DEMAND FORECASTING FOR CONSUMER NON-DURABLE

GOODS LIKE EGGS & SOAP

Introduction:

Eggs are one of the popular items of food for non-vegetarians and semi-vegetarians. The present study

tries to use regression technique of demad forecasting to estimate the demand fuction of eggs for Raigarh

district of Chhatisgarh for various occupational groups in rural and urban areas. In this study we consider

variables like size and composition of family, family income, occupation, number of earning members etc.

Likewise for soaps we choose variables like growth in population and increase in per capita income for

regression.

Demand Forecasting for Eggs: Eggs are one of the popular items of food for non-vegetarians and semi

vegetarians. We estimate demand function for eggs for Raigarh district of Chhatisgarh for various

occupational groups in rural and urban areas. However we consider here the results for all groups

combined. In our aggregated demand functions we consider the following variables:

1. Quantity of eggs consumed (the dependent variable),

2. Size and composition of family,

3. Family income,

4. Occupation and

5. Number of earning members in the family.

In our annual demand function we include only two variables viz., (i) quantity of eggs consumed, and (ii)

per capita disposable income, for lack of data and problems of specification. The data on quantity of eggs

and per capita disposable income have been taken arbitrarily for 20 years, from 1990-2010. We estimate

the following forms of demand function:

(i) Y=a + b X (Linear)

Y= quantity of eggs consumed,

a = constant

b = intercept

X= per capita disposable income

Here we have used single equation regression model, which carries two variables one is dependent and

another one independent variable.

The form equation is like this:

(i) Y= 3.0085 + 0.0619 X R2 = 0.8569

(13.5301) (2.76)

The linear function gave a ‘consistently better fit to the data. From the above equation we find that

there is positive impact of egg consumption with rise of the income. When the income increases, there is

significant rise in the consumption demand of egg. Now, if per capita income projections are available,

the demand for eggs can be forecast for the successive years.

Demand Forecasting for Soap: For the purpose of forecasting future demand of soaps in India, the two

variables which affect the consumption significantly, namely, growth in population and increase in per

capita income can be chosen for regression. Unfortunately, the true consumption levels for soap in the past

years are not available. Table-1 shows the consumption built up only on the basis of indigenous

production from the organized sector and from imports.

Regression equation Y = - 425.5541 + 1.1756 X1 + 6.4544X2

Where Y is consumption of soap,

X1 is population, and X2 is per capita income.

Coefficient of multiple correlation R123 = 0.848

In India, quite a substantial portion of the demand for soap is met from the production in the small scale

sector and therefore any projection based on the data furnished in Table-1 cannot be truly meaningful.

Nevertheless, a multiple regression equation was obtained with the data contained in Table-1 as shown

below.

Table-1 (Apparent Consumption of Soap in India) (Tonnes)

Years Production Import Total (2 + 3) Export Apparent Consumption

(4-5)

(1) (2) (3) (4) (5) (6)

1980-81 77,255 174.50 77,429.50 282.30 77,147.20

1981-82 87,747 122.30 87,869.30 1606.20 86,263.10

1982-83 86,772 117.00 86,889.00 - 86,889.00

1983-84 82,492 96.70 83,588.70 634.30 82,954.40

1984-85 90,765 62.80 90827.80 566.00 90,261.80

1985-86 104,304 100.20 104,402.20 472.00 103,930.20

1986-87 115,198 251.60 115,449.60 310.50 115,139.10

1987-88 112,689 110.30 112,799.30 293.90 121,006.70

1988-89 127,195 43.20 127,238.20 365.90 126,872.30

1989-90 134,799 45.00 134,844.00 464.50 134,379.50

1990-91 143,805 21.70 143,826.70 628.10 143,198.60

1991-92 147,922 13.50 147,935.50 591.60 147,343.90

1992-93 151,721 1.40 151,722.40 563.10 151,159.30

1993-94 164,468 0.50 164,468.50 640.40 163,828.10

1994-95 164,402 1.50 164,403.50 920.60 163,482.90

1995-96 164,130 1.20 164,131.20 1932.00 162,199.20

The analysis of variance for the multiple regressions is as follows:

Source of Variation

Degree of Freedom

Sum of squares Mean sum of squares

F. Ratio

Due to regression

2 2233.26 1116.63 -

Residual about regression

5 872.66 174.53 6.3979

Total 7 3105.9

Since F is significant at 5 percent level, one may accept the hypothesis that the regression of Y on x 1 and

x2 is jointly linear. The coefficient of multiple correlations 0.848 indicates that the variables together

explain 72 percent of all the variance in consumption of soap during the period under review. Since the

variables are logically consistent so as to be related to the dependent variable, soap projections have been

made using the above equation and the results are given below.

Years ‘000 tonnes

1995-96 133.25

2000-01 170.42

2005-06 192.62

The projection for the year 1995-96 as given is lower than the result obtained by the trend method.