demand for milk labels in germany: organic milk, conventional brands, and retail labels

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Demand for Milk Labels in Germany: Organic Milk, Conventional Brands, and Retail Labels Astrid Jonas Abbott GmbH & Co. KG, Ludwigshafen, Germany Jutta Roosen Technische Universita ¨t Mu ¨nchen, Alte Akademie 16, 85350 Freising-Weihenste- phan, Germany. E-mail: [email protected] ABSTRACT German milk brands have come under significant price pressure due to the introduction of retail labels at the lower price end and of organic milk as a premium product. This analysis provides elasticity estimates by milk types and analyzes sociodemographic determinants of demand. A censored system of German household demand for organic and conventional milk, further separated into retail-label and brand milk, is estimated using a two-step procedure on data from the 2000–2003 German GfK ConsumerScan Houshold Survey. Own-price elasticities of conventional milk are around unity, but the demand for organic milk is very price-elastic. Results suggest that the price of organic milk should be considered as an important marketing instrument. [JEL-Code: D12, Q11]. r 2008 Wiley Periodicals, Inc. 1. INTRODUCTION The German market for fluid milk has undergone significant changes in recent years. The supply of private labels 1 in food retailing has strongly increased over the last years, and Germany counts among the five European countries with the largest share of private labels in consumer retailing (ACNielsen, 2005). By offering private labels, retailers can distinguish themselves from their competitors (Berge` s-Sennou, Bontems, & Re´quillart, 2004). While retail brands were originally introduced on a price- competitive level as so called ‘‘no name’’ products in the early 1970s, retailers have used them recently in an attempt to place themselves in the premium-quality segment. There also is an increasing demand for premium-quality products. German general-retail markets have become very active in listing organic products on their shelves. Traditionally, specialized natural-product stores have been the primary outlet for organic food in Germany, and in 2004, they were still responsible for about 34% of organic sales (Richter, 2005). While general grocery stores are the most important shopping location for food for German consumers, selling 67% of all food (IFAV, 2001), supermarkets and discounters combined garnered only 36% for organic sales in 2004, up from 28% in 1997 (Richter, 2005). Milk is an important organic product group in general retailing. Between 2001 and 2006, the sales of organic milk increased from 39.7 Mio. liters to 92.5 Mio. liters. This is equivalent to a share of organic milk in the market for fluid milk in 2006 of about 2.7% (Personal communication, data information based on ACNielsen Retail 1 According to Berge`s-Sennou, Bontems, and Re´quillart (2004), private-label products encompass all merchandise sold under a retailer’s brand. In this article, we use the terms ‘‘private label,’’ ‘‘retail label,’’ and ‘‘retail brand’’ synonymously whereas the term ‘‘brand’’ refers to manufacturer brands only. Agribusiness, Vol. 24 (2) 192–206 (2008) r r 2008 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/agr.20155 192

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Demand for Milk Labels in Germany: Organic Milk,Conventional Brands, and Retail Labels

Astrid JonasAbbott GmbH & Co. KG, Ludwigshafen, GermanyJutta RoosenTechnische Universitat Munchen, Alte Akademie 16, 85350 Freising-Weihenste-phan, Germany. E-mail: [email protected]

ABSTRACT

German milk brands have come under significant price pressure due to the introduction ofretail labels at the lower price end and of organic milk as a premium product. This analysisprovides elasticity estimates by milk types and analyzes sociodemographic determinants ofdemand. A censored system of German household demand for organic and conventional milk,further separated into retail-label and brand milk, is estimated using a two-step procedure ondata from the 2000–2003 German GfK ConsumerScan Houshold Survey. Own-priceelasticities of conventional milk are around unity, but the demand for organic milk is veryprice-elastic. Results suggest that the price of organic milk should be considered as animportant marketing instrument. [JEL-Code: D12, Q11]. r 2008 Wiley Periodicals, Inc.

1. INTRODUCTION

The German market for fluid milk has undergone significant changes in recent years.The supply of private labels1 in food retailing has strongly increased over the lastyears, and Germany counts among the five European countries with the largest shareof private labels in consumer retailing (ACNielsen, 2005). By offering private labels,retailers can distinguish themselves from their competitors (Berges-Sennou, Bontems,& Requillart, 2004). While retail brands were originally introduced on a price-competitive level as so called ‘‘no name’’ products in the early 1970s, retailers haveused them recently in an attempt to place themselves in the premium-quality segment.There also is an increasing demand for premium-quality products. German

general-retail markets have become very active in listing organic products on theirshelves. Traditionally, specialized natural-product stores have been the primaryoutlet for organic food in Germany, and in 2004, they were still responsible for about34% of organic sales (Richter, 2005). While general grocery stores are the mostimportant shopping location for food for German consumers, selling 67% of all food(IFAV, 2001), supermarkets and discounters combined garnered only 36% fororganic sales in 2004, up from 28% in 1997 (Richter, 2005).Milk is an important organic product group in general retailing. Between 2001 and

2006, the sales of organic milk increased from 39.7 Mio. liters to 92.5 Mio. liters.This is equivalent to a share of organic milk in the market for fluid milk in 2006 ofabout 2.7% (Personal communication, data information based on ACNielsen Retail

1According to Berges-Sennou, Bontems, and Requillart (2004), private-label products encompass all

merchandise sold under a retailer’s brand. In this article, we use the terms ‘‘private label,’’ ‘‘retail label,’’

and ‘‘retail brand’’ synonymously whereas the term ‘‘brand’’ refers to manufacturer brands only.

