260 N. S. S. Narayana and B. P. Vani
and incomes, in addition to the usually desirable Equations 6, 7, and 8. See appendix for algebraic expressions for h
kj
, m
kj
and deri- vation of an important relation between them, namely Eq. 17:
m
kj
2 h
kj
5 p
j
· E
k
17
The above model avoids imposing any behavior on S possibly as a function of incomes; S is treated merely as a residual see
equation 4 here. Also, note that equation 5 could be expanded, and estimated as a linear function in Y
j
. However then the struc- tural parameters of the basic model remain unidentified.
3. DATA
We applied the above model for two separate cases: a rural non-cultivating wage earner households in the year 1970–71
NCW; b cultivating and non-cultivating rural agricultural and non agricultural labor households CNL in the year 1974–75.
These are the available latest data sources giving details both on consumer expenditure and sources of income. Households under
a are essentially non-cultivators; whereas, in b some of them are also poor cultivators with some land. Hence, the results be-
tween a and b cannot strictly be compared. Data problems precluded the possibility of considering households with exactly
the same characteristics between groups a and b. The data are from the surveys of National Sample Survey Organization and
Labor Bureau of the Ministry of Labor and Rehabilitation. Further data details are given in appendix.
4. RESULTS AND ANALYSIS
Table 1 gives statewise data on total number of households under NCW and CNL, their per capita total income and total
expenditure. Except in the States Punjab, Haryana, Jammu and Kashmir and Himachal Pradesh, the total income is quite less than
the expenditure level, which indicates the extent of borrowing. Besides, even with borrowing for consumption purposes these
poor labor households in many states still remained below the poverty line.
Consumption expenditure data was aggregated into the follow- ing commodity groups: 1 cereals CER, 2 all other food
OFD, 3 clothing and footwear CLF, 4 fuel and light FUL and 5 services and miscellaneous SNM. SNM includes utensils,
EARNINGS AND CONSUMPTION BY INDIAN LABORERS 261
Table 1: Number of Labour Households, Average Earnings, Consumption Expenditure and Poverty Line
NCW 1970–71 ESCH
ENCHWH AHS
MPCI MPCE
PL States
9000 9000
nos Rs.
Rs. Rs.
Andhra Pradesh 283.50
1296.70 3.75
22.75 28.83
30.42 Assam
89.20 135.00
4.47 31.00
34.93 37.39
Bihar 477.80
879.10 4.14
20.16 28.14
33.43 Gujarat
221.30 660.60
4.66 24.76
29.41 39.18
Haryana 51.00
201.60 4.67
36.47 34.18
33.94 Jammu and Kashmir
22.50 6.70
5.18 35.09
31.57 25.06
Kerala 144.50
58.50 5.04
25.73 25.79
38.38 Madhya Pradesh
381.00 492.60
3.76 22.41
27.49 28.44
Maharastra 293.30
1179.90 4.54
23.58 25.42
37.45 Karnataka
236.70 792.80
4.11 21.96
27.13 36.23
Orissa 202.30
393.00 3.64
18.64 21.15
31.37 Punjab
53.20 381.50
4.89 44.30
42.70 33.94
Rajasthan 159.00
84.60 3.61
29.57 25.22
28.80 Tamil Nadu
249.50 1403.50
3.64 20.87
24.16 31.36
Uttar Pradesh 634.10
876.20 4.10
25.87 26.18
26.69 West Bengal
309.00 1468.50
4.30 23.12
23.33 39.17
Goa, Dui and Daman 2.10
17.50 4.05
31.62 38.32
36.23 Himachal Pradesh
21.30 2.20
2.18 67.44
78.11 33.94
Manipur 10.30
7.60 3.84
28.30 35.22
37.39 Pondichery
1.60 29.30
4.73 22.51
28.56 31.36
Tripura 9.50
21.30 3.23
32.02 32.92
37.36 Meghalaya
– –
– –
– –
All India 3852.70
10388.70 4.13
23.85 26.83
32.45 NCW: Non-cultivating wage earners. ESCH: Estimated small cultivator households.
ENCWH: Estimated NCW households. AHS: Average household size. MPCIMPCE: Monthly per capita incomeexpenditure. PL: Poverty line monthly per capita.
medicines, transport, schooling, house rent, etc. The different in- comes, receipts, and loans in net terms are as follows: 1 wage
income WG, 2 garden and orchard produce, etc., for NCW 1970–71, plus some self-cultivation income for CNL 1974–75
GC, 3 livestock, poultry-farming, and fishing LP, 4 other household enterprise resources OR, and 5 cash and kind loans
and sale of assets DS.
