0\epter 6

nECIONAL VMIA'l'IONS IN SOHE EMPLOYMEUT at.MAC1'E.RI• STICS OF FEMALE NJRKERS 1 AN EXPWRA'roRY ANAIHSIS

In the preceding dlapters sane insight was go!ned into

the level and structure of fanale employment, the anployment

end .tnccme of agricultural labour wc:men and certa.1n aspects

of female unemployment fran various secondary data sources.

In this chapter en attenpt is made to study regional variations

in the labour force in egricul ture end the average daily wage

earnings of casual worlters in agric:ult\U"al operations. It 1s

en exploratozy analysis of how certain demond end supply

·factors ere a8sociated with these features of the labour· market

at the regional level. 'Jhe labour force includes those who are

c:ur.rently employed as well as the unemployed. Regional varia­

tions 1n the labour force ere studied 1n te.tm& of the overall

participation and 1nterud. ty of employment. unemployment, and

the proportion of agric:ul tural lebourers in the work force.

It is a C%088 sectional analye.t.a for the year 1971•78, based

on the 32nd Round reo1onal level data. 'l!le 15 major states of

the country ere divided into 56 more or less banogeneous agro­

cUmatic zones which are caJ.led the regions.

~e method of study adopted is a regression analysis of

the enployment variables on various independent demand end

supply variables. ~e purpose 1s to escertain factors associ­

ated t~i th variouo aspects of employment and to test certain

postulated relationships between the anployment related

191

~cpondent verJ.ab leo end the .lndepenacnt varinbles. stncc

sevu-al independant vcrloblQO were found to be cottelotod \11th

~ech othOr. un ln~ex o! these vcrioolca tiCS c::onot.tuctca using

the technique of factor analysis. ~a ineiex wElD then uae4 ca

ta inder,enoent wrieble in the rcg..r:eaeion aoalysJ.s.

~he chept.cr in c:U v1C!od into thJ:'(:e secUcns. lo ilect.lcn

1 tbo z;ol.oveut 11tcratw:o .lD nYie\.'Cd in ~<lcr to unecr~tand

the ct:.ccrvcu rcleUonahips bot:t:een var.touu fcc:toro and tho

C!mploym.ent varieblCIII to te etuc.Uect. In section 2 tho specifi­

cation of the CeJ;enc:lont and .f.ndepaxlent vari~lEO nne DC'r.lG of

tho Um1tnt1ona of tho aata uceil are outUDed. ccrtcln 1lYPQ­

thot1cill reloticMhlpa to be tested in the otuc!y oro alDO

postu.lfltea in thiS Gcetioc. 1be emp.lrieal results chtcincd

and tho inferences drawn frcrn tho regressicn enalyclD nro

presented in aecticn 3. 'Ibe major c::onclusions ero cu:.:.u:u:1zea

at the end of tho chapter. Appenc:lix VI.l p%'0viclea a brief

easessment cf vcrioua aethcds of constructing e compoal te

:f.nclex. 'ltle methOO. of constructing tho index of agr:!c:ul tural

c1evolo.r;ment used in tho chapter to olso outline4 hc.r:c.

certdn problema of obtuin1l'l9 dctc ot the regional level aro

dlac::ussea iD appema.tx vz.2. 'lbe detoilD of ccljustmonta made

in order to obtain sane ~:egJ.onal level es UmatcD aro alao

.npo.r:ted here.

1. D!.'T!'J>.Z·il11JJ~~:;;; OF l,I:.:·L'\1.£ fl·tJ.?Wl!·!t:!iZ a ,\ Ot~c·ri V!i! l'J;;VliAI Oi' Ll~.t:UA1URE

A brief review of the relevcmt U terature is unccrtaken

here to get an idea of the )"-J.ndD of relationsh1r4 that have

192

been observed by various authors between tho employment

characteris t1co outlined Qbovc Md sane socio-econanic variables

on the basiS of this, the relationships p.roposed to be tested

through the regression analya1B are fo.tmuloted.

l§!U!le left122U£, PQFtA¢Pat1on

A number of studies 1n recent years h~ve show:l that

funsle labour participation tendS to decline w1 th economic

developnent end the consequent increase in 1ncane levels. Dn::­

gupta ( 1977) using Agro-Econom1c Research centres (l~RC) data

at the village level found that the overall participation rate

declincd in advanced villegeo, aeopting the ncn., • green revolu­

tion• technology. 'lllis ves due to the withdrawal Of intermit­

tent workers, such e.s wocnen and children. sawant and Dewan

(1979) noted for 1!le.nc district of Uaharashtra, that ·tho work

participation of both males and females were lover in tho

developed villa9es. Other studies using various proxies for

economic development also arrived ot similar results. Incane

meaaurEXi in te.tma of veluo of output per worker was found to

heve a negative effect on fanale participation rate in .rural

uttar Pradesh (Papola end Misra, 1980) • ~e proportion of

irrigated erca to net sown area was found to be negatively

associated with female participation nt t:he stat~ level and at

the tlistrict level for Mai!hya Pre.desh (Reddy, 1975). ~is \Ja:J

e:tt.ribu.ted to the hiQher income levels in tho irrigated

regions. Labour productivity being used as a proxy for per

capita income was found to be negatively associated with fanale

participation at the state level (Reddy, 1975). Low lnbour

193

p~ctiv.lty wna asooclated with s~anty r:&infcll. tlOOr iaiga­

tion,. clepcnck nee on dJ:y crop end ouch regicns bed !'..1gb or fecole

pertid.pctiofl.

ucc::tore t:bic:b were p~o1t1wly QOeoeict~ tilth fo:Jele

lebeur pt..niclpc.ticn ~rc the rorcc:nt!]tJC Of 091'1Cultur~.

labow.·ers irl t!'le population ( PCl)OlO t'l"ld f1lDt"U1 17'Q{)) 1 Ct"O:l

u::C:cr ~ jetter cult1vr:.t1cn (RC'..lly., 1915) Mel thn proportion of

sdleeuled caste end echewled tribe househel(l:l (eerdlon. 1!i<:4).

thlle the new t\9Z'icu1 t.urel technology led to n fnll in

tho ovcall pezt1d.P!'t10D rate. the dUration of ~ployment

.lnc:reaaeo.. ~ot 1a, in ~:egiona e(lop~ the new technology

tiork vna concenu-etcd in the honda of fcwr more ~t

members of tho labcur force (Dasgupta. 197?).

etciO&"ol etudlo:~ ebow a pco1Uve inC.I'ea&e in lebOur use

I:G.t' bectuo t;ltb tbo y1elt:l increasing t.ecbno10gic.::1 i.":lpro'Ve­

mmt=a »uch cs itti9at1on, u~v. fe.rUUzcr~ plant p.rOtecUon

chc:miccla end mul t1p1o c.t(lpptng (for 6otnilud n:view of thcso .. ctud!ea, oee nasent. 100 .. 1) • UO\.;avar,. there ct'O wry few

otu~ai1 ~.tc:h enalyso tho impact of the new tec:hnol<x;y on

fa.1ale etn!"l.oymont. IJ.I.ngh et..el (1901) noted o fall in i:anole

lr.bour per hccto.ro w1 tb the 1otl:Oduct1c·n of uw techno low,

usin{; panel dat:a for threo t:J.Ito po1.nt:s .!n ee:ttcrn Utt:;r Prnticah.

Dhollo ( 1!it1) • \1h11o CQ:n~"'\rlng five egro-cl!mt~tic eones of

194

Ha.ryena, note4 that almost the entire positive impact of

irrigation and HYV use waa on male employment. l .. ceortlirlg to

e t-:cM:R ( 19SO) study, tractor use '":as associated with lcsa

fenele labOur use per acre. 'lbus, whilo the introduction of

the new technology incUcated all increase in the intensity of

male enployment per unit of land ita impact on the intensity

of fsnale employment per hectaro was not very clear.

'lbe factors effecting the ttJO major canponenta of the

female lebow: force, cul t1 vators and ag.ricul tural lebourer:s,

in a particular region could be qui to different. Paday is

coaaidezed a female 1nteru:J1ve c:xop. sen (1983) using census

Clsta noted that the proportion of cul t1 vators in the fenale

population tendS to ba low in the paddy grotd.ng regions. ~a

.to pem~ we to the difference in caste between 'WCI'Qen of

lena holding c::lass(B ana the schcd.uled caste/tribe \.TOmen who

work as labourero in tho paddy regions. However, she elso

found an el::IGence of correlation between aren under paddy end

proportion of agr1c:ulturol l®ourers !n .rurel fene.le population

or the acx ratio of agr.iculturcl labourers. ~e census dnta

p.robobly only tell us if the tJQnen of a particulc.r .region work

as agric:ul t:ural labourers 1 they d.o not tell us about the

presence of ~anen (from other '"eg1ono) working as agricul turel

lebow:'ers in the region. she hypothesized that tho e.gricul tu­

.ral labour wanen who work in the padcly .regions really migrate

from other regions and were not captured by tbe census of that

195

region. Hence, the relevent question would be, what causes

wanen of o region to beca.ne agricultural ll!bourers? she

identified c:este end thG extent of regionel impoverishment ss

pos31blo factors. A high i~c1Gcnco of female agricul~~al

le.bourero in the fe.:al~ population was found to ba c.ssocitoted

wi tb a high p.roportion of COilrSO cc.roals in g.ros.:J cropped urea~

low i:lgricult:~t~.l pro®.ctivity • poor csricultu.rcl <;;J:Oi.Jth end

high land inequalit.i meczure:G in tenno of g1n1 coefficient of

0"-'1101! lut:d (SEn, 1905) •

Chopra ( 1982) studied female participation in thr~

crop te9ions of wheat;, na.lllet and rice, using cenDus date. of

1951, 1961 ond 1971. She rmalysed various explanatoxy vari­

ables likely to hove an impact on dG!lend ana supply of fannle

employment. using value of output per worker as a proxy for

illcane, she found a negative association with fenale c:ultiva­

tora end a positive association with fenale agricultural

labourers, in the wheat Md rice regions. ln the millet

region, however, there was a conflicting esaociation with c:ul­

ti vators in different years and a negative association vi tb

agricultural lebeurers. on the demand side, proportion of net

irrigated area to net sown area bad a negative association with

fenale cultivatorD in tho "heat region and a positive associa­

tion wi tb female ag.rJ.wl t.urill lal:lourero in tbe ~eat and rice

region. On the supply side, the PL"'port.ion of scheduled caste

population had a positive asoociation with fenale agriculi;urul

labou.Z'ers.

196

ynanR).oyment BAte

'lhe relationship between unemployment, agricultural

growth or p.roducti vi ty and poverty has been (li.scussed conside­

rably. Lak4awale (19'78) noted that in msny parts of the

countty low etenderds of livinq of le.ndless leboureJ:s are

eDSociated 1~ith fairly let: uncmpl~YTncnt rot~?s end t..'l(.!Je

regione al:lo have flXtrcmElly low cgricultural P!'Oductivity.

Certain other ctudie:J,. ho~..:ovor, c:ontredicted thwo ::wults

(OMtwclc, 19'7!>).. :::au ( 1973) using Lel:r.!a\z~la' s dat.; &Jttived

at tho conel us ion that poverty and high une::tployccnt nlso co­

exist .tn mcny pleceo. no also found the relation bett.:ccn out.

put por hectare and par c£I)i ta monthly consumer expendi tore to

be Dixed. He concluded that if pove~J seemed to coaxiot with

low rate of unemployment., it only ocant that anployrnent in the

region wos not "~.;Orth the n~e. Des ides, there 1a no mechenicnl

connection bob:ec:n low p.roductiv1ty and ucute poverty., oince

social .rolat.ionsk have immenoo bearil'lg on these 1asues • Partha­

sarathy (1979) examined the oomo set of data as Lakdavala and

concluaod tho.t thcr~ ~w no fi::m relation betvoon unenployment

ratas ena level or g.rowtll .rntos of output par hectare. Aggn­

get1on of region/district of .relevant states is also likely

to vitiate compar18ona and therefore the findings (Dantwale.,

1979).

FOr t,h(?l 5G r~ions of r11ral India a. post ti ve correla-

tion of o. 30 (significunt at 5 par cent level) is observed

between unemployncnt end avcrcgo agricultural outp-..:t per hectare.

19'7

visaria (1981 e) fel't that perhaps valuo of agricultural out­

put per hectare it.; cot o good index of the extent of poverty

1n a region. Also, regior.n with high ogricul tural producti­

vity mey attract unanploycd labour from neighbouring areas.

KriBbna ( 1984) baD more recently found a positive association

bet\fecn poverty end unemployment, particularly in le.ndle.ss oud

small farmer bowsehold:J.

Mishra (19C4) tried to explt.d.n the incidence of poverty

for ~6 NSS regions of tho country (19'12-73) using factors l.L~e

unemployment rate, vnlue of output: per hectsre4' proportion of

t:nge lcbcur houaehol&l, 11verage wage carning:s end proportion

of eaeet poor householas. He divided the regions into high

poverty end low poverty regionS. Unemployment rate failed to

show eny significant relation with incidence of poverty in low

poverty and all ngions, while in high poverty regions it sho­

wed significant negetive easod.ation. However., the model had

low explcnntory power. in t-he high poverty regions. 'lbe euthor

euggested that povert"-J is perhaps so deep rooted htee that

these fectors fo not ex~rt e.""l'./ s1gn1ficent influence.

U::ing t!~.s hcuseho ld level dcte for GUj a.rot <::nCl t-;cllara­

Bhtra, Visru:la (1981 Ci)· noted a clear inverse association bot•

woe..'l monthly per c~.p1 ta consumer ex[)efldi tu.re end the daily

~tstu.o uner:'q?loyment rt~.to. 'l'bc poor. reported non-nvni.:tn...,i11 ty

of oppo::-tuni ties for work to connidcr.ebly groat<':'r extent tba11

the avcrngo level in the two atates.

