Directory UMM :Journals:Journal Of Policy Modeling:Vol22.Issue2.2000:

Earnings and Consumption by Indian Rural

Laborers: Analysis with an Extended Linear

Expenditure System

N. S. S. Narayana and B. P. Vani,Economic Analysis Unit, Indian Statistical Institute, Bangalore, India

This paper analyzes consumption pattern of agricultural and nonagricultural labor households in rural India using a new extension of the familiar Linear Expenditure System (LES) model. This extension accounts for changes in commodity consumption not only due to changes in prices and total expenditure but also due to changes in certain other “status” variables. The analysis extends to assess differential impacts on consumption pattern due to different welfare programmes. 2000 Society for Policy Modeling. Pub-lished by Elsevier Science Inc.

1. INTRODUCTION

In India, rural population forms nearly 78% of the total. More than one-third of the rural population is poor. The majority of them are either landless agricultural laborers or marginal and small cultivators (who also work as laborers). This paper deals with consumer behavior of agricultural and nonagricultural poor labor households in the rural areas whose wage income, on aver-age, is above 50% of their total income. Because wage incomes are not adequate enough to meet their consumption needs, these households earn supplementary incomes from other activities such as livestock operations (selling milk, dung-cakes, etc., from cattle and buffaloes, hiring out bullocks, etc.), minor cultivation (garden Address correspondence to N. S. S. Narayana, Economic Analysis Unit, Indian Statistical Institute, R. V. College P.O., 8th Mile, Mysore Road, Bangalore, 560059, India.

Very useful comments and suggestions from several critics helped us in improving an earlier draft of this paper. We thank all of them and in particular Professors Sanjit Bose, Kirit S. Parikh, and A. Vaidyanathan. Thanks are also due to A. K. Ganesh for helpful discussions and H. M. Rajashekara for typing the manuscript.

Received February 1996; final draft accepted November 1996. Journal of Policy Modeling22(2):255–273 (2000)

2000 Society for Policy Modeling 0161-8938/00/$–see front matter Published by Elsevier Science Inc. PII S0161-8938(97)00068-9

and orchard produce, etc.), and other household enterprises. They also resort sometimes to selling away their assets and quite often to borrowing (cash and kind loans). Underlying this situation is a distinction between incomes on one hand and expenditure on the other. This distinction, generally ignored in consumer demand models, is retained in this paper and the Linear Expenditure System (LES) model is extended in order to evaluate the individ-ual effects on commodity demands due to different incomes and dissavings along with the usual price and expenditure effects.

This issue of individual effects on household consumption due to different types of income has been dealt with earlier by Hol-brook and Stafford (1971) and Benus et al. (1976). However, these studies are not based on estimation of complete demand systems as we do here.

2. EXTENDED LINEAR EXPENDITURE SYSTEM MODEL

The familiar LES model, based on utility maximization subject to a budget constraint (SPk·Qk<E) leads to commodity demand

equations

Qk5ck1

bk

Pk

1

E2

o

k

Pk·ck

2

(1)

where Qk and Pk are demand and price of kth commodity, and

E, total expenditure. Also, 0 ,bk ,1 and

o

k

bk 51.

The bracketed term in Equation 1 is referred to as “marginal budget” and coefficientbkas marginal budget share (m.b.s). The

coefficientckis usually interpreted as minimum consumed quantity

or commitment (m.c.q). The model does not spell out how the m.c.q get determined. It treats them as mere parameters. Strictly speaking, empirical estimates ofckcould become negative in which

case interpretation problems would arise. In this paper, the tradi-tional LES model is extended by specifying the commitments as

ck5

o

j

akjyj (2)

i.e., minimum consumed quantity, m.c.q, is a linear function of levels of certain “state” variables yj. These state variables are

specified as the income earning patterns of the labor households. More precisely; consider a labor household earning incomes from m sources. LetYjbe the income from jth source (j51, 2, . . .m).

EARNINGS AND CONSUMPTION BY INDIAN LABORERS 257 Then jth income proportion is

yj5

Yj

o

j

Yj

(3) and m.c.q are specified to be a function ofyj.

The rationale behind Equation 2 is:

(a) The minimum (not total) consumed quantities of various commodities depend essentially on the pattern of the vari-ous incomes earned. For example, the minimum quantity of foodgrains consumed by a household with a higher farm income and lower dairy income is likely to be more com-pared to that of another household with lower farm income and higher dairy income.

