research_milk_consumption.docx

7/30/2019 research_milk_consumption.docx

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Arokia Rexton C, PGDMB130281stApril 13

Research on Factors

influencing Milk

Consumption


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Introduction

The global milk consumption has been rising significantly in the past few years and

is expected to rise by a compound annual growth rate (CAGR) of 2.2% over the next

three years also, according to a report by Tetra Pak, a food processing and

packaging solutions company. And India, where around 65% of the population areDeeper in the Pyramid, still majorly consumes loose milk, but that is changing,

particularly in cities. India is one of the worlds biggest milk consumer. And the milk

consumption in India is expected to notch up a compound annual growth rate

(CAGR) of 2.9% in 2011-2014, according to forecasts. Part of that growth is

expected to come from less affluent consumers buying dairy snacks and drinks in a

country where white milk sales still account for the bulk of consumption.

The report focuses on analyzing the factors affecting the milk consumption in

Chennai, since Chennai has been one of the major consumers of dairy products

among all the metropolitans in India. Chennai metro region has been expanded and

its population is growing. In order to fulfill the increasing demand, Tamil Nadu

Cooperative Milk Producers Federation (TCMPF) is planning to increase the sale of

milk by another one lakh litre per day. It is proposed to sell 11 lakh litres of milk per

day in Chennai Metro and 10.00 lakh litres of milk per day in District Unions in

various pack size and varieties.


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Selling price of Milk Products (2012-2013)

Study ObjectivesThe main objective of this study is to demonstrate the way in which econometric

analysis can be used for three different specific purposes:

(1) historical analysis - quantifying the factors which determine demand for milk and

evaluating the effects of price increases, the effectiveness of advertising, and

other marketing activity;

(2) forecasting annual Consumption

(3) Regression analysis Studying how the consumption of packaged milk changes

with the changes in the independent variables like income level, family size,

beverage drinking habit and socio-economic status.

The focus has been on analyzing the consumption pattern of the packaged milk by

the households. And how factors like income level, family size, beverage drinking

habit and socio-economic status affect the overall demand of packaged milk in

Chennai. As we know, that an increase in demand leads to an increase in the prices,which has been a common phenomenon in case of Chennai. However, data shows

that the sales has increased with the prices of the packaged milk products, which

makes the whole study an interesting subject as it is a contradiction to the law of

demand. One of the purposes of the report is also to understand the level of relation

between the milk consumption and the standard of living of the consumers, which is

clearly depicted by the income levels and the socio-economic status of the people.


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Preliminary Examination of DataThe variables analyzed in the report are as follows:-

Dependent Variable

Milk Consumed by the households in Chennai The packaged milk

consumption has been rising steadily in Chennai even though the prices of

milk are rising, which makes it an interesting area of study. The econometrics

research specifies the affect of the independent variables chosen on the

consumption of packaged milk. The milk consumption is measured in litres.

And the scale of measurement for the variable is a ratio scale.

Independent Variable

Income of the Households The monthly income is a factor which affects the

level of consumption of food items. However, studies indicate that milk is

regarded as a necessity by consumers and so is not affected by income

changes. The average income data has been used and the scale of

measurement is ratio scale. The unit of measurement used is Indian National

Rupee INR.

Socio Economic Status It is a combination of the education level, assetholdings, and the standard of living of the individuals. It is an experience in

many developed nations that an increase in the socio economic status leads

to an increase in the consumption of the individuals, some part of which is

the increase in the consumption of the necessity. So we used this variable to

study its impact on the consumption of the milk, which is a necessity. The

scale of measurement for this variable is Nominal Scale.

Tea/ Coffee drinking habits This variables states the addiction of the people

towards caffeine drinks like tea and coffee and how does it affect the demandof tea and coffee which in turn affects the demand for milk. Since Tea and

coffee are the major beverage drinks consumed by the people in India and so

it has been used as a variable to study how does it affect the consumption

pattern of packaged milk products. This is a dummy variable added to the

regression model that we are studying. And it is a categorical variable.


