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Keywords:

  • Food product claims;
  • choice experiments;
  • eggs;
  • consumer preferences;
  • production methods;
  • origin

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
  8. References
  9. Supporting Information

This paper provides an analysis of consumer preferences for product claims, specifically about origin and production methods. In particular, it addresses two important questions: i) whether consumers are willing to pay a premium for food products carrying these claims; and ii) whether local and organic claims are complements or substitutes. A choice experiment designed to estimate two-way interactions was undertaken in Spain for eggs. The findings show first, that consumers are willing to pay a positive premium price for an enhanced method of production (that of barn, free-range and/or organic instead of cage produced eggs) as well as for the proximity of production (local, regional and national over imported). Second, the findings show that consumer preferences for the claims are heterogeneous with two consumer segments being identified: “origin preference”, the larger segment, and the “production method preference”. Results show that organic and local claims were complements for the larger first segment but that free-range and local/regional claims were substitutes for the second smaller segment. These results provide the marketing chains with insights applicable for pricing strategies.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
  8. References
  9. Supporting Information

Consumers' food choice is not only influenced by food products' prices and consumers' income, as stated by traditional economic theory, but increasingly by quality attributes of the product including sensory, health, process and convenience aspects (Grunert, 2005). As a result, there is an increasing differentiation of products in the food market as food processors try to adapt to the increasing complexity and heterogeneity of consumer demand (Grunert, 2006). This has led to a large number of food products in the market with different information communicating the distinctive product attributes demanded by today's consumers. Among the different claims seen in the food market, those of the method of production and origin are increasingly being used. This has also generated a significant amount of literature analyzing consumers' perception, attitudes and preferences for food products carrying specific method of production and origin claims, organic and local.

Hughner et al. (2007) in their review of organic product demand identify five main purchase motives: (i) health concerns (including nutritional and safety); (ii) better taste; (iii) environmental concerns; (iv) animal welfare concerns; and (v) support of the local economy. Consumers purchase organic foods because they are perceived as healthier, tastier, more environmentally friendly, more animal welfare friendly and supportive of the local economy. On the other hand, Born and Purcell (2006) indicate that consumers are interested in local food products because they are perceived as having: (i) higher quality (fresher, tastier, healthier, safer, etc.); (ii) higher environmental sustainability (including the use of sustainable production methods, minimal transport); and (iii) higher social and economic justice (including the support of local economies, community stability, etc.).

The question arises of which of the two claims (organic or local) do consumers value most? Taking into account the overlap in purchase motivation for each of the food products, an additional important question is whether organic and local origin claims are substitutes or complements. While both claims address similar consumer needs (health, taste, environmental, social, etc.) each of them separately may satisfy, to a greater or lesser extent, particular ones. The answer will inform food producers and marketers about opportunities to market foods as both organic and locally produced or as either organic or locally produced.

While several empirical papers have tried to answer the first question, that of consumer willingness to pay for organic and locally produced food products (Bond et al., 2008a; Campbell et al., 2012; Costanigro et al., 2011; de Magistris et al., 2012; Hu et al., 2009 and 2012; James et al., 2009; Loureiro and Hine, 2002; Onozaka and McFadden, 2011; Shi et al, 2013 and Yue and Tong, 2009), to our knowledge only the last three studies have calculated the valuation for the combination of both attributes for American consumers. The valuation of these attributes used different methods (e.g. stated choice experiments, field experiments) and was applied to a wide variety of products with a predominance of fresh products (i.e. strawberries, potatoes, apples and blueberries) and focused on northern American markets (USA or Canada).2 Overall the results of these studies show that consumers are willing to pay more for a local product than for an organic product. Our paper provides a double contribution to the knowledge of the relationship between organic and local origin claims. Firstly, it provides an additional estimate of willingness to pay (WTP) for local and organic foods for a fresh product in a geographical area for which not many studies have been done (the EU, Spain). Secondly, it looks at the complementarity or substitution relationship between the claims. Other papers have also investigated the complementarity and/or substitution relationships between attributes (Bond et al., 2008b; Bernard and Bernard, 2009, 2010). However, those studies analyzed the complementarity and/or substitution between the organic attribute and its parts (Bernard and Bernard, 2009, 2010) instead of claims for different attributes, as in this case. Bond et al. (2008b) do consider different types of claims, however they focus on organic and health attributes, concluding that organic and vitamin C claims are complements, rather than organic and local.

We use a choice experiment with two-way interactions to answer these questions. This design provides the flexibility to not only calculate the main marginal WTP for each of the claims, organic and local, but also the total marginal WTP for the joint provision of both claims. If the sum of the main marginal WTP for each of the claims is lower than the total marginal WTP for the joint provision of both of them, we conclude that organic and local claims are complements.