(b) Equation 2 based on the distribution of different incomes easily accounts for differences in occupational characteris-tics of the households.

(c) For poor households, cash and kind borrowings play a very important role in their subsistence (sustenance). Note,

o

j

Yj?E

but

o

j

Yj1S5E (4)

where S is savings (2) or, usually for poor, dissavings (1). Equation 2 relating m.c.q to basic incomes and receipts explicitly accounts for the effects of borrowings on con-sumption.

Now, the modified commodity demand equations are as follows: Qk5

o

j

akjyj1

bk

Pk

3

E2

o

k

Pk(

o

j

akjyj)

4

(5)

with 0,bk,1 andSbk51 wherej51, . . .mincomes andk5

1, . . . ncommodities.

Admittedly, LES with linear Engel curves is somewhat a restric-tive model, where the income effect dominates over substitution effect; see Theil (1975). However, it has its advantages too. The model is parsimonious in the parameters with easy estimability and interpretability. The assumption of additive preferences suits our data at the available commodity aggregation. Besides, house-hold level LES demand equation can be shown with minor assump-tions to be valid even at the mean level data aggregated over households.

Based on the usual desirable properties, namely Engel aggrega-tion, zero-degree homogeneity in prices and expenditure, and symmetry for commodity substitutions, a “complete” demand sys-tem satisfies certain restrictions involving all own and cross price elasticities and expenditure elasticities. These are generally speci-fied as follows:

(a) Engel aggregation:

o

k

akEk51 (6)

(b) Homogeneity restriction:

o

l

ekl1Ek50 (7)

(c) Symmetry restriction:

ak(ekl1 alEk)5 al(elk1 akEl) (8)

where ak: average budget share ofkth commodity,

Ek: expenditure elasticity ofkth commodity demand,

ekl: elasticity ofkth commodity demand with respect

to the price of lth commodity,

kand l: commodity subscriptsk, l51, 2 . . . ,n.

It is easy to see that in the context of the modified LES (Equa-tion 5), Equa(Equa-tions 6, and 8 still hold. However, the modifica(Equa-tion brings forth certain additional relations (derivations avoided here) through the homogeneity assumption. These relations, quite meaningful and intuitively clear, are as follows:

We get, in comparison to Equation 7,

o

l

ekl1

o

j

hkj1Ek50 (9)

whereekland Ekare as defined above and hkjare partial income

elasticities (partial, because E and Pk are kept constant while

differentiatingQkwith respect toYjimplying savings would

corre-spondingly adjust). However in our specific case, yj, but not Yj

directly, entered into the demand equations.1_{It follows then that}

1_Ify

jis replaced byYjin Equation 5, then the function would not satisfy the homogeneity

restriction unlessYjremain fixed. Note that Equation 5 satisfies this restriction in prices,

EARNINGS AND CONSUMPTION BY INDIAN LABORERS 259

o

j

hkj50 (10)

implying, for each commodity, all the partial income elasticities would add up to zero. Thus, Equation 7 also holds (see Equa-tion 9).

However when incomes (Yj) change, usually total expenditure

(E) also changes. Then a different (from the partial) income elas-ticity could be computed which accounts for changes in demand due to simultaneous changes in Yjand E. These elasticities are

referred to as “complete” income elasticities and denoted asmkj

mkj5

dQk

dYj

· Yj Qk

(11) Now, suppose only a few of themincomes go up with a simultane-ous corresponding increase in total expenditure. Let the changing

Yjbe j5 1, . . . gand unchanging Yjbe j5g 11, . . . m

Let pj5

Yj

E and ps5 S

E (12)

Then, using Equation 4 and noting that dYj/dE 5 1, it can be

shown that

Ek5

dQk dE · E Qk 5

o

g 1 mkj

o

g 1 pj (13)

If allYj,Sand accordinglyEalso change simultaneously, then,

because by definitionSpj1ps51, Equation 13 reduces to

Ek5

o

g

1

mkj1sk (14)

wheresk is the (dis)savings elasticity similar to complete income

elasticitymkj. Further, it follows from Equations 9 and 10 as,

o

l

ekl1

o

j

mkj1sk50 (15)

Also, note thatEkin equation 13 is equal tobk(E/PkQk) (see

Equa-tion 5).