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Family Size The size of the family directly correlates with the consumption

of milk theoretically. However the study focuses on studying whether the

theory states right or not. The scale of measurement for this variable is again

ratio scale.

Frequency of milk purchase This variable measures the number of times the

households purchases milk. It is a clear indicator of the milk consumption

pattern among the households in Chennai. Frequency of milk purchase can

have a increasing as well as decreasing affect on the litres of milk consumed,

depending on the volume bought by the households. The scale of

measurement for this variable is a ratio scale.

Preliminary Data DescriptionThe describe command gives a detailed description about the data used for the

analysis. The output tabular column shows us that there are 304 observations in the

data. The variables list includes average milk usage per day in a household, the

total family members present, how frequent do they buy milk?, do they have the

habit of drinking tea / coffee, their socio-economic classification, their average

monthly income. The table also includes the variable labels and the data format in

which they are saved.

Summary StatisticsThe summary statistics gives us the complete statistical details about the variables

used in the analysis. The statistics shows that there are 304 observations of each

variables, their mean, standard deviation, minimum value and the maximum. The

categorical variable, indicating, whether the family members have the habit of

drinking tea or coffee acts as the dummy variable. 0 indicates that they do not have

the habit of drinking tea / coffee and 1 is vice versa. It is found that the dummy

variable dont have a significant influence in determining the variance in the

amount of milk consumed in the later part of the report. The details are listed below.

CorrelationsThe correlation matrix computes the correlation coefficients of the columns of amatrix. That is, row i and column j of the correlation matrix is the correlation

between column i and column j of the original matrix. The diagonal elements of the

correlation matrix will be 1 since they are the correlation of a column with itself. The

correlation matrix is also symmetric since the correlation of column i with column j

is the same as the correlation of column j with column i.


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The correlation matrix shows that there is a small negative correlation of .108

between the frequency of milk purchased and the amount of milk purchased and a

strong positive correlation between the income and the amount of milk consumed in

a house hold.

Base line modelAmount of milk consumed = 0 + (1 * family size) + (2 *frequency of milk

purchase) + (3 *SEC classification) + (4 *Average monthly Income)

Result of multiple regression in STATA shows that the regression model is able to

explain 58.61% of variance in the dependent variable, ie 58.61% of amount of milk

consumed per household depends on the listed independent factors at 90%

confidence interval. It is also found that frequency of milk purchase remains

insignificant in the regression model at 90% confidence interval. The result also

shows us that any change in the family size will have a significant impact on the

amount of milk consumed.

Omitting Frequency of Milk PurchaseAmount of milk consumed = 0 + (1 * family size) + (3 *SEC classification)

+ (4 *Average monthly Income)

The above model is a result of omitting the influence of frequency of milk purchase

from the base line model. There is no significant change in the value of R 2 , but

there is a small increase of .0014 in the adjusted R square and it reaches 0.5819

from 0.5805. All the other variables are significant even at 95% confidence interval.

Therefore in the following models we proceed without incorporating the effects of

the frequency of milk purchase on the amount of milk consumed.

Log-Log ModelLog(Amount of milk consumed) = 0 + log(1 * family size) + log(2

*frequency of milk purchase) + log(3 *SEC classification) + log(4*Average monthly Income)

In this model, all the variables are significant except the frequency variable, just like

the lin-lin model. The R square and the adjusted R square decreases to 0.5426 and

0.5365 respectively and the effectiveness of the model is reduced.

Log-Lin ModelLog(Amount of milk consumed) = 0 + (1 * family size) + (2 *frequency

of milk purchase) +(3 *SEC classification) + log(4 *Average monthly

Income)

The model estimates the percentage of variance in the amount of milk consumed

that is caused by i% change in the average monthly income of the family. In other


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words it is the measurement of elasticity between the milk consumption and the

income factor. Test result shows that 1% increase in their average monthly income

would lead to 1.6 % increase in the average amount of milk consumed every day at

95% confidence level.

