P REFACE The Workshop on International Marketing and International Trade of Quality Food Products was held at the Faculty of Agriculture in Bologna on March 8-10, 2007. It was endorsed as the 105th seminar by the European Association of Agricultural Economists (EAAE), organized by the Department of Agricultural Economics and Engineering, and reflected the framework of the activities foreseen by the BEAN-QUORUM project (Building a Euro-Asian Network for Quality, Organic, and Unique food Marketing - TH/Asia-Link/006). The BEAN-QUORUM was funded by the European Union's Asia-Link Programme. This program is dedicated to the promotion of regional and multilateral networking between higher education institutions in European Union Member States and South Asia, South-East Asia and China. The BEAN-QUORUM consortium is composed by 5 Asian and European Universities, aiming at setting up long-term relationships and enlarging the initial network to other higher education institutions, companies, governmental bodies, and others in the food industry who are interested in the topic of marketing for quality food. The EAAE Seminar was held in concurrence with the third meeting of the BEAN-QUORUM consortium. The main motivation for this workshop initiative was the increasing relevance for agricultural and food products specific to the proposed keywords “Marketing”, “Quality”, and “International Trade” in the scientific debate among agricultural economists. Marketing and quality are perceived to be intimately linked. Additionally, specificities within the food industry have highlighted the need to develop a conceptual approach to be more consistent with the specifications of the food supply and distribution sector and the demands of food product consumers. In this respect, many controversial issues may be raised by the three keywords. Marketing may be interpreted in many ways depending on the context in which agricultural and food marketing is emphasized. Quality improvement of agri-food products is another strategic task for agriculture and rural economic development and has become a policy priority. Presently, key words used when talking about agriculture and food products are “quality” and “safety”. These two terms are often used together, although some consider safety to be part of the quality concept. However, it may be argued that safety is not linked with marketing, but this may be true only at a national level. On an international level, the existence of different safety standards may play a role both in marketing and in international trade issues. In this respect, Europe has drastically reformed its Agriculture Policy with the aim of guaranteeing quality standards, sustainability of production, and safety for consumers. This topic is also important outside of the European borders and increasingly important for developing nations. Under the pressure of increasing international and EU demand for safe, highquality and environmentally-friendly food products, developing countries are beginning to open the doors to new concepts such as organic and EU geographically-labelled foods. The domestic and international marketing of quality food products generates significant import and export opportunities, both in Europe and abroad. These opportunities - in addition to the growing opportunities created by world trade flows and markets for organic and quality foods – have increased the need for specific competencies and skills in the marketing and trading of specialized food products. III
Currently, a great debate exists regarding geographically-indicated foods and the push towards the globalization of markets. Some discussions regard the ability of these marketing tools to increase consumer welfare, induce local development, and provide benefits for producers and local communities. However, the marketing of quality food products may be more demanding than traditional agricultural and food commodities. In this respect, the adoption of food value enhancement tools (such as, EU food quality marks and regulations, or Thailand's OTOP- “One Tambon One Product” initiative) may play a key role, in guaranteeing food safety and quality, as well as promoting traditional food specialities which stimulate the development of rural areas and local markets. The requirements for setting up successful initiatives regarding opportunities in the quality food markets are high. The Italian market has been an important local arena for this kind of issues. For example, the adoption of food-value enhancement tools such as EU food quality marks and regulations has certainly been successful in some cases, but has been frustrating and disappointing in others. The reason for these mixed results may be due mainly to the lack of a comprehensive marketing strategy which is considered necessary for managing such kinds of tools. Moreover, the adoption of common food-value enhancement tools increases the scope of the food supply networks in international markets and affects consumer confidence and subsequent willingness-to-pay. For this reason, it is becomes necessary to explore new methodologies and techniques regarding consumer preferences, purchase intentions, and willingness to pay, as well as to analyze food network behaviours in contexts that may be unfamiliar to the company management. It may be in these instances when a different cultural environment may create problems with the usual way of conducting business. The seminar has raised a good deal of interest and many proposals consistent with the theme have been submitted. More than one hundred scholars from twenty-five countries attended program sessions, and approximately sixty contributed papers and posters were presented. The scope of the seminar concentrated mainly on differentiated foods and related management issues. Submitted papers analyzed a broad range of research issues that have been raised by the global commerce of Quality Food Products (QFP). Focal areas resulting from the final program include organic food, local and traditional food specialities, functional foods, branding of quality food, and many other topics, most of them framed in an international perspective. The variety and range of the topics, methods from different countries and geographic areas provided us with interesting and lively discussion. Moreover, it was the occasion for networking among researchers who are involved in the same broad area of interest. Hopefully, some of the papers will be further developed so that they may be published in international journals, possibly taking advantage of the comments and the questions raised during the paper presentation and discussion. We are grateful to all of our colleagues who contributed to the success of this seminar and to the institutions that gave a tangible support to the initiative; namely, the European Commission, the Italian Ministry of Agricultural, Food and Forestry Policies, and the Alma Mater StudiorumUniversity of Bologna. Many thanks also to the Italian Society of Agricultural Economics and to its President's representative, Gervasio Antonelli, and to the delegate of the United Nations Food and Agriculture Organization, Prabhu Pingali, who opened the seminar and made an introduction to the general topic. We also gratefully thank the members of the International Scientific Committee and several other external referees who reviewed and evaluated the extended abstracts and helped us to select IV
interesting proposals for papers and posters, as well as the colleagues at the Department of Agricultural Economics and Engineering who helped us in the seminar organization. Finally, we wish to thank Elisabetta Iogna and the entire staff of Avenue media for their professional services and the patient and reassuring assistance during hectic days of the seminar. We hope this event will be the starting point for the creation of a fruitful and long-lasting network of scholars, willing to contribute, exchange, and disseminate knowledge on this intriguing and challenging topic. The editors Maurizio Canavari, Domenico Regazzi and Roberta Spadoni
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I NTERNATIONAL SCIENTIFIC COMMITTEE Paolo De Castro (Ministry of Agriculture, Italy) Prabhu Pingali (UN Food and Agriculture Organization) Lee Ann Jackson (World Trade Organization) Walter Schiebel (University for Natural Resources and Applied Life Sciences, Austria) Bill Slee (Macaulay Institute, UK) Lionel Hubbard (University of Newcastle-upon-Tyne, UK) Gerhard Schiefer (University of Bonn, Germany) Harald von Witzke (Humboldt University Berlin, Germany) Julian Briz (Polytechnic University Madrid, Spain) Konstadinos Mattas (Aristotle University of Thessaloniki, Greece) Terhi Latvala (Pellervo Economic Research Institute, Finland) Eugenia Serova (Institute for the Economy in Transition, Russia) Sedef Akgüngör (Dokuz Eylül Üniversitesi, Turkey) Shahidur Rashid (International Food Policy Research Institute, Ethiopia) Willis Oluoch-Kosura (African Economic Research Consortium, Kenya) Chen Tong (Xinjiang Agricultural University, China) Zhang Lubiao (Chinese Academy of Agricultural Sciences, China) Masaru Kagatsume (Kyoto University, Japan) Bundit Anurugsa (Thammasat University, Thailand) Riccardo Scarpa (The University of Waikato, New Zealand) Robert P. King (University of Minnesota, USA) Rodolfo M. Nayga, jr. (Texas A&M University, USA) Ellen Goddard (University of Alberta, Canada) Decio Zylbersztajn (Universidade de São Paulo, Brazil) Elsa Rodríguez (Universidad Nacional de Mar del Plata, Argentina)
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ORGANISATION COMMITTEE The seminar was organised at the Department of Agricultural Economics and Agricultural Engineering, Alma Mater Studiorum-University of Bologna, Italy, by:
Maurizio Canavari (Chair) Aldo Bertazzoli Alessandra Castellini Rino Ghelfi Claudio Malagoli Carlo Pirazzoli Alessandro Ragazzoni Domenico Regazzi Andrea Segrè Roberta Spadoni Luigi Vannini Davide Viaggi
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Canavari M., Regazzi D., Spadoni R. (eds., 2007). International Marketing and International Trade of Quality Food Products. Proceedings CD-ROM of the 105th Seminar of the European Association of Agricultural Economists, Bologna, March 8-10, 2007. Bologna: Avenue Media. ISBN: 978-88-86817-30-1.
BibTeX citation data: @PROCEEDINGS {, title = {International Marketing and International Trade of Quality Food Products. Proceedings CDROM of the 105th Seminar of the European Association of Agricultural Economists, Bologna, March 8-10, 2007}, editor = {Canavari, Maurizio and Regazzi, Domenico and Spadoni, Roberta}, year = {2007}, publisher = {Avenuemedia}, address = {Bologna, Italy}, isbn = {978-88-86817-30-1}, }
RIS citation data TY - CONF T1 - International Marketing and International Trade of Quality Food Products. Proceedings CDROM of the 105th Seminar of the European Association of Agricultural Economists, Bologna, March 8-10, 2007 ED - Canavari, Maurizio ED - Regazzi, Domenico ED - Spadoni, Roberta Y1 - 2007 PB - Avenuemedia AD - Bologna, Italy SN - 978-88-86817-30-1 ER -
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Copyright 2007 by the Authors. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.
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CONTENTS
Papers Food quality and product export performance. An empirical investigation of the EU situation Christian Fischer
Pag. 1
Home Bias in U.S. Beer Consumption Rigoberto A. Lopez, Xenia Matschke
Pag. 27
Quality Assurance and other Marketing Management Elements as Key Success Factors for Entering a New Market: a Case Presentation of Functional Food Market in Indonesia Jofi Puspa, Rainer Kühl
Pag. 37
Determinants of Consumer Preferences for Regional Food Meike Henseleit, Sabine Kubitzki, Ramona Teuber
Pag. 55
The influence of label on wine consumption: its effects on young consumers’ perception of authenticity and purchasing behavior Renaud Lunardo, Richard Guerinet
Pag. 69
What Impact Are EU Supermarket Standards Having on Developing Countries Export of High-Value Horticultural Products? Evidence from Kenya Solomon Asfaw, Dagmar Mithöfer, Hermann Waibel
Pag. 85
Potential income gains for rural households in North Eastern Thailand through trade with organic products Ulrich B. Morawetz, Rungsaran Wongprawmas, Rainer Haas
Pag. 111
A Principal-Agent Model for Investigating Traceability Systems Incentives on Food Safety Moises Resende-Filho
Pag. 127
Effectiveness of European Appellations of Origin on the International wine market Giulio Malorgio, Luca Camanzi, Cristina Grazia
Pag. 139
How to make money by feeding the tourists: the case of Fiji Cristina Salvioni
Pag. 157
Consumer Perception of Organic Food in Urumqi Zhou Lili, Chen Tong
Pag. 173
Willingness to pay for organic food in Argentina: Evidence from a consumer survey Elsa Rodríguez, Victoria Lacaze, Beatriz Lupín
Pag. 187
Milk-Marketing: Impact of Perceived Quality on Consumption Patterns Carola Grebitus, Chengyan Yue, Maike Bruhn, Helen H. Jensen
Pag. 215
Impact of international organic markets on small U.S. producers Shelly Grow, Catherine Greene
Pag. 233
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Exploring hybridity in food supply chains Bill Slee, James Kirwan
Pag. 247
The UK Consumer’s Attitudes to, and Willingness to Pay for, imported Foods Matthieu Arnoult, Alexandra Lobb, Richard Tiffin
Pag. 261
Competitive positioning and value chain configuration in international markets for traditional food specialties Odd Jarl Borch, Ingrid H. E. Roaldsen
Pag. 277
China: A Tiger only in the East or a World Player in High Quality Fresh Produce Exports? Brian J. Revell, Liu Xue
Pag. 293
Relationship between ethics and Fair Trade supply chain organisation and performance: the case of Italian Alternative Trade Organisations (ATO’s) Cesare Zanasi, Lorenzo Paluan
Pag. 309
Evolution of trade flows for sheep milk cheese: an empirical model for Greece George Vlontzos, Marie-Noelle Duquenne
Pag. 329
Consumers’ Willingness To Pay for Value-Added Food Products Efthimia Tsakiridou, Konstadinos Mattas, Zoi Mpletsa
Pag. 341
Study on Problems Faced by Xinjiang Organic Producers and Solutions Chai Jun, Zhang Xia, Zhang Jianlei, Chen Tong
Pag. 343
Agrifood safety standards, market power and consumer misperceptions Eric Giraud-Héraud, Cristina Grazia, Abdelhakim Hammoudi
Pag. 351
Vocational training in the field of agriculture: empirical results Achim Spiller, Julian Voss
Pag. 369
People, trade and training: the needs of Morocco agricultural enterprises facing EU markets Mohamed Ismaili, Meri Raggi, Davide Viaggi
Pag. 371
Analysis of Italian High Quality Wine Exports using the Gravity Model Approach Giuseppe De Blasi, Antonio Seccia, Domenico Carlucci, Fabio Santeramo
Pag. 379
International traveling and trade: further evidence for the case of Spanish wine based on fractional VAR specifications Luis A. Gil-Alana, Christian Fischer
Pag. 395
Competitiveness of Turkey’s Organic Exports in the European Union Market R. Funda Barbaros, Sedef Akgüngör, Osman Aydoğuş
Pag. 419
The challenge of the international organic certification: a new opportunity for agricultural trading? Maurizio Canavari, Nicola Cantore
Pag. 441
Organic Consumers: A Demographic Portrayal of Organic Vegetable Consumption within the United States Rachel L. Dettmann, Carolyn Dimitri
Pag. 453
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Does taste beat food safety? Evidence from the “Pêra Rocha” case in Portugal Pierre Combris, Alexandra Seabra Pinto, Antonio Fragata, Eric Giraud-Héraud
Pag. 465
Consumer Willingness to Pay for Organic Products in Urban Turkey Sedef Akgüngör, Bülent Miran, Canan Abay
Pag. 481
What does the ‘New Quality’ mean in view of Polish dairy cooperatives? Jon Hanf, Agata Pieniadz
Pag. 493
Indirect Effects of Eco-labelling of agricultural products on Natural Resources Maurizio Prosperi, Rosaria Viscecchia
Pag. 507
The roles of geographical indications (PDO and PGI) on the internationalisation process of agro-food products Giovanni Belletti, Tunia Burgassi, Elisabetta Manco, Andrea Marescotti, Alessandro Pacciani, Silvia Scaramuzzi
Pag. 517
Evaluation of the potential interest of Italian retail distribution chains for Kamut-based products Maurizio Canavari, Pamela Lombardi, Roberta Spadoni
Pag. 541
Impact of export control policy measures in an attempt to tame Argentina’s inflation Paula Rossi, Masaru Kagatsume, Maurizio Prosperi
Pag. 559
Channel Management and differentiation strategies: A case study from the market for fresh produce Valeria Sodano, Martin Hingley
Pag. 577
Ecological characteristics and new competitiveness strategies in fresh vegetables market Luigi Cembalo, Gianni Cicia, Teresa Del Giudice, Riccardo Scarpa, Carolina Tagliafierro
Pag. 595
Food Safety in Agricultural International Trade: The Spanish experience in Mediterranean products Julian Briz, Isabel De Felipe, Marian García
Pag. 605
Analogous Products and Food Quality Products: Two Poles of the Competition Iveta Bošková
Pag. 623
Geographical Indications of Origin as a Tool of Product Differentiation: The Case of Coffee Ramona Teuber
Pag. 633
Objectiveness in the Market for Third-Party Certification: Does market structure matter? Sven Anders, Diogo M. Souza Monteiro, Elodie Rouviere
Pag. 651
The acceptance of GMO Seeds in German agriculture: empirical results Achim Spiller, Julian Voss
Summary The relationship between product quality (as indicated by unit value) and export performance, both measured in absolute (per capita) and relative terms, is investigated. Five EU countries (DE, UK, FR, ES, IT), three product categories (cheese, meat preparations and wine), three export destinations (intra EU, extra EU and world) and two time periods (1995-1999 and 2000-201) are analysed. The estimation results show that the connection between quality and export performance is positive for Italy, Spain and France but depends on the product category (but not on the period), and differs (but not in all cases) according to the export destination. While the signs of the estimated slope coefficients are stable, the obtained statistical significance levels for these parameters depend on the measure used (relative or absolute) and on the estimation method (OLS or GLS). The main implication arising from this analysis is that it may be justified to introduce 'marketing of high-quality products' as a new academic discipline, teaching students and professionals in it and thus to contribute to the enhancement of EU agribusiness competitiveness in increasingly liberalised markets. KEYWORDS: Food quality, international trade, EU.