Further, it can also be shown that

o

j mkj Ek 5

o

j Yj E (16)

Thus, the demand functions (Equation 5) satisfy the Equations 10, 14, and 16 arising due to the distinction between expenditure

and incomes, in addition to the usually desirable Equations 6, 7, and 8. See appendix for algebraic expressions forhkj,mkjand

deri-vation of an important relation between them, namely (Eq. 17):

mkj2 hkj5pj·Ek (17)

The above model avoids imposing any behavior onS(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 inYj. 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. MPCI/MPCE: Monthly per capita income/expenditure. 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

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 househouse-holds. AHS: Average household size. MPCI/ MPCE: Monthly per capita income/expenditure. 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 R2_{, 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, theR2_was

EARNINGS

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263

Table 2: Estimation Results

Traditional LES (Equation 3) Extended LES (Equation 10)

bk ck R2 bk ak1(WG) ak2(GC) ak3(LP) ak4(OR) ak5(DS) R2

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): bk ck R2 _{bk ak1(WG) ak2(GC) ak3(LP) ak4(OR) ak5(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.akj: Coefficients corresponding to income sources indicated in the brackets. Figures in the bracket are asymptomatic t-values.R25

1-[Sui2u)2_/(SYi2Y)2_{] whereuiandYiare 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 ofx2

(0.05)with 27 and 15 degrees of freedom. Theil’s measure51/n[SSwitlog(wit/wˆit)]

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 sharebk, and,

hence expenditure elasticities, Ek, 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 ofckas 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 R2_{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. Particuparticu-larly, 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

R2_{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

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Table 3: Expenditure, Own and Cross Price Elasticities

Traditional LES Extended LES

Commodities Ek Own and Cross Price Elasticities Ek 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

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)

Commodities/Incomes WG CG LP OR DS C/E WG GC LP OR DS C/E

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

C/E: Average total consumption share in total expenditure.

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 and/or 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

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 themkjare 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 themkj, the quantitative importance of the supplementary

incomes and borrowings in maintaining the livelihood can be noted. For example, the sum ofmkjcorresponding 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 GC1LP1OR DS GC1LP1OR 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

Opportunities for all incomes to go up simultaneously are rare. Various rural development programs initiated for poverty eradica-tion differ in the way the addieradica-tional incomes are created for the

EARNINGS

AND

CONSUMPTION

BY

INDIAN

LABORERS

269

Table 5: Partial and Complete Income Elasticities

Partial Income Elasticities (hkj) Complete Income Elasticities (mkj)

Commodities/Incomes WG GC LP OR DS WG GC LP OR DS

NCW (1970–71)

Cereals 0.1011 20.0196 20.0762 0.0058 20.0111 0.4739 0.0265 20.0367 0.0584 0.0325

Other food 20.0370 20.0147 0.1287 20.0771 0.0001 0.8450 0.0945 0.2221 0.0473 0.1031

Clothing and footwear 20.5464 0.2304 20.1022 0.3504 0.0679 0.2690 0.3314 20.0159 0.4654 0.1632

Fuel and light 20.2600 0.3393 20.1560 0.0773 20.0006 0.5869 0.4442 20.0663 0.1967 0.0983

Services and miscellaneous 0.2549 20.3295 0.0200 0.0366 0.0180 1.3333 20.1961 0.1342 0.1887 0.1440

CNL (1974–75)

Cereals 20.0675 0.1334 20.0961 0.0537 20.0235 0.2660 0.1598 20.0804 0.0950 0.0639

Other food 0.0686 20.0742 0.0630 20.0010 20.0563 0.9752 20.0024 0.1056 0.1113 0.1813

Clothing and footwear 20.2628 20.3034 0.3220 20.0327 0.2768 0.5367 20.2400 0.3596 0.0663 0.4864

Fuel and light 0.2081 20.0267 20.0173 20.1003 20.0638 0.9848 0.0349 0.0192 20.0041 0.1398

Services and miscellaneous 0.0802 20.3126 0.1699 20.2051 0.2677 1.5211 20.1984 0.2376 20.0266 0.6454

N.

S.

S.

Narayana

and

B.

P.