198

In U2 illtereeting study on the int~egional va.rictiono

in povert~; and unemployment, t.undcr.~r.a t:.nd 'l'cndul'kt:r (19Sn)

hypothesised thet ~esc t\:o vcr1t'.blc3 oparate on ench other

( ae reletcaX through tho avercge value and dis trilY\ltJ.On of

esc eta, the producti v1 ty of the main asset land, ani tho over­

age velue ot ccnaumption oxpenai ture.

sanyal ( 1984) stuc3y.:l.ng .tnt.exregional variation bett·1een

agricultural pr~tctivity- land holdirl{rs er:d uncrnploynent

aeress the 56 NSS regions, did not find any s.S.~ificent associ­

ation between agr!cul tural product! vi ty end unenployment :rata.

unemployment rate in the reference weel( for a casual

fum labourer in rural west Bengal was found to be negatively

associated with farm siZe, dependency .ratio of the household,

and dun;nys reprenenting village level irrigation ~nd the busy

&:eason (Bardhan, 1964).

'lbe average wage oarnings wore found to be pooi ti vely

associated with varieoles indicating agricultural dcvclo~ncnt

both at the household level for .rurnl t.'SO t Bengal nnd for the

52 agro-cl!matio regions of the country (Barahon, 190,;:). At

the household level, the:Je wore the du."t'lmy vcriables represen­

ting village irrigation, a district agricultural develo~nent

index., use of n1troueneous fertilizer end lower deficit in

rainfall. At the regional level, they were use of ff~tili:;:cr •

.. _~ .. .-~:-~-... ~·-~ .... r.~. _.. '"1 ·.; . 1 .

/ ~ .,.. .'·/. ··\ . . ' ' .. ~

. ..,· ,·.

199

soil quality index, low ,deficit in rain and proportion of large

farm householdS. At the regional level, average wage tlarnings

waa found to be negaUvely associated with variables indicating

excess supply of labour such es uncployment rate ena propor­

tion of asset poor householdS.

Pepola end Miahra (1980) studying interdiatrict varia­

tions in male end female wage rates for rural uttar Pradesh

observed that supply conditions came out more sharply as deter­

minants of the female wage rote, 1. e., supply of funnle laboUr

and pxoport.ton of a.grieul tural labourers in the population

ahowcxl neg aU ve associations with femele wage rate.

2. SPECinCAUOt~ OF VARIABLES AND DATA Lll1ITATICUS

'lbis section provides the deteils of the specification

of dependent and independent varieblea used in the regression

analysis. The dependen~ variables ere d18cusoed first. '.Ibis

is followed by a discussion of the independent variables and

the likely relet1onahips between the employment (dependent)

variables and various socio-economic characteristics (indepen­

dent variables) • \~erever aec:csoary, tho problens of apecifi­

cction arising fJ:om the inadequacy of tbe available data aro

highlighted.

PCR!Dsen' XA[l@blea

LM9ur PpEtlclRe1£~on and Iptens&gr of flllJ210Xffignta TWO import­

ant dimensions of the enployment situaticn are the overall laboul

force put1cJ.pat1on and intensity of employment in a .region.

200

(me dependent variables and their notations arc lJ.otcd in

'l'eble 6,1). Ld;)o-~r participation captures the proportion of

persons who were working during an agricultural year. ~o

intensity of anploymcnt is defined etJ tha days of employmont

in an etjricultural year available r.er worker ond tho day.J of

employment availcblc per unit of land. ~eDe variableS have

been specified on the bt~Dia ot the !13S 32nd nouncl data, 1977-70.

!be overall labour participation iD canputed as tho

worker population ratio separately fer males and females

(Wl?BM, WPRF) using the usual status data. The usual status

data at the a tate level includeD bOth principal and subaidi&y

worke.t&. However, at tho regional level these dntn do not

ioc:lude subsidiary workers. In order to obtain ~timates

of sutsidie.ry vorlcers at the regional level I have CS!:il.m1cd that

the d1s tribut1on of subS1dlary workers at the regional level

is the name tiS the distribution of non-worl\.Grs. ~ usucl

s tatua estimate of worl-..en~, thus computed captures worJ.:c~ by

usual activity as vell es those not usually working b'_llt

engaged in sana subsidiary activity during the year.

The usual status data do not capture the 1ntenoi ty of

employment. some measure of the intensity of anploymont can

be obtained frc::2 the aaily stottm clata t~hich incorporate

intensit¥ in half day units 'tO arrive at th& person days of

employment on en average day in tho reporting yeer. f!owever,

201

-:able 6.1

List of Dependent variables w1 tb Mesa and standard Deviation~·

I

viideble Mean Stlmdard Nota-~on ~;Ugtion

t-JPRF worker population ratio, female (%.) 43.0 15.4 l'<'PRM \-:orker population ratio. male (%) 65.2 4.5 Db eli' Days of employment in egri~l ture

in a year per hectare (GCA) fe:nale 123.0 ?1.0 DhcH Days of employmant in agricul t.u.ro

in a yecr per hectare (GCA) mala 234.0 8%1.2 AWF Days of employment 1n agriculture 238.0 58.6

in a year per worker, female AVtH Days of empl~ent in agriculture 333.0 21.2

in a year per worker, male PrAJ..F Female agriculture labourers to 30.5 15.0

female workers ( ~ Prl-W Male sgricul tural lt!tour&rs to 23.0 9.6

male workers (~ URI' Persondays of unemployment to s.o 6.?

persondays in the l~ur force, female (%)

Unl-1 Persondays of unemployment to persoD- 7.1 s.o days in tho labour force, male (%)

AWF Average wage earnings in agriculture of fenale casual workers (es)

3.1 1.1

AVtii Average wage earnings in agriculture 4.0 1. 3 of male casual workers (!>$)

a limitation of the daily status data is that while moat of the

principal workers by usual ~tetua ~e captured by the daily

status. a large proportion of the llUbs1d1axy workers get left

out of tho daily status count. According to the 38th Round

( 1903) cross tabulations between usual status end daily status

data :for ell India. while 90 end 73 per cent respectively of

male and female principal workers are \forking by daily a totus

also. only 23 end 18 per cent respectively of male nnd female

subsidiary workers by usual status ere working by Claily otatus.

since a luge proportion of the sub.U.<U.ary workers ere not

captured by the daily atatus • 1 t can be assumed that the data

202

on intensity of employment refer to only principal workers.

unfortunately, such anta are not available for sublic:U.e.ry worl,ers.

It .18, therefore, necessary to estimate the days of employment

availeble to both principal end sublidiary workers taken togothcr.

Again accordino to the 38th ROund (1988) data the sub­

sidiary workers, both malo end female, \iork for only one-fourth

of the days \forked by principal workers in a ye~r nt the all

India level. ~eking this aD a otand&J:d it is possiblo to obtain

b;o different specifications of the intensity of employment

computed from the 32nd Jound date separately for males and

females.

(a) ~e days of agricultural employment available per worker,

incluaing both principal end subSidiary worlcers in a region

(AVDF or AVDM). 2.111s .ts obtained in the following waya

(X11) X hlmmi) X • (X:u.) X (Wsuq! ) t~here, AVDF or •----~------~~---------------------•----=----------AVU-1 '1pus1 + • \-1aua.t.

nyear

• .., •ti ~ Number of pr:lnc:1pal workers by usual status in region 1.

~Number of subSidiary workers by uoual :Jtatus in region 1.

(b) '!be days of egricul tur el Employment available (to o. pr in­

c! pel Eilld subSidiary workers) in e yoer per hectare of GCA

DhCF or Db eM

203

• (XU) X (W~si) + (Xzi) X (W§USi)

GCA 1n reg on 1.

persentage of·agr1cultu£gl LQbgurwos ln the wo;k Forcea Agricul­

tural lnbOurera foz:m en important component of the agricultural

lnbour force. A lcrge proportion of the female worlt force also

consists of agricultural labourers. How the proportion of male

and female agricultural labOurers in a region is .-elated to

vil%'iOUS demand ana supply factors 1:3 another aspect studied in

this chapter.

Using the 32nd Round date., the variable has been speci­

fied as follow, percentage of agric;;ultural labourers (r-tale/

female) to total male/femele workers (principal plus subsidia.r;y)

. (PrAia• PrALM) • !be problem of the non-availability of estimate%

of subaicUary wor.kera at the regional level wet~ mention~ earlieJ

matimates of subsidiary workers who work as agricultural labOureJ

WliS obtained by assuming t'1nt the cless of worker distr-ibution

of subsidiary workers at the regional level was the S&'ile as· that

at the state level.

ynemployme"sa In Chepter 5 V::t.t"ious dimeno:t.ons of the problan of

unsnployment and underemployment were stuc:lieCS. M atter:tpt 1a

made bere to analyse the relatior.ships beblean unenploymcnt and

various factors such as poverty, agricultural developnent and

pz:oductivity in a region. In most of the studies on unemploy­

ment and !X)verty zeviewed earller, unemployment is taken ea the

casual variable. 1be logic presumably 18 that the irlcome level

of a hous cllold is dotcrmin'ld ~~ the employcent available to the

204

housahola members or CC.IflWrooly ty tho l~c:l: of 1 t, 1. c.,

unecployoent. t.n'1 str.ce povcrt7 ia me.,aurcd ~1 u.o1ng o cut-off

potnt of the level cf consw:ea: t;:,.verJdt turc, e prexy fer 1ncune,

unetis>lo~l•:lCnt bect..t~c~ n c~suc;l ft.tctor tn explcining t;~Ncrt~r· ltl

th~ r('t~C~t'~cn e•r-r.c.!::;e in tbf.o nt'1'~f' hot;evrr, f'l'\!J~.or.ol vr..r!o­

t.lc:ns 1n utu,mple~lnent err" sought to be t'Y.Plt:ined tn ter1~a of

r:'Qglonnl vnric'!!or.s i~ o t~ellGurc cf 1/.ivcrt;r. 71'lc t.~ccrlytno

J.den iD that ,r.ovett'J, mc~;.Z\lt'f<i 1n tc~ of cor..sur:cr mc~nt11tw:"e

~low e ccrtt!lr: lev~l~ io. oetccical l:'f tho lc.~cl end accct

Dtructtll'e of. tho rOQJ.On (LUDdu'~.J t:ntl ~·:.::Cdulke.t\t !OOC) • J:::)tJ.•

mctco of l.4"l•~1z;loy::1ent. rnte.o conputru froo the u.;;.;~ ~~~~ renll:,•

cc.pt'.tt'c e:lli" o:,.on :mtl "V"'..s.iblc tm~..)lCl~::cnt. ::u"*"'i c:;c:t t:nc=~!Cl'J•

ment wulcl be 1o\li~r 1n r~:t;icr~a t..har<~ th-C>.rc u lean cnec:tlel

ai:;tibutit')n of lc:1t; i'!':~ n::=:ote 01' t::.orc: ac:cr;:o !or self ~;~::)lC:t•

~t~Cnt. oven unc:nplt:t~~nt \~Culd be \;t'eatc.r 1n rcu.ton:1 t.tlc rc thora

iu u IJO!:C' u••ec;u.al aJ.otr1bltlen of lc~nu mil riSaet=., ~o:.·t: \.:o.t-u

ocr;onu~:nt hou::Jdlolc.!:- er.t:. bJ.glu:: lcr.~elo of t.JOvort-J. rz!1c 1nc.1ccnc1

of pov(:r~J f.:on thuti l'C te.,e:en es e liroxtJ varieble for t-...ot!1 tho

proportior. of! weQo c1C"pond~nt hcuach~lda ca •11 c.u tho lQ:!u t.n=l

c«iDctD ouuc:ture of tho .region tlnd used to explain rcctent!l

vartet1C.."'lll to the unco,lt."'Yt:ent rate.

~le WlC!C.I,)loy;u:mt .c;cto beo t(:{:n <let.1:lcd WJ1r..g ~o x:.ez·ccn

<Jt:y daily otntt . .-..s conce:>t of the 32nd noune cf tho t~~:;, dinco 1't

i.e tho mout. CCCliJ.~tchc:nt.i.1¥c oeeew:o ~t:.:.t:1t:g to en c.:•tc.,."lt toU1

uoe'.'lploJ:ncnt nnd undcrc:plOf::-.(nt (O(:c Cb.e:>tr:r Et) • 'J.hc er.r.: ~ley­

meat &etc \JOU Ol7ecifJ.o4 eo vcrcent~o o! r;:clo/fcuclc vo:.~<.n acyu

205

of unemployment to male/fanele person days in the labour force

( UR·1, RUF) •

averag~ Deilx Wsge ~~;nings to casual LftbOU& in ASKiculturC2

Finally some fectors associated with differentials in the \Inge

rates of casual labourers across regions are explored. 'Ibo 32nd

Round does llOt give data on the wuge rate for eccb tigricultural

operation. 1 t only provides tho average C.aily wage ccrnings

of ct¥iual loboureJ:s ln agrlcult.uro, com,putcd by dividing the

aggregute wG~tly wage earnings for agricultural operations br

the corresponding number of days of casual employment of full

intenai ty. ihe aver ago wage earnings of casual la.bOw:crs in

agriculture (Aft;7• AvK•l) aa:e speoifiecl accordingly separately

tor males and f e:naJ.es.

~e limitation of this specification of tho Vilritlblo 1G

that it 1a not reully the wage rnte accruing to casual worlter

for a specific agricultural operation, but en avarCCJc daily uege

earnino dependent on the total earnings and days of employment

available.

!be independent vuiables used in the r.egression analysis

and their notations ere listed in Table 6.2.