The analysis is undertaken for the egg market as eggs are considered an important fresh product in terms of supply and demand in Spain, as well as showing prevalent use of origin and production claims. Spain is the second most important egg producing country in the European Union (EU) after France, accounting for 12% of the EU total egg production. Egg production represented 7% of the animal production and 2.3% of the total agricultural production in 2010 (MERCASA, 2011). In terms of demand, annual per capita consumption in 2010 is currently 131 eggs, of which 12% are organic, with an associated expenditure of €16.20, with nearly 99% of total consumption and expenditure associated with hen eggs. It should be noted that the consumption of eggs in Spain is falling sharply (by 41% from 2000 to 2010) (MAGRAMA, 2012). However, this fresh produce market is one where claims about nutritional benefits have been widely used, (e.g. omega 3, vitamin E).

The rest of the paper is structured as follows: section 'Methodology' describes the methodology including the choice experiment design and the data collection. Section 'Model Specification' presents the economic model specification, and section 'Estimation and Results' the estimation and results. Finally, the main economic implications and conclusions are presented in section 'Conclusions'.

2. Methodology

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
  8. References
  9. Supporting Information

2.1. The choice experiment

We opted to use a choice experiment (CE) to conduct our analysis for a number of reasons: the ability to value multiple attributes simultaneously; the consistency of CE with random utility theory; and the similarity of the choice task asked to participants with real purchase decisions (Adamowicz et al., 1998; Lusk et al., 2003; Lusk and Schroeder, 2004).

The choice experiment is characterized by the provision of several options of the same product with different attributes and prices in a series of multiple choice tasks. In each task consumers must choose which, if any, of the offered products is preferred (i.e. which one they would purchase). The similarity of this task with actual purchase situations is the main advantage of the choice experiment method compared with the other commonly used valuation methods, and can explain the high number of empirical papers on valuing food products using this technique.3

2.2. Choice experiment design

The first step to implement a choice experiment is to select the specific product to be analyzed, in our case fresh eggs, and second to choose the attributes and levels to be used. The selection of the three attributes considered in our study was straightforward: price, because it allows the calculation of the willingness to pay, and the method and origin of production because they are the aim of the study. Other important attributes affecting egg demand such as size or nutritional benefits (i.e. omega 3) were fixed as they were not the objective of this research.4

Regarding the attribute method of production, EU Commission Regulation EC/589/2008 implementing the marketing standards of eggs (EC, 2008) and the Spanish Royal Decree 226/2008 (BOE, 2008) define four types of egg production systems which can be used to label eggs: caged, barn, free-range and organic. Organic eggs are also regulated by Council Regulation EC/834/2007 on organic production and labeling of organic products (EC, 2007) which also includes standards for animal welfare. We have used these four egg production systems to define the levels of the production systems attribute.

As far as the attribute origin of production is concerned, the first step in identifying its level is to define the term “local”. Local food clearly refers to a geographic production area that is circumscribed by boundaries and in close proximity to the consumer (Hand and Martinez, 2010). However, definitions of local food remain many and varied (Hein et al., 2006). Some authors use the distance between the production and the consumption of the product as the basis for the definition of “local”. They consider that local foods are produced and sold within a 30–150 mile radius of a consumers' home (La Trobe, 2001; Chambers et al., 2007). On the other hand, administrative boundaries (counties, states, regions) are often used to describe local foods (Wilkins et al., 2000). We use the latter approach and consider that a food product is local if it is produced and distributed within the province in which the final product is marketed. In Spain province (provincia) corresponds to the European NUTS-3 definition. The remaining levels defined for this attribute correspond to region (NUTS-2, Comunidad Autónoma), country (NUTS-1, Spain) and the broadest concept of European origin.5

Last, the price attribute is included with four levels. The lowest level corresponds to the minimum price for half-dozen extra large eggs that could be found in the Spanish market at the time of the survey (€0.75 /half dozen). The next level was set at the average price of eggs (€1.25 /half-dozen) and the other two levels were set at €2.0 /half-dozen and €2.5 /half-dozen, respectively, with the highest price corresponding to the average organic price in the market. Table 1 shows the attributes and the levels used.

Table 1. Attributes and levels used in the choice design
AttributesLevels
Note
  1. Levels in bold are reference levels in the model estimation. *Cages that were allowed by EU regulation at the time of the experiment.

