Household&Consumption&Expenditure&Survey · 2016. 1. 7. ·...

REPORT&OF&THE&

66th&Round&of&NSS&(Socio6Economic&Survey)&

Household&Consumption&Expenditure&Survey&(July&2009&–&June&2010)&

Results based on Pool ing of Central and State Sample Data

&&&&&

Directorate&of&Economics&&&Statistics&Department&of&Planning&

Government&of&Uttarakhand&100/6&Neshvilla&Road,&Dehradun&&Uttarakhand&(INDIA)&6&248001&

Report No. (66/1.0/1)

! i!

Acknowledgement--

It#gives#me#immense#pleasure#to#note#that#the#Directorate#of#Economics#and#

Statistics# (DES),# a# constituent# of# the# Department# of# Planning,# has# been#

complying# one# of# the# important# recommendation# made# by# the# National#

Statistical# Commission# (NSC)# is# respect# of# pooling# central# and# state# sample#

data#from#66th#Round#of#the#National#Sample#Survey#onwards.#

#

DES#has#prepared#a#publication#titled,#“Household#Consumption#Expenditure#

Survey:#Results#based#on#Pooling#of#Central#and#State#Sample#Data”#that#is#an#

outcome#of#66th#Round#of#NSS#(SocioIEconomic#Survey).#The#technical#support#

and#guidance#provided#by#the#Data#Processing#Division##(DPD)#of#the#National#

Sample# Survey#Office# (NSSO)# in# the# form#of# software# and# handsIon# training#

has#been#the#main#driving#force.##

As# survey# itself# is# an# extensive# field# job# requiring# greater# intelligence# and#

devotion,# I#must# communicate#my# sincere# gratitude# to# the# field# staff# of# the#

DES.# However,# a# good# report# can# be# prepared# only# with# vision# and#

understanding# of# the# topic# under# consideration,# and# for# that,# efforts# of# the#

officials# of# the# DES# engaged# in# this# activity# are# praiseworthy.# Under# the#

guidance#and#supervision#of#Shri#Pankaj#Naithani,#Joint#Director,#a#small#team#

of# Shri# GS# Pandey,# Programmer# and# Shri# Alok# Kumar,# Assistant# Statistical#

Officer,# has# meticulously# prepared# this# document.# Their# efforts# are# duly#

acknowledged.##

Enlightened# readers# are# requested# to# kindly# communicate# their# comments#

and#suggestions,#which#will#help#DES#to#cater#to#the#specific#needs#of#planning#

and#research.#

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! ii!

Foreword--

The# Directorate# of# Economics# and# Statistics# (DES)# has# been# participating# in#

various# Round# of# the# National# Sample# Surveys# (SocioIEconomic# Surveys)# on#

the# basis# of# ‘equal# sampleIsize’.# DES# is# complying# one# of# the# important#

recommendation#made#by#the#National#Statistical#Commission#(NSC)#is#respect#

of# pooling# central# and# state# sample# data# from# 66th# Round# of# the# National#

Sample# Survey,# and# in# this# direction# it# has# prepared# its# publication# I#

“Household# Consumption# Expenditure# Survey:# Results# based# on# Pooling# of#

Central#and#State#Sample#Data”.#This# is#first#publication#based#on#66th#Round#

of#NSS#(SocioIEconomic#Survey).###

An#attempt#has#been#made#to#make#use#of#the#software#provided#by#the#Data#

Processing# Division# (DPD)# of# the# National# Sample# Survey# Office# (NSSO)# for#

pooling# the# data# from# central# and# state# samples.# State# has# gathered#

information# from# 224# FSUs# and# the# centre# also# surveyed# equal# number# of#

FSUs.#Consumer#Expenditure#of#households#on#Food#and#NonIfood#items#has#

been# captured# by# both# the# agencies# on# Schedule# 1.0:# TypeI1# and# TypeI2.#

Apart# from# various# other# important# estimates,# the# report# basically# presents#

districtIwise#pooled#estimates#of#Monthly#Per#Capita#Expenditures#(MPCEs)#on#

Food#and#NonIFood.###

I# would# like# to# appreciate# the# fieldIwork# done# by# the# staff# positioned# in#

various# regional# units# of# the# DES.# The# supervision# provided# by# the# District#