1.
Introduction
Food quality has become an increasingly important topic during the last decades. In developed countries, driven by aging populations and growing diet-related health concerns, consumer demand now seems to shift towards higher quality, more natural and healthier food (Regmi, 2001). Given today's globalised markets, it can be assumed that – in line with rising consumption – international trade of quality food products (QFPs) is increasing. Yet, the concept of food quality is elusive. Overall, quality may be seen as an abstract construct, multidimensional in nature. More specifically, in the literature, at least four different quality definitions are discussed. It is either referred to as "excellence or superiority", as "value", as "conforming to specifications" or as "meeting or exceeding customer expectations" (Verdú Jover et al., 2004; Reeves and Bednar, 1994). The first approach defines quality as 'best in class', judged on some to-be-specified criteria. The "value" approach is an economic one (higher monetary value reflects higher quality), while the "conforming to specifications" is a technological one (a quality product is a product which fulfils some pre-defined technical standards however low they may be). The fourth approach defines quality from the point of view of the final consumer: as long as s/he is happy with it, it is a quality product. Either way, in order to measure quality objectively, a generally accepted reference system is necessary, otherwise "the term quality is very subjective and means very little" (Satin, 2002). Ninni et al. (2006) state that the quality difference between competing products can be easily analysed for measurable characteristics such as reliability, durability, various indicators of performance and health and safety. However, it becomes more subjective when it refers to intangible characteristics such as design, taste and flavour. Here the boundaries of vertical and horizontal differentiation are blurred. Obviously, the intangible characteristics are of particular importance for food products. Therefore, for these products, it may perhaps be most useful to accept them having different qualities rather than one quality. Despite its increasing importance, and probably partly due to the difficulties of objectively defining quality, not much research has been done so far on the particularities of the
1
international trade of QFPs. Thus, for example, it is unclear whether the nature of the international trade of QFPs is similar or potentially structurally different as compared to the one of low or average-quality food products. Since many QFPs may be more perishable, more sensitive to external influences (e.g., temperature, vibration, light etc.) and often of higher (monetary) value, more demanding logistics and insurance issues may affect the international trade of these products, potentially resulting in different export patterns. The objectives of the following analysis are threefold. First, to review the recent literature dealing with international trade in QFPs. Second, to generate empirical insights into the export patterns (levels and destinations) of food products, depending on their quality level. Third, based on the obtained results, to derive at conclusions of what this could potentially mean for agribusiness management and/or policy making. This paper's structure is as follows. After the introduction, section two reviews the previous literature related to the topic. Section three describes the procedure of the empirical investigation. Section four discusses the obtained results. Section five concludes by summarising and pointing at some implications which arise from the findings. Annex A provides a technical treatment on the generalised least squares estimation method used in this study.
2.
Previous studies
The literature on the relationships between quality and trade performance is sparse. The few existing studies have in common that they investigate inter-country quality competition in the sense that they try to find out whether a country's exports of certain products have higher quality vis-à-vis other countries producing similar goods. Aiginger (1997) suggested a method of how to use unit values (UV) in order to discriminate between price and quality competition in international markets, using the case of German exports of industrial goods. Gelhar and Pick (2002) applied Aiginger's framework to US food trade flows and found that almost 40% of US food exports could be characterised as dominated by quality competition. For imports, the share amounts to 60%. However, the results for bilateral trade flows are much lower, which points to problems involved when using unit values and net trade figures of economic aggregates. Ninni et al. (2006) explore the role of quality of Italian food products in international markets. They regress relative market shares of the Italian products in the import market of several different countries on a quality indicator based on UVs and other variables. The obtained results suggest that "the quality image of Italian goods offers protection for some traditional products, but that this protection is not strong enough to counteract price competition" (p. 2). No previous studies (to my knowledge), however, have addressed the issue of intra-country product export performance. That is, whether a country tends to specialise in exporting QFPs or rather in low and/or average products among the whole range of highly differentiated products it manufactures. In other words, within a nearly defined product category (e.g., cheese), is a country a high-quality exporter (which indicates that the country's high quality products are internationally appreciated and sought after) or that the country is rather a low or average/quality exporter (implying that its QFPs are more regionally preferred but that international demand for them is weak).
3.
Procedure
The general research approach is an inductive, empirical investigation. That is, international trade data are analysed econometrically and conclusions are drawn from the findings. The operationalisation of the variables under consideration is as follows. First, food quality (within a homogeneous product category) is measured by price (i.e., unit value as a price proxy). In other words, it is assumed that among similar products quality is positively related to price. Thus, the above mentioned "value" approach is adopted here for defining
2
quality. Second, trade performance is assessed by per capita exports and by relative export shares.
3.1
Measuring quality
As price proxies, UVs (in € per kg) are used, obtained by dividing export values by export quantities. UVs are known to be imperfect price indicators (King, 1993; Shiells, 1991; Holmes, 1973). Their main problem is related to the fact that an observed change in the unit value may not necessarily be a result of an underlying price change, but may simply reflect a change in the composition of the goods within the class of exports under consideration. Another problem relates to invoicing practices. The existence of a lag between the time of contract and the delivery of goods can result in differences between the contract value (i.e., the real price) and the UV calculated from customs declaration when a good is actually delivered, in cases where the exchange rate changes in between. However, the magnitude of these measurement problems is not clear. For instance, while Shiells (1991) finds that for US trade data import UVs are good import price proxy, Holmes (1973) shows for Canada that domestic UVs (national production values divided by output quantity) do not well represent industrial selling prices obtained by means of manufacturer surveys. Despite these shortcomings, UVs have been suggested and used as an indicator of quality content (Aiginger, 1997; Gehlhar and Pick, 2002). Since the UV is output per units of input (material measured in kilograms), for homogenous and comparable goods the value can indicate differences in quality if unit production costs can be assumed to be equal across the considered countries. However, the UV will also reflect differences in costs and thus high UVs can indicate relative high product quality and/or relative high unit costs. In order to distinguish between these two cases, Aiginger (1997) suggests looking at the net trade position of the good (aggregate) under consideration. If within a country a good's UV is high and the good's net trade position is positive, cross-country UV differences must then be due to superior quality. However, if a good's UV is high but the corresponding net trade postition is negative, this indicates a cost disadvantage. One problem with this approach is that it requires aggregate data (e.g., 'cars', 'cheese', or 'ice cream') or trade data on commodities (i.e., homogenous, undifferentiated goods) such as 'meat of sheep, frozen', 'flat fish, fresh or chilled', etc. in order to be able to calculate net trade positions. For highly disaggregated data of certain agricultural products (e.g., Gorgonzola cheese, Bordeaux red wine, etc.) no such trade balance can be calculated (because no other countries produces such goods and thus no imports can exist). Thus, in general, the higher the level of disaggregation, the more accurate UVs may be as price proxies, but the more difficult it is to determine whether high UVs reflect high quality or high production costs. Yet, if exports of products with high UVs are comparatively high, then this may nonetheless be an indicator of quality (due to an apparent international willingness to pay relative high prices for goods with many close substitutes). UVs are calculated in absolute terms, and in relative terms (RUV) as symmetrised and normalised deviations from a category's mean unit value. That is, UVkcpt = Export value cpt (in €) / Export quantity cpt (in kg) (1)
⎛⎛ k k RUVcpt = ⎜ ⎜ UVcpt ⎜⎜ ⎝⎝
1 nck
⎞ k ⎟ −1 ∑ UVcpt ⎟ p =1 ⎠
⎛ ⎜ UV k cpt ⎜ ⎝
n ck
1 nck
⎞ ⎞ k ⎟ UV ∑ cpt ⎟ + 1⎟⎟ *100 p =1 ⎠ ⎠ n ck
(2) where k refers to the category (i.e., cheese, wine or meat products, see below), c to the country, p to a particular product within k (e.g., Roquefort cheese), t to the period (19951999 or 2000-2005, see below) and nck is the number of products in a particular category for a certain country. Note that the range of RUV is [–100; 100], where positive (negative) values indicate above (below) average UVs.
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3.2
Measuring export performance
Trade performance is assessed by per capita exports and by relative export shares. The latter measure is a modified version of Balassa's index of revealed comparative advantage. It is defined as the deviation from the expected export share of a product within a product category, again symmetrised and normalised as suggested by Laursen (1998). The index of revealed comparative advantage (RXA) was defined by Balassa (1965) as a measure for "the export performance of individual industries in a particular country…" (p. 105) which can be evaluated by "… comparing the relative shares of a country in the world exports of individual commodities […] where the data have to be made comparable through appropriate 'normalisation'". Thus, the original index was constructed in a form such as
⎛ ⎞ RXA ct = ⎜⎜ x ct / ∑ x ct ⎟⎟ c ⎝ ⎠
⎛ ⎜ X ct / ∑ X ct ⎜ c ⎝
⎞ ⎟ ⎟ ⎠,
(3) where xct stands for the exports (in €) of the food processing sector of a country c in year t, and Xct stands for total country exports of country c in year t. Σ xct designates total sector exports, while Σ Xct refers to total aggregate exports in a particular year t. The RXA, therefore, is an index of the share of a country in the international market of a particular economic sector, corrected (i.e., normalised) for the size of the country to which the sector belongs. The correction is necessary since larger countries can a priori be assumed to have larger market shares simply due to their size. Bowen (1983) showed that Balassa's RXA may also be interpreted as the deviation of actual exports, xct, from expected exports, E(xct), where E(xct) can be defined as Σ xct * (Xct / Σ Xct), assuming that all countries engage in all economic activities in equi-proportional shares. The (theoretical) range of the RXA index is from [0; ∞[, with scores ∈[0; 1[, indicating a comparative disadvantage and scores ∈]1; ∞[ showing a comparative advantage of an economic sector of a particular country relative to the other sectors within this country. Thus, the RXA allows determining the strong and weak sectors within one country, by ranking them by descending RXA scores. Unfortunately, however, the RXA may fail to reliably indicate comparative (or competitive) advantages within the same sector relative to other countries. The main problem in cross-country comparisons is the different frequency distribution of the RXA scores in each country.1 In addition, De Benedictis and Tamberi (2001) show that the effective upper bound of the RXA for each country is different. This upper bound is equal to world total trade divided by total trade of country c (Σ Xct / Xct), and thus is in general relative small for large and very high for small countries. Consequently, the >1 index scores can not directly be compared across differently sized countries (implying the above mentioned normalisation for country size does not work perfectly in the standard notation of the RXA). Thus, because the conventional RXA range is inconvenient for interpretation, a "symmetrisation" has been suggested by Laursen (1998), which yields a range for the RXA ∈[–1; +1] with values below (above) zero indicating below (above) average relative exports:2 SRXAct = (RXAct – 1) / (RXAct + 1). (4) The main problem with using the RXA in the context of this research is again that it can only be applied to aggregate data or trade data on commodities (i.e., homogenous, undifferentiated goods) in order to be able to calculate 'world sector exports'. However, for highly disaggregated data of certain agricultural products (e.g., Gorgonzola cheese, Bordeaux red wine, etc.) no such sums can be calculated because these products are only produced in one single country and thus country exports equal 'world sector exports'. For this reason, a modified version of the RXA is used in the following, which is related to 1
See Hinloopen's and Van Marrewijk's (2001) study on the empirical distribution of the Balassa Index and the problem of the incomparability of RXA scores across countries. 2 However, it should be noted that despite symmetrisation the problem of unequal distribution and different country specific upper bounds remains.
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Bowen's (1983) interpretation of the Balassa index. Here the relative export performance (REP) is assessed by the share of the exports of a particular product p in category (k) exports as deviation from the expected export share (1/nck, where nck is the number of p in k for a particular country c), assuming that preferences for all products p are equal. That is,
k REPcpt
⎛⎛ ⎜⎜ ⎜⎜ xk cpt = ⎜⎜ n ck ⎜⎜ k ⎜ ⎜⎜ ∑ xcpt ⎜ p =1 ⎝⎝
⎞ ⎟ 1 ⎟ ⎟ −1 nck ⎟ ⎟⎟ ⎠
⎛ ⎜ k ⎜ xcpt ⎜n ⎜ ck k ⎜⎜ ∑ xcpt ⎝ p =1
⎞ ⎞ ⎟ ⎟ 1 ⎟ ⎟ ⎟ + 1⎟ *100 nck ⎟ ⎟ ⎟⎟ ⎟⎟ ⎠ ⎠
(5) where t refers to the period (1995-1999 or 2000-2005, see below). Note that the range of REP is also [–100; 100], where positive (negative) values indicate above-(below-)average relative export performance.
3.3
Data
The raw data were taken from Eurostat's COMEXT "EU25 Trade Since 1995 By CN8" database and combined with Eurostat population figures. The trade data are at the highest available level of disaggregation (8-digit level). The export data (€ values and kg quantities) are factored in four dimensions. First, reporters: the five largest EU countries (DE, ES, GB, FR and IT) were selected. Second, as destination area three different locations were chosen: within the EU-15, outside the EU-15 and world (i.e., the sum of the former two). Third, three product categories (cheese, meat preparations and wine) were selected. Each category contains a large number of individual products. The maximum types of products for each category is: cheese 66, meat preparations 54 and wine 70.3 However, the actual number of types included into the analysis depends on the individual country (see next paragraph). The categories were selected based on their importance to the EU food industry (meat, beverages and dairy products are the three most important sub-sectors as measured by their shares in total sector value added, see Lienhardt (2004)). The fourth dimension is time. The years 1995 to 2005 were included into the analysis. Since the used trade data are volatile, the 11-years were averaged over two periods (1995-99 and 2000-05). The first covers the period before the Euro introduction as a general currency, the second the period after its introduction. Overall, some 26,804 observations were used in the analysis. For the UK, the wine category was not analysed because the country is not a significant wine producer. For Spain, no (or only a few) wine export quantities for the second period were available, thus only period-one exports could be included into the analysis. Despite these few missing values, the dataset covers a whole target population rather than being of sample nature. This needs to be kept in mind when interpreting the statistical significance (i.e., deviations from zero larger than those to be expected due to sampling error) of the estimation results later in this paper. Data preparation involved the removal of re-export data and of outliers.4 Unfortunately, the raw data also included export flows of products which could not have been produced in a certain country (e.g., Navarra red wines in German exports, or Gorgonzola cheese in French exports). These re-exports were removed from the original data set where possible. 3
The covered product codes are: cheese: 4061010–4069099; meat preparations: 16010010– 16029099 and wine: 22041011–22042199. The exact description of the individual products can be found on the internet through the Eurostat Comext database or be obtained on request by the author. 4 Only two product types (16022011 and 16022019: preparations of goose or duck liver) were removed in the meat preparations category. In almost all countries, these products displayed extremely high unit values and very low export performance, thus making them very influential outliers.
5
However, for many products (e.g., processed cheese, uncooked sausages, etc.) where the production is not restricted to a certain geographical area, no potentially existing re-exports could be eliminated. Table 1 lists the actual number of products included in the empirical analysis for each category and country (the nck) [Table 1 around here].