Price (€ per half dozen)0.75, 1.25, 2.0 and 2.5
Method of production Caged*
Barn
Free-range
Organic
Origin of production Local (Province)
Regional (Comunidad Autónoma)
Country (Spain)
Europe

Choice sets include three alternatives: two unlabeled alternatives consisting of the different egg options as a combination of the different levels of the attributes (alternative A and B) and the no-buy option (alternative C). The choice set design is created following Street and Burgess (2007) for a design corresponding to three attributes with four levels each. As the research objective is to estimate the main effects and the two-way factor interaction, we start with a full factorial design with 64 profiles to ensure that all effects are uncorrelated. The second option in the choice sets is then created using one of the generators derived from the suggested difference vector (1, 1, 1) by Street and Burgess (2007) for 3 attributes with 4 levels and two alternatives. We obtained 128 pairs, with this design being 96.4% D-efficient. To avoid respondents having to respond to a large number of choice sets, thus increasing the risk of a fatigue effect, the total number of choice sets was randomly split into 21 blocks. Respondents were randomly allocated to one of the blocks. An example of a choice card is shown in Figure 1.

image

Figure 1. Example of a choice card

Download figure to PowerPoint

2.3. Data collection and survey

Data were collected from a survey conducted in two medium-sized Spanish cities, Córdoba and Zaragoza, in January 2009. These cities were selected as being representative of the north (Zaragoza) and the south (Córdoba) of the country, while their socio-demographics are similar to the Spanish Population Census (Table A1 in the online supporting information). A stratified random sample of consumers was drawn on the basis of district and age. Sample size in both cities was set at 400, resulting in a sampling error of ±5%, and a confidence level of 95.5% (k = 2) when estimating proportions (p = q = 0.5).

A number of grocery stores and supermarkets were selected in each district. The target population was the primary food buyer in the household and interviews were carried out face-to-face. Interviewers were instructed to select each third customer leaving the food outlet. Interviewers approached the selected individuals asking them whether they were a main household food shopper.6 In the case of a negative response, interviewers selected the second next customer belonging to a given age group, until they obtained a positive response to the question. In order to take into account the changes in shopper characteristics that occur between different times and days of the week, interviews covered the full range of opening hours from Monday to Saturday at each food outlet.

The questionnaire contained the choice experiment questions and questions on socio-demographic characteristics (i.e. gender, family size and composition, age, educational level, income range) and knowledge of organic foods. Before the final questionnaire was administered, a pilot survey was conducted with a small sample of respondents (N = 20) to test for understanding and interview length. A description of the experiment was presented to participants, indicating the selected attributes and levels. In addition, before asking the choice experiment question, participants were asked to read a cheap talk script (Cummings and Taylor, 1999) to encourage and motivate respondents to reveal their real preferences in order to minimize possible hypothetical bias. The cheap talk script explains to the respondents the problem of hypothetical bias, reminds them of their budget constraint and asks that they respond as if they were in a real-life setting.

Summary statistics for the characteristics of the full sample are presented in Table 2. More than half of the respondents were female (55%) on average 45 years old and living in households with three members. Approximately 61% of respondents stated that their household monthly net income was between €600 and €2,500 and around one third of the sample belong to each of the three different educational levels considered (primary, secondary and tertiary).

Table 2. Socio-demographic characteristics
CharacteristicVariable definition(% unless stated)
Sample size (individuals) 803
Age (mean years of age)AGE (Continuous)45.5
20 to 34 years old 28.3
35 to 50 years old 31.1
51 to 65 years old 26.0
More than 65 years old 14.6
Household size (mean number of members)HSIZE (Continuous)3.3
Net Household Income  
High (≥ 2,501€ month) (1 = Yes)HINCOME (Dummy)34.0
Medium (Between 600€ and 2,500€ month) 61.4
Low (≤ 600€ month) 4.6
Educational Level  
Elementary education 25.8
High School education 37.9
Higher education (1 = Yes)HIGH_EDUCATION (Dummy)36.3
Gender  
Male 45.5
Female (1 = Yes)FEMALE (Dummy)54.5

3. Model Specification

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
  8. References
  9. Supporting Information

Choice modelling assumes that consumers choose between alternative products, with a number of attributes of differing levels, so as to maximize their utility. Following Lancaster (1966), total consumer utility associated with the provision of a good can be broken down into separate utilities for their component characteristics or attributes. However, consumer utility cannot be observed by the researcher, who can only observe some attributes but not the components of the individual utility. These are treated as stochastic, thus fitting a random utility model (McFadden, 1974). Utility is taken as a random variable, where utility from the nth individual facing a choice among j alternatives within choice set J in t choice occasions can be represented as,