Quadratic VariableAmount of milk consumed = 0 + (1 *( family size)2) + (2 *frequency of

milk purchase) + (3 *SEC classification) + (4 *Average monthly Income)

In the above mentioned model the family size has a better quadratic fit in the

model. This leads to the betterment of the effectiveness of the model with adjusted

R square reaching 58.80 % but the variable explains less of the variation in the

dependent variable.

Impact of Dummy VariableAmount of milk consumed = 0 + (1 * family size) + (2 *frequency of milk

purchase) + (3 *SEC classification) + (4 *Average monthly Income)+ (5 *Tea/Coffee)

The newly added variable shows whether or not the respondent has an addiction

towards coffee. This model checks whether there is an increase in the milk

consumption if there in an addiction to caffeine.

The result shows that there is a slight increase in the R square and the Adjusted R

square values due to the inclusion of the dummy variable. Thought they are not

significant, they are negatively correlated with the dependent variable.

ConclusionThe model consist of 6 independent variables explaining the variance in the

dependent variable. The factors analysed in the research constitute only for around

60% variance in the dependent variable whatsoever variations incorporated in the

model. The effect of transformations in the variables are analyzed and the results of

all the regression models are interpreted. The result shows strong relation betweenthe income and the amount of milk consumed in every household, which may not

be the case in reality. The family size, which had a positive correlation with the

amount of milk consumed in simple regression model, has a negative influence on

the dependent variable as other factors are included in the system. The paper also

proves that there is no significant relation between the addiction to caffeine and the

amount of milk consumed. The frequency of milk purchase has also proved to have


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no significant influence in the amount of milk consumed by the family. This gives an

insight that people who buy milk on alternative days or only twice in a week, have

the habit of storing and using milk. SEC category of the customer is seen to have

better influence on the amount of milk consumption than the average monthly

income. If better the standards of living more is the amount of milk and other

healthy products consumption, was the assumption behind including the incomeand SEC variables, then the standard of living should be determined by the SEC

classification rather than income at 90% confidence level.

Description

NOteacoffee byte %10.0g NO tea/coffeeIncome long %14.2f IncomeSEC byte %14.2f SECTEACOFFEE byte %14.2f TEA/COFFEEfrequency byte %14.2f frequencytotalfamilyme~s byte %14.2f total family memberstotaldailymil~e int %14.2f total daily milk usagevariable name type format label variable label

storage display valuesize: 3,344vars: 7

obs: 304Contains data

. describe

Summary Statistics

Income 304 31111.84 6470.388 21200 64000

SEC 304 6.269737 1.549919 1 8

TEACOFFEE 304 .8157895 .388295 0 1

frequency 304 4.526316 1.587689 3 7

totalfamil~s 304 4.009868 1.238772 1 11

totaldaily~e 304 1058.882 346.4388 200 3000

Variable Obs Mean Std. Dev. Min Max

. summ


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Correlation matrix explained by Scatter Plot

Distribution of Independent Variables with respect to Milk consumption

Charts


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Regression

_cons 634.3832 62.50432 10.15 0.000 511.3841 757.3823totalfamilymembers 105.8634 14.89526 7.11 0.000 76.55177 135.1751totaldailymilkus~e Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 36366019.7 303 120019.867 Root MSE = 321.19Adj R-squared = 0.1405

Residual 31155067.1 302 103162.474 R-squared = 0.1433Model 5210952.63 1 5210952.63 Prob > F = 0.0000

F( 1, 302) = 50.51Source SS df MS Number of obs = 304

. reg totaldailymilkusage totalfamilymembers


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_cons -238.9393 100.928 -2.37 0.019 -437.5612 -40.31743Income .0524321 .0030275 17.32 0.000 .0464741 .0583902

SEC 16.26448 8.522425 1.91 0.057 -.5072791 33.03624TEACOFFEE -51.53908 33.40568 -1.54 0.124 -117.28 14.20185frequency .1036065 8.342478 0.01 0.990 -16.31403 16.52124

totalfamilymembers -98.21698 16.0407 -6.12 0.000 -129.7844 -66.64958totaldailymilkus~e Coef. Std. Err. t P>|t| [95% Conf. Interval]




