European consumers’ willingness to pay for red meat labelling attributes

animals Article European Consumers’ Willingness to Pay for Red Meat Labelling Attributes Emilia Cubero Dudinskaya 1,* , Simona Naspetti 2,* , Georgios Arsenos 3, Emmanuelle Caramelle-Holtz 4, Terhi Latvala 5, Daniel Martin-Collado 6, Stefano Orsini 7, Emel Ozturk 1and Raffaele Zanoli 1,*   Citation: Cubero Dudinskaya, E.; Naspetti, S.; Arsenos, G.; Caramelle-Holtz, E.; Latvala, T.; Martin-Collado, D.; Orsini, S.; Ozturk, E.; Zanoli, R. European Consumers’ Willingness to Pay for Red Meat Labelling Attributes. Animals 2021,11, 556. https://doi.org/doi:10.3390/ ani11020556 Received: 20 January 2021 Accepted: 17 February 2021 Published: 20 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Agricultural, Food and Environmental Sciences (D3A), UniversitàPolitecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy; [email protected] 2 Department of Materials, Environmental Sciences and Urban Planning (SIMAU), UniversitàPolitecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy 3Faculty of Veterinary Medicine, Aristotle University of Thessaloniki, P.O. Box 393, GR-54124 Thessaloniki, Greece; [email protected] 4The French Livestock Institute/Institut de l’Elevage (IDELE), Campus INRA—Chemin de Borde Rouge, CEDEX, BP 42118-31321 Castanet Tolosan, France; [email protected] 5Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00791 Helsinki, Finland; ter[email protected] 6 Animal Production and Health Unit, Agrifood Research and Technology Centre of Aragon (CITA), Gobierno de Aragón, Avenida Montañana 93, 050059 Zaragoza, Spain; [email protected] 7Organic Research Centre, Trent Lodge, Stroud Road, Cirencester, Gloucestershire GL7 6JN, UK; [email protected] *Correspondence: [email protected] (E.C.D.); [email protected] (S.N.); [email protected] (R.Z.); Tel.: +39-07-1220-4929 (R.Z.) Simple Summary: Given the decrease in red meat consumption in the last decade, it is crucial for red meat producers to understand consumer preferences. This international study analyses the European consumer preferences for red meat (beef, lamb and goat) in seven countries: Finland, France, Greece, Italy, Spain, Turkey and the United Kingdom. Through a survey with hypothetical choice situations (choice experiment), 2900 responses were collected. Advanced econometric models were estimated to identify the diversity of preferences among consumers at the country level. The results indicate substantial differences between the most relevant attributes for the average consumer in each country. Nevertheless, national origin and organic labels were highly valued in most countries. Abstract: Food consumption in Europe is changing. Red meat consumption has been steadily decreasing in the past decades. The rising interest of consumers for healthier and more sustainable meat products provides red meat producers with the opportunity to differentiate their offers by ecolabels, origin and health claims. This international study analyses the European consumer preferences for red meat (beef, lamb and goat) in seven countries: Finland, France, Greece, Italy, Spain, Turkey and the United Kingdom. Through a choice experiment, 2900 responses were collected. Mixed multinomial logit models were estimated to identify heterogeneous preferences among consumers at the country level. The results indicate substantial differences between the most relevant attributes for the average consumer, as well as their willingness to pay for them in each country. Nevertheless, national origin and organic labels were highly valued in most countries. Keywords: consumer preferences; discrete choice experiment (DCE); organic food and farming; country-of-origin; fat; Halal; carbon footprint; protein content; convenience 1. Introduction Food production and consumption tendencies in Europe have shown significant changes in recent years, especially regarding meat consumption. While beef and lamb meat production have decreased since the beginning of the nineties, poultry and pig meat production have increased during the same period [ 1 , 2 ]. Concerns about health issues and Animals 2021,11, 556. https://doi.org/10.3390/ani11020556 https://www.mdpi.com/journal/animals Animals 2021,11, 556 2 of 16 environmental sustainability have driven away beef consumers [ 3 – 6 ]. Its peculiar taste, with consumers perceiving it as fatty, difficult to cook, and sold in family-sized portions, has negatively impacted lamb meat consumption. Nevertheless, when new lamb meat products were introduced for younger consumers (e.g., single portions), lamb meat sales increased [ 7 ]. Addressing specific consumer needs through product differentiation could help revitalise the European red meat market [8]. In Europe, the rising interest in societal benefits (e.g., sustainability, biodiversity) as