5.2 the Swiss Wine Market

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Doctoral Thesis

Four Essays in Wine Economics: An Empirical Approach with Swiss Panel Data

Author(s): Mondoux, Alexandre

Publication Date: 2018

Permanent Link: https://doi.org/10.3929/ethz-b-000266501

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ETH Library Dissertation Series

Four Essays in Wine Economics: An Empirical Approach with Swiss Panel Data

Alexandre Mondoux

Diss. ETH No. 24887 KOF Dissertation Series, No. 33, 2018

KOF Swiss Economic Institute Imprint

Publisher

KOF Swiss Economic Institute, ETH Zurich

FOUR ESSAYS IN WINE ECONOMICS: AN EMPIRICAL APPROACH WITH SWISS PANEL DATA

A thesis submitted to attain the degree of

DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich)

presented by

ALEXANDRE MONDOUX Master of Arts in Economics, University of Fribourg born on 06.04.1984 Citizen of Châtonnaye (FR)

accepted on the recommendation of

Prof. Dr. Peter Egger, examiner Prof. Dr. Marko Köthenbürger, co-examiner

2018

“Nothing more excellent or valuable than wine was ever granted by the gods to man.” Plato, Greek philosopher, (c. 427-347 BC)

Acknowledgements

First of all, I would like to thank Professor Peter Egger for the valuable opportunity to write my PhD thesis under his supervision at one of the best universities in the world, as well as for his continued support and advice in my academic research. I am also grateful to have shared time for discussion and friendship with all his team of the Chair of Applied Economics: Innovation and Internationalization. At the same time, I am very thankful to my thesis committee, Professors Marko Köthenbürger and Massimo Filippini of ETH Zurich, for taking the time to read and comment on the dissertation presented here.

I would especially like to thank Professor Michael Graﬀ, as well as his team from the Research Division Economic Forecasting, where I was an economic researcher in parallel to preparing my doctorate. I acknowledge with gratitude Dr. Samad Sarferaz and Gabriel Loumeau for their precious help and advice. I am very grateful to Katha- rina Bloch, who helped and supported me during the whole period of my dissertation, in both the good times and the diﬃculties that I encountered; my thanks go also to Professor Jan-Egbert Sturm, director of the KOF, who created the ideal working conditions for conducting my doctoral studies. I thank also Susan Gilbert for carefully reading my text.

During my dissertation, I had the chance to meet extraordinary people at national and international conferences, in particular within the American Association of Wine Economists (AAWE) and the European Association of Wine Economics (EuAWE). Among others, I met Philippe Masset and Jean-Philippe Weisskopf, both Professors at Ecole hôtelière de Lausanne, with whom I wrote in co-authorship the last research paper of this dissertation. All the seminars organized by the KOF, including the KOF Brown Bag by Heiner Mikosch, gave me a unique opportunity to present my research or attend presentations by well-known international professors in economics in my academic area of expertise.

I am very grateful to the Swiss Wine Market Institute (OSMV), established at the University of Applied Sciences Western Switzerland (HES-SO) in Changins (Nyon), where I was scientiﬁc partner, for funding my dissertation. I would like to thank in particular Conrad Briguet and Philippe Delaquis for their trust and support all through the writing of my PhD, as well as Anne Planquart, Marie-Clémence Mouron, Caroline Schaub and Zeltia Rodriguez for the excellent teamwork. This experience went beyond all my expectations giving further fruitful cooperation. In the same way, I am truly thankful to Thierry Walz, Jean-Marc Amez-Droz and Olivier Savoy for giving me the keys to understanding the Swiss wine market.

I would like to thank my wonderful wife Oleksandra, who always encourages me to go beyond my limits and without whom I would never be where I am today. My father Michel, my mother Isabelle and my brother Christophe have always helped and believed in me, each with their own competences and strong support from the beginning of my studies until the completion of my doctorate. To all of them I will always be extremely grateful.

Zurich, December 2017 Alexandre Mondoux Contents

List of Figures ...... v List of Tables ...... ix Abstract ...... xi Résumé ...... xiii

1 Introduction 1 1.1 Issues and Goals ...... 2 1.2 Data and Methodology ...... 5 1.3 Findings ...... 7 1.4 Contribution ...... 9

2 Price Endogeneity and Demand for Swiss Wines 11 2.1 Introduction ...... 12 2.2 Literature ...... 13 2.3 Data ...... 14 2.3.1 Descriptive statistics ...... 14 2.3.2 Statistical analysis ...... 16 2.3.3 Transformation and tests on the data ...... 17 2.4 Identiﬁcation strategy: instrumental variable estimation ...... 18 2.4.1 Price endogeneity ...... 18 2.4.2 Discussion of the instruments ...... 20 2.4.3 Econometric model ...... 21 2.5 Results ...... 23 2.5.1 Baseline model ...... 23 2.5.2 Graphical analysis ...... 26 2.5.3 Pseudo panel and cross-price elasticity ...... 28 2.5.4 Robustness checks ...... 29

i 2.6 Conclusion ...... 30 2.7 Appendix ...... 32 2.7.3 Figures ...... 34 2.7.4 Tables ...... 52

3 Scenarios and Prospects for the Swiss Wine Market 67 3.1 Introduction ...... 68 3.2 Literature ...... 69 3.3 Data ...... 70 3.4 Identiﬁcation strategy: Panel VAR ...... 72 3.4.1 Supply and demand shocks in the wine market ...... 72 3.4.2 Econometric model ...... 74 3.4.3 Lag order selection and stability of the model ...... 74 3.4.4 Granger causality test ...... 75 3.4.5 Impulse Response Function (IRF) ...... 76 3.4.6 Cholesky IRF ...... 77 3.5 Results ...... 78 3.5.1 Panel VAR ...... 78 3.5.2 Impulse response function ...... 78 3.6 Robustness checks ...... 80 3.6.1 Cholesky IRF ...... 80 3.6.2 Forecast ...... 81 3.7 Conclusion ...... 82 3.8 Appendix ...... 84 3.8.1 Tables ...... 84 3.8.2 Figures ...... 86

4 Should We Put Ice in Wine? A Diﬀerence-in-Diﬀerences Approach from Switzerland 97 4.1 Introduction ...... 98 4.2 Literature ...... 100 4.3 Data ...... 101 4.3.1 Data description ...... 101

ii 4.3.2 Descriptive statistics ...... 102 4.3.3 Statistical analysis ...... 103 4.4 Identification strategy: difference-in-differences ...... 104 4.4.1 Identifying assumptions and definitions ...... 104 4.4.2 Exogenous supply and demand shocks ...... 107 4.4.3 Econometric model (baseline) ...... 108 4.4.4 Econometric model (extension) ...... 109 4.5 Results ...... 110 4.5.1 Parallel time trend: visual evidence ...... 110 4.5.2 Baseline regression results ...... 112 4.6 Robustness checks ...... 115 4.6.1 Baseline regression results ...... 115 4.6.2 Extension of the regression results ...... 116 4.7 Conclusion ...... 117 4.8 Appendix ...... 119 4.8.1 Tables ...... 119 4.8.2 Figures ...... 130

5 Pricing Strategies for High-End Experience Goods in a Very Com- petitive and Opaque Market – The Case of Swiss Fine Wines 137 5.1 Introduction ...... 138 5.2 The Swiss wine market ...... 142 5.3 Data ...... 145 5.4 Methodology ...... 149 5.5 Results ...... 152 5.6 Robustness checks and extensions ...... 154 5.7 Conclusion ...... 155 5.8 Appendix ...... 157