3.4
Estimation methods
The relationship between export performance and food quality was estimated using regression analysis. First, ordinary least squares (OLS) estimators of the slope coefficients were obtained separately for each country and product category, controlling for a potential period effect. Second, all data were pooled and feasible generalised least squares (GLS, which allows for heteroscedasticity across and correlation between different panels, see Cameron and Trivedi, 2005) was used to estimate the (nested) fixed effect of food quality on export performance, controlling for country, product category and time. A more detailed description of the GLS estimation procedure is provided in Appendix A.
4.
Results
The estimation results are displayed in Figures 1 to 10 (OLS) and in Tables 2 and 3 (GLS). Overall, it emerges that the direction (and the significance) of the relationship between food product quality and its export performance is not systematic but depends on the country, the product category and the export destination. However, the direction (i.e., the sign of the slope coefficient) is in most cases independent of the measurement approach (per capita or relative), yet the significance levels are not. In the OLS estimations, export performance (per capita and relative) was regressed on unit values (absolute and relative) and a dummy variable was included in all cases (except for wine in Spain), in order to control for a potential period effect. However, in no case the dummy coefficient turned out to be significant at least at the 95% confidence.
4.1
Country-specific results
In Germany (Figures 1 and 2), for all investigated product categories and export destinations, the relationship between product quality and export performance is negative. The slope coefficient is statistically significant (at least at the 95% confidence level) for total and intra-EU per capita cheese exports and in addition for wine exports to all three export destinations using relative export performance measurement. There does not seem to be a major difference between the intra- and extra-EU situation, except for perhaps the case of meat preparations where the slope coefficient seems to be slightly larger than for intra and total exports. Overall, it appears that Germany exports relatively very few highquality food products, where high quality is meant relative to the other products in the respective categories [Figures 1 and 2 around here]. In Italy (Figures 3 and 4), the situation is almost completely different as compared to the one of Germany. The estimated slope coefficients are positive for cheese and meat preparations and negative for wine to all export destinations. Here the significance levels depend on the applied measurement approach: in the case of per capita exports the slope coefficients are statistically significant (at least at the 95% confidence level) for cheese and meat preparations to all destinations but not for wine. In the case of relative measurement, they are significant for cheese extra-EU exports and for wine exports to all destinations. Overall, it appears therefore that Italy is predominantly a quality exporter, except for wine [Figures 3 and 4 around here]. The situation in France (Figures 5 and 6) is the exact mirror image to the one in Italy: a negative quality-performance relationship for cheese and meat preparations and a positive one for wine. For intra-EU per capita exports of meat preparations there seems to be a slight positive relationship. However, when measured in relative terms, the relationship comes out as slightly negative. As for the significance levels, both measurement approaches yield significant (95% confidence level) slope coefficients for extra-EU exports of meat preparations and wine exports to all destinations. Overall, France seems therefore
6
to be a quality exporter of wine only. In the other product categories, export performance is highest for low- and average-quality products [Figures 5 and 6 around here]. In Spain (Figures 7 and 8), the results are less uniform. There appears to be a difference in the direction of the quality-performance relationship between the intra- and extra-EU export situation for cheese and meat preparations. For cheese, the relationship is negative for the total (but slightly positive in per capita terms) and intra-EU situation, but positive for extraEU exports. For meat preparations, the situation is almost vice versa: a positive relationship for the total and intra-EU situation, and a neutral (or only slightly positive one) for extra-EU exports. For wine, the slope coefficients are (except for the relative measurement of the intra-EU situation) positive in all cases. As for statistical significance levels, only the per capita coefficients for extra-EU cheese exports and total and intra-EU wine exports exceed the 95% confidence-level threshold. Overall, Spain clearly seems to be a high-quality wine exporter, a high-quality exporter of meat products and a high-quality exporter of cheese to extra-EU destinations [Figures 7 and 8 around here]. The UK situation (Figures 9 and 10) finally, is also different according to the export destination. For cheese, the estimated slope coefficients are negative or only slightly positive for total and intra-EU exports, but definitely positive for extra-EU exports. For meat preparation, the situation is vice versa: a positive relationship for total and intra-EU exports and a negative one for extra-EU exports. However, none of the coefficients turned out to be statistically significant (again 95% confidence level). Thus, the UK appears to be a high-quality exporter for cheese to extra-EU countries and for meat preparations to intraEU countries only [Figures 9 and 10 around here].
4.2
Results from the pooled estimation
As the previous section has shown, there does not seem to be a systematic relationship between product quality and export performance. The signs of the estimated slope coefficients vary widely across countries and sometimes even across export destinations. The only exception is perhaps for meat-preparations exports to extra-EU destinations, where the slope coefficients are mostly negative. Thus, regressions run on the overall (i.e., pooled) dataset can be expected to not perform well. Nonetheless, the results from these GLS estimations are reported in Tables 2 and 3. For the absolute (per capita) measurement approach (Table 2), the tests for fixed effects show that there are significant differences between the included factors (country, category and period). This implies that the absolute level of per capita exports is significantly different across all three factors which makes the pooling of the data problematic. As a result, the estimates for the included covariates (here unit value) may be biased. From the fixed-effect parameter estimates it can be seen that relative to the reference category Italy, France has (across all included product categories and the two periods) the highest per capita exports, followed by the UK, Spain and Germany. As compared to the reference category wine, the per capita exports of meat products are (across all countries and the two periods) the smallest, followed by cheese. Given a total dataset size of 744 observations, the differences between the two included periods turn also out to be statistically highly significant. The absolute level of per capita exports is higher in the latter period, which can perhaps be explained by inflation-induced increases (neither export nor unit values were deflated) or the trade-enhancing effect of the common market from 1999 onwards. The overall effect of quality (unit value) comes out as being positive for all three analysed export destinations, however it is only statistically significant (95% confidence level) for extra-EU exports (but the estimate may not be reliable as argued above). All variance components were estimated as statistically highly significant. There seems to be a considerable difference between the error variance of the first and the second period, implying that heteroscedasticity may be present in the data. Residual autocorrelation also seems to be present. Thus, on statistical grounds, the use of feasible GLS seems to be justified. The overall fit of the model is not very high, as can be seen from the comparatively high values of the information criteria restricted –2 log likelihood,
7
AIC and BIC (see Appendix A for a theoretical discussion of these measures) [Table 2 around here]. For the relative measurement approach (see Table 3), only the results from the final estimated model are reported. Since deviations from means are used in the relative measures, differences in levels across countries or periods are no longer present in the data. However, there is still a significant difference in levels between the analysed product categories. Relative to wine, meat products seems to be structurally exported least (although the difference is only statistically significant for total and intra-EU exports). Given these differences in levels, the unit-value slope coefficient were estimated in nested form, i.e., as category-specific. The results show that for cheese exports, the slope coefficient is negative for all export destinations (but it is not statistically significant). For meat preparations, the unit-value slope coefficient is negative for total and extra-EU exports but positive for intra-EU exports. It is highly significant (and largest in magnitude) for extra-EU export only. For wine, the situation is exactly vice versa as compared to the one of meat preparations: positive unit-value slope coefficient for total and extra-EU exports, but a negative one for intra-EU ones. However, none of these coefficients is statistically significant at the 95% confidence level. As for the estimated variance components, the degree of heteroscedasticity seems to be less severe, but autocorrelation is still present across the two panels. All information criteria statistics have comparatively high values which indicates that overall model fit is low [Table 3 around here].
6.
Conclusions
This paper has investigated the relationship between product quality (as indicated by UV) and export performance, both measured in absolute (per capita) and relative terms. The estimation results show that the connection between quality and export performance clearly depends on the product category and country (but not on the period) and differs, but not in all cases, according to the export destination. While the signs of the estimated slope coefficients are stable, the obtained statistical significance levels depend on the used measures (relative or absolute) and estimation methods (OLS or GLS). Overall, it emerges that in Italy export performance is positively related to product quality (i.e., exports of higher-quality products are relatively higher) for cheese and meat preparations, but not for wine. In France, the situation is exactly vice versa: a negative relation for cheese and meat preparations but a positive one for wine. In Spain, relative export performance rises with quality for meat products and wine, but decreased for cheese (however, it increases for extra-EU exports). In the UK, for both analysed product categories, meat preparations and cheese, the slopes are only slightly positive, indicating that export performance is roughly the same for all quality levels (although there are differences between the intra and extra-EU export situation). Finally, in Germany, there is a negative connection for all three product categories, implying that high-quality products only play a minor role in the country's exports. The findings from this analysis suggest that while there does not seem to be a systematic relationship between food quality and export performance high-quality products clearly play a considerable role in the food exports of at least some countries (here Italy, France and Spain). This underlines the point that it is justified, and even necessary, to give special attention to this sort of trade. One possibility to do this, for instance, could be the introduction of specialised courses in 'marketing of quality products'. Today, a few other highly specialised post-graduated management courses already exist (e.g., Essec Business School in Paris offers an MBA in Luxury Brand Management while the University of South Australia offers a Master of Wine Marketing), which may serve as a model. Another possibility would be to offer specialised short courses (or other training programmes) to already practicing agribusiness professionals. Given that the issue seems to be of relevance for several EU countries, an integrated, pan-European teaching/training programme and thus the pooling of the expertises from different countries may perhaps be most useful. In any case, such specialised capacity-building initiatives for current and/or future agribusiness leaders can clearly be expected to contribute positively to the enhancement of
8
the competitiveness of EU food companies, which operate in increasingly liberalised markets. Future research may investigate why some countries in some product categories perform better in exporting high-quality products than others. One possible reason could be that exports simply reflect production proficiency, i.e., that countries which produce relative more high-quality food products also export more of these. However, this hypothesis needs empirical confirmation.
Appendix A Given the special nature of the data (i.e., a short unbalanced panel, or, more accurately, a nested cross-section with one repeated measurement), a general linear model (GLM) was fitted for estimating the slope coefficient of product quality with regard to product export performance. Formally, the GLM (i.e., a fixed-effects specification which is assumed to be linear in parameters; Verbeke and Molenberghs (1997)) can be specified as
(
)
l yckt = µ + φc + φk + φt + ∑ γ (lc, k , t ) ⋅ xckt + ε ckt l
.
(A.1)
φ µ is the overall (grand) mean (a fixed parameter, equivalent to the usual intercept); .. the fixed main-effect parameter for the three categorical predictors (i.e., 'factors') country (c = 1, …, C), category (k = 1,…, K) and time period (t = 1,…, Tck); parameters of l included (non-stochastic) covariates
γ (lc, k , t )
the fixed
l ε xckt (l = 1,…, L); and ckt the
y
random error (disturbance) of ckt . (In this case, C = 5, K = 3 and Tck ∈ [1, 2], depending on the country and category; and L can potentially be high, since nested effects were considered.) In the given specification, product categories (level one) are nested in
γl
countries (level two), and covariate parameters ( c , k , t ) are allowed to vary by included factor, i.e., across countries, categories or periods. (If the parameter is estimated and presented as not factor-specific, then it represents the overall mean effect across all
γl
.) In this way, eq. (A.1) can also be seen as a countries, categories and periods, i.e., two-level hierarchical linear model (HLM) (Cameron and Trivedi, 2005). Equation (A.1) can be simplified to
yckt = x′ckt β ( c, k , t ) + ε ckt
, where
(row) vector of m inputs (factors, covariates and interactions) and
x′ckt
β (c, k , t )
denotes a
the (column)
φ vector of corresponding parameters (including the .. ). Rewritten in full matrix notation, eq. (A.1) becomes y = X β (c, k , t ) + ε
,
(A.2)
∑ c =1 I cTc ×1 response vector, X is a ∑ c =1 I cTc × m input matrix, β is a C m ×1 parameter vector, and ε is a ∑ c =1 I cTc × 1 disturbance vector. C
C
where y is a
Given the panel structure of the data, where the different product types are treated as subjects,
E (y ) = X β (c, k , t )
σ , and Var ( y ) = Var (ε ) is assumed to be i.i.d. N(0, t ), 2
9
thus disturbances in the two panels may be contemporaneously correlated and potentially
β
heteroscedastic (i.e., displaying non-constant variance). As a consequence, ( c , k , t ) in eq. (A.2) cannot efficiently be estimated by pooled ordinary least squares (OLS) regression. Instead, a method that takes into account autocorrelated and non-constant residual errors needs to be used. One such method is feasible generalised least squares (FGLS), where:
[
]
ˆ −1X −1 X′V ˆ −1y βˆ FGLS = X′V
(A.3) (see Cameron and Trivedi, 2005), and where implementation requires the consistent estimation of V, the variance-covariance matrix of disturbances ε . In order to consistently estimate V, one usually needs to make explicit assumptions about the underlying structure of its variance components, but it is possible (and was done in this case) to treat V as being
⎡ σ 12 σ 12 ⎤ ⎢ ⎥ σ 21 σ 22 ⎦⎥ ⎢ ⎣ completely unstructured, i.e., . The estimation of variance components can be done in different ways. Under the i.i.d. multivariate normal assumption for ε , maximum likelihood estimation methods are usually employed, with two possible options: maximum likelihood (ML) or restricted maximum likelihood (REML). A weakness of the ML method is that the estimates are biased in small samples (Cameron and Trivedi, 2005). Moreover, since REML does explicitly take into account the loss of the degrees of freedom involved in estimating the fixed effects, it is the recommended option in models containing many fixed-effect parameters (Verbeke and Molenberghs, 1997). The –2 times log-likelihood of REML is (Cameron and Trivedi, 2005)
(
)
⎡ ⎛ 2π ⎞⎤ ⎟⎟⎥ + ln X′V −1X − 2l REML (V ) = ln V + ( NT − p ) ln r ′V −1r + ( NT − p ) ⎢1 + ln⎜⎜ ⎝ NT − p ⎠⎦ ⎣ (A.4)
[
′
−1
]
−1
′
−1
V
denotes the determinant of V, N the number where r = y − X X V X X V y , of subjects, and p is the rank of X. The variance components of V can be computed by maximising eq. (A.4), however in general, there are no closed-form solutions. Therefore, Newton and scoring algorithms are usually used to find the solution numerically, starting with some initial value for residual error variance σ2. Assuming i.i.d. N(0, σ 2), residual sum of squares from OLS regression usually yields σˆ to be used as starting value. Once V has been estimated, original data X and y are then accordingly transformed and OLS regressions are run on the adjusted data, yielding autocorrelation and 2
βˆ
heteroscedasticity-adjusted ( c , k , t ) and respective panel-robust standard errors. For the significance tests of the included factors, ANOVA (i.e., 'method of moments'-type) estimators, which equate quadratic sums of squares to their expectations and solve the resulting equations for the unknowns, are used. Baltagi et al. (2001) showed that ANOVA methods perform well in estimating the regression coefficients in unbalanced nested errorcomponent regression models. Given that the dataset in this paper is unbalanced, and that I am interested in the significance of the remaining differences in the factor category (or marginal) means, Type III sums of squares are used (Hill and Lewicki, 2006). In maximum likelihood-based FGLS regressions, conventionally only the final value of the (restricted) –2 times log-likelihood function and derived information criteria (such as the Akaike information criteria, AIC, and the Bayesian information criteria, BIC) are calculated (see Cameron and Trivedi, 2005 for a discussion), on the basis of which appropriate (nested) models are selected. However, for assessing the overall fit of a model to the underlying data, theses statistics are less useful, since they cannot be compared across different non-nested models.
10
7.
