Demand and Market Competitiveness of Almond Milk as a Dairy Alternative Beverage in the United States. Senarath Dharmasena. Oral Capps, Jr.

Transcription

1 Demand and Market Compettveness of Almond Mlk as a Dary Alternatve Beverage n the Unted States Senarath Dharmasena Oral Capps, Jr. Brooke Kosub Department of Agrcultural Economcs and Agrbusness, Food and Consumer Economcs Research Center (AFCERC) Texas A&M Unversty Emal: sdharmasena@tamu.edu, ocapps@tamu.edu, bkhornet2012@tamu.edu Phone: (979) Selected Paper Proposal to Southern Agrcultural Economcs Assocaton Annual Meetng, Atlanta, Georga, January 31-February 3, 2015 Copyrght 2015 by Senarath Dharmasena, Oral Capps, Jr and Brooke Kosub. All rghts reserved. Readers may make verbatm coped of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 Demand and Market Compettveness of Almond Mlk as a Dary Alternatve Beverage n the Unted States Abstract There are many dfferent types of nonalcoholc beverages avalable n the Unted States today as compared to a decade ago and the functonalty and health dmensons of beverages have changed over the years. Recently, calcum and vtamn fortfed dary alternatve beverages, such as almond mlk and soymlk have entered the market to compete wth conventonal mlk. Knowledge of prce senstvty, substtutes/complements, and demographc proflng wth respect to consumpton of dary alternatve beverages s mportant for manufacturers, retalers, advertsers, nutrtonsts, and other stakeholders from a compettve ntellgence and strategc decson-makng perspectve. Usng natonally representatve household level data from 65,000 households (Nelsen Homescan), and tobt econometrc procedure, factors affectng the demand for almond mlk for all households and households grouped by race, ethncty, regon, and ncome status wll be determned. Moreover, own-prce, cross-prce, and ncome elastctes for almond mlk delneated by selected demographc segments wll be estmated. Prelmnary analyses reveal that the own-prce elastcty of demand for almond mlk s Soymlk s found to be a substtute for almond mlk. Ths nformaton wll be useful for almond mlk manufacturers, wholesalers, and retalers for strategc prcng decsons as well as government polcy makers to mplement polces related to food consumpton and nutrton. Keywords: Almond mlk, soymlk, conventonal mlk, consumer demand, tobt, Nelsen Homescan JEL Classfcaton: D11, D12 1

3 Demand and Market Compettveness of Almond Mlk as a Dary Alternatve Beverage n the Unted States Introducton There are many dfferent types of nonalcoholc beverages avalable n the Unted States today compared to decade ago. Functonalty and health dmensons of beverages have changed over the years. On top of conventonal hydraton and refreshment functons, beverages now are fortfed wth numerous vtamns, mnerals, protens, antoxdants, favorable fatty acds, etc (BMC, 2010; 2011, 2012). Currently, calcum and vtamn fortfed dary alternatve beverages are enterng the market to compete wth dary mlk, provdng consumers an alternatve, specfcally for those who are lactose ntolerant. To strengthen the poston of ths, the new food gudelnes developed under the ChooseMyPlate, placed dary alternatves such as soymlk, rce mlk and almond mlk n the Dary Group (USDA, 2014). Ths placement rased eyebrows of dary producers and marketers n the Unted States, and t s of nterest for them to know the compettveness of dary alternatves n the dary marketplace. Dary-alternatve products represented roughly fve percent of dary launches n 2012, wth soy beng the prmary or secondary ngredent n 78 percent of them (Innova Market Insghts, 2013). However, ths trend wth respect to soy s changng as nterest s growng n dary alternatves made wth ngredents ncludng almonds, rce, oats, barley, hazelnuts and walnuts. Accordng to Chcago based market research frm, Mntel, almond mlk has overtaken soymlk over the past two years and has become Amerca s most popular plant-based mlk alternatve accountng for 4.1% of total mlk sales (KCT.org, 2014). Almond mlk now 2