Bibliography 165

Curriculum Vitae 175

iii

List of Figures

2.1 Swiss AOC wine price ...... 15 2.2 Swiss AOC wine ln(price) ...... 15 2.3 Seasonality of Valais wines (monthly frequency) ...... 16 2.4 The six Swiss wine regions (SWP, 2015) ...... 34 2.5 Proportion AOC color ...... 35 2.6 Proportion AOC region ...... 35 2.7 Proportion AOC region color ...... 35 2.8 Proportion AOC color region ...... 35 2.9 Proportion VDP color ...... 36 2.10 Proportion VDP region ...... 36 2.11 Proportion VDP region color ...... 36 2.12 Proportion VDP color region ...... 36 2.13 Proportion Foreign color ...... 37 2.14 Proportion Foreign country ...... 37 2.15 Proportion Foreign country color ...... 37 2.16 Proportion Foreign color country ...... 37 2.17 Proportion Total color ...... 38 2.18 Proportion Total type ...... 38 2.19 Proportion Total type color ...... 38 2.20 Proportion Total color type ...... 38 2.21 Dôle rouge ...... 39 2.22 Dôle rouge (covariates) ...... 39 2.23 Fendant ...... 39 2.24 Fendant (covariates) ...... 39 2.25 Pinot noir rosé ...... 39 2.26 Pinot noir rosé (covariates) ...... 39

v 2.27 Standardized normal probability plot of residuals (Swiss AOC) . . . . . 40 2.28 Quantiles of residuals against quantiles of normal distribution (Swiss AOC) ...... 40 2.29 Pooled OLS regression (Swiss AOC) ...... 41 2.30 Pooled OLS regression by color (Swiss AOC) ...... 41 2.31 Pooled OLS regression by Swiss AOC wine region ...... 41 2.32 Fixed effects regression (Swiss AOC) ...... 42 2.33 Fixed effects regression by color (Swiss AOC) ...... 42 2.34 Fixed effects regression by Swiss AOC wine region ...... 42 2.35 Between effects regression (Swiss AOC) ...... 43 2.36 Between effects regression by color (Swiss AOC) ...... 43 2.37 Between effects regression by Swiss AOC wine region ...... 43 2.38 Regression Switzerland AOC by color ...... 44 2.39 Regression Valais AOC by color ...... 45 2.40 Regression Vaud AOC white by sub-region ...... 45 2.41 Regression Geneva AOC by color ...... 46 2.42 Regression Ticino AOC by color ...... 46 2.43 Regression Neuchâtel AOC by color ...... 47 2.44 Regression German-speaking region AOC red by Canton ...... 47 2.45 Regression French-speaking region (VDP) by color ...... 48 2.46 Regression Goron red (VDP) ...... 48 2.47 Regression German-speaking region (VDP) by color ...... 49 2.48 Regression Italian-speaking region (VDP) by color ...... 49 2.49 Regression France by color ...... 50 2.50 Regression Italy by color ...... 50 2.51 Regression Spain by color ...... 51 2.52 Regression Rest of the World by color ...... 51

3.1 Correlation Dôle blanche (rosé) – temperature maximum ...... 71 3.2 Examples of supply and demand shocks ...... 72 3.3 Exchange rate EURO/CHF in 2012-2016 (SNB, 2016) ...... 73 3.4 IRF for Swiss wine (90%, 95% and 99% conﬁdence intervals) ...... 79

vi 3.5 Vardec for Swiss wine ...... 79 3.6 Cholesky IRF ...... 81 3.7 Forecast for Swiss wine ...... 82 3.8 Stability of the Panel VAR ...... 86 3.9 IRF VDP wines ...... 87 3.10 Vardec VDP wines ...... 87 3.11 IRF foreign wines ...... 88 3.12 Vardec foreign wines ...... 88 3.13 IRF and Vardec by wine colors ...... 89 3.14 IRF by wine regions (part 1) ...... 90 3.15 IRF by wine regions (part 2) ...... 91 3.16 IRF by bestsellers (part 1) ...... 92 3.17 IRF by bestsellers (part 2) ...... 93 3.18 IRF by bestsellers (part 3) ...... 94 3.19 IRF by bestsellers (part 4) ...... 95 3.20 IRF by bestsellers (part 5) ...... 96

4.1 Seasonality of three treated wines (monthly frequency) ...... 104 4.2 Time trend for quantity ...... 112 4.3 Time trend for income ...... 112 4.4 Estimated shock effect over time (semester) ...... 117 4.5 Radar view on the intensity of the hail shock (20 June 2013) ...... 130 4.6 Vineyard damaged in Cortaillod, Canton Neuchâtel ...... 130 4.7 Volume Three Lakes (trimester) (Delaquis et al., 2016) ...... 131 4.8 Volume Controls (trimester) (Delaquis et al., 2016) ...... 131 4.9 Volume Three Lakes (monthly) ...... 132 4.10 Volume Controls (monthly) ...... 132 4.11 Whole quantity of wine (treated group) (FOAG, 2016) ...... 133 4.12 Whole quantity of wine (control group) (FOAG, 2016) ...... 133 4.13 Stocks difference of white wines in percentage terms (FOAG, 2016) . . 134 4.14 Stocks difference of red and rosé wines in percentage terms (FOAG, 2016)134 4.15 Estimated shock effect over time (trimester) ...... 135

vii 4.16 Estimated shock eﬀect over time (month) ...... 135 4.17 Volume by treatment and colors (monthly frequency) ...... 136

5.1 Price evolution by Swiss wine region (CHF/L) ...... 157

viii List of Tables

2.1 Price elasticity (IV) for Swiss AOC wines ...... 25 2.2 Number of individuals (types of wines) ...... 52 2.3 Name of individuals (types of wines) ...... 53 2.4 Descriptive statistics (AOC wines) ...... 54 2.5 Descriptive statistics (non-AOC and foreign wines) ...... 55 2.6 1st stage of the 2SLS model for ln(price) ...... 56 2.7 Price elasticity (static 1) ...... 57 2.8 Price elasticity (static 2) ...... 58 2.9 Price elasticity with control variables ...... 59 2.10 Price elasticity (dynamic) ...... 60 2.11 Price elasticity by color and region (Swiss AOC) ...... 61 2.12 Price elasticity by color and region (Swiss non-AOC) ...... 62 2.13 Price elasticity by color and country (foreign) ...... 63 2.14 Price elasticity by bestsellers (types of wines) ...... 64 2.15 Cross-price elasticity by bestsellers ...... 65

3.1 Panel VAR-Granger causality Wald test ...... 76 3.2 Panel VAR ...... 78 3.3 Lag order selection ...... 84 3.4 Forecast-error variance decomposition ...... 85

4.1 Regression results for quantity ...... 114 4.2 Number of individuals by group ...... 119 4.3 Name of individuals (treated group) ...... 120 4.4 Name of individuals (control group) ...... 121 4.5 Descriptive statistics (total) ...... 122 4.6 Descriptive statistics by treatment and control group ...... 123

ix 4.7 Regression results for price ...... 124 4.8 Regression results for income ...... 124 4.9 Placebo pre-post treatment regressions (quantity) ...... 125 4.10 Placebo control regions regression (quantity) ...... 125 4.11 Regressions for diﬀerent conﬁgurations of the control group (quantity) . 126 4.12 Regressions by color type (quantity) ...... 126 4.13 Leads and lags (semester) ...... 127 4.14 Leads and lags (trimester) ...... 128 4.15 Leads and lags (month) ...... 129

5.1 Descriptive statistics per region of production (CHF/0.75L) ...... 146 5.2 Descriptive statistics for white wine per varietal and wine type (CHF/0.75L)158 5.3 Descriptive statistics for red wine per varietal and wine type (CHF/0.75L)159 5.4 List of variables used in the hedonic regression ...... 160 5.5 Hedonic regression results ...... 161 5.6 Year ﬁxed eﬀects (time dummies) ...... 162 5.7 Parker - Wine advocate (visibility) ...... 162 5.8 List of selected wine producers in Switzerland ...... 163

x Abstract

This dissertation consists of four research papers discussing economic aspects of wine in Switzerland. Following the introductory chapter, the next three chapters analyze price elasticity, forecasting and climatic shocks using a panel data set with quantity and price from Swiss supermarkets. The last chapter explores Swiss ﬁne wine pricing determinants using price lists individually extracted from wine producer websites.

Chapter 2 examines the determinants of the demand for Swiss wines, estimating price elasticity in the retail market through a panel data structure. In order to eliminate unobserved heterogeneity at the wine type level, different econometric specifications such as fixed effects (FE) are used. An instrumental variable (IV) approach is defined to deal with price endogeneity, allowing a causal interpretation of price variation in wine consumption. We find that globally Swiss wine is price elastic (-2.63 for FE and -1.71 for IV estimations), confirming consumer sensitivity to price changes. Several wine specifications (i.e. color, region of origin and grape varietal) are included, confirming the statistical significance of the estimated effects as well as the initial economic assumptions.