References
Aiginger, K. (1997). The use of unit values to discriminate between price and quality competition. Cambridge Journal of Economics, 21, pp. 571-592. Balassa, B. (1965). Trade Liberalization and 'Revealed' Comparative Advantage. The Manchester School of Economic and Social Studies 33, pp. 99-123. Baltagi, B., Song, S. and Jung, B. (2001). The unbalanced nested error component regression model. Journal of Econometrics, 101, 357-381. Bowen, H. (1983). On the Theoretical Interpretation of Indices of Trade Intensity and Revealed Comparative Advantage. Weltwirtschafliches Archiv 119 (3), pp. 464472. Cameron, C. and Trivedi, P. (2005). Microeconometrics: methods and applications. Cambridge University Press, New York. De Benedictis, L., & Tamberi, M. (2001). A note on the Balassa Index of Revealed Comparative Advantage. Retrieved November 2, 2004, from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=289602 Gelhar, M. and Pick, D. (2002). Food Trade Balances and Unit Values: What Can They Reveal About Price Competition? Agribusiness: An International Journal, 18(1), pp. 61-79. Hill, T. and Lewicki, P. (2006). Statistics: Methods and Applications. StatSoft, Inc., Tulsa, US. Hinloopen, J. and C. Marrewijk (2001). On the Empirical Distribution of the Balassa Index. Weltwirtschaftliches Archiv 137 (1), pp. 1-35. Holmes, R. (1973). The Inadequacy of Unit Value Indexes as Proxies for Canadian Industrial Selling Price Indexes. Review of Income and Wealth, 19(3), pp. 271-277. King, A. (1993). A note on export unit values indices in competitiveness variables. Bulletin of Economic Research, 45(1), pp. 69-77. Laursen, K. (1998). Revealed Comparative Advantage and the Alternatives as Measures of International Specialisation. Druid Working Paper 98(30). Retrieved November 12, 2004, from http://www.druid.dk/wp/pdf_files/98-30.pdf Lienhardt, J. (2004). Statistics in focus: the food industry in Europe. In: http://epp.eurostat.cec.eu.int/portal/page?_pageid=1073,1135281,1073_1135295&_ dad=portal&_schema=PORTAL&p_product_code=KS-NP-04-039. Accessed on 21 Nov 2004. Ninni, A., Raimondi, M. and Zuppiroli, M. (2006). On the success of "made in Italy": an appraisal of quality-based competitiveness in food markets. University of Parma working paper WP10/2006, Serie: Economia e Politica Economia. http://ideas.repec.org/p/par/dipeco/2006-ep10.html Regmi, A. (ed.) (2001). Changing Structure of Global Food Consumption and Trade. Economic Research Service, US Department of Agriculture. http://www.ers.usda.gov/Publications/wrs011/ Reeves, C. and Bednar, D. (1994). Defining quality: alternatives and implications. Academy of Management Review, 19 (3), pp. 419-445. Satin, M. (2002). Food quality and international trade. FAO, http://www.fao.org/ag/AGS/Agsi/gtz_html.htm Shiells, C. (1991). Errors in import-demand estimates based on unit-value indexes. The Review of Economics and Statistics, 73(2), pp. 378-382. Verbeke, G. and Molenberghs, G. (eds.) (1997). Linear Mixed Models in Practice. A SASOriented Approach. Springer-Verlag, New York. Verdú Jover, A., Lloréns Montes, F. and del Mar Fuentes, M. (2004). Measuring perceptions of quality in food products: the case of red wine. Food Quality and Preference, 15(5), pp. 453-469.
11
Tables and Figures Table 1. Number of product types included in the empirical analysis for each product category and country (nck) DE
IT
FR
ES
GB
Cheese
27
25
31
13
15
Meat preparations
36
35
41
34
36
Wine
17
25
25
24
–
Figure 1. Germany, absolute measurement (per capita exports in € (y-axis) versus unit values €/kg (x-axis))
Total per capita exports
Intra-EU per capita exports
4.0
Extra-EU per capita exports
4.0
1.0
Cheese
0.8
0.6 2.0
2.0 0.4
0.2
0.0
0.0
0.0 0.0
2.0
4.0
6.0
8.0
0.0
2.0
4.0
6.0
8.0
0.0
2.0
4.0
6.0
8.0
2.5
2.5
Meat preparations
0.14 2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.12 0.10 0.08 0.06 0.04 0.02 0.00
0.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
0.0
12.0
2.0
4.0
6.0
8.0
10.0
0.0
12.0
1.0
2.0
4.0
6.0
8.0
10.0
12.0
0.6
1.2 0.5
0.8 1.0
0.4
Wine
0.6 0.8
0.3 0.4
0.5
0.2 0.2
0.2
0.0
0.1
0.0 0.0
5.0
10.0
15.0
20.0
25.0
0.0 0.0
5.0
10.0
15.0
20.0
25.0
Note: bold regression lines indicate that the slope coefficient is statistically significant at the 95% confidence level.
13
0.0
5.0
10.0
15.0
20.0
25.0
Figure 2. Germany, relative measurement (deviations from expected values (y-axis) versus deviations from average unit values (x-axis))
Cheese
Total relative exports
Meat preparations
Extra-EU relative exports
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-30.0
-20.0
-10.0
0.0
10.0
20.0
30.0
40.0
-30.0
-20.0
-10.0
0.0
10.0
20.0
30.0
40.0
-30.0
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-40.0
Wine
Intra-EU relative exports
-20.0
0.0
20.0
40.0
-40.0
-20.0
0.0
20.0
40.0
-40.0
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-20.0
0.0
20.0
40.0
60.0
80.0
-20.0
0.0
20.0
40.0
60.0
80.0
Note: bold regression lines indicate that the slope coefficient is statistically significant at the 95% confidence level. 14
-20.0
-20.0
-10.0
0.0
-20.0
0.0
10.0
0.0
20.0
20.0
20.0
40.0
60.0
30.0
40.0
80.0
40.0
Figure 3. Italy, absolute measurement (per capita exports in € (y-axis) versus unit values €/kg (x-axis))
Total per capita exports
Intra-EU per capita exports
6.0
Extra-EU per capita exports
4.0
2.5
5.0
2.0
Cheese
4.0 1.5 3.0
2.0 1.0
2.0 0.5
1.0
0.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
2.5
0.0
2.0
4.0
6.0
8.0
10.0
0.0
2.0
4.0
6.0
8.0
10.0
0.6
Meat preparations
1.5 0.5
2.0
1.2 0.4 1.0
1.5
0.3
0.8 1.0 0.5
0.2
0.2
0.1
0.5
0.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
7.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
4.0
4.0
2.0
2.0
6.0
Wine
5.0
4.0
3.0
2.0
1.0
0.0
0.0 0.0
5.0
10.0
15.0
20.0
25.0
30.0
0.0 0.0
5.0
10.0
15.0
20.0
25.0
30.0
Note: bold regression lines indicate that the slope coefficient is statistically significant at the 95% confidence level. 15
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Figure 4. Italy, relative measurement (deviations from expected values (y-axis) versus deviations from average unit values (x-axis))
Cheese
Total relative exports
Meat preparations
Extra-EU relative exports
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-40.0
-20.0
0.0
20.0
-40.0
-20.0
0.0
20.0
-40.0
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-75.0
Wine
Intra-EU relative exports
-50.0
-25.0
0.0
25.0
-75.0
-50.0
-25.0
0.0
25.0
-75.0
50.0
50.0
90.0
25.0
25.0
60.0
0.0
0.0
-25.0
-25.0
-50.0
-50.0
-75.0
-75.0
-100.0
-100.0
-20.0
-50.0
0.0
-25.0
20.0
0.0
25.0
30.0
0.0
-30.0
-60.0
-90.0
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
Note: bold regression lines indicate that the slope coefficient is statistically significant at the 95% confidence level. 16
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
Figure 5. France, absolute measurement (per capita exports in € (y-axis) versus unit values €/kg (x-axis))
Total per capita exports
Intra-EU per capita exports
Extra-EU per capita exports
6.0
3.0
5.0
2.5
4.0
Cheese
4.0
2.0
3.0
1.5 2.0
2.0
1.0
1.0
0.5
0.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
12.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
12.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
2.5
5.0
Meat preparations
0.8 1.5
1.4
1.2
1.2 0.6 1.0
1.0 0.8
0.4
0.8 0.6 0.5 0.4 0.2
0.2
0.2
0.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
12.0
0.0 0.0
2.0
4.0
6.0
8.0
10.0
12.0
30.0
12.0 15.0
25.0
10.0 12.5
20.0
8.0
Wine
10.0 15.0
6.0
7.5
10.0
5.0
0.0
5.0
4.0
2.5
2.0
0.0 0.0
2.5
5.0
7.5
10.0
12.5
15.0
0.0 0.0
2.5
5.0
7.5
10.0
12.5
15.0
Note: bold regression lines indicate that the slope coefficient is statistically significant at the 95% confidence level.
17
0.0
7.5
10.0
12.5
15.0
Figure 6. France, relative measurement (deviations from expected values (y-axis) versus deviations from average unit values (x-axis))
Cheese
Total relative exports
Meat preparations
Extra-EU relative exports
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-25.0
0.0
25.0
50.0
-25.0
0.0
25.0
50.0
-25.0
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-50.0
Wine
Intra-EU relative exports
-25.0
0.0
25.0
50.0
-50.0
-25.0
0.0
25.0
50.0
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-50.0
-25.0
0.0
25.0
50.0
-50.0
-25.0
0.0
25.0
50.0
Note: bold regression lines indicate that the slope coefficient is statistically significant at the 95% confidence level.
18
0.0
25.0
50.0
-50.0
-25.0
0.0
25.0
50.0
-50.0
-25.0
0.0
25.0
50.0
Figure 7. Spain, absolute measurement (per capita exports in € (y-axis) versus unit values €/kg (x-axis))
Total per capita exports
Intra-EU per capita exports
1.0
Extra-EU per capita exports
0.8
0.6
0.5
0.8 0.6
Cheese
0.4 0.6 0.4
0.3
0.4 0.2 0.2 0.2
0.1
0.0
0.0 2.0
4.0
6.0
0.0 2.0
4.0
6.0
2.0
4.0
6.0
2.0
Meat preparations
1.4 1.2
0.4
1.5 1.0 0.8
1.0
0.6
0.2
0.4
0.5
0.2 0.0
0.0 1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0.0 1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
4.0 1.5 4.0
1.2
Wine
1.0 2.0
0.8
2.0 0.5
0.2
0.0
0.0 0.0
2.0
4.0
0.0 0.0
2.0
4.0
Note: bold regression lines indicate that the slope coefficient is statistically significant at the 95% confidence level.
19
0.0
2.0
4.0
Figure 8. Spain, relative measurement (deviations from expected values (y-axis) versus deviations from average unit values (x-axis))
Total relative exports
Intra-EU relative exports
50.0
50.0
25.0
25.0
0.0
0.0
-25.0
-25.0
-50.0
-50.0
-75.0
-75.0
Extra-EU relative exports 90.0
60.0
Cheese
30.0
0.0
-30.0
-60.0
-90.0
Meat preparations
-30.0
-20.0
-10.0
0.0
10.0
20.0
30.0
40.0
-30.0
-20.0
-10.0
0.0
10.0
20.0
30.0
40.0
-30.0
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-50.0
-25.0
0.0
25.0
-50.0
-25.0
0.0
25.0
-20.0
-50.0
-10.0
-25.0
0.0
10.0
20.0
0.0
30.0
40.0
25.0
20.0 90.0
90.0 0.0
Wine
60.0
60.0
30.0
-20.0
30.0
0.0
-40.0
0.0
-30.0
-60.0
-30.0
-60.0
-60.0
-80.0
-90.0
-90.0 -100.0
-80.0
-60.0
-40.0
-20.0
0.0
20.0
-80.0
-60.0
-40.0
20
-20.0
0.0
20.0
-80.0
-60.0
-40.0
-20.0
0.0
20.0
Figure 9. UK, absolute measurement (per capita exports in € (y-axis) versus unit values €/kg (x-axis))
Total per capita exports
Intra-EU per capita exports
Extra-EU per capita exports
1.2
0.4
1.4 1.0
1.2
0.8
Cheese
1.0 0.8
0.6
0.2
0.6 0.4 0.4 0.2
0.2
0.0
0.0
Meat preparations
2.0
3.0
4.0
2.0
3.0
4.0
5.0
2.0
0.6
0.6
0.1
0.5
0.5
0.0
0.4
0.4
0.0
0.3
0.3
0.0
0.2
0.2
0.0
0.1
0.1
0.0
0.0
0.0 0.0
Wine
0.0
5.0
2.0
4.0
6.0
8.0
10.0
3.0
4.0
5.0
0.0 0.0
2.0
4.0
––
––
21
6.0
8.0
10.0
0.0
2.0
4.0
––
6.0
8.0
10.0
Figure 10. UK, relative measurement (deviations from expected values (y-axis) versus deviations from average unit values (x-axis))
Total relative exports
Intra-EU relative exports
60.0
60.0
30.0
30.0
0.0
0.0
-30.0
-30.0
Extra-EU relative exports 90.0
60.0
Cheese
30.0
0.0
-30.0 -60.0
-60.0
-90.0
-90.0
Meat preparations
-30.0
-10.0
0.0
10.0
20.0
30.0
-90.0
-30.0
-20.0
-10.0
0.0
10.0
20.0
30.0
-30.0
90.0
90.0
90.0
60.0
60.0
60.0
30.0
30.0
30.0
0.0
0.0
0.0
-30.0
-30.0
-30.0
-60.0
-60.0
-60.0
-90.0
-90.0
-90.0
-80.0
Wine
-20.0
-60.0
-60.0
-40.0
-20.0
0.0
20.0
40.0
-80.0
-60.0
-40.0
––
-20.0
––
22
0.0
20.0
40.0
-80.0
-20.0
-60.0
-10.0
-40.0
0.0
-20.0
––
10.0
0.0
20.0
20.0
30.0
40.0
Table 2. Summary regression results (feasible GLS estimates) for a two-level hierarchical linear model with one repeated measurement; absolute measurement
Dependent variable: per capita exports Regressors
Total
Intra EU
Extra EU
Tests of fixed effects (F-values) Intercept
33.5**
45.2**
10.5**
COUNTRY
7.9**
7.3**
5.0**
CATEGORY
6.4**
15.5**
3.0*
29.2**
33.3**
10.3**
2.3
1.6
5.6**
PERIOD Unit value
Fixed effect parameter estimates Intercept
.646**
.638**
.137
Country = DE
.141
-.020
.094
Country = ES
.254
.037
.149
Country = FR
.786**
.451**
.301**
Country = UK
.271
.117
.104
Country = IT
0
a
a
a
Category = CHEESE
-.361*
-.354**
-.140*
Category = MEAT PR
-.510**
-.526**
-.154
0
a
Category = WINE
0
Period = 0 (1995-99)
-.142**
Period = 1 (2000-05) Unit value
0
a
0
-.094** 0
.020
a
a
.011
0
0a -.043** 0a .017*
Variance/covariance matrix components†
σ12 σ22 σ12
2.17**
.787**
.382**
3.46**
1.22**
.646**
2.69**
.957**
.482**
Model statistics # of obs.
744
744
744
Restr. –2 log lik.
1,650.0
996.0
581.8
AIC
1,656.0
1,002.0
587.8
BIC
1,670.0
1,015.8
601.6
Notes: ** statistically significant at least at 99% confidence level; * statistically significant at least at 95% confidence level. Significant levels are based on panel-robust standard errors. a Reference category. † Restricted maximum likelihood estimate. Source: author's calculations from Eurostat data.
23
Table 3. Summary regression results (feasible GLS estimates) for a two-level hierarchical linear model with one repeated measurement; relative measurement (non-significant factors removed)
Category = CHEESE Category = MEAT PR Category = WINE Relative unit value (CHEESE) Relative unit value (MEAT PR) Relative unit value (WINE)
-26.13**
.776 -13.13 0
-34.923**
-4.92
-7.45
-19.80**
-14.20*
a
0
a
0a
-.161
-.156
-.040
-.113
.024
-.356**
.017
-.111
.048
Variance/covariance matrix components†
σ12 σ22 σ12
2,922.5**
2,785.4**
2,966.6**
2,826.8**
2,725.5**
2,961.7**
2,666.1**
2,516.1**
2,662.0**
Model statistics # of obs.
