80
CHAPTER IV
SOCIO ECONOMIC ASPECTS OF SAMPLE TAPIOCA
CULTIVATORS AND THEIR PROBLEMS IN PRICE FIXATION – AN
ANALYSIS
The quality of social research is judged on the basis of how the sample is
selected and whether it represents the population. Hence an attempt is made to
analyse the sample regarding its representation of various socio economic
characteristics of the society. The profile of sample would reveal how well the
sample has been, in representing cross- sections of the population.
AGE WISE CLASSIFICATION OF RESPONDENTS
Tapioca cultivators are classified on the basis of age group and presented
in Table 4.1.
81
TABLE 4.1
AGEWISE CLASSIFICATION OF SAMPLE TAPIOCA CULTIVATORS
Age
Group
Small Cultivators
Using
Using
Total
irrigated dry
land
land
Below 20
3
4
7
Row %
(42.86) (57.14)
(100)
Column%
(4.41)
(7.41) (5.74)
20 – 40
57
40
97
Row %
(58.76) (41.24)
(100)
Column% (83.82) (74.07) (79.50)
40-60
6
8
14
Row %
(42.86) (57.14)
(100)
Column%
(8.83) (14.81) (11.48)
60 &
2
2
4
above
Row %
(50)
(50)
(100)
Column%
(2.94)
(3.71) (3.28)
Total
68
54
122
Row %
(55.74) (44.26)
(100)
Column%
(100)
(100)
(100)
Source: Primary Data
Medium Cultivators
Using
Using
Total
irrigated dry
land
land
2
3
5
(40)
(60)
(100)
(12.50)
(15) (13.89)
7
11
18
(38.89) (61.11)
(100)
(43.75)
(55)
(50)
5
4
9
(55.56) (44.44)
(100)
(31.25)
(20)
(25)
2
2
4
(50)
(50)
(12.50)
(10)
16
20
(44.44) (55.56)
(100)
(100)
Note: Figures in parentheses represent percentage to total
(100)
(11.11)
36
(100)
(100)
Large Cultivators
Using
Using
Total
irrigated dry
land
land
2
3
5
(40)
(60)
(100)
(12.50) (11.54) (11.90)
9
8
17
(52.94) (47.06)
(100)
(56.25) (30.77) (40.48)
3
13
16
(18.75) (81.25)
(100)
(18.75)
(50) (38.10)
2
2
4
(50)
(12.50)
16
(38.10)
(100)
(50)
(7.69)
26
(61.90)
(100)
(100)
(9.52)
42
(100)
(100)
Total
Using
Using
Total
irrigated dry
land
land
7
10
17
(41.18) (58.82) (100)
(7)
(10)
(8.5)
73
59
132
(55.30) (44.70) (100)
(73)
(59)
(66)
14
25
39
(35.90) (64.10) (100)
(14)
(25) (19.5)
6
6
12
(50)
(6)
100
(50)
(100)
(50)
(6)
100
(50)
(100)
(100)
(6)
200
(100)
(100)
82
Age wise classification of sample tapioca cultivators is shown in table
4.1. Among the sample small cultivators using irrigated land, 4.41 per cent of
them are below 20 years old; 83.82 per cent of them are 20-40 years; 8.83 per
cent of them are 40-60 years and the rest of them are 60 years and above.
Among the sample small cultivators using dry land, 7.41 per cent of them are
below 20 year old; 74.07 per cent of them are 20-40 years and 3.71 per cent of
them are 60 years and above.
Among the sample medium cultivators using irrigated land, 12.50 per
cent of them are below 20 years old; 43.75 per cent of them are 20-40 years;
31.25 per cent of them are 40-60 years and the rest of them are 60 years and
above. Among the sample medium cultivators using dry land, 15 per cent of
them are below 20 years old; 55 per cent of them are 20-40 years; 20 per cent
of them are 40-60 years and the rest of them are 60 years and above.
Among the sample large cultivators using irrigated land, 12.50 per cent
of them are below 20 years; 56.25 per cent of them are 20-40 years; 18.75 per
cent of them are 40-60 years and the rest of them are 60 years and above.
Among the sample large cultivators using dry land, 11.54 per cent of them are
below 20 years; 30.77 per cent of them are 20-40 years; 50 per cent of them are
40-60 years and the rest of them are 60 years and above.
In general, among the total sample cultivators using irrigated land, 7 per
cent of them are below 20 years old; 73 per cent of them are 20-40 years; 14
per cent of them are 40-60 years and the rest 6 per cent of them are 60 years
and above. Among the total sample cultivators using dry land, 10 per cent of
them are below 20 years old; 59 per cent of them are 20-40 years; 25 per cent
of them are 40-60 years and the rest 6 per cent of them are 60 years and above.
83
EDUCATIONAL QUALIFICATION OF RESPONDENTS
Tapioca cultivators are classified on the basis of educational
qualification and presented in Table 4.2.
84
TABLE 4.2
EDUCATIONAL QUALIFICATION OF SAMPLE TAPIOCA CULTIVATORS
Educational Small Cultivators
Qualification Using
Using
irrigated dry
land
land
SSLC
Row %
Column %
HSC
Row %
Column %
Graduates
Row %
Column %
Post
Graduates
Row %
Column%
Technical
Row %
Column %
Total
Medium Cultivators
Using
Using Total
irrigated dry
land
land
Large Cultivators
Using
Using
irrigated dry
land
land
Total
Total
Using
Using
irrigated dry
land
land
Total
12
2
(85.71) (14.29)
(17.65)
(3.70)
6
11
(35.29) (64.71)
(8.82) (20.37)
38
25
(60.32) (39.68)
(55.88) (46.30)
10
12
14
(100)
(11.48)
17
(100)
(13.93)
63
(100)
(51.64)
22
7
(70)
(43.75)
2
(20)
(12.50)
2
(40)
(12.50)
2
3
(30)
(15)
8
(80)
(40)
3
(60)
(15)
4
10
(100)
(27.78)
10
(100)
(27.78)
5
(100)
(13.89)
6
1
(33.33)
(6.25)
9
(47.37)
(56.25)
2
(25)
(12.50)
3
2
(66.67)
(7.69)
10
(52.63)
(38.47)
6
(75)
(23.08)
4
3
(100)
(7.14)
19
(100)
(45.24)
8
(100)
(19.05)
7
20
(74.07)
(20)
17
(36.96)
(17)
42
(55.26)
(42)
15
07
(25.93)
(7)
29
(63.04)
(29)
34
(44.74)
(34)
20
27
(100)
(13.5)
46
(100)
(23)
76
(100)
(38)
35
(45.45)
(14.71)
2
(33.33)
(2.94)
68
(55.74)
(100)
(100)
(18.03)
6
(100)
(4.92)
122
(100)
(100)
(33.33)
(12.50)
3
(60)
(18.75)
16
(44.44)
(100)
(66.67)
(20)
2
(40)
(10)
20
(55.56)
(100)
(100)
(16.66)
5
(100)
(13.89)
36
(100)
(100)
(42.86)
(18.75)
1
(20)
(6.25)
16
(38.10)
(100)
(57.14)
(15.38)
4
(80)
(15.38)
26
(61.90)
(100)
(100)
(16.67)
5
(100)
(11.90)
42
(100)
(100)
(42.86)
(15)
6
(37.5)
(6)
100
(50)
(100)
(57.14)
(20)
10
(62.5)
(10)
100
(50)
(100)
(100)
(17.5)
16
(100)
(8)
200
(100)
(100)
Total
Row %
Column %
Source: Primary Data
(54.55)
(22.22)
4
(66.67)
(7.41)
54
(44.26)
(100)
Note: Figures in parentheses represent percentage to total
85
Table 4.2 shows the educational qualification of sample tapioca
cultivators. Among the sample small cultivators using irrigated land, 26.47 per
cent of them have studied up to higher secondary level; 55.88 per cent of them
are graduates; 14.71 per cent of them are post graduates and the rest of them
have possessed technical qualification. Among the sample small cultivators
using dry land, 24.07 per cent of them have studied up to higher secondary
level; 46.30 per cent of them are graduates and 7.41 per cent of them have
possessed technical qualification.
Among the sample medium cultivators using irrigated land, 56.25 per
cent of them have studied up to higher secondary level; 12.50 per cent of them
are graduates; 12.50 per cent of them are post graduates and the rest 18.75 per
cent of them have possessed technical qualification. Among the sample
medium cultivators using dry land, 55 per cent of them have studied up to
higher secondary level, 15 per cent of them are graduates, 20 per cent of them
are post graduates and the rest 10 per cent of them have possessed technical
qualification.
Among the sample large cultivators using irrigated land, 62.50 per cent
of them have studied up to higher secondary level; 12.50 per cent of them are
graduates; 18.75 per cent of them are post graduates and the rest 6.25 per cent
of them have possessed technical qualification. Among the sample large
cultivators using dry land 46.16 per cent of them have studied up to higher
secondary level; 23.08 per cent of them are graduates; 15.38 per cent of them
are post graduates and the rest 15.38 per cent of them have possessed technical
qualification.
86
In general, among the total sample cultivators using irrigated land, 37
per cent of them have studied up to higher secondary level; 42 per cent of them
are graduates; 15 per cent of them are post graduates and the rest 6 per cent of
them have possessed technical qualification. Among the total sample
cultivators using dry land, 36 per cent of them have studied up to higher
secondary level; 34 per cent of them are graduates; 20 per cent of them are post
graduates and the rest 10 per cent of them have studied technical qualification.
SOCIAL CLASS OF RESPONDENTS
Though caste system in India is fast disappearing, it is still being used
for administrative purposes. People are identified by their caste system. The
government has grouped the numerous castes into four categories (i) scheduled
castes and scheduled tribes (ii) most backward class (iii) backward class and
(iv)others. The sample tapioca cultivators are grouped on the basis of caste and
presented in Table 4.3.