Agribusiness, Vol. 24 (2) 192–206 (2008) rr 2008 Wiley Periodicals, Inc.

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/agr.20155

192

Panel, May 3, 2007). In 2004, the market share of organic products for all foodproducts combined was estimated at 2.5% (Richter, 2005). The competition betweenorganic milk as a premium product and of retail brands as low-price alternatives is asignificant challenge to traditional milk brands in the German market for fluid milk.The objective of this article is to analyze the demand for milk labels of differenttypes, especially focusing on organic and conventional brands.The demand for a particular product depends on its own price, the prices of close

substitutes, income, and demographic effects. The demand for organic products hasmostly been analyzed based on data from consumer interviews (e.g., Bruhn, 2002;Connor & Christy, 2004; Fricke, 1996; Govindasamy & Italia, 1999; Loureiro,McCluskey, & Mittelhammer, 2001; Thompson & Kidwell, 1998). These articlesmost often show that demand for organic products increases with household size andincome, and for Germany, Bruhn (2002) and Fricke (1996) showed that age has apositive impact on the likelihood of organic-product purchase.The use of real purchase data as collected in household panels, however, offers

important advantages over consumer interviews. In consumer demand analysis withpanel data, detailed information about actual household purchases by product iscollected together with detailed demographic information, and therefore alldeterminants of demand can be considered.The purpose of this research is to estimate the expenditure and price elasticities of

household demand for milk with or without retail label and/or organic label usinghousehold panel data covering the period 2000–2003. We distinguish conventional milkinto retail-label and branded products and separate conventional milk from organicmilk. Because organic milk is at an expenditure share of about only 1 to 2% in thedataset, organic milk was not further distinguished by retail label and brand. Thecensored demand system is estimated in a two-step procedure using a linear approximatealmost ideal demand system (LA/AIDS). To the authors’ knowledge, householddemand for organic milk has not been analyzed using German household panel data.Econometric demand system studies on organic products also are scarce in the

international context. Glaser and Thompson (1999, 2000) analyzed the demand oforganic and conventional milk and frozen vegetables using U.S. scanner data.Jorgensen (2001) analyzed the demand of organic products with Danish householdpanel data, and Wier and Smed (2000) analyzed the demand for organic productswith GfK household panel data ConsumerScan in Sweden. However, the studies ofJorgensen and Wier and Smed are not available in English. Heien and Wessells(1988, 1990) and Dong, Chung, and Kaiser (2004a) estimated econometric modelsfor conventional dairy products. Dhar and Foltz (2005) estimated demand for rBST-free and organic milk using retail scanner data in a quadratic AIDS system.The structure of this article is as follows. The next section presents the LA/AIDS

and the estimation procedures. The following section describes the data. Results arediscussed, and the conclusion is presented.

2. THE MODEL

2.1 The LA/AIDS

The demand system used for the estimation is the Almost Ideal Demand System(AIDS; Deaton & Muellbauer, 1980). We consider a two-stage budgeting approach.

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After deciding on the optimal expenditures allocated to fluid milk consumption, thehousehold allocates shares to different types i5 1,y , n of fluid milk to maximizeutility subject to the milk budget. The expenditure share of Household h on Product iin Period t, wiht, results as

wiht ¼ai þXnj¼1

gij log pjht þ bi logðxht=PhtÞ i ¼ 1; 2; . . . ; n ð1Þ

where pjht are prices, xht measures total milk expenditure, and Pht denotes the priceindex for Household h. Consumer theory imposes the following constraints on theAIDS expenditure-share equation (1):X

i

ai ¼ 1;X

i

bi ¼ 0;X

i

gij ¼ 0 ðadding upÞ ð2Þ

Xj

gij ¼ 0 ðhomogeneityÞ ð3Þ

gij ¼ gji ðsymmetryÞ ð4Þ

To incorporate sociodemographic variables into the demand system, we use themethod of demographic translation, which preserves the linearity of the system. Itassumes that the constant terms in the share equations vary across households.Hence, the constants in the share equations for goods i5 1, y , n change to

ai ¼ ri0 þXKk¼1

rikdk ð5Þ

where ri0 and the riks are parameters to be estimated, and dkt, k5 1,y , K are thesociodemographic variables. For adding up to hold, it is required that

Pni¼1ri0 ¼ 1

andPn

i¼1rik ¼ 0:

The full AIDS specification uses the translog price index defined as

ln Phtð Þ ¼ dþXN

m¼1

am ln pmhtð Þ

þXN

m¼1

XNj¼1

gmj ln pmhtð Þ ln pjht

� �ð6Þ

Using Equation 7 in Equation 1 yields a nonlinear system of equations. Because ofthe large number of sociodemographic (i.e., dummy) variables used in the systemestimation, we reduce the computational burden by using the linear approximatedAIDS (LA/AIDS). Following Moschini (1995) and Buse and Chan (2000), weconsider a linear approximation based on the Stone index (for recent applications,also see Lambert, Larue, Yelou, & Criner, 2006; Meyerhoefer, Ranney, & Sahn,2005) defined as

logP�Lht ¼Xnj¼1

wjt log pjht ð7Þ

Including the demographic translation, the finally estimated LA/AIDS forHousehold h in Period t results in a system of share equations to be estimated:

wiht ¼ri0 þXKk¼1

rikdkht þXnj¼1

gij log pjht

þbi log xht=P�Lht

� �þ eiht 8i ð8Þ

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Because the demand system is estimated for different types of fluid milk, a largepercentage of zero observations results. We correct for this by adopting the two-stepprocedure of Shonkwiler and Yen (1999).