Before proceeding with the estimations, correlations among the different incomes and loans exogenous variables other than
prices have been observed not reported here and found that for both NCW 1970–71 and CNL 1974–75 none of the incomes
262 N. S. S. Narayana and B. P. Vani
Table 1: Continued
CNL 1974–75 ETRH
ERLH AHS
MPCI MPCE
PL States
9000 9000
nos Rs.
Rs. Rs.
Andhra Pradesh 7455.00
2939.00 4.37
29.04 49.68
57.27 Assam
2159.00 476.00
4.81 46.52
43.76 62.99
Bihar 8896.00
3239.00 4.80
34.13 40.92
61.99 Gujarat
3404.00 1007.00
5.21 39.26
45.11 72.25
Haryana 1372.00
222.00 5.01
42.48 59.45
49.82 Jammu and Kashmir
656.00 32.00
4.98 54.64
61.80 59.38
Kerala 3234.00
1364.00 5.56
32.54 47.05
68.68 Madhya Pradesh
6081.00 1460.00
4.60 28.84
40.79 59.61
Maharastra 6085.00
2232.00 4.92
31.55 39.31
71.00 Karnataka
4063.00 1454.00
4.87 30.63
44.27 66.29
Orissa 4147.00
1517.00 4.66
23.25 34.80
58.74 Punjab
1820.00 465.00
5.15 54.81
72.20 59.96
Rajasthan 3967.00
257.00 4.81
41.01 52.99
61.43 Tamil Nadu
6304.00 2792.00
4.26 33.67
48.44 72.99
Uttar Pradesh 15148.00
2887.00 4.83
33.99 45.84
55.14 West Bengal
5896.00 2286.00
4.81 33.54
36.17 64.27
Goa, Diu and Daman 121.00
15.00 5.22
40.72 44.62
66.29 Himachal Pradesh
561.00 24.00
4.12 60.52
60.17 59.96
Manipur 162.00
4.00 4.27
58.96 55.31
62.99 Pondichery
57.00 30.00
4.53 41.00
52.67 72.99
Tripura 252.00
81.00 4.79
38.15 43.32
62.99 Meghalaya
169.00 35.00
4.16 48.98
58.49 62.99
All India 82009.00
24818.00 4.70
33.37 44.57
62.20 CNL: Cultivating and non-cultivating laborers. ETRH: Estimated total rural house-
holds. ERLH: Estimated rural labor households. AHS: Average household size. MPCI MPCE: Monthly per capita incomeexpenditure. PL: Poverty line monthly per capita.