,Ag£ic;ultyrel neyelepnent u..,en.IDdQ2~dent yer11ffilea (a) l!l.@

c~sual Linkaa Agricultural dcvelopnent has been fairly rapid

in sane Z"OJions .la recent years and baa been associated l'lith

the edvent of the new agricaltural technology. ~is has had

206

1 ts impact on tho employment characteristics outlined nbove.

It has been sug9estcd that higher incane levels liSBOciatcd with

agriculture! development hnvc led to o d~~linc in tile overall

participation rete. 'Ih1s waa due tlO the 1171thdrawal of inter­

mi ttent tlOrkers such ~ wancn Md children (Dasgupta, 1977) •

The dUrution or rley~ of ~ploymcnt per worker has, however,

lncret.Dedt the t-;ork tiCS coccentrctC'.l in the handS of fc~r more

pe.rmnr.ent wor~~ers. in the agr.tcu~turnlly developed regions

(Dcsgupta, 1977). aevernl studies have clearly f.nc1icmtcd the

increase ln int.onni t"J of male employrr.ent per hectare \'71th the

introduction of the h1gn.y1el~ng seed-fertilizer teennol~J·

tiOtJCver, ita impact on fc:nale 1ntmsity of etnployment per

hectare \1Qa not very clear,

t'\t the crcss-aect1cn~l lo-..,-cl e.t \-Jhich this enolysi£ he.s

been uneert\:'.:ken, one "'ould expect tho participation rato of

both .sexes to bo lot~'tlr in the agr1cul turelly developed rccion:;,

while the Guration or 1ntensi ty of c:nployr..ent per worker 18

expected to be higher. on the other hend, while the r.tale

intensity of anployzoont par beotaro would be higher in egricul­

t.urally developed regionu the relationship with female .tntensity

of employment per hectare ce.nnot be predicted definitely as

noted earlier in section 1.

~studies revie~n:d earlier present contraclicto.:y

evice.tlcc oD the ~ociotio1~ bot\ieen varioua indicator.:J of c.gri­

cul tural dGvelopuent and the 1nc1dence of fan ale agricultural

labourers. \'.bile Gell (1985) found a negative association with

201

'l'able 6.2

Liat of Independent Variables Used in the Regression Analysis

Notation

.IJ\D vrAL

t:'.t'OPSO

UICl:: cc:r LIC A val P.rPOOR PrS~

Prsc

PEale Af! 'l'nhe PrUJ AVLAW

variables

lndex of agricultural development t-griC".llturcl lt'bo'J.r hc:.!.S~hol~ to tot:.·l rurnl householdS (%) .rxea. operate·;; :bJ the bott.:cm SO pe1.· <;e'1t of hold­ings (~ l:Xt.:a unC~.t: .1:.t.ce to total GC/t. {~ Area ur.:cler CXltton to total GCh (:Q t.roa under labour inte~i-..-o crops to total GCA (~ TOtal bovines per household I?opulation J:;elow the poverty line (~ SChedUled t-ribe househol<'S to total rural house­holdS (}_, scheduled caste houadlolds to total rural house­holds ('~ ProdUction of 18 major crops per hectare (nz> Actual raiufa!l (mrr.) Tractors per • oo hectare Landless householdS to t:otal rural households (~ NSA per agric:ul tural worker

agricult"Ura.l productivity. Chopra (1982) observed a positive

assoc1o.tJ.or: ~1th value of output. per t10rker end nat irrigated

area 1n whea-t e.na rice regic.ns. Obviously, the entJ:y of wanen

into the a.gricul tural work force is <Ufficul t to predi t at the

aggregativc level in terms of varioua indicators of clevclopnent.

i'inally, \l..~ilu wn;e: .c-atGD eccruing to casual l~ur in

as.riculturc arc ~<pectcd. to be highar in agriculturally deve­

loped region:.s (Bar6.han, 1984), the 853ociat1c;~ bat\-;em unenploy­

me:nt rt.ttas ana etf.riculturcl G.evclopmcnt ic not so clEK·r c.nd

obvious.

(b) Nerd for ccnst;uction og '• £2ml?2Site tnd§X of A9z:icult:uraJ.

1Je"!llopnenta A number of veriable.s can be considered to indi-

208

cate egric;:ul tural developnent of a region. such es agricultural

productivity, availebUity of \Fater thrOugh irrigation and rain­

fall, various technology factor& such as fertili:er use and

mechanisation, the cropping pattern, multiple CJ:"Opping, etc.

No single variable can be taken to zoepresent the level of agri­

cultural <ievelopnent fully.

Wbile it is possible to hypo~esize the relation be~1een

several variables end various aopects of female pertic:ipntion,

it 1s rather difficult to isolate the influence of eny one

single variable. 'lbis ia due to the fact that many of these

variebles are related to each other, that 18, there is problem

of mul ticollineBri ty.

In regression enalys18, one practice often followed to

tackle thiB problsn is to drOP one or more of the correlated

variables. However, this an unsatisfactory method since the

effect of tile variable droppEd may be compounded in the

.retained variable end the obServed association ca~mot J:Je really

categorized as the sole effect of the retained variable

(Vaidyanathen, 1978). 'lbere is, therefore, a need to consider

a group of variables which ere related to each other and vary

more or less together. rather than_ to lay emphe.a1s on nny one

variable or diecard possibly crucial infozmat1on at the begin­

ning of the exercise itself. 'lbiS can be done 1¥ constructing

e canposi te index of the inter-related vuiables which can b!

used ea one of the independent variables explaining female

participation. 'lbia method vea adopted and en index of several

209

relevant variables was coM tructed to represent agricul ture.l

developnent el¥1 tackle the problsn of mul ticolllneari ty.

'lhc cona truction of a c:ar.posi te index involves the p.ro­

blma of assigning weights to each of the variables to combine

tbe:a L'"lto one single canpoDito varieble. Various methods of

C.'Onstruct1ng a composite index were attempted end finally the

tedhnique of factor analysis was Chosen to construct the com­

posite inaax of agricultural development. ~ different methOdS

of construction of ccmpoaite indices end the relative advantages

of tho factor analytic method ere outlined in APPendix Vl.1.

( c:) J'sd.gbJ!ifs Included in the <¢!PPQ!i$e Jnc:ex of &0r1cu1tural

EOvelopoenta Eleven variables were chosen to be included in

the index of agrtcul tural devolopaeni:. '!he choice of these

variables was bued on sane a priori J.nfoz:mation fran the cxJ.s t•

ing literature end the eveilability of. de.ta nt the district

level. 1be sources of data and specification of these eleven

variables ere outlined belowt certsin problems faced in the

collection of these data end the methods used to rcaol vo thEm

are discussed in Appendix VI. 2.

Productivity of Agric:ul tu.rea A direct 1nd1cotor Of the

agricultural development of a region 1s agricultural produ­

ctivity. 'lbe main problem in the specifienUon of this factor

is the problem of nggregatiag across different crops. t-311le

meet crops are measured quentitati"-ely in tonnes sana of the

commercial cJ:OpG are measured ill different un1 ts. If they are

to be aggregated in value tenm, the problem iD which prices

210

should one use fazm harven t price, retail price or wholes ale

price? ~ variable was specified as production of 18 major

crops per hectare of GCA, Rs/hectare (PRhe) • Gross cropped

area (GCA) and not net sown area (NtJA) was uaed since the

effect of cropping intensity is not reflected in the GCA.

cropping intensity is coDaidc:ed separately cs a variable.

Value of outpUt of 18 major crops refer to the average

for the 5 year period ending 1979-00, the mid-point of 'Which

is 1977-78 (Centro for Monitoring the Indian EcOnaay, 1982).

'J.'he 18 crops acC:ount for 86 per cent of the l.eights of the

officiel index of Agricultural Production and 68 per cent of

the value of output of all agricultural crops. In tho cuse of

Kcrale... these 18 crop~ account for a much smaller proportion of

total output, end therefore, ten other crops have been nddcd to

compute the value of output. 'lbe ell India average unit price:s

in respect of these 18 crops were derived fran the cso•s reports

on l~atioael Accounts StatiStics, 1970-71 to 1978-79., '!bus, the

prices are uniform for all districts end relate to 1978-79.

'.lbe employment data relata to 1977-78 whereas the value

of output of 18 major crops .relate to a five year average with

1977-78 as ita mid-point. 'lbe days of anployment in the year

computed from the current atatua 4ata .reflect partly the

actual agricultural production in the reference year. 'lbe value

of output date have a eiaacivantage 1n that they average the

agricultural production over a 5 year period. '.ltle impact of

agricultural production in the reference year on employment is

211

not c:apturetl. '!be f;p.R:s computed fran the usual status data,

hOwever, ao not noc:easarily reflect tho exigencies of the given

year, even though the latter may affect responses. 'lberefore,

the use of egricul tural production_ date of the qu112quenn1um .ts

acceptable.

Actual Reinfalla 1be rest of the variable& ere mainly

those that influeDce agricultural productivity in a re91on. one

such factor 1s the ava1lab1li ty of water which 1s datemined by

the actual rainfall. 1'be ava1le])111 ty of irrigation wnter in

canalS ana tbe groundwater avall&ble tlu:'ougb wella/tubewells is

al.Bo dependent on the actual rainfall. Besides, a large part

of agric.ulture in India is still dependent on· rainfall. xn

certain parts of the counay w1 th asaw:ed. rainfall, e. g., Kerala.

normally U:rigated crops like rice ere grown under rninfcCl con­

di tiona. -me lo):)our use and agricultural producti v.l ty are

dependent on actual rainfall (AR) 1n a particular yeor. ~eso

data are also obtained nt the diStrict level from the fltetiatica~

l'J:xltracts of various states.

Irrigationl Irrigation lee(ls to agricultural Clevclopnent

of a region. It also affects labour use by introducing new

operations. leads to shifts in cropping pattern to more labour­

intcnsivo c&"Ops anci also makes multiple cropping possible. 1be

impact of irrigation would depend not only on its extent but

also on the intensity and source of irr!c;,ation. It 1a argued

that controlled is:r1gat1on thrOugh walla and tu.bewells he:a a.

l

212

greater impact on le.bour utilization (Voidyanathen. 1986) •

~o variables are specified ee followsa

Extent of Irrigation - gross irrig;Dted @li'!§ X 100 (GleN Gross cropped area '

Irrigation Intensity - gross .S.qiqated area x 100 (II) Net irrigated e.rea '

source of Irrigation -(Irrigation Quality)

Area under well(tubewel~ i;[igatio"xlOO Net Irrigated area

(t·.~

flhese data are twailo.ble in the ctat!Sticnl 1\lXltrects of various

states.

Multiple croppinga AJJ stated abOve, irrigc.tion increaseD

the possibility of multiple cropping l-bich hes a direct impact

on the total labour use in agriculture. methe~: 1 t would

incrcuae or decrease female lcbour use would depend on the crops

sown Sld the neture of operatiorw involved. fJho varicble \1SP

Sp!cified as cropping IntcruJity o ~~ x 100, (CI).

cropping Pett~rna The prevalent cropping pattern in a

region is en indicator of agricultural development. nenides, a

shift in cropping pattern towards more labour intensive crops

can lead to an increase in overall labOur use, introduction of

new operations ond change in the canposition of family/hired

labour use. cropping pattern in favour of non-food c.rops

iqplies greater market-involvement and commercialisation. In

general, non-foOd cro;r;e are also the high valued crops. ~e

croppin~ pattern variable specified aD area under non-food

C.tOIXJ tD total CCA (~ (NFC) W3S included in the egricul't:ural

213

dcvelo.r;rnent index. Ulese data were clao aveilable from the

:;;tatiotical ~tracw.

Fertilizer uses l,'hilo the data on ores under 1-zn' seedS

are not available for all the ctatcs ct the dintrict level, ti1cso

en fertilizer use are indeed ava116ble. However, fertilizer

u.sc is closely disociated tdth HYV seedS and is r.ecdcd to obtnin

the full potcntiel of the new seed technology in t~ of r:gri­

cul tural prodUcU vi ty. It al[o 1ncrc;:sca overcll lcbour Wle.

'lhe three nain ccnstituents of fertilizers ere nitrogen (l~) 1

potuSoium (K) , and Phosphorous (P). Data on fertilizer use

c cpnrc.tely in toms of these three ccmponen~ nrc evililo.blc ot

tho district level from the publication t~rtilizer §tatiDtics of

Indi.:;. J:'ertilizer 1\Soociation of India. The varieble can be ..... specified as NPK in tonnes/• 00 hecteres of GCA. (NPK) •

l-1echanisaticna Another irnportnnt component of the new

technology is mechrutisation. 'lhere ore confUcting opinions on

the exact impact of this on lebour use. l:hilo the use of oil

engines end electric pumps reduces the use o£ labour in the

actual operation of irrigation, it may inc:reose tho overall

labour use through controlled irrigation. Evidence on tho impact

of trectors on labOur use 1s also not conclusive. use of other

machinos such GS herveater combines is not so prevalent. oil

engines end electric pwnps are supplementary to each other,

i.e., water cen be pumped either with an oil engine or en

electric pump. Per en index of agricultural development, the

total effect of these two ahould be considered. The variables

214

,,;ere thus specified oa. follows a number of tractors per 1 000

hectares, ('J.'Rhe), end number of oil en9ines + electric pumps

per •oo hectares, (OEPhe). 'these data ara available at the

district level in the Livestock census of India which was con­

ducted in 1977.

acvinef.i per 1-:0WJcholdt ;J. though thio varialllo docs not

directly affect egricltlturnl vrooocti~.rity or agricul~u.ral clcve­

lo~nt, 1 t wns inc-..ludcd fer the t.~c.~o:."l;J g.~.vcn bclcw. '!he

1~ u.oc data of tho zws !rom uhidl t.'le dctcndcnt vcriilble

't:CZ dc.ri ved relate to the broad cutcgo~-y of atJricul turo inclu­

ding animal husbandcy. women opond a considerable ar.tO"'Jnt of

tim in loold.l'lg e.f ter cattle ana in dairy fiU:ming ( nisodia,

1985) • ~us, the \WOrk participation end days of employtv.lnt of

women are affected by the number of animals in the houocholda.

since my concem was for time spent by WaDeD in n household on

animals, the final specification used wesa total bovines per

household, (AvBH.l. 'lbese data MJrc also obtained free the Live·

stoCk Census of 1917.