  • display math(1)

where β is a vector of parameters to estimate and ɛnjt is an independent identically distributed (i.i.d.) error term over time, people and alternatives. Traditionally, it has been assumed that consumers are homogeneous in terms of taste, and conditional logit models were fitted (McFadden, 1974). However, most empirical papers using choice experiments have found that consumer preferences for food products are heterogeneous (i.e. Burton et al., 2001; James and Burton, 2003; Alfnes, 2004; Rigby & Burton, 2005; Loureiro and Umberger, 2007; Barreiro-Hurlé et al., 2008; Andersen, 2011). Two alternatives have gained popularity in choice modelling literature when addressing this issue of heterogeneity: the random parameter logit model (RPL) and the latent class logit model (LC), both of which are versions of a mixed logit model (Hynes et al., 2008). For the RPL, each individual is considered to have a unique set of preferences, reflected in the individual parameters of a general utility function. When estimating the choice model an additional vector of parameters is included to incorporate individual preference deviations with respect to the mean preference values.7 The RPL has been widely used in several applications of discrete choice modelling in different disciplines and, in particular, in agro-food marketing (Rigby and Burton, 2005; Gao and Schroeder, 2009; Tonsor et al., 2009; Tonsor, 2011). However, if preferences are assumed not to be “unique” for each individual but rather distinct for a determined number of individual classes, the LC model is an appropriate choice. In the LC model, consumers are assumed to belong to different segments or classes, each of them characterized by different class-specific utility parameters. In other words, within each segment, consumer preferences are homogeneous but preferences vary between segments, reflecting a “lumpy” spread of preferences (Hynes et al., 2008). This modelling approach has also been used to better understand consumer preferences for agricultural products by identifying distinct valuation and behaviour patterns for each market segment (Hu et al., 2004; Kontoleon and Yabe, 2006; Nilsson et al., 2006; Cortiñas et al., 2007; Liljenstolpe, 2011). We use the latent class model as we anticipate differing valuation patterns, which may result in a differentiated marketing strategy according to consumer segments.

In the LC model, utility of the individual n choosing alternative j in the tth choice occasion is:

  • display math(2)

where βs is the parameter vector of class s associated with the vector of explanatory variables Xnji, εnjt|s are error terms that follow a Type I (or Gumbel) distribution. The choice probability that individual n, conditional to belonging to class s (s = 1, …, S), chooses alternative i from a particular set J, comprised of j alternatives, in a particular choice occasion t, is represented as:

  • display math(3)

The allocation of individual n to class s, and the probability of class s, is unknown and various formulations have been used. For this application, the convenient multinomial logit is assumed (Greene and Hensher, 2003):

  • display math(4)

where Zn are individual-specific characteristics and inline image the class-specific utility parameters.

We assume that, given the allocation of individuals into classes (segment membership), the tth choice occasions are independent (Greene and Hensher, 2003). Thus, for the given segment membership, the choice probability that individual n, conditional to belonging to class s (s = 1, …, S), chooses alternative i from a particular set J, comprised of j alternatives is represented as:

  • display math(5)

In order to derive a model that simultaneously accounts for choice and segment membership, equations (4) and (5) are combined to construct a mixed-logit model that expresses the joint probability that individual n belongs to segment s and chooses alternative i as:

  • display math(6)

The number of segments can be endogenously determined jointly with the utility coefficients. The latent class model has been estimated using NLOGIT 4.0.

In the LC model two groups of variables require further specification, those that enter the utility function and those which explain the segmentation. The utility function is comprised of the product attribute levels (variables, interactions amongst attributes) and an alternative-specific constant (α) associated with the designed options (alternative A and B). The utility function is then specified as follows:

  • display math(7)

where, n is the number of respondents, j denotes each of the three options available in the choice set, t is the number of choice occasions, k is the number of attribute-levels for the production method attribute, l is the number of attribute-levels for the origin of production attribute, and m is the number of interactions among attributes-levels. α is a dummy variable describing the designed options which takes value 1 for the alternatives A and B, and 0 for the no-buy option. It was expected that this parameter would be positive and significant, indicating that consumers will gain a higher utility from choosing any given alternative than from the no-buy option. The price variable (PRICE) is defined as the price level given to respondents for each egg option. Price is expected to have a negative impact on utility while the effects of the other variables are the focus of interest. The Xknjt represent the variables for the production method attribute that were defined as dummy variables. Because we have four levels for this attribute, three dummy variables were created (BARN, FREERANGE and ORGANIC considering the caged system as the reference level). Similarly, Zlnjt represents the variables for the origin of production attribute that were defined as dummy variables. Because we have four levels for this attribute, three dummy variables were created (LOCAL, REGIONAL and COUNTRY, leaving the European origin as the reference level). Finally, interactions among those dummy variables were also calculated by multiplying the dummy variables for the system and place of production (e.g. ORGANIC*LOCAL).

For the specification of the segment membership function, the socio-demographic variables defined in Table 2 have been included together with the self-reported level of knowledge about organic production. Consumers were asked to indicate their self-reported level of knowledge in three levels (low, medium and high (KNOW_ORGANIC)).