Economic#and#Statistics#Officers,#Divisional#Deputy#Directors#and#the#Officials#

of#the#NSS#unit#in#the#DES#is#appreciated.#Pains#taken#by#Shri#Pankaj#Naithani,#

Joint#Director,#and#his#small#two#member#team#consisting#of#Shri#GS#Pandey,#

Programmer# and# Shri# Alok# Kumar,# Assistant# Statistical# Officer# needs# special#

mention.# It# is# due# to# their# efforts# that# this# report,#which# is# first# of# its# kind,#

could#be#prepared.###

I# sincerely# urge# honourable# readers# to# kindly# send# their# comments# and#

suggestions.#

### #

! iii!

Prologue--

“Household# Consumption# Expenditure# Survey:# Results# based# on# Pooling# of#

Central#and#State#Sample#Data”#is#first#publication#based#on#66th#Round#of#NSS#

(SocioIEconomic# Survey).# Through# this# document# the# Directorate# of#

Economics#and#Statistics#(DES)#is#complying#an#important#recommendation#of#

the# National# Statistical# Commission# (NSC).# NSC# has# desired# that# from# 66th#

Round#onwards#states#should#attempt#pooling#central#and#state#sample#data#

in#order#to#prepare#districtIwise#estimates.####

This#particular#Report# is#an#outcome#of# the#data#collected# through#Schedule#

1.0:# TypeI1# and# TypeI2# canvassed# for# the# collection# of# ‘Consumer#

Expenditure’#during#the#66th#Round.#There#are#132#rural#units#(villages)#and#92#

urban# units# (blocks)# surveyed# by# the# state,# and# equal# numbers# of# units# are#

covered# by# the# centre# as# well.# Data# entry# has# been# done# at# districts# itself,#

whereas# validation# checks# have# been# administered# at# the# Directorate.#

Software# provided# by# the# Data# Processing# Division# (DPD)# of# the# National#

Sample#Survey#Office#(NSSO)#has#been#used#for#pooling#of#data#pertaining#to#

the#state#and#central#samples.#Consumer#Expenditure#of#households#on#Food#

and#NonIfood# items# captured#by#both# the#agencies#on#Schedule#1.0:# TypeI1#

and# TypeI2# have# been# used# to# prepare# districtIwise# pooled# estimates# of#

Monthly# Per# Capita# Expenditures# (MPCEs)# on# Food# NonIFood# and#

Combined.#Document#prepared#by# the#DPD# for#handsIon# training#using#data#

for#Bihar#and#Odissa# is#strictly# followed.#However,# titles#of#Tables#have#been#

edited#suitably,#and#Notes#provided#wherever#needed.####

The#Report#is#prepared#in#three#parts##

• Main# Text:# Describing# the# need# of# the# Report,# the# Test# used# for#Poolability,#Methods#applied#for#pooling#of#data#etc.###

• Tables:#In#all#23#Tables#are#prepared#and#produced.#These#are#focused#on# general# information# about# sample# sizes,# results# of# various# tests#

applied,# estimating# certain# parameters# e.g.# number# of# households,#

number#of#persons##sex#ratio,#MPCEs#for#Food#NonIFood#items#and#

Combined.#

• Annexure:#Schedule#1.0:#TypeI1#and#TypeI2#have#been#annexed#for#kind#perusal#of#the#readers.##

! iv!

DES#is#indeed#indebted#to#the#field#staff#that#has#helped#gathering#data#from#

the# respondents.# It# is# because# of# their# hard#work# and# dedication# that# state#

data# passed# its# poolability# with# the# central# data# in# most# of# the# cases.#

Contribution# of# Economic# and# Statistics# Officers# and# Divisional# Deputy#

Directors# in# supervising# and# inspecting# NSS# units# is# equally# acknowledged.#