87
TABLE 4.3
SOCIAL CLASS OF SAMPLE TAPIOCA CULTIVATORS
Social
class
Small Cultivators
Using
Using
irrigated dry
land
land
SC/ST
8
9
Row %
(47.06) (52.94)
Column% (11.76) (16.67)
MBC
4
9
Row %
(30.77) (69.23)
Column%
(5.88) (16.67)
BC
40
24
Row %
(62.50) (37.50)
Column% (58.83) (44.44)
Others
16
12
Row %
(57.14) (42.86)
Column% (23.53) (22.22)
Total
68
54
Row %
(55.74) (44.26)
Column%
(100)
(100)
Source: Primary Data
Total
17
(100)
(13.93)
13
(100)
(10.66)
64
(100)
(52.46)
28
(100)
(22.95)
122
(100)
(100)
Medium Cultivators
Using
Using
Total
irrigated dry
land
land
2
7
9
(22.22) (77.78)
(100)
(12.50)
(35)
(25)
2
3
5
(40)
(60)
(100)
(12.50)
(15) (13.89)
7
2
9
(77.78) (22.22)
(100)
(43.75)
(10)
(25)
5
8
13
(38.46) (61.54)
(100)
(31.25)
(40) (36.11)
16
20
36
(44.44) (55.56)
(100)
(100)
(100)
(100)
Note: Figures in parentheses represent percentage to total
Large Cultivators
Using
Using
Total
irrigated dry
land
land
2
5
7
(28.57) (71.43)
(100)
(12.50) (19.23) (16.67)
7
2
9
(77.78) (22.22)
(100)
(43.75)
(7.69) (21.43)
4
13
17
(23.53) (76.47)
(100)
(25)
(50) (40.47)
3
6
9
(33.33) (66.67)
(100)
(18.75) (23.08) (21.43)
16
26
42
(38.10) (61.90)
(100)
(100)
(100)
(100)
Total
Using
Using
Total
irrigated dry
land
land
12
21
33
(36.36) (63.64) (100)
(12)
(21) (16.5)
13
14
27
(48.15) (51.85) (100)
(13)
(14) (13.5)
51
39
90
(56.67) (43.33) (100)
(51)
(39)
(45)
24
26
50
(48)
(52) (100)
(24)
(26)
(25)
100
100
200
(50)
(50) (100)
(100)
(100) (100)
88
Table 4.3 shows the social class of sample tapioca cultivators. Among
the sample small cultivators using irrigated land, 11.76 per cent of them belong
to scheduled caste and tribe; 5.88 per cent of them belong to most backward
class, 58.83 per cent of them belong to backward class and the rest 23.53 per
cent of them belong to other category. Among the sample small cultivators
using dry land, 16.67 per cent of them belong to scheduled caste and tribe;
16.67 per cent of them belong to most backward class; 44.44 per cent of them
belong to backward class and the rest 22.22 per cent of them belong to other
category.
Among the sample medium cultivators using irrigated land, 12.50 per
cent of them belong to scheduled caste and tribe; 12.50 per cent of them belong
to most backward class; 43.75 per cent of them belong to backward class and
the rest 31.25 per cent of them belong to other category. Among the sample
medium cultivators using dry land, 35 per cent of them belong to scheduled
caste and tribe; 15 per cent of them belong to most backward class; 10 per cent
of them belong to backward class and the rest of them belong to other category.
Among the sample large cultivators using irrigated land, 12.50 per cent
of them belong to scheduled caste and tribe; 43.75 per cent per cent of them
belong to most backward class; 25 per cent of them belong to backward class
and the rest 18.75 per cent of them belong to other category. Among the
sample large cultivators using dry land, 19.23 per cent of them belong to
scheduled caste and tribe; 7.69 per cent of them belong to most backward class;
50 per cent of them belong to backward class and the rest of them belong to
other category.
In general, among the total sample cultivators using irrigated land, 12
per cent of them belong to scheduled caste and tribe; 13 per cent of them
89
belong to most backward class; 51 per cent of them belong to backward class
and the rest 24 per cent of them belong to other category. Among the total
sample cultivators using dry land, 21 per cent of them belong to scheduled
caste and tribe; 14 per cent of them belong to most backward class; 39 per cent
of them belong to backward class and the rest 26 per cent of them belong to
other category.
MARITAL STATUS OF RESPONDENTS
The sample Tapioca cultivators are classified on the basis of marital
status and presented in Table 4.4.
90
TABLE 4.4
MARITAL STATUS OF SAMPLE TAPIOCA CULTIVATORS
Marital
Status
Small Cultivators
Using
Using Total
irrigated dry
land
land
Married
46
41
87
Row %
(52.87) (47.13)
(100)
Column%
(67.65) (75.93) (71.31)
Unmarried
22
13
35
Row %
(62.86) (37.14)
(100)
Column%
(32.35) (24.07) (28.69)
Total
68
54
122
Row %
(55.74) (44.26)
(100)
Column%
(100)
(100)
(100)
Source: Primary Data
Medium Cultivators
Using
Using Total
irrigated dry
land
land
8
17
25
(32)
(68)
(100)
(50)
(85) (69.44)
8
3
11
(72.73) (27.27)
(100)
(50)
(15) (30.56)
16
20
36
(44.44) (55.56)
(100)
(100)
(100)
(100)
Note: Figures in parentheses represent percentage to total
Large Cultivators
Using
Using Total
irrigated dry
land
land
12
11
23
(52.17) (47.83)
(100)
(75) (42.31) (54.76)
4
15
19
(21.05) (78.95)
(100)
(25) (57.69) (45.24)
16
26
42
(38.10) (61.90)
(100)
(100)
(100)
(100)
Total
Using
irrigated
land
66
(48.89)
(66)
34
(52.31)
(34)
100
(50)
(100)
Using Total
dry
land
69
135
(51.11)
(100)
(69) (67.50)
31
65
(47.69)
(100)
(31) (32.5)
100
200
(50)
(100)
(100)
(100)
91
Marital status of sample tapioca cultivators is shown in table 4.4. Among
the sample small cultivators using irrigated land, 67.65 per cent of them are
married and the rest 32.35 per cent of them are unmarried. Among the sample
small cultivators using dry land, 75.93 per cent of them are married and the rest
24.07 per cent of them are unmarried.
Among the sample medium cultivators using irrigated land, 50 per cent
of them are married and the rest 50 per cent of them are unmarried. Among the
sample medium cultivators using dry land, 85 per cent of them are married and
the rest 15 per cent of them are unmarried.
Among the sample large cultivators using irrigated land, 75 per cent of
them are married and the rest 25 per cent of them are unmarried. Among the
sample large cultivators using dry land, 42.31 per cent of them are married and
the rest 57.69 per cent of them are unmarried.
In general, among the total sample tapioca cultivators using irrigated
land, 66 per cent of them are married and the rest 34 per cent of them are
unmarried. Among the total sample tapioca cultivators using dry land, 69 per
cent of them are married and the rest 31 per cent of them are unmarried.
92
ANNUAL INCOME OF RESPONDENTS
Income is one of the main factors influencing tapioca cultivation. As
income increases, production of tapioca increases. Hence, an attempt is made
to segregate the sample tapioca cultivators on the basis of income classification
proposed by the National Council of Applied Economic Research (NCAER)
New Delhi and presented in the table 4.5.
93
TABLE 4.5
ANNUAL INCOME OF SAMPLE TAPIOCA CULTIVATORS
Annual
Income
Range (₹)
Upto
20000
Row %
Column%
2000140000
Row %
Column%
4000162000
Row %
Column%
6200186000
Small Cultivators
Using
Using
Total
irrigated dry
land
land
26
2
28
(92.86)
(38.24)
25
Medium Cultivators
Using
Using Total
irrigated dry
land
land
4
5
9
Large Cultivators
Using
Using
Total
irrigated dry
land
land
1
5
6
Total
Using
Using
Total
irrigated dry
land
land
31
12
43
(7.14)
(100)
(3.70) (22.95)
26
51
(44.44) (55.56)
(25)
(25)
5
4
(100)
(25)
9
(16.67) (83.33)
(100)
(6.25) (19.23) (14.29)
7
12
19
(72.09) (27.91)
(31)
(12)
37
42
(100)
(21.5)
79
(49.02) (50.98)
(100)
(36.76) (48.15) (41.80)
10
13
23
(55.56) (44.44)
(31.25)
(20)
2
4
(100)
(25)
6
(36.84) (63.16)
(100)
(43.75) (46.16) (45.24)
4
3
7
(46.84) (53.16)
(37)
(42)
16
20
(100)
(39.5)
36
(43.48) (56.52)
(100)
(14.71) (24.07) (18.85)
5
9
14
(33.33) (66.67)
(100)
(12.5)
(20) (16.67)
2
3
5
(57.14) (42.86)
(100)
(25) (11.54) (16.67)
1
2
3
(44.44) (55.56)
(16)
(20)
8
14
(100)
(18)
22
(33.33) (66.67)
(6.25) (7.69)
3
4
(36.36) (63.64)
(8)
(14)
8
12
(100)
(11)
20
Row %
(35.71)
Column%
(7.35)
Above
2
86000
Row %
(33.33)
Column
(2.94)
%
Total
68
Row %
(55.74)
Column%
(100)
Source: Primary Data
(64.29)
(100)
(16.67) (11.48)
4
6
(40)
(12.5)
3
(60)
(100)
(15) (13.89)
4
7
(100)
(7.14)
7
(66.67)
(7.41)
(100)
(4.92)
(42.86) (57.14)
(100)
(18.75)
(20) (19.44)
(42.86) (57.14)
(100)
(18.75) (15.38) (16.66)
54
(44.26)
(100)
122
(100)
(100)
16
20
(44.44) (55.56)
(100)
(100)
16
26
(38.10) (61.90)
(100)
(100)
Note: Figures in parentheses represent percentage to total
36
(100)
(100)
42
(100)
(100)
(40)
(8)
(60)
(12)
(100)
(10)
100
(50)
(100)
100
(50)
(100)
200
(100)
(100)
94
The annual income of sample tapioca cultivators is shown in table 4.5.