:he z:elaticr.shil;Xl of a f~• c! thol vor!i:~lo;: 1ncl~ed in

the agricul·tural ecvelcpJJGnt ind(;.X 1111 th the 1ndq,,~n,cnt vari­

oblet~ vero aJ;; o c;Q?lored G(;parataly. 'lbey tiara the prouuett­

vity of agriculture in a r~gion, bovin~.s :a.JQr h::WJ·~hold, actual

rainfall and tractor wa. l3e!Jidc.J, theiio vor1ables included

in tho ugricul tural development index, the rclationsllip of tho

employment variables to other a:;lpects of demanj and supply of

labour ~-ere also studied separately. some such variwlcs -were

215

specific cropping pattern variables, lLnd r~lated factor~, the

percentage cf sch~dule4 cr.sto Md tribe bo~1eholdS and c anetl­

aure of poverty of the reqion. 'Ihe3e rolntior.ships end the

sourcC3 ~oo:d 3!0C1f1cr.tJ.cn of the V{:r1i::bles £U":e cutUL.ed balcw.

aopping Pgttemc variations in the lllbour use per cropped

area 1D dependent on the particul&r: crop gro'Wll ond Ul.o propor­

t1cn of u.rca acvoted to the 41ffCJ:cnt ctopa. A oh1ft iu tllo

CJ;opping p&ttc.rn in favour of labour intensive cro~ C{lll lend

to an inc:.r:a$Je in overall l~w: USE:, J.nb;c.luuction o~ new

oporaUcns a.."'ld chf'llge in the cowpositio.n of f8llily/iliJ:ed lubeur

usc. A variable indicnUng the r..arccntage oz ltibOur intcr~ive

crops wus oleo specified taking U1e p.r.·opol:Uon of £Jrco unilc:l:

pa.&:y t.nd nugoreane, both l;no;:n tv bo more lcbaw:' intcn=iv~

then ClOSt other crops. t-.h1le oevcral otl.le.r.: Cl."Of>t·• o.{;.,

tobe~-o cUIJ be iucntificcl cs lllLow: intc;:nsive, their import.unce

in tc.rr~ of ~rcenteyc area nllr:;t:e.te<l is not ve.ry atgr.i.t:iccnt

(Vai«!yanathan, 1986). ~us, in regicn~ t~ith a ~rec::.tcr );Orcen­

t<lt;;t· of lal;our intensive c..tor-s or.e \o .. -oulu ~ct h19her lclx>ur

uae Plr hectare.

cer..:;(l.!n op.oratio~ in the c.-ultJ.vaUon of .rico ancl

cot~n urc unecrtu.:.cn lJy foncl.(! l~ur ~uch ~ trcnsplant.ir.g

enu ha...""'Ves d.ng of .~o:ice (Uur.i.i# 1S81) on'1 cotton picki:.;g

(c:hattopadhyc.{, 1932). •.rhQo o.vr:.ruti".us ure cl.ao ~c.?JcrcJ.ly

done 1:1.{ hired lnbour:. (EV1dc:ace Zvr lhe hiroo lab.;:>ur cor;l!JO­

nunt in riee c:ult.!vc:t!cn cc,n be obt~uaG in U.!Ut1, 1ro1; !o::

216

ev14cnc:c on hired lebcrur una 1n cotton cultivation, ceo Jocb1

and Alshi, 1995) • A predominance of these crope 1n 8 region

is likely to be sasociated with a higher proportion of hired

fennle egricul turel workers and lower proportion of female

fanily wort. ... ~s. sen (1985) notcCl 8 low proportion of female

cultivntorn in paddy grc'Wing regions. valuo of ou1..-put pe.r

worker in rice regions was found to be negatively c.ssoc.toted

uith female cultivators and positively with fc:nnle c.gricultural

lebOurers (Chopra. 1982) • 'l'bus, in regions with o high propor­

Uon of rice c:ul t1 vation one would expect a higher proportion

of fanelo agricu~tlJral labOurero end lower fanale eel£ employed

liorkers. 'Ibo net effect on overall female participation would

depend on the balance of tl1ese two opposing effects. The

incioonco of. fenele agric.\,lturel lnbourers in re9ions with a

high proportion of cotton cultivation would clso be higher if

the operations ore done mainly by fOil ale hired ~.rkers.

~o three croppi~g pattern V£~riabl~3 ~re specified as

foll~-s s.nd these dnta w~o collected fran the statiStical

abl tnic:ts a percentage of! area tmder lto.bour_ intensive c:r:opa to

total Gel. ( LIC) r percentage of erea under rico to total GCA

(t\l:C£) 1 and paroentage of area un<ler cotton to total GC.\ (COT).

JtHJ6 pi;;b:lbutioga ~lcl ).'}cing the cost irr.,portant .rosource in

u9riculturo it; availwil1ty llnd eistribution detc~dne tho

agriclll turol devalopn3nt al'lt.l overall. lul;X)\U" us a in a region.

It would bo of interest to tieo how the distribution of l~<l

in a z:egion affects the overall work participation. T.:J::ing

217

per~ntage area operated by the bottan 50 per cent of holdings

as an indicator of land distribution in the region, the rela­

tionship was explored. However, data on land distribution is

not very reliable because of the problem of land ceiling,

benami holdings and various other unreported attempts at con­

cealing the actual size of the holding. Any attempt at test­

ing the relationship of the employment variables with a proxy

variable to capture land distribution is subject to the limi­

tations of the data. ~ese data were obtained from the

Agricultural census of 1976-77.

A,Ssetless HouseholdS a Members of assetless households have

no other source of income but the sale of their labour power.

such persons constitute a large and pexmanent chunk of the

labour force in the country. A high proportion of such house­

holdS in a reqion is likely to increase the supply of labour.

(Papola and Misnra, 1980.) Even the female members of these

households are forced to hire out their labour for a 11 ving.

'l'WO variables have been specified to capture such householdS&

percentage of agricultural labour households to total rural

householdS (PrAlH) 1 and percentage of landless rural householdS

to total rural households (PrUi).

Given the demand for labOur in a region, a high propor­

tion of agricultural labour households would be associated

with higher labour participation, lower days of employment per

worker, greater unemployment and lower average wage earnings.

A high p.:oportion of landless rural households in a region

would normally mean a greater proportion of agricultural

218

lal:xrur$rs in the t~ork force. ,lo.-.

AI!ilabilitz of Landa 1he pressure of population on land or

availability of land per worker would affect employ.ment/unmplo­

yment in e number of ways. 11Us factor was specified as net

sow area per agricultural wrker (AvLAM end its relatioaship

with ununployment was tested. It is expected that in regions

with more land aval~eb.tl.t. ty reportocl unemployment would be lower.

§dledUlf4 'as$e pnd fr1be nousehol<!ga DUO to the low social

status esaocioted with work in the agricultural fields, thiS

work is often done by members of the scheduled caste householc5B

1n the south and ache&tlcd tribe householdS in various parts of

the counU'y (sen, 1995) • ~e work participation of scheduled

tribe householdS, particululy of its female members, is much

higher than that of all householdS. TO the extent that members

of these households hire out their labour, a greater proportion

of such houscholda 1n a region may lead to higher WPRB and a

greater proportion of agricul turel labourers (Bardhan, 1904) •

'.lbe variables \:Ue sped.fied separately as percentage of .sche­

duled tribe and scheduled caste households to total rural bouse­

holdS, PrST end PrBC zoespectiwJ.y.

A Meas'!'e gf Poye.qy of the ,Rcgiona ~e poverty of a household

1110uld have a definite impact on the onployment/unemployment of

1 ta membera. 'Jhe 1mpo\18r1Bbment of a region would hove en

impact oa the anployment and unemployment of tbe reoion. In

order to explore these relationships o mea$ure of poverty of

the reoion was included in the study. 'l'he apecification of the

219

varitlble wu ho\':ever limited by the availability of regional

level data. 'ltle variable was specified ass the percentage of

populotion below the poverty line (PrPOOR).

'lhe well known poverty no.r:m of £s.1S per capita per 1.10nth

at 1960-61 rural prices ~aa adjusted by the consumer Price Index

for Agricultural Lcbourers end worked out to be ~48.45 at the

ell India level for 1977-78. 'Ibis ranged fran t:;.42. 75 for

Gujarat to r.s.S1. 75 for Madhya Pradesh. It was apPJ:OximatEXl to

as.49.99 per capita per month. the upper limit of the expenditure

class interval 1n which the poV4Q:ty line is located. 1!\e source

of data was tho NSS 32nd ROund estimates of consumption expendi­

ture by monthly per capita expenditure groups at the regional

level. However. certain pJ:Oblems of double counting appeared

in these estimates at the regional level and the t;ss put out

new estimates at tho state level (NSSO, sarvekshana, vol,IX,

l~o.3. 1986). 'lbis difference was adjusted for by multiplying

the ratio of the new to old es timntcs at the state level by the

regional level data.

It is expected that a high percentage of t....,_e population

below the po-verty Une t·:ould bo positively esaociated with the

participation rate end percentage of agricul tu.ral. labourers in

a region. In the 11 tereture there ere conflicting views on

the relationship bet\t~een poverty ena unemployment. 'lhiS rela-

. tionsbip WGS also explored in the analysis.

3. RESULTS OF t.IHE REGRESSION ANALYSIS

'lbo possible relationship between the employment variable

end various demand and supply variables were outlined in the

220

previous section. 'I'he empirical ovidence obtained from the

.regressicn analysis and their interpretation are discussed in

this section. sane of the independent variables ,.,ere correlated

with each other. since it was consiClered important to study the

relationship of these variables with the eraployment 1fariabl•,

more then one equation was specified for each dependent variable

taking tho correlated variables in separate equations. 'l'hus,

the probleu1 of rnul t1c:oll1near1 ty between independent variables

was avoided. since the major foc:us is on female employment,

each equation is specified separately for males end females in

order to sec if there c;.re sex c11fferential.s in tllo responses to

various factors at the regional level. Givan the nggregati ve

level of tho data and the exploratory nature of the analysis,

even a 10 per cent level of significance 1B considered to be a

o1gn1ficant result.

lf0£ke[ J!2pulaticm Ratio

It 1s postulated the.t the req~-onal v~1at1ons in WPRs

e.re related to the levels of egric:ult.ural development of the

region, lend distribution, the percentage of agricultural lebou.z

end scheduled tribe households 1 area under rice c:ul ti vation,

number of bovines per household end the percentage of populatiotl

below the poverty line. t-4\era tho regression equation for funale

l'.JPR was epecified as determined by the index of agricultural

developnent (lAD) 1 the percentage of agricultural labOur house­

holdS (PrAUI) end the percentage of area operated by tho l:lOttan

fifty per cent of hold1nos (PJ:OP50) only, the varimce expleine~

\laB veey low end the regression equation :1. tself was insign1fico.n

221

(P statistic wan 1.1). However, when the additional variable

percentage area under rice cul ti vction (RICE) wes included in

the equation the variance explained improved considercl>ly ena

the regression equation became s1gnificMt. 'lhe inclusion of

P.ICE elso improved the pe.rformenco of the t.wo non-significel'lt

variobles in the equat1cn. In the c;ese of the male t:PR the

former opccification of the equation we3 significant in tc~~

of F statistics (i'c4.1). However, percentage variance expl&ined

improved ~DSiderebly with the inclusion of the area under rico

cul t1 vation ( 'l'eble 6. 3) • 'Ihus, tho latter apecif1cat1on ~us

included in the m1alya1s.

'!\«' more equations ~ra specified to explsin rq;ional

variations in t~P..'l. In the .s eccntl equation, the cxplanatoz:y

vu1eb1Ps included were the number of bovines per household

(AVBH) and the percentage of population below the poverty line

(PrPOOB) • In the third equation the explanatory variables were

the percentage of scheduled tribe heuSGholds (Prs'l? en;! the

percentage of e.oricult.ural labour households (PrAUi).

'lbe association. between agricultural developnent and

WP.Rs was as llypothesised. 'lbe level of agr1culturcl. dcvelopnent

was inversely related to male and fanale participation rate!J.