4. Estimation and Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
  8. References
  9. Supporting Information

Different LC models were estimated. Initially equation (7) was estimated including all consumers' socio-demographic characteristics as defined in Table 2 as well as the level of knowledge of organic production as independent variables in the class membership function. This specification was used to estimate models with one to four latent classes.8 Most consumer characteristics in the class membership were not statistically significant. Thus, equation (7) was redefined to include only the socio-demographic variables found to be significant. This new specification was estimated again for models considering one to four latent classes.9

To select the number of segments in the LC model, we calculate the negentropy statistic following Ramaswamy et al. (1993)10 to measure the separation of segments. This statistic is similar to the R2 statistic in that the model is said to identify the segments “better”, the closer the value is to unity. A negentropy value of 0.8 or higher indicates that the segments are well separated. Based on the negentropy value (0.96, 0.89 and 0.91 for the two, three and four segments models, respectively) we selected the two segment model. The results for the LC model with two segments are presented in Table 3, and the parameters estimates for the one-segment model are included for comparison.

Table 3. Parameter estimates: Latent class choice model with two segments
VariableOne-segment modelLatent classes
Segment 1Segment 2
Coeff.z-ratioCoeff.z-ratioCoeff.z-ratio
Notes
  1. *, and ** denote statistically significant differences at 10% and 5%, respectively

ASC 2.09319.46**4.16124.03**2.2144.46**
PRICE –1.599–38.05**–1.557–29.33**–3.604–19.59**
BARN 0.2863.74**0.4004.25**3.40311.75**
FREERANGE 0.7525.74**1.0496.38**4.9888.73**
ORGANIC 0.4153.07**0.6864.04**4.2137.42**
LOCAL 1.10910.83**1.70213.05** 1.6843.74**
REGIONAL 0.7866.98**1.2678.48**1.3292.68**
COUNTRY 0.6966.61**0.8986.66**1.0041.81*
FREERANGE * LOCAL –0.182–1.14–0.021–0.11–1.952–3.32**
ORGANIC * LOCAL 0.1811.120.3751.86*–0.138–0.24
FREERANGE * REGIONAL –0.319–1.85*–0.233–1.14–1.625–2.66**
ORGANIC*REGIONAL 0.2221.270.3341.540.0320.05
FREERANGE * COUNTRY –0.123–0.75–0.050–0.26–1.056–1.52
ORGANIC * COUNTRY –0.095–0.57–0.004–0.21–0.348–0.51
Class probability 0.764 0.236 
Class function Coef. t-ratio   
CONSTANT  –0.042–0.17  
HIGH_EDUCATION   0.392 2.02**  
KNOW_ORGANIC  0.8024.45**  

Looking at the one-segment model, the effect of the price in the utility function was statistically significant and negative indicating that increments on the price decrease the consumers' utility level. The positive and statistically significant value of the parameter estimate for BARN, FREERANGE, ORGANIC, LOCAL, REGIONAL and COUNTRY indicated that the utility for the eggs produced under the barn, free-range or organic systems and from the same province, region or country (Spain) was higher than the utility derived by caged eggs and those coming from Europe.11

However, only one of the interaction terms parameter estimate was statistically different from zero at the 10% significance level. The negative value of the parameter estimate for the interaction between the free-range system and the regional production (FREERANGE*REGIONAL) indicated that the utility derived from free-range and regional-produced eggs was lower than the sum of the utilities associated with the free-range eggs or the regionally produced eggs. This implies that both attribute levels (claims) are partial substitutes because the benefits associated with the free-range production method are already implicit in the regionally-produced system (and vice versa). However, we note that the results for the one-segment characterization of the market are not the best representation of consumer behaviour. The LC model with two classes has better statistical properties.

The LC model with two classes identifies two segments, the first of which includes 76% of respondents. The segment membership function coefficients for this segment show that the probability of belonging to this segment is positively influenced by education (HIGHER_EDUCATION) and knowledge of organic food products (KNOW_ORGANIC).12

In the first segment the parameter estimates for BARN, FREERANGE, ORGANIC, LOCAL, REGIONAL and COUNTRY are positive and statistically significant, indicating that the utility for the eggs produced under the barn, free-range or organic system and from the same province, region or country (Spain) is higher than the utility derived by caged eggs and those coming from Europe. The only interaction term estimated parameter statistically different from zero at the 10% significance level is ORGANIC*LOCAL. The positive value for the interaction between organic and local production (ORGANIC* LOCAL) indicates that consumer utility for organic and locally produced eggs is higher than the sum of the utilities derived by either organic or locally produced eggs. Thus, combining organic and locally produced claims in eggs could be a more successful strategy rather than to focus individually on either organic or locally produced claims.13 Organic and locally produced claims can be considered complements and both together can be used to differentiate eggs in the market to reach consumers in the largest segment.