Chapter 3 provides a better understanding of prospects for the Swiss wine market, through modeling and forecasting analysis. Results show a strong heterogeneity for impulse responses functions (IRF) as well as supply-demand ratios in explaining forecasting error terms among different wine specifications such as region of origin, color and grape varietal. Panel vector autoregressive model (Panel VAR) and forecasting estimation allow for longitudinal analysis as well as cross-section data for specific types of wines. Sign restrictions strategy enables us to disentangle demand and supply shocks effect on two main variables: quantity and price equilibrium. Finally, this study provides an important tool for understanding price change persistence for both producers and consumers.

Chapter 4 analyses the treatment effect of a hail weather shock in a specific Swiss wine region, using a difference-in-differences approach. We exploit a natural experi- ment from the Swiss wine region “Three Lakes” in 2013 on the outcomes of the Swiss retail market. We find statistically significant (1%-level) negative effects of -22.8% and

xi +2.8% for respectively the volume and price of wine consumed. These effects can be interpreted as average treatment effect, which is the difference in outcomes between treatment and control groups in a pre- and post- shock methodology. Several robustness checks are provided, confirming the statistical significance of the estimated effects as well as the initial assumptions.

Chapter 5 examines the price determinants of wine on the Swiss market, using a hedonic regression approach. We find that grape varietals and wine growing regions have a large impact on prices. Growing Petite Arvine or growing wine in Valais or the Swiss German part of the country triggers a premium. Cultivating Pinot Noir or Gamay, or being located in Vaud or Geneva, prompts a discount. Positioning and being located in a renowned wine-growing region further lead to higher prices. Information on producers, on the other hand, only marginally explains wine prices. Strong competition and high production costs, coupled with the limited visibility of Swiss wines, result in prices that depend mostly on collective reputation effects and on specific types of cuvees/blends. Our results suggest that Swiss producers price their wines fairly uniformly.

xii Résumé

Cette dissertation est composée de quatre papiers scientiﬁques traitant des aspects économiques du vin en Suisse. En commençant par un chapitre introductif, les trois chapitres suivants analysent l’élasticité-prix, la prévision et les chocs climatiques en utilisant des données de panel avec les quantités vendues ainsi que les prix des su- permarchés en Suisse. Le dernier chapitre explore les déterminants des prix des vins suisses de haute qualité en extrayant manuellement les données depuis le site Internet de chaque producteur.

Le chapitre 2 examine les déterminants de la demande pour les vins suisses en es- timant l’élasticité-prix en grande surface par le biais d’une structure de données en panel. Afin d’éliminer l’hétérogénéité non observée au niveau du type de vin, nous utilisons différentes spécifications économétriques comme celle des effets fixes (EF). Une approche de variable instrumentale (VI) est définie pour traiter de l’endogénéité des prix, permettant une interprétation causale de la variation des prix sur la con- sommation de vin. Nous constatons généralement que le vin suisse est élastique (-2.63 pour EF et -1.71 pour les estimations VI) confirmant une sensibilité des consommateurs aux variations de prix. Plusieurs spécifications du vin (c’est-à-dire la couleur, la région d’origine et les cépages des raisins) sont incluses, confirmant la signification statistique des effets estimés ainsi que les hypothèses économiques initiales.

Le chapitre 3 permet de mieux comprendre les perspectives du marché vitivinicole suisse, grâce à la modélisation et à l’analyse des prévisions. Les résultats montrent une forte hétérogénéité pour les fonctions de réponse impulsionnelle (FRI) ainsi que les ratios offre-demande pour expliquer les termes d’erreur de prévision entre différentes spécifications de vin, telles que la région d’origine, la couleur et le cépage des raisins. Le modèle de vecteur autorégressif en panel (Panel VAR) et l’estimation de prévision permettent une analyse longitudinale ainsi que des données transversales pour des types spécifiques de vins. La stratégie de la restriction des signes nous permet de séparer l’effet des chocs de la demande et de l’offre sur deux variables principales: la quantité et le prix d’équilibre. Enfin, cette étude constitue un outil important pour

xiii comprendre la persistance de modiﬁcation des prix tant pour les producteurs que pour les consommateurs.

Le chapitre 4 analyse l’effet de traitement d’un choc de grêle dans une région viticole suisse spécifique, en utilisant la méthode économétrique des doubles différences. Nous exploitons une expérience naturelle de la région viticole suisse des “Trois Lacs” en 2013 sur les performances du marché de détail suisse. Nous trouvons des effets né- gatifs statistiquement significatifs (seuil <1%) à hauteur de -22.8% et de +2.8% pour respectivement le volume et le prix du vin consommé. Ces effets peuvent être inter- prétés comme un effet de traitement moyen, ce qui signifie la différence des résultats entre les groupes de traitement et de contrôle dans une méthode pré- et post- choc. Plusieurs contrôles de robustesse sont fournis confirmant la signification statistique des effets estimés ainsi que les hypothèses initiales.

Le chapitre 5 examine les déterminants des prix du vin sur le marché suisse en utilisant une approche de régression hédonique. Nous constatons que les cépages et les régions viticoles ont un impact important sur les prix. Le cépage Petite Arvine ou la viticulture en Valais ou en Suisse alémanique déclenche une prime. Cultiver du Pinot Noir ou du Gamay, tout en étant situé dans le Canton de Vaud ou à Genève, entraîne un rabais. En étant situé dans une région viticole renommée, cela provoque d’avantage de prix plus élevés. Les informations sur les producteurs, d’une part, n’explique que de façon marginale les prix du vin. La concurrence élevée et les coûts de production, conjugués à la visibilité limitée des vins suisses, se traduisent par des prix qui dépendent principalement des effets de réputation collective et des types spécifiques de cuvées/assemblages. Nos résultats suggèrent que les producteurs suisses fixent les prix de leurs vins de manière assez uniforme.

xiv Chapter 1

Introduction 1 Introduction

This dissertation consists of four studies discussing Swiss wine market issues with an empirical panel data approach. The second chapter deals with the price elasticity con- cept, which is a fundamental economic indicator in a range of economic fields, in order to understand the consumer reactivity to price changes. The third chapter discusses the quantity and price prospectives of wine in Switzerland, using an innovative approach to disentangle demand and supply shocks on the market. The fourth chapter analyzes the particular event of a supply weather shock in a specific Swiss wine region and focuses on economic consequences through wine market performances. The three chapters following the introduction use monthly data from supermarkets in Switzer- land, with about 80 types of wines aggregated in panel along with other economic, agricultural and climatic variables. The fifth chapter focuses on the determinants of wine prices from different aspects and wine specifications, using a prices data set individually collected from selected Swiss fine wine producer websites.

1.1 Issues and Goals

The ﬁeld of wine economics is quite recent; Professor Ashenfelter from Princeton University was one of the pioneers, in parallel with the creation of the American Association of Wine Economists (AAWE). See Storchmann (2012) and Marks (2015) for an introduction to wine economics.

Swiss vineyards have a long history, as part of the European context, with the ﬁrst references to wine production in 3,000 to 1,800 B.C. and especially during the Middle Ages. This was when monks developed a production system in the area of Lake Geneva as well as in the Valais region (Masset and Philippe-Weisskopf, forthcoming). More recently, between 1950 and 1980, Swiss vineyards beneﬁted from favorable conditions for domestic production. Indeed, through customs quotas, imports were regulated and domestic consumption was encouraged. At this time, consumer demand was such that overproduction began, especially for white wines, with the consequent reduction of quality due to very high yields (Mondoux et al., 2017). The 1990s saw a gradual

2 decline in the overall consumption of wine, mainly attributed to national campaigns to prevent problems related to alcohol consumption as well as the effect of a maximum alcohol level permitted while driving. The main effect on consumers was to drink in smaller quantities but better quality. As early as 1993, quota limitations came into effect, with the aim of adapting production in the face of lower consumption, increasing the overall quality level and diminishing the large accumulated stocks of wine. This date corresponds to the end of the overproduction of wine in Switzerland (Mondoux et al., 2017).

Since the opening up of borders and the liberalization of wine imports in 2001, when the annual import quota was set at 170 million liters (which has never been reached to date), Swiss wines have faced increasing competition (Herminjard, 2017a). This event marks in fact the beginning of real foreign competition for Swiss wines, resulting in a continuing decline in their production. If we compare the current situation, we notice that production declined from about 120 million liters in 2001 to less than 100 in 2015 (FOAG, 2016). Another reason for this signiﬁcant decrease is the introduction by the Confederation in 2002 of grubbing-up premiums to diversify vineyard grape varieties in order to meet market demand, as well as the reduction of quota production in order to gain in quality (Mordasini, 2014). In recent years, the trend of wine-producing estates has been to move towards sustainable viticulture which is respectful of the environment, such as integrated, organic and biodynamic production (Mondoux et al., 2017).