well as the growing concerns on more “ethical” food production methods [ 9 , 10 ] provide red meat producers with the opportunity to compete through differentiation. However, societal benefits are usually embedded in credence cues that the consumers cannot evaluate before or even after consumption, given the lack of expertise or practical possibilities [ 11 ]. In this case, meat producers must find alternative ways to communicate such benefits. The use of ecolabels and specific claims represent tools to inform the concerned consumer about attributes of interest. However, even if a specific label or claim is perceived positively by consumers, willingness to pay for that attribute might vary, as consumer preferences and purchase intentions are heterogeneous between countries [12]. This study presents the results of an econometric analysis of consumer preferences and willingness to pay for health-related and ethical claims in the red meat European market. Previous studies have evaluated consumer preferences for either beef, lamb or goat meat individually and mainly focused on specific national contexts [ 8 , 13 , 14 ]. However, comprehensive trans-national research comparing preferences for all red meat types and their key attributes was still missing. This study aims to fill this gap through a discrete choice experiment (DCE) on six European countries and Turkey. Based on a systematic literature review on Scopus and Web of Knowledge, 458 papers were identified using keywords related to consumption, meat, choice experiments/choice model or conjoint analysis/conjoint design. Only studies published in English in peerreviewed journals were considered. Articles in which the main subject under study was not the consumer or the consumer’s perception of meat attributes were excluded. Additionally, the definition of meat was restricted to fresh meat, avoiding processed products (e.g., sausages, ham) and fish. Only studies using choice experiments or conjoint analysis were taken into account, retrieving 92 articles. After analysing these articles, the most significant attributes for the consumer when selecting meat were identified as: origin [ 15 – 17 ], price [ 18 , 19 ], animal welfare [ 20 – 22 ], fat content [ 13 , 23 , 24 ], type and cut of meat [ 25 – 27 ], organic [ 28 – 30 ], carbon footprint [ 31 – 33 ], certifications such as Protected Geographical Indication (PGI) and Protected Denomination of Origin (PDO) [ 8 , 34 , 35 ], nutritional/health claims [ 36 – 38 ] and seasoning/natural presentations [39,40]. According to previous studies, consumers tend to prefer meat of local or national origin [ 16 , 41 ]. However, other studies found that attributes related to quality and traceability are often more important than the meat’s origin [ 19 ]. In Europe, origin certifications often embed a quality assurance scheme, for example, PDO and PGI in the European Union [ 42 ]. The PGI label emphasises that the quality, reputation or other characteristics of the product itself is primarily attributable to its geographic origin. The Protected Designation of Origin (PDO) label identifies a product whose characteristics or quality are originated by having every part of the production, processing and preparation process taking place in a particular geographical location [ 43 , 44 ]. Nevertheless, studies involving these quality schemes are limited [45,46]. The organic label is also perceived as a proxy for product quality. In the European Union, the mandatory organic logo (Euroleaf) guarantees that the products have been produced following the best environmental practices, a high level of biodiversity, the preservation of natural resources, the application of high animal welfare standards, and a production method using natural substances and processes [ 47 ]. Although organic products have gained high interest by consumers [ 28 ], perceiving them as healthier, safer and with better organoleptic qualities [ 29 , 48 – 51 ], there are still conflicting results regarding Animals 2021,11, 556 3 of 16 consumers’ willingness to pay for them [ 27 , 39 , 52 , 53 ]. The carbon footprint label also shows consumers the commitment to environmental sustainability that the producers have [ 54 ]. However, previous research reported contradictory results on the importance that consumers give to this label when making their choices [9,33,55]. Although the average consumer is concerned about animal welfare [ 28 , 56 , 57 ], previous studies showed heterogeneous preferences [ 32 , 37 ]. According to de Jonge and van Triip [ 21 ], this variety of results is a product of the complexity of the animal welfare concept, which is highly dependent on consumer perception, their inferences on the diverse animal welfare levels, as well as how the message is communicated. In this