Male ana female participation was positively aasocJ.ntcd with

tho number o~ bovines per houee.~old and with tho perc:9ntago of

schedUled trite households aD wos e:cpeetcd ('rablo 6. 3). 'l'ha

regre9s1on coefficients indicate thet the nu:r.ber of bovines ,t:er

household had o rnuC".h greater impact on fenale \JPR:l than Qn male

Table 6.3

netel'minants of tiOrker POpulation Ratios separately I:Jy sex- 1977-?S (Linear Regression)

DePendent Variable constant

BSQ£p1on £qs&£icients for XAD PrAW PrOPSO 1-.ZCi.; -2 R

E (df)

1. ~ t~Rr 138.749 -o.S33•* 0.191 -o.679 -o.290*** 0.210 4.654 (2.891) t-1.'722) (1.104) (-1.240) (•3.52'7) . (4.51)

D. WPRM 90.277 -o.229• 0.167*+* -o.382** -o.059** 0.244 5.442 (6,539) (-1.§f7) ~~. 351) (-2.f29) J-2.482) (4,51)

Dependent segreamion QO!ffiqien$! for _2 F xariable COM tent AyBH PrPOOR R s df)

2. A. WPRF 27.871 4.241*• 0.097 0.0'79 3.375 (3.661) (2.435) (0.810) (2.53)

s. !\'PElf 5'1.556 1.598*** 0.075•• 0.221 a. ?SG (29,454) ( 3, 389) (2. 333) (2,531

Depelldent .R!Sir!fSi9tl coeffis&ents for ..2 F VV1§ble ccms t§!!lt P.r§~ Pi@i Ir l dfl

3. A. tJPJU' 42.4.10 0.358••• -0.104, 0.115 4.570 ( 7.679) (3.015) (..0.606} (2.53)

a. 1\PRM 61.27• o.104••• o.os9•• o.1a9 7.396 (39.392) (3.117) (1.842) (2.53)

NOtes 1. Figures ill parenthesis below tile regression coefficients arc t values 2. *** significant at one per cent lovel

•• significant at five per cent level • significant at ten per cent level

N N I;)

223

t.:Pf\.'1. 'Ibis Hns trua (to n lec.ser fl.>:tent )of the parccntago of

scheduled tribe housohold$ nlso.

eontrcu:y to the initial hypothesia percentage area under

rice cultivation 1S negatively relctcd to both mnl.Et and female

effect of the a::lBociation cf are.:l unC:cr rice cultivation with

percentage of oclf o:rlPlC']eci an~ percentage of egricul tt"trnl lr.­

bourcra in tho region. M hypothc:~iscd earlier the proportion

of rnale snd f~alo self G>lploycd workers iS lower end U1ot of

agricul turel labourers is higher in rogior.s with a higher pro­

portion of e.t"ea under rice c:ult1vnt1on (Tru:>le 6.4). since the

self employed form a much higher proportion of the work force

t:han agricultural lsbourers# the overall t•'PRs 1 both cula o.nd

fenalo. are negatively related to c.rea under rice cultivation.

Tsbl.O 6.4

!?erc:enta~c of oelf Employed (Prsr.) and Agricultural Labourers (Prl\L) by cox end 'lhc:ir correletion with tho Independent Veriables R.lCE, PrAIH and Pr~R.

PrS~1 i'rSli.:i' Pr~ Pr~

Hean(~ 53.2 56.1 23.0 30.5 RICE -0.46* -o,Sl• 0.40• o.21 J?rA1U -0.79• -o.o6• 0.97• o.94* PrPOOR -0.31 -0.47* o.s2• 0.54*

Note a * oenotes significe.nce upto 5 per cent level

Female ~~Rs wcs positively but insignificantly related

with tho percentage ox agricultural l~~ houseboldD in o

region end the percentage of population below the poverty line,

tllouyh male \:PR was positively illld significantly relntcii in

both cases (Tublo G.3). ~iv was again due to the fuct that

the association with overall participation is the net effect

I I

224

of association with the percentage of self employed and agricul­

tural lal:lourers. The high negative association of female self

employed with both PrAU-1 and PrPOOR appears to danino.te over the

_ positive association with fanale agricultural labourers, oo that

there iS no significMt relation vi th the overall participation

rate (~able 6.4).

Percentage area operateti by the bOttom 50 pe~: cent of

holdings ( P.rOPSO) is negat1 vely associated with male '·JPIW alone.

1'besc regions were alSo more cgricul turnlly developed in terms of

higher productivity of the mojor 10 crops, PE:be (correlation

coefficient between ProPSO end PMe we.s o. 35 significant et 1 per

cent) and had greater c.rea under non-foocl crops, t~PC (correlation

coefficient between PrOP50 and Ht='C was 0.49 aignj,ficant at 1 per

cent) • 'lbesc charecte.risti09 implying higher income levels, per­

ha}:G led to lower male \';Pa, though theoretically it should also

lead to lower fsnale WPRS. Inaccuracies in data on lend distri­

bution, noted earlier, also makes interpretation of this result

difficult.

'l'he explenetory variables which have a significant asso­

ciation with the regional variation in fsnale l-.'PRs e:ro thus the

agricultural developraent of the region, area under rico cultiva­

tion, the number of bOvines per household and the proportion of

scheduled tribe householdS. 'l'he important features of the

nature of the relationship need to be noted. First# the predo­

minance of self employment in the work force eppenx:s to influ­

ence the relationshit:B with the overall participation rete.

secondly# the only variable whose relationship to overall fanale

participation which 1a significant dUe to i tB influence on the

demand for labour is tho nu.-:Jber of bovines per household. ~e

225

.relationship to the supply of labo~ appears to dominate ever

the O.emand tor lll.beur rolntionship in the case of the other

three significant variables. ~o el&bOratc, any increase in the

daunnd for labour in agriculturally developed regions is over­

shndel.;ed by the ·ui thdrawal of intermittent femnle \.'O:tl;orc.

Increase in the danend for egricul turel labourero in .rice regions

is eccompanied by the decline of fEr.tule solf o:1plcycd -;torkers

in tho some region:J end the lette.t· o!fcct dominatro. Arxl

fiually, a hi9her proportion of schooult.::d tribe houschold.s in a

region affects ovorall female {lart1c1pat1on pooitively by

increo~ing the supply of labour.

Jntensity of Effiplpymegt per Hectare end per WO[~er

I§Y& of Ao£1cul tural ~c.nt, Ear ogsters: 'lt1c 11 tc.r~ture

on l~bour ab.:.aoJ:Ption in agriculture has vary closely studied

the determinenta of the intensity of employment per unit of

cropped area. However, the differential impact of various

factor::; on male and female intensity of emp!oyment has not been

stuOied sufficiently I!IIJ was indicated earlier in section 1.

1 t is reasonable to O..'tpcct that lf'.bour 1ntens.1t.y woula

be higher in region:::. t.'hich gro._,; rJoro labour 1nt€l1S1 va c:ops nnd

have higher yield. aut given the level of productivity and crop

pattern the extent of mochaniaation could cu:ako a :;;i~li=ico.nt

difference to li:lbour inten£1ty (Vaidyanathan, 1906). '.rhus, the

first equaticm to explain regional variationa in daya of fiQ.t-1-

cultural employm~t par hcetarc included the varicl:ileo, produ­

ction of the major 18 crops per hectC!re of GCA (P.Rhe} , the

226

percentage of lebou.r: intens1 ve crops to GCA (LIC) end tractors

per • 00 bectere ( '.t'Rhc) (Table 6. 5) •

Mother important factor influencing lebour intensity

per hectare is the availebility of water. '11118 would be depen­

dent on tho actual rainfall in the area ana the extent of irri­

gation. 'lbe inclu:U.on of the variable percentage of area

irrigated in the EQUation cUd not produce eny significant

results. Ho\:cver. the inclusion of actual rainfall (AR) mndc

a significant difference to the eQUation for female inten~i~

of employment -the percentege cf variance explained improved

considerably. 'the inclusion of J\R also improved the performance

of the other variables in the equation. ttence the &ceond equa­

tion incluuecl the expl~.nntoty variclllen, production cf tho

major 16 crops per hectare (P&le), tho percentage Of lt..bour

in tens iva crops to GCA (LIC) , tractors per • 00 hectc:lre ('l·Rhe)

Gnd actual rainfall (AR) •

And f.i.nally, a third eQUation was specified which inclu•

cicd the explanato.cy variables, the index ot agricultural. dcve­

lo~ent (lAD) , the percentage of lElbour intenSive crops to GCA

(LXC) end actual rainfall (AR).

In general, these fnc::torc.:J £~xple1ned a very 1~~ percen­

tage of the tot.l'll vr-rif'\neo in the c&~e of isnole 1ntcr.si ty of

employment rer hectare. ~e vuriencc Pxplaincd rGngcd trom 7

to 15 per cent for ferra'!le 1nte:naity lillile it w&D moro then 60

percmt for mcle inter.!.' 1 'ly of employment.

L

Table 6,5

~inaDts of Days of l.gricul tural Employment Per neetere separately by sex. 1977•78 (Linear Regression)

.Dependent Variable constant

Reoressiop 9;?eff1d!!lt§ foE Plilhe '.l'Rhe LIC · r F

(df)

1, A. Dhe~ 137,622 0,004 -128,044••• 0,003 0,087 2,759 (6,653) (0,239) ( -2.523) (0.007) (3.52)

B, DheM 143,820 0,026** -84,535•• 2,458••* 0.635 32.942

Dependent Verieble

<e.§ft> 12.oem t-2.11m <6.864} <J,S2>

oonstant RIW'slop coefficients for .• P e T.Ri'ie Lie AA a2 F

(elf)

2, A DheF 113.461 -o.015 -86,634* -CJ,1S5 0,039~* 0,148 3, 302 (4.953) (..O,S53) (-1.645) (-o,413) (2.161) (4,51)

B,DbeM 144.161 0,026•• -65,119** 2,461••• -D,OOl 0,627 24,159

Dependent Varieble

<7.6Eo> <a.sot> <-1.9671 < 6,6741 f:P..037l <1,sn

constant Beare§!1on eoeff1e1ent fO£ XAD LlC AR n2 F

(df)

3, A, DheP 346.870 -2,682 -c,C66 0,037••• 0,066 2.296 ( !.4 36) (-1.104) (-0,149) (2,282) ( 3.52)

B, DheM 41.44? 0,994 2,726*'** 0.014 0,604 29,922 (0,212) (0,506) (7a64 3)

0 • (!• 067) ( 3,52}

Note!;: 1. Figures 1n parenthesiS below the regression coefficient:s ere t values 2. ••• significant at one per cent level

•• significant at five per cent level • signi fJ.cant at tea per cent level.

N .., ~

~ilo male days of u~.riculturel employcent pe.r hcctcro

\Jere positively aGsociated ,,·!th productivity of t.l)o region

(P.Rhe) end the proportion of labour intensive crops (LtC),

these factors did not influence the fe:nulA dcys of C1;;ployment.

'ltlo latter \IJDD positivell' associated only with actual rninfall

(~. trtle nu.':lhcr of tractors per hectore h~ver, wa:J negativell

am:societed with both mule o:.nd female Employment per hectare.

Tht:~ im,r-act of agri,"UlJ.;ure.l. dcvclorxnent CIAO) ou intcn•

city of er..ployrncnt per hectoro is indicated in cquution 3A m:d

n ('l'e.b1e: 6.5) • ~bough tl1e .a;t.!3UltLi ere not significant the

tDsoc1nt1on in nognti~~ 1n the case of feJele end positive for

cole intenD 1 ty of un~loyr.~ent. 'Ibo vnluo of the regrCLlsion

coefficient .S.n also nuch larger for fanalu dcys per hoctnrc os

co.-npared to m~lc days per hcctnrc. cnc posoible c:pl<:."'lcticn

t.or thiS pbcr.zomenc·n cou:t.Cl be thr·.'t; the C..."'fC groll:a in t.'lc lcsa

developed regions (meinly eonrse cereals) have a greater inten­

sity of \~a of fo:1nle labour.

Reg1cnal variations in the inten9i~J of fcmclo employ.oont

are positively &Ssocittte:l l-.iith the tlCtual rainfoll in t."lc

rc-gicn but not with the productivity of the region or thc: pro­

por-tion of lubow: inter.sivc cz:opa which pc;sitiv£\ly influence

ml.ll~ .1ntcns1 li- of anplo.-&t~nt •

.w~rrs of r.grtcul tnrtll f!n:'l£mf"n$ pgr, .. l<'<"Ir~t": It t-:~ r.~tulctod

that the regional varictJon~ in the intcn~i~~ of ~~pl~~2nt

cvailable por \10rke?r are ralat.od to the level of ag-ricultural

devel.o~cnt of the region and/or productivity of the region,

the percentage of agricultural labour households end the nu1;lber

229

of bovines per household. TWO equations were specified to

explore these relationships.

'lhe explanatory variables included in the first equation

were. the index of agrieul tural development (lAD) , percentage

of agricul turel ltl.bour households ( PrAill) and Dur.'l>er of bovines

per household (AvaH). Alternatively~ in the necond equation

IAD wes replace.<! by the production of 18 major crops per hectare

(Pl1he) (Teble 6.6).

t:hile the intensity of employment per hectare studied

earller. the variables included here explained e greater per­

centage of the total variation of female days per ~rker then

that of tnale deyo per \I.'Orker. Desides. the inclusion of PP.he

in place of IAD improved the percentage variance explained,

particularly in the case of malo days per worker.

o:»ntrac.y to the initial hypothesis the days of mule and

fanale e.'":lployment per worker were not higher in tho ngriculture­

lly developed regions or in the more producUve I"eOions. In

fact both !AD and PRhe were negatively eASsociated with male

and female days per worker. !taus# the hypothesis that the more

agricuJ.t.1rally developed regie-ns with lower participation rates

( WPa) vou.J.d have more days of employment available to the remain·

ing workers is not aubatentiated. '!be negative impact of

ngricul tural developnent ·(lAD) on days of employment per worker

was much higher for females than for males.