2.2 The Two-Step Estimation of a Censored System

The two-step estimation by Shonkwiler and Yen (1999) is a corrected form of theHeien and Wessels (1990) procedure. There exist alternative procedures for dealingwith censoring in demand system estimations, such as the Amemiya-Tobin approachused by Dong, Gould, and Kaiser (2004b), the General Method of Momentsestimation procedure adjusting for panel structure in the data by Meyerhoefer et al.(2005), and the minimum distance estimation by Perali and Chavas (2000).In the approach by Shonkwiler and Yen (1999), the estimation involves two steps.

First, a probit regression is computed that determines the probability that a givenhousehold will consume the good in question. The probability is then used as aninstrument that incorporates the censoring latent variables in the second-stageestimation of the LA/AIDS.The first stage of the system is modeled as a dichotomous choice problem: The

endogenous variable is Yiht 5 1 if Household h consumes the i-th food item in Periodt and is Yiht 5 0 if the household does not consume the item in question. Thisendogenous variable is a function of exogenous variables governing the purchasingdecision for milk of Type i. In two-step estimations of censored demand systems, thepurchasing decision typically results as a function of the sociodemographiccharacteristics of the household.We acknowledge household-specific heterogeneity. As Gould and Dong (2000)

argued, accounting for heterogeneity is important for several reasons: First, whenusing microdata, heterogeneity may persist over time, as many variables such asfamily status or income do not vary over the periods considered in a panel. Thisheterogeneity can be accounted for by using sociodemographic variables or byacknowledging that the current utility level that can be achieved also depends on pastconsumption decisions. This may lead to a type of habit formation, where pastconsumption decisions serve as predictors of future purchase decisions (Keane, 1997).Brand purchase history is introduced into the analysis through consumption

quantities of each milk type in the previous year, Qiht�1, as an explanatory variable.The purchase decision is hence modeled as a function of sociodemographic variablesand past consumption:

Yiht ¼f d1ht; . . . ; dkht;Q1ht�1; . . . ;Qnht�1ð Þ i ¼ 1; . . . ; n: ð9Þ

In contrast to Gould and Dong (2000), who used dummy variables to describe pastconsumption, we use actual quantities because the system here is estimated usingannual consumption data whereas they used weekly data. In their model, purchasedecisions may be a combined effect of consumption frequency, storage, and productloyalty. Aggregating data to annual observations reduces brand purchase behaviorto a question of product loyalty.The equation is estimated as a probit model using a fixed-effect specification to

adjust for multiple observations coming from a single household. The fixed-effectspecification was chosen as the results of the Hausman test favor a fixed against

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random effects specification for the data at hand. Because of the dependence onlagged consumption, Equation 9 is estimated for the Years 2001–2003.For each Household h and Period t, the normal probability density function (pdf,

fiht) and the normal cumulative distribution function (cdf, fiht) are estimated as fiht

and Fiht, in a preliminary step to the second-stage estimation.The second step involves the consistent estimation of the corrected share equations

of the LA/AIDS system via seemingly unrelated regression (SUR) of the averagebudget share that Household h spends on Good i in Time Period t:

wiht ¼Fiht

� ri0 þXk

rikdkht þX

j

gij log pjht þ bi log xht=P�Lht

� �" #

þdifiht þ eiht; i ¼ 1; 2; 3 ð10Þ

where eiht ¼ wiht � E wiht½ �. It holds that E½eiht� 5 0, but Eiht is heteroscedastic andmay be autocorrelated. Nevertheless, the two-step procedure by Shonkwiler and Yen(1999) and Yen, Kan, and Su (2002) yields consistent, albeit inefficient, estimates.We follow Shonkwiler and Yen (1999) and estimate the system with (n�1)equations.2 This imposes the demand restrictions on latent expenditure shares, butnot on observed expenditure shares.3 A seemingly unrelated regression procedure isused for the pooled dataset.4

The elasticities are estimated following an approach suggested in Green andAlston (1990). The formulas are given next, where F is evaluated at the mean of theexplanatory variables for the first-step estimation. Details on the derivation ofelasticities can be obtained from the corresponding author:5

Expenditure elasticity:

Zi ¼ Fi �bi

wi

þ 1 ð11Þ

Uncompensated own-price elasticity:

eii ¼ Fi �gii

wi

� bi

� �� 1 ð12Þ

2When estimating the system on n�1 equations, one may face the problem that the results are not

invariant to the equation that is omitted. We therefore checked the robustness of the elasticity estimates by

rotating the product excluded. Price and expenditure elasticities of the estimated equations were stable.

Other researchers such as Yen et al. (2002) and Lambert et al. (2006) noted that the demand system is not

necessarily singular when using a two-step procedure estimate the second step SUR system for all n

equations. However, convergence of the FGLS estimator on all n equations was not assured for the dataset

at hand.3For a discussion, see Yen and Huang (2002). An alternative approach that imposes adding-up on latent

and observed expenditure shares that is based on the Amemiya-Tobin method can be found in Dong et al.

(2004b).4Panel data estimators are discussed in Dong et al. (2004a) and in Meyerhoefer et al. (2006), but are not

considered here because of the limited number of periods. Empirical robustness to the assumption was

checked by estimating the system for each of the 3 years of data separately, which yielded similar results.5These are short-term elasticities, as the impact of price and expenditure changes on future consumption

likelihood is not included in the elasticity formulas.

