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BEERONOMICSCONFERENCE 2009

A Simple Hedonic Model of Beer Prices for Major Cities around the World

Guenter SchamelFree University of Bozen-Bolzano

School of Economics and ManagementVia Sernesi 1, I-39100 Bozen-Bolzano, Italy

Tel.: +39 0471 013170 / Fax: +39 0471 013009E-mail: [email protected]

17 May 2009

Abstract:

We present a simple empirical model of beer pricing in 40 major cities around the world. Databy patrons having visited a city and reporting actual prices paid for a pint of beer are used toestimate their willingness to pay. Key explanatory variables are the city's attractiveness beingmeasured by Anholt's City Brand Index (CBI), a full-day per diem reimbursement ratewithout lodging costs, and an indicator of the relative price level being measured by the Big-Mac Index (The Economist). We estimate that a one point increase in the CBI raises patron'swillingness to pay by about 8 cents or 2.2% at average prices. A 10 Euro increase in the perdiem rate will raise patrons willingness to pay for a pint by 16 cents or 4.3% at averageprices. Moreover, one point increase in the Big-Mac Index will on average raise patronswillingness to pay for a pint by 23 cents or 6.3%.

Key Words: hedonic pricing, beer, city brands.

Paper presented at the 1. Beeronomics Conference held in Leuven, Belgium May 27-29, 2009.
mailto:[email protected]:[email protected]


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A Simple Hedonic Model of Beer Prices for Major Cities around the World

1. Introduction

We observe that prices for a pint of beer differ significantly in major cities around the world.Patrons report eye-watering price differences up to a factor of seven on pintprice.com (e.g.Rio de Janeiro vs. Oslo). We try to explain this phenomenon using a hedonic beer pricingmodel for 40 major world cities from Amsterdam to Warsaw. Pint prices paid by pub visitorsreflect their willingness to pay and can be used in a hedonic pricing model assumed to be inequilibrium to estimate implicit prices for beers drunk in bars and pubs. We propose toexamine the drinking behavior of out of town visitors on official business trips. Wehypothesize that the key variables explaining their willingness to pay for a pint of beer drunkin a pub are the attractiveness of the city visited measured by Anholts City Brand Index(CBI) and the full day per diem reimbursement rate without lodging costs. As a controlvariable, we also include the relative price level as measured by the Big-Mac Index which ispublished annually by The Economist Intelligence Group.

2. Brief L iterature Review

Empirical studies involving hedonic pricing models typically follow Griliches (1971) orRosen (1974). Both studies imply that the price of a good is a function of its underlyingcharacteristics. Griliches approach is to estimate marginal prices for the characteristics that aproduct encompasses while Rosens procedure uses hedonic pricing to estimate the price ofcharacteristics and then uses this to estimate implicit market prices for attributes. For aconcise review on the theory of hedonic pricing models we refer to Nerlove (1995).

Numerous studies have applied pricing hedonic models to estimate implicit prices for wine.For a review on such applications on wine we refer to Schamel (2006). On the contrary, thereare only a few studies that attempt to do this for beer. Ruttanajarounsub (2007) adopts ahedonic model to study beer prices in the U.S. Beer prices are modeled as a function of beercharacteristics with the primary goal to delineate the attributes lead to premium beer prices.Estimated results show that consumers pay a premium for the country of origin as well as onthe type of beer.

Other strings of the economic literature on beer consumption have looked at advertising,market concentration, or alcohol taxes to counter the negative consequences of excessiveconsumption. Pipoblabanan (2008) analyzes the market structure and the relationship between

beer taxes and prices in the U.S. finding that the U.S. brewing industry is quite competitiveand that raising the federal taxes rather than uncoordinated policies by individual states wouldbe more effective in reducing the negative consequences alcohol consumption. Gruenewald etal (2006) examined price effects on alcohol consumption using a seemingly unrelatedregression model the impacts of price increases. Their results show that consumers respond toprice increases by altering their total consumption and by varying brand choices. Significantreductions in sales were observed in response to price increases, but these effects weremitigated by significant substitutions between quality classes. Tremblay and Martins-Filbo(2001) analyze the impact of advertising and quality decisions on price competition showingthat high quality firms will advertise more intensively than low quality firms and providing anexplanation for the coexistence of name brands (heavily advertised) vs. generic brands(sparsely advertised).