study, given the difficulty of communicating animal welfare claims beyond the EU-wide animal welfare standards, animal welfare was not included in the attributes. Instead, the Halal certification label was included. Though Islam does not tolerate animal abuse, most halal certification does not incorporate specific animal welfare requirements, while pre-slaughter stunning is still a contentious issue [ 58 ]. Islamic rules require the animals to be alive, healthy, fed and watered before the slaughtering. The animal must be killed incurring the least pain possible, and the carcass must be drained of blood [ 59 ]. Some European Union countries (i.e., Belgium and Denmark) require that all animals be previously stunned before Halal slaughtering, while a recent EU Court ruling has established that all organic meat needs to be stunned, including that bearing the Halal label [ 60 , 61 ]. Research on reversible pre-slaughter stunning is still ongoing to improve animal welfare during slaughter yet still meet the spiritual requirements for Halal [ 58 ]. Previous research also identified that many consumers, including non-Muslims, perceive the Halal label as a credence quality attribute [ 62 , 63 ], and this is a growing trend [ 64 ]. There is not much evidence on consumer perception of Halal certification in Europe, though one study showed that some non-Muslims question animal welfare issues in relation to the Halal certification in the UK [ 65 ]. However, a recent EU-funded study concluded that since “there is little expressed use of animal welfare as a purchase criterion, little understanding of the slaughter process and an inability to distinguish between different methods of stun, providing additional information on the different methods used would not appear to aid a consumer decision” [66]. There is also a wide variety of nutritional and health labels used for diverse purposes. As red meat remains the most important dietary source of protein [ 67 ], a “high protein content” claim was selected for the present study. There is little previous literature reporting studies on consumers’ preferences of protein content in meat products [ 36 , 68 ], showing significant differences in cross-cultural settings. Moreover, there is a growing trend to use ready to cook/eat foods [ 69 ] and to look for healthier products [ 56 ] with less fat. However, the previous literature does not offer clear and homogeneous results in both cases, as consumers present opposite views regarding these attributes’ desirability, according to their culture and depending on the meat cut [38]. Given the variability of results from prior studies, all the previously mentioned attributes were included in this research. In addition, to account for cross-cultural differences, the willingness to pay (WTP) for each attribute was estimated at country level. Previous studies have established that product differences can be product differentiators only if they create valued benefits that consumers are willing to pay for [ 70 ]. Understanding the value that consumers give to each meat attribute and their WTP will provide essential information to producers, allowing them to identify which labels and quality claims consumers are willing to pay for. The present paper is organised as follows. In the next section, there is a description of the methods applied, followed by the presentation of the results for each country. Then, the results are discussed, and conclusions are presented in the final section. Animals 2021,11, 556 4 of 16 2. Materials and Methods 2.1. The Discrete Choice Model A DCE is a survey-based methodology widely used and well established for modelling consumers’ preferences [ 71 , 72 ]. The method simulates a trading market with different attribute combinations. “Different levels of item attributes are combined, and these combinations are configured into a choice set according to the unbiased and efficient principles of statistical estimation” [ 73 ]. In each choice set, respondents select the option they like the most, after comparing the given alternatives. All alternatives in a choice set are described by the same attributes, and each of these attributes can take one level from a set of possible levels (e.g., the presence or absence of the organic label) [ 74 ]. Moreover, by including price/cost as one of the attributes of the good, the willingness to pay of a respondent for a specific attribute can also be calculated. The theoretical model is based on the Lancastrian consumer theory [ 75 ] and the random utility model (RUM) framework [76], in which the utilities of different goods can be broken down into separate utilities for their attributes. Faced with a set of Javailable alternatives, rational decision-makers would select the alternative with the highest utility to them. This utility