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Uncompensated cross-price elasticity:

eij ¼ Fi �gij � biwj

wi

!ð13Þ

Compensated own-price elasticity:

eCii ¼ eii þ�w

iZi ð14Þ

Compensated cross-price elasticity:

eCij ¼ eij þ�w

jZi ð15Þ

3. DATA

This article analyzes the demand for organic and conventional milk consumed byGerman households. We divide milk consumption not only into organic andconventional milk but conventional milk further into retail-label and brandedproducts.Data were taken from the 2000–2003 GfK ConsumerScan conducted by the GfK

Group. The data contain households’ daily milk purchases as well as socioeconomicand demographic characteristics of the households (see Table 1). Observations havebeen aggregated to yearly data to control for the large number of zero observations.The final sample covers 7,768 households after excluding households with missinginformation on important variables and households not reporting over the entire 4-year period. As lagged consumption is used as an explanatory variable in the firststage of the estimation, demand is estimated for the Years 2001–2003. The panel isbalanced, yielding 23,304 observations included in the analysis.For each product, the quantity (in liters) consumed and the amount (in euros)

spent during a year are recorded. Price is derived as the unit value. Prices fornonconsuming households are not available. While procedures exist to accommo-date missing prices (e.g., Erdem, Keane, & Sun, 1999; Griffiths & Valenzuela, 1998),they greatly complicate the implementation of the current estimation procedure. Asimple approach is taken by replacing the missing prices through regional averagescalculated on data for consuming households.6

The demographic variables include the number of household members (i.e.,household size), the age of the person heading the household in six categories (under25 years of age, 25–34, 35–44, 45–54, 55–64, and over 65), the income per month infive categories (0–999 euros, 1000–1999 euros, 2000–2999 euros, 3000–3999 euros,and more than 4000 euros), dummy variables for the profession of the householdhead (employees & civil servants, self-employed & farmer, free professions (e.g.,lawyers, medical doctors), worker and dummy variables for the lifecycle phase of thehousehold (family with children, family without children, young single, senior

6We use the regional categories of ACNielsen that partition Germany into eight regions. These regions

aggregate administrative regions (Bundeslander) into consumption relevant areas. The regions are (1)

Hamburg, Bremen, Schleswig-Holstein, and Lower Saxony; (2) North Rhine-Westphalia; (3a) Hesse,

Rhineland-Palatinate, Saarland; (3b) Baden-Wuerttemberg; (4) Bavaria; (5) Berlin; (6) Brandenburg,

Saxony-Anhalt, and Mecklenburg-West Pommerania; and (7) Thuringia and Saxony.

197DEMAND FOR MILK LABELS IN GERMANY

Agribusiness DOI 10.1002/agr

TABLE 1. Sample Statistics: Household Consumption of Milk in Germany and

Sociodemographic Characteristics, 2001–2003

Variables M SD

Quantities (liters/year)–Consuming households

Organic milk 10.65 28.58

Conventional branded milk 64.26 81.02

Conventional retail-label milk 56.37 77.25

Budget Shares–All households

Organic milk 0.01 0.08

Conventional branded milk 0.56 0.36

Conventional retail-label milk 0.43 0.36

Prices (euro/liter)–Consuming households

Organic milk 78.89 21.68

Conventional branded milk 58.24 11.11

Conventional retail-label milk 53.97 5.64

Household composition and demographics

Household size 2.62 1.21

Age of household head (dummy variables)

Under 25 (base cat.) 0.01 0.10

25–34 0.14 0.35

35–44 0.21 0.41

45–54 0.20 0.40

55–64 0.20 0.40

Over 65 0.23 0.42

Income per month (euros) dummy variables)

0–999 (base cat.) 0.08 0.26

1000–1999 0.43 0.50

2000–2999 0.34 0.47

3000–3999 0.14 0.34

More than 4000 0.02 0.13

Profession of household head (dummy variables)

Employees & civil servants (base cat.) 0.31 0.46

Worker 0.17 0.38

Self-employed & farmer 0.44 0.50

Free professions 0.01 0.09

Lifecycle of the household (dummy variables)

Family without children (base cat.) 0.47 0.50

Family with children 0.37 0.48

Young single 0.04 0.20

Senior single 0.12 0.33

Lagged Consumption (liters per year)–All households

Conventional retail-label milk 47.60 75.17

Conventional branded milk 61.59 82.60

Organic milk 0.58 7.51

Note. The source of these data is GfK Household Panel 2000–2003.

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single).7 The categories left out in the estimation are age under 25 years, incomebelow 1000 euros, employees & civil servants, and families without children.Among the consuming households (excluding zero observations), the mean annual

quantities consumed are 10.65 liters for organic milk, 64.26 liters for conventionalbranded milk, and 56.37 liters for conventional retail-label milk. The average pricesof milk are 0.79 euros/liter for organic milk, 0.58 euros/liter conventional brandedmilk, and 0.54 euros/liter for conventional retail-label milk.The average household of the sample has 2.62 members. Compared to the general

German population, there are few senior and young single households and many inthe self-employed category.8

The zero-purchase counts for the three product groups are shown in Table 2. Forconventional private-label milk, 14.2% of the observations are zero observations.That means that in more than 80% of the annual observations, households haveconsumed conventional retail-labeled milk. The zero observations for conventionalbranded milk are less in number (8.0%), but the share of zero observations fororganic milk is large.