Furthermore, Swiss wine consumption in Switzerland has continued its downward trend over the last few years due to multiple causes such as bad harvests affecting production or unfavorable exchange rates, which tend to give an advantage to foreign wines in real terms. In the current economic situation, weakened by the strong Swiss franc, Swiss wine has managed with some success to maintain its market share (OSMV, 2016). Local Swiss wines should therefore focus on the price intervals where comparative advantages and potential growth exist, given the fact that they do not necessarily share the same playing field as foreign wines in terms of price classes and are not competitive under a certain price per liter. Different targets for specific price categories could be thus more relevant for the Swiss wine industry.

3 Put in a world context, the Swiss wine-growing sector is quite small, with only 14,780 hectares, which corresponds to only 0.2% of the world’s wine-growing areas (OIV, 2016). Meanwhile in terms of wine consumption per capita, Switzerland ranks in fourth position, with 35 liters/capita (SAB, 2015). According to our panel data, the retail market share represents about 25% of overall Swiss wine consumed in Switzerland (FOAG, 2017), the other channels of distribution being direct sales, Horeca (HOtel, REstaurants ans CAfé) and wholesalers. Swiss wine production is very heterogeneous among regions on various levels: exposure to the sun, terroirs, grape varieties and altitudes. Sloping vineyards that are not easily mechanized require more labor and higher production costs, not to mention that climate change will require adaptation of the vineyard, moving for example to more suitable areas (Herminjard, 2017b). This heterogeneity is also linked to the economic context of Swiss viticulture, with each of the 26 cantons making up the Swiss confederation having its own legislation governing wine production, giving as many AOC1 legislations as there are cantons (Herminjard, 2017b).

About two-thirds of Swiss wine consumption comes from imports, mainly from big wine-growing neighbors such as Italy, France and Spain, to compensate for insufficient local production. Exportation of Swiss wine is tiny, at about 1% of the domestic production and unfortunately Swiss exportation data do not differentiate between Swiss and foreign wine exported from Switzerland (Swiss-Impex, 2015). The latter figure is overvalued because of the phenomenon of re-exportation (for example, a wine broker in Switzerland who buys Bordeaux wines and re-exports them to England or Hong-Kong), which is not possible to isolate. An explanation for these two facts is that most wines produced are sold on the domestic market, covering only one third of consumption needs and leaving a very small opening for exportation, confirming that this is a matter of relatively low supply availability on the market. Meanwhile there are some notable successes for a few exporters, such as for example the niche market for Chasselas Vaud AOC wine along with Sushi in Japan (Eskenazi, 2016) and the growing number of medals that specific Swiss wines have been awarded abroad in international competitions (Vinea, 2010).

1Appellation d’origine contrôlée (“controlled designation of origin” in English).

4 In addition, there are no reliable data about purchasing tourism beyond Swiss borders, especially in regions such Geneva, Basel or Ticino. A rough estimation gives a range between 14 and 26 million liters, which corresponds to 5-10% of total consumption in Switzerland (OSMV, 2016). From 2014 a revision of an ordinance adopted by the Federal Council has allowed individuals to import duty-free 5 liters per person (only one liter/capita was allowed before) into Switzerland, which in addition to the unfavorable exchange rate puts high pressure on Swiss wine producers.

The goal of this dissertation is therefore to understand and model the Swiss wine market in order to contribute to the Swiss project “Observatoire Suisse du Marché des vins” (OSMV, Swiss Wine Market Observatory) which is based at the “Haute école de viticulture et Œnologie” in Changins. The project is ﬁnancially supported by the OSMV in collaboration with the KOF Swiss Economic Institute, Department of Management, Technology and Economics at the ETH Zurich.

Chapters 2 and 3 are a ﬁrst attempt to analyze and understand the demand side of wine in Switzerland. Most of the statistics from the Federal Oﬃce of Agriculture and the cantonal report for each AOC legislation provide quantity of stocks (beginning and end of year), harvests and imports. Wine consumption is actually an accounted deduction on the prior indicators, which stays only on the supply side and at a yearly frequency. The current dissertation provides demand side prospectives, adding the fundamental variable of price into a monthly frequency design.

Chapter 4 focuses on a speciﬁc situation, in order to understand producer and consumer behavior through Swiss supermarkets facing an unexpected hail storm shock. Chapter 5 studies price determinants in order to control and estimate which variables have an impact and which indicators matter at the time of producer price setting.

1.2 Data and Methodology

For the dissertation, we gained access to a unique data set from the Nielsen Company, which provides data on all the types of wine sold in the major supermarket chains in Switzerland as scanned at the till. The Nielsen Company has provided monthly prices and quantity data (more precisely 4-weekly data) from the year 2012, and these

5 data continue to be updated every semester. It is possible to track about 80 types of wine, identiﬁed by color, region of origin and grape varietal. The ﬁrst step of this dissertation is to construct a panel data set from Swiss supermarket data as a basis for the research papers. Then, depending on the chapters and in order to start proper analysis in the context of this doctorate, other variables are added, such as those from other wine sources (FOAG, 2015), socio-economic indicators, and import and export of wine (Swiss-Impex, 2015).

Coming back to the comparative advantage of the OSMV and therefore to the econometric analyses in this dissertation, it is certainly the introduction of the variable price. Along with the quantity, prices, in a monthly frequency design allow us to estimate and put into correlation these two main economic variables. Looking at methodology, this dissertation goes beyond correlations between economic variables and tries in each chapter to discuss and apply a speciﬁc aspect in dealing with causal analysis.

Chapter 2 starts with estimating quantity-price correlations with a wide range of Swiss wine speciﬁcations. In order to deal with price endogeneity, we use an instrumental variable (IV) method that allows us to infer a causal interpretation of price variation on wine consumption. Diﬀerent candidates for IV are selected, discussed and estimated. Some of these are already integrated into the retail market panel, such as information about price and market share promotions, others are integrated ex-post in the data, such as economic, agricultural and climatic variables.

Chapter 3 uses the Panel VAR approach in order to forecast and give prospectives for wine consumption in Switzerland, disentangling supply and demand shocks through sign restrictions strategy. Impulses responses functions (IRF) allow us to study in detail the persistence of a price variation due to an exogenous shock and its respective return to an equilibrium. Forecasting methods based on technical analysis, factor of inﬂuence and mainly the forecasting error term, complete the methodology.

Chapter 4 begins with a focus on identifying qualitatively and descriptively time data about a hail shock from different sources of information. Once the time data are clearly established and after initial assumption of what we expect the effect could look like, we then start to implement the difference-in-differences (DID) method that seems

6 to be the most appropriate to this situation. The treated group is clearly identiﬁed, and we were able to measure the data of the hail storm as well as its initial consequences on the retail market.

Chapter 5 relies on a unique “manually” collected set of prices from the websites of 163 selected fine wine producers. This collection of data was initiated by the two co- authors, Professors Masset and Weisskopf, then assisted and completed by myself. The method of hedonic regression is applied to the price of wine on wine characteristics such as color, producer, region and grape varietal. Other econometric approaches such as Propensity Match Score (PMS) are also implemented in order to evaluate, for example, the introduction of Parker score ratings into the Swiss wine market. Due to the panel structure of the data (four years’ observations for each type of wine) and a majority of time-invariant dependent variables, we also contributed to this research paper by applying an appropriate random effects (RE) model, instead of pooled OLS, fixed effect (FE) or between effects (BE) models.

1.3 Findings

As explained in the summary, in Chapter 2 we find that Swiss wine is price elastic at -1.71 for the IV approach, which allows us to interpret it as causal effect. This means that a decrease/rise of 1% in the prices seems to cause a rise/decrease of 1.71% in the volume of wine sold. For different specifications, IV method with causal inference of price on wine consumption confirms price elasticity but with a lower extent than when we estimate correlation between quantity and prices through FE regression (price elasticity of -2.63). A modified structure of the panel data permits us to estimate a wide range of cross-price elasticities by selecting 10 wine bestsellers. This configuration puts the analysis back to economic theory, with substitute and complementary goods giving new prospectives. All the detailed results can be found in this chapter.