Vani

Table 6: Consumption Effects from Alternative Earning Schemes

WG GC LP OR DS E CRL OFD CLF FUL SNM

NCW (1970–71)

Reference: 941.7 116.6 99.7 132.8 110.1 1400.8 588.3 532.8 71.9 109.9 97.8

Change in

all incomes 1008.9 124.9 106.8 142.3 117.9 1500.8 612.1 518.9 78.1 119.4 109.3

only WG 1041.7 116.6 99.7 132.8 110.1 1500.8 618.1 580.0 74.2 116.7 111.8

only GC 941.7 216.6 99.7 132.8 110.1 1500.8 602.7 575.8 91.3 148.2 82.9

only LP 941.7 116.6 199.7 132.8 110.1 1500.8 569.3 645.1 71.2 103.9 111.2

only OR 941.7 116.6 99.7 232.8 110.1 1500.8 614.5 553.6 95.7 125.2 111.9

only DS 941.7 116.6 99.7 132.8 210.1 1500.8 606.4 581.9 82.2 119.4 110.8

CNL (1974–75)

Reference: 1896.5 150.4 89.1 234.8 497.1 2867.9 1383.8 911.9 120.0 201.6 250.7

Change in

all incomes 1962.8 155.6 92.2 243.0 514.5 2967.9 1408.1 955.9 124.9 209.8 269.2

only WG 1996.5 150.4 89.1 234.8 497.1 2967.9 1403.4 959.2 123.4 211.9 270.2

only GC 1896.5 250.4 89.1 234.8 497.1 2967.9 1527.1 912.0 102.3 206.3 220.3

only LP 1896.5 150.4 189.1 234.8 497.1 2967.9 1263.5 1018.9 165.4 205.9 314.1

only OR 1896.5 150.4 89.1 334.8 497.1 2967.9 1438.9 955.6 123.4 201.6 248.5

only DS 1896.6 150.4 89.1 234.8 597.1 2967.9 1401.8 945.9 131.2 207.3 281.8

EARNINGS AND CONSUMPTION BY INDIAN LABORERS 271 poor. Some schemes are direct employment oriented; some pro-vide cattle and buffaloes; some subsidize food consumption, farm-development, land-development etc., and so on. A number of studies earlier analyzed different social welfare programmes. See Taylor (1980) for a survey on the experiences gained by different countries in this context and Sah and Srinivasan (1988) for an assessment of the impact on different social groups of a food redistribution program. Here we take up the issue such as: as far as ultimate impact on food and nonfood consumption pattern is concerned, are all such programmes same? To anayse such an issue, the estimated equations could be used. Suppose an incre-ment ofRs 100/- per household is to be provided in annual total expenditure over its current level of these poor households. Sup-pose this additional sum is provided through alternative welfare schemes: either as wages (WG), or garden and orchard produce plus cultivation if any (GC), or livestock and poultry farming (LP), etc. The resulting commodity consumptions under alternative schemes are shown in Table 6. Maximum increase over the refer-ence average level, for example, in cereals consumption was brought by additional wage income (WG) in the case of NCW and by additional cultivation income in the case of CNL. For both NCW and CNL, additional income from livestock and poultry brings maximum increase in other food consumption and even reduces the cereal consumption. Coming to non food commodities other than fuel and light, additional incomes in other household enterprises (OR) for the NCW, and in livestock and poultry opera-tions (LP) for the CNL provide for maximum increase in consump-tion of these commodities. The results suggest that employment oriented and land related welfare programmes have an edge over the other programmes in improving the staple food, cereals, intake by the poor.

6. CONCLUSIONS

Our analysis of earnings and consumption by Indian rural la-bourers was based on a new extension of the familiar LES model providing analytical scope for multiple sources of income. Individ-ual effects of different incomes and borrowings on commodity consumptions were estimated along with the usual price and total expenditure effects. In this light, different rural welfare programs have been assessed.

REFERENCES

Benus, J., Kmenta, J., and Shapiro, H. (1976) The dynamics of household budget allocation to food expenditures.Review of Economics and Statistics58:129–138.

Glejser, H. (1969) A new test for heteroscedasticity.Journal of American Statistical Associa-tion64:316–323.

Holbrook, R., and Stafford, F. (1971) The propensity to consume separate types of incomes: A generalised permanent income hypothesis.Econometrica39:1–22.

Sah, R.K., and Srinivasan, T.N. (1988) Distributional consequences of rural food levy and subsidised urban rations.European Economic Review32:141–159.