4. RESULTS

The results of the probit estimation by maximization of the likelihood function foreach product group are presented in Table 3. Many of the variables are significant atthe 5% level.The older the household head, the higher the probability was to consume

conventional, retail-label milk or organic milk. The probability of consumingorganic milk is significantly higher when the household head is older than 55. Forconventional branded milk, there was no significant impact of age. Bruhn (2002) alsofound that the older the household head, the higher the probability of consumingorganic products.Families with children were more likely to consume retail-labeled milk while they

are less likely, compared to the families without children, to consume organic milk.Workers were more likely to consume conventional branded milk and less likely to

TABLE 2. Frequencies of Zeros

Product group

No. of zero

observations

Share of all

observations

Organic milk 21,497 92.2%

Conventional branded milk 1,874 8.0%

Conventional retail-label milk 3,297 14.2%

Total number of observations 23,304 100.0%

Note. The source of these data is GfK Household Panel 2000–2003.

7Note that the GfK defines a senior single household as a single household with a household head older

than 55 years. All other single households are young single households.8By restricting the analyzed sample to households reporting over 4 years, the sample holds fewer young

single households in employee positions. For all other sociodemographic variables, sample structure is

hardly affected.

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buy organic milk than the base category of households of employees and civilservants.Past consumption was quite significant in explaining current purchase behavior.

For both types of conventional milk, past consumption increased the purchaselikelihood in a given year. However, for organic milk, a household was less likely topurchase organic milk if it consumed organic milk in the previous period. There maybe two reasons to explain this surprising result. First, availability of organic milk ingeneral retailing was still quite sporadic over the period analyzed; hence, consumersmay not always find the product when shopping. Those who shop to specifically buyorganic milk may directly turn to specialized natural product stores, which were notcovered in the current dataset. Second, milk is a largely homogeneous product, andconsumers of organic milk may be disappointed by the lack of differentiation inproduct attributes as opposed to process attributes and hence not buy organic milkagain. The large price difference may be too high to be seen as justified.

TABLE 3. First-Step Probit Estimates

Organic milk Conventional BMd Conventional RLMc

Coeff. t Coeff. t Coeff. t

Household size �0.097 �0.64 0.014 0.10 �0.190 �1.51

Age

Under 25a

b b b

25–34a 0.720 1.22 �0.453 �1.03 0.022 0.07

35–44a 0.931 1.48 �0.733 �1.50 0.301 0.79

45–54a 1.003 1.51 �0.847 �1.53 1.058�� 2.46

55–64a 2.790�� 3.67 �0.325 �0.53 1.030�� 2.09

more than 65a 3.430�� 4.31 �0.518 �0.80 1.059�� 2.01

Income (euros) o1000 b b b

1000–1999 �0.439 �1.28 �0.618�� �2.23 �0.196 �0.89

2000–2999 �0.315 �0.86 �0.824�� �2.61 �0.307 �1.22

3000–3999 0.090 0.23 �0.810�� �2.13 �0.107 �0.36

Z4000 1.117�� 2.25 �1.076�� �2.20 �0.389 �1.06

Employees & civil servants b b b

Worker �0.857�� �2.42 0.743�� 2.51 �0.226 �0.92

Self-employed & farmer �0.061 �0.26 0.296 1.33 0.084 0.46

Free professional 11.176 0.01 6.105 0.00 5.839 0.00

Family without children b b b

Family with children �0.507� �1.77 0.183 0.71 0.425�� 2.20

Young single �0.353 �0.84 0.103 0.23 �0.606 �1.49

Senior single �0.504 �1.18 �0.465 �1.41 �0.184 �0.56

Q(t�1) Organic milk �0.024�� �2.99 �0.019�� �1.64 �0.010� �1.73

Q(t�1) Conv. BM �0.001 �1.60 0.005�� 3.78 �0.001� �1.88

Q(t�1) Conv. RLM 0.001 1.12 �0.003�� �3.44 0.003�� 2.62

Note. The source of these data is GfK Household Panel 2000–2003.��significant at 0.05; �0.10.aAge of household head.bDummy category not included in the estimation to avoid dummy variable trap.cRetail-label milk.dBranded milk.

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Table 4 presents the results of the LA/AIDS estimations. The system (10) wasestimated for organic milk and conventional branded milk. Parameters forconventional retail-labeled milk are calculated from the constraints on the demandsystem. Note that the method of demographic scaling requires the parameters on thesociodemographic variables to sum to zero across all three equations.

TABLE 4. Second-Step Estimates of LA/AIDS

Variable Organic milk Conventional BM Conventional RM

Coeff. t Coeff. t Coeff.c t

Constant 0.0116�� 16.84 0.4955�� 34.28 0.4928�� 25.33

Log(price conv. RLM) �0.1295�� �13.30 0.0055 0.72 0.1240�� 2.96

Log(price conv. BM) 0.0055 0.72 0.0999�� 7.38 �0.1053�� �5.33

Log (price org. milk) 0.1240�� 13.56 �0.1053�� �7.69 �0.0187 �0.47

Log (X/P) �0.0075�� �5.70 �0.0248�� �10.85 0.0323�� 8.80

Household size 0.0076�� 2.29 0.0154�� 4.25 �0.0231�� �2.43

Age

under 25 b b b

25–34a 0.0634�� 3.32 0.1842�� 11.26 �0.2477�� �4.69

35–44a 0.0572�� 3.38 0.2268�� 13.83 �0.2841�� �7.08

45–54a 0.0132 0.91 0.2638�� 16.59 �0.2769�� �6.25

55–64a 0.0088 0.73 0.1938�� 11.73 �0.2026�� �5.60

older than 65a �0.0135 �1.13 0.2311�� 13.42 �0.2176�� �6.02

Income (euros)

o1000 b b b

1000–1999 0.0170�� 3.19 0.1553�� 16.91 �0.1723�� �11.892000–2999 0.0188�� 3.26 0.2015�� 20.22 �0.2203�� �14.283000–3999 0.0136�� 2.17 0.2000�� 17.49 �0.2136�� �12.77Z4000 0.0045 0.51 0.2628�� 12.35 �0.2674�� �9.63