'!be hypothesis that a greeter percentage of e.gricul tural

1~ householdS in a region 1110uld reduce the days available

~able 6.6

Dete%minents of nays of Agricultural Dnployment per tJOrl<er separately by sex~ 197?-78 (Linear RegressJ.on)

Dependent Variable

1. A. ATIIJF

B. Av.tft

, ~>epenttent VariablE

constant

916.522 (5.170)

497.428 (6.2gS)

(X)nstan~

2. A. AVDP 215.829 (7 ... 248)

B. AVDM 350.983 (30.619)

~:t~ni\:!2!! £2!ff!&&in!a! f!2£ XAD PrAUi AVBH

-1.105•• 1.544•* 16.S29** (-4.697) (2.938) (2.711) -1.748• -0.148 5.522• (•2.3~1) (..0.631) (2.017)

.• essioD_£9.tJ:..~is;:1£nt:s fer _ P PrAUI AvDH

-o.oso•• (-6. 312) -o.020**

(-6.302)

1.621** (3.472} -1.199

(•1.095)

14.932'** (5.249) 2.546

(1.232)

Notesa 1. Pigu.rea in parerathee.ts below the re~-reasion coefficients 2. ** significant at one per c:ent level

* signi~icant at five per cent level

-~ R

o.S16

0.301

n2 0.610

o.S39

e.re t values

F (df) -

20.5~ (3,52) 7.603 {~!;2)

I' (df) ..,

~ 29.623 (3.52) 22..420 ( 3,52)

~~

231

per ~rlcer to al.Do contradicted et least 1n the ccse fct:1\llo

worked. 1\ Ul~ely explanation for thiS pbanomet'lOD 1o that

r:egions \lith n greater proporUon of e.g.l'iculturel l~bOUr house­

holes ateo have o greeter dcmcnd for hired female labOUr, per­

ho,ps Oue to tho apeciflc czcppillg pat tun of tho rC9ior..s.

certain oporot1ons aro not. done by fernily members and cro lo't

to the h1J:et1 female worker-s.

'lbo d~.s of employment e.vc11ablo per fancle \o'O.tkor J.a

higher in the reg.lcno .more the nu:~:ber of bOvines per household

ere greo.tar. llOwcNcr. the r'dat1on~h1p of AWH witb c~lc <leya

per wor).-.cr is po.sltlw end oigc1ficant 1n e~cu .. ~n 10 but 1o

not a1gnif1cont in 2B ('Icble 6.6). 'l"hc re.greasior. cooff!cicnts

1nc1lcnte that the aunbor of bovines per houaehold:J hcil o much

oreoter impact on fo:nala day& of employment per vor~:.cr thun on

&!ele days per t.-.orkO 1:. ·

overell the re9ional v.nr1ot1on:s in fa:aele emplOiCCl'lt per

worker ore IX)S1t1vely nlcted to the ow:i:ler of bOVinoo per

bouseltold end tho percentege of ~cultural labour bousahol6G

in the reKJlcn, but ore nesat1voly CDcoclatcd with nor1culturol

developnenVpi'O(..~ct1vit<J of the region •

.£srcen$age of Ag£1culSY£tl Jd'b2Y£!rp in tbc ,,;ork. [S?[S!

~nc rogreaaioo e~atlcn::J .-re spec1f.t.ed to explore the

nature of the rolnt1onoh1p bett;ef.n .rcg.tono.l vcr1o.t1or.s .t.n r.ar­

centace of agricultural labourers (~rt.U1 8lld t>rr.Ll:) Mel o.

nWlbor of v:.r1cbles (Table 6.1) • In the first equa.tion b"1e

"fable 6. *I

Detezm!nents of Perce:2togo of ,fUJr1cul tural t~cs 1ft ~ t :ort: Fercc Gc;?clreto "!.? tTJ nex. 19'17-?e (t;1near R«'.ressien)

Depeftden~ Vnriatle

t • .a. Pr~t,F Be i"rAU-1

constt.mt

52.679 (1.056) 9.955

(0.3!1)

{~S!!S¥£ff!J:amtaf'>Z£,. , , . AD RICE COT

-o.311 {..0.626)

o.o37 (0.133)

0.231•• (2.572) 0.252"•

(5.066}

o.M6~:• · (2.328) o.G24•••

(4.0!9)

n2· o.oSJG o.32a

F (df)

2.941 (3.52) 9.935

(3.S2) ---------------------------------------------------------- ------'>!~ it 1 4' .. u • ... •. ,..., =

Depeadent Variable ccwtant

~cs;r;;i.?!i c:e::tficJ.tts f(\s • PrLH PrST 'ft2

F (df)

----------------------------------------------------------------------------------------2. A. I'rAU

a. PrAll-!

uependelt Varieble

c.999 (1.'n2) 9.435

(2. '794)

CXJnstent.

3. A. PzALg -2.002 (..0.301)

a. PrAUt •3.246 (.0.726)

0.615••• (4.3C%) o.3CS.-•

(4.2CC)

o.1a1• (1.?25) 0.120•

(1. ?09)

. . ~£r,geg,ms-n...,m~(i!£i.tmt~ f.:~ • ••. l?Rba P% PrJ?OO.tt ~.r~

..0.001 (-D.3SS)

0.003•• (2.197)

o.623*iht tS.4?S} 0.354 ......

(4.802i

0.44-3••• (4.910) 0 .. 315•~·

\5.452.)

-c.2£3 {•1.711) •0.1~5

t-1.26\J)

o.23G

0.22S

1(2

0.524

o.s,_6

t~otes 1. F1gur¢a in parcnthcsu ~lcr*' tho ~leo coeffiet...enw ora t "aluc.c 2. ••• a1gn1f.1~ at cme per emt level

•• a1g1'11f1cant at f1Te per cent level • c.tgDif.f.cent ot tm per cent level.

9.591 (2.53) 9.1G1

(2,53)

F (df)

16.134 {4.51) 15.69? (4.51)

f,) c..; w

233

explanatory variables included tJere the index of egricul tural

development (II~), percentage area under rice cultivation (RICE)

and porcentage aren unaer cotton cultivation (~. ~be percen­

tage variance •plained by this equation w~ very low, particu­

larly for the perc:e:ntage of fenale agric:ul tural labourers (P=ALF).

Only two variables, the percentage of landless householdS (PrUI)

ancl the percentage of scheduled tribe households (PrS'l?, were

included in the second equation, but the total variance explained

\la3 corJJidarebly higher than equation 1 for PrALF, but not for

PrAL!i. 'Ihe third equation included the explanatory variwles

production of 10 mnjor crops per hectare (PAAc) percentage of

lancUeso householdS (PrW), percentage of population below the

poverty line (PrPOOn) and percentage of scheduled caste househol&

(Prsc.) • 'l'hc total variance explained was the highest for this

equation.

Doth the index of agrieul tural development (IAO) and

ogricul tural p.r:oductivi ty of the region (Proto) were not associ a•

ted with the percentage of female agricultural labourers. How­

ever, a.gricultural productivity (PRhe) of the region 'W~ posi­

tively essocinted with the percentage of male agricultural

leboure.r:s in the t~:ork force.

J..S expected, the percentages of bOth male end fe.:ulle

agriCUltural labourers ere high 1n regions uith e greater per­

centage of erea undc~ rice end cotton cultivation. Prl\LF and

PrAIM ere positively related to the percentage of landless rural

households (PrUl) u.nd scheduled tribe householdS (Pr~1? es

hypothesised. However,. the percentage of female agricultural

234

labourers was surprisingly negatively related to tho percentage

of scheduled caste householdt.J (Prsc) in the reQ1on. And

finally, the percentage of population below the poverty line

(PrPOOR) was positively related to percentage of both male and

fanele agricultural leboure.r:s (~able 6.1) •

~ sum up, while the .incidence of female cgri~ltural

labourers is higher 1n regions with a predominance of rice end

cotton cultivation a very low percente.qe of varionce is explainec

by these factors. In regions with higher aorieulturel producti­

vity, the incidence of male agricultural labourers is high#. but

the relationship 18 not clearly identified for female egricul­

tural labOurers. It t.Ould eppe.:1r that male agricultural labour­

era enter the work force in response to tho higher dernt'.r.d for

labOur in these regions. on the other hond, the level of

impoverishment of a region, e.s depicted by tho percentage of

londlcss rurel housebolclS and the percentage of population below

the poverty line, is a significant explc.natoxy factor of the

regional variation in fenale ngricultural labourers. Regions

with a higher p%'0portion of scheduled tribe householdS also

have a greater incidence of fenale aoricultursl labourers. 'lbe

regional variations in the incidence of female agricultural

lalJOuret-s are expleined better in equations 2 and 3 ('l'ablo 6. 7)

which include meinly these supply variables. 1bus, it eppears

that female agr.tcul tural labourers* do not respond to en overall

increase in the demand for labour, but only in a Urnitc~ feshion

to a very crop specific demand. Otherwise, it is the greater

235

supply of fe..'llale hired -workers in the more impoverished regions

end regions with a greater proportion of schedUled tribe house­

holes which raises the incidence of female ngricultural labourers

y_nernployment Rate

It is pes tulated that .regional variations in tbe unemplo

ment rate ( U.EM end uty are releted to the level of agricultural

Clevelopnent and e.gricul tural proaucti vi ty of the region, the per­

centage of agric:ul tural labour householdS, avaiil.abili ty of l~.nd

and the percente9e of population below the poverty line in the

region. TWO rcgrc:ssion equations were spe fied to study the

relationships. In tho first equation, the explanatory variebleo

included were, the indoc of agricul'\.-ural davelopaent (IJ\D), per­

centage of agricultural labour householdD (PrAUi) und not sown

area per agricultural tttOrker (AvLAl:). In the second equation,

tho value of production of 18 major crops per hecte~:e (PRhe) £llld

percentage of the population below the poverty line (PrPOOR)

were included. 'l'he explanatory power of the equation was

relet! vely good.

Both the index of agricultur&l developaent (IAD) and

agricultural productivity (PRhe) nre positiwly and aignificantll

~latcd to both male and female URS (Table 6.8). 'lbiS might

appear surprising to the extent that one expects unemployment to

be less in such regions t.1here the demand for labour is higher.

However. the URB derived from the NSS data capture only visible

end reported unEtnploymont. such unemployment is perha.r::s hi~her

in the moro developed regions since opportunities for ernploymen1

arc greater. It is al.Do easier to perceive oneself £13 uncrnplo-~c

in o situation where agriculture is r.;cre carw.19rcialized.

236

Male and fan~le URs are also higher in regions with c

greater percentage of agricultural labour householdS (PrAUI) ar.d

percenta.Qe of population below the poverty line (PrPOOR). 'Ihero

is no si;nifieant associction between URs oneS the net sotm area

per worr..er ( PrLA\'l) ( 'l'eble 6. 9) •

In the l1tcr~ture, no concensus wcs arrived nt regarding

the rclc.tion bct\.zccn poverty. a~ricultur~l productivity ~d

unemployment. ~o·rom tho Qbove rcaul tv 1 t would cppea.r the.t URS •

both r.tale and female, ore higher in region with CJrcutcr ugricul­

tural productivity, agricultural development and higher levelsof

poverty. 'Ihe agriculturally developed regions al!lo have higher

levels of poverty (correlation be~een IAD end P.rPOOR is 0.46,

significant at 1 per cent and between Pnhe and PrPOOR is o.2S).

1bis is possible, as diBcusscd abOve, es the UIW really refer to

reported or visible unemployment.

avc,rngo peily t·~sg Earninqa in t:gr!cultJU.:e to s=asual &QJ2our

only one regression equation was speciflecl to stuGy tho

.relotior.ship of regional variations in aver£lge daily tlaqe earn­

ings in agriculture to casual labour (1'.-~~·a.f and Av\~') lJith tho

level of agricultural development (lAO) and the percentage of

agricultural ltlbour houocholds (PrAUl) in the region ('fable 6.9),

~e average daily wage earnings in agriculture of both

male end fcmole cesual workers were positively related to the

level of agric:ul tural development (IAD) of the region end

negat1 vcly to tho percenteye of egricul tu.ral labOur housdlold3

i>rALH ( Tuble 6. 9) • tthe demand for lebour would be higher in

Table &.o -

Deteminants of unemployment Rates separately t¥ sex~ 1971-78 (Linear: nugression)

Dependent Regres@!oJl.._coe£fiqegts f9&: i' Variable constant. .Il.D ?rAi~l l>rL!'~t: §2 (df)

1. A.. URF

a. UBM

-5S.G4S (-S.044) -!;t..1B8 {-4.116)

0.52911' (3.086) 0.619•

(4.502)

o. 331"' tS.6tk!) 0.157~

(3.30')

-1.427 (0.27~) -1.~1-l

(-1.Z67)

0..463

0.899

17.129 (3.,52} 1~399 { :i.S2) --------------... -- .. .., ______ _

oependent variable

2. A. URF

B. UJ:i

CenAt:ent

-7.310 (-2.177) -.t.soe

(-2.851)

negrension Q9€fficients tq~ Pfhe PrPOOR

o.oo6* (5.874) 0~006*

(9.610)

o.160t.­(3.976) o.aa3t:

( -2.851}

fi2

0.439

0.633

~ote: 1. i'igures iri--parenthesis below tho · regrension cco££j.cl ents-ere -tvnlues 2. * significant at one per cent level.

'.rnble 6.9

I' (df)

2<'.630 (?..53) 48.477 (2.53)

Detezmi.nants of Average Daiq wage Earnings 1D Agriculture, separately by sex, Avtm' end .r.vv:-t., 1977-78 (Linear Regression)

Dependent [<egres§1cn Q?ef&J;.i.~t! for _2 F !'mable ~st;:~$ XAp ·- . .P&;.AUi 1 R (gi\ 1. A. AVI:F ~.e1a o.t04• -o.oso o.so9 1o.s1

(-1.969) (3.516) (-4.998) - (2.53) B. AV\:l'1 -16.160 0.224 * -o.OS6* 0.649 51.935

(-6.122) (0.234) (-6.075) (2.53)

Not:ea 1. Figures in parenthesis below the regression coefficients are t values 2. • significant at one per cent level.

.., ~

238

agriculturally developed regions thus raising the average daily

waoe earnings. It wc.s noted earlier thnt twns were lower 1n

these regions. 'I'his would also lead to high average daily wa<Jes

in these regions. on the other hand, in regions with a greater

percentage of agric:.ultu.r:al labOur households, the supply of hirec

workers would be greater, thereby depre~sing the average deily

wage earnings accruing to the."n.

,kgngluniono

ne;!oncl variatiorJ= in certoin features of tho lcbour

market related to women have been studied in this chaptat'. D~nd.

the :following questions -were examineda t~bnt factors influence th1

overall work participation and intcnsi ty of employt:lcnt of wanen?

l!hat causes wanen to hire themnolvoa out for agricultural work?