Chapter 3 shows price persistence variation for selected types of wines. This is a major ﬁnding that could help producers or supermarkets to be more precise when using price promotions in order to trigger more sales. This operation can actually give a bad price signal image to the customers by making it diﬃcult to convince them that

7 a further rise in price is justified. High response heterogeneity across individuals to supply or demand shocks demonstrate that these two phenomena should be treated in a separate way, allowing for different adapted answers. Coming to the forecasting analysis, we noticed that it is more difficult to make projections with aggregate panel data than a forecasting estimation individual by individual. The latter statement could seem to be trivial, but it shows that longitudinal data are not always a panacea and cannot resolve all economic and econometric issues.

From a theoretical point of view, when a negative supply shock occurs in a market, we expect a decrease of the quantity and a rise in prices. Chapter 4, which analyses a specific supply hail shock in a defined Swiss wine region (“Three Lakes”) shows statistically significant (1%-level) negative effects of -22.8% for volume and +2.8% for price of wine consumption produced in that region of origin. Doing further estimation with the turnover, which is simply quantity times price, we realize that higher prices did not compensate for the decrease of wine sales for the “Three Lakes” region. Allowing the DID coefficient to vary over time, with monthly, trimester and semester specifications, we confirm more rigorously and econometrically the descriptive statistics found in the FOAG (2016) report.

Using a hedonic regression design on the price of Swiss fine wines, we demonstrate in Chapter 5 that producers price their wines in a quite uniform way. Specific region or grape varietal can have a strong impact on wine prices. Two recent events, the dropping of the CHF/EUR peg by the Swiss National Bank and the beginning of regular coverage by the Wine Advocate (Robert Parker TWA Rating System), which has dramatically increased the visibility of some producers, allow us to deepen the analysis. For the first event, we see from a descriptive and econometric point of view that Swiss producers did not actually change the trend of price rising due to the end of the peg. The Swiss wine market is therefore in a situation where Swiss entry-level wines are very expensive compared to those from foreign countries, but increasingly attractive when we analyze high-end Swiss wines. They offer indeed good quality and a very competitive price compared to good French or Italian wine. For the second event, we note a positive impact on prices for producers that were rated by Parker, showing an influence of the recognition of their wines.

8 1.4 Contribution

The general contribution of this dissertation is to study the Swiss wine market beyond the traditional descriptive statistics lines, moving to demand side and econometric analyzes, as well as extending previous literature in wine economics. The aim is to create a statistical tool which can contribute to wine market understanding for Switzerland and give an impetus for further academic research in the area.

Chapter 2 adds to previous statistics on the Swiss wine market by analyzing the demand side, from a customer point of view, which completes the traditional supply side and “accounting” approach of consumption. The panel integrates some unique price promotion data that are a convincing candidate for IV estimations. Seasonality is taken into account to control for monthly variations, while FE estimation allows us to deal with unobserved heterogeneity. This Chapter extends the previous literature in wine economics by treating wine as a product oﬀering a complex experience in all its characteristics such as region of origin, color and grape varietal. It goes even further in creating a pseudo panel in order to estimate cross-price elasticity, disentangling substitute and complementary products inside the Swiss wine world.

Chapter 3 extends previous analysis in VAR approaches by dealing with forecasting and taking into account several economic, climatic and agricultural factors. Disentan- gling supply and demand shocks with sign restriction in wine economics is also quite innovative, and this method allows us to go beyond current knowledge on wine price persistence. This research paper can help producers in taking a decision whether to decrease their prices with the aim of producing a positive reaction from Swiss consumers.

Chapter 4 adds to the applied literature of DID methodology, a concrete model which can be applied to other regions when a similar exogenous supply shock occurs, or that can be applied to other commodities where similar data from the supermarkets are available. The DID model can help to predict future supply shocks in the Swiss wine market that could be grounds for a quicker price-setting reaction. It furthermore allows for discussion about climatic wine stocks “reserves” in order to compensate for bad harvests with better ones, on a yearly basis. Using the Autor (2003) approach that

9 allows a DID coeﬃcient to vary over time, we can be more rigorous when analyzing supply shock in agricultural or wine markets.

Chapter 5 is a first attempt to understand price determinants in the Swiss wine market. Studying the price strategy of Swiss wine producers is important because they deal with a product which offers highly differentiated experiences in a very competitive and opaque market. There is indeed recent interest by academics in studying lesser-known wine regions (Storchmann, 2017). The novelty of this econometric approach is of unifying hedonic regression with the panel structure of the data. The propensity match score analysis of the inference of the introduction of Parker scores in Switzerland, as well as the price evolution trend after the end of the CHF/EUR peg, as previously mentioned, make study of this research paper interesting. Some of the conclusions regarding price setting may be found surprising but are very helpful towards an increased understanding of Swiss wine producer behaviors.

10 Chapter 2

Price Endogeneity and Demand for Swiss Wines 2.1 Introduction

The goal of this paper is therefore to estimate the signiﬁcant determinants (economic, climatic and agricultural indicators) for the demand of wine in Switzerland with a focus on price elasticities, in order to better anticipate the factors inﬂuencing supply and demand. Wine producers and traders in the Swiss market can actively regulate the supply, while consumers are on the demand side (see preferences of consumers, exchange rate EUR/CHF, etc.). This paper will also deal with the evolutions of Swiss wine consumption, to better understand consumer expectations and needs in order to strengthen the marketing and sales strategy of Swiss wines. The structure of the data allows us to construct a panel of 98 types of wines in addition to control variables such as climatic (temperature), macroeconomic (exchange rate) and agricultural (harvest).

The panel scanner data set used in this paper allows us to identify purchasing by consumers and interpret it as an equilibrium between the demand and supply of wine. In this data set, we have all the types of wines sold (quantity) and prices per liter in the major supermarket brands1 in Switzerland, as scanned at the till.

The main motivation of the study is to set up a tool for analyzing price elasticities on the Swiss wine market. It should help the Swiss wine sector in estimating the sensitivity of consumers to a variation of prices. This paper can make an important contribution by providing different scenarios which can help wine professional associa- tions at the macroeconomic level, as well as the producers at the microeconomic level, in adopting a suitable pricing policy. In fact, the estimation of price elasticities with causal interpretation, i.e. the effect of a 1% price variation on the percentage change of wine consumption can be of great added value to the Swiss wine industry, particularly in order to maximize the profit of the wine sector for different wine specifications.

This paper proceeds as follows: the next section surveys the literature on determinants of wine consumption and IV method. Section 3 presents the dataset and provides descriptive statistics on the quantities and prices of Swiss wines in the retail market. In section 4, we describe the identiﬁcation strategy and the econometric methodology,

1Which include Coop, Denner, Manor, Globus and Volg, with the exception of Landi, Lidl and Aldi (Delaquis et al., 2015a).

12 while section 5 presents and analyses the results with a speciﬁc focus on robustness checks. Section 6 concludes the study.

2.2 Literature

The general wine economics literature considers wine as a homogeneous commodity or integrates it into more general alcohol consumption. More recent works propose detailed analysis and look at wine as a heterogeneous item. For example, Bentzen et al. (2013) analyse the effect of life satisfaction on alcohol consumption in the OECD Countries; Fogarty (2013) looks at alcohol demand and maximizing tax, while Hyunok et al. (2013) estimate the value of wine names in the US market for Champagne and Sparkling wine. Masset and Henderson (2010) analyse wine as an alternative asset class and Hoffmann et al. (2013) estimate the demand for alcoholic and non-alcoholic bever- ages in France and the impact of advertising (Giraud-Héraud et al., 2013). Ashenfelter (2008) uses climatic data, such as temperature and rainfall, to predict the quality and prices of Bordeaux Wine. Ashenfelter and Storchmann (2014) and Ashenfelter and Storchmann (2010) analyse the effect of weather and more precisely climate change on the wine market in general.

Roberts and Schlenker (2013) present a new approach to identifying supply and demand elasticities of four main storable commodities (corn, rice, soybeans and wheat) using past shocks as exogenous price shifter. In a way more closely related to our study, a “plausible identiﬁcation requires instruments that shift prices in ways that are plau- sibly unrelated to unobservable shifts in each curve” (Roberts and Schlenker, 2013). Weather factors could be good candidates as natural instruments for agricultural supply shifts in order to identify an unbiased demand estimation. Such identiﬁcation strategy, which we will subsequently move on to consider, is one of the potential solu- tions to eliminate endogeneity and reverse causality between the two variables quantity and price (Roberts and Schlenker, 2013).