Among the sample small cultivators using irrigated land, 38.24 per cent of
them have income up to ₹20000; 36.76 per cent of them have income between
₹20001 and 40000; 14.71 per cent of them have income between ₹40001 and
62000; 7.35 per cent of them have income between ₹62001 and 86000 and the
rest of them have income above ₹86000. Among the sample small cultivators
using dry land, 3.70 per cent of them have income up to ₹20000; 48.15 per cent
of them have income between ₹20001 and 40000; 24.07 per cent of them have
income between ₹40001 and 62000; 16.67 per cent of them have income
between ₹62001 and 86000 and the rest 7.41 per cent of them have income
above ₹86000.
Among the sample medium cultivators using irrigated land, 25 per cent
of them have income up to ₹20000; 31.25 per cent of them have income
between₹20001 and 40000; 12.5 per cent of them have income between
₹40001 and 62000; 12.5 per cent of them have income between ₹62001 and
86000 and the rest 18.75 per cent of them have income above ₹86000. Among
the sample medium cultivators using dry land, 25 per cent of them have income
up to ₹20000; 20 per cent of them have income between ₹20001 and 40000; 20
per cent of them have income between ₹40001 and 62000; 15 per cent of them
have income between ₹62001 and 86000 and the rest 20 per cent of them have
income above₹86000.
Among the sample large cultivators using irrigated land, 6.25 per cent of
them have income up to ₹20000; 43.75 per cent of them have income between
₹20001 and 40000; 25 per cent of them have income between ₹40001 and
62000; 6.25 per cent of them have income between ₹62001 and 86000 and the
95
rest of them have income above ₹86000. Among the sample large cultivators
using dry land, 19.23 per cent of them have income up to ₹20000; 46.16 per
cent of them have income between ₹20001 and 40000; 11.54 per cent of them
have income between ₹40001 and 62000; 7.69 per cent of them have income
between ₹62001 and 86000 and the rest 15.38 per cent of them have income
above ₹86000.
In general, among the total sample cultivators using irrigated land, 31
per cent of them have income up to ₹20000; 37 per cent of them have income
between ₹20001 and 40000; 16 per cent of them have income between ₹40001
and 62000; 8 per cent of them have income between ₹62001 and 86000 and the
rest of 8 per cent of them have income above ₹86000. Among the total sample
cultivators using dry land, 12 per cent of them have income up to ₹20000; 42
per cent of them have income between ₹20001 and 40000; 20 per cent of them
have income between ₹40001 and 62000; 14 per cent of them have income
between ₹62001 and 86000 and the rest 12 per cent of them have income above
₹86000.
AMOUNT SPENT ON TRANSPORT(MONTHLY)
Though income is one of the determinants of fixing price for a produce,
transport cost incurred for transferring a produce from the field to mandi or
market also plays an important role in fixing price of a produce. Hence, an
attempt is made to categorise sample respondents on the basis of amount spent
on transport services and presented in the table 4.6.
96
TABLE 4.6
AMOUNT SPENT ON TRANSPORT BY SAMPLE TAPIOCA CULTIVATORS
Spending Small Cultivators
Range
Using
Using
Total
(Monthly) irrigated dry
land
land
(₹)
Below
44
35
79
500
Row %
(55.70) (44.30)
(100)
Column%
(64.71) (64.81) (64.75)
500-1000
20
13
33
Row %
(60.61) (39.39)
(100)
Column%
(29.41) (24.07) (27.05)
10002
3
5
1500
Row %
(40)
(60)
(100)
Column%
(2.94) (5.56) (4.10)
15001
3
4
2000
Row %
(25)
(75)
(100)
Column%
(1.47) (5.56) (3.28)
Above
1
0
1
2000
Row %
(100)
(0)
(100)
Column %
(1.47)
(0) (0.82)
Total
68
54
122
Row %
(55.74) (44.26)
(100)
Column%
(100)
(100)
(100)
Source: Primary Data
Medium Cultivators
Using
Using Total
irrigated dry
land
land
4
7
11
Large Cultivators
Using
Using
Total
irrigated dry
land
land
2
8
10
(36.36) (63.64)
(100)
(25)
(35) (30.56)
5
8
13
(38.46) (61.54)
(100)
(31.25)
(40) (36.11)
5
3
8
(20)
(80)
(100)
(12.50) (30.77) (23.81)
7
10
17
(41.18) (58.82)
(100)
(43.75) (38.46) (40.48)
4
3
7
(62.5)
(31.25)
1
(57.14) (42.86)
(100)
(25) (11.54) (16.67)
1
3
4
(37.5)
(100)
(15) (22.22)
2
3
Total
Using
Using
Total
irrigated dry
land
land
50
50
100
(50)
(50)
(50)
(50)
32
31
(50.79) (49.21)
(32)
(31)
11
9
(55)
(11)
3
(100)
(50)
63
(100)
(31.5)
20
(45)
(9)
8
(100)
(10)
11
(33.33) (66.67)
(6.25)
(10)
1
0
(100)
(8.33)
1
(25)
(75)
(6.25) (11.54)
2
2
(100)
(9.52)
4
(27.27) (72.73)
(3)
(8)
4
2
(100)
(5.5)
6
(100)
(0)
(6.25)
(0)
16
20
(44.44) (55.56)
(100)
(100)
(100)
(2.78)
36
(100)
(100)
(50)
(50)
(12.50) (4.69)
16
26
(38.10) (61.90)
(100)
(100)
(100)
(9.52)
42
(100)
(100)
(66.67) (33.33)
(4)
(2)
100
100
(50)
(50)
(100)
(100)
(100)
(3)
200
(100)
(100)
Note: Figures in parentheses represent percentage to total
97
Amount spent for transport per month by sample cultivators is shown in
table 4.6. It is clear from this table that among the sample small cultivators
using irrigated land, 64.71 per cent of them have spent below ₹500 per month
for transport; 29.41 per cent of them have spent between ₹500 and 1000; 2.94
per cent of them have spent between ₹1000 and 1500; 1.47 per cent of them
have spent between ₹1500 and 2000 and the rest 1.47 per cent of them have
spent above ₹2000. Among the sample small cultivators using dry land, 64.81
per cent of them have spent below ₹500 per month for transport; 24.07 per cent
of them have spent between ₹500 and 1000; 5.56 per cent of them have spent
between ₹1000 and 1500 and 5.56 per cent of them have spent between ₹1500
and 2000.
Among the sample large cultivators using irrigated land, 12.50 per cent
of them have spent below ₹500 per month for transport; 43.75 per cent of them
have spent between ₹500 to 1000; 25 per cent of them have spent between
₹1000 and 1500; 6.25 per cent of them have spent between ₹1500 and 2000
and the rest 12.50 per cent of them have spent above ₹2000. Among the sample
large cultivators using dry land, 30.77 per cent of them have spent below ₹500
per month for transport; 38.46 per cent of them have spent between ₹500 and
1000; 11.54 per cent of them have spent between ₹1000 and 1500; 11.54 per
cent of them have spent between ₹1500 and 2000 and the rest 4.69 per cent of
them have spent above ₹2000.
In general, among the total sample cultivators using irrigated land, 50 per cent
of them have spent below ₹500 per month for transport; 32 per cent of them have
spent between ₹500 and 1000; 11 per cent of them have spent between ₹1000 and
1500; 3 per cent of them have spent between ₹1500 and 2000 and the rest 4 per cent
of them have spent above ₹2000. Among the total sample cultivators using dry land,
98
50 per cent of them have spent below ₹500 per month for transport; 31 per cent of
them have spent between ₹500 and 1000; 9 per cent of them have spent between
₹1000 and 1500; 8 per cent of them have spent between ₹1500 and 2000 and the rest
2 per cent of them have spent above ₹2000.
REASONS FOR CHOOSING TAPIOCA CULTIVATION
India is an agricultural country. Majority of population are engaged in
agriculture. They are producing different crops. Hence an attempt is made to
know the reasons for choosing tapioca cultivation. Some of the cultivators are
producing tapioca due to long experience in the cultivation of tapioca while
others are saying that it is the major agricultural produce of their area.
Therefore the sample tapioca cultivators are classified on the basis of reasons
for choosing tapioca cultivation and presented in Table 4.7.
99
TABLE 4.7
REASONS FOR CHOOSING TAPIOCA CULTIVATION
Reasons
Major
agricultural
produce of
the area
Row %
Column%
Gives more
profit than
other crops
Row %
Column%
Long experience in the
cultivation of
tapioca
Row %
Column%
Suitability of
soil and
climate
Row %
Column%
Other reasons
Row %
Column %
Total
Row %
Column%
Small Cultivators
Using
Using
irrigated
dry land
land
10
45
Total
55
Medium Cultivators
Using
Using
irrigated
dry land
land
4
6
Total
10
Large Cultivators
Using
Using
irrigated
dry
land
land
8
8
16
Total
Using
irrigated
land
22
Total
Using
dry
land
Total
59
81
(18.18)
(14.71)
6
(81.82)
(83.33)
3
(100)
(45.08)
9
(40.0)
(25)
2
(60.0)
(30)
1
(100)
(27.78)
3
(50)
(50)
3
(50)
(30.77)
1
(100)
(38.10)
4
(27.16)
(22)
11
(72.84)
(59)
5
(100)
(40.5)
16
(66.67)
(8.82)
21
(33.33)
(5.56)
1
(100)
(7.38)
22
(66.67)
(12.50)
6
(33.33)
(5)
7
(100)
(8.33)
13
(75)
(18.75)
2
(25)
(3.85)
2
(100)
(9.52)
4
(68.75)
(11)
29
(31.25)
(5)
10
(100)
(8)
39
(95.45)
(30.88)
22
(4.55)
(1.85)
2
(100)
(18.03)
24
(46.15)
(37.50)
2
(53.85)
(35)
4
(100)
(36.11)
6
(50)
(12.50)
2
(50)
(7.69)
10
(100)
(9.52)
12
(74.36)
(29)
26
(25.64)
(10)
16
(100)
(19.5)
42
(91.67)
(32.35)
9
(75)
(13.24)
68
(55.74)
(100)
(8.33)
(3.70)
3
(25)
(5.56)
54
(44.26)
(100)
(100)
(19.67)
12
(100)
(9.84)
122
(100)
(100)
(33.33)
(12.50)
2
(50)
(12.50)
16
(44.44)
(100)
(66.67)
(20)
2
(50)
(10)
20
(55.56)
(100)
(100)
(16.67)
4
(100)
(11.11)
36
(100)
(100)
(16.67)
(12.50)
1
(16.67)
(6.25)
16
(38.10)
(100)
(83.33)
(38.46)
5
(83.33)
(19.23)
26
(61.90)
(100)
(100)
(28.57)
6
(100)
(14.29)
42
(100)
(100)
(61.90)
(26)
12
(54.55)
(12)
100
(50)
(100)
(38.10)
(16)
10
45.45)
(10)
100
(50)
(100)
(100)
(21)
22
(100)
(11)
200
(100)
(100)
Source: Primary Data
Note: Figures in parentheses represent percentage to total
100
Table 4.7 shows the reasons for choosing tapioca cultivation by sample
cultivators. Among the sample small cultivators using irrigated land, 14.71 per
cent of them have stated that tapioca cultivation is the major agricultural
produce of the area; 8.82 per cent of them have stated that it gives more profit
than other crops; 30.88 per cent of them have stated that they have long
experience in the cultivation of tapioca; 32.35 per cent of them have stated that
due to suitability of soil and climate and the rest 13.24 per cent of them have
stated other reasons. Among the sample small cultivators using dry land, 83.33
per cent of them have stated that tapioca is the major agricultural produce of
the area; 5.56 per cent of them have stated that it gives more profit than other
crops; 1.85 per cent of them have stated that they have long experience in the
cultivation of tapioca; 3.70 per cent of them have stated that due to suitability
of soil and climate and the rest 5.56 per cent of them have stated other reasons.