744
744
744
Restr. –2 log lik.
7,313.9
7,344.6
7,453.0
AIC
7,319.9
7,350.6
7,459.0
BIC
7,333.7
7,364.4
7,472.8
Notes: ** statistically significant at least at 99% confidence level; * statistically significant at least at 95% confidence level. Significant levels are based on panel-robust standard errors. a Reference category. † Restricted maximum likelihood estimate. Source: author's calculations from Eurostat data.
24
Acknowledgements The author gratefully acknowledges financial support from the H. Wilhelm Schaumann Stiftung, Hamburg, Germany
Contact information Christian Fischer University of Bonn Institute for Food and Resource Economics Nussallee 21 D-53115 Bonn, GERMANY Phone: +49-228-73-3582 Fax: +49-228-73-3582 Email: [email protected]
25
Home Bias in U.S. Beer Consumption Rigoberto A. Lopez, Xenia Matschke University of Connecticut, USA
Summary We apply the Berry, Levinsohn and Pakes (1995) market equilibrium model (BLP) to data from 30 brands of beers sold in 12 U.S. cities over 20 quarters (1988-92) to estimate the consumers’ taste for beer characteristics (price, alcohol content, and calories) as well as for the cultural region of origin (USA, Anglo-European, Germanic, and countries bordering the U.S.). Consumer heterogeneity is allowed with respect to age, income and gender. Overall we end up with 7,200 beer brand observations (30x12x20) and 13,920 (58 random draws x 12 x 20) consumer observations. Empirical results indicate that indeed there is home bias with respect to European beers and somewhat less so with respect to beers from bordering countries (Mexico and Canada). Home bias is more accentuated among older males who are more affluent. Furthermore, the own-price elasticities and the cross price elasticities of demand are higher for foreign beers, indicating a higher degree of loyalty and differentiation for domestic beers. KEYWORDS: Home bias, beer, country of origin, demand, differentiated products
1.
Introduction
We apply the Berry, Levinsohn and Pakes (1995) market equilibrium model (BLP) to 30 brands of beers sold in 12 U.S. cities over 20 quarters (1988-92) to estimate the consumers’ taste for beer characteristics (price, alcohol content, and calories) as well as for the cultural region of origin (USA, Anglo-European, Germanic and bordering countries). Overall, we confirm the existence of a home bias effect, but also decompose it by consumer types. For instance, older males with higher income tend to be more loyal to U.S. beers and be turned off by Germanic beers, regardless of price, promotion, alcohol content or calories. Thus, the results provide a detailed picture of the American consumer home bias toward home vs. foreign made beers.
2.
Background
A growing trade literature finds that nations trade far less internationally than they do within their borders, an empirical regularity that has been commonly referred to as the border or home bias effect. Empirical studies on home bias employ the highly successful gravity equation using rather aggregate levels of data and asserting supply-side causes by relating this phenomenon to, e.g., transportation costs, co-location of intermediate inputs, and increasing returns (Davis, 1998; Hillberry and Hummels, 2002; Head and Ries, 2001). Yet, such studies tend to ignore domestic consumer preferences for the products in question, let alone the fact that consumer preferences in a country like the United States are not monolithic as there is a large variation in consumer characteristics which might influence the degree of home bias.
3.
Objectives
This paper examines the effects of domestic consumer heterogeneity on choices of foreign and domestic beers using data at the product brand level. Beers provide an interesting case study for examining home bias. First, the country of origin can be easily identified by
27
consumers. Second, beer comes in differentiated brands. Third, consumer heterogeneity can play a crucial role in shaping home biases. Fourth, foreign beers play a growing role in terms of their share in the American beer market. Last, home bias has not been tested at the product brand level for beer or any other product.
4.
Data and methodology
In the BLP model (summarized here for expository purposes), the consumer, in choosing a beer brand among competing products, maximizes utility driven by the brand characteristics as well as his/her own characteristics. The indirect utility of consumer i from buying the brand j is given by (1) U ij = β i x j + α i p j + ζ j + ε ij , i = 1,...n; j = 1,..., J where x j is a vector of the observed characteristics of brand j (excluding price), p j is the price of the brand j ,
αi
ζj
denotes unobserved (to the researcher) product characteristics,
and β i are parameters that depend on individual i’s taste, and
ε i j represents
the
distribution of consumer preferences around the unobserved product characteristics with a probability density function f (ε ) . Following BLP, let
α i = α + λDi + γvi
and β i =
β + ϕDi + ρvi ,
where Di denotes
observed consumer characteristics (i.e., demographics) with a probability density function h(D) , vi denotes the unobserved consumer characteristics with a probability density function
g (v) assumed to be normally distributed; and θ1 = (α, β) and θ21 = (λ,ϕ,γ , ρ)
denote fixed parameters. Substituting into (1) yields:
U ij = βx j + αp j + ζ j + φDi x j + ρvi x j + λDi p j + γν i p j + ε ij .
(2)
µij
δj
The indirect utility given in equation (4) is decomposed into two parts: a mean utility term δ j , which is linear (common to all consumers), and a brand- and consumer-specific deviation from that mean
µ ij . Let k = 0
denote an outside good if the consumer decides
not to buy any of the J brands in the set of brands (j=1,…,J). As each consumer purchases a unit of the brand that yields the highest utility or the outside good, aggregating over consumers, the market share of the is chosen.
j th brand corresponds to the probability the j th brand That
is,
s j (δ , x, p,θ 2 ) = ∫ I {( Di , vi , ε ij ) : U ij ≥ U ik ∀k = 0,.., J }dH (D)dG (v)dF (ε ), (3)
where H(D), G(v) and F(ε) are cumulative density functions for the indicated variables and are assumed to be independent. The price elasticities of the market shares for individual brands are:
28
⎧ pj α i sij (1 − sij )dH ( D)dG (ν ), for j = k , ⎪ ∂s j pk ⎪ s j ∫ =⎨ η jk = ∂pk s j ⎪ pk − α s s dH ( D)dG (ν ), otherwise. ⎪ s j ∫ i ij ik ⎩
(7)
We use data from 30 brands of beer in 12 cities over 20 quarters (1988-1992). In total, 7,200 beer brand observations are used (30 brands x 12 cities x 20 time periods). The cities are: Atlanta, Buffalo, Chicago, Cincinati, Cleveland, Columbus, New Orleans, New York, Omaha, San Antonio, San Diego and St. Louis. The data consist of two types of information: product characteristics and consumer characteristics. The product characteristics data include the brand-level market share, the retail price and percent volume sold under promotion. These data came from the Information Resources, Inc. (IRI) Infoscan database at the Food Policy Marketing Center of the University of Connecticut. The potential market size for each was computed by multiplying the statespecific per capita consumption of beer in a given quarter (from the Brewer’s Almanac) times the population. Market shares were then computed by dividing brand dollar sales by the potential market size. The retail price (dollars per case of 24-12 oz. containers) was deflated by the city or region specific Consumer Price Index (December 1992=1). In addition, the percent calorie and alcohol contents as well as the region of origin were obtained online. Four regions of origin are considered: USA, Germanic (Germany and the Netherlands), Anglo (Great Britain and Ireland) and border countries (Canada and Mexico). Observable consumer characteristics were obtained from 58 random draws from the Current Population Survey for each city market and quarter (National Bureau of Economic Research, 2002). These variables are age, income, and gender. Another 58 draws from a normal distribution with zero mean and unit variance are obtained for the unobservable characteristics. Instrumental variables are used to control for potential endogeneity of retail prices arising from their correlation with product characteristics (e.g., imported beers tend to be more expensive). Following Nevo (2001), 120 interactions between 30 brand dummies and four input prices are used as instruments. Input prices include the city-specific wages for supermarket workers, petroleum prices, 3-month interest rates and the price of malt. In addition, state taxes on beer and ale/lager dummies are used as additional instruments. For estimation purposes, we define a market as a city-quarter combination, resulting in 240 markets, each with 30 brands of beer and 58 consumer observations. Overall we end up with 7,200 beer brand observations and 13,920 consumer observations. We adapt the MATLAB algorithm of Nevo (2000) to the beer case. This algorithm minimizes the distance between observed and estimated market shares, using the Generalized Method of Moments. The results are presented in the following section.
5.
Results
Table 1 shows the BLP parameter estimates and their distribution statistics. One should keep in mind that we obtain a distribution consisting of 13,920 parameters, one for each individual consumer in the sample (58 draws x 20 periods x 12 cities). Thus, the ‘standard errors’ represent standard deviations rather than the usual interpretation for fixed point estimates. The parameter estimates of the mean utility (δj), which are common to all individuals, are (jointly) statistically significant at the 5% level and most have the expected
29
signs. Price has a negative effect independent of consumer characteristics and promotion has a positive effect on the mean utility, as expected. The mean utility results clearly point to a home bias in U.S. beer consumption with respect to Anglo and bordering countries’ beers but not with respect to Germanic ones. Taking into account consumer heterogeneity, the taste parameter for price becomes smaller (less price elastic) with age, higher income and for the male gender. Higher income consumers tend to view beers less favorably that are high in calories or alcohol content. On the other hand, alcohol content and calories follow exactly opposite patterns with respect to age and gender. While older males tend to appreciate a higher calorie content, these same consumers tend to stay away from high alcohol content. In terms of the cultural region of origin of the beers, although the mean consumer tends to prefer USA beers (a la par with Germanic ones), this preference tends to be accentuated as consumers get older and wealthier, particularly among males. This group of consumers is generally turned off by foreign made beers, particularly those of European origin. Overall, we calculated 10,800 price elasticities of demand for beers (the square of 30 brands x 12 cities), side-stepping the problem of dimensionality that plagues differentiated product demand estimation. As Table 2 shows, all the estimated own-price elasticities are negative, as illustrated for the city of Chicago, Illinois. The own price elasticities seem a bit high for most beers relative to estimates in the literature, although most estimates are done at a more aggregate level. Nonetheless, domestic beers tend to have much lower price elasticities than foreign ones. The elasticities of substitution with respect to the price of Budweiser (the leading beer) are lower for domestic than for foreign beers. The elasticities of substitution with respect to Harp (the most similar beer to Budweiser in terms of alcohol content, calories and lager type) are much lower in spite of its similarities to Budweiser which is a domestic beer. Also note that the cross price elasticities of substitution are higher for foreign beers than for domestic ones, attesting that in the eyes of the Chicago consumer, foreign beers are closer substitutes among themselves than with respect to American ones.
6.
Final remarks
Although the presented results are preliminary, the methodology of Berry, Levinsohn and Pakes (1995) seems promising in analyzing consumers’ taste for home vs. foreign products. Applying such methodology to a large data set involving 12 cities and 30 brands of beers, the results point to home bias with respect to U.S. beer consumption. Furthermore, this bias appears to be more accentuated in male consumers who are older and have higher income. The estimated price elasticities of demand further attest that American consumers are less sensitive to the prices of domestic beer and that they more easily switch to domestic beers than foreign ones in spite of common physical beer characteristics. This shows that the payoff to go beyond the common aggregate studies of home bias in international trade is potentially high as one tests not only for home bias, but also gets a detailed insight into consumer behavior and consumer heterogeneity with respect to home bias.
7.
References
Berry, S., Levinsohn, J. and Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica, 63: 841-890. Davis, D. R. (1998). The home market, trade, and industrial structure. American Economic Review, 88: 1264-1276.
30
Head, K. and Ries, J. (2001). Increasing returns versus national product differentiation as an explanation for the pattern of U.S.-Canada Trade. American Economic Review, 91: 858-876. Hillberry, R. and Hummels, D. (2002). Explaining home bias in consumption: The role of intermediate input trade. NBER working paper 9020, Cambridge, MA. National Bureau of Economic Research, (2002). Current population survey, merged outgoing rotation groups, 1979-2001, Cambridge, MA. Nevo, A. (2000). A practitioner’s guide to estimation of random coefficients logit models of demand. Journal of Economics and Management Strategy, 9: 513-548. _____(2001). Measuring market power in the ready-to-eat cereal industry. Econometrica, 69: 307-342.
31
Table 1. Demand Parameter Estimates
Variable
Parameter Notation
BLP Estimate S.E.
Logit Estimate S.E.
Mean Utility Price Alcohol Calories Ale Promotion USA Anglo German Border
pj X1 j X2j X3j X8j X4j X5j X6j X7j
α β7 β2 β3 β8 β4 β5 β6 β7
D1i D1i p j D1i X 1 j D1i X 2 j D1i X 3 j D1i X 4 j D1i X 5 j D1i X 6 j D1i X 7 j D2 i D2 i p j D2 i X 1 j D2 i X 2 j D2 i X 3 j D2 i X 4 j D2 i X 5 j D2 i X 6 j D2 i X 7 j D3i D3i Pj D3i X 1 j D3i X 2 j D3i X 3 j
Deviations Age Age x Price Age x Alcohol Age x Calories Age x Ale Age x USA Age x Anglo Age x German Age x Border Income Income x Price Income x Alcohol Income x Calories Income x Ale Income x USA Income x Anglo Income x German Income x Border Male Male x Price Male x Alcohol Male x Calories Male x Ale
32
0.085
1.660
-0.185
1.5424
0.544
22.6878
-0.366
NA
Male x USA Male x Anglo Male x German Male x Border Unobserved Unobs. x Price Unobs. x Alcohol Unobs. x Calories Ubobs. X Ale Unobs. x USA Unobs. x Anglo Unobs. x German Unobs. x Border
ϕ 34 ϕ 35 ϕ 36 ϕ 37 γ1 γ2 ρ1 ρ2 ρ3 ρ4 ρ5 ρ6 ρ7
D3i X 4 j D3i X 5 j D3i X 6 j D3i X 7 j
νi νi pj ν i X1 j νiX2 j νiX3j νiX4 j vi X 5 j vi X 7 j νiX7j
33
0.031
NA
-0.471
NA
-0.669
NA
0.226
7.466
0.654
2.5084
-0.012
0.1981
-0.080
0.2154
0.511
2.2845
0.474
0.9532
-0.885
3.67
-0.699
2.2773
-0.941
2.9821
-0.359
4.4523
Table 2. Price Elasticity Estimates for Beer Brands in Chicago
Brand Amstel Light Bass Becks Budweiser Budweiser Light Busch Colt 45 Coors Coors Extra Gold Coors Light Dos Equis Guinness Harp Heineken Kaliber Labatt Lowenbrau Michelob Michelob Light Miller Miller Light Milwaukees Best Molson Molson Golden Moosehead Old Milwaukee Rolling Rock Schaefer Schlitz St. Pauli Girl Average: Home Average: Foreign
Country of Origin
Own Price Elasticity
Holland England Germany USA USA USA USA USA USA USA Mexico Ireland Ireland Holland Ireland Canada USA USA USA USA USA USA Canada Canada Canada USA USA USA USA Germany
Acknowledgements We wish to acknowledge funding from the Food Policy Marketing Center at the University of Connecticut via CSREES Special Grant No. 2006-34178-15694. via the Food Policy Marketing Center and from the Storrs Agricultural Experiment Station. We also wish to thank Ronald Cotterill for making the data available. The content of the paper, however, is the sole responsibility of the authors.