13 2.3 Data

2.3.1 Descriptive statistics

The Nielsen company (Nielsen, 2016) provides monthly prices and quantities (more precisely 4-weekly data) from the year 2012. It was possible to track 98 types of wine (see Tables 2.2 and 2.3 for details), identiﬁed by color and by region of origin2. Furthermore, along with dependent variables (income, quantity, price and promotions), diﬀerent types of covariates such as economic- (exchange rates, Swiss Consumer Price Index (CPI), wine import prices) and climatic- (temperature, sunshine and rainfall) variables are added to the panel.

Section 4.3 describes in detail the structure of Swiss AOC wines, which is divided into six wine regions of origin, identified in regioni. In this panel data structure, we can furthermore identity the colori of the wine (red, white, rosé) and the country of origin with the variable countryi (Switzerland, France, Italy, Spain and “Rest of the world” (RoW)) aggregated by color. Along with the Swiss AOC wines, the Swiss non- AOC wines (Swiss country and table wines)3 are also included in our data set and identified by the variable region − naoci which defines four different Swiss non-AOC wines regions (French-, German-, Italian- parts of Switzerland and Goron4).

In Figures 2.1 and 2.2, we can observe in graphic form the distribution of prices (in CHF/L) and ln(prices) per liter pooled across all AOC wine labels and monthly time observations. Figure 2.2 shows a distribution which tends to be normally distributed and supports the use of natural logarithm in our next econometric estimations. As we see in Table 2.4, which provides descriptive statistics for all the variables in the AOC context (see Table 2.5 for descriptive statistics concerning non-AOC and foreign wines), the mean prices is 16.30 CHF/L with a minimum of 5.82 CHF/L and maximum of 70.00 CHF/L at a monthly level. It is interesting to note that because of the structure of this panel, we overestimate the mean prices (weighted average price over quantity

2According to Swiss Wine Promotion (SWP), in Switzerland we have six diﬀerent wine regions origin: Valais, Vaud, Geneva, Ticino, Three Lakes and German part of Switzerland. 3Also known in Switzerland as wines of second category (see the French terminology: “Vins de pays” and “Vins de table”). 4The label Goron is a red non-AOC wine produced in region Valais only.

14 = 12.00 CHF/L and median price = 15.86 CHF/L). As expected, Swiss non-AOC and foreign wines have lower mean prices at 10.37 CHF/L (weighted average price over quantity = 7.18 CHF/L and median price = 10.97 CHF/L).

Figure 2.1: Swiss AOC wine price Figure 2.2: Swiss AOC wine ln(price)

In Figures 2.5 and 2.6, we note the proportion of the quantity of wine sold in the Swiss retail market (2012-2016) by region and wine color for Swiss AOC wines (see Figures 2.9 and 2.10 for non-AOC-, Figures 2.13 and 2.14 for foreign- as well as Figures 2.17 and 2.18 for total- wines). The white wines represent a little more than half, the red wines a little less than a third and rosé wines 17.2% of the Swiss AOC wines sold in supermarkets.

Comparing the Nielsen data, which include only the wine sold in Swiss supermarkets, with those of the FOAG (2015), which include the whole consumption of Swiss wine (see above), we can observe some interesting differences5. The region Valais is clearly overrepresented in the Swiss retail market; Vaud and Ticino seem to have about the same proportion, while Geneva, Three Lakes and German part of Switzerland are underrepresented. This is due to the fact that the former has a higher proportion of the sales in the retail market while the two latter have a higher proportion in other channels of distribution, such as direct sale, Horeca (HOtel, REstaurant and CAfé) and wholesalers.6 5The region Valais takes 51.6% (FOAG: 37%), Vaud 33.0% (FOAG: 32%), Geneva 2.3% (FOAG: 10%), Ticino 4.5% (FOAG: 5%), Three Lakes 2.2% (FOAG: 4%) and the German part of Switzerland 6.4% (FOAG: 12%). 6The launching of the Mercuriale (“representation of current prices of foodstuffs published peri- odically” (Delaquis et al., 2015a)) of the Swiss wine market observatory (OSMV) will hopefully help to understand better the distribution of Swiss wine from the producer to the final consumers outside the retail market channel.

15 In Figures 2.7 and 2.8, we can observe that the proportion of Swiss AOC red, white and rosé wines diﬀers from one region to another. For example, Vaud has almost exclusively white wine, Ticino produces mostly red wine and Three Lakes oﬀers mainly white and rosé wines. Valais is clearly the leader for red and rosé Swiss AOC wine and Vaud for white wines. We provide the same analyses for non-AOC- (Figures 2.11 and 2.12) foreign- (Figures 2.15 and 2.16) and total- (Figures 2.19 and 2.20) wines.

2.3.2 Statistical analysis

In order to show the seasonality of Swiss wine consumption in supermarkets, Figure 2.3 includes three different types of wine, one for each color, from the wine region Valais (VS). We can observe that «Pinot noir VS (rosé)» sees peaks in summer as we would normally expect for rosé wines in general7. «Fendant VS (white)» is consumed in larger quantities during the winter, while «Dôle VS (red)» seems to be sold in larger quantities at the beginning and end of the year. This evidence about time-fixed effects will need to be addressed in the following analyses.

Figure 2.3: Seasonality of Valais wines (monthly frequency)

In order to analyse the predictive power of the model when adding control variables, we include three types of wine, one for each color: Dôle red (Figures 2.21 and 2.22), Fendant (Figures 2.23 and 2.24) and Pinot noir rosé (Figures 2.25 and 2.26). We

7Delaquis et al. (2015) have dedicated a chapter in the OSMV Report No. 4 to the correlation between rosé consumption and temperature.

16 can note that observations approach signiﬁcantly the 45 degrees line, which compares the predicted ln(quantity) with the actual ln(quantity), when we add covariates to the regressions. This means that the predictions of the model increase when control variables are added, at least for the three label speciﬁcations in question.

2.3.3 Transformation and tests on the data

As a reminder, the panel data set consists of 98 individuals (types of wine) observed over five years (13 times 4-weekly data), thus 65 time observations per individual. Monthly-frequency covariates data are transformed from 12 to 13 observations per year through extrapolation. Yearly-frequency covariates, of which some are potential IVs, such as average climatic data and grape harvest, are “repeated” 13 times along each year in the panel structure. At this stage, mixed-frequency issues could potentially avoid much of the variation and consequently allow for explanation of only a small effect within the variable. From an econometric point of view it is still possible to use the fixed effects (FE) model, as lower-frequency variables remain (rarely) time-variant variables.

Before going in more detail into the identiﬁcation strategy and econometric estimations, we provide several tests on the data:

Breusch-Pagan Lagrange multiplier test We strongly reject the null hypothesis that variance across individuals is zero (no panel eﬀect) and conclude that random eﬀects (RE) would be more appropriate compared to a restricted pooled OLS model (Breusch and Pagan, 1980).

Hausman test Carrying out a Hausman test to identify whenever we should consider random effects (RE) versus fixed effects (FE) model, we find that we can significantly reject (<1%- level) the null hypothesis (H0: “difference in coefficients between FE and RE is not systematic”). Following this test we should therefore use the FE model (Hausman, 1978).

17 Time fixed effects We strongly reject the null hypothesis (<1%-level) that the coefficients for all time periods (65 time dummies) are jointly equal to zero, thus time FE are needed in this case to control for seasonality of wine consumption (see Figure 2.3).

Heteroskedasticity We reject the null hypothesis and conclude that there is heteroskedasticity in the error term (Greene, 2003). We should therefore cluster standard errors at the individual level (i) to allow for autocorrelation and heterogeneity inside each cluster. Two distribu- tional diagnostic plots are provided in the appendix: Figure 2.27 graphs a standardized normal probability plot, while Figure 2.28 plots the quantiles of a variable against the quantiles of a normal distribution.

Test for autocorrelation Carrying out the Wooldridge test for autocorrelation in panel data (Wooldridge, 2002), the null hypothesis being no serial correlation (H0 : ρ = 0), we reject H0 and conclude that data have ﬁrst-order autocorrelation (see Table 2.10 with Arellano Bond estimations).

2.4 Identiﬁcation strategy: instrumental variable estimation

2.4.1 Price endogeneity

Given that we want to estimate the effect of price variation on wine consumption, there is an evident problem of endogeneity through omitted-variable bias, measurement error of the dependent variable and reverse causality. In the case of quantity and price, we would not know which variable has an influence on the other or if both variables influence each other. We will thus apply an econometric solution through instrumental

18 variable (IV) that is strongly correlated with price but not with wine consumption, in order to be able to infer a causal eﬀect.

