How to capture regional values? The power of names. Maria L. Loureiro Universidade de Santiago de Compostela, Spain maria.loureiro@usc.es
Introduction The role of regional values and collective reputation will be studied employing two case studies: Denominations of quality of wine in Spain Real quality versus Perceived Quality: Hedonic market price study of Spanish wines: do markets recognize and value PDO labels? And if so, which ones? Assessing the spillover effects of wine producing areas: What are tourists looking for? The case of Tuscany
Case Study I Hedonic Market Price Analysis: PDO and PGI wines in Spain Assessing whether denominations of origin in wine carry a price premium
Introduction Wine labeling is a complicated matter in Spain. Different quality cues are expressed: wine harvest, type of grape, alcohol level, origin Different regional designations and classifications
When labeling is too much
Large Geographical Diversity Spanish wine producing areas
Different Geographical Labeling Types Vinos de Pago Protected Designations of Origin Designations of Origin Vinos de la Tierra Wines without geographical identification
Protected Geographical Indicators: Some Popular Examples Many others.
Are these labels signals of quality? PGI labels are denoting credence attributes Although there is an experience component, in order to buy the product for the first time, consumers have to believe the claim made. These attributes are more difficult to market: information delay
Are these labels signals of quality? The reasons credence claims are though to market are because: Consumers have to believe the appelation conveys some extra quality level. Consumers have to believe that the claim is true.
Label proliferation and noise Labeling proliferaton is a serious concern with European wines More than 300 appelations of origin in France 62 in Spain regional designations of origin Some of them very new Moderately EU wines are losing markets to other new world wines.
New Labels and Noise? We may expect more recent PDOs to gather lower price premiums than more stablished PDOs. Less consumer recognition More volatile demand/supply Less established
Recent PDOs with success SF Chronicle, Albarino is in the air: Perfect springtime wine from Spain makes a pilgrimage to the New World The taste of youth Albariño wines and Rias Baixas
Case Study I: Data Data come from popular wine guide 300 Mejores Vinos de España, 2003. From this guide, information was collected about: wine price, harvest year, type of grapes, whether the wine has a PDO/DO label, degrees of alcohol, harvest, number of bottles produced, and the quality score assigned by the authors. Wine search in other two wine guides to check if each wine is recommended. The construction of a papel data would have been desirable
Data Description Quality Indicators-The role of percevied quality Crianza wines are those that have been aged for two years Reservas are aged three years Gran Reservas (also known as Reserva Especial) are aged at least five years PDO/DO or no geographical indication Author s Guide Scores (1-10) Listed in other wine lists: Peñín wine list; Proensa wine list
1. Methods: Hedonic Models 1. Hedonic Model: Value of PDO :Rioja and Penedés Price Age QualityScore PDO Alcohol Bottels Crianza Re dwine Whitewine Roséwine Sparlingwine 9 6 0 1 7 2 8 3 4 9 5 We hypothesized that the DO label's effectiveness in obtaining a premium depends on the wine type that it is associated with.
1. Results PDO Logprice Coef. Std. Err. t P> t Age.2759397.0550674 5.01 0.000 Age2 -.0051028.0014542-3.51 0.001 Pdo.164598.0862545 1.91 0.058 Score.3345403.0904691 3.70 0.000 Alcohol.0428509.0277718 1.54 0.125 Bottles -2.19e-07 1.19e-07-1.84 0.068 crianza.3642734.1204063 3.03 0.003 reserva.9119954.3922861 2.32 0.021 pen -.0082888.1268402-0.07 0.948 Red.2279233.1254234 1.82 0.071 White.3211606.1334163 2.41 0.017 _cons -2.925.719.7816914-3.74 0.000
Results: Comments Wines with a PDO designation of origin carry a premium over the rest (16%). Older harvests carry higher premia Reserva wines (aging) before going to the market carries a premium Perceived quality (ratings) are important determining prices.