as well as the borrowings have any strong correlations between them. Thus, for estimation purposes problem of any multicollin-
earity did not exist. The empirical analysis starts with estimating both the traditional
LES model [equations—system 1] and the extended LES model [equations—system 5] and comparing first their estimation re-
sults by means of R
2
, Theil’s measure and likelihood ratio test Table 2. The estimations were based on full-information maxi-
mum likelihood method with a full variance-covariance matrix of errors accounting for the correlations across equations Zellner’s
seemingly unrelated system. Because in the nonlinear estimation, the estimated error-mean need not be equal to zero, the R
2
was
EARNINGS
AND CONSUMPTION
BY INDIAN
LABORERS
263
Table 2: Estimation Results
Traditional LES Equation 3 Extended LES Equation 10
b
k
c
k
R
2
b
k
a
k1
WG a
k2
GC a
k3
LP a
k4
OR a
k5
DS R
2
NCW 1970–71: Cereals
0.2121 184.94
0.46 0.2267
163.53 644.83
105.47 151.59
141.84 0.54
7.74 8.25
7.09 2.91
1.86 0.48
0.76 1.69
Other food 0.5479
261.76 0.86
0.5101 2151.68
908.21 822.85
2269.21 20.63
0.89 21.28
22.27 18.21
22.19 2.12
2.66 20.88
20.01 Clothing and footwear
0.0540 4.10
0.32 0.0541
247.51 263.03
43.24 153.26
50.93 0.50
4.83 0.46
4.15 22.45
1.92 0.52
2.27 1.79
Fuel and light 0.0817
3.34 0.73
0.0893 238.48
389.03 27.06
41.76 6.87
0.79 16.96
1.41 14.98
22.79 3.51
20.16 0.84
0.30 Services and miscellaneous
0.1043 214.05
0.63 0.1198
234.74 25.12
138.32 215.29
24.8 0.71
21.58 21.48
0.15 1.51
20.16 20.13
Theil’s measue 0.02855
0.2090 over all commodities
22 log likelihood L 2300.51
2602.26 CNL 1974–75:
b
k
c
k
R
2
b
k
a
k1
WG a
k2
GC a
k3
LP a
k4
OR a
k5
DS R
2
Cereals 0.2851
344.67 2ve
0.2573 216.32
2537.45 25278.42
3191.04 160.37
0.34 8.32
6.12 9.83
2.63 4.27
24.25 4.70
1.21 Other food
0.4178 217.07
0.79 0.4390
2145.73 267.07
24237.44 3297.91
2364.93 0.90
17.08 21.31
20.31 22.08
20.12 22.74
4.29 22.55
Clothing and footwear 0.0485
5.25 0.45
0.0461 249.10
2318.43 14.90
524.89 97.52
0.71 4.90
0.35 4.70
21.68 22.11
0.04 2.71
2.65 Fuel and light
0.0818 4.51
0.37 0.0816
4.85 106.49
21760.02 987.07
2113.46 0.56
11.90 0.29
10.57 0.14
0.47 23.48
3.39 22.44
Services and miscellaneous 0.1668
287.23 0.71
0.1760 2169.48
2695.67 22260.3
1577.71 6.27
0.90 23.07
23.91 21.97
22.66 3.43
0.08 Theil’s measure
0.02365 0.01104
over all commodities 22 log likelihood L
1975.52 2233.22
: Significance at 5 level. a
kj
: Coefficients corresponding to income sources indicated in the brackets. Figures in the bracket are asymptomatic t-values. R
2
5 1-[Su
i
2 u
2
SY
i
2 Y
2
] where u
i
and Y
i
are error and dependent variables. Number of observations: 56 1970–71 and 44 1974–75. The likelihood ratio statistics difference between the values of L for the traditional and extended models, following a chi-square distribution comes to 301.74 and 257.71 for the NCW and CNL
cases respectively. These are far higher than the critical values 40.11 and 25.99 of x
2 0.05
with 27 and 15 degrees of freedom. Theil’s measure 5 1n[SS w
it
logw
it
w ˆ
it
] where w
it
and w ˆ
it
are observed and estimated budget shares.
264 N. S. S. Narayana and B. P. Vani
computed adjusting for the error-mean. Asymptotic t statistics have been computed for determining the significance of the esti-
mated parameters. In addition, heteroscedasticity has also been tested out using Glejser’s test.
First, note that the estimates of marginal budget share b
k
, and, hence expenditure elasticities, E
k
, are quite close between the traditional model and extended model for both NCW 1970–71
and CNL 1974–75 cases Table 3. Even own and cross price elasticities for most of the commodities are close. Whenever, they
appeared to be different magnitudewise or signwise, such price elasticities themselves are very close to zero. These results indicate
that the modified specification of c
k
as in Equation 5 did not affect the size of the marginal budget. Thus, there were no mutually
distorting spill-overs between minimum and marginal budgets, while moving from the traditional to extended model estimation.