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3. Model and Data

We propose to examine the drinking behavior of out of town visitors on official business trips.We hypothesize that the key variables explaining their willingness to pay for a pint of beerdrunk in a pub are the attractiveness of the city visited measured by Anholts 2008 City BrandIndex (CBI) and a measure of the full day per diem reimbursement rate without lodging costs.

As a control variable, we include the relative price level in the city as measured by the Big-Mac Index which is published annually by The Economist newspaper.

Patrons around the world provide a useful service for empirical economists and their beer-drinking comrades at PintPrice.com. The prices of pints of beer (lager) are reported on thebasis of first-hand evidence from beer-drinkers around the world. The site is regularly updatedand provides a measure of the average price for a pint of beer in major cities around the world.

Visitors expect to pay more for food and drinks in an attractive location. There are variousindicators that measure the attractiveness of a destination. For this purpose, we have identifieda number of suitable indicators including Quality of Life (QoL), Peace, and DemocracyIndexes published by The Economist Intelligence Group as well as City Brand Index (CBI)published by Anholt-GfK Roper. Because the CBI indicator is available on a city (location)specific basis and measures indicators of particular relevance for visitors we choose to includethe CBI in our estimation. In the next paragraph, we describe what the CBI index intents tomeasure in some detail.

The CBI index is constructed through an annual survey on six dimensions, defining six sub-indexes for Presence, Place, Potential, Pulse, People, and Pre-Requisites. Presence measuresa citys international status and standing. People are asked how familiar they are with a city,whether they have actually visited or not, what the city is know for and whether it has madeimportant contributions to the world in culture, science, etc. Place examines the perceptionsabout the physical aspects of each city, e.g. how it feels to be in and to travel around the city,

how beautiful it is and what the climate is like. Potential deals with economic and educationalopportunities that each city offers. Pulse considers the appeal of a vibrant urban lifestyle as apart of each citys image. The People dimension measures the perceptions about friendliness,hospitality, and safety within a city. Pre-requisites consider the standard of basic qualities:accommodation, public goods (schools, hospitals, public transport, sports facilities etc.).

We can also reasonably expect that visitors in a city are willing to pay more for food anddrinks when they receive higher per diem reimbursement rates. Tax authorities in manycountries publish official per diem reimbursement rates for many different locations aroundthe world. These rates are typically available for lodging costs, food, drinks and incidentals aswell as for full-day, half-day, and short visits. For our estimation, we used the German full-

day per Diem for the year 2008 (24h rate, in units of 10) without lodging costs.We posit the hypothesis that economic fundamentals, i.e. a city's overall attractiveness tovisitors and/or its economic potential are key variables explaining beer prices paid in bars andpubs. Thus, we employ a full-day per diem rate and Anholts City Brand Index for 2007/08 asa composite measure for the attractiveness of world cities in our estimation. The CBI rankscities based on six sub-dimensions: presence (familiarity with the city), place (physical andclimatic attributes), pre-requisites (basic amenities such as housing, schools, hospitals etc.),people (friendliness, safety), pulse (variety of activities available), and potential (economicopportunities). Our analysis also includes a control variable in order to account for anygeneral price level effects in each city. For this purpose, we include the 2008 Big-Mac-Index(BMI) published by The Economist Intelligence Group. We estimate two log-linear models:Model 1 (Equation 1) includes the overall City Brands Index while Model 2 (Equation 2)includes thePotential sub-index of the CBI (i.e. just reflecting economic opportunities):

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(1) Log(Price) =1 +1 (CBI) +1 (BMI) +1 (per Diem) +

(2) Log(Price) =2 +2 (Potential) +2 (BMI) +2 (per Diem) +

Log-Linear forms have been applied in a number of hedonic studies including Oczkowski(1994), Nerlove (1995), and Combris et al. (1997). Moreover, an examination of thecorrelation matrices for the coefficient estimates suggests that no serious degree of multi-collinearity is present in the data set.