4 domnates dary alternatve beverage market wth a staggerng 60% market share, whle soymlk has only about 30% share (Food Navgator, 2014). Growth n almond mlk has been attrbuted to mproved health-related clams and consumer perceptons, a flurry of almond mlk brands, appealng and convenent packagng, and a plethora of flavors avalable. Sales of dary alternatve beverages reached nearly $2 bllon n 2013, drven up largely as a result of popularty of almond mlk (The Washngton Post, 2014). Gven ths backdrop, knowledge of prce senstvty, substtutes/complements and demographc proflng wth respect to consumpton of dary alternatve beverages s mportant for manufacturers, retalers, advertsers, nutrtonsts and other stakeholders from a compettve ntellgence perspectve as well as from a strategc decson-makng perspectve. We are not aware of any past study pertanng to demand for dary alternatve beverages focusng on almond mlk n the extant lterature. The most comprehensve market research on dary alternatve beverages thus far was conducted by Dharmasena and Capps (2014). However, they focused on soymlk, and dd not consder almond mlk n ther study. Therefore, to our knowledge, our study s the frst to examne the market compettveness and demographc factors determnng U.S. demand for almond mlk. Objectves: The general objectve of ths study s to develop models that uncover the demand for almond mlk by a dverse set of consumers. The specfc objectves of ths study are to: (1) determne the factors affectng the decson to purchase almond mlk for all households as well as for households grouped by race, ethncty, regon and ncome/poverty status; (2) to estmate own- and cross-prce and ncome elastctes for almond mlk delneated by selected 3

5 demographc segments; and (3) to determne the compettveness of almond mlk vs-à-vs dary mlk and soymlk. Data and Methodology: Household purchases of soymlk, almond mlk, whte mlk and flavored mlk (expendture and quantty) and soco-economc-demographc characterstcs wll be generated for each household n the Nelsen Homescan Panel for calendar year These data represent the most recent data avalable to us. Expendture and quantty data for each household wll be aggregated to form quarterly observatons for each type of mlk and dary alternatve beverages, subsequently generatng a panel dataset. In other words we wll be usng pooled tme-seres (four quarters) and cross-sectonal data (61,000 households). Quantty data wll be standardzed n terms of lqud ounces and expendture data wll be expressed n terms of dollars. Then takng the rato of expendture to volume, we wll generate unt values (prces n dollars per ounce) for each beverage. Tryng to use a standard Heckman-type (Heckman, 1979) sample selecton correcton for censored panel data could nduce problems wth respect to calculaton of the nverse mlls rato (fxed effects or random effects model nverse mlls rato) as well as for the second-stage condtonal demand model (Vella, 1992; Vella, 1998; Verbeek and Njman, 1992; Wooldrdge, 2002). Therefore, usng the aforementoned panel n the presence of censored observatons, we wll estmate demand models for dary alternatves usng panel tobt specfcaton takng care of censorng ssue assocated wth ths data (Wooldrdge 2002). Panel tobt procedure avalable n the Stata statstcal package wll be used to model aforementoned tobt specfcatons. Quantty of soymlk, almond mlk, whte mlk and flavored mlk are consdered as dependent varables n each demand equaton. Prce of mlk and dary alternatve beverages, and host of demographc 4

6 varables (age of household head, educaton, household ncome, race, ethncty, regon, presence of chldren, poverty status) are consdered as explanatory varables n each tobt specfcaton. Fnally, we wll generate forecasts of quanttes of mlk and dary alternatve beverages consumed for all households, and households grouped by selected demographc segments. Prelmnary analyss was performed usng the tobt procedure (Tobn, 1958) for almond mlk purchases for the year 2011.We generated both condtonal and uncondtonal demand estmates pertanng to almond mlk. Heckman (1979) model only wll be able to speak to condtonal demand estmates, although n the frst stage probt analyss wll provde nformaton on consumer s probablty to purchase or not to purchase the product. Also, we use the decomposton of the beta coeffcent estmates of tobt model suggested by McDonald and Mofftt (1980) to shed lght on changes n probablty of beng above the lmt (lmt beng zero n ths paper) and changes n the value of the dependent varable f t s already above the lmt. Ths s the McDonald and Mofftt decomposton assocated wth tobt parameter estmates. For all those transactons assocated wth zero quanttes and hence zero expendtures, we do not observe any unt value or prce. However, snce we are expectng to use prce of each beverage category as explanatory varables n the tobt model, we have to mpute prce for those observatons where no prce s observed. Prce mputaton s done usng an auxlary regresson, where observed prces for each beverage are regressed on household ncome, household sze and regon where the household s located. These varables are used extensvely n the prce mputaton lterature as good nstruments n mputng prces. Once the prce for almond mlk s mputed, we use them and aforementoned explanatory varables to estmate the tobt model pertanng to almond mlk consumpton. Table 1 shows dfferent categores of explanatory varables used n ths study along wth base categores for dummy varables. 5