Employees & civil servants b b b

Worker �0.0286� �1.87 �0.0941�� �13.90 0.1227�� 5.46

Self-employed & farmer �0.0111�� �2.20 �0.0362�� �5.12 0.0474�� 4.25

Free professional �0.0584�� �6.88 �0.2904�� �12.52 0.3488�� 10.07

Family without children b b b

Family with children �0.0163 �1.62 �0.0406�� �4.53 0.0570�� 2.97

Young single �0.0153 �0.82 0.0117 0.88 0.0036 0.12

Senior single 0.0306�� 5.48 0.1082�� 11.76 �0.1388�� �8.95

Year

2001 b b b

2002 0.0127�� 3.65 0.0030 0.55 �0.0157� �1.92

2003 0.0063� 1.78 �0.0271�� �4.83 0.0286�� 2.48

f �0.0630�� �12.03 �1.0746�� �43.68 1.1375�� 32.79

Note. The source of these data is GfK Household Panel 2000–2003.

Organic milk: R2 5 0.024, Conv. BM: R2 5 0.0254.��significant at 0.05; *0.10.aAge of household head.bDummy category not included in the estimation to avoid dummy variable trap.cCoefficients calculated from imposing demand system restrictions. The t values were obtained in

bootstrap with N5 500.

201DEMAND FOR MILK LABELS IN GERMANY

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Household size had a positive impact on the budget share of conventional brandedand organic milk. Previous studies also have found that household size is positivelycorrelated with buying propensity for organic foods (Wier, Hansen, MoerchAndersen, & Millock, 2002).Families with children had a higher budget share for conventional retail milk and a

lower budget share for conventional branded milk. Senior single households had ahigher demand for organic milk and conventional branded milk. This effectreinforces the age effect for conventional branded milk. Compared to the group ofemployees and civil servants, all other professional groups exhibited a higher budgetshare for conventional retail-labeled milk. Income had a significant positive impacton the budget share allocated to organic milk and branded milk. The impact of thedemographic variables confirms that organic milk and conventional branded milkare competing for the same type of consumers, who have higher income, live in largerhouseholds, and are older.The elasticities are calculated at the sample means of explanatory variables (see

Equations 11–15). For statistical inference, standard errors for elasticities arecalculated by bootstrapping with n5 500. The price and expenditure elasticities forall milk groups are presented in Table 5. Most of the estimated elasticities aresignificant at the 5% level. The own-price elasticities are all negative and vary aboutunity for the conventional milk with brand and retail label. The own-price elasticityof organic milk is very large in absolute value at �10.166. Glaser and Thompson(1999, 2000) and Wier et al. (2002) also found that high-price sensitivity exists fororganic products, and and Dhar and Foltz (2005) found the same for rBST-free milk;however, Joergensen (2001) estimated lower own-price elasticities. The result can beexplained by two observations. First, remember that demand for fluid milk is highly

TABLE 5. Price and Expenditure Elasticities

Price elasticities

Product groups Organic milk Conv. BM Conv. RLM Expenditure elasticities

Uncompensated

Organic milk �10.166�� 1.595 7.841�� 0.730��

(3.565) (1.258) (2.877) (0.166)

Conv. BM 0.009 �0.955�� �0.043�� 0.989��

(0.007) (0.008) (0.009) (0.001)

Conv. RLM 0.151�� �0.181�� �1.010�� 1.041��

(0.056) (0.028) (0.056) (0.005)

Compensated

Organic milk �9.853�� 1.909 8.249��

(3.579) (1.263) (2.851)

Conv. BM 0.434�� �0.402�� �0.032��

(0.007) (0.008) (0.009)

Conv. RLM 0.732�� �0.170�� �0.562��

(0.056) (0.028) (0.056)

Note. The source of these data is GfK Household Panel 2000–2003.

Asymptotic standard errors obtained in a bootstrap with N5 500 are reported in parentheses.��significant at 0.05; *0.10.

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disaggregated in this analysis. Demand for a particular type of milk may be moreelastic than demand for milk in general because close substitutes exist.9 Second, theresult demonstrates the sensitivity to the price premium charged for organic milk andthat the price corridor in which a market share for organic milk can be expanded ingeneral retailing is very narrow. Remember that the data used here only coverpurchases in general retailing, which represent just 36% of organic sales in Germany.Dedicated consumers of organic products may hence be underrepresented in thisdataset. Together with the results from the first-step estimation, this suggests thatconsumers shop for organic milk if it is reasonably low priced. If the price premiumis too high, demand will collapse. Given that the convinced consumers of organicproducts often buy their products in specialized organic stores (not analyzed here),the consumer segment identified here are the occasional buyers of organics. Thoseoccasional organic buyers are, as the results show, extremely price sensitive.Cross-price elasticities were calculated, and most of them are statistically

significant. The cross-price elasticities show that mainly conventional retail-labeledmilk exerts a strong competition to organic milk, with a cross-price elasticity of7.841. In addition, the cross-price elasticity to conventional branded milk is fairlyhigh at 1.595, but not statistically significant. The other cross-price elasticities arequite small, even if most of them are significantly different from zero.The expenditure elasticities for all milk groups are positive and statistically

significant at the 5% level. They also vary about unity, and only the expenditureelasticity of organic milk is at 0.730, considerably smaller than 1.