Is the level of unemployment related to the levels of agricultu­

ro.l development or poverty in a region? And finally, given

agricultural developnent of the region, bow doe~ the demand for

labour and the supply of agricultural labourers atfect the ~:~e

rata accruing to casual labour in agricul ture'l certain bypothes

regarding these questions can be derived from the results of the

study. I SU.'lunar1ze below the results of tho exercise end put fo.

ward p.r:obablc explanations of the o~erved phenomena es

hypothes '!IS.

'!tlo ov~ra\1 pnrticipntion of \JCX:\en in the "~or!! force is

affected by the level of agricultural devolopment of tha region,

the c.rca under rice cultivation, the nu-nber of bovines per house.

hold nnd the proportion of sc;hedulcd tribe households. In

agriculturally developed regions overall participation rates of

239

both males and females are lower. ~e higher ineomo levels

associated with egricul tural devclopnent perhaps let!d to the

wi thdratml of intermittent workers such as vomen and children.

A almile.r phenomenon appears to be working in ereus with n pre­

aomincnc:e of rice cultivation so that greater dernend for fo:nale

workers for the cultivation of rice does not increase tho over­

all wrk participaticn of \>/omen. However, female participntion

t:es higher in regions with more bovines per household and a

higher proportion of scheduled tribe householdS. ~nding cattle

and dairy fat:ming are often done by wanen in the hour;ehold as

nn extension of their houaehold work. 'Ihe dunand for labour of

\.ror.len in these activitie-s end the greoter supply of women fran

schedUled tribe hou.seholda ere both reflected in higher overall

wrk participation.

~he intensity of fs:inle e:nplOtpent per hectare is higher

in regions with more actual rainfall, ana lower in region with

greater cgricultursl productivity. ~1c intensity of e~pl~;ment

per l>.'Orker is hi~hcr in rcQions with nore bovines por hoW"Jehold

and o ~renter proportion of egriculturnl labour houst::holcs,

1·:hile it ia lcx-. .. cr in tho agriculturally more productive regions.

!he proportion of femnle agricultural labourers is posi­

tively related to the area under rice and cotton cultivation.

the percentage of landless rural households• percent~ge of

population below the poverty line. and the percentage of sche­

duled tribe householdS in o rc9ion. certain or~roticns in the

cul t1 vation of rice end cotton are c.neierteken by fer::ale hired

lnbcur. 1'berefore, though the ovcroll female participation 1n

rice regions WeD low, the incJ.c!cnce of fen ale agricultural

240

labourers wea hi~h in regions ~ith o predominance of both rice

end cotton cultivcticn. 'l'he ir.cidcncc of f£'mele agricultural

labourers wtlS higher 1n tho more impoverished regions auc:h ao

regicns with n greater proportion of landless rural householdS

and greater proportion of population below the pove~ line. In

thC3c impovari3hcd regions even the female metibers of the house­

holdS ere p:·cibcl>ly forced to hire out their lobour due to low

levelD of inco11c er.d lack of facilities for self employment.

similarly, the proportion of female agricultural lilbeu.res:s J.s

also higher in regions with a greater proportion of ochcdulcd

tribe householdS.

~e relationship betveen unemployment, agricultural deve­

lorxnent or prodUctivity and poverty seems to 'bo aff£:cted by the

fact that the unempl~{r.~nt rnte captures only open end visiblo

unemployment. such U..'1e·nplO'IJLlt:nt ,.,.~s hi9her in rcgicr.::: ~i th

greater egricultural proeuctivityf higher levels of ugricultural

development end hi9her levels of poverty. 'Ihc uneT.ployr::cnt rate

't!CG ~leo positively a..'1soc1cttJd with the perc~nt~gc of rtrricul­

tural labour hcusehcldS since U.'"lC.:t}.)lC<Jm·.:mt t;ould be aT.Orc cpon

end ~iblc in auch hou3choldS.

And finally, ~hile agricultural dcvelopnent presumably

through incre~sed demcnd for labour positively effected the

e.vere.~o d~ily wc.9e earninc;;o of bOth mele c,nd fc.ma.le casual

wor1'..ers, more supply of hired lebour in regions with a high

proportion of a~riful tural let-our householdS, depressed them.

In most cases the rclnt1cr-sh1p oot.wccn tho ~-nployment

variables end the various explanatory fcctora was r:ot markedly

241

different for male end female workers. A fat: differences which

were observed were QUite reveallng and need to be recapitulated.

'lbe intensity of employment of male tiOrkers alone wiJS positively

essocleted with agricultural prOductivity of the region. simi•

lo.rly, hi9h ngr1cultural productivity :;?COitively ~f~cts the

incidcuce of male agricultural lClbou.rera only and not that of

fe-rtale agricultural labourers. In other wordS, n genornl increas

in the aemand for lc.bour aue to the i.Pcrease in e.griculturnl pro­

ductivity neither increaseD the intensi~I per hectw:o of female

employment nor raiaes the incidence of feoele egrlc~ltural

lP.bourcrs. The latter, however, was found to be influenced by

e high crop specific demand for labour (like that of rice ana

cotton) • on the other hi!nd,. supply condi tiona came out mora

sherply as determinants of the proportion of female agricultural

labourers in a region. The push factors oper~ting in regions

with a greater percentage of lendless rural househol~, percen­

t~go of population bel~ the poverty line and in regions with a

greeter percentage of ccheduled tribe households have a positive

impact on tho proportion of fe:nale cgriculturol ltbourars.

'lha <.Zominance of the supply fcct(;,rs in influeneiDg

regicnol vuriQticns in the overall f~uale particip~tion rate

i~ also r.o~eL~rthy. TO rccapituln~, any incre~a in dc~tnd for

lGbour in agriculturally developed regior~ did not positively

influoncc fc.-nale ~£--R, aue to the \<11 thdrawel of intCl.'m! ttcnt

fEtnG.lO \.'Orkeru. Incre£1Se in the ec:nand for hl.t·oo fer1ule wrl"e.rs

in predOmint~ntly rice growing regions \JuS ecc:c:mpaniccl by loucr

proportion of f~11ale self anployod workero in the region so that

242

overall participation rata wus lower. In both cases, the dcclln

1n tbe supply of faaale 'WCrkors wt.B the dominant factor affect­

ing overall fem~le participation.

243

Appendix VI. 1

In this appenc11x 1 outllne the different methoc;ls of

constructing a composite index and the relative advantages of

the factor analytic methcd. I alco describe briefly the con­

struction of the index of agricultural development used in the

regression analysis

J;lE!thOdg of £Onsyucting a comppsitg InQex

'lhe cons trucUon of a composite index implies that seve­

ral variables have to be combined together in some fashipn to

. obtain one single composite variablo which 1a representative of

the whole group. since each variable focuses on different

espects, there arises the problem of assigning weights to eadl

varieble. some of the methods of assigning such weights are

outlined bel0\1:.

pverall Rank Jndexa In this method all the regions ere ranked in

descending order for each variable. 'l'hese ranks ore added up to

constitute the composite index. '!'he method gives equal \¥eight

to each variable. 'lbe main advantage of this type of a composit•

index is its simplicity in computation. Ho'Wavcr, such on index

does not take into consideration the variation within each

variable end the extent of correlation between the variables.

xnxerse of the coefficient of variation: '-be coefficient of

variation measures the emount of variation within different

series of variables 1r1ith wi"ely deffering averages. Each va.ri­

~;:ble is given the weight of the inverse of the coefficient of

variation and adaed together to obtain the c:ompoai te index.

HO\-JSver, the method ignores the effect of correlation o£ each

variable with the others. secondly, tho magnitude of the

244

variables g.Una prominence with variables having higher values

naturally dominating the index. ~is can be corrected thrOugh x1 - x

a tandardiaation of the variable, x1 • x •

lndex using the Q?rrelotion Matrix: \lith the help of the

correlation matrix tbe variebleo are assigned weights 4epending

on their correlation with other variables. The assumption behinc

the method ie that the various indicators of development arc

inter-related with eadl other and vary 1n a similar fashion.

'or;elgtion Mat(ix sum o' $bs now§ ~-'lx x1 x2 ~ ' ixj

x1 Jt ~ ..9] xl"n

~_, x1x1 x1x2 I x1xj

~ x2 "-;x1 91"2"2 ,"2"n I )) "2Xj

• n •

J1 "nxl ~Xn"2 )1~~ ,£

xn ' .'l "nxj

In this method each element in eech row (or column) Of the

correlation matrix is squared and added. ?:he surJ of ec.ch cow

(or colwnn) is diVided by the total number of variables end

this figure ia used as a wight of the concerned variable. 'lhe

method givea maximum weight to tho varil'.blc V11ch shows maximum

correlation (1n magnitude) -with the other variables.

l,nd~.x by Method of Factor Analy:si~a 'lhe OJ.stinguishing feature

of the factor analytic approach is the assumption that the

observed co-variation a.rnong variables is due to sane underlying

'ltle correlation between two variables is e!':su:ned

to be a result of their sharinq common sources or factors end

not as a result of one being the direct causa of the other. 'Ihe

245

factor analytic approach di videa the total variation of a vari­

eble into t1«> canponent parts, 1. e. • the variation vi thin tho

varieble o.nd covarion.:e or the variation of ee.ch varitblo with

other variables. ~e indices outlined earlier consider only

one of the:Je t\.'0 components of variation.

~c &1m of this method is to construct, out of the

total aet of veriablea. now varigblcs callec1 factors whic:b axe

linear combinations of the foxmar variables. ?.be loading ere

chosen such that the constructed factors catisfy two cond1 tionsa

(il) the factors are orthogonal or uncorrclated to each othert

and (b) the first factor abeorbs and account~ for tile oaxicrum

possible proportion of the total variation in the set of ell

variablesr tho second factor accounts for or absorbS the maximum

of the remaining variation in the variables (after allowing for

the variation accounted for in the first fector) end so on.

In computillg the factor loading from the correlation

matrix~ the 11 s in the diagonal are replacetS by tho communali­

ties h~. ?.be communality of a variable is a measure of the

correlation of each variable vi th the rest of the variables 1n

the set. :rt can be measured as the squared multiple co.rrelatiotl

of each variable with tho re.-nainder of the variables in the

set or the ~gluwt absolute correlation in a row of a correla­

tion matrix. Each loading of the first factor is the oum of

the row (or column) of c:ottolat1on coefficients in o correlation

matrix, cU. v1ded by the square root of the grand total of

cor-relation coefficients. ~ereforc, the loading arc a ccmbinec

form of correlation coefficients thEmSelves. fJ.be sU:n of the

squares of the f C~cto~ loadings for each factor 1D celled the

latent root or eigen value or chra-actoristic root. 'lhe factor

loading of the second factor ia calculated fr:om the new • resi•

duel correlation Uble•. 'IbiD is computed from the original

one, by removing a part of the total variance which bas been

eb3orbecl by tbe fire t factor.

~c composite index is c:or.atructed. by applying these ....

factor loading as weights to the veriables. 'lhis index, though

relatively more c:Ufficult to compute end requiring the uoc o:f

computera, bes a number of advantages. 'lhe factor loaaings or

weights ere assigned taking into consiCleretion both the inter­

correlation among variables and the variation within the vari­

able. Index eonstJ:ucted from the correlation matrix tokos into

cons1dezat1on only correlation of one variable \Ji th other

variables in essigning weights. Factor loadings, hot-7ever, takes variation

into considerat1on_Lwith1n the varieble (atandardlaed so that

varience 1a equal to one) and also across tho va.r:iobleo. In view

of the abOve positive features thiS mothod has been adopted in

the analysis.

Jnggx Of hQ£iCUltufal neYJlOP!eDt In order to construct an ind.ox of agricultural develop.

ment.. factor enalys1s ~- done using the eleven variables

t.icect:ibed in the chapter. 'lbree new factors emerged explaining

69 pur cent of tho variation. ROtation by the varimax method

wee attempted to see tthether e bet;tcr or simple structure of the

factors would energe. -.rtais did not improve the pictur~, in fact

the fect.or structure wes c:U.sturbed. Hence, wu:ot.ate:l factor

matrices, indicating a air.npler and more easily inteJ:Pretcl:>le

structure of facto.re, wea used in the analyeis.

247

Eight of tho eleven variables obtained their highest

factor loadings in Factor 1. They ~.re product! vi ty of tho 10

major crops, proportion of e.rca irrigated, irrigation 1ntens1 ty,

cropping intElnBit.y, all tho three tcchnolow c;oupcncnt.:> of fer­

tilizer uoe, trnctoro and oJ.l.enginea + electric pumps per

hectare and bovine-s per household. 'lhio factor s t.ructuro may be

tcmoo the productivity cum new technology factor ana c..cplained

35 per cent of tho total variation. ~c second fnctor had the

lone varieble percenta9e area under well/~ewell 1t:riga.tion.

'.lhis source of iz:rigation faetor explnincd 18 per cent of the

variation. ibe third factor included percentage tu:eo under non.

fOOd f;:l:OP e.nd act.ual rainfall and explained 16 per cent .of the

variation. ~e foetor loadings of these factors were used as

~ights to construct three basic indices. Index 1 represents

the product! vi ty cum new technology factor • Index 2 is tho

source of irrigation fee tor end Index 3 repres ent:B cropping

pattern and rainfall. However, these three basic indices are

based on factor matrices which exploin only 35, 18 end 16 per

cent reapecti vely of tho total variation. A cri t1cism against

each of them is that they are not fully representative of the

total variance in the set of variables. In order to see bow

representative these bc:l81c factors are of the total variation I

constructed .oome composite inCU.ces combining the three basic

indices TNhidl explain 69 per cent of the total variation. 'lbree

meth0d3 of combining tho basic indices were used to obtain

composite indices 4, 5 end 6.