Among the sample medium cultivators using irrigated land, 25 per cent
of them have stated that tapioca cultivation is the major agricultural produce of
the area; 12.50 per cent of them have stated that it gives more profit than other
crops; 37.50 per cent of them have stated that they have long experience in the
cultivation of tapioca; 12.50 per cent of them have stated that due to suitability
of soil and climate and the rest 12.50 per cent of them have stated other
reasons. Among the sample medium cultivators using dry land, 30 per cent of
them have stated that tapioca cultivation is the major agricultural produce of
the area; 5 per cent of them have stated that it gives more profit than other
crops; 35 per cent of them have stated that they have long experience in the
cultivation of tapioca; 20per cent of them have stated that due to suitability of
soil and climate and the rest 10 per cent of them have stated other reasons.
101
Among the sample large cultivators using irrigated land, 50 per cent of
them have stated that tapioca cultivation is the major agricultural produce of
the area; 18.75 per cent of them have stated that it gives more profit than other
crops; 12.50 per cent of them have stated that they have long experience in the
cultivation of tapioca; 12.50 per cent of them have stated that due to suitability
of soil and climate and the rest 6.25 per cent of them have stated other reasons.
Among the sample large cultivators using dry land, 30.77 per cent of
them have stated that tapioca cultivation is the major agricultural produce of
the area; 3.85 per cent of them have stated that it gives more profit than other
crops; 7.69 per cent of them have stated that they have long experience in the
cultivation of tapioca; 38.46 per cent of them have stated that due to suitability
of soil and climate and the rest 19.23 per cent of them have stated other
reasons.
In general, among the total sample cultivators using irrigated land, 22
per cent of them have stated that tapioca cultivation is the major agricultural
produce of the area; 11 per cent of them have stated that it gives more profit
than other crops; 29 per cent of them have stated that they have long
experience in the cultivation of tapioca; 26 per cent of them have stated that
due to suitability of soil and climate and the rest 12 per cent of them have
stated other reasons. Among the sample cultivators using dry land, 59 per cent
of them have stated that tapioca cultivation is the major agricultural produce of
the area; 5 per cent of them have stated that it gives more profit than other
crops; 10 per cent of them have stated that they have long experience in the
cultivation of tapioca; 16 per cent of them have stated that due to suitability of
soil and climate and the rest 10 per cent of them have stated other reasons.
102
PROBLEMS OF PRICING BY SAMPLE TAPIOCA CULTIVATORS
With the growing commercialisation in agriculture, marketing of tapioca
is becoming more important. National planning committee1 is of the opinion
that the cultivators in general sell their produce at unfavourable terms. The
cultivators are forced to sell their produce as soon as the harvest is over. The
reasons for forced sales are cultivator’s poverty and prior indebtedness and
need of finance to meet various obligations. These constraints are termed as
exogenous factors which affect the pricing of tapioca by cultivators. There are
many such constraints like time, availability of specific mode of transport for
transporting the tapioca from field to market, harvesting cost, cultivation cost,
interest on capital invested, income expected from sale of tapioca etc. Hence,
the researcher has attempted to identify major factors which directly influence
the pricing of tapioca cultivators in case of direct channel of marketing, that is,
cultivator to consumer.
Some of the exogenous factors are quantifiable while others are not. But
by analysing quantifiable factors, the pricing of tapioca by cultivators can be
predicted in exact terms. Through content analysis the researcher could identify
four quantifiable exogenous factors which seem to affect pricing of tapioca by
cultivators. They are
1. Return (or) income from tapioca cultivation
2. Transport cost
3. Harvesting cost and cost of cultivation
4. Interest on working capital.
Pricing as a dependent variable is influenced by the above mentioned
four independent or explanatory variables singly or collectively.
1
National Planning Committee Report, 2010
103
FACTORS INFLUENCING PRICING OF TAPIOCA BY
CULTIVATORS OF TAPIOCA
1. RETURN (OR) INCOME FROM TAPIOCA CULTIVATION
Return or income expected from sale of a produce plays a major role in
influencing pricing of a produce. The return expected by cultivators may differ
from person to person. Generally, income from a produce is the reminder after
cost of goods sold, other variable costs and fixed costs have been subtracted
from sales revenue.
2. TRANSPORT COST
In India, the means of transport are not adequate. Transport facilities
from the field to the village and from the village to the mandi are often
extremely poor and defective. There are bad roads which lead to loss during
transportation. The freight charges fixed by lorry owners are also varying from
time to time. This results in fluctuation in transport cost from time to time. 2
3. HARVESTING AND CULTIVATION COST
Cost of cultivation and harvesting cost play important role in
determining the price of a produce. Cultivation and harvesting costs are
fluctuating due to shortage of power. Cultivators are paying high wages to
labourers to harvest their produces at the time of shortage of power.3
4. INTEREST ON WORKING CAPITAL
Most of the financial needs of the cultivators in India are met by village
money lenders, who in turn are financed by arhatiya and the indigenous
bankers. The rate of interest charged by the money lenders is always high, 24
per cent to 40 per cent, and that too on compound interest basis. The money
2
PandurangaRao, D, Trends in Indian Transport System, Inter India Publications, New Delhi, 2009, p.35.
Thinamalar – Vivasaya Malar, 17th April 2010, p.III
3
104
has to be paid along with interest at the time of harvest. This will increase the
interest on working capital. This, in turn, will affect pricing of agricultural
produce.4
ANALYTICAL TOOL
The present study attempts to examine the influence exerted by the
above mentioned four exogenous variables only. From statistical point of view,
fewer variables lead to more stable models. Inclusion of irrelevant variables
would increase standard errors of the estimate without improving prediction.5
Hence only four variables are included in the study.
To find out the impact of explanatory variables on pricing of tapioca,
multiple linear regression analysis is used. Multiple linear regression analysis6
is the statistical technique used to derive estimates of future pricing where two
or more independent factors are suspected of simultaneously affecting the
pricing. A functional relationship is established between dependent variable
and independent variables. The proposed regression equation is,
1nY= β0+ β11nX1 + β21nX2+ β31nX3 + β41nX4+ ε
Where
ε is the error term which is assumed to be normally distributed with
mean zero and variance σ2
The coefficients βi are estimated by the method of Least Square as
4
Bagavathi and Pillai, R.S.N., Modern Marketing, S. Chand and Company Ltd., New Delhi, 2007, p.356
Marija, N., Norusis, studentware, SPSS Inc., Chicago, 1991, p.302
5
6
Jhonston, J., Econometric Methods, McGraw Hill Book Company, Singapore, 1984, pp.121-151
105
^
β = (x’x)-1xiy
where
xi = 1nXi and
y = 1nY
To analyse the net effect of explanatory variables Xi, their coefficient βi
are tested with the help of students t-test statistic under the following Null
hypothesis.
Hypothesis (Ho) The effect of Xi is not significant on Y.
The test statistic is
^
βi - βi (Ho)
t = ---------------------------- Vi = 1,2,3,4
√∑ei2/n – K √aii
Where aii is the appropriate diagonal element in (x’x)1 matrix, and
^
^
^
^
^
ei = 1nYi= β0- β11nX1i - β21nX2i - β31nX3i - β41nX4i
Multiple correlation coefficient (R2) is calculated to find out percentage
variation of the dependent variable (Y) explained by independent variables
included in the regression equation. Multiple correlation co-efficient (R2)
measures the proportion of total variation about the mean Y explained by the
106
regression. It is often expressed as a percentage multiplying by 100.7 The
multiple correlation coefficients R2 is calculated by using the relation
Β’x’y = y’yR2 which gives the alternative test as
R2/K-1
F = ---------------(1-R2)/n-k
Where
n is the number of observations collected and k is the number of independent
parameters to be tested.
Stepwise regression analysis is used eliminating the least significant
variable in each step and F ratios are calculated to verify the significant
variation of Y due the remaining explanatory variables individually and
collectively.
To identify the existence of multi-collinearity and to test the significance
of correlation coefficients between Y and Xi simple correlation coefficients
between these variables are calculated and their significance are tested by
forming the following Null hypothesis.
Ho: The correlation coefficients are not significant
To test the above Ho, students t-test statistic is used
Where
r√n-2
t = -----------------√1 – r2
7
Draper, N.R and Smith.H., Applied Regression Analysis, John Willy and Sons, USA, 1981, p.33.