is known to the decision-maker, but not to the researcher [77]. Since there are aspects of utility that are unknown to the researcher, the total utility for the alternative j for the respondent i is decomposed as: Uij = Vij +εij (1) where the researcher can only observe V ij , the deterministic part of the utility. The remaining part of the utility ( εij ) is unobservable for the researcher and treated as random. The systematic component (V ij ) can be approximated by a linear function of observed attributes in the vector Xij and the utility parameters of each attribute collected in the vector β: Vij =βXij (2) Assuming heterogeneous individual preferences across respondents [ 78 ], the discrete choice model may be specified as a mixed multinomial logit (MMNL) model that allows the unobserved, random part of the utility to follow any distribution [79]. Thus, the utility of individual i from alternative jis specified as [77]: Uij =β0 iXij +εij (3) where β ’ i , is a vector of unobserved coefficients that varies between individuals but not over alternatives, X ij is a vector of observed variables that relate to each alternative jand respondent i, and εij is a random term that is i.i.d. extreme value over individuals and alternatives. For the present research, the linear specification of the utility for an individual i for the alternative j is: Uij =αij +βprice Price + βhalal Halal + βnational origin National origin + βeu origin EU origin + βPGI PGI + βorganic Organic + βcarbon Carbon + βLow fat Low fat + βprotein Protein + βformat Format + εij (4) The random parameters β ’ (except the price) were assumed to be normally distributed to allow both positive and negative preferences for each attribute. A negative lognormal distribution was assumed for the price parameter to attain better behavioural fit to microeconomic theory. Including the monetary cost in the choice experiment offers wide information on the trade-offs that consumers make among the benefits provided by the different alternatives (with diverse attributes) and their price, allowing the estimation of the WTP for each attribute. Animals 2021,11, 556 5 of 16 The utility was specified directly in the WTP space [ 80 ] to obtain better results and reduce the range of behavioural implausibility [81]: Uij =αij −λipij + (λiγi)’ xij +εij (5) where λi = ( βi price / µi ). βi price and µi are, respectively, an individual-specific coefficient for price and an individual-specific scale parameter; while γi= (ci/λi), where ci= (βi/µi). Estimating an MMNL model directly in the WTP-space offers the direct advantage of obtaining parameters and estimated standard errors that can be immediately interpreted as marginal WTP values [ 80 , 82 , 83 ]. This means that the estimated results are already presented in the currency used to make the choice experiment, simplifying the interpretation of the results. 2.2. Product and Attributes Selection A labelled DCE was developed, including four labelled red meat alternatives (lamb leg, lamb chops, goat chops, T-bone steak) and a no-choice option. The type of meat and cuts were based on the most consumed cuts of lamb and goat meat in the countries investigated, as they often present less variety than beef. As the most consumed lamb and goat cuts were chops and legs (for lamb), a T-bone steak was considered as the most comparable beef cut, to avoid bias for more convenient cuts or with no presence of bones. Based on the literature review results and a qualitative study [ 84 ], nine credence attributes were selected (Table 1). Given the high number of attributes, the number of levels for each attribute was kept low to avoid excessive participants’ cognitive burden [85]. Table 1. Attribute levels. Attributes Levels Considered Price Average price (country-specific) −30% +30% Origin National EU New Zealand (reference) PGI/PDO PGI/PDO No label (reference) Organic label Organic label No label (reference) Halal label Halal label No label (reference) Carbon footprint label Low carbon footprint label No label (reference) Protein content label High protein content label No label (reference) Fat content Low-fat Fatty (reference) Format (convenience) Ready-to-cook Plain (reference) All alternatives were presented to the respondent simultaneously with all their attributes (Figure 1). The product name, origin and price were introduced through text in each alternative, while the other attributes were presented graphically either using labels or by modifying the original image (see below an explanation for each attribute). To avoid any biases, only the attributes under study were modified (e.g., fat), keeping all the other characteristics (e.g., colour, size) unchanged between the images of the same product. The base price was calculated as the average price for each meat type and cut