Besides, goodness of fit as measured by the adjusted R
2
is far better in the extended model for all equations for both NCW
1970–71 and CNL 1974–75 cases. This improvement is particu- larly impressive in the case of CNL for all equations. Particularly,
while the traditional model could not fit at all for the cereals, the extended model performed at least reasonably. In the case of
NCW too, while the extended model fitted much better than the traditional model for all equations, the improvements in adjusted
R
2
for cereals and clothing and footwear are substantial. Theil’s measure as well as likelihood ratio test also resulted in favour of
the extended model. The Glejser test on heteroskedasticity estimation results not
reported here for the extended model turned out negative as coefficients of none of the income variables regressed on absolute
estimated errors is significant. Also the fits of these regressions were very poor.
a NCW: The results indicate: While the wages, the main source of income, explain the m.c.q of all commodities ex-
cept services and miscellaneous, the supplementary in- comes, derived from garden and orchard produce fruits,
pulses, vegetables, straw etc. are used for providing the m.c.q of other food consisting of pulses, oils, fruits and
vegetables, stimulants and intoxicants, etc., and fuel and light. Income from livestock products milk, poultry, meat,
fishing, etc. also provides for the m.c.q of other food milk, meat, fish, and eggs, etc.. However, income from other
EARNINGS
AND CONSUMPTION
BY INDIAN
LABORERS
265
Table 3: Expenditure, Own and Cross Price Elasticities
Traditional LES Extended LES
Commodities E
k
Own and Cross Price Elasticities E
k
Own and Cross Price Elasticities
NCW 1970–71 CER
OFD CLF
FUL SNM
CER OFD
CLF FUL
SNM Cereals CER
0.4773 20.5145
0.0366 20.0042
20.0024 0.0073
0.5547 20.5158 20.0404
20.0069 20.0104
0.0011 Other food OFD
1.4739 20.3839
21.0920 20.0129
20.0075 0.0224
1.3120 20.3561 20.9010
20.0164 20.0247
0.0025 Clothing and footwear CLF
1.0158 20.2645
0.0779 20.8394
20.0052 0.0154
1.2129 20.3292 20.0883
20.7701 20.0228
0.0024 Fuel and light FUL
1.1397 20.2968
0.8740 20.0100
20.9377 0.0173
1.2599 20.3419 20.0917
20.0158 20.7746
0.0024 Services and Misc. SNM
1.4803 20.3855
0.1136 20.0129
20.0076 21.1879
1.6042 20.4354 20.1168
20.0201 20.0302
21.0241 CNL 1974–75
Cereals CER 0.4705
20.5057 0.0080
0.0038 20.0050
0.0283 0.5043
20.5496 0.0166
20.0015 20.0027
0.0302 Other food OFD
1.3688 20.4187
21.0291 0.0111
20.0144 0.0823
1.3710 20.3951 21.0574
20.0040 20.0072
0.0820 Clothing and footwear CLF
1.5562 20.4760
0.0264 21.1838
20.0164 0.0936
1.2091 20.3484
0.0399 20.9315
20.0064 0.0724
Fuel and light FUL 1.1017
20.3370 0.0187
0.0090 20.8586
0.0663 1.1746
20.3385 0.0387
20.0034 20.9299
0.0703 Services and Misc. SNM
2.2065 20.6750
0.0374 20.0179
20.0233 21.5636
2.1791 20.6280
0.0719 20.0064
20.0116 21.5755
E
k
: The expenditure elasticity for the extended LES is as per Equation 13.
266
N. S.
S. Narayana
and B.
P. Vani
Table 4: Average Shares of Incomes and Commodities and Significance in Explaining M.C.Q.
NCW 1970–71 CNL 1974–75
CommoditiesIncomes WG
CG LP
OR DS
CE WG
GC LP
OR DS
CE
Cereals –
– –
0.4274 –
0.4854 Other food
– –
– 0.3750
– 0.3203
Clothing and footwear –
– 0.0504
– –
0.0396 Fuel and light
– –
– 0.0761
– –
0.0701 Services and miscellaneous
– –
– –
– 0.0711
– 0.0847
Income share in total exp. 0.6722
0.0832 0.0712
0.0948 0.0786
1.0000 0.6613
0.0524 0.0311
0.0819 0.1733
1.0000 CE: Average total consumption share in total expenditure.
: Indicates significance of the corresponding coefficients at 510 level.