Contact information Rigoberto Lopez Department of Agricultural & Resource Economics University of Connecticut 1376 Storrs Rd., U-4021 Storrs, CT 06268 Phone: +001-860-486-1921 Fax: +001-860-429-1932 Email: [email protected]
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Quality Assurance and other Marketing Management Elements as Key Success Factors for Entering a New Market: a Case of Functional Food Market in Indonesia Jofi Puspa, Rainer Kühl University of Justus Liebig, Germany
Jofi.Puspa@ernährung.uni-giessen.de
Summary Based on its distinctive profiles functional-food (FF) can be considered as a mixture of food and pharmaceutical items. Apparently, these different characteristics that exist beyond conventional food products contribute success of the commercialization of new innovative FFs. Therefore, assumption can be made by arguing that for the marketing a FF a distinctive marketing-strategic beyond the one usually used for the conventional food products must be employed. This study was pursued with the main aims to understand the consumer’s psychological factors and to find out elements important to setting up the marketing strategy. These two findings will be then used as basis for designing a distinctive marketing strategy for a FF. We found that consumer’s psychological set varied across different sample groups. Therefore, segmentation plays a significant role. Due to the fact that most of the consumers had a medium to high involvement level, communication strategy becomes a salient means for the marketing of FFs. According to the respondents some important extrinsic/intrinsic quality based elements of FF can be generally incorporated into the communication platforms for FF. From the industry’s point of view this study showed that internal organizational and management, market attractiveness and trade capability are the most important elements supporting a firm in developing a new innovative FF in Indonesia. KEYWORDS: product quality, communication, segmentation, key success factors for market entry, functional food in Indonesia.
1.
Introduction
In the last two decades international product innovation projects have been directed to redesign conventional food and beverage products in such a way that they can provide additional health benefits beyond their basic nutritive function. As an example, researchers have tried to influence the systemic immune reaction by specific nutrients and food ingredients directly (e.g. by triggering immune cell activation or altering immune cell interactions) or indirectly (e.g. by changing substrate for DNA synthesis, altering energy metabolism, changing physiologic integrity of the cells or altering signals or hormones) (Deibert and Berg, 2002). This innovative development is triggered by improvement of people’ interest in the nutrition and health prevention. Researchers and industry have continuously pursued discussions that try to find out about clear definition of”functional food”. In short, functional food describes products similar in appearance to conventional food (and beverage) products, consumed as a part of the usual diet. They come from natural sources or have been enriched with natural substances or other components with a specific physiological preventive and/ or health promoting effect (Backgrounder, 1998; ILSI, 1999; Health Canada, 2004). However, there has been a long-standing debate about the definition and limitations of health promoting effects of functional foods and about how to prove
37
those effects. Apparently, these issues relate to legal aspects, especially, concerned with the commercial uses of health claims for marketing of functional food products. In many Western countries there is a strict dividing line between food and medicine. Health effects such as prevention-, treatment- and reduced risk of chronic diseases are, more or less, considered as the domain of medical and not of nutritional intervention. Principally, through establishing a legal regulation the government or food authority intends to protect the consumers from the abuse of a health claim. But from the view of industry, this legal regulation is often being seen as a rigid construct limiting their marketing creativity. It is not surprisingly, that industry pays much attention to this issue, because, it is a fact that the presence of a health claim for functional foods endows an important distinctive trademark, which differentiates a functional food from conventional food items. For marketing purposes a health claim is a valuable unique selling proposition, which is beneficial for establishing product positioning and segmentation. The other distinctive characteristics of functional food over conventional foods beyond the above mentioned features are (1) the presence of disparity in terms of consumers’ psychological set (perception, knowledge, attitude, motivation and involvement level) in the society; (2) the present of moderate to high cost of investment, (3) the presence of an asymmetrical market structure. The concept of functional food especially for finished and packed food and beverage products is relatively newly introduced and communicated to the public. Apparently, consumers’ reactions towards this concept depend on their perception and motivation towards general health- and prevention of diseases concept. Possibly, we can argue that consumers, who have a relatively high awareness level or interest on a healthy life style and who have a high motivation level towards disease prevention measures will tend to have a positive attitude and acceptance towards functional foods. And contrarily, consumers with low awareness and low motivation level towards a healthy life style and diseases prevention measures will ignore or pay no (or less) attention to functional food products. Beyond that, we can find that quite a lot of people, who show their negative or skeptical perceptions about functional food, will possibly have a low acceptance level towards the consumption of functional food. Due to these facts we can argue that people have different psychological settings towards functional food and that this issue can be considered as one specific characteristics of the functional food field. According to Henderson and Clark’s (1990) classification of innovation, functional food can be classified as a ``strategic innovation`` group, because functional food is perceived as having a new design but being otherwise based on established technologies. To develop such innovation a firm may require higher financial and management capability, especially when this is compared to the financial means required for developing a conventional food product. The main cost-created-activity in developing a functional food is related to R&D activities, especially to the investigation of the ingredient supposedly delivering the health effect and to prove its efficacy in that respect. Developing a functional food product carries a much higher risk than developing a conventional one especially when and if the product faces a flop. As has been mentioned above, an improvement of awareness of general health problems can be observed, although, people’s inherent knowledge concerning medical perspectives of health and nutritional sciences varies. Considering its distinctive design and function, we can argue that the functional food represents a mixture of food and pharmaceutical product. In order to understand the concept of functional food and its offered health effects, a certain level of medical knowledge is absolutely required. Considering the variety of people’s inherent knowledge level and the sophistication of the functional background of functional food, we further observed the presence of asymmetrical information in the field of functional food. There seems to be a large discrepancy between medical experts and industry as information holders and consumers on the likely effects and benefits of the consumption of functional food.
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Understanding the clear distinctive features of functional food, therefore for the view of marketing, we argue that a firm needs a different marketing approach than the one used for marketing of conventional food products, because, obviously a distinct strategy will ensure the success of the commercialization of the newly developed functional food products. For that purpose this study has the main aim to understand and study elements important for the marketing of functional food products. In this study two main anchors, i.e. consumers’- and company’s perspectives will be deliberately investigated.
2.
Background
Since year 2000 plenty of food and beverage products were marketed with different health claims in Indonesia. Most of them are produced and marketed by locally or even regionally based companies. The significant trend of the functional-food market in Indonesia follows the fast market development of supplement products and it is strongly influenced by international functional food market developments. Increasing people’s awareness of following a healthy life style through nutrition consumption upholds the positive movement of the functional food market. Actually, for the Indonesian people the concept of functional food is not really new because of the fact that consuming food which provides health benefits is a part of their ancient traditional eating habit. An example of a traditional functional food worthy to be mentioned is a fermented product of soybean called Tempeh. There are many published scientific studies that confirmed the health benefits of Tempeh and those studies supported Tempeh to be classified into the functional food category (Karyadi & Lukito, 2000, Wilcox, et al., 1990 and Sudarmadji et al. 1997). However, due to the absence of a consistent regulation, which is supposed to be issued by the Indonesian food authority, we find that many so-called functional food products have been improperly marketed. The usage of a health claim as marketing instrument was wrong and many advertising campaigns were misleading and elusive. Without any single supportive scientific argument or relevant scientific finding some companies used health claims as product selling propositions. Therefore, it is not surprising that one can find a product, which is claimed to be beneficial for treating a broad range of diseases such as from skin infection to cancer. This development is aggravated by a strong trajectory competition in this market. Consumer behavior studies have emphasized the importance of consumers’ psychological factors such as attitude, perception; knowledge, involvement level and attitude are fundaments for developing the marketing strategy for a product (Solomon, 1996; Chisnall, 1994; Foxall and Goldsmith, 1994). Attitude is defined as a learned predisposition to respond in a consistently favorable and unfavorable manner with respect to a given object (Sarnoff, 1960 and Thurstone, 1931 in Fishbein & Ajzen, 1975). It is typically viewed as a latent or underlying variable that is assumed to guide or influence behavior. The individual attitude is predisposed toward performing a type of behavior, all of which are either favorable or unfavorable with respect to the object. Unfortunately, till now a consumer study concerned with attitude towards functional food in Indonesia is absent. Our assumption regarding people’s attitude towards functional food suggests that due to massive deceptive marketing programs launched by companies the consumer’s acceptance and attitude towards functional food products may be negatively influenced. We assumed that consumers will have a negative perception towards functional foods. This tendency can hamper the development of new functional food market. While, involvement level is perceived as level of perceived personal importance, feeling of interest, enthusiasm, familiarity, normative, commitment, excitement about product specific that is evoked by a stimulus within a specific situation (Solomon, 1996; Bloch and Richins, 1986; Freedman, 1964; Howard and Sheth, 1969, Lastovicka and Gardner, 1979).
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Depending on the importance of the product, significant risk, emotional appeal and norm identification, the consumer involvement can be classified i.e. low, medium or high involved consumers). In this study, attitude and involvement level of prospected consumers towards functional food were investigated. The results will be then used as prominent platforms for developing the other marketing concepts. Communication is a core in the marketing program and it can play different roles determining the product success. The important purpose of the communication is to contribute to brand equity (Keller, 1998). Communication is a media that helps to position the product image in the consumers’ mind. Communication can put something in consumers’ mind or change consumer’s attitude or get consumer into a buying action. It is a bridge that connects between product and consumer and it is a means by which it can establish dialog and build relationship with consumers. Through communication marketer can be seeking a cognitive, affective or behavioral response from the target consumers. Researcher have been argued that understanding target consumers’ perception, attitude and inherent knowledge level is required as basis for set up appropriate, efficient and interesting communication messages. Communication message can be established through emphasizing what the targeted consumer want to reach and what response they want (Kotler, 1994). Consumers will hear the messages that fit into their belief system. Communication effects are greatest where the message is in line with the existing opinions, beliefs, and dispositions of the consumers (receivers) (Fiske and Hartley, 1980 in Kotler 1995). In the food and beverage market, basically, we can observe that, communication platform for food and beverage products follows the one usually used for the commodities. In this case consumers are assumed to have low involvement towards all products. Most of consumers buy a product without comparing actively all available brands or product alternatives and without making a long process of decision making (inertia). They just pick a familiar brand or make a decision based on trial and error principal. Therefore communication of these products is usually simpler and more animated, which suitable for passive learning consumers. It has little opportunity for reflection or making connection, and it often deals with unimportant matter (Assael, 1995). Further, apparently, for currently marketed foods and beverage products the main communication messages often relate to emotional or affective issues such as taste, freshness, great pleasure or enjoyment, and newness. Due to the fact that functional foods have clear distinctive comparisons over the conventional foods, therefore, consequently, we can further argue that communication strategy is a prevalent marketing tool. Through communicating the unique features and benefits a firm can position a functional food differently from conventional food products. In this study we aimed to find out idea concerned with communication messages suitable in general for functional food from the view of consumers. Consumers’ psychological set is not only important as a basis argument for establishing product communication, but it is also useful to be used as a platform for segmenting the products. Our above mentioned assumptive preposition stated that consumers of functional food have a wide variation in term of their behavior, personal characteristic and value with regards to the healthy life style and prevention measures. Therefore, we further assumed that market segmentation is relevant for a functional food, because it will give an opportunity for a firm to base its strategy on a more relevant and prospective segment and to allocate more efficiently their resources and capability on focused target segment. In the case of functional food, when a firm want cover a mass market as they usually do to market conventional foods it seems that a substantial financial resources is required especially for improving or upgrading people’s awareness and acceptance. According to its capacity and capability a firm may cover segment which is large enough to bring an optimal turn over. Marketing theory has emphasized that the traditional segmentation model, which bases on consumers’ social-, economical and demographical-characteristics such as age, income,
40
gender, location, often fails to perform an optional return on investment because, apparently, consumers can not be easily segmented based on those profiles. Nondemographic segmentation, which is based on people’s need-want, perception, and value, may serve as a good basis for marketing strategy (Yankelovich and Meer, 2006). Reynolds (2006) argued that essentially focusing on the potential "need-and-want hot buttons" is most likely to be successful for product extension development and new product development. Understanding consumers’ need, value, and behavior associated with the product can help the marketer to develop segments around brand loyalty, price-sensitive, or feature-sensitive respondents (Feldman, 2006). Understanding the different market structure and different characteristics of functional food, therefore in this study we assumed that segmentation plays important role for the success of marketing of a functional food. This study is aimed also to find out and propose a segmentation model required for marketing of a functional food especially in the Indonesian market. Although communication and segmentation are considered as the most central issues for marketing strategy, intrinsic and extrinsic product profiles also contribute significantly to the success of marketing of a product. Intrinsic (such as taste, color, texture, appearance, quality) and extrinsic profiles (such as packaging, brand name, name of producer, price, labeling) are relevant for differentiating a product from their competitors and for defining the unique selling proposition of the product. Apparently, these factors are attributed as indicators for general product quality. Consequently, intrinsic and extrinsic quality cues can be used as one possible communication themes, depending on the consumers’ attention, interest, awareness and desire towards the product. Since continuous and consistence communication messages or called product-positioning will occupied the consumers’ top of mind, therefore, it will help the consumer to easily recognize the product and finally it will influent consumer’s buying decision. Considering the importance of intrinsic and extrinsic quality cues, in this study we intended to identify the consumers’ perception regarding the importance of intrinsic and extrinsic elements related to buying decision of functional food products.
3.
Objectives
Understanding the past and current development of functional foods and beverages in Indonesia this study was designed to investigate the following research objectives: • finding out the consumers’ attitude towards functional food concept, • investigating marketing elements (intrinsic and extrinsic quality cues, possible communication message and segmentation) importance for influencing consumers’ buying decision of functional food and, • understanding marketing management elements important for developing innovative functional food from the view of the industry (firms).
4.
Data and methodology
In order to accomplish the research objectives, this study’s framework was designed, which consists of a combination of an exploratory and a descriptive research. We conducted a consumers’ study using an in-depth personal interview method. This field study was being conducted in Indonesia (Jakarta and its surroundings) starting in October 2002 and was finished in September 2003. Jakarta was selected, because it represents a rapidly developing city where the market for such functional products is widely open up. Respondents were recruited based on a judgmental sampling method, which selects certain respondents according to the presumable representation of the population of interest (target segment) (Dillon et al., 1994). In general, the inclusive criteria for selecting respondents were as follows:
41
• Persons with ages of 16 years and older. • Persons, who are living in the selected region. • Persons, who have selected diseases such as CHD, hypertriglyceridemia, obesity, medical care providers and a healthy group. In recruiting respondents we used a priory segmentation method, which targets respondents prior to the study by establishing the following groups (A) patients with coronary heart disease (confirmed by angiography, PTCA, bypass operation or myocardial infarction. (B) patients with high serum triglyceride level (all hypertriglyceridemic patients with plasma triglyceride levels of > 200 mg/dl- or according to the NCEP (National Cholesterol Education Program ATP II guideline), with or without the metabolic syndrome. (C) patients with obesity (according to the ITFO (International Task Force on Obesity) guideline, overweight and obese patients is defined when the patients have BMI (Body Mass Index) more than 30 kg/m2) with or without other metabolic diseases. (D) medical experts, including Physicians (Internists, Cardiologists and General Practioners), Nutritionists, and Pharmacists, and (E) healthy persons as controls. Groups of patients were selected as respondents in order to represent a direct target group for functional foods and to represent motivated consumers, who are dealing directly with the diseases and are aware of fooddiseases relation and who have direct access to the scientific information. A Healthy person group as control represents unmotivated consumer, it was our initial assumption, who are less aware of disease prevention and healthy diet patterns. In this study we intended to compare these two groups of consumer especially in term of their attitude toward functional foods and to compare their response toward product innovation. Medical experts were also included in this study, because of their important role in passing through the scientific and product information and in influencing the patients’ diet pattern. Respondents were interviewed by using a prepared questionnaire. A total number of recruited respondents were 237. Attributes and beliefs evaluation necessary for attitude evaluation was measured according to the Fishbein model. It was further confirmed by the five scale model of Likert’s attitude measurement technique. Functional food attributes necessary for the attitude measurement were evaluated and determined prior to the study by using the full rank method of attributes selection, thus providing a basis for finding out and presenting fairly all of the important attributes (which included positive and negative characteristics) of functional foods to the respondents. In order to evaluate the correlation between attitude and intention to buy we used a non-parametric correlation analysis of Kendall-Tau-b and Spearman-Rho. Involvement level was determined by measuring certain indicators such as (1) people’s intention to search for information (2) type and number of sources of information usually used and (3) quality of information usually collected. Involvement level towards functional foods was measured using a theoretical construct model presented by some authors (Assael, 1995). People’s motivation level towards healthy life style and disease prevention was measured by using some life style parameters such as (1) sport activity, (2) healthy food and supplement consumption, (3) vegetarian behavior. Factor analysis was used in order to find out some important new factors, which could be extracted from collected multi-attributes of functional foods. The new factors found by factor analysis would then be useful to determine further the communication concept of a functional food. Further, using a multivariate regression model all new factors were analyzed in order to obtain information regarding the importance of each factors for influencing the buying behavior. Cluster analysis using K-means clustering was used in order to segment the market of functional food.