I12dex 4a using the factor loading of the first three factors the coaposi te 1ndic£:S were constructed. The factor loneings of eeeh of the three bnsic indices were ranked in <Iescending order

248

and addition of these ranks was considered as the composite Index 4. Index 5 1 'ltle factor loading for each region by Indices 1, 2 and 3 were added to obtain a composite Index 5. Index 6: The factor loading of each of the three basic indices are given a weight according to the proportion of variation explained by that factor matrix. Thus, loadings of the basic Index 1 obtainea a weight of o. 35 1 Index 2 obtained a weight of 0.18 and Index 3 obtained a weight of O.l6.Therweigh~ factor loadings for each region were then added to obtain the third combined compos! te Index 6.

Each of the 3 basic indices were then correlated sepa-

rately to the indices 4, 5 and 6 (Table VI.l)

Table VI.l

Index 4 5 6

correlation Matrix of Three Basic Indices snd '.Ibree combined Indices

Index 1

-0.5160 0.8235 o. 9456

Index 2

-0.5338 0.4282 0.2509

Index 3

-o.S353 o. 3851 0.2108

Index 1 correlates best with the three combined canposite

indices. Indices 2 and 3 correlates equally well with index 4,

but not so well with indices 5 and 6. Since all the combined

canposite indices based on the three basic indices, obtained

from the factor analysis, correlate well with Index 1, the

latter is considered the most representative index. Thus, even

though Index 1 is bcsed on a factor matrix whiCh explains only

35 per cent of the variation it appears to represent the group

of variables best. Hence Inde.x 1 is chosen as the basis for

classifying the 56 NSS regions. Table VI.2 gives the ranking

of the NSS regions ~ this index of agricultural development.

1 ? , 3 4 5 6 7 B 9 10 11 12 13 14 15 16

17 10 19 20 21 22 23 24 25 26 27 26 29 30 31 32 33 34

35 36 37 38 39 40

249

Table VI.2

nanking of NSS Regions by the Index of! Agric:ul• tural Devolopnent (Index 1)

• Region

tiost JJgvci2Rsm PUnjab North em Punjab Bouthem 'f;-.mil Naau consto.l northern Hc.ry~na Eastern 'l'amil Nadu coastal f~erala soutbem Heryane t-:estern Uttar Pradesh \ties tern 'lemil Nedu Inland TE!!:lil ~e.du southern Kerala Northern Karneteke. coastal/Chats Andhre Prade~h coastal Bihar central Uttar Prcdcsh Himalayan ~.nC!hra Prndesh Inlsnd southem Qevelop!n~ Gujarat P eins Northern Uttar Prildeah E<:.S tern Uttar Predesh centrel Bihar Northem west Bengal centrel Pl.ldna

-Himachal Pradesh _Gujerat seureshtra Orissa coastal Hcllorashtl'u Inlcnc ltorthcm Knrnotaka Inland £;outhem Komateka Inlnnd Eestem Ancllu."a Pradesh lnlcnfl t~orthe.m tJest Bengel EaGtem Plains GUjarat Plaint. t:;outhem Mcllar~htra tnl~d NcsteJ:·n \/CS t Den gal .;cs tern P la1na t:cst nengcl H!maloycn Gujarat Eastem

le£E£S$ pgyclriP£4 Andhra PracleDh south western MQherasha·o Eastern orisse r~orthcm Karno taka Inlan<l r;orthem nnjasthan south £~stern Maharashtra coastal

LOadings by . Jn{.tex 1

10.6654 10.2994 9. 77.21 7.01SS s.S77o 5.1520 4.7220 4.5446 4.1279 4.0714 3.7066 2.9799 2.11266 2.1993 1.8595 1.7181

0.9509 o.e920 o.eJ64 o.61S6 0.4206 o. 3256 0.2026 0,1934 o.oJeo

-o.2019 -0.3149 -o.6256 -o,7632 -1.0108 -1.2790 •1.3660 •1,5776 -1.0440

-2.3459 -2. 3G2:: -2.6175 -2,6705 -2,6807 -2.022~

contd.

250

41 Rajasthan southern 42 I·tahcrcshtra !nlend Eastern 43 P.ajasthc.n l!orth Eastern 44 Uttar Prnrlash t;~.Jthern 45 t-!~a.reshtra Inlend central 46 Oricaa couthern 47 Guj~rat D.r:y Arceo 48 Biber southern 4 9 nadhya Prcccsh 11orthern SO lU.t.jaathan t-:estem 51 l:acllya Prildesh Chctiegarh 52 Medhya Pradesh Malwa Plateau 53 nadhyn Pr{;dc>oh south t.;cstcrn 54 Madhya Pradesh r.outh central 55 Meclhya Pradesh central 56 Madhya Pradesh Vindhyes

Loedingo 'by Inqcas 1 -2.911? -2.9162 -3.1-'73 ... 3.200? -3. 3·: 32 -3.5240 -3.525!> -3.6745 •3.C53~ -4.0859 -4.1195 -4.1560 -4. ZQ.16 -4.3951 ~1.5967 -5 .. 112"1

•

251

.. !jppend1x VI • 2

In this appenaix I discuss the prob+ano faced in t.~e

collection of dnta und the methods used to oolve thGn. I·1einly

two kinds of problems arose. one, since data for several vari­

ebles \:Jetro collected fran different sources at tho discggregated

level of the diD tricts, there were sane gaps in the info.rmat1on.

~~1c:-;e 9£~p:; t;t;:re either due to nc.n-Q.vail<iliility of data for the

ycr.r of ~tucly or due t) data not being c.vailnble in tho to::m in

\;hic.'l the variable was s~cifisd for tho cxcrciso. Ifl both

casos OJrtain e:usumpticr.: were; r:1aoo to fill in the gripo t:hich

c.rc G.iacussol ncrc. 'lhe second proble:n \1~ specific ·· to GUjnrnt

Ciistrl.ct as the unit of grouping. HC\<,:evcr, in Gujo.rat, tho o.gro­

c.:llmatic ~:ones clearly cut acrcss &strict& and hence for 7 of

t."lc 19 cU.stricts,. the t~luka vtc~ the unit of grouping to for.a

Cujnrat it was neccasazy to collect d.'lto at the taluket lnvcl

fer the :evan districts but the required information was not

cvailable nt the tcluJ~a level. certain assumpticns hcd to be

made to fill in the OilPB•

Ule state governments oro r~po:wiblo for the collection,

cornp:Liation ena. often. tho publication of the data for di:J tric:ta

tll'ld their eubdl~sicr.a such £:S tnlul~r:o or the.nos. Therefore,

the avt&ilab111 ty of data depend:: on the icport~cc attached to

then by the state gov._rnmcnt::: concerned. Evon t.~ougb the year

1977-7C ll:aB not very rcc:cnt, the tc:sk p~-od to be difficult.

'252

~ atatea for ~~ch only very l~tcd data ~zo cvalloble for

sny recent year wero J~ and l~oohmir ma Mlltw. 1\lOo# on

clo:Jer exe:ninaticn, oven tho <:rnploymcnt tlatn ava.tlo.i:lc fot thetJe

t."W ctatoo frc;, thcr !u.;n Gcon (l1f:'tinctly rliffcrcnt ~s th~o for

tl'le oth~r =tot:~. F!nclly the t\1o Btnte~: hac.1 to he <!l';:-,i ':;od fran

tl1c tnttl'![$1:.;:. rox· t<'=.nt r.cncnl, a p~f:cl<">m!,nently r~cn C'.lltivating

!'lt:)to t:~.th -1m fr.r.nlc par.tie!J'<~t!~, the dato for n t;l1uhtly

c~llcr y(;::-.:: then ,.'.>7?-19 h~ to tlC used t~!th c:f!rtBln c.'"!Dump.

ll{ttQ em WYS9$0)i!i Q!1dt'§1(h9f1~B£}t~Cf. , .. W.YJ&'itOC{; Qei.'QiJ <~( JPQ.&I

~e c!Lctw for the Uvcs\.'o<.:l<. CUl~~us of 1977 woro !'.10t ovoi-

le..~lc for the otnte o£ \Jc~t nongtJ.. ~n~ cnrl1or Livc.stoc.:l: ccn:sua

tion c~tc1nec f1or.1 tl'liD su::r.Jcy \JC.re l:djt~a ted by ua$.r:] t.J,~, annual

r;:-cmth r,::t~ p.;=to~.r.!ng t~ c·ll !r.d~~) b-::;b;~cn !972 ~~{~ 1tl77. In

o~hcr t.'t)rcl:J,. the nt:r.:be.t' o! ~:otnl botr!tJoo, i;rr~ctf,ro~ f)il t,.z:gioee

1 !l"1~ nnc1 ! f)77 •

. ,

concluctw in tho otatc uw:iDg tha l.uot 20 '!.~oro. ••;1o ~::.t;~e::l

given !n the statietica! Ab:ltracte f<:;;.t t;t.nt t:.cngal Dtoto 6nd

its <!iDuibutJ.on 1'1 oource in tho plm-: f..oc~nt~ er.c.t ~tinntcs.

253

Ule latest es tJ.matcs available on tUA and the soureca of irri­

gation relate to 1967-60. 'rtle all India estimates fer 196'7-68

er1d 1977-78 1o0ere used to obtain the annual growth rnto of lliA,

GIA end source of 1r:c1gnt!on. It. is ~ssu:ncd (a) that t;cst Dengel',

!l~~ m:~ and .t.rrie:;ati('ln by source 2rew at the snmc :ato c.<J those

fer India ;:;s ~ whole: w<l (b) that the eiotrict level dietribu­

tior: cf NI ~. una the source of irr1«Jet1on rane.ined the cal:"':o in

1t'77-18· r.s 1n 196?-68. l"'Urther, since the district level figures

for Gl.l. ..-:ere r.ot: evail~.l)le fo:- 1957-80, they were SSC'Jmed to be

aistributcd in the srmo ~ay as UII ...

se~r&l problems exict \>lith the:Jc esourupt1Cl.1E:. Qui to likel

the growth rote of the extent of 1ttigat1cr, f.lne its diotribution

by source in t:f'..S't Dent;;;el L~ M.f.ft:rent ff."C'}m ·thnt in Iwlia no a

tJhole. ThE dintr.ict level d1otribution can nlso bcccm~ conside­

rably d!f!crer.t cwr a deC'~de,. '!ho third scsumpticn t'l1~t intcn•

sity of !rri{Jnt!ct~ hc:!1 re~c-1nro the scn1e fe:r oll the di:::tr!cts

of tho stnto aurir,g 1"1C.7-70 il u.l.So <r..tite un.catisfc.ctr,cy.

Oris:JOJ uata on CilA were not nvaileble for Ute s tutc or crissa

for 19'17-78. 'l'ho \JIA f.i.gures available for 197G-79 \<;t:t-c • cye­

es'tirnatoo•. lienee in computing thu c.'ttf.:.:llt of !rrigute.l cu.---oa by

nourco \~re not uvailable acparately for canal,lt.rull<, .-~ell/tube­

wall irrigation. ~1tay tzere evailoble C$ w:ea undt:r: .a). Hujor

a.."ld. Hi:1o.r Irrigation Projects, b) Ninor :irrigation P.roje:c·;EJ,

which ~-e.-a giV"'...tt ecpara~oly "" • FlO\:' ~"16. • Lift• irri~ati•::n,

end c) Chock D\J'I.\'J and otnor oourcca, l~inor Irrig\lt!c,n r-;:ojocts

and Tradi t1onal l·10thod. I have assur.led tl~ut n) t;ao canal/taW;

irrigation while c) was well/tubct.,ml.l i.r:rig(Jtion. Jirea cnder

254

Hinor lttigaUon ~rojeet3 p.;~ed le~fl of a problan, whue • Flow•

irrigat1;,;n -~~..s ca.Lul/t.,;s.nk cnc. •r~t:tt• Wl'.G "Well./tubcw(·ll ir.-i',Jn•

tt:<i e.ree.s •

f!i'hS?;tA@lltr~o Aroa t"J source of irrigiltion in Hahar,:;sh·:.;.:a was

9iwn as :nu-fnco UJ:"ig~t-..ion :-.J:d \'iCll irrigation. uar.ca nurfac:e

ir.rigaticn Tt;i::S prcsumc-d to be eanal/tan1~ ir.r1gat1oo •

. ~yjar!)tc Ytt§£ rr£d~h nng Meghya Prcdes,ha For GUjl'ret and

Utter Pradesh ir:rigetion date c;:ould be obtained only tor 1916-

7"1, 'ttlhilo for Ncclhya Predesh, the data on the source or ir-riga­

tion referred to 1975-76. 1~0 att~pt waa made to update these

e:sUmatce t,.:.) 1977-"18 on the ueouit\l,>tion that the Uificrcnce

would biJ small.

~s ~-,entionl:d enrU.e:-, d1tn hef.! to be collecto~ ot the

t.~luJ~;q. lc~rcl fc:: £1(~-on eiatr1.ct•l o! Gujarr~t. Dnta. oo c.oricul•

tural !>r<:lduct1 vi ty :~nd de t·1 on the prod,Jcti en of c.ro.ro ,,,cr•:)

not r.vn1.l.-:blP. .:Jt th~J tC'lul:c. lovcl. Value of total o~r.!cnltural

output Ha:l availa'blo 0-t t.ho ~!strict lcve!l. :tn other \·:or·:~s no

c.llO\'•;_>..~Ce'1 ~~uld J:--o me de for int()r-tc~luka ve~i '3t1c-ns '·n proch­

et1v1ty. 'lho !'J:OI'OX'tion of CIA in the talultu was u:~ro to

split the district level estimates of production eccord.tng tx>

~:ss regions.