107
For analysing exogenous factors a purposive sample of 200 tapioca
cultivators were contacted and the information is obtained from them by
personal enquiry. This sample is stratified into three groups based on their size
of land owned and presented in the table 4.8.
TABLE 4.8
CLASSIFICATION OF SAMPLE RESPONDENTS ON THE BASIS OF
SIZE OF LAND OWNED
Classification Size of land* owned
(Acres)
Small
Less than 2.5 acres of
Cultivators
irrigated land or less
than 5 acres of dry land
Medium
2.5 to 5 acres of irrigated
cultivators
land or 5 to 10 acres of
dry land
Large
More than 5 acres of
cultivators
irrigated land or 10
acres or more of dry
land
Total
Source: Primary Data
Using irrigated
land (Number)
68
Using dry land
(Number)
54
16
20
16
26
100
100
*As per Directorate of Economics and Statistics, Ministry of agriculture,
Government of India
Out of the 100 tapioca cultivators using irrigated land for tapioca
cultivation, 68 belong to small cultivators, 16 belong to medium cultivators and
16 belong to large cultivators. In case of tapioca cultivators using dry land, of
the 100, 54 belong to small cultivators, 20 belong to medium cultivators and 26
belong to large cultivators. From each strata a sample of 15 is taken at random
and subjected the relevant data for analyzing the impact of explanatory
variables on the dependent variable Y-pricing made by tapioca cultivators.
108
I. SMALL CULTIVATORS USING IRRIGATED LAND
To analyse the significance of explanatory variable (Xi) on Y by using
single and multiple regression analysis, the estimated relation is,
InY = -20.7078 + 1.89661nX1 – 0.6927 1nX2 + 1.7403 1nX3 – 0.4140 1nX4 +ε
From the above relation, the following results are obtained.
TABLE 4.9
ANOVA – Small cultivators using irrigated land
Source of
variation
(SV)
Due to Xi
Residual
Total
Degree of
freedom (df)
Sum of
squares (SS)
4
10
14
4.2611
4.3214
8.5825
Mean Sum of F-value
Squares
(MSS)
1.0653
F=2.4653
0.4321
As a resultant F value is 2.4653 with (4, 10) degrees of freedom it may
be concluded that no significant variation is caused by the explanatory variable
(Xi) on the dependent variable Y-pricing of tapioca. To analyse the net effect
of the explanatory variable (Xi), the coefficients βi are calculated and tested
with the help of student’s t test and multiple correlation coefficient is
calculated. The following results are obtained.
109
TABLE 4.10
NET EFFECT OF THE INDEPENDENT VARIABLES
L-S estimation of
coefficients
SE
^
β1=1.8967
^
SE(β1)
= 2.2122
0.8573
b1=3.5651
^
β2=0.6927
^
SE (β2)
= 0.4615
1.5010
b2=-0.3789
t-values
Simple regression
estimates
Multiple
R2 = 0.4965
correlation
coefficient
F-value
F(4,10) = 2.4653
* Significant at 5% level
^
β3=1.7403
^
SE(β3)
= 0.9254
1.8806*
b3=1.9467
^
β4=0.4140
^
SE(β4) =
0.5078
0.8152
b4=-0.3412
From table 4.10, it is clear that the net effect of the independent variable
X3- harvesting and cost of cultivation alone is significant. The net effect of the
other three variables X1 – Income from tapioca cultivation, X2- Transport cost
and X4-Interest on working capital are not significant on the dependent
variable. To test the existence of multicollinearity, simple correlation between
the variables are calculated and their significances are tested with the help of
students t-test.
110
TABLE 4.11
CORRELATION MATRIX BASED ON (y; x1, x2, x3, x4)
Y
Y
1
X1
0.4429*
(1.7811)
X2
-0.1891
(0.6943)
X3
0.5778
(2.5525)
X4
-0.1527
(0.5571)
*significant at 5% level
X1
X2
X3
X4
1
0.1742
(0.6378)
0.5603*
(2.4390)
0.1183
(0.4296)
1
0.2117
(0.7810)
0.0333
(0.1201)
1
-0.0132
(0.0476)
1
Note: The values in parenthesis are their corresponding t-values
It is clear that two variables, X1- income from tapioca cultivation and X3
– harvesting and cultivation cost are positively and significantly related to the
dependent variable Y. Likewise, X1, and X3 are found to have positive
correlation whose t-value is significant. The percentage of individual
contributions of the variables is 33, 6, 57 and 4 respectively.
ANALYSIS OF INDIVIDUAL CONTRIBUTION OF Xi ON Y
The significance of Xi alone and the additional effect of the remaining
variables are tested with the F-values. The results are as under:
111
TABLE 4.12
INDIVIDUAL AND COMBINED EFFECT OF XI, X2, X3, X4 ON Y
SV
Due to X1, X2, X3, X4
X1 alone
Additional effect of X2,X3, X4
X2 alone
Additional effect of X1, X3, X4
X3 alone
Additional effect of X1, X2, X4
X4 alone
Additional effect of X1, X2, X3
* Significant at 5% level
Df
(4, 10)
(1, 13)
(3,10)
(1, 13)
(3, 10)
(1, 13)
(3, 10)
(1, 13)
(3,10)
F-value
2.4653
3.1720
1.9884
2.0735
3.0502
6.5159*
1.0766
3.2240
3.1421
It could be seen from table 4.12 that the individual effect of X 3 –
harvesting cost and cost of cultivation alone is significant in causing variation
on the dependent variable Y-pricing of tapioca. But its effect is not felt when it
acts in combination of other three independent variables.
The same analysis is conducted in a stepwise fashion to analyse the
separate contribution of each variable by eliminating the least significant
variable in each stage.
STAGE 1
In this stage of analysis, the insignificant effect X4 is eliminated and the
regression equation fitted is of the following form.
1nY = -19.576 + 1.61821 1nX1 – 0.06768 1nX2 + 1.8082 1nX3 +ε
Based on stepwise regression techniques the individual and additional
contribution of variables X1, X2, X3 on Y are analysed and tested with Fstatistic. The results are shown in table 4.13.
112
TABLE 4.13
INDIVIDUAL AND COMBINED EFFECTS OF X1, X2, X3 ON Y
SV
Due to X1, X2, X3
X1 alone
Additional effect of X2,X3
X2 alone
Additional effect of X1, X3
X3 alone
Additional effect of X1, X2
R2=0.46
* Significant at 5% level
Df
(3, 11)
(1, 13)
(2,11)
(1, 13)
(2,11)
(1, 13)
(2, 11)
F-value
3.1614
3.1720
2.7334
2.0735
4.376*
6.5159*
1.323
It is clear from table 4.13 that X3 alone and in combination with X1 has
significant impact on the dependent variable Y. The factors X1, X2, X3 together
explained 46 per cent variation on Y-pricing of tapioca.
STAGE 2
In this stage of analysis X2 is eliminated. The regression equation of Y
on the determinants X1 and X3 is of the following form
InY = -20.2944 + 1.3978 1nX1 + 1.6189 1nX3 + ε
Based on stepwise regression techniques, the individual and combined
contribution of variables of X1 and X3 on Y are analysed, tested with F –
statistic. The results are given in Table 4.14.
113
TABLE 4.14
INDIVIDUAL AND COMBINED EFFECTS OF X1, X3 ON Y
SV
Due to X1, X3
X1 alone
Additional effect of X3
X3 alone
Additional effect of X1
R2=0.3546
* Significant at 5% level
Df
(2,12)
(1, 13)
(1,12)
(1, 13)
(1, 12)
F-value
3.2961
3.1720
2.945
6.5159*
2.601
It is clearly evident from Table 4.14 that the variable X3-harvesting cost
and cost of cultivation has fared as the most dominant variable whose F-value
is significant in this group. No other factor seems to have any significant
impact on Y. The factors X1 and X3 explained 35 per cent variation on the
dependent variable Y-pricing of tapioca.
II. MEDIUM CULTIVATORS – USING IRRIGATED LAND
To analyse the significance explanatory variables (Xi) on the dependent
variable Y by using single and multiple regression analysis, the estimated
relation is,
1nY = -20.7036 + 0.5978 1nX1 – 0.2726 1nX2 + 2.6550 1nX3+ 0.7829 1nX4+ ε
From the above fit, the following results are obtained.
TABLE 4.15
ANOVA – MEDIUM CULTIVATORS USING IRRIGATED LAND
SV
Df
Due to X1,
4
X2, X3, X4
Residual
10
Total
14
* Significant at 5% level
SS
5.5587
MSS
1.3897
2.577
8.1357
0.2577
F-value
F=5.3926*
114
As the result of F-value is 5.3926 with (4, 10) degrees of freedom, it may
be concluded that there is significant variation in dependent variable Y-pricing
of tapioca caused by the four explanatory variables. To analyse the net effect of
explanatory variables (Xi) the coefficient βi are calculated and tested with the
help of students t test and multiple correlation coefficient is calculated. The
following results are obtained.
TABLE 4.16
NET EFFECT OF THE INDEPENDENT VARIABLES
L-S
estimation of
coefficients
SE
^
β1=0.5978
^
β2=-0.2726
^
β3=2.6550
^
β4=0.7829
^
SE(β1)
= 1.3321
b1=2.3320
R2 = 0.6833
^
SE(β2)
= 0.3273
b2=-0.3429
^
SE(β3)
= 0.8907
b3=2.2506
^
SE(β4)
=0.8328
b4=-1.4561
t-values
Multiple
correlation
coefficient
F-value
F(4,10) = 5.3926*
*Significant at 5% level
Of the four explanatory variables considered, only the net effect of X3 –
harvesting cost and cost of cultivation is significant. The net effect of the other
three variables X1-income from tapioca cultivation, X2-transport cost and X4Interest on working capital are not significant. The four explanatory variables
taken together explained 68 per cent variations on Y. To test the significance of
multicollinearity, simple correlation between the variables is calculated and
their significances are tested with the help of students t-test.
