Scotland ' s Rural College Revealing additional preference heterogeneity with an extended random parameter logit model : The case of extra virgin olive oil

Methods that account for preference heterogeneity have received a significant amount of attention in recent literature. Most of them have focused on preference heterogeneity around the mean of the random parameters, which has been specified as a function of socio-demographic characteristics. This paper aims at analyzing consumers’ preferences towards extra-virgin olive oil in Catalonia using a methodological framework with two novelties over past studies: 1) it accounts for both preference heterogeneity around the mean and the variance; and 2) it considers both sociodemographic characteristics of consumers as well as their attitudinal factors. Estimated coefficients and moments of willingness to pay (WTP) distributions are compared with those obtained from alternative Random Parameter Logit (RPL) models. Results suggest that the proposed framework increases the goodness-of-fit and provides more useful insights for policy analysis. The most important attributes affecting consumers’ preferences towards extra virgin olive oil are the price and the product’s origin. The consumers perceive the organic olive oil attribute negatively, as they think that it is not worth paying a premium for a product that is healthy in nature. Additional key words: preference heterogeneity; attitudinal factors; discrete choice model; extra-vigin olive oil; willingness to pay. * Corresponding author: chema.gil@upc.edu Received: 20-12-13. Accepted: 09-07-14 This work has 1 supplementary table that does not appear in the printed article but that accompanies the paper online. Abbreviations used: BCAT (Catalonian Manufacturer Label); CAT (Catalonia); CE (choice experiments); CFA (Confirmatory Factor Analysis); CL (Conditional Logit); GHR-RPL (Greene-Hensher-Rose Random Parameter Logit model); HL (Health); IIA (independence from irrelevant alternatives); LCM (Latent class model); MNL (Multinomial Logit); NL (Nested Logit); ORG (organic); ORG-AGE (interaction variable of organic with age); ORG-KNW (interaction variable of organic with knowledge); PDO (protected designations of origin); PRIV (Private Label); RPL (Random Parameter Logit Model); RUM (Random Utility Modeling); RUT (Random Utility Theory); SD (standard deviation); SLL (simulated log-likelihood); SML (simulated maximum likelihood estimator); TRT (trust); TS (town size); UNIV (university education level); WTP (willingness to pay). Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Spanish Journal of Agricultural Research 2014 12(3): 553-567 http://dx.doi. org/10.5424/sjar/2014123-5501 ISSN: 1695-971X eISSN: 2171-9292 RESEARCH ARTICLE OPEN ACCESS 554 A. Yangui et al. / Span J Agric Res (2014) 12(3): 553-567 Greene & Hensher, 2013). Among the most relevant we can cite: 1) the use of segmentation strategies (Shen, 2010); 2) the inclusion of interaction effects to explain sources of heterogeneity (Mtimet & Albisu, 2006); 3) the use of random parameter estimates, assuming preference coeff icients to be randomly distributed across individuals (Revelt & Train, 1998); and 4) the combination of interaction effects and random parameters (Hensher & Greene, 2003), or segmentation strategies and random parameters (Greene & Hensher, 2013). Among these approaches, the estimation of a Random Parameter Logit Model (RPL) (Revelt & Train, 1998) to obtain WTP estimates for food attributes has become increasingly popular. The RPL model relies on the relaxation of the three main limitations of conventional logit models: 1) it allows for random preference variation across individuals through the distribution of random parameters; 2) it relaxes the assumption of independence from irrelevant alternatives (IIA), and 3) it allows for correlation among unobserved factors over time (Train, 2003). However, the RPL also has some limitations. Indeed, although the RPL model uses continuous (i.e., normal, log-normal or triangular) distributions for individual tastes to account for preference heterogeneity, it does not identify the heterogeneity source. Additionally, the RPL estimated parameters (βki = βk + σkθki), depend on the so-called individual specific heterogeneities (θki) which follows a normal distribution with zero mean and a standard deviation of one (Hensher & Greene, 2003), and σk, the standard deviation of the distribution of the estimated parameter around its mean. Therefore, if the product σkθkiis small, the estimated parameters across individuals will be very similar. Furthermore, Lenk & DeSarbo (2000) and Scarpa & Thiene (2005) have shown that although the RPL model provides an interesting way to account for preference heterogeneity, it might be inadequate if different groups of individuals with different group-specif ic preferences exist. To overcome such limitations, Greene et al. (2006) extended the Random Parameter Logit model (GHRRPL) to account for heterogeneity around both the mean and the variance of the parameter distributions and illustrated the implications on the moments of the WTP. The traditional approach to identify the preference heterogeneity is to directly include the individual observable socio-demographic characteristics in the utility function. However, Morey & Rossmann (2003) showed that this procedure could be very restrictive as it assumes that some segments, which seem to have the same characteristics, have the same preferences. Although improving the model goodness-of-fit, Scarpa & Thiene (2011) also argued that this approach was relatively poor in giving some insight about the source of heterogeneity. McFadden (1986) and Ben-Akiva et al. (2002) discussed the important role of attitudes and beliefs to understand and estimate individuals’ preferences, and to what extent they were conceptually important in choice decision protocols. However, in spite of the potential benefit of introducing attitudinal factors to explain individuals’ preferences heterogeneity, up to our knowledge, only a few papers have addressed this issue (Moore, 2008; Stolz et al., 2011a,b). The aim of this paper is to assess consumer’s preferences heterogeneity for extra-virgin olive oil in Catalonia (North-East Spain). Spain is the first producer and exporter country of extra-virgin olive world-wide. Additionally, olive oil constitutes a fundamental component of the Spanish diet. As a consequence, the vast majority of Spanish consumers are knowledgeable about this product, and all of them are aware of market prices and product characteristics. Catalonia is the second region within Spain in terms of total olive oil consumption (the Catalonian per capita consumption was 9.93 liter in 2011). Moreover, Catalonia has a quite heterogeneous population with an adequate combination of urban (Barcelona is the second largest town in Spain) and rural environments which seems to be adequate for the purpose of this study. To tackle with this objective, the methodological framework adopted is based on the estimation of the GHR-RPL model. More precisely, we intend to account for preference heterogeneity in two ways: (i) by identifying further behavioral information associated with the mean of the random parameter distributions by the parameterization of its heterogeneity through attitudinal factors such as health awareness, environment awareness, organic olive oil trust, subjective norms, organic olive oil purchasing 1 The use of the Latent class model (LCM) could also lead to inefficient estimates because it might oversimplify the population’s preferences, especially when a small number of classes are defined and the distribution of preferences is continuous within classes (Allenby & Rossi, 1998). 2 Extra-virgin indicates that the olive oil has been produced by using mechanical means only, without any chemical treatment and contains no more than 0.8% free acidity. It is considered as the highest quality olive oil. Preferences heterogeneity of extra virgin olive oil consumers 555 intention and knowledge, and (ii) by providing more information about the variance, allowing it to be expressed as a function of individual specif ic observed characteristics. The performance of the GHRRPL with attitudinal factors is evaluated against three alternative RPL models: 1) the conventional RPL model without accounting for heterogeneity around the mean (RPL1); 2) the RPL model taking into account the heterogeneity around the mean of the random parameters as a function of socio-demographic characteristics (RPL2); 3) same than RPL2 but in this case heterogeneity is a function of attitudinal factors (RPL3). The paper also illustrates the implications of each model on the moments of the WTP distribution.