in each country. Then, it was pivoted in three levels, with variations of ± 30%. The price was expressed in local currencies. Animals 2021,11, 556 6 of 16 Animals 2021, 11, 556 6 of 16 Figure 1. Example of a choice set. The origin attribute was set to three levels: national, EU and New Zealand, as most lamb meat imports to the EU come from this country [86]. New Zealand was set as the reference category for differences in utility in the choice model. As a consequence, the estimated willingness to pay (WTP) for both national and EU origin needs to be interpreted as a premium price above the New Zealand origin. The PGI and PDO labels were presented through their official labels in the primary language of each country. The choice to choose either PGI or PDO was based on the presence/absence or predominance of each certification scheme for lamb meat in each country. In Finland, there exists only one PDO for red meat. In Turkey, being outside the EU, PGI and PDO regulations do not apply. Therefore, in both countries, the PGI and PDO labels for national meat were omitted as there are no certifications available in these countries. The organic label was also adapted by country. Turkish respondents were presented with their local logo, while, for the European countries, the mandatory EU organic logo was used. The Halal certification, the carbon footprint and the “high protein content” labels were also adapted to each country. With respect to the carbon footprint attribute, the Carbon Trust label was used, which has gained popularity in the UK in recent years. The label was presented in the primary language of each country. In the case of the “high protein content” claim, a simple label of red letters on a white background was created for the present research, as there was no homogeneous label used across the countries under study. The attributes fat content and format (convenience) were introduced to the respondents by modifying the alternative’s image. For example, for fat content, the base image of each cut was modified using image-editing software by adding or removing the presence of visual fat, as shown in Figure 2. No additional claims (or labels) referring to the fat content or format were added in any way. The objective was to keep it closer to reality, as people in the supermarket usually see the meat cut and the visual presence of fat (as a proxy to healthiness and meat quality) [23,87,88] when making their choice. In the case of format, the image was modified by adding additional ingredients (e.g., rosemary, pepper) to feature a ready-to-cook product. Figure 1. Example of a choice set. The origin attribute was set to three levels: national, EU and New Zealand, as most lamb meat imports to the EU come from this country [ 86 ]. New Zealand was set as the reference category for differences in utility in the choice model. As a consequence, the estimated willingness to pay (WTP) for both national and EU origin needs to be interpreted as a premium price above the New Zealand origin. The PGI and PDO labels were presented through their official labels in the primary language of each country. The choice to choose either PGI or PDO was based on the presence/absence or predominance of each certification scheme for lamb meat in each country. In Finland, there exists only one PDO for red meat. In Turkey, being outside the EU, PGI and PDO regulations do not apply. Therefore, in both countries, the PGI and PDO labels for national meat were omitted as there are no certifications available in these countries. The organic label was also adapted by country. Turkish respondents were presented with their local logo, while, for the European countries, the mandatory EU organic logo was used. The Halal certification, the carbon footprint and the “high protein content” labels were also adapted to each country. With respect to the carbon footprint attribute, the Carbon Trust label was used, which has gained popularity in the UK in recent years. The label was presented in the primary language of each country. In the case of the “high protein content” claim, a simple label of red letters on a white background was created for the present research, as there was no homogeneous label used across the countries under study. The attributes fat content and format (convenience) were introduced to the respondents by modifying the alternative’s image. For example, for fat content, the base image of each cut was modified using image-editing software by adding or removing the presence of visual fat, as shown in Figure 2. No additional claims (or labels) referring to the fat content or format were added in any way. The objective was to keep it closer to reality, as people in the supermarket usually see the meat cut and the visual presence of fat (as a proxy to healthiness and meat