anis case study of soft drink demand estimation

7/25/2019 Anis Case Study of Soft Drink Demand Estimation

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CASE STUDY OF SOFT DRINK DEMAND ESTIMATION

Demand can be estimated with experimental data, time-series data, or cross-section data. Sara

Lee Corporation generates experimental data in test stores where the effect of an NFL-licensed

Carolina Panthers logo on Champion sweatshirt sales can be carefull examined. Demandforecasts usuall rel on time-series data. !n contrast, cross-section data is appear in "able #. Soft

drin$ consumption in cans per capita per ear is related to six-pac$ price, income per capita, and

mean temperature across the %& contiguous in the 'nited States.

"able #

Cans(Capita()r

*-Pac$ +Price

!ncome+(Capita

ean"emp. F

labama /00 /.#1 #2 **ri3ona #40 #.11 #5 */

r$ansas /25 #.12 ## *2

California #24 /.41 /4 4*

Colorado #/# /./1 #1 4/

Connecticut ##& /.%1 /5 40

Delaware /#5 #.11 /& 4/

Florida /%/ /./1 #& 5/

6eorgia /14 #.&1 #% *%

!daho &4 /.21 #* %*

!llinois ##% /.24 /% 4/!ndiana #&% /.#1 /0 4/

!owa #0% /./# #* 40

7ansas #%2 /.#5 #5 4*

7entuc$ /20 /.04 #2 4*

Louisiana /*1 #.15 #4 *1

aine ### /.#1 #* %#

arland /#5 /.## /# 4%

assachusetts ##% /./1 // %5

ichigan #0& /./4 /# %5

innesota #0& /.2# #& %#

ississippi /%& #.1& #0 *4

issouri /02 #.1% #1 45

ontan 55 /.2# #1 %%

Nebras$a 15 /./& #* %1

Ne8ada #** /.#1 /% %&

New 9ampshire #55 /./5 #& 24

New :erse #%2 /.2# /% 4%


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New exico #45 /.#5 #4 4*

New )or$ ### /.%2 /4 %&

North Carolina 220 #.&1 #2 41

North Da$ota *2 /.22 #% 21

;hio #*4 /./# // 4#

;$lahoma #&% /.#1 #* &/;regon *& /./4 #1 4#

Pennsl8ania #/# /.2# /0 40


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ultiple


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Ioth temperature and price are statisticall significant with expected signs while income is

insignificant in its effect on soft drin$ demand.

for the log-linear model J2.#/.

ean P B#04.5/ ( %&

B /./0/4

ean A B 541% ( %&

B #4&./0&2

KA(KP B -/%/.15

Price elasticit ?D B EKA(KP Eean P(ean A

?D B E-/%/.15 ( E /./0/4 ( #4&./0&2

?D B E - 2.2& elastic

Interpretation on Price Elasticit! Iased on the calculated price elasticit, the consumption on

soft drin$ is price elastic in nature. "his means that for a #M increase in price will result in more

than #M decrease in Guantit demanded for soft drin$s.

"his point elasticit at the mean price and Guantit across the states is in the elastic range, as

expected. "hese are mar$et-le8el price elasticities, so no firm beha8iour is directl implied b

this estimate. n elastic demand at the mar$et le8el does impl elastic firm-le8el demand at

comparable prices, comparable price sensiti8it, and the smaller Guantities facing each firm.

# "he coefficient for demand for soft drin$ and price of soft drin$ is in8erse relationship.

/ "he Guantit demand for soft drin$ per capita will change in opposite direction as the price of

soft drin$ change.


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2 Demand for soft drin$ will reduce b /%/.15 when price of soft drin$ change in the opposite

direction or in8erse direction.

% "he coefficient for demand for soft drin$ and income and demand for soft drin$ and

mean temperature is positi8el relationship.

4 "he Guantit demand of soft drin$ will change in same direction as the income and mean

temperature change. So that, demand for soft drin$ will increase b #.// when income per

capita increase, and demand for soft drin$ also will increase b /.12 when mean temperature

increase.

QUESTION "

;mit price from the regression eGuation and obser8e the bias introduced into the parameter

estimate for income.


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Inco#e elasticit

A B 4#%.&1 - /%/.&&P #.//) /.1/"

!ncome elasticit, ? B A() x )(AB #.//OE#5.&1(#*0.5*

B 0.#%

LogA B #.0* - 2.#1LogP 0.//Log) #.##Log"

!ncome elasticit, ? B 0.//

Interpretation on Inco#e Elasticit! Iased on the calculated income elasticit, a positi8e

income elasticit indicates that soft drin$ is a normal goods.

log ADB J 0.#* #.5/ log "?P J 0.#4/ log !NC;?

hen the independent 8ariable of Price is remo8ed from the eGuation, the h or wh not


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Dependent =ariable@ CNethod@ Least SGuaresSample@ # %&!ncluded obser8ations@ %&

%aria&le Coe''icient St() Error t*Statistic Pro&)C /4%.4*/1 %#.010&/ *.#14#/1 0.0000

!NC -4.25#*&2 /./2# -/.%0*&*5 0.0/0/


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5 10 15 20 25 30

0

50

100

150

200

250

300

350

f(x) = - 5.23x + 254.32

R = 0.11

INCOME

Q

Linear (Q)

"he graph abo8e shows the wea$ relationship between !ncome and Auantit Demanded. "hus,

the mar$eting plan should not be designed based on the income per capita factor as it does not

strongl correlated with the demand of soft drin$ cans.

>hether the mar$et the product at low income groups or otherwise, it will not affect the

Guantit demanded that much. >e strongl belie8e that the compan should not design theirmar$eting plan to relocate most canned drin$ machines into low-income neighbourhood.

!n addition, as some 8ariables i.e. price and temperature were remo8ed from the eGuation, it is

unwise to rel solel on income factor to design on mar$eting plan as there exists a bias.

!nstead of wasting resources in tring to influence a 8ariable that is wea$l related to the

dependant 8ariable, the compan should focus on other 8ariables such as pricing as the critical

component of their mar$eting plan. Since price is strongl related to Auantit Demanded, the

compan can stimulate the demand for their soft drin$ b gi8ing discounts and Qbu one, free

oneQ EI;6; promotions.

"he Rbest demand specification


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1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7

0

50

100

150

200

250

300

350

f(x) = - 311.85x + 845.67

R = 0.57

PRICE

Q

Linear (Q)

For Price, the


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30 40 50 60 70 80 90

0

50

100

150

200

250

300

350

f(x) = 4.91x - 104.03

R = 0.46

TEMPERATURE

Q

Linear (Q)

For "emperature, the

anis case study of soft drink demand estimation

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