115
TABLE 4.17
CORRELATION MATRIX BASED ON (y: X1, X2, X3, X4)
Y
Y
1
X1
0.4149
(1.6442)
X2
-0.2120
(0.7822)
X3
0.7921*
(4.6790)
X4
-0.5044*
(2.1062)
*significant at 5% level
X1
X2
X3
X4
1
0.3492
(1.3437)
0.5303*
(2.2553)
0.4124
(1.6325)
1
-0.0781
(0.2825)
-0.0279
(0.1006)
1
-0.3813
(1.5134)
1
Note: The values in parentheses are their corresponding t-values
It is inferred from Table 4.17 that while the variable X3-harvesting cost
and cost of cultivation is positively and significantly related to the dependent
variable Y-pricing of tapioca, interest on working capital is negatively related
to the dependent variable. Significant multicollinearity is found among X1 and
X3 in this group. Among the four explanatory variables X2 is the least
significant variable. The percentage contribution of the variables is 16, 4, 57
and 23 respectively.
ANALYSIS OF INDIVIDUAL CONTRIBUTION OF Xi ON Y
The significance of Xi alone and the additional effect of the remaining
variables are tested with the F values. The results are given in Table 4.18.
116
TABLE 4.18
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X3, X4 ON Y
SV
Due to X1, X2, X3, X4
X1 alone
Additional effect of X2,X3, X4
X2 alone
Additional effect of X1, X3, X4
X3 alone
Additional effect of X1, X2, X4
X4 alone
Additional effect of X1, X2, X3
* Significant at 5% level
Df
(4, 10)
(1, 13)
(3,10)
(1, 13)
(3, 10)
(1, 13)
(3, 10)
(1, 13)
(3,10)
F-value
5.3926*
2.7039
5.3784*
1.6339
6.717*
21.894*
1.7032
4.4373
4.5122*
Table 4.18 shows that X3 – harvesting cost and cost of cultivation alone
has significant individual impact in influencing the dependent variable Y –
pricing of tapioca. Where X3 is combined with other independent variable,
there is significant variation in dependent variable and vice versa.
The same analysis is conducted in a stepwise fashion to analyse the
separate contribution of each variable by eliminating the least significant
variable in each stage.
STAGE 1
In this stage of analysis, the insignificant effect of X2 is eliminated and
the regression equation fitted is.
1nY = -19.5042 + 0.0943 1nX1 + 2.8833 1nX3 + 0.8556 1nX4
Based on stepwise regression technique, the individual and combined
contribution of X1, X3 and X4 on Y are analysed. The results are given in Table
4.19.
117
TABLE 4.19
INDIVIDUAL AND COMBINED EFFECT OF X1, X3, X4 ON Y
SV
Due to X1, X3, X4
X1 alone
Additional effect of X3,X4
X3alone
Additional effect of X1, X4
X4 alone
Additional effect of X1, X3
R2=0.6633
* Significant at 5% level
Df
(3, 11)
(1, 13)
(2,11)
(1, 13)
(2,11)
(1, 13)
(2, 11)
F-value
7.1591*
2.7039
7.9429*
21.8941*
1.8192
4.4373
6.6064*
As the F-value is 7.1591 with (3, 11) degrees of freedom, it is concluded
that there is significant association between the group X1, X3, X4 on Y. It is
evident that X3 alone and in combination with other factors has significant
impact on Y. These three factors combined together explained 66 per cent
variation on the dependent variable Y-pricing of tapioca.
STAGE 2
At this stage of analysis, the effects of X1 and X2 are suppressed. The
regression equation of Y on the determinants X3 and X4 after eliminating X1
and X2 is,
1nY = -18.8212 + 2.9031 1nX3 + 0.8084 1nX4 + ε
Based on stepwise regression technique, the individual and combined
contribution of variables X3 and X4 on Y are analysed and tested with Fstatistic. The results are given in table 4.20
118
TABLE 4.20
INDIVIDUAL AND COMBINED EFFECT OF X3, X4 ON Y
SV
Due to X3, X4
X3 alone
Additional effect of X4
X4 alone
Additional effect of X3
R2=0.6611
* Significant at 5% level
Df
(2,12)
(1, 13)
(1,12)
(1, 13)
(1, 12)
F-value
11.7023
21.8941*
1.1910
4.4373
14.3956*
It is clear from table 4.20 that X3 is the most dominant variable and
alone has emerged as the significant factor in the medium cultivators using
irrigated land. The variable X3 and X4 together explained 66 per cent variation
on Y.
III. LARGE CULTIVATORS USING IRRIGATED LAND
The significance of explanatory variables X1 on Y is analysed by using
single and multiple regression analysis. The equation fitted is,
1nY = 1.2023 + 0.5518 1nX1 – 0.9896 1nX2 + 0.2179 1nX3 – 0.3537 1nX4 + ε
From the above fit, the following ANOVA table is formed.
TABLE 4.21
ANOVA – LARGE CULTIVATORS USING IRRIGATED LAND
SV
Df
Due to X1,
4
X2, X3, X4
Residual
10
Total
14
* Significant at 5% level
SS
2.6954
MSS
0.6738
1.4320
4.1274
0.1432
F-value
F=4.7057*
As the result of F-value is 4.7057 with (4, 10) degrees of freedom, it is
concluded that there is significant association between explanatory variables
(Xi) and pricing of tapioca (Y). To analyse the net effect of explanatory
119
variables (Xi) the coefficients βi are calculated and tested with the help of
student’s t test and multiple correlation coefficient is calculated. The results are
shown in table 4.22
TABLE 4.22
NET EFFECT OF THE INDEPENDENT VARIABLES
L-S
estimation of
coefficients
SE
^
β1=0.5518
^
β2=-0.9897
^
β3=0.2179
^
β4=-0.3537
^
SE(β1)
= 1.1996
0.4600
b1=-1.3929
^
SE(β2)
= 0.4743
2.0852*
b2=-0.9095
^
SE(β3)
= 0.3663
0.5946
b3=-3.7273
^
SE(β4)
=0.4646
0.7613
b4=-0.8349
t-values
Simple
regression
estimates
Multiple
R2 = 0.6531
correlation
coefficient
F-value
F(4,10) = 4.7057*
*Significant at 5% level
It is interesting to note from the table 4.22 that, unlike the other two
types of cultivators discussed earlier, the net effect of X2-transport cost, alone
is significant on the dependent variable Y - .pricing of tapioca. The net effect
of the other three variables is not significant. The four explanatory variables
taken together explained 65 per cent variations on the pricing of tapioca.
In order to find out the existence of multicollinearity between the
variables, simple correlation between the variables are calculated and tested
with the help of student’s t test.
120
TABLE 4.23
CORRELATION MATRIX BASED ON (y: X1, X2, X3, X4)
Y
Y
1
X1
-0.4081
(1.6118)
X2
-0.7549*
(4.1502)
X3
-0.0308
(0.1111)
X4
-0.4834*
(1.991)
*significant at 5% level
X1
X2
X3
X4
1
0.7564*
(4.1694)
0.5852*
(2.6021)
0.2361
(0.8761)
1
0.2672
(0.9998)
0.4117
(1.6288)
1
-0.4111
(1.6260)
1
Note: The values in parentheses are their corresponding t-values
As in the case of net effect, the independent variable X2 -transport cost
has significant and positive correlation with the dependent variable Y-pricing
of tapioca.
Among the four variables X3 is least insignificant factor. The percentage
contribution of these variables is 17, 58, 1 and 24 respectively.
ANALYSIS OF INDIVIDUAL CONTRIBUTION OF Xi
The significance of Xi alone and the additional effect of the remaining
variables are tested with the F- values. The results are given in Table 4.24.
121
TABLE 4.24
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X3, X4 ON Y
SV
Due to X1, X2, X3, X4
X1 alone
Additional effect of X2,X3, X4
X2 alone
Additional effect of X1, X3, X4
X3 alone
Additional effect of X1, X2, X4
X4 alone
Additional effect of X1, X2, X3
* Significant at 5% level
Df
(4, 10)
(1, 13)
(3,10)
(1, 13)
(3, 10)
(1, 13)
(3, 10)
(1, 13)
(3,10)
F-value
4.7057*
2.5973
4.6746*
17.2203*
1.2505
81.3313
6.2654*
3.9643
4.0286*
Table 4.24 clearly shows that there is only one variable, that is, X2 –
transport cost, whose individual effect is significant on the dependent variable
Y – pricing of tapioca. It is this variable when combined with other
independent variables causes significant variation on the dependent variable Y.
The same analysis is conducted in a stepwise fashion to analyse the separate
contribution of each variable by eliminating the least significant factor in each
stage.
STAGE 1
In this stage of analysis, the least significant factor X3 is eliminated. The
regression equation of Y on the determinants X1, X2 and X4 is,
1nY = -2.5440 + 1.1942 1nX1 – 1.1558 1nX2 – 0.1891 1n X4 + ε
Based on stepwise regression technique, the individual and combined
contribution of X1, X2 and X4 on Y are analysed and tested with F statistic. The
results are given in Table 4.25.
122
TABLE 4.25
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X4 ON Y
SV
Due to X1, X2, X4
X1 alone
Additional effect of X2,X4
X2alone
Additional effect of X1, X4
X4 alone
Additional effect of X1, X2
R2=0.6406
* Significant at 5% level
Df
(3, 11)
(1, 13)
(2,11)
(1, 13)
(2,11)
(1, 13)
(2, 11)
F-value
6.5398*
2.5972
7.2604*
17.2203*
1.0861
3.9643
6.2330*
It could be seen from table 4.25 that significant variation is caused by
X1, X3, X4 on the dependent variable Y. These three variables explained
together 64 per cent variation on Y.
STAGE 2
In this stage of analysis, X1 and X3 are eliminated. The regression
equation of Y on the determinants X2 and X4 when X1 and X3 are eliminated is,
1nY = 6.5116 – 0.8092 1nX2 – 0.28229 1n X4 + ε
The following are the results obtained.