The second segment (24% of respondents) is characterized by a lower level of education and organic knowledge. The estimated parameters of the main effects for this second segment are statistically significant and with the same sign as for the first segment, although of higher value in absolute terms. In this segment, two of the estimated parameters for the interaction effects are statistically different from zero at the 5% significance level. The negative estimated parameters for FREERANGE*LOCAL and FREERANGE*REGIONAL indicate that the consumer utility for free-range and local/regional eggs is lower than the sum of the utilities derived by both free range and locally/regionally produced eggs.

To assess consumer valuation for each of the attribute levels, we calculated the marginal WTP for each of the attributes, the marginal WTP for each of the interactions and the total marginal WTP for a combination of claims that include the interaction factors terms (Table 4). The WTP is calculated by determining the price difference that generates utility equivalence between eggs with different attributes levels. Mean main marginal WTP values for each attribute level are calculated by taking the ratio of the mean parameter estimate for each non-monetary attribute level to the mean price parameter multiplied by minus one for each class.

Table 4. WTP estimates for attributes (€ per half dozen of eggs)
 Segment 1 “Origin lovers”Segment 2 “Method lovers”
Notes
  1. Confidence intervals are in parentheses. aThese WTPs are relative to the cage method of production; bThese WTPs are relative to the EU origin; cThese WTPs for the interactions are relative to cage*EU; dThese are the sum of the individuals WTP (e.g. WTP(FREERANGE) + WTP(LOCAL)' eThese are the total WTP (e.g. WTP(FREERANGE) + WTP(LOCAL) + WTP(FREERANGE*LOCAL). *, and ** denote statistically significant differences from zero at 10% and 5%, respectively.

% of respondents76.323.7
Main WTP    
BARN a 0.26**(0.14,0.38)0.94**(0.79,1.10)
FREERANGE a 0.68**(0.47,0.88)1.38**(1.09,1.68)
ORGANIC a 0.44**(0.23,0.65)1.17**(0.86,1.48)
LOCAL b 1.09**(0.92,1.26)0.47**(0.22,0.71)
REGIONAL b 0.81**(0.62,1.00)0.37**(0.10,0.64)
COUNTRY b 0.58**(0.41,0.75)0.28**(–0.02,0.58)
FREERANGE * LOCAL c –0.01(–0.25,0.22)–0.54**(–0.86,–0.23)
ORGANIC * LOCAL c 0.24*(–0.01,0.49)–0.04(–0.35,0.27)
FREERANGE * REGIONAL c –0.15(–0.41,0.11)–0.45**(–0.78,–0.13)
ORGANIC*REGIONALc 0.21(–0.06,0.49)0.01(–0.32,0.34)
FREERANGE * COUNTRY c –0.03(–0.27,0.21)–0.29(–0.67,0.08)
ORGANIC * COUNTRY c –0.00(–0.26,0.25)–0.10(–0.46,0.27)
Total WTP    
 Without interactionsdWith interactionseWithout interactionsdWith interactionse
FREERANGE * LOCAL 1.77** (1.46,2.07)1.75** (1.51,2.00)1.85** (1.35,2.35)1.31** (0.98,1.64)
ORGANIC * LOCAL 1.53** (1.22,2.09)1.77** (1.54,2.01)1.64** (1.13,2.15)1.60** (1.30,1.89)
FREERANGE * REGIONAL 1.49** (1.17,1.66)1.34** (1.09,1.58)1.75** (1.23,2.28)1.30** (0.97,1.63)
ORGANIC*REGIONAL 1.25** (0.93,1.58)1.47** (1.23,1.71)1.54** (1.01,2.07)1.55** (1.25,1.85)
FREERANGE * COUNTRY 1.25** (0.95,1.55)1.22** (0.91,1.52)1.66** (1.11,2.21)1.37** (1.04,1.69)
ORGANIC * COUNTRY 1.02** (0.71,1.33)1.01** (0.78,1.24)1.45** (0.89,2.00)1.35** (1.05,1.65)

From Table 4, we can conclude that consumers were willing to pay a positive premium for barn, free-range and organic eggs over the caged eggs and also for local, regionally and nationally produced eggs over imported eggs. However, the premium differs by consumer segment. The first segment presented a higher willingness to pay for origin related attributes than the second segment. The difference in intensity in preference is such that the WTP for national origin of segment 1 is higher than that of local origin for segment 2. In both segments this premium decreased as the origin become less restrictive (i.e. local is more valued than regional and regional more valued than national). However, the main marginal WTP values for the production method claims are higher for segment 2 than for segment 1. Again the difference in preference intensity is such that the WTP for the lowest level of the production method attribute (BARN) of segment 2 is higher than the WTP for the highest level of production method (FREERANGE) of segment 1. Accordingly, segment 1 can be denoted as “origin of production lovers” and segment 2 as “method of production lovers”. Both segments show the same ranking, with free-range being the most valued, followed by organic and last by barn within the method of production attribute. This ranking of WTP is similar to the one found by Kehlbacher et al. (2012) who conclude that respondents' WTP increases as the level of animal welfare also increases. Moreover, within the origin of production attribute, local is the most valued followed by regional and country, and finally by imported eggs from Europe. This last finding differs from the one obtained by Tonsor et al. (2012) for meat who found that consumers value meat produced in the country (US) approximately the same as meat produced in North America (Canada, Mexico and US).