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In order to understand the internal and external elements important for a firm or company in developing functional food products, opinions of the selected managers responsible for product launching in some food companies (such as Business Development Manager, Marketing Manager, Research and Development Manager, Product Manager) were collected. The company study was focused on the evaluation of success and risk factors when launching a new food product. We have done in-depth interviews with some managers and staff using a guided questionnaire. The main theme declared in the questionnaire was concerned with the evaluation of success and risk factors in developing and launching a new product. It was designed in accordance with Standard Performance Measures from Cooper and Cooper (1984), Kleinschmidt and Cooper (1991), Weiss et al., (1994), and the Risk Evaluation List from Keizer et al., (2002) and self modification. A total of 11 managers from two companies were interviewed. The selected companies had different or opposite profiles in terms of size, organizational and managerial capability and culture.
5.
Results
5.1
Consumers’ psychological set
Although both attitude models are using different and separate questioning techniques and different measurement scoring the final calculation of both methods showed a quite identical result of measuring consumer’s attitude. The mean value of the Likert model indicated that people’s overall attitude toward functional food was close to the quite positive value (mean value of 3.6) and the Fishbein analysis agreed (see FIGURE 01 and 02). In spite of aggressive and negative marketing campaign concerned with the health claims for food and beverage products done in the past, consumers apparently still displayed a positive feeling, desire and perceptual tendencies towards functional food products. They learned to evaluate functional foods in a consistently favorable way. The Fishbein attitude evaluation basically measures a person’s perception regarding the importance of all possible attributes of an object and a person’s belief in that object. This model showed that people have a positive perception towards the attributes of functional foods, but that their belief was inadequate. The respondents are in favor of the health features of functional foods, but they, apparently, did not have an adequate belief that such food and beverage products can perform those health benefits. The low belief in benefits of the product was as result of an aggressive misleading and elusive health campaign done in the past. As the belief in a product is a key success factor determining future consumption behavior, therefore in the future industry needs to improve the image of the functional food concept through empowering people’s belief. Improving people’s belief will give a positive impact on attitude and finally on intentional buying behavior. The healthy control group showed the lowest attitude score as compared to the other groups. The attitude of healthy people in this group was varying. Most of the healthy respondents had a lower level of awareness of the necessity of prevention than participants of the patient groups. The correlation analysis showed that there is a pertinent relationship between attitude and people’s intention to buy in a medium scale (Table 01). People with a positive attitude tend to consume functional food better than people with a negative attitude. The other findings concerned with consumers’ psychological factors such as involvement level showed that quite a number of respondents had a medium to high involvement toward functional foods. Only 3% of the Indonesian respondents showed to have a low involvement (FIGURE 03). This finding indicates that most of the respondents paid attention toward product information and that they spent some time to search for, process, evaluate, and learn the product profiles. The above-mentioned discussion allows us to accept our previously stated hypothesis of this study, which mentions that the involvement
43
level towards functional foods was medium to high and that it is different from the involvement level towards conventional foods, which is usually perceived as low. The consequence of this finding for a firm is that product information and communication will be beneficial for the consumers’ buying decision process. Moreover, this study argued that respondents, who have a higher motivation, tend to have a better knowledge level concerning health aspects as compared to respondents with a lower motivation level. This study showed that the actual and perceived knowledge level was relatively good, but that product class familiarity was low. In general, the before mentioned group had a more positive attitude towards different attributes that can possibly be offered by functional foods. For marketing purposes this finding suggests that people vary in term of their psychological set towards functional food. Patients groups have a higher motivation level, a more positive attitude and a higher knowledge level than healthy young respondents. Consequently, this group is supposed to have a higher readiness to consume functional food. For a firm, this segment is an adequately potent target group of consumers for such functional foods, because they already have positive behavior foundation such as knowledge, motivation, attitude and perception important for determining product acceptance.
5.2 Marketing elements important for the buying decision of functional foods (from the viewpoint of consumers) Product profile is one of the important elements in a marketing mix. Especially for functional food and beverage products both intrinsic and extrinsic elements contribute significantly to the influence on consumers’ acceptance. In general, product quality can be represented by both intrinsic and extrinsic features. Examples of extrinsic profiles of functional food importance to be mentioned are labeling, brand name, packaging size and outlook, and price. In this study we intended to evaluate whether some of these elements provide equal or different roles for influencing consumers’ buying decision. We did not intend to compare the role of each element. According to the respondents quality and safety guarantees provided by the manufacturer and the presence of a logo or a label as proof of quality from external institutions were the most important profile items determining the buying decision (positive correlation coefficients of 0.300 and 0.107 respectively). The Indonesian respondents perceived that brand name and company name as the second most important extrinsic profile item of functional food. This study confirmed that product quality and safety are the most important issue determining consumers’ trust. Consumers can easily recognize the product quality through brand name and company name. The consumers’ demand for product quality guarantee is mainly due to the negative perception and low confidence in food quality control measures currently implemented by the government. People do not see that there is any significant and serious improvement in the government’s or food authority’s engagement with regard to food quality assurance and control. Therefore, it is not surprising that many food scandals are now emerging there in the public such as the use of formalin and borax (sodium borate) as a preservative components and the use of prohibited coloring materials such as Rhodamin for some traditional food products. A low level of sanitary and quality control of traditional foods contributes to decreasing people’s confidence ad trust in food and beverage products. Thus, this development is a challenge for local, regional wide-, and home-made- food companies, because they are asked to improve and establish a high quality image in order to maintain their position and to win the consumers’ trust. While international food companies, which are perceived to a have high sanitary standard and adequate quality assurance measurement will have a better opportunity to market new functional food products through the company’s name umbrella-branding. Taste is another one of the important elements for functional food. We did a trade off analysis by asking the respondents about the importance
44
of the health claim versus the taste. They argued that although, a product may have important health effects but it will no longer be attractive (32.9% of total respondents) or may be not attractive (52.7%) when its taste is marginal. Therefore, we further assumed that taste, quality, safety and efficacy are the most importance intrinsic elements of functional foods, while, labeling, company name, brand name are importance extrinsic features. For the marketing process this finding can offer some ideas how to position a functional food product in the Indonesian market. Possibly, these are also some issues which consumers want to hear about from the industry. In the previous section this study has identified a medium to high involvement level of respondents towards functional foods. Consumers pay much attention to product information. It is different with the involvement level toward conventional food products, which is suggested to be low. In order to make a buying decision in favor of such functional foods products, the consumers went through a medium intensity high buying decision process. They needed more products’ information in order to perform an intensive attribute evaluation and for establishing a positive motivation, belief and attitude toward the object. Finally this will direct the buying decision. These findings confirm the importance of the communication strategy within the whole marketing process of functional foods. Factor analysis of several attributes of functional foods has identified 9 new factors of the communication messages appropriate for a functional food according to the respondents’ opinion. In general we have classified two major communication themes in relation to the functional foods attributes: (1) affective components, which cover all communicative messages emphasizing the emotional aspects of consumers, such as feeling, passion, fear, happiness etc. (2) cognitive components, which cover all logical reasons for buying or consuming a product. The affective components cover two major issues (a) internal usage consequences, which cover all emotional factors, resulting from the functional consequence of having used or consumed a functional food product. Examples of this are the feeling of satiety (feeling full), feeling good, and looking good (cosmetic feeling) and (b) immediate intrinsic association, which includes other emotional factors resulting from the intrinsic features of functional food, such as confidence of outlook (resulting from good product appearances), confidence of efficacy (resulting from product effectiveness in performing the health claim), confidence of safety (resulting from product assurance of minimal or no side effects), confidence of usage (resulting from the presence of guarantee of quality and appropriate applications). The cognitive components are covering two main other issues (a) health benefits sought (relating to all health benefits offered by the functional food product). The health benefit sought can be divided into two parts based on the length of the product reaction time to achieve the health claim, i.e. (a1) short term effects, such as achieving expected nutritive the expected nutritive value, improving health and fitness, improving the body’s defense mechanisms etc., and (a2) long term effects, such as lowering the risk of disease, prevention of disease, treatment of disease and reduction of the cost of drugs, and (b) non-health benefits sought. This relates to all other aspects beyond the offered health aspects, which are perceived as product features important for influencing consumers’ belief. The non-health benefits include many other cognitive components such as (b1) providing a natural base of foods, (b2) convenience of usage including features, which offer ease of handling of the product (easy pouring or opening, easy packaging design for keeping purposes, easy disposing of, (b3) freedom of choice, presenting functional food in a variety of end products, (b4) potency and efficacy, the product offering a strong effectiveness regarding the health claim (as potent as a drug), (b5) fast onset of action, performing health benefits in a short period of time. For a new product launching, the cognitive components seem to be more important to be emphasized in the communication message rather than the affective components. According to our multiple regression analysis the affective components did not successfully
45
influence the intentional buying behavior. In the first launching period of a functional food product it appears that the affective components would fail to build the consumers’ belief toward the product. They could not clearly explain and convince the consumers to what extent, the product can solve what the consumer tries to get done. The cognitive components seem to be more appropriate for convincing the consumer to intentionally make a choice decision. This finding confirms the existing theories, i.e. consumer-response stage models or response-hierarchy-models such as ``Hierarchy-of-Effects-``, ``InnovationAdoption-``, ``Communication-`` and ``AIDA model``. All of these models assume that consumers pass through a cognitive, affective and then behavioral stage, in that order (Kotler, 1994). In these models a cognitive stage leads to the establishment of attention, awareness, reception and knowledge level towards an object. The result of the cluster analysis indicated that segmenting the market of functional foods is still beneficial. Cluster analysis has confirmed that there were some specific differences in the consumers’ characteristics among different clusters. The consumers with a high intentional buying behavior can be separately defined from groups of consumers with medium or low buying intention. Each cluster has a certain consistent bundle of characteristics, which is totally different from alternative clusters. Segmentation can still be considered as an efficient method in selecting a target consumer group and focusing the promotion for a product. However, our findings suggest that the marketers can not base their segmentation solely on the basis of the traditional segmentation methods, such as geographic location and demographic factors, socioeconomic, psychological and organizational, because those factors did not form the cluster in a consistent manner. As a consequence of this result we would like to suggest another way of segmentation, i.e. based on consumers’ perspectives and not based on consumers’ characteristics. We called this segmentation method ‘’Instrument-Value (I-V) Segmentation’’. ‘’Instrument’’ means that a product is being seen as a tool or a stuff that helps the consumer’s job to be done. While ‘’Value’’ refers to the relative importance of a product perceived by the consumers. Therefore, the ‘’Instrument-Value Segmentation Model’’ is based on the product as an instrument and on how the consumers value the product. This IV segmentation emphasizes the necessity to regard the consumers’ perceived value of a product or service. It concentrates more on what consumers really do with their product. This covers all cognitive and affective demands. A certain segment can consist of some consumer characteristics such as differences in age (young or old), different education levels and family status etc., but all groups of consumers may have ‘’the same job to be done’’ (Christensen and Raynor, 2003). The advantage of using the I-V Segmentation Method is that the firm can cover prospective consumers with broad characteristics, who have the same way of thinking about the value of the product. The I-V Segmentation Method will avoid being misled by determining sets of consumers’ characteristics, especially, when those characteristics are complex. However, if the usage of this I-V segmentation covers too large a market segment, which the firm cannot afford to reach, then hybrid segmentation can be selected. A hybrid segmentation model combines I-V segmentation with other segmentation model using one or two factors related to demographic, geographic, socio-economic profiles with the purpose to limit market coverage. Depending on its capability and goals the firm can decide how broad they want to cover the market. This study suggests the following relationship between a firm’s capability and resources and the range of market coverage (see FIGURE 06). To enter a small and limited market segment can be considered as an appropriate strategy, especially when the firm has only limited financial and non-financial resources, because -in our opinion- entering a large market requires big resources. Without enough support it would be inefficient. For functional food products, segmenting a product to patients, who
46
can value the product’ health benefit, is an example of a narrow but reasonable segmentation strategy. In other cases, e.g. when a firm has higher (moderate) financial resource, covering a larger target market will make much more sense. Based on our I-V segmentation model, a firm can target its functional food to consumers, who a have high level of risk factors of certain related diseases. This segmentation can be further developed based on the firm’s capability. The final stage will be reached, when plenty of financial capabilities and resources are available. In that situation covering a very huge segment like all healthy people- including those who are unaware of a healthy life style- may be possible. In this last segmentation model the firm is assumed to be willing and ready to invest into changing people’s awareness, perception, attitude and acceptance towards a healthy life style, disease prevention measures as a prerequisite for their acceptance of functional food products (see FIGURE 06).
5.3. Marketing management elements important for developing innovative functional foods (from the view of industry or firms) The result of an in-depth interview with managers and staff working at the selected companies showed that, although the two recruited companies had quite different profiles especially in terms of size, organizational and managerial capabilities, they were in agreement with regards to analyzing important factors for developing and launching new functional food products. Company A represents a small to medium sized company, which has the main business in the food market. Company B represents a big enterprise, which has a wide business interest, such as in the food market, pharmaceutical market, packaging materials, distribution, etc. They perceived that organizational and management problems are the most important internal factors, while market attractiveness and trade capability were the important external elements. In our study organizational and managerial issues consisted of for examples of the following themes: cooperation, team work spirit, leadership and communication and motivation within team members in the development process of new product, collaboration and cooperation with other departments, support from top management, effective sales force to support newly launched product. Market attractiveness covers issues such as degree of competition, number of competitors, size and growth of the market, impact of launching of innovation towards market and company, the consumers’ loyalty towards competitor products, timing of product launching, reaction of competitors towards new coming innovative products. Trade capability means trade (wholesaler, retailer) readiness to absorb, sell or distribute new product, company power vs. trade power issue, coverage profile of distributor, financial capability of trade, managerial and man power of trade, procurement management of distributors or retailers.
6.