quality) [ 23 , 87 , 88 ] when making their choice. In the case of format, the image was modified by adding additional ingredients (e.g., rosemary, pepper) to feature a ready-to-cook product. Animals 2021,11, 556 7 of 16 Animals 2021, 11, 556 7 of 16 Figure 2. Examples of fat content presented in red meat cuts. 2.3. Data Collection and Analysis The data were simultaneously collected through an online survey from March to May 2019 in seven countries: Finland (FI), France (FR), Greece (GR), Italy (IT), Spain (ES), Turkey (TR) and the United Kingdom (UK). The respondents were selected by a third party (Qualtrics) using a quota sampling approach to achieve between-country comparability [89,90]. The quotas were established according to age and occupation by gender [91]. Only red meat consumers (at least once a year) were sampled. Respondents were between 18 and 64 years old, fully or partially responsible for the grocery shopping in their household and did not work or had a close relative working in the meat or catering industry. Two thousand nine hundred responses were collected. After removing incomplete or lowquality surveys (e.g., speeders, line-responses), 2866 usable responses were left (approximately 400 per country—see Table 2). Table 2. Collected responses per country. Country Total Responses Valid Responses Finland 417 413 France 416 414 Greece 403 400 Italy 419 417 Spain 420 417 Turkey 405 391 UK 420 414 Total 2900 2866 Figure 2. Examples of fat content presented in red meat cuts. 2.3. Data Collection and Analysis The data were simultaneously collected through an online survey from March to May 2019 in seven countries: Finland (FI), France (FR), Greece (GR), Italy (IT), Spain (ES), Turkey (TR) and the United Kingdom (UK). The respondents were selected by a third party (Qualtrics) using a quota sampling approach to achieve between-country comparability [ 89 , 90 ]. The quotas were established according to age and occupation by gender [ 91 ]. Only red meat consumers (at least once a year) were sampled. Respondents were between 18 and 64 years old, fully or partially responsible for the grocery shopping in their household and did not work or had a close relative working in the meat or catering industry. Two thousand nine hundred responses were collected. After removing incomplete or low-quality surveys (e.g., speeders, line-responses), 2866 usable responses were left (approximately 400 per country—see Table 2). Table 2. Collected responses per country. Country Total Responses Valid Responses Finland 417 413 France 416 414 Greece 403 400 Italy 419 417 Spain 420 417 Turkey 405 391 UK 420 414 Total 2900 2866 The questionnaire was developed in English. Then, it was translated and backtranslated to the primary language of each country [ 92 , 93 ]. Active collaboration and feedback from international researchers allowed cross-country conceptual, functional, and category equivalence [ 94 , 95 ]. The questionnaire included consumer sociodemographic and geographic data, as well as a hypothetical discrete choice experiment (DCE). It was pilot tested in each country before launch. Animals 2021,11, 556 8 of 16 2.4. DCE Design and Estimation A fractional D-efficient design with priors consisting of twenty-four choice sets in two blocks was generated (D-error = 0.256, A-error = 0.754) using the Ngene software [ 96 ]. In contrast to orthogonal designs, efficient designs do not merely try to minimize the correlation in the data for estimation purposes, but aim to result in data that generate parameter estimates with as small as possible standard errors. Each respondent was presented with twelve choice sets of five alternatives. This means that each respondent saw twelve different combinations of the five alternatives (lamb leg, lamb chops, goat chops, T-bone steak and a no-choice option) with different attribute levels (e.g., different prices, origins) defined according to the design. All choice sets and alternatives were presented randomly to avoid order-effect [ 97 ]. A “cheap talk” was introduced to respondents before answering the DCE, aiming to reduce the hypothetical bias [ 98 , 99 ]. Participants were introduced to all labels, their meanings and definitions [ 100 ]. They were also shown an example of a choice set. Respondents had unlimited time to answer each choice set [ 101 ]. A total of 34,392 choices were collected. The data were analysed using the APOLLO package in R [ 102 ]. For each country, an MMNL model in WTP-space was estimated based on the preference-space MMNL model’s priors. The log-likelihood was attained by Monte-Carlo simulation-based integration using Halton draws with 1000 replications. A scaling factor was used to ease convergence in the WTP-space. 