However,#Shri#Sunder#Lal#(Deputy#Director,#Kumaon#Division),#Shri#VP#Ramola#

(Retd.# E# St# O)# and# Shri# BS#Miyan# (E# St# O,# Urban#Development# Department)#

need#special#appreciation#as# they#served#NSS#Unit#of# the#DES#at# the# time#of#

conduct# of# training# and# survey,# its# supervision,# data# processing# etc.#

Contribution#of#Smt.#Nalini#Dhyani# (Additional#Statistical#Officer,#DES)# is#also#

praised.###

The# help# and# support# extended# by# the# DPD,# NSSO# in# the# form# of# technical#

guidance# and# software# is# gratefully# acknowledged.# DPD# helped# us# through#

two#training#programmes.#The#document#provided#by# it#after#processing#and#

testing#poolibility#of#data#from#Bihar#and#Odissa#has#been#very#handy.##

It#will#not#be# fair#on#my#part# if# I# fail# to#appreciate# the#sincere#work#done#by#

teamImembers#I#Shri#GS#Pandey,#Programmer#and#Shri#Alok#Kumar,#Assistant#

Statistical# Officer# I# in# preparing# the# tables# and# preliminary# Report.# It# is#

because#of#their#hard#work#and#dedication#that#this#Report#could#be#prepared#

in#the#present#form.###

I# would# like# to# make# sincere# appeal# to# our# honourable# readers# to# kindly#

provide#their#valuable#comments#and#suggestions,#and#also#to#point#out#errors#

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Report-prepared-by:--Shri#Pankaj#Naithani,#Joint&Director&

Shri#GS#Pandey,#Programmer#

Shri#Alok#Kumar,#Assistant&Statistical&Officer#

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Web#Information#Manager:## # Shri#Pankaj#Naithani,#Joint&Director&

Asstt.#Web#Information#Manager:## Shri#GS#Pandey,#Programmer#

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! vi!

Content--

Title# Page#No.#

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Main-Text- 01#–#09#

Tables- 10#I#32#

Annexure- 33#Onwards#

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1!

66th ROUND OF NSS (SOCIO-ECONOMIC SURVEY)

Household)Consumption)Expenditure)Survey:)Results based on Pool ing of Central and State Sample Data )

)

1.0 The'Directorate'of'Economics'and'Statistics'(DES)'participated'in'the'66th'Round'of'the'National'Sample'Survey'(NSS)'on'the'basis'of'‘equal'sampleCsize’'i.e.'the'number'of'rural'and'urban'units'surveyed'by'both'the'bodies'of'the'centre'and'state'were'equal.'There'were'132'villages'(rural'units)'and' 92' urban' blocks' (urban' units)' surveyed' by' each' of' the' body.'Combined' number' of' unit' was' 448.' The' Round' was' focused' on'‘Household' Consumer' Expenditure’' and' ‘Employment' and'Unemployment’.'However,'household'consumer'expenditure'survey'was'done'canvassing'two'types'of'Schedule'–'Schedule'1.0:'TypeC1'(Similar'to'one'canvassed' in'61st'Round)'and'Schedule'1.0:'TypeC2' (7Cday'reference'period'for'some' items'of' food,'pan,' tobacco'and' intoxicants).' Interested'reader'may'refer'Annexures'for'details'about'these'Schedules.''

2.0 The' survey' was' conducted' in' four' subCrounds' (July' –' September' 2009;'October'–'December'2009;'January'–'March'2010'and'April'–'June'2010).'State' surveyed' 1782' Households' (1048' –' Rural' and' 734' –' Urban)' for'Schedule' 1.0:' TypeC1,' and' 1784' Households' (1049' –' Rural' and' 735' –'Urban)'for'Schedule'1.0:'TypeC2.'Centre'surveyed'1779'Households'(1048'–'Rural'and'731'–'Urban)'for'Schedule'1.0:'TypeC1,'and'1775'Households'(1045'–'Rural'and'730'–'Urban)'for'Schedule'1.0:'TypeC2.'''

Note:)Please refer Table-1A and 1B detailed below.'