It follows that the response to our first research question is that the majority of our consumers value the local claim more than the organic one (i.e. segment 1). However, there is a smaller group of consumers (segment 2) who value the organic claim more than the local one. Previous studies have consistently found that consumers are more willing to pay for the local origin than for organic production method (Bond et al., 2008a; Costanigro et al., 2011; de Magistris et al., 2012; Hu et al., 2009, 2012; James et al., 2009; Loureiro & Hine, 2002). Only Campbell et al. (2012) find higher WTP values for organic than for local albeit both lower than values reported in the other studies, and Yue and Tong (2009) conclude that WTP values for organic and local food products are the same. Thus, while corroborating previous findings our results suggest that preference heterogeneity can lead to segments where consumers place more value on the organic method of production than the local origin.

Looking at the interaction terms marginal WTP, the differences between segments persist. The difference lies in type, number and sign of the interactions. While only one interaction WTP is statistically significant at the 10% significance level in segment 1 (ORGANIC*LOCAL), two are statistically significant at the 5% significance level in segment 2 (FREERANGE*LOCAL and FREERANGE*REGIONAL). The positive value for the marginal interaction WTP ORGANIC*LOCAL in segment 1 indicated that the sum of individual WTP values for each of these claims was lower than the total marginal WTP which includes the interaction effect. Both claims, organic and local, had a positive impact on the joint consumer valuation and can be considered complements.

The negative value in segment 2 for the marginal interaction WTP FREERANGE*LOCAL and FREERANGE*REGIONAL indicated that the sum of individual WTP for each of the claims was higher than the total WTP which includes the interaction effects. Providing both claims (free-range plus local and free-range plus regional) had a negative impact on the joint consumer valuation for the two claims indicating that free-range can be considered a substitute claim to local and regional.

Responding to the second research question of the paper, we found that for the largest section of our sample the local and organic claims were complements when provided in eggs. For a minority of consumers however, origin (both at local and regional level) captures part of the production method attribute FREERANGE (and vice versa). Different results are found by Yue and Tong (2009), their WTP values for the joint provision of both organic and locally produced tomatoes are lower than the sum of each of the individual WTP. As this study focused on tomatoes in the US, until further evidence is available, we can conclude that the complementarity or substitutability of the local and organic claims are also product and country dependent.

Finally, taking into account all marginal WTP, we can conclude that the least valued eggs for consumers in both segments are those produced in cages and in Europe. On the other hand, the most valued eggs for consumers differ between segments. In the first segment, the most valued eggs are the organic and locally produced (€1.77) followed by the free-range and locally produced (€1.75).14 Hence, these results imply that consumers in segment 1 value the local claim most when provided in isolation but when combining two claims, the most valued combination is local plus organic closely followed by local plus free-range. In the second segment, the most valued eggs are also the organic and locally produced followed by the organic and regional. These results indicate that consumers in segment 2 value the free-range claim the most when provided in isolation, but where two claims are combined the negative impact of the interaction terms means that the most valued combination does not include this claim. In particular, the most valued combination of claims always included the organic claim (organic plus local and organic plus regional).

Taking into account the WTP values and the size of the segments, we can conclude that the best marketing strategy for egg producers would be to market the eggs labelled with both organic and locally produced claims because all consumers present the highest WTP for those eggs. However, the final decision must account for the cost of implementing the different claims.

5. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
  8. References
  9. Supporting Information

There is an increasing differentiation of food products in the marketplace. Two of these differentiated products, organic and locally produced, have seen a dramatic increase in availability and demand, creating new marketing opportunities for local producers. However, while consumer preferences and attitudes towards organic food products have been extensively studied, there is relatively less empirical work on consumer preferences for locally produced foods and even less on consumer preferences for both types of food products (organic and locally produced). The aim of this paper is to analyze consumer preference and valuation for these two claims taking into account the valuation of the joint provision of the two claims for the same food product.

Our results indicate that consumers are willing to pay a positive premium for eggs with organic and locally produced claims. However, we found that consumers had heterogeneous preferences for the analyzed claims and two segments according to these preferences were found. The largest segment was called “origin lovers”, those that value the origin of production claims more than the method of production, and the second segment, “method lovers” for those that value the method of production more than the origin of production claims. When comparing our findings with those previously reported we conclude that most consumers value the local claim more than the organic claim but differences among consumer preferences for the local and organic attributes exist. Further research on this topic should take into account this difference in consumer preferences when calculating the willingness to pay for organic and local production.