7 The Tobt Model The stochastc model underlyng the tobt model can be expressed as follows: (1) y = X β + u, X β + u > 0 0, X β + u 0 where = 1,2,3,.., N, the number of observatons. y s the censored dependent varable; X s the vector of explanatory varables; β s the vector of unknown parameters to be estmated; E[u X] = 0 and u ~N(0, σ 2 ). The uncondtonal expected value for y s expressed n equaton (2) and the correspondng condtonal expected value for y s shown n equaton (3), where the normalzed ndex value z s shown as z = Xβ. Also, F(z) s the cumulatve dstrbuton functon σ (CDF) assocated wth z and f(z) s the correspondng probablty densty functon (pdf). (2) E(y) = XβF(z) + σf(z) (3) E(y ) = Xβ + σ f(z) F(z) The uncondtonal margnal effect s represented by, (4) E(y) X = βf(z) The condtonal margnal effete s shown by, (5) E(y ) X f(z) = β(1 z f(z)2 F(z) F(z) 2) Furthermore, the McDonald and Mofftt (1980) decomposton relatng both change n condtonal expectatons and uncondtonal expectatons can be shown below. In other words, the total change n uncondtonal expected value of the dependent varable, y can be represented by the sum of the change n the expected value of y beng above the lmt, weghted by the 6

8 probablty of beng above the lmt and change n probablty of beng above the lmt weghted by the expected value of y beng above the lmt. (6) E(y) X = F(z) Ey + X E(y ) F(z) X Emprcal Estmaton We tred several functonal forms such as lner, quadratc and lnear-log to fnd that Lnear-Log model (we used logged prce varables n the model) outperforms other functonal forms as far as the model ft, sgnfcance of varables and loss matrces such as AIC and Schwarz crtera are concerned. The tobt model for almond mlk can be represented as follows, (7) ( Q _ Almond _ Mlk) = β + β log PRICE _ AL + β log PRICE _ SOY + β log PRICE _ WMILK 3 β AGEHH β AGEHHGT64 11 β EDUHHU 15 β WEST + β BLACK 19 β MHONLY + β FHONLY + β INCOME + β PRICE _ FMILK + β AGEHH3544 β OTHER + β HISP _ YES + β AGEPCLT 6 _ ONLY β EDUHHPC + β MIDWEST + β SOUTH + β AGEPCLT 6 _ 6 _12ONLY 23 ASIAN + β AGEHH β EMPHHPT + β EMPHHFT + β EDUHHHS β AGEPC6 _12ONLY 1 + β β AGEPC13_17ONLY β AGEPCLT 6 _13_17ONLY + β AGEPC6 _12AND13_17ONLY + β AGEPCLT 6 _ 6 _12AND13_ β AGEHH β AGEHH As such, we wll calculate both condtonal and uncondtonal margnal effects assocated wth each explanatory varable. The level of sgnfcance we wll be usng n ths study s We further conduct an F-test for demographc varable categores to fnd statstcally sgnfcant demographcs. The equatons for uncondtonal and condtonal margnal effects for the Lnear- Log model and correspondng uncondtonal and condtonal own- and cross-prce elastcty estmates are explaned below. 7

9 The uncondtonal margnal effect for the Lnear-Log model s as follows, (8) E(y) X = β F(z) PU where P U s the average prce of all observatons (uncondtonal prce) consdered. The condtonal margnal effect for the Lnear-Log model s as follows, (9) E(y ) X = β f(z) (1 PC z f(z)2 F(z) F(z) 2) Where, P C s the average prce of non-censored sample (condtonal prce). The uncondtonal own- and cross-prce demand elastctes are represented by equatons (9) and (10) respectvely. β U Q P (10) ε U = P U β U Q P (11) ε j U = P j U U F(z) U F(z) The condtonal own- and cross-prce demand elastctes are represented by equatons (11) and (12) respectvely, β C Q P (12) ε C = P C β C Q P (13) ε j C = P j C C (1 z f(z) C (1 z f(z) f(z)2 F(z) F(z) 2) f(z)2 F(z) F(z) 2) Expected Results and Dscusson: Once the condtonal demand functons are estmated for all households, and households grouped by race, ethncty, regon and ncome/poverty status, we are n poston to calculate own- and cross-prce and ncome elastctes for these segments. Ths nformaton wll reflect 8