3. Results The respondents’ sociodemographic characteristics and meat consumption frequency are presented in Table 3. In all countries, most consumers declare to consume beef at least once a month. However, the percentage of consumers that consume lamb or goat meat with the same frequency varies in each nation. Turkey is the country with higher lamb and goat meat consumption, followed by the United Kingdom and Spain. Finland is the country with lower consumption frequency for both lamb and goat meat. Table 3. Sociodemographic characteristics of the sample in percentages by country. Sociodemographic Variables FI FR GR IT ES TR UK Gender Female 49.4 50.7 48.5 49.6 50.4 49.1 50.0 Male 50.6 49.3 51.5 50.4 49.6 50.9 50.0 Age group 18–24 years old 14.0 13.3 12.5 11.3 11.3 19.2 14.3 25–34 years old 21.3 20.8 19.5 18.2 19.2 28.1 21.5 35–44 years old 21.3 22.5 25.2 23.5 26.4 23.3 21.5 45–54 years old 22.3 23.4 23.8 26.4 23.5 16.4 23.4 55–64 years old 21.1 20.0 19.0 20.6 19.6 13.0 19.3 Occupation Employed 66.6 68.1 56.5 61.6 63.8 56.3 73.7 Unemployed 10.4 8.5 18.5 11.3 17.7 5.4 3.4 Inactive (retired + students) 23.0 23.4 25.0 27.1 18.5 38.3 22.9 Regular red meat consumers (at least once a month) Beef 77.2 87.2 76.5 89.9 82.3 76.2 79.0 Lamb 12.8 37.9 25.0 33.6 45.8 61.4 51.2 Goat 7.0 7.7 21.8 18.0 21.6 24.6 14.0 Total number of respondents 413 414 400 417 417 391 414 The consumers’ choices regarding the meat cut also varied between countries ( Table 4 ). Beef T-bone was the preferred choice in Finland. Beef T-bone was the preferred cut in Greece too, though it was a close race with lamb chops; on the contrary, in Italy, the lamb chops were the preferred cut, closely followed by beef T-bone. In Spain, Turkey and the UK, there was a clear preference for the lamb chops over other meat types. Only in France, Animals 2021,11, 556 9 of 16 the lamb leg was the preferred cut. In all countries, most consumers preferred to choose a meat alternative than selecting none. Table 4. Percentages of selected cuts in the discrete choice experiment (DCE) per country. Cuts Selected in the DCE FI FR GR IT ES TR UK Beef T-bone 31 20 24 27 24 24 15 Goat chops 11 8 16 12 11 18 10 Lamb chops 18 21 23 29 29 29 30 Lamb leg 17 29 19 18 20 18 26 None 23 22 18 14 16 11 19 Total respondents 413 414 400 417 417 391 414 Willingness to Pay for Each Country The results of the MMNL in WTP-space are presented in Table 5. National origin and the organic label are the only statistically significant and positive attributes for all countries. The attributes EU origin, PGI/PDO, carbon footprint and low fat had positive coefficients when significant, while the attributes ready to cook and high protein content presented negative coefficients when significant. The Halal attribute was statistically significant and positively valued only in Turkey, while in the other countries where it was significant, it was perceived negatively. However, it is worth mentioning that the statistical significance of the standard deviation of the Halal attribute is relatively high in several countries, implying a high heterogeneity of taste for this attribute within those countries. Table 5. Estimated willingness to pay (WTP) for all countries in local coin. Estimates Countries FI (EUR) FR (EUR) GR (EUR) IT (EUR) ES (EUR) TR (TRY) UK (GBP) LL a−6587.84 −6864.38 −6984.07 −7018.80 −7071.07 −6696.65 −6858.30 BIC b13,379.88 13,933.02 14,171.57 14,242.03 14,346.58 13,596.18 13,920.87 Adj. Rho-square 0.1711 0.1385 0.0928 0.1255 0.119 0.11 0.1392 Mean estimates (normal distribution) Halal −0.693 (0.011) −2.041 (0.000) 0.183 (0.493) −0.542 (0.008) −1.335 (0.000) 13.230 (0.000) −0.713 (0.001) National origin 2.277 (0.000) 3.737 (0.000) 2.299 (0.000) 3.052 (0.000) 2.584 (0.000) 11.070 (0.000) 0.433 (0.038) EU origin 0.636 (0.022) 1.695 (0.000) 0.082 (0.783) 0.557 (0.018) 1.068 (0.002) −0.993 (0.575) 0.143 (0.449) PGI/PDO 0.035 (0.895) 0.357 (0.138) 0.973 (0.000) 0.815 (0.000) 0.472 (0.058) 6.857 (0.000) 0.302 (0.032) Carbon footprint 0.330 (0.056) 0.495 (0.015) 0.412 (0.027) −0.032 (0.827) 0.516 (0.022) 3.853 (0.001) 0.047 (0.681) Organic 0.839 (0.000) 2.058 (0.000) 1.265 (0.000) 0.657 (0.000) 0.463 (0.036) 4.458 (0.000) 0.491 (0.004) Low fat 0.330 (0.102) 1.134 (0.000) 0.181 (0.245) 0.554 (0.002) 0.357 (0.069) 0.242 (0.856) 0.137 (0.310) High protein −0.332 (0.049) −0.147 (0.496) −0.417 (0.011) 0.183 (0.257) −0.150 (0.405) −3.048 (0.001) −0.136 (0.260) Ready to cook 0.310 (0.101) −0.705 (0.043) −0.816 (0.000) −0.200 (0.287) −1.300 (0.000) −1.646 (0.222) −0.285 (0.097) Standard deviations estimates (normal distribution) Halal 2.634 (0.000) 6.167 (0.000) 2.746 (0.000) 1.920 (0.000) 3.802 (0.000) 20.804 (0.000) 2.613 (0.000) National origin 3.350 (0.000) 4.050 (0.000) 3.296 (0.000) 3.561 (0.000) 3.545 (0.000) 21.133 (0.000) 0.231 (0.826) Animals 2021,11, 556 16 of 16 85. 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