2. Hedonic Models-Results for all DO log(price) Coef. Std. Err. t P> t age.7500706.1538297 4.88 0.000 age2 -.0358042.0093001-3.85 0.000 rioja.4824563.1040034 4.64 0.000 riasbaixas.5635015.1264375 4.46 0.000 penedes -.0153809.1035031-0.15 0.882 riberaduero.320217.1297192 2.47 0.015 navarra -.3413559.1564804-2.18 0.031 jumilla -.4212146.2210881-1.91 0.058 cava -.4587617.2038983-2.25 0.026 bierzo -.1805291.3135862-0.58 0.566 lamancha -.2756449.1889969-1.46 0.147 valdeorras.0475697.3051616 0.16 0.876 rueda.0447202.1815752 0.25 0.806 ribeirasacra.5105668.3089679 1.65 0.100 alcohol.0067348.0238634 0.28 0.778 crianza.3087041.1104563 2.79 0.006 reserva.8570652.321152 2.67 0.008 peñinguide -.1545266.1059542-1.46 0.147 R-adjusted=0.74 Score-points.2489508.0711519 3.50 0.001
2. Hedonic Results for Red Wine logprice Coef. Std. Err. t P> t age.7950823.2187821 3.63 0.000 age2 -.038374.0126714-3.03 0.003 rioja.5561508.1372264 4.05 0.000 penedes.0583517.2143916 0.27 0.786 riberaduero.3113695.1609193 1.93 0.056 navarra -.295606.2738859-1.08 0.283 jumilla -.3940464.2647589-1.49 0.140 bierzo.0397566.3754527 0.11 0.916 lamancha -.1271227.2500182-0.51 0.612 ribeirasacra.6522183.5080504 1.28 0.202 terralta.0313811.3603652 0.09 0.931 cari na -.1285979.3146625-0.41 0.684 priorato 2.143.235.5066697 4.23 0.000 somontano -.0890063.3231969-0.28 0.784 conca 1.458.868.5051589 2.89 0.005 alcohol.2164172.0998988 2.17 0.033 botellas -3.02e-07 1.24e-07-2.44 0.017 crianza.3054269.2069962 1.48 0.143 reserva.6053888.3968602 1.53 0.131 peñín guide -.1606351.1269496-1.27 0.209 score-points.2154453.1062169 2.03 0.045 R- adj:0.72
2. Hedonic Results for Wine Wine logprice Coef. Std. Err. t P> t age 3.917.293 255.789 1.53 0.133 age2 -.3407437.2274973-1.50 0.142 rioja -.1728773.2702173-0.64 0.526 riasbaixas.2462114.141532 1.74 0.089 penedes -.1125887.1344152-0.84 0.407 navarra -.2316736.3246642-0.71 0.479 cava -.6662762.2457078-2.71 0.010 valdeorras -.0882742.2397206-0.37 0.715 rueda -.3925942.1921562-2.04 0.047 ribeirasacra.3016412.3330513 0.91 0.370 terralta.0528261.3243866 0.16 0.871 somontano -.2043541.2397898-0.85 0.399 alcohol -.000489.0224373-0.02 0.983 bottels -6.92e-08 1.70e-07-0.41 0.686 crianza.3657295.1275812 2.87 0.006 Score-points.1634869.1218481 1.34 0.187 R-adjusted:0.44
3. Conclussions Consolidated PDO/DOs designations such as Rioja for red and Rias Baixas are signals of good wines Other geographical identifications suffer due to their lack of recognition and decrease the average wine price. Marketing strategies may be employed and selective use of labels should be recommended.
Conclusions: Case Study I Price premiums should be contrasted with costs required to protect these food names. Need to look for potential alternatives to promote areas if food names do not carry sufficient reputation (natural endowment, biodiversity, landscapes, Bundle-type of goods) Perceived quality and rankings by third parties are useful to promote agricultural areas.
Case Study II: The influence of agricultural landscapes on tourism flows: An application to Tuscany Paulo A.L.D Nunes and Maria Loureiro
Outline: Case Study II 1. Tourism demand and landscape amenities 1. Agricultural landscape, high-quality wine production and tourism in Tuscany 2. Economic valuation of the impacts of agricultural landscape and quality wine production on tourism flows: who values what?
Landscape Amenities and Tourism Environmental amenities, among which landscape features, are an important determinant of tourism demand (Wunder, 2000; Naidoo and Adamovicz, 2005; Green, 2001). Tourists attach different values to different types of landscapes (Hamilton, 2006). Agricultural land provides natural habitats, open spaces, pleasant scenery and cultural preservation, thus having a significant impacts on tourist perception. Tourists are also attracted by local products, such as high-quality wine.
2. Landscape metrics Landscape has been defined as a spatially heterogeneous area presenting at least one factor of interest (Turner et al., 2001). How can we measure landscape characteristics? 1. Composition indicators refer to the number and occurrence of different landscape types 2. Configuration indicators reflect their physical or spatial distribution (McGarigal et al. 1994). Composition elements seem to be more easily perceived by tourists, therefore those metrics appear to be more appropriate to measure the influence of landscape on tourism flows.
2. Landscape metrics: an application to the Tuscany case study Among other possible composition indicators, landscape richness has been chosen as the main indicator of landscape diversity for the purpose of this study. The CORINE Land Cover Inventory has been chosen as the main data source, being the only dataset providing a synoptic overview of land cover at European level. Landscape richness: number of different land cover categories recorded in each municipality out of the total number of categories recorded in the whole region. Advantages: Easy to compute and to aggregate at different geographical scales Limitations: Landscape richness does not provide information on the surface covered by the different patches, nor it reflects the relevance of agricultural landscape.
2. Landscape metrics To overcome these limitations, the relative abundance (i.e. % of municipal surface) of complex cultivation patterns has also been computed. Complex cultivation patterns: mixed parcels of permanent crops (fruit trees, berry plantations, vineyards and olive groves) with scattered house and villages, creating the characteristic patchwork structure.