10 the market compettveness and profles of demographcs consumng almond mlk n the Unted States. Prelmnary analyss of almond mlk data reveal that the own-prce elastcty of demand for almond mlk n the Unted States s The cross prce elastcty of almond mlk wth soymlk s 0.22, makng soymlk a substtute for almond mlk n consumpton. In the end, these results are useful for almond mlk manufacturers, wholesalers and retalers for strategc prcng decsons as well as government polcy makers to mplement polces related to food and nutrton. Also, ths nformaton wll be useful for dary processors to understand the compettve poston of dary mlk n the dary alternatve beverage marketplace. 9

11 Table 1 Descrpton of the Rght-Hand Sde Varables Used n the Econometrc Analyss Varable Explanaton PRICE Prce of Almond mlk AGEHHLT25 Age of Household Head less than 25 years (Base category) AGEHH2529 Age of Household Head between years AGEHH3034 Age of household Head between years AGEHH3544 Age of household Head between years AGEHH4554 Age of household Head between years AGEHH5564 Age of household Head between years AGEHHGT64 Age of household Head greater than 64 years EMPHHNFP Household Head not employed for full pay (Base category) EMPHHPT Household Head Part-tme Employed EMPHHFT household Head Full-tme Employed EDUHHLTHS Educaton of Household Head: Less than hgh school (Base category) EDUHHHS Educaton of Household Head: Hgh school only EDUHHU Educaton of Household Head: Undergraduate only EDUHHPC Educaton of Household Head: Some post-college EAST Regon: East (Base category) MIDWEST Regon: Central (Mdwest) SOUTH Regon South WEST Regon West WHITE Race Whte (Base category) BLACK Race Black ASIAN Race Orental RACE_OTHER Race Other (non-black, non-whte, non-orental) HISP_NO Non-Hspanc Ethncty (Base category) HISP_YES Hspanc Ethncty

12 Table 1 Contnued. Varable Explanaton NPCLT_18 No Chld less than 18 years (Base category) AGEPCLT6_ONLY Age and Presence of Chldren less than 6-years AGEPC6_12ONLY Age and Presence of Chldren between 6-12 years AGEPC13_17ONLY Age and Presence of Chldren between years AGEPCLT6_6_12ONLY Age and Presence of Chldren less than 6 and 6-12 years AGEPCLT6_13_17ONL Age and Presence of Chldren less than 6 and years Y AGEPC6_12AND13_17 Age and Presence of Chldren between 6-12 and years ONLY AGEPCLT6_6_12AND1 Age and Presence of Chldren less than 6, 6-12 and years 3_17 FHMH Household Head both Male and Female (Base category) MHONLY Household Head Male only FHONLY Household Head Female only 1

13 References: BMC, 2010, 2011, 2012, Beverage Marketng Corporaton Multple Beverage Marketplace Reports Dharmasena, S., and O.Capps, Jr. (2014) Unravelng Demand for Dary Alternatve Beverages: The Case of Soymlk. Agrcultural and Resource Economcs Revew, 43(1): Food Navgator, (2014), Almond Mlk Accounts for Two-Thrds of Sales n Plant-Based Mlk Category. Internet access: mlk-accounts-for-two-thrds-of-sales-n-plant-based-mlk-category-says-whtewave- Foods (accessed on September 3, 2014). Heckman, J. J., (1979). Sample Selecton Bas as a Specfcaton Error. Econometrca 47: Innova Market Insghts, FoodBusnessNews.net, accessed January 7, KCT.org, (2014) Demand for Almond Mlk s Growng, but s t Bad for the Planet? Internet access: (accessed on September 3, 2014) Soyfoods Assocaton of North Amerca, (2013) Soy Informaton: Sales and Trends. Internet Access: (accessed on February 12, 2013) STATA, 2013, STATA Statstcal Software, College Staton, USA The Washngton Post, (2014), Got Mlk? From a Cow or a Plant? Internet access: (acceded on September 3, 2014). USDA, (2014). What foods are ncluded n the Dary Group?. ChooseMyPlate, (nternet access on September 3, 2014: Vella, F., (1992). Smple Tests for Sample Selecton Bas n Censored and Dscrete Choce Models, Journal of Appled Econometrcs 7: Vella, F., (1998). Estmatng Models wth Sample Selecton Bas: A Survey. Journal of Human Resources 33: Verbeek, M., and T. Njman, (1992). Testng for Selectvty Bas n Panel Data Models, Internatonal Economc Revew 33: Wooldrdge, J.M., (2002). Econometrc Analyss of Cross Secton and Panel Data. The MIT Press, Cambrdge, Massachusetts