The aim of this project is furthermore to estimate price elasticities of the demand for wine in Switzerland in general, by color (red, white, rosé), by region of origin and by principal labels (For example: Red Merlot from Ticino or White Fendant from Valais). Following Angrist et al. (2000), to address endogeneity concerns and infer a causal effect of the influence of prices on the volume of wine sold, we plan to use two strategies in an FE model. The first strategy relies on the IV price promotion, which captures the prices of selected wines under price promotion8. The second strategy relies on IV such as climatic data (temperature, rainfall, sunshine), annual grape harvest using data from the FOAG (2015), at the Swiss wine region level (Ashenfelter, 2008) and individual-invariant variables (exchange rates, Swiss CPI and imported wine prices). As we will explain below, these IVs should be present only in the supply- and not in the demand- equation for wine. This framework should allow IVs to be good “supply shifters” helping in identifying the demand curve given all others factors ceteris paribus (Greene, 2003). Since we do not have access to the vintage information of the wine bottles, we assume that the harvest campaign of the year t should affect the wine market in year t + 1.

We consider a classical supply-demand simultaneous equations model for the wine market:

Supply: ln(Qi,t) = β0 + β1 ln(Pi,t) + β2Zi,t + εs i,t (2.1)

Demand: ln(Qi,t) = γ0 + γ1 ln(Pi,t) + εd i,t (2.2)

Qi,t, Pi,t and Zi,t are respectively the quantity, the mean price of the wine sold and a potential IV, where the index i design the type of wine and t the time at which it was sold. ln term prior each variable indicates that we take the natural logarithm of it. β0, β1, β2 as well as γ0, γ1, γ2 are the parameters to estimate and εs i,t, εd i,t two idiosyncratic error terms.

Given classical economic theory, we should expect that β1 > 0 because supply of wine increases and γ1 < 0 as demand for wine decreases with higher prices. In the demand

8According to Nielsen (2016), they represent the types of wines that experienced a lowering in their prices of at least 20% for a maximum of 4 weeks and then returned to the previous price.

19 equation we see that ln(Pi,t) is endogenous, and correlated with the error term by definition as an exogenous demand shock εd i,t will alter jointly the quantity and price equilibriums in the market, violating the zero-conditional-mean assumption and OLS would be biases and inconsistent (Baum, 2006). The idea is therefore to find an IV which influences the supply but is not part of the demand equation. We assume that

Zi,t is not included directly in the demand equation, so it should be a good supply shifter in order to identify the demand curve.

2.4.2 Discussion of the instruments

In order to identify a causal effect of price on wine consumption, we will test different types of IVs, as previously discussed. We treat ln(Pi,t) as endogenous and therefore we try to look at an external IV that has an effect on ln(Qi,t) but only through ln(Pi,t). We assume that all our observations are equilibriums (Becker, 2010), so potential IVs should have an influence and shift only the supply curve, which will allow us to move along the demand curve in order to identify it. As examples, Cuellar and Huffman (2008) use grape price as IV for estimating the demand for wine in the United States, while Sarsons (2015) instruments rainfall data to estimate a causal effect of income on war-conflicts in agriculturally-dependent regions.

Conversely, we could also take into account a potential demand IV shifter, which would allow us to move along the supply curve (Roberts and Schlenker, 2013). These “potential candidates”, on the demand side, could be exchange rates CHF/EUR and CHF/USD (FTA, 2015). Given the fact that the Swiss national bank applied a ﬁxed lower end bound exchange rate of 1.20 CHF for one Euro until January 2015, there will consequently be only a limited variation for that post-period. Import prices of red and white wine for the three main wine importers, which are Italy, France and Spain (Swiss-Impex, 2015) as well as Swiss CPI will also be provided, as they can be considered both as covariates or potential IVs.

To sum up, we disentangle two types of IVs as demand and supply shifters:

• Climatic data (supply): as previously described, we take the average temperature (March-May, June-October) and the average rainfall (March-May, June- October). These variables should inﬂuence only the harvest and production

20 and therefore inﬂuence the supply of wine in the retail distribution market only through the price.

• Grape harvest (supply): FOAG (2015), we will provide directly the harvest by region of origin in quantity, distinguished by red (aggregated with rosé) and white grapes.

• Climatic data (demand): we take the monthly data for temperature, sunshine and rainfall, because consumption and wine demand are connected to the data in the same month period. For example, we can see that in the descriptive statistic of rosé wine, consumption is much higher in the summer time and highly correlated with the temperature (see Delaquis et al. (2015)).

• Individual-invariant variables (demand): we could reasonably expect that these previously described variables (such as exchange rates, Swiss CPI and imported wine prices) should have an inﬂuence on wine consumption, but principally through the price.

• Price promotion (supply): this seems to be the most convincing IV to deal with price endogeneity. It is strongly correlated with wine prices but not with wine consumption; furthermore it is jointly high individual- and time- variant (not the case with other potential IVs described above).

2.4.3 Econometric model

We start with a general two-way fixed effects (FE) model for total wine, by wine region and by color, in order to estimate different price elasticities specifications captured by coefficient β1 (quantity and prices in natural logarithms):

ln(Qi,t) = β0 + β1 ln(Pi,t) + Si,tβ2 + Ztβ3 + ui + δt + εi,t (2.3)

Qi,t, Pi,t and Si,t are respectively the quantity, the mean price of the wine sold and a vector of control time-variant variables, where the index i design the type of wine and t the time (month) at which it was sold. Zt and ui are respectively a vector of control individual-invariant variables and the unobserved heterogeneity. δt is the time-ﬁxed eﬀects (monthly level) and εi,t the idiosyncratic error term.

21 We can easily prove that β1 can be interpreted as price elasticity. Proceeding as follows from Equation 2.3 :

∂Q β ∂Q β i,t ln(Qi,t) 1 i,t 1 = e ∗ ⇔ = Qi,t ∗ (2.4) ∂Pi,t Pi,t ∂Pi,t Pi,t

We therefore obtain the price-elasticity, namely the percentage change in quantity relative to a 1% percentage change in the price:

∂Qi,t β = Qi,t (2.5) 1 ∂Pi,t Pi,t

Following Angrist et al. (2000) and Greene (2003), assuming that the price (Pi,t) is endogenous, diﬀerent IVs will be used and tested to identify the demand and the supply curve respectively. We thus estimate the ﬁrst step of the 2SLS model as:

ln(Pi,t) = γ0 + IVi,tγ1 + Si,tγ2 + Ztγ3 + ui + δt + τi,t (2.6)

where IVi,t are different tested IVs with the respective coefficient γ1 and τi,t the idiosyncratic error term. Note that in equations (2.3) and (2.6), due to the within transformation (FE), the intercepts (β0 and γ0), the time-invariant regressors (not present in the aforementioned equation) and the individual specific effects (ui) cancel. Mundlak (1978) proposes an alternative to RE model, which includes time-invariant unit averages of the explanatory variables. This strategy allows us to estimate time- invariant variables such as color, region of origin and grape varietal but taking into account the unobserved heterogeneity (ui) at the same time. The advantage of this approach is that we would obtain the same coefficient results as in an FE model without discarding time-invariant variables (see Subsection 2.7).

22 2.5 Results

2.5.1 Baseline model

The key issue of a 2SLS model is the first stage regression, where we test IVs that could be ideally correlated with price and not correlated with the error term. Table 2.6 shows the results of the first stage for four IVs specifications (climatic-supply, grape harvest, climatic-demand, individual invariant variables and price promotion) and confirms that they are relevant. The goal of this process is to eliminate price endogeneity, isolating the useful variation which allows us to infer a causal effect of price on quantity. At this stage, Specification (6), i.e. price promotion, seems to be the best instrument given its variation jointly across time and individuals. Specifications from (2) to (5), even if they can give statistically significant effects, they remain collinear with covariates and time dummies because their specific IVs are mostly individual-invariant.