5. CONCLUSION

Food manufacturers and retailers undertake great efforts to differentiate theirproduct lines. They employ different forms of labels describing specific processattributes such as organic or region of origin, or concurrently sell retail labels andmanufacturer brands. The demand for organic products has increased over the lastfew years in German general retailing; however, empirical studies on organicproducts estimating theoretically consistent demand models with actual purchasedata remain scarce. Most of the existing literature is based on consumer interviews.This study presents the first results on German organic-milk demand usinghousehold scanner data. Our results show that consumers exhibit very differentprice sensitivities regarding organic and conventional milk. Demand for organic milkis highly price-elastic whereas price elasticities for conventional milk are around �1.The analysis of sociodemographic determinants shows that both conventionalbranded milk and organic milk compete for the same households shopping forpremium products: the older, high-income households.Estimated elasticities show that the expenditure elasticities for milk across

conventional brands and retail labels are quite similar at unity, but lower for organicmilk. The own-price elasticities of conventional milk vary about �1. The resultsconform closely with other food demand estimations in Germany. For example,based on data of the German household income and expenditure survey of 1993,Thiele (2001) found milk own-price elasticities for Germany of �0.99 and as an

9Dhar et al. (2005) also found quite large price elasticities when considering demand for specific

beverage brands.

203DEMAND FOR MILK LABELS IN GERMANY

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expenditure elasticity, 1.06. Results based on newer data for 2003 yield estimates of�0.98 and 0.95, respectively (Thiele, 2007). The demand for organic milk, however,is highly price-elastic. Organic milk is a new premium product introduced in Germangeneral retailing, but it is priced at a level that makes consumers very reactive toprice changes.We also have identified a habit-formation effect in the consumption of branded

and retail-labeled milk. For organic milk, our results indicate that past consumptionreduces the likelihood of current consumption. In conclusion, if retailers wish toincrease the segment of organic milk in general retailing, more needs to be done tokeep the households who once tried organic milk as consumers. The large priceelasticity shows that price will be an important marketing instrument that needs tobe considered.

ACKNOWLEDGMENTS

We gratefully acknowledge data provided by the GESELLSCHAFT FURKONSUMFORSCHUNG (GfK GROUP) within the project ‘‘Food Retail Pricing’’at the University of Kiel. We thank S.T. Yen, C. Weiss, J.P. Loy, and twoanonymous reviewers for helpful comments on earlier drafts of the article. Allremaining errors are our own.

REFERENCES

Berges-Sennou, F., Bontems, P., & Requillart, V. (2004). Economics of private labels: Asurvey of literature. Journal of Agricultural & Food Industrial Organization, 2, 1–23.

Bruhn, M. (2002). Die Nachfrage nach Bioprodukten. Eine Langzeitstudie unter besondererBerucksichtigung von Verbrauchereinstellungen. [The demand for organic products. A long-term study with particular consideration to consumer attitudes.]. Frankfurt am Main,Germany: Peter Lang.

Buse, A., & Chan, W.H. (2000). Invariance, price indices and estimation in almost idealdemand systems. Empirical Economics, 25(3), 515–539.

Conner, D., & Christy, R. (2004). The organic label: How to reconcile its meaning withconsumer preferences. Journal of Food Distribution Research, 35(1), 40–43.

Deaton, A.S., & Muellbauer, J. (1980). An almost ideal demand system. American EconomicReview, 70(June), 312–326.

Dhar, T., Chavas, J.-P., Cotterill, R.W., & Gould, B.W. (2005). An econometric analysis ofbrand-level strategic pricing between Coca-Cola Company and PepsiCo. Journal ofEconomics and Management Strategy, 14(4), 905–931.

Dhar, T., & Foltz, J. (2005). ‘‘Milk by any other namey. Consumer benefits from labelledmilk. American Journal of Agricultural Economics, 77(1), 214–228.

Dong, D., Chung, C., & Kaiser, H.M. (2004a). Modelling milk purchasing behaviour with apanel data double-hurdle model. Applied Economics, 36, 769–779.

Dong, D., Gould, B.W., & Kaiser, H.M. (2004b). Food demand in Mexico: An application ofthe Amemiya-Tobin approach to the estimation of a censored food system. AmericanJournal of Agricultural Economics, 86(4), 1094–1107.

Erdem, T., Keane, M.P., & Sun, B. (1999). Missing price and coupon availability data inscanner panels: Correcting for the self-selection bias in choice model parameters. Journal ofEconometrics, 89, 177–196.

Fricke, A. (1996). Das Kauferverhalten bei Okoprodukten. Eine Langsschnittsanalyse unterBerucksichtigung des Kohortenkonzeptes. [The purchasing behavior for organic products.A cross-section analysis using the concept of cohorts.]. Frankfurt am Main, Germany:Peter Lang.

204 JONAS AND ROOSEN

Agribusiness DOI 10.1002/agr

Glaser, L.K., & Thompson, G.D. (1999, August). Demand for organic and conventionalfrozen vegetables. Paper presented at the annual meeting of the American AgriculturalEconomics Association, Nashville, TN.

Glaser, L.K., & Thompson, G.D. (2000, June 29–July 1). Demand for organic andconventional beverage milk. Paper presented at the annual meeting of the WesternAgricultural Economics Association, Vancouver, BC.