3. Agricultural landscape, high-quality wine production and tourism in Tuscany The administrative territory of Tuscany is divided into ten provinces and agricultural land covers a significant portion of the various provincial territories, ranging from 16% to 58%. Complex cultivation patterns cover between 20% and 38% of agricultural land in seven out of ten provinces. Countryside tourism was particularly significant for Siena, Florence, Pisa and Pistoia provinces High-quality wine production is important across the entire regional territory, including 44 top-quality wines which have been awarded the denomination of origin. 14 Wine routes itineraries are organized across the provinces and this is a major tourism attraction factor for the whole region. The influence of agricultural landscape on tourism flows: An application to Tuscany 30
3. Agricultural landscape, high-quality wine production and tourism in Tuscany A model has been constructed according to the specification: lny i 0 1X1 i 2 X 2i 3X 3i 4 X 4i 5 X 5i 6 X 6i u i The dependent variable is number of tourist arrivals in each municipality. The selected explanatory variables are: Socio-demographic and geographical characteristics of each municipality Accommodation availability and price levels Share of protected area on the municipal territory and the proximity of other protected sites Landscape metrics DOCG and DOC wines produced in each municipality Types of tourism attraction factors (e.g. art and seaside destinations) The influence of agricultural landscape on tourism flows: An application to Tuscany 31
3. Agricultural landscape, high-quality wine production and tourism in Tuscany Total tourists Coefficient P> t Surface (ha) 0.0000416 0.000*** Accommodation availability.0.0001471 0.000*** Consumer s Price Index -0.0308177 0.048* Natura 2000 (% area ) 0.9075294 0.078* Landscape richness 2.600392 0.093* Complex cultivation patterns ( % area) 0.2064509 0.028* No. DOCG 0.0626771 0.000*** N2000 within 25 km 0.453212 0.007** Art 0.8991645 0.000*** Seaside 0.8928514 0.002** Other 0.7433117 0.000*** Constant 10.86247 0.000*** R2 0.60 Statistical significance of 0.1%. 5% and 10% is indicated by ***. **. * respectively The influence of agricultural landscape on tourism flows: An application to Tuscany 32
3. Agricultural landscape, high-quality wine production and tourism in Tuscany Differences between the international and domestic tourists preference patterns*: *The dependent variable, the number of total tourists, has been regressed against the difference between international and domestic arrivals and this difference was significantly different from zero. The influence of agricultural landscape on tourism flows: An application to Tuscany 33 International tourists Coefficient P> t Domestic tourists Coefficient P> t Area 0.0000321 0.020**.0000492 0.000*** Accommodation availability 0.0001585 0.000***.0001325 0.000*** Consumer s Price Index -.0300402 0.126 -.0342442 0.029* Natura 2000 (%) 0.8693671 0.179.992435 0.055* Landscape richness 3.335082 0.087* 2.768308 0.075* Complex cultivation patterns ( % area) 0.3249696 0.006**.1361711 0.149 No. DOCG 0.0798978 0.000***.209011 0.212 N2000 within 25 km 0.6375517 0.003**.053343 0.001** Art 0.7716575 0.001** 1.069137 0.000*** Seaside 0.3741198 0.306*** 1.317361 0.000*** Other 0.7834912 0.002**.6617854 0.001** Constant 9.83585 0.000*** 10.41817 0.000*** R2 0.50 0.61
4. Economic valuation of the impacts of agricultural landscape and quality wine production on tourism flows The provinces can be ranked on the basis of the monetary value of the impacts of their agricultural landscape and high-quality wine production on tourism flows. Province Agricultural landscape ( / year) Province DOCG wines ( / year) Florence 103,702,920 Florence 36,889,950 Lucca 23,403,987 Siena 20,235,749 Siena 15,263,926 Pisa 2,507,084 Livorno 10,763,168 Arezzo 1,181,911 Pisa 8,736,871 Grosseto 1,002,834 Arezzo 6,768,081 Pistoia 626,771 Grosseto 3,008,194 Prato 411,878 Pistoia 2,994,829 Livorno 0 Massa-Carrara 2,733,104 Lucca 0 Prato 997,138 Massa-Carrara 0 The influence of agricultural landscape on tourism flows: An application to Tuscany 34
Conclussions Landscape richness is positively correlated to the number of tourism arrivals for both international and domestic tourists This outcome suggests that tourists are attracted by the visual component of landscape diversity, regardless their origin. The production of top-quality wines in the territory of a municipality exerts a positive influence on total and international tourist arrivals. International visitors appear to be positively influenced by the high-quality and internationally renowned wines. By contrast, these characteristics do not seem to exert a significant influence on domestic tourists.
Conclussions Attaching a monetary value to the impact of agricultural landscape and quality-wine production on tourism flows towards the different provinces has important policy implications: Helps defining priorities in terms of valorisation and branding of two of the main touristic resources in Tuscany, wine and agricultural landscape Helps identifying the type of tourist attraction factor generating the highest revenues in each province, and helps to understand heterogeneous preferences.
Thank you for your attention maria.loureiro@usc.es