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.
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