Final remarks
The result of attitude analysis showed that most of the respondents are actually still in favor with the health features possibly can be offered by a functional food, but, apparently they did not have an adequate belief in the efficacy of related health effect supposed to be performed by a functional food. People with a medium and high motivation (patients groups) tend to have a more positive attitude and higher motivation level than the young and healthy control group. However, an adequate number of respondents tend to have a medium to high involvement towards functional foods. These two findings indicate that the prospected consumers of functional food vary in term of their psychological factors especially attitude and motivation. The finding regarding respondent’s involvement level enable us to further suggest that basically, a comprehensive product communication program is important for the marketing of a functional food in order to satisfy the needs on information requested by the medium to high consumer group. Based on the collected
47
consumer’ opinions the two major communication themes suitable in general for a functional food can be classified, i.e. (1) affective components, which cover all communicative messages emphasizing the emotional aspects of consumers, such as feeling, passion, fear, happiness etc. (2) cognitive components, which cover all logic reasons for buying or consuming a product. The finding regarding variations in term of people’s psychological set suggest that new product introducing can be efficiently achieved through an appropriate market segmentation strategy. Cluster analysis has confirmed the presence of some specific differences in the consumers’ characteristics performed by different clusters. This study suggests segmenting the market based on the consumers’ perspectives and not based on consumers’ characteristics. This study proposed using the ‘’InstrumentValue`` (I-V) Segmentation model for the marketing a functional food. This model offers possibility that a firm targets its prospected consumers based on the similarity of the consumer’s ways of thinking towards a product. With this model a firm can segment the market based on how the consumers value the product and how the product as instrument can fulfill the consumer’s demand and need (and how the product can finish the consumer’s job to be done). Moreover, it emphasizes the necessity to regard the consumers’ perceived value of a product or service and concentrates more on what consumers really do with their product. Concerning the quality profiles of functional food important for a buying decision process, this study found that taste, quality, safety and efficacy are the most important intrinsic elements of functional foods. While, labeling, company name, brand name are the important extrinsic features. This finding suggests that intrinsic and extrinsic quality based profiles offer some benefits that are relevant for setting up a communication and position strategy for a functional food product. From the point of view of industry to develop a functional food a firm may require adequate organizational and management capabilities. Moreover, the success of marketing of an innovative functional food will also depend on the market attractiveness and the trade capability.
7.
References
Assael, H. (1995). Consumer behavior and marketing action. The International Thompson Publishing. Ohio. 1995, p. 267-285. Backgrounder. (1998). Functional foods. In: Food Insight Media Guide. Washington, DC: International Food Information Council Foundation. Bloch, P.H., and Richins, M.L. (1986). After the new wears off: the temporal context of product involvement. Journal of Consumer Research. 13: 280-285. Chisnall, P.M. (1994). Consumer behavior 3rd. McGraw-Hill Book Co. London. Cooper, R.G. (1984). How new product strategies impact on performance. Journal of Product Innovation Management. 1:15-18. Christensen, C.M. and Raynor, M.E. (2003). The innovator’s solution; creating and sustaining successful growth. Harvard Business School Publishing Corporation. Boston. Deibert, P. and Berg, A. (2002). Functional Food. Der Lipid Report. 200;1. Dillon, W.R., Madden, T.J., Firtle, N.H., (1994). Marketing research in a marketing environment, 3rd edition.. Illnois. Irwin Engel, F.J., Blackwell, R.D., Minirad, P.W. 1995. Consumer behavior, 6th edition. Binarupa Aksara, Indonesia. Feldman, D. (2006). Segmentation building blocks. Marketing Research. Summer. 23-29. Freedman, J.L. (1964). Involvement, discrepancy and change. Journal of Abnormal and Social Psychology. 69:290-295. Fiske and Hartley, (1980) in Kotler (1994). Marketing management – analysis, planning, implementation and control, 8th edition. Prentice Hall International Editions. New Jersey.
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Foxall, G.R. and Goldsmith, R.E. (1994). Consumer psychology for marketing. Int. Thomson Business Press. London. Health Canada. (2004). Final policy paper on nutraceuticals/ functional foods and health claims on foods. http://www.hc-sc.gc.ca. 6.02. 2004. Henderson, R., and Clark, K. (1990). Architectural innovation: the reconfiguration of existing product technologies and the failure of established firms. Administrative Science. 35 (1): 9-30. Howard, J.A. and Sheth, J.N. (1969). The theory of buyer behavior. New York. John Wiley. ILSI. (1999). Safety assessment and potential health benefits of food components based on selected scientific criteria. ILSI. North America Technological Committee of Food Components for Health Promotion. Critical Review of Food Scientific Nutrition 39, 203-316. Karyadi, D and Lukito, W. (2000). Functional food and contemporary nutrition-health paradigm: tempeh and its potential beneficial effects in disease prevention and treatment. Nutrition, 16 (7/8). Keizer, J.A., Halman, J.I.M., Song, M. (2002). From experience: applying the risk diagnosing methodology, Journal of Product Innovation Management. 19:213-232. Keller, K.L. (1998). Strategic branding management. New Jersey. Prentice-Hall, Inc. Kotler, P. (1994). Marketing management – analysis, planning, implementation and control, 8th edition. Prentice Hall International Editions. New Jersey. Kleinschmidt, E.J. and Cooper, R.G. (1991). The impact of product innovativeness on performance. Journal of Product Innovation Management. 8: 240-151. Lastovicka, J.L. and Gardner, D.M. (1979). Components of involvement, in Attitude Research Plays for High Stakes, John, C. Maloney and Bernard Silverman, eds. Chicago: American Marketing Association, 53-73. Raynolds, T.J. (2006).Methodological and strategy development implications of decision segmentation. Journal of Advertising Research. 12: 445-461. Sarnoff, 1960 and Thrustone (1931) in Fishbein, M. and Ajzen, I. (1975). Belief, attitude, intention and behavior, an introduction to theory and research. Addison-Wesley Philippines. Solomon, M.R. (1996). Consumer behavior, 3rd edition. London. Prentice-Hall. Sudarmadji, S., Suparmo, Raharjo, S. (1997). Reinventing the hidden miracle of tempeh. Indonesian Tempeh Foundation. Jakarta. Weiss, M. M., and Calantone, R. (1994). Determinants of new product performance: a review and meta-analysis. Journal of Product Innovation Management. 11(12):397-417. Wilcox, G., Wahlqvist, ML., Burger, H., Medley, G., (1990). Estrogenic effects of plant food in post menopausal women. British Medical Journal, 301-905. Yankelovich and Meer, (2006). Rediscovering market segmentation. Harvard Business Review. 2:122-131.
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Tables TABLE 01. Correlation of attitude and intention to buy
Correlation method
Attitude measurement Fishbein's scale
Kendall's tau_b
Likert's scale
Fishbein's scale
Spearman's rho
Likert's scale
Correlation coefficients
Intention to buy
Correlation Coefficient
.248(**)
Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient
.000 236 .219(**) .000 237 .309(**)
Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N
.000 236 .239(**) .000 237
** Correlation is significant at the 0.01 level (2-tailed). Source: Model re
Graphs and Diagrams Figure 01. The mean value using the Fishbein model
very negative
-104
negative
neutral
-52
0
positive
+52
34,6
very positive
+104
Source: Model result Figure 02. The mean value using the Likert model
very negative
-8
negative
neutral
-4
0
positive
3.6
Source: Model result Source: Model result
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+4
very positive
+8
FIGURE 03. Respondent involvement level towards functional food
Involvement level (Indonesian group) medium involvement 32,5%
high involvement 64,5%
low involvement 3,0%
FIGURE 04. The importance of other marketing elements T h e im p o r ta n t o f o th e r m a r k e tin g e le m e n ts
Q u a lity a n d s a fe ty q u a r a n te e fr o m m a n u fa c tu re r
1 .3 6
th e
0 .8 3
N a m e o f th e m a n u fa c tu re r 1 .0 1
P ric e 0 .4 8
D is c o u n t 0 .2 2
P ro d u c t b o n u s 0 .7 4
P ro d u c t va rie ty 0 .9 4
P ro d u c t h a n d lin g 1 .2 1
L o g o o r s e a l o f q u a lity 0 .8 1
B ra n d n a m e 0 .5 6
D e s ig n o f th e p a c k a g in g 0 .5 6
S iz e o f th e p a c k a g in g
0
0 ,2
I n d o n e s ia
0 ,4
0 ,6
m e a n
Source: Author’s field study
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0 ,8
v a lu e s (m in
1
-2 a n d
1 ,2
m a x . + 2 )
1 ,4
Figure 05. Communication Messages for functional foods Functional Foods
Affective Components
Intentional usage consequence
Cognitive Components
Immediate intrinsic association
Feeling good/satiety (factor 7a)
Confidence of outlook (factor 3)
Looking good/cosmetic function (factor 7b)
Confidence of efficacy (factor 1a) Confidence of safety (factor 1b) Confidence of usage (factor 9)
Health benefits sought
Short term
Non -health benefits sought
Long term
Nutritional Value (factor 8b)
Lowering risk of disease (factor 2b)
Normalise organ function (factor 2b)
Prevention of disease (factor 2a)
Improves healthiness, makes fit (factor 2f)
Natural basis (factor 8) Convenience of usage (factor 4a)
Treatment of disease (factor 2d)
Reduce cost of drugs (factor 2d)
Improves body defence (factor8c)
Source: Author’s presentation
Figure 06. Segmentation model for functional foods A firm’s resources high
Example of market segmentation model for a functional food:
Abundant resources – very wide market coverage High resources – wide market coverage Medium resources-medium coverage Low to medium
Healthy – unaware people
Consumers who value their health highly and try to solve their demand for Consumers with high awareness of doing disease prevention motivated consumers with high risk factors consumers suffering certain diseases (including secondary prevention)
Low
low Market coverage
Source: Model result
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Freedom of choice (factor 4b) Potency or efficacy (factor 5b) Fast onset of action (factor5a)
FIGURE 07. Important elements for defining the success of new product development
The important elements for product success
Public acceptance and regulation Market attractiveness and competition Trade capability Sourcing viability Technological support and manufacturing viability Organizational and management capability Financial, commercial viability and promotional effort Product assortment strategy and image Product characteristics and advantages
0
20
40
60
80
100
importance score
company A
company B
Source: Author’s field study
Contact information Jofi Puspa Department of Agricultural Economics Chair of Food Economics and Marketing Management Justus Liebig University Senckenbergstrasse 3 Giessen 35390 Germany Phone. no.: +49-641-9927378 Fax no.: +49 – 641-9927379 Email: [email protected]
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Determinants of Consumer Preferences for Regional Food Meike Henseleit, Sabine Kubitzki, Ramona Teuber University of Giessen, Germany
Summary In recent years an increasing consumer preference for regional food can be observed, both in Germany and in other European countries. Regression models investigating this regionof-origin effect are rare and in most cases the region or sample size under consideration is quite small. Different from that, the present study is based on a representative data set for Germany. Our objective is to identify and quantify the determining factors of consumers’ preferences for regional food. Therefore, a theoretical construct is proposed and tested empirically by using a binary logit model. The results indicate that cognitive and normative factors are the main determinants on consumer preference for regional food, whereas affective and socio-demographic variables only are taking marginal influence. Especially consumers’ perceptions, that regional food has better product attributes and offers a higher food safety, are significant and important determining factors. The same is true for the idea to support the domestic agriculture by purchasing locally grown food. Contrarily, no significant influence could be examined for most of the socio-demographic variables, e.g. level of education, location size, and level of income. KEYWORDS: Consumer preferences, willingness to pay, region-of-origin effect, regional food, binary logistic regression model
1.
Introduction
Regional food is defined as food which is grown in the surrounding region and which is usually unprocessed (Dorandt 2005). In Germany most of the consumers define their home federal state as their home region (ZMP 2003: 9ff.). In recent years an increasing interest in regional food can be observed both in Germany and in other European countries. Several studies have been carried out on this phenomenon already. However, in most surveys, either the study region is relatively small or the sample size is rather limited, so that results seldom are statistically representative. In addition, only a few researchers have applied explorative methods like regression analyses to investigate the so-called region-of-origin effect (ROOE). Thus, the level of knowledge about the main reasons and the magnitude of preferences for regional food is still quite low. In Germany as well as in many other European countries regional cooperations have been established to promote the sale of regional food. For those cooperations it is important to understand the determinants of preferences for regional food, because knowing the impact factors can help to promote locally grown products more successfully. Our study is based on a representative survey for Germany. We used the survey data to examine and quantify the impact factors for the preference for regional food by applying a binary logit model. The article is structured as follows. Chapter two contains a literature review of studies that have investigated the demand for regional food by means of regression analyses. In chapter three we explain the theoretical construct of our research and in chapter four we characterise the data we used. A summary of our empirical results is given in chapter five and the last chapter contains our conclusions and recommendations for further research.
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2.
State of research
Many surveys in Germany as well as in other European countries and in the United States have already attempted to identify a consumer segment with preferences for food of their home area (among others Becker 2000; Dorandt 2005; Schröder et al. 2005). However, there are only few studies which applied advanced econometric methods to survey the determinants for preferences for regional food, especially in German speaking countries. Most of them have been conducted in the United States. In nearly all of the studies the focus was either on psychographic or socio-demographic factors.1 • a) Psychographic factors for preferences towards regional food Consumers’ perceptions of product quality and food safety belong to this group of factors. It seems to be empirically proven that in consumers’ perception of regional food is linked with higher food safety as well as with higher quality and, thus, regional food is preferred to other products. Furthermore, the awareness of health and nutrition as well as environmental concerns and the willingness to support the economy of the home region are supposed to take influence on the preference for regional food. In connection with that it is expected that emotions like sympathy for the home region are promoting the demand for regional products. To our knowledge no survey has been conducted so far including all mentioned psychographic factors. • b) Socio-demographic factors for preferences towards regional food Age, sex, class of income, education and the number of children in the household are the most surveyed factors in this field. The impact of the time period someone has already lived in a region as well as the size of the home region (urban versus rural areas) are included scarcely. While correlation analyses and non-parametric methods often show significant relations between socio-demographic variables and preferences for the origin of food products, causal analyses seldom show statistically significant impacts. Furthermore, many causal analyses in this context provide contradictory results. While some studies proved a positive influence of the number of children per household on the preference for regional food, other survey results showed a negative influence. Only in terms of sex there is consistency across different studies: women usually have got a higher preference for regional food than men. All in all the results regarding the influence of socio-demographic factors on the preference for regional food are not stable across different studies. Moreover, results indicate that socio-demographic have got only a marginal effect on the preference for regional food.
3.
Theoretical Construct
The main point of criticism on all of the published studies we surveyed is, that none of them has considered the full range of possible impact factors in the causal analysis. To give an overview about the plurality of possible factors influencing the preference for regional food the theoretical framework proposed by Obermiller and Spangenberg to explain the effects of country-of-origin labels is presented (Obermiller and Spangenberg 1989, 456ff.). Von Alvensleben applied this concept to the region-of-origin-effect and groups the determinants into cognitive, normative and affective processes (Von Alvensleben 2000a: 6ff.).
a) Cognitive factors The geographical origin might be used as a quality cue by consumers who are unsure about the quality of a product. This effect may result from two processes. First, the region of origin is a “signal” for the general product quality. Based on this there might be a positive 1
A review of causal analytical studies considering psychographic and socio-demographic factors is presented in Annex 1 and 2.
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bias in the consumer’s perception of other attributes that are not necessarily linked to the region-of-origin. Second, regional food is perceived to be fresher, healthier and more environment-friendly (Darby et al. 2006:2ff.).
b) Normative factors A preference for regional food as a consequence of environmental-friendly consumption aspects (e.g. short ways of transportation, sustainability) can also result from normative processes. Thus, social norms instead of consumers’ own beliefs may lead to environmental-friendly behavior. Social norms influence the purchase intension independent of cognitive and affective processes directly. Van Ittersum specifies this theory. He assumes that the preference for regional food is influenced by consumer ethnocentrism (Van Ittersum 1999: 46ff.). Consumer ethnocentrism is defined as the beliefs consumers hold about the moral appropriateness to favor domestic products (Shim and Sharma 1987: 280ff.). Therefore, consumers feel constrained to support the local economy by their selective purchase decision.
c) Affective factors The impact of ethnocentric and patriotic norms might also be influenced by emotional aspects. Thus, emotions like pride of and sympathy to the own region may be transferred directly to the product. Moreover, Von Alvensleben suggests that sympathy to the region leads to a positive bias in the perception of the product and its attributes (Von Alvensleben 2000a). The contact-affect-phenomenon is discussed as the cause of this positive image transfer from the region to the product (Von Alvensleben 2000b: 401): The pure contact to an object leads to familiarity and finally to sympathy to the object (Kroeber-Riel and Weinberg 2003: 624ff.)