EARNINGS AND CONSUMPTION BY INDIAN LABORERS 267
resources perhaps only occasional, provides for the m.c.q of clothing and footwear. For these poor households, none
of the incomes is exclusively devoted for providing the m.c.q of services and miscellaneous medicine, schooling, housing,
etc.; i.e., this consumption is explained through marginal budget only after the m.c.q of all other commodities is
provided for. Loans do not explain m.c.q of any commodi- ties. Two possibilities exist here: i the various m.c.qs the
certainty component of consumption themselves are too small for this group that no need to borrow for meeting
minimum consumption needs; ii with little credit worthi- ness of these poor households, loans are not an assured
source of expenditure. Loans, if any, would however con- tribute to marginal consumptions of all commodities
through the marginal supplementary budget.
b CNL: CNL households possess land which can be culti- vated andor leased out and possibly other fixed property
too. Income from such fixed assets other than from cultiva- tion is accounted under OR other resources. OR also
includes other remittances and transfers. Due to ownership of at least some assets they have credit worthiness for bor-
rowing. The results indicate that while wages provide for the m.c.qs of cereals and services and miscellaneous, OR
income provides for the m.c.qs of all commodities. Livestock and poultry income from rearing cows, buffaloes and bul-
locks for cultivation, and poultry operations also provides for the m.c.qs of all commodities milk, meat, eggs, cereals,
dung, etc. except clothing and footwear. Cultivation in- come provides for the m.c.q of not only cereals but also for
clothing. They borrow they could, perhaps due to their credit worthiness even for minimum consumptions of other
food, clothing and footwear, and fuel and light.
In general there seems to be a relation between the type of income and the m.c.q of type of commodity group. For example
income from livestock and poultry contributes to the m.c.q of other food consisting of milk, meat, eggs, etc.; garden and orchard
produce income contributes to otherfood consisting of fruits and vegetables and fuel and light consisting of straw, etc. A summary
of these results is given in Table 4.
Estimated partial and complete income elasticities are pre- sented in Table 5. These along with the price and expenditure
268 N. S. S. Narayana and B. P. Vani
elasticities Table 3 satisfy all the relations discussed earlier. These elasticities were all computed using the sample average
values of the corresponding variables. Most of the m
kj
are positive. However it may be noted that a negative income elasticity does
not imply that corresponding commodities k are inferior. No commodity here is inferior because all expenditure elasticities are
significantly positive. In fact LES does not allow for inferior goods.
From the m
kj
, the quantitative importance of the supplementary incomes and borrowings in maintaining the livelihood can be
noted. For example, the sum of m
kj
corresponding to incomes GC, LP and OR for different commodities as a percentage of the
corresponding expenditure elasticities see Equation 14 are as follows:
NCW 1970–71 CNL 1974–75
Commodity GC 1 LP 1 OR
DS GC 1 LP 1 OR
DS
Cereals 9.01
5.63 33.70
12.99 Other food
27.64 7.94
14.80 13.39
Clothing and footwear 69.81
14.55 18.70
42.39 Fuel and light
45.07 7.68
5.38 11.76
Services and miscellaneous 8.16
8.82 0.38
29.54
That is, if all incomes and expenditure were doubled simultane- ously keeping prices constant, 70 of the total rise in clothing
and footwear consumption for the NCW would be due to the change in supplementary incomes GC, LP, and OR. Similarly,
34 of the total rise in cereals consumption in the case of CNL would be due to the change in these supplementary incomes; and
so on. Similar computation with regard to loans and asset sales DS reveal that the corresponding figures for different commodities
range between 5 for cereals to 15 for clothing and footwear in the case of NCW; and between 11 for fuel and light to 43
for clothing and footwear in the case of CNL. The percentages under DS can be attributed almost to the loans since the other
component, asset sales, under DS is too small about 8.
5. DIFFERENTIAL IMPACTS OF WELFARE PROGRAMS