Taylor, L. (1980)Food Subsidy Programmes: A Survey Ford Foundation. New York. Theil, H. (1975)Theory and Measurement of Consumer Demand.Amsterdam:

North-Holland.

APPENDIX

For the year 1970–71, NSSO (National Sample Survey Organisation) reports [Numbers 232, and 241 (vol. 1 and 2) of the 25th round] gave data on incomes and consumer expenditure of the economically weaker sections of the rural population (small cultivators and wage earners) households. Our analysis is restricted to only wage earners. These data were available in averages for 63 geographical regions in the rural areas. The total wage earner households sur-veyed were 8714.

For the year 1974–75, Rural Labour Enquiry 1974–75 (Final report on income and consumption expenditure of rural labour households) presents average data for 23 states in India. These data on incomes and expenditure relate to agricultural labour and all rural labor households with all the associated information on number of households, household size, age-gender composition etc.. From this information the figures for rural nonagricultural labor households were worked out. With respect to the incomes and receipts in kind form, the data sources reported only their cash equivalents. Cash loans were separately recorded and shown under “incomes and receipts.” Kind loans were converted into their cash equivalents and were included under expenditure. Thus, we treated the difference between total expenditure and total incomes and receipts as due to kind loans. However, this could be a source of error to the extent the prices assumed by the surveyors and actual consumer prices differ. We believe this is only a minor measurement error. Receipts under sale of assets, cash, and kind loans were lumped together (DS).

That these are average data does not mean that one individual labourer was gettingY1and anotherY2so that averaging over the two shows, the “average”

laborer to be earning both Y1 andY2. To confirm that many labourers were

earning supplementary incomes other than the main source wage income, data were checked to the most disaggregated detail available.

Instead of prices, we used price indices in our estimation. Food and general consumer price indices are separately available Statewise. Using the consumption weight of cereals in total food, and supplementary data on statewise cereals’ price indices, other food price index was derived. Paucity of Statewise data on

EARNINGS AND CONSUMPTION BY INDIAN LABORERS 273 the non food items called for some compromises. The Statewise price indices of the net domestic product of manufacturing was used as the price index for clothing and footwear and that of transport, communications, trade and hotels, etc., for the services and miscellaneous. Due to inadequate data, the price index of electricity, gas and water supply was used as the price index of fuel and light. The base year for all these indices is 1960–61 for the NCW (1970–71) and 1970–71 for the CNL (1974–75). This derived data on price indices revealed considerable variation across states both the years 1970–71 and 1974–75.

The algebraic expressions for partial and complete income elasticities are as follows:

hkj5

3

Pkakj

TY 2 Pk

o

j

(akjYj)

(TY)2 2bk

o

k

Pkakj

TY 1bk

o

k

Pk

o

j

(akjYj)

(TY)2

4

Yj

QkPk

(A.1)

mkj5

3

Pkakj

TY 2 Pk

o

j

(akjYj)

(TY)2 1bk2bk

o

k

Pkakj

TY 1bk

o

k

Pk

o

j

(akjYj)

(TY)2

4

Yj

QkPk

(A.2) whereTY5

o

j

Yj. Substitute Equation (A.1) into Equation (A.2) and simplify

to get

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 themkjare 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 themkj, the quantitative importance of the supplementary incomes and borrowings in maintaining the livelihood can be noted. For example, the sum ofmkjcorresponding 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 GC1LP1OR DS GC1LP1OR 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

Opportunities for all incomes to go up simultaneously are rare. Various rural development programs initiated for poverty eradica-tion differ in the way the addieradica-tional incomes are created for the

AND

CONSUMPTION

BY

INDIAN

LABORERS

269

Table 5: Partial and Complete Income Elasticities

Partial Income Elasticities (hkj) Complete Income Elasticities (mkj) Commodities/Incomes WG GC LP OR DS WG GC LP OR DS

NCW (1970–71)

Cereals 0.1011 20.0196 20.0762 0.0058 20.0111 0.4739 0.0265 20.0367 0.0584 0.0325 Other food 20.0370 20.0147 0.1287 20.0771 0.0001 0.8450 0.0945 0.2221 0.0473 0.1031 Clothing and footwear 20.5464 0.2304 20.1022 0.3504 0.0679 0.2690 0.3314 20.0159 0.4654 0.1632 Fuel and light 20.2600 0.3393 20.1560 0.0773 20.0006 0.5869 0.4442 20.0663 0.1967 0.0983 Services and miscellaneous 0.2549 20.3295 0.0200 0.0366 0.0180 1.3333 20.1961 0.1342 0.1887 0.1440 CNL (1974–75)