Gould, B.W., & Dong, D. (2000). The decision of when to buy a frequently purchased good: Amultiperiod probit model. Journal of Agricultural and Resource Economics, 25(2),636–652.

Govindasamy, R., & Italia, J. (1999). Predicting willingness to pay a premium for organicallygrown fresh produce. Journal of Food Distribution Research, 30(2), 44–53.

Green, R., & Alston, J. (1990). Elasticities in AIDS models. American Journal of AgriculturalEconomics, 72(2), 442–445.

Griffiths, W., & Valenzuela, M.R. (1998). Missing data from infrequency of purchase:Bayesian estimation of a linear expenditure system. In T.F. Fomby & R.C. Hill (Eds.),Messy data-missing observations, outliers, and mixed-frequency data: Advances ineconometrics (Vol. 13, pp. 75–102). Stamford, CT; London: JAI Press.

Heien, D.M., & Wessells, C.R. (1988). The demand for dairy products: Structure, prediction,and decomposition. American Journal of Agricultural Economics, 70, 219–228.

Heien, D.M., & Wessells, C.R. (1990). Demand systems estimation with microdata: Acensored regression approach. Journal of Business and Economic Statistics, 8(July),365–371.

IFAV. (2001). Verbraucherverhalten beim Lebensmittelkauf. Koln. ITC. (2002). Overviewworld markets for organic food and beverages (Estimates). Retrieved April 21, 2006, fromhttp://www.intracen.org/mds/sectors/organic/overview.pdf

Jorgensen, C. (2001). Prisbildning och Efterfragan pa Ekologiska Livsmedel. [Price formationand demand for organic food.]. Livsmedelsekonomiska institutet, Lund.

Keane, M.P. (1997). Modeling heterogeneity and state dependence in consumer choicebehavior. Journal of Business and Economic Statistics, 15(3), 310–327.

Lambert, R., Larue, B., Yelou, C., & Criner, G. (2006). Fish and meat demand in Canada:Regional differences and weak separability. Agribusiness, 22(2), 175–199.

Loureiro, M., McCluskey, J., & Mittelhammer, R.C. (2001). Assessing consumer preferencesfor organic, eco-labeled, and regular apples. Journal of Agricultural and ResourceEconomics, 26(2), 404–416.

Meyerhoefer, C.D., Ranney, C.K., & Sahn, D.E. (2005). Consistent estimation of censoreddemand systems using panel data. American Journal of Agricultural Economics, 87(3),660–672.

Moschini, G. (1995). Units of measurement and the Stone index in demand system estimation.American Journal of Agricultural Economics, 77(1), 63–68.

Nielsen, A.C. (2005). The power of private labels 2005. A review of growth trends around theworld. Retrieved April 21, 2006, from http://www.acnielsen.de/pubs/documents/ThePowerofPrivateLabel2005.pdf

Perali, F., & Chavas, J.-P. (2000). Estimation of censored demand equations from large cross-section data. American Journal of Agricultural Economics, 82(4), 1022–1037.

Richter, T. (2005). The organic market in Germany—Overview and information on marketaccess. Geschaftsstelle Bundesprogramm okologischer Landbau in der Bundesanstalt furLandwirtschaft und Ernahrung. Retrieved May 17, 2007, from http://www.oekolandbau.de/fileadmin/redaktion/bestellformular/pdf/031105.pdf

Shonkwiler, J.S., & Yen, S.T. (1999). Two-step estimation of a censored system of equations.American Journal of Agricultural Economics, 81, 972–982.

Thiele, S. (2001). Ausgaben- und Preiselastizitaten der Nahrungsmittelnachfrage auf Basis vonQuerschnittsdaten: Eine Systemschatzung fur die Bundesrepublik Deutschland. [Expendi-ture and price elasticities of food demand on the basis of cross-section data: A systemanalysis for Germany.]. Agrarwirtschaft, 50, 108–115.

Thiele, S. (2007). Elastizitaten der Nachfrage nach Nahrungsmitteln. [Elasticities of thedemand for food.]. Unpublished manuscript, University of Kiel, Department of FoodEconomics and Consumption Studies.

205DEMAND FOR MILK LABELS IN GERMANY

Agribusiness DOI 10.1002/agr

Thompson, G., & Kidwell, J. (1998). Explaining the choice of organic produce, cosmeticdefects, prices and consumer preferences. American Journal, 7–287.

Wier, M., Hansen, L.G., Moerch Andersen, L., & Millock, A. (2002, September). Consumerpreferences for organic foods, OECDWorkshop on Organic Agriculture, Washington, DC.Retrieved January 5, 2005, from http://www.akf.dk/organicfoods/conference/OECDpaper.pdf

Wier, M., & Smed, S. (2000). Forbrug af økologiske fødevarer. Del 2: Modellering afefterspørgslen. [Consumption of organic food. Part 2.]. Rapport nr. 319, DanmarksMiljøundersøgelser, København.

Yen, S.T., & Huang, C.L. (2002). Cross-sectional estimation of U.S. demand for beefproducts: A censored demand system. Journal of Agricultural and Resource Economics,27(2), 320–334.

Yen, S.T., Kan, K., & Su, S.-J. (2002). Household demand for fats and oils: Two-stepestimation of a censored demand system. Applied Economics, 14, 1799–1806.

Astrid Jonas is product manager at Abbott GmbH & Co. KG, Ludwigshafen, Germany.

Jutta Roosen is a professor, Chair of Marketing and Consumer Research at the Technical

University of Munich.

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