. reg totaldailymilkusage totalfamilymembers frequency TEACOFFEE SEC Income

Regressing without frequency factor

_cons -285.0403 80.11384 -3.56 0.000 -442.6966 -127.3841Income .0521273 .0030225 17.25 0.000 .0461793 .0580754

SEC 16.63666 8.443745 1.97 0.050 .0201895 33.25313totalfamilymembers -95.30542 15.82341 -6.02 0.000 -126.4444 -64.16649totaldailymilkus~e Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 36366019.7 303 120019.867 Root MSE = 224Adj R-squared = 0.5819



. reg totaldailymilkusage totalfamilymembers SEC Income

Log-Log Model

_cons -8.238261 .9732664 -8.46 0.000 -10.15358 -6.322941

l_income 1.486477 .0996683 14.91 0.000 1.290337 1.682618l_SEC .1086376 .0398637 2.73 0.007 .0301886 .1870866

l_frequency -.0571002 .0396761 -1.44 0.151 -.1351799 .0209796l_totalfamilymembers -.2311422 .0578603 -3.99 0.000 -.3450072 -.1172772

l_totaldailymilkus~e Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 31.8446192 303 .105097753 Root MSE = .22071Adj R-squared = 0.5365

Residual 14.5650141 299 .048712422 R-squared = 0.5426Model 17.2796051 4 4.31990128 Prob > F = 0.0000


. reg l_totaldailymilkusage l_totalfamilymembers l_frequency l_SEC l_income


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Log-Lin Model

_cons -17996.65 1047.332 -17.18 0.000 -20057.72 -15935.57

l_income 1875.339 106.4918 17.61 0.000 1665.771 2084.908SEC 13.39921 8.4704 1.58 0.115 -3.269945 30.06836

frequency 4.38183 8.270006 0.53 0.597 -11.89296 20.65662totalfamilymembers -103.4793 16.0361 -6.45 0.000 -135.0372 -71.92137totaldailymilkus~e Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 36366019.7 303 120019.867 Root MSE = 221.82

Adj R-squared = 0.5900Residual 14712483.6 299 49205.6307 R-squared = 0.5954

Model 21653536.1 4 5413384.04 Prob > F = 0.0000F( 4, 299) = 110.02

Source SS df MS Number of obs = 304

. reg totaldailymilkusage totalfamilymembers frequency SEC l_income

Quadratic Variable- Family Size

_cons -523.3907 100.1633 -5.23 0.000 -720.5049 -326.2765sq_size -10.7708 1.668011 -6.46 0.000 -14.05332 -7.488266Income .0534463 .0030424 17.57 0.000 .0474591 .0594335

SEC 16.55755 8.444634 1.96 0.051 -.0608914 33.176frequency 1.172303 8.250041 0.14 0.887 -15.0632 17.4078

totaldaily~e Coef. Std. Err. t P>|t| [95% Conf. Interval]


Residual 14786446 299 49452.9966 R-squared = 0.5934Model 21579573.8 4 5394893.44 Prob > F = 0.0000


. reg totaldailymilkusage frequency SEC Income sq_size

Dummy Variable Tea / Coffee

_cons -238.9393 100.928 -2.37 0.019 -437.5612 -40.31743Income .0524321 .0030275 17.32 0.000 .0464741 .0583902

SEC 16.26448 8.522425 1.91 0.057 -.5072791 33.03624TEACOFFEE -51.53908 33.40568 -1.54 0.124 -117.28 14.20185frequency .1036065 8.342478 0.01 0.990 -16.31403 16.52124

totalfamilymembers -98.21698 16.0407 -6.12 0.000 -129.7844 -66.64958

totaldailymilkus~e Coef. Std. Err. t P>|t| [95% Conf. Interval]




. reg totaldailymilkusage totalfamilymembers frequency TEACOFFEE SEC Income

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