We hypothesize positive price effects for all independent variables included in the models andthat the per diem and BMI coefficients should not be statistically different in both models(Hypothesis 1: 1 =2 and1 = 2). Moreover, we would expect that the CBI coefficient as acomprehensive measure defining the overall attractiveness of cities (including non-economicfactors) is significantly smaller than the coefficient for the Potential sub-index reflectingpurely economic factors (Hypothesis 2: 1


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Table 2: Model 2 Estimates Dependent Variable LOG (Price)

Variable Coefficient Std. Error t-Statistic Prob.

CONSTANT -0.78475 0.3314 -2.3679 0.0234

POTENTIAL INDEX 0.09316 0.0310 3.0084 0.0048

PER DIEM RATE 0.03851 0.0183 2.1050 0.0423

BICMAC INDEX 0.06876 0.0178 3.8675 0.0004

R-squared 0.5212 Mean dependent variance 0.5215

Adjusted R-squared 0.4813 S.D. dependent variance 0.1979

S.E. of regression 0.1425 Sum squared residual 0.7311

Log likelihood 23.283 F-statistic 13.063

Method: Least Squares (Included observations: 40)

White Heteroskedasticity-Consistent Standard Errors & Covariance

Source: Own calculations.

We can use Model 1 to perform hypothesis tests on the estimated coefficients. For example, totest the hypothesis that the coefficient on the per diem and BMI terms in Model 1 are equal tothose in Model 2, we may perform a Wald test. There is no evidence to reject the nullhypothesis, suggesting that the model is robust and that a city's overall attractiveness tovisitors or its economic potential are indeed key variables explaining beer prices paid in barsand pubs of major cities around the world (confirming Hypothesis 1). While the CBI is acomprehensive measure defining implicit price for the overall attractiveness of cities, itsestimated coefficient is smaller that the coefficient for the Potential sub-index definingimplicit prices based purely on economic opportunities of cities (confirming Hypothesis 2). In

Table 3 we report the estimated price changes in the various cites in response to a one unit

change in the independent variables.

5. Summary and Conclusions

We observe that beer prices differ significantly in major cities around the world. We try toexplain this phenomenon using a hedonic beer pricing model to analyze the drinking behaviorof out of town visitors on official business trips in 40 major cities around the world. We usepint prices reported by visitors of bars and pubs to estimate their willingness to pay for beersactually purchased in these cities. We hypothesize that the key variables explaining theirwillingness to pay for a pint of beer are the attractiveness of the city visited measured byAnholts City Brand Index (CBI) and the full day per diem reimbursement rate without

lodging costs. As a control variable, we include the relative price level as measured by theBig-Mac Index based on the concept purchasing power parity (PPP).

We estimate that a one point increase in the CBI raises patron's willingness to pay by about 8cents or 2.2% at average prices paid. A 10 Euro increase in the per diem rate raises a patronswillingness to pay for a pint by 16 cents or 4.3% at average prices. Moreover, a one pointincrease in the Big-Mac Index will on average raise prices for a pint by 23 cents or 6.3%.

Our model shows that a city's attractiveness and/or economic potential are key determinantsexplaining beer prices paid in bars and pubs around the world; a sound economic result whichalso holds when the impact of the current financial crisis is taken into account.

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Table 3: Estimated Price Changes by City

Average Estimated Price Changes

Country City Price CBI Potential Per Diem BMI

Australia Melbourne 3.92 0.09 0.37 0.16 0.26

Australia Sydney 3.57 0.08 0.33 0.15 0.23 Belgium Brussels 2.54 0.06 0.24 0.10 0.17