Our findings demonstrate that local egg producers could ask for a higher price if the products are marketed with the locally produced claim rather than the organic ones (if they prefer to use only one claim) because this target segment is the largest. Moreover, this strategy might be less costly to implement since the organic claim usually encounters higher costs due to the additional cost of obtaining organic certification. Hence, the decision of whether to sell the product undifferentiated or to use either of the two labels will partly depend on the cost of production (including the cost of certification and transaction costs) for the different egg products.

Related to the second aim of our paper, to establish whether local and organic claims are complements or substitutes we found that that the claims seem to be complements. Evidence from other countries and products on the relationship between organic and local claims show different findings (Yue and Tong, 2009). Without further evidence on this issue we cannot generalize our results. Complementarity or substitutability of organic and local claims appears to be product and country dependent requiring new assessments for specific products and specific target populations. However, for the majority of our sample, free-range and local/regional claims are partly substitutes. In this sense, the more stringent the limitations associated with the claims the clearer the differentiation of the benefits and the added value of their simultaneous presence. For the smaller second segment of our sample, the emphasis on origin seems to implicitly include the benefit associated with enhanced production method claims, such as free-range.

In the case of eggs in Spain, we found that although differences in preferences between segments existed, the most valued combination of claims for eggs for both segments were the organic and locally produced claims. Thus, we can conclude that the best marketing strategy for local producers in Spain would be to market the eggs labelled as being both organic and locally produced instead of either organically or locally produced.

Our results provide more evidence for the debate on whether production method or origin claims are most valued by consumers and it is among the first to provide an answer to the question of whether organic and local labels are complements or substitutes. However, our study does have some limitations that should be taken into account for further research. Firstly, our study was limited to a specific country within the European Union and should be replicated in other European countries to further validate the results. Further, although we used a model specification that takes into account the possible heterogeneity of preferences and two consumer segments were detected, future research should identify the reasons for this heterogeneity. In other words, we should be able to characterize the profile of those consumer segments in order to provide targeted marketing recommendations. Finally, although Yue and Tong (2009) found that hypothetical bias was not so high when analyzing the consumer valuation for local and organic tomatoes in the US, the use of a real choice experiment would be advisable to avoid hypothetical bias and further corroborate the findings.

Notes
  1. 2

    Only de Magistris et al., 2012 has been conducted in a European context.

  2. 3

    Burton et al. (2001); Burton and Pearse (2002); James and Burton (2003); Scarpa and Del Giudice (2004); Scarpa et al. (2005); Barreiro-Hurlé et al. (2008); Hu et al. (2009); James et al. (2009); Pouta et al. (2010); Menapace et al. (2011); Tonsor (2011) and Pozo et al. (2012) to name a few.

  3. 4

    The eggs presented in the choice experiment where extra large and had no added functional ingredient.

  4. 5

    As the field work was carried out in two different locations, province and region varied across location; Zaragoza and Aragón for the Zaragoza sample and Córdoba and Andalusia for the Córdoba sample.

  5. 6

    The interviewer questioned whether interviewees always, almost always, occasionally, hardly ever or never buy the food for the household. For a response of ‘never buy the food’, the interviewer selected another customer at random belonging to the same age group, and repeated the screening question until a participant matching this requirement was found.

  6. 7

    β in equation (1) is not constant but varies across individuals, βn.

  7. 8

    We also estimated a model with five latent classes but we found that two of the five classes had less than one per cent of the total sample and thus discarded it.

  8. 9

    This strategy of dropping variables could have introduced some statistical bias and, to avoid it, the model was first estimated including all the variables and sequentially dropped those that were not significant. Second, starting from the restricted model the previously rejected variables were added one by one. Either approach drives the same final model, indicating that our statistical process is consistent.

  9. 10

    inline image where N is the number of observations, S the number of clusters and Pns are all the posterior probabilities.

  10. 11

    When dummy coded variables are used, the estimated parameters measure the change in utility of this attribute in relation to the reference attribute

  11. 12

    As the segment membership coefficients for the second segment were normalized to zero in order to identify the remaining coefficients of the model, first segment coefficients must be interpreted relative to the second segment.

  12. 13

    An overall assessment of this strategy would depend on whether the cost of implementing both claims is higher or lower than the additional price that can be obtained.

  13. 14

    Taking into account that the average price for half-dozen eggs was 0.75€ at the time of the experiment, these values represent more than double the average market price

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  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Model Specification
  6. 4. Estimation and Results
  7. 5. Conclusions
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
jage12036-sup-0001-AppendixS1.docxWord document13KTable A1. Population distribution by gender and age in Spain, Córdoba and Zaragoza in 2011.

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