In general, as we can see in Table 2.1, Swiss wine is consistent with the law of demand and therefore with the economic theory, namely that a lowering/raising of prices is associated with a rise/fall in wine consumption, meaning that the correlation between the two variables of interest is negative. In the FE specification (1), we can note that the price elasticity is -2.63, which implies that a variation in percentage of the prices of +1% (-1%) is associated with a variation in percentage of the quantity sold of -2.63% (+2.63%) on monthly average. Specifications (2) to (6) present the results of a 2SLS model with four different IVs: clima-s (climatic supply), grape harvest, clima-d (climatic demand), individual-invariant variables and price promotion, as discussed in Section 2.4.2.

In specification (2) of Table 2.1, we use climatic data for each region that should shift the supply curve of wine and therefore influence the demand for wine but only through the prices. These instruments are shifted back by one year, because the climatic data should have an influence on the harvest (supply of wine) and then enter the market the year after. In specification (3), we use the harvest (quantity) of white and red (aggregated with rosé) wine for each region given by the FOAG (2015), shifted back also by one year, because we assume that the harvest should have an influence on

23 the supply of wine the subsequent year. Specification (4) and (5) with respectively climatic demand and individual-invariant IV are also provided as demand shifters. Specifications (2) to (3) give acceptable results in line with law of demand and as expected Specifications (4) and (5) follow the law of supply with positive coefficients. Specification (5), which is on a demand-shifter design, i.e. an identification of the supply curve, gives us a positive and statistically significant (<1%-level) price elasticity of +1.90%. In general, Specifications (2) to (5) are not very stable when we add covariates and Time FE as well as for different configurations across colors and regions.

Speciﬁcation (6) seems therefore to be the most convincing, given the fact that as previously mentioned, price-promotion IV is jointly time- and individual- variant, and it will be used as a baseline for the next IVs estimations. We can therefore note that the price elasticity is -1.71, which implies that a variation in percentage of the prices of +1% (-1%) is associated with a variation in percentage of the quantity sold of - 1.71% (+1.71%) on monthly average. Four post-estimations that test the validity of this instrument are presented in Subsection 2.7). Using IV strategy as causal analysis method, we show that price elasticity of Swiss AOC wines, in absolute value, is lower (-1.71) than in the FE model (-2.63).

Table 2.9 shows explicitly estimated coefficients of covariate for four selected specifications. We can explicitly look at the covariates results in a two-way FE model, i.e. taking into account jointly individual and time FE. Adding control variables tends to lower the price elasticity in the Pooled OLS and IV specification, while in the RE and FE model, the price elasticity tends to be higher. Quantity and price under promotion give statistically significant effects, because they are by construction strongly correlated with our main two variables: quantity and price. Note that in specification (4), price promotion is intentionally removed from the list of covariates because it is used as IV.

Economic variables such as exchange rates, Swiss CPI and imported prices, which are individual invariants by definition, fail to give significant results. Therefore, except for French imported wine prices, this is mainly due to time FE, which principally captures that kind of variation. Climatic variables are very significant in the pooled OLS model, while in the other specifications, temperature (minimum and maximum),

24 Table 2.1: Price elasticity (IV) for Swiss AOC wines

(1) (2) (3) (4) (5) (6) IV IV IV IV IV FE (clima-s) (harvest) (clima-d) (II-var.) (promo)

ln(Pi,t) -2.6255*** -2.0728** -2.1492*** 1.7050*** 1.0948*** -1.7068*** (0.2580) (0.7569) (0.7444) (0.5170) (0.4189) (0.1435)

Covariates TV yes no no no no yes

Covariates TI no no no no no no

Individual FE yes yes yes yes yes yes

Time FE yes no no no no yes

Constant 27.979 no no no no no (113.3147) Observations 4010 4010 4010 4010 4010 4010 No. of labels 76 76 76 76 76 76 R-squared 0.3033 0.0692 0.1754 0.1856 0.2451 0.3617 Note: *** p<0.01, ** p<0.05, * p<0.1; clustered robust standard errors (individual) in parentheses; TV=time variant; TI=time invariant; FE=fixed effects. wind (mean), sunshine and pressure (minimum) seem to be weakly but negatively and statistically significantly associated with wine consumption.

Estimations are then disaggregated in Table 2.11 by region and color for Swiss AOC wines. All coefficients are estimated in a panel structure with individual FE as well as 2SLS methodology. We can observe a strong heterogeneity among different specifications and combinations. For example, consumption of rosé wine tends to be more sensitive to price variations (-2.65), followed by white (-1.58) and red (-1.56) wines. Tables 2.12 and 2.13 show in the same way price elasticity for non-AOC wine as well as foreign wine.

We then isolate the 10 Swiss bestselling wines (Table 2.14) estimated in time series OLS and IV regression. These types of wine represent about 75% of the total consumption of Swiss AOC wines. In IV regression, all 10 labels, except Merlot red TI, are strongly signiﬁcant, from Dôle red VS (-0.49) to Lavaux white VD (-3.63). The bestsellers altogether have a price elasticity of -1.83, very close to global Swiss AOC wines (-1.71).

25 2.5.2 Graphical analysis

For graphical analysis, we run a first regression with ln(Qi,t) as the dependent variable and ln(Pi,t) as the independent variable. In Figure 2.29 we can find a graphical presentation of the observed equilibriums between the two variables in blue and the fitted line in green. As we can observe, the coefficient is -4.15, which corresponds to a pooled OLS regression, shown in Table 2.7. In Figures 2.30 and 2.31, we then disaggregate respectively the regression by color and by region.

In Figure 2.32, applying a within transformation (demeaning) on the data, we get graphical results and a coeﬃcient of -2.09, which correspond to the FE model regression. The data are therefore demeaned as follows:

ln(Qi,t) = β0 + β1 ln(Pi,t) + ui + εi,t (2.7)

ln(Qi,t) − ln(Qi,t) = (ln(Pi,t) − ln(Pi,t))β1 + εi,t − εi (2.8)

1 PT 1 PT 1 PT where ln(Qi,t) = T t=1 ln(Qi,t), ln(Pi,t) = T t=1 ln(Pi,t), εi = T t=1 εi,t. In Figures 2.33 and 2.34, we disaggregate respectively the demeaned observations with the ﬁtted values by color and region.

Finally, in Figure 2.35, we collapse the data, keeping only the mean of each individual (between transformation) in order to get a graphical presentation, and the coeﬃcient of -4.51 corresponds to the BE model regression. Following Cameron and Trivedi (2009), the between estimator uses the cross-sectional variation, transforming the database into a short panel. Averaging over all periods, the BE model can be rewritten as:

ln(Qi,t) = β0 + ln(Pi,t)β1 + ui + εi (2.9)

In Figures 2.36 and 2.37, we then disaggregate respectively the between transformed observations by color and region.

Figures 2.38 to 2.44 give an interesting overview of the total observations, price classes as well as ﬁtted values (estimated coeﬃcient and price elasticity) for selected

26 AOC types of wines. Figure 2.38 is more difficult to interpret, given the fact that actually each observation is aggregated by color and region. We can see that the three bestsellers of Valais (Figure 2.39) have quite similar coefficients and their price elasticity varies between -2.11 for Fendant and -3.73 for Pinot noir rosé. Concerning Vaud wines (Figure 2.40) we focus logically on white wines and we see a quite different shape of price classes between the three main sub-regions by ascending order (with estimated price elasticities): La Côte (-1.82), Lavaux (-3.91) and Chablais (-1.40). For Geneva (Figure 2.41), we can note that Gamay rosé is strongly dispersed, while Gamay red and Chasselas have a very similar price interval, even if price elasticity differs quite a lot. Except for Merlot white (Bianco di Merlot), Ticino wines (Figure 2.42) do not give any significant result. This is due to the fact that there is a lot of heterogeneity (aggregation of different labels) across different supermarket brands inside Merlot red and Merlot rosé labels. Pinot noir rosé from Neuchâtel experiences a big variation in consumption in a relatively narrow price band (Figure 2.43), quite typical for a rosé wine. Chasselas seems to be in the low- and Pinot noir red in the high- interval price. Concerning the Swiss German-speaking part of the country, Grisons red wines, mostly made of Pinot noir wine grape, are well known for their quality and somewhat higher prices (Figure 2.44). Red wines from Schaffhausen and Zurich are priced in a relatively similar interval.

Switching to Swiss non-AOC wines (“country” and table wines), we note a quite high elasticity for red (-3.34) and rosé (-2.9) wines from the French-speaking part of Switzerland (Figure 2.45) but statistically insigniﬁcant for white with a lower price interval9. Goron (Figure 2.46) has a high price elasticity with a price band between 7.40