Brazil Rio-de-Janeiro 1.11 0.02 0.10 0.05 0.07

Canada Montreal 3.59 0.08 0.33 0.15 0.24

Canada Toronto 3.95 0.09 0.37 0.16 0.26

Canada Vancouver 3.48 0.08 0.32 0.14 0.23

China Beijing 1.18 0.03 0.11 0.05 0.08

China Hong-Kong 4.18 0.09 0.39 0.17 0.27

Czech Prague 1.36 0.03 0.13 0.06 0.09

Denmark Copenhagen 4.56 0.10 0.42 0.19 0.30

Finland Helsinki 4.69 0.10 0.44 0.19 0.31 France Paris 7.00 0.16 0.65 0.29 0.46

Germany Berlin 2.32 0.05 0.22 0.09 0.15

Hungary Budapest 1.85 0.04 0.17 0.08 0.12

Iceland Reykjavik 5.31 0.12 0.49 0.22 0.35

Ireland Dublin 5.00 0.11 0.47 0.20 0.33

Italy Milan 5.67 0.13 0.53 0.23 0.37

Italy Rome 4.78 0.11 0.45 0.20 0.31

Japan Tokyo 4.29 0.10 0.40 0.18 0.28

Korea Seoul 1.98 0.04 0.18 0.08 0.13

Latvia Riga 2.81 0.06 0.26 0.11 0.18

Netherlands Amsterdam 4.79 0.11 0.45 0.20 0.31

Netherlands The-Hague 2.40 0.05 0.22 0.10 0.16

Norway Oslo 7.14 0.16 0.67 0.29 0.47

New Zealand Auckland 2.59 0.06 0.24 0.11 0.17

Poland Warsaw 2.09 0.05 0.19 0.09 0.14

South Africa Johannesburg 1.46 0.03 0.14 0.06 0.10

Russia Moscow 2.96 0.07 0.28 0.12 0.19

Singapore Singapore 4.70 0.10 0.44 0.19 0.31

Spain Barcelona 3.76 0.08 0.35 0.15 0.25

Spain Madrid 3.41 0.08 0.32 0.14 0.22

Sweden Stockholm 4.79 0.11 0.45 0.20 0.31

Switzerland Geneva 5.68 0.13 0.53 0.23 0.37

Turkey Istanbul 3.11 0.07 0.29 0.13 0.20

UK Edinburgh 3.67 0.08 0.34 0.15 0.24

UK London 3.98 0.09 0.37 0.16 0.26

UK Manchester 2.96 0.07 0.28 0.12 0.19

USA Chicago 3.27 0.07 0.30 0.13 0.21

USA New-York 3.62 0.08 0.34 0.15 0.24

World Average 3.64 0.08 0.34 0.15 0.24

Source: Own Calculation.

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References:

Anholt S. (2006). The Anholt-GMI City Brands Index: How the world sees the world's cities.Place Branding and Public Diplomacy, 2, 18-31.

Combris, P., Lecocq, S. and Visser, M. (1997), Estimation of a Hedonic Price Equation for

Bordeaux Wine: Does Quality Matter? Economic Journal, 107, 390-402.Griliches Z. (1971). "Hedonic price indexes for automobiles: an econometric analysis ofquality change" inPrice Indexes and Quality Change: Studies in New Methods ofMeasurement, Harvard University Press, Cambridge, MA.

Gruenewald, P., W. Ponicki, H. Holder, and A. Romelsj (2006). "Alcohol Prices, BeverageQuality, and the Demand for Alcohol: Quality Substitutions and Price Elasticities."Alcoholism: Clinical and Experimental Research. 30(1): 96-105.

Nerlove, M. (1995): "Hedonic Price Functions and the Measurement of Preferences: The Caseof Swedish Wine Consumers." European Economic Review, 39: 1697-716.

Oczkowski, E. (1994), A Hedonic Price Function for Australian Premium Table Wine,Australian Journal of Agricultural Economics, 38, 93-110.

Pintprice.com (2008) The price of beer in 206 countries. Accessed December 28th, 2008.

Pipoblabanan C. (2008). Essays on Parametric and Nonparametric Estimation of MarketStructure and Tax Incidence in the U.S. Brewing Industry. Ph.D. Thesis. Oregon StateUniversity.

Rosen S. (1974). "Hedonic Prices and Implicit Markets: Product Differentiation in PureCompetition."J ournal of Political Economy, 82: 35-55.

Ruttanajarounsub R. (2007). Hedonic Prices and Country of Origin Bias in the U.S. Brewing

Industry. M.Sc. Thesis. Oregon State University.Schamel, G. (2006). Geography versus Brands in a Global Wine Market. Agribusiness: AnInternational J ournal, 22(3), 363-374.

Tremblay V. and C. Martins-Filbo (2001). A model of vertical differentiation, brand loyalty,and persuasive advertising. Economics Department Working Paper. Oregon State University.

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