TABLE 4.26
INDIVIDUAL AND COMBINED EFFECT OF X2, X4 ON Y
SV
Due to X2, X4
X2 alone
Additional effect of X4
X4 alone
Additional effect of X2
R2=0.5907
* Significant at 5% level
Df
(2,12)
(1, 13)
(1,12)
(1, 13)
(1, 12)
F-value
8.6577*
17.2203*
1.6353
3.9643
10.4652*
123
It is clear from table 4.26 that even after suppressing the effects of X1
and X3, the independent variables X3 and X4 cause significant variation on the
dependent variable Y – pricing of tapioca. These two variables combined
together explained 59 per cent variation on the dependent variable Y-pricing of
tapioca
IV. SMALL CULTIVATORS – USING DRY LAND
To analyse the impact of X1 on Y in case of small cultivators using dry
land by using simple and multiple regression analysis, the estimated relation is,
1nY = -15.2572 + 0.6780 1nX1 – 0.3594 1n X2 + 1.7709 1n X3 + 0.8817 1nX4
+ε
From the above relation the following tabulated results are obtained.
TABLE 4.27
ANOVA – SMALL CULTIVATORS USING DRY LAND
SV
Df
Due to X1,
4
X2, X3, X4
Residual
10
Total
14
* Significant at 5% level
SS
4.6628
MSS
1.1657
2.2170
6.8798
0.2217
F-value
F=5.2580*
As the resultant F-value is 5.2580 with (4, 10) degrees of freedom, it is
concluded that there is significant association between the explanatory
variables (Xi) and pricing of tapioca. To analyse the net effect of the
explanatory variables (Xi) their coefficients βi are calculated and tested with
the help of student’s t test and multiple correlation coefficient is calculated.
The results are shown in Table 4.28.
124
TABLE 4.28
NET EFFECT OF THE INDEPENDENT VARIABLES
L-S
estimation of
coefficients
SE
t-values
^
β1=0.6780
^
β2=-0.3594
^
β3=1.7709
^
β4=0.8817
^
SE(β1)
= 2.2231
0.0304
^
SE(β2)
= 0.2729
1.3168
^
SE(β3)
= 0.6814
2.5988*
^
SE(β4)
=0.5871
1.5018
b3=2.2257
b4=1.5421
Simple
b1=3.2320
b2=-0.4946
regression
estimates
Multiple
R2 = 0.6778
correlation
coefficient
F-value
F(4,10) = 5.2580*
*Significant at 5% level
Of the four variables, the net effect of X3 – harvesting cost and cost of
cultivation alone has significant impact on the dependent variable Y. The other
three variables βi and their corresponding t-values are not significant indicating
their insignificant net effect on the dependent variable Y. The four explanatory
variables together explained 68 per cent variation on the pricing of tapioca. To
identify the multicollinearity between the variables, simple correlation between
the variables are calculated and tested with the help of students t test.
125
TABLE 4.29
CORRELATION MATRIX BASED ON (Y; X1, X2, X3, X4)
Y
Y
1
X1
0.3068
(1.1622)
X2
-0.3374
(1.2923)
X3
0.7177*
(3.7161)
X4
0.5580*
(2.4245)
*significant at 5% level
X1
X2
X3
X4
1
-0.1647
(0.6021)
0.4127
(1.6336)
-0.1090
(0.3954)
1
-0.0556
(0.2008)
-0.1622
(0.5927)
1
0.3461
(1.3301)
1
Note: The values in parentheses are their corresponding t-values
Unlike the other previously discussed different types of cultivators, there
is no multicollinearity among the independent variables. Besides the factors
X3- harvesting cost and cost of cultivation, the factor X4 – interest on working
capital also has significant and positive relation with the dependent variable Y.
Among the four variables, X3 is the most dominant variable. The percentage
contribution of the explanatory variables is 9, 11, 51 and 29 respectively.
ANALYSIS OF INDIVIDUAL CONTRIBUTION ON Xi ON Y
The significance of Xi alone and the additional effect of the remaining
variables are tested with F values. The results are shown in Table 4.30.
126
TABLE 4.30
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X3, X4 ON Y
1
Due to X1, X2, X3, X4
X1 alone
Additional effect of X2,X3, X4
X2 alone
Additional effect of X1, X3, X4
X3 alone
Additional effect of X1, X2, X4
X4 alone
Additional effect of X1, X2, X3
* Significant at 5% level
Df
(4, 10)
(1, 13)
(3,10)
(1, 13)
(3, 10)
(1, 13)
(3, 10)
(1, 13)
(3,10)
F-value
5.2580*
1.3513
6.0365*
1.6703
5.8331*
13.810*
1.6829
5.294*
4.0171*
It is clearly apparent from the Table 4.30 that like the correlation matrix,
the individual effect of X3 – harvesting cost and cost of cultivation and X4 –
interest on working capital are significant in exerting influence on the
dependent variable Y – pricing of tapioca.
By following the same model, a step wise analysis is conducted to verify
the separate contribution of each variable by eliminating the least significant
factor in each stage.
STAGE 1
In this stage of analysis X1 is eliminated as it is the least insignificant
factor. The regression equation of Y on the determinants X2, X3 and X4 is,
1nY = -10.2806 – 0.3771 1nX2 + 1.8730 1nX3 + 0.8232 1nX4 + ε
Based on stepwise regression technique, the individual and additional
contribution of X2, X3 and X4 on Y are analysed and the results are shown in
Table 4.31.
127
TABLE 4.31
INDIVIDUAL AND COMBINED EFFECT OF X2, X3, X4 AND Y
SV
Due to X2, X3, X4
X2 alone
Additional effect of X3,X4
X3alone
Additional effect of X2, X4
X4 alone
Additional effect of X2, X3
R2=0.6748
* Significant at 5% level
Df
(3, 11)
(1, 13)
(2,11)
(1, 13)
(2,11)
(1, 13)
(2, 11)
F-value
7.6077*
1.6703
9.4860*
13.8082*
2.7006
5.2947*
6.5172*
It is evident from Table 4.31 that there is significant variation caused by
the group X2, X3 and X4 on the dependent variable Y. The three variables
together explained 67 per cent variation on Y.
STAGE 2
In this stage X2 is eliminated. The regression equation fitted is of the
following form
1nY = -12.5085 + 1.8726 1nX3 + 0.9429 1nX4 + ε
Based on stepwise regression technique, the individual and combined
contribution of variables X3 and X4 on Y are analysed, tested with F statistic
and the results are shown in table 4.32.
TABLE 4.32
INDIVIDUAL AND COMBINED EFFECT OF X3, X4 ON Y
SV
Df
F-value
Due to X3, X4
(2,12)
9.3977*
X3 alone
(1, 13)
13.8082*
Additional effect of X4
(1,12)
2.9330
X4 alone
(1, 13)
5.2947*
Additional effect of X3
(1, 12)
9.8827*
2
R =0.6103
* Significant at 5% level
128
Both X3 and X4 have significant impact in exerting influence on the
dependent variable Y. The factor X3 is the most dominant factor. These two
variables combined together explained 61 per cent variation on the dependent
variable Y – pricing of tapioca.
V. MEDIUM CULTIVATORS USING DRY LAND
To study the impact of Xi on Y in case of medium cultivators using dry
land by using single and multiple regression analysis, the estimated relation is,
1nY = -6.6609 + 2.0086 1nX1 – 1.3979 1nX2 + 0.1179 1nX3 – 1.6047 1nX4 + ε
From the above relation, the following table is formed.
TABLE 4.33
ANOVA – MEDIUM CULTIVATORS USING DRY LAND
SV
Df
Due to X1,
4
X2, X3, X4
Residual
10
Total
14
* Significant at 5% level
SS
4.8086
MSS
1.20215
4.4033
9.2119
0.44033
F-value
F=2.7301
As the calculated F value (2.7301) is less than the table value, it is
inferred that no significant variation is caused by the explanatory variables on
the dependent variable Y – pricing of tapioca.
To analyse the net effect of the explanatory variables (Xi) their
coefficients βi are calculated and tested with the help of student’s t test and
multiple correlation coefficient. The results are shown in Table 4.34.
129
TABLE 4.34
NET EFFECT OF THE INDEPENDENT VARIABLES
L-S
estimation of
coefficients
SE
t-values
^
β1=2.0086
^
β2=-1.3979
^
β3=0.1179
^
β4=-1.6047
^
SE(β1)
= 0.2086
3.0269*
^
SE(β2)
= 0.4396
3.1799*
^
SE(β3)
= 0.5414
0.0218
^
SE(β4)
=0.8898
1.8035*
b3=6.4783
b4=-0.5226
Simple
b1=-0.5107
b2=-1.0084
regression
estimates
Multiple
R2 = 0.5220
correlation
coefficients
F-value
F(4,10) = 2.7301
*Significant at 5% level
It is interested to know from the table 4.34 that the net effect of X 1 income from tapioca cultivation, X2 – transport cost and X4 – interest on
working capital are significant. The four explanatory variables together
explained 52 per cent variation on the pricing of tapioca. To identify the
multicollinearity between the variables, simple correlation between the
variables are calculated and tested with the help of students t test.
130
TABLE 4.35
CORRELATION MATRIX BASED ON (Y;X1, X2, X3, X4)
Y
Y
1
X1
-0.0652
(0.2356)
X2
-0.5823*
(2.5825)
X3
0.0308
(0.1111)
X4
0.1476
(0.5381)
*significant at 5% level
X1
X2
X3
X4
1
0.3474
(1.3358)
0.4003
(1.5750)
0.1407
(0.5124)
1
-0.0597
(0.2156)
-0.3069
(1.1627)
1
0.3886
(1.5206)
1
Note: The values in parentheses are their corresponding t-values
It is inferred from table 4.35 that there is no significant multicollinearity
among the independent variables. The variable X2 – transport cost and
dependent variable Y-pricing of tapioca are negatively but significantly
correlated. Among the explanatory variables X2 is the dominant variable. The
percentage contribution of the explanatory variables is 1, 92, 1 and 6
respectively.