Cereals 20.0675 0.1334 20.0961 0.0537 20.0235 0.2660 0.1598 20.0804 0.0950 0.0639 Other food 0.0686 20.0742 0.0630 20.0010 20.0563 0.9752 20.0024 0.1056 0.1113 0.1813 Clothing and footwear 20.2628 20.3034 0.3220 20.0327 0.2768 0.5367 20.2400 0.3596 0.0663 0.4864 Fuel and light 0.2081 20.0267 20.0173 20.1003 20.0638 0.9848 0.0349 0.0192 20.0041 0.1398 Services and miscellaneous 0.0802 20.3126 0.1699 20.2051 0.2677 1.5211 20.1984 0.2376 20.0266 0.6454

270

N.

S.

S.

Narayana

and

B.

P.

Vani

Table 6: Consumption Effects from Alternative Earning Schemes

WG GC LP OR DS E CRL OFD CLF FUL SNM

NCW (1970–71)

Reference: 941.7 116.6 99.7 132.8 110.1 1400.8 588.3 532.8 71.9 109.9 97.8 Change in

all incomes 1008.9 124.9 106.8 142.3 117.9 1500.8 612.1 518.9 78.1 119.4 109.3 only WG 1041.7 116.6 99.7 132.8 110.1 1500.8 618.1 580.0 74.2 116.7 111.8 only GC 941.7 216.6 99.7 132.8 110.1 1500.8 602.7 575.8 91.3 148.2 82.9 only LP 941.7 116.6 199.7 132.8 110.1 1500.8 569.3 645.1 71.2 103.9 111.2 only OR 941.7 116.6 99.7 232.8 110.1 1500.8 614.5 553.6 95.7 125.2 111.9 only DS 941.7 116.6 99.7 132.8 210.1 1500.8 606.4 581.9 82.2 119.4 110.8 CNL (1974–75)

Reference: 1896.5 150.4 89.1 234.8 497.1 2867.9 1383.8 911.9 120.0 201.6 250.7 Change in

all incomes 1962.8 155.6 92.2 243.0 514.5 2967.9 1408.1 955.9 124.9 209.8 269.2 only WG 1996.5 150.4 89.1 234.8 497.1 2967.9 1403.4 959.2 123.4 211.9 270.2 only GC 1896.5 250.4 89.1 234.8 497.1 2967.9 1527.1 912.0 102.3 206.3 220.3 only LP 1896.5 150.4 189.1 234.8 497.1 2967.9 1263.5 1018.9 165.4 205.9 314.1 only OR 1896.5 150.4 89.1 334.8 497.1 2967.9 1438.9 955.6 123.4 201.6 248.5 only DS 1896.6 150.4 89.1 234.8 597.1 2967.9 1401.8 945.9 131.2 207.3 281.8 Reference: At the sample averages of WG, GC, LP, OR and DS.

vide cattle and buffaloes; some subsidize food consumption, farm-development, land-development etc., and so on. A number of studies earlier analyzed different social welfare programmes. See Taylor (1980) for a survey on the experiences gained by different countries in this context and Sah and Srinivasan (1988) for an assessment of the impact on different social groups of a food redistribution program. Here we take up the issue such as: as far as ultimate impact on food and nonfood consumption pattern is concerned, are all such programmes same? To anayse such an issue, the estimated equations could be used. Suppose an incre-ment ofRs 100/- per household is to be provided in annual total expenditure over its current level of these poor households. Sup-pose this additional sum is provided through alternative welfare schemes: either as wages (WG), or garden and orchard produce plus cultivation if any (GC), or livestock and poultry farming (LP), etc. The resulting commodity consumptions under alternative schemes are shown in Table 6. Maximum increase over the refer-ence average level, for example, in cereals consumption was brought by additional wage income (WG) in the case of NCW and by additional cultivation income in the case of CNL. For both NCW and CNL, additional income from livestock and poultry brings maximum increase in other food consumption and even reduces the cereal consumption. Coming to non food commodities other than fuel and light, additional incomes in other household enterprises (OR) for the NCW, and in livestock and poultry opera-tions (LP) for the CNL provide for maximum increase in consump-tion of these commodities. The results suggest that employment oriented and land related welfare programmes have an edge over the other programmes in improving the staple food, cereals, intake by the poor.

