.43 0.33

This result is consistent with the declarations of the interviewees who indicated that animal feed and environmental quality are the two main factors that influence milk quality. As stressed by Verlegh and Steenkamp (1999), the preference for food country of origin has three components: cognitive, affective and normative. According to the cognitive component, the country of origin represents a ‘‘signal for overall product quality’’ (p. 524). The affective component has a symbolic meaning of national pride. Finally, the normative component relates to the tendency to favour the local economy and national production. We can infer that the premium price given for the milk produced in north-centre Italy is mainly due to the normative component, given that the cognitive component is already included in the models in the other attributes (type of rearing, area of production). The results can be considered consistent with those of other authors. A preference for one’s national products has been found in several studies (Alfnes, 2004; Alfnes & Rickertsen, 2003; Bolliger & Réviron, 2008; Carpio & Isengildina-Massa, 2009; Loureiro & Umberger, 2007; Umberger, et al., 2003). The trend to differentiate the premium price among different areas of origin in the same country is also not new (Burchardi et al., 2005; Darby, Batte, Ernst, & Roe, 2006). The same applies to the positive WTP for production that takes into consideration the animal well-being (Conner & Oppenheim, 2008; Lagerkvist et al., 2006; Napolitano, Pacelli, Girolami, & Braghieri, 2008). On the other hand it is noticeable that about half of the interviewees prefer the milk produced on the plain and not on the mountains as we expected. This result can be explained by two factors: first that nowadays the rearing method on mountain farms is similar to that on plain farms and second that the large majority of

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T. Tempesta, D. Vecchiato / Food Quality and Preference 27 (2013) 35–43

interviewees live on the plain. Mountain is probably not synonymous with grazing for a large part of the population. This is coherent with the evolution of the livestock sector in Italy. The traditional alpine summer pasture has been abandoned in the last decades or its use has changed. Today a large part of the mountain pasture is used to graze beef and not dairy cattle. With reference to the market strategies and in particular to the possibility that people pay a premium price for some milk attributes, the study highlighted some interesting elements. First an inverse relationship has been found between the quantity consumed and the WTP. This result suggests that to design correct market price strategies it is not possible to consider only the average WTP or the distribution of frequencies of the individual average WTP, given that the premium price can reduce both the number of consumers and the quantity that each consumer buys. The analysis of the distribution of WTP cannot provide a good measure of the market share of each level of premium price. Using the results of the base MNL model (Table 7) to calculate the premium price for milk from north-centre Italy for the mean quantity of milk purchased weekly by our sample (4 l/week, excluding outliers), we found that 58% of our respondents are able to spend an extra 0.72 €/l. The problem is that they account for only 35% of the milk purchased. Moreover the latent class model results highlighted that the preferences and WTP can vary widely among different groups of consumers. This increases the difficulty in finding a unique relationship between price and market share. Furthermore for nearly one third of the interviewees it was not possible to estimate the WTP since the coefficient of the price was not significant. It can be concluded that the choice experiments are a useful instrument in order to investigate consumer preferences, at least with reference to the Italian milk market. This approach to a certain extent helps in defining market strategies that could be useful to the Italian milk producers to widen their market share. On the other hand, considering the results of our experiment, it is difficult to affirm that through this approach it would be possible to quantify a single/unique premium price that people are willing to pay for a certain characteristic of the milk: several models should be applied and the results obtained from different models should be carefully considered. Therefore CE WTP estimates should be considered carefully in real market contexts because different models provide not always convergent results.

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