ANALYSIS OF INDIVIDUAL CONTRIBUTION OF Xi ON Y
The significance of Xi alone and the additional effect of the remaining
variables are tested with F values. The results are given in table 4.36.
131
TABLE 4.36
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X3, X4 ON Y
SV
Due to X1, X2, X3, X4
X1 alone
Additional effect of X2,X3, X4
X2 alone
Additional effect of X1, X3, X4
X3 alone
Additional effect of X1, X2, X4
X4 alone
Additional effect of X1, X2, X3
* Significant at 5% level
Df
(4, 10)
(1, 13)
(3,10)
(1, 13)
(3, 10)
(1, 13)
(3, 10)
(1, 13)
(3,10)
F-value
2.7301
17.9998
3.6105
6.6696*
1.2756
81.3722
3.6334
3.452
3.4881
Among the independent variables, X2 alone is the significant variable.
By following the same model, a step wise analysis is conducted to verify the
separate contribution of each variable by eliminating the least significant factor
in each stage.
STAGE 1
In this stage of analysis, X3 is eliminated. The regression equation of Y
on the determinants X1, X2 and X4 is
1nY = -7.3866 + 2.1819 1nX1 – 1.4080 1nX2 – 1.5448 1nX4 + ε
From the above fit, the result obtained is shown in table 4.37.
Based on step wise regression technique, the individual and additional
contribution of X1, X2 and X4 on Y are analysed.
132
TABLE 4.37
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X4 ON Y
SV
Due to X1, X2, X4
X1 alone
Additional effect of X2,X4
X2alone
Additional effect of X1, X4
X4 alone
Additional effect of X1, X2
R2=0.5197
* Significant at 5% level
Df
(3, 11)
(1, 13)
(2,11)
(1, 13)
(2,11)
(1, 13)
(2, 11)
F-value
3.9679*
17.9998
5.9033*
6.6696*
2.0689
3.4520
5.7024*
As the F-value is 3.96 with (3, 11) degrees of freedom, it is inferred that
there is significant association between X1, X2 and X4 and dependent variable
Y. After eliminating X3, the effect of X2 is more felt. The three explanatory
variables together explained 52 per cent variation on pricing of tapioca.
STAGE 2
In this stage X3 is eliminated. The regression equation after eliminating
X1 and X3 is of the following form.
1nY = 9.5316 – 1.1999 1nX2 – 1.2754 1nX4 + ε
Based on step wise regression technique, the individual and combined
contribution of variables X2 and X4 on Y are analysed and tested with F
statistic. The results are shown in Table 4.38.
133
TABLE 4.38
INDIVIDUAL AND COMBINED EFFECT OF X2, X4 ON Y
SV
Due to X2, X4
X2 alone
Additional effect of X4
X4 alone
Additional effect of X2
R2=0.4567
* Significant at 5% level
Df
(2,12)
(1, 13)
(1,12)
(1, 13)
(1, 12)
F-value
5.0432*
6.6696*
2.5975
3.4520
9.6049*
There is no significant association between the variables X2, X4 and Y.
These two explanatory variables together explained just 46 per cent variation
on the dependent variable Y – pricing of tapioca. In this group the effect of X2
is so overwhelming that the other factors pale into insignificant.
VI. LARGE CULTIVATORS USING DRY LAND
To analyse the impact of Xi on Y in case of large cultivators using dry
land by using single and multiple regression analysis, the estimated relation is
1nY = 21.2064 – 1.7072 1nX1 – 0.5322 1nX2 + 0.02656 1nX3 – 1.1158 1nX4 +
ε
From the above fit, the following results are obtained.
TABLE 4.39
ANOVA – LARGE CULTIVATORS USING DRY LAND
SV
Df
Due to X1,
4
X2, X3, X4
Residual
10
Total
14
* Significant at 5% level
SS
3.1505
MSS
0.787625
3.4836
6.6341
0.3484
F-value
F=2.2607
134
As the calculated F-value is 2.2607 with (4, 10) degrees of freedom, it is
inferred that there is no significant association between the explanatory
variables and pricing of tapioca.
To analyse the net effect of the explanatory variables (Xi) their
coefficient βi are calculated and tested with the help of student’s t test and
multiple correlation coefficients is calculated. The results are shown in Table
4.40
TABLE 4.40
NET EFFECT OF THE INDEPENDENT VARIABLES
L-S
estimation of
coefficients
SE
t-values
^
β1=-1.7072
^
β2=-0.5322
^
β3=0.0266
^
β4=-1.1158
^
SE(β1)
= 1.288
1.3255
^
SE(β2)
= 0.3611
1.4738
^
SE(β3)
= 0.4493
0.0592
^
SE(β4)
=0.5913
1.8870*
b3=-0.3966
b4=-0.8491
Simple
b1=-1.6762
b2=-0.7189
regression
estimates
Multiple
R2 = 0.4749
correlation
coefficient
F-value
F(4,10) = 2.2607
*Significant at 5% level
Of the four explanatory variables considered, the net effect of X4-interest
on working capital alone is significant. The net effect of the other three
variables (X1, X2 and X3) is not significant.
The four explanatory variables together explained 47 per cent variation
on the pricing of tapioca.
135
To find out the existence of multicollinearity among the variables,
simple correlation between the variables are calculated and tested with the help
of students t-test.
TABLE 4.41
CORRELATION MATRIX BASED ON (Y: X1, X2, X3, X4)
Y
Y
1
X1
-0.3625
(1.4024)
X2
-0.4931*
(2.0437)
X3
-0.2220
(0.8209)
X4
-0.3537
(1.3634)
*significant at 5% level
X1
X2
X3
X4
1
0.3645
(1.4113)
0.3612
(1.3966)
-0.2891
(1.0889)
1
0.1735
(0.6352)
-0.0084
(0.0303)
1
0.086
(0.3012)
1
Note: The values in parentheses are their corresponding t-values
It is clear from table 4.41 that the variable X2 – transport cost is
negatively but significantly related to the dependent variable Y-pricing of
tapioca. Likewise, no multicollinearity is found among the explanatory
variables of large cultivators using dry land. Among the four explanatory
variables, X2 is the dominant variable. The percentage contribution of the
variables is 24, 44, 9 and 23 respectively.
ANALYSIS OF INDIVIDUAL CONTRIBUTION OF Xi ON Y
The significance of Xi alone and the additional effect of the remaining
variables are tested with F-values. The results obtained are shown in table 4.42.
136
TABLE 4.42
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X3, X4 ON Y
SV
Due to X1, X2, X3, X4
X1 alone
Additional effect of X2,X3, X4
X2 alone
Additional effect of X1, X3, X4
X3 alone
Additional effect of X1, X2, X4
X4 alone
Additional effect of X1, X2, X3
Df
(4, 10)
(1, 13)
(3,10)
(1, 13)
(3, 10)
(1, 13)
(3, 10)
(1, 13)
(3,10)
F-value
2.2607
1.9668
2.1803
4.1758
1.4710
1.4837
2.7038
1.8585
2.2204
No factor has significant individual impact on the dependent variable Y.
By following the same model, a step wise analysis is conducted to verify the
separate contribution of each variable by eliminating the least insignificant
factor in each stage.
STAGE 1
In this stage of analysis, X3 is eliminated as it is the least insignificant
factor. The regression equation of Y on the determinants X1, X2 and X4 is
1nY = 21.1109 – 1.6789 1nX1 – 0.5316 1nX2 – 1.1085 1nX4 + ε
Based on stepwise regression technique, the additional and individual
contribution of X1, X2 and X4 are tested with F-values. The results are given in
Table 4.43.
137
TABLE 4.43
INDIVIDUAL AND COMBINED EFFECT OF X1, X2, X4 ON Y
SV
Due to X1, X2, X4
X1 alone
Additional effect of X2,X4
X2alone
Additional effect of X1, X4
X4 alone
Additional effect of X1, X2
R2=0.4747
Df
(3, 11)
(1, 13)
(2,11)
(1, 13)
(2,11)
(1, 13)
(2, 11)
F-value
3.3137
1.9668
3.5945
4.1758
2.4249
1.8585
3.6608
It is evident from Table 4.43 that there is no significant association
between the group X1, X3 and X4 on Y. The three variables explained only 47
per cent variation on pricing of tapioca.
STAGE 2
At this stage of analysis, X4 is eliminated. The regression equation after
eliminating X3 and X4 is of the following form.
1nY = 13.4635 – 0.9746 1nX1 – 0.6069 1nX2 + ε
Based on step wise regression techniques, the individual and combined
contribution of X1 and X2 are analysed and tested with F statistic.
138
TABLE 4.44
INDIVIDUAL AND COMBINED EFFECT OF X1, X2 ON Y
SV
Df
Due to X1, X2
(2,12)
X1 alone
(1, 13)
Additional effect of X2
(1,12)
X2 alone
(1, 13)
Additional effect of X1
(1, 12)
2
R =0.2817
There is no significant variation in Y caused by X1 and
F-value
2.3527
1.9668
2.5099
4.1758
1.5536
X2 individually
and collectively. These two explained only 28 per cent variation on Y. Though
individual effect of none of the variables is significant, X2 – transport cost
remains the dominant variable among the four independent variables.
In this chapter, the profile of sample tapioca cultivators, such as age
group of sample tapioca cultivators, educational qualification, social class,
marital status, annual income, amount spent for transport are analysed. The
reasons for choosing tapioca cultivation are also analysed. The effect of
exogenous factors – income from tapioca cultivation, transport cost, harvesting
cost and cost of cultivation and interest on working capital – in influencing the
pricing of tapioca among small cultivators, medium cultivators and large
cultivators using irrigated land and small cultivators, medium cultivators and
large cultivators using dry land are analysed in this chapter. Only in three of
the six sample type of cultivators, the effect of exogenous variables is
significant. Transport cost and harvesting cost and cost of cultivation are the
dominant variables whose impact on Y is significant except in case of large
cultivators using dry land.
The next and fifth chapter deals with analysis of price trend of tapioca
from the year 2003 to 2012.