6. CONCLUSIONS

Our analysis of earnings and consumption by Indian rural la-bourers was based on a new extension of the familiar LES model providing analytical scope for multiple sources of income. Individ-ual effects of different incomes and borrowings on commodity consumptions were estimated along with the usual price and total expenditure effects. In this light, different rural welfare programs have been assessed.

272 N. S. S. Narayana and B. P. Vani

REFERENCES

Benus, J., Kmenta, J., and Shapiro, H. (1976) The dynamics of household budget allocation to food expenditures.Review of Economics and Statistics58:129–138.

Glejser, H. (1969) A new test for heteroscedasticity.Journal of American Statistical Associa-tion64:316–323.

Holbrook, R., and Stafford, F. (1971) The propensity to consume separate types of incomes: A generalised permanent income hypothesis.Econometrica39:1–22.

Sah, R.K., and Srinivasan, T.N. (1988) Distributional consequences of rural food levy and subsidised urban rations.European Economic Review32:141–159.

Taylor, L. (1980)Food Subsidy Programmes: A Survey Ford Foundation. New York. Theil, H. (1975)Theory and Measurement of Consumer Demand.Amsterdam:

North-Holland.

APPENDIX

For the year 1970–71, NSSO (National Sample Survey Organisation) reports [Numbers 232, and 241 (vol. 1 and 2) of the 25th round] gave data on incomes and consumer expenditure of the economically weaker sections of the rural population (small cultivators and wage earners) households. Our analysis is restricted to only wage earners. These data were available in averages for 63 geographical regions in the rural areas. The total wage earner households sur-veyed were 8714.

For the year 1974–75, Rural Labour Enquiry 1974–75 (Final report on income and consumption expenditure of rural labour households) presents average data for 23 states in India. These data on incomes and expenditure relate to agricultural labour and all rural labor households with all the associated information on number of households, household size, age-gender composition etc.. From this information the figures for rural nonagricultural labor households were worked out. With respect to the incomes and receipts in kind form, the data sources reported only their cash equivalents. Cash loans were separately recorded and shown under “incomes and receipts.” Kind loans were converted into their cash equivalents and were included under expenditure. Thus, we treated the difference between total expenditure and total incomes and receipts as due to kind loans. However, this could be a source of error to the extent the prices assumed by the surveyors and actual consumer prices differ. We believe this is only a minor measurement error. Receipts under sale of assets, cash, and kind loans were lumped together (DS).

That these are average data does not mean that one individual labourer was gettingY1and anotherY2so that averaging over the two shows, the “average”

laborer to be earning both Y1 andY2. To confirm that many labourers were

earning supplementary incomes other than the main source wage income, data were checked to the most disaggregated detail available.

Instead of prices, we used price indices in our estimation. Food and general consumer price indices are separately available Statewise. Using the consumption weight of cereals in total food, and supplementary data on statewise cereals’ price indices, other food price index was derived. Paucity of Statewise data on

of the net domestic product of manufacturing was used as the price index for clothing and footwear and that of transport, communications, trade and hotels, etc., for the services and miscellaneous. Due to inadequate data, the price index of electricity, gas and water supply was used as the price index of fuel and light. The base year for all these indices is 1960–61 for the NCW (1970–71) and 1970–71 for the CNL (1974–75). This derived data on price indices revealed considerable variation across states both the years 1970–71 and 1974–75.

The algebraic expressions for partial and complete income elasticities are as follows:

hkj5

3

Pkakj

TY 2

Pk

o

j (akjYj) (TY)2 2bk

o

k

Pkakj

TY 1bk

o

k

Pk

o

j (akjYj) (TY)2

4

Yj

QkPk

(A.1)

mkj5

3

Pkakj

TY 2

Pk

o

j (akjYj)

(TY)2 1bk2bk

o

k

Pkakj

TY 1bk

o

k

Pk

o

j (akjYj) (TY)2

4

Yj

QkPk

(A.2) whereTY5

o

j

Yj. Substitute Equation (A.1) into Equation (A.2) and simplify to get