3.0 The' Professional' Committee' constituted' by' the' National' Statistical'Commission'(NSC)'on'pooling'of'central'and'state'sample'data'under'the'chairmanship'of'Professor'R.'Radhakrishna,'ExCChairman'of'NSC,'has'given'the'theoretical'framework'of'pooling'of'NSS'data'and'its'methodology'as'well.'The'NSC'has'accepted'the'report'of'the'Committee'in'its'meeting'on'9.11.2012.' Consequently,' the' Data' Processing' Division' (DPD)' of' the'National'Sample'Survey'Office' (NSSO)'has'undertaken'pooling'of'central'and'state'sample'data'of'two'states'namely'Bihar'and'Odissa.'

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4.0 The'DPD,'NSSO'organised'first'training'programme'on'01C02'January'2013'at'New'Delhi' in'which' Shri'GS' Pandey' (Programmer)' and' Shri' BS'Miyan'(Eco.'&'Stat.'Officer)'participated'from'the'state.'Preliminary'material'on'pooling' was' provided' in' the' programme.' However,' DPD' has' recently'organised' a' detailed' training' programme' on' 25C26' August' 2014' at'Chandigarh.' Shri' Alok' Kumar' (Asstt.' Stat.' Officer)' and' Shri' Gopal' Gupta'(Addl.'Stat.'Officer)'from'the'DESCUttarakhand'attended'this'programme.'The' DPD' has' provided' a' report' titled' –' Pooling' of' Central' and' State'Sample' data' of' the' NSS' 66th' Round:' Consumer' Expenditure' and'Employment' &' Unemployment' Survey' (Bihar' and' Odissa' State).'Methodology'of'pooling'has'been'demonstrated'and'states'are'motivated'to'undertake'the'pooling'exercise'from'66th'Round'onwards.''

5.0 This' report' is'an'outcome'of' the'support' that' is' received' from'the'DPD,'NSSO' in' the' form' of' technical' guidance' and' software' availability.' DESCUttarakhand'has'used'the'same'central'software'for'data'entry'and'data'validation.'Consequently,'there'has'been'no'difficulty' in'pooling'the'two'sets' of' data' –' one' each' from' central' and' state' sample.' Considering' the'smaller'sample'size'at'district'level'following'broad'parameters'have'been'considered'for'pooling.'

a) Monthly' Per' Capita' Expenditure' (MPCE)' of' Food,' NonCFood,' and'Combined' MPCE' derived' from' detail' item' for' Uniform' Reference'Period' (URP),' Mixed' Reference' Period' (MRP)' and' Modified' Mixed'Reference'Period'(MMRP)'

b) Household'Size'and'Sex'

c) District'Level'Computations'

6.0 DistrictCwise'following'tests'are'undertaken'to'test'poolability:'

a) District'wise'WaldCWolfowitz'Run'Test'for'MPCE'(URP,'MRP,'MMRP)'for'central'and'state'sample'[NonCParametric'ZCtest]'

About&the&Test&(Text&adopted&from&DPD&Document):&&&

Suppose'X'and'Y'are'independent'random'samples'with'Cumulative'Distribution'Function' (CDF)'as' Fs(x)' and'Fc(y).'Null'Hypothesis' to'be'

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3!

tested' is'H0:'Fs(x)'=' 'Fc(x)' for'all'x'against'alternative'Hypothesis,'H1:'''Fs(x)'

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4!

),) )based' on' subCsample' 1'&' 2'

estimates.' Where;' stands' for' summing' over' stratum' x' subC

stratum'level'variance'at'the'domain'of'pooling.'

Let' rc' and' rs'be' the'estimate' of' population' rates' Rc'and' Rs' i.e.' ' Y/X'based'on' central' and' state' sample' respectively'with' corresponding'mean'square'error'MSE(rc)'and''MSE'(rs).'For'large'sample,'making'all'assumption' of' parametric' test,' one'may' use' ZCStatistic' to' test' the'null'hypothesis'H0'E(rc)=E(rs).'Where;'E'stands'for'expectation.'

Z=) )

MSE(rc)'and'MSE(rs)'are'estimated'as'follows:'

mse(rc)''='( (tyc)'–'2'*'rc' '(tyc,'txc)'+'rc2'* '(txc))/'txc2'

mse'(rs)''='( (tys)'–'2'* rs' '(tys,'txs)'+'rs2'* '(txs))/'txs2'

Where;'

),) ))

),) ))

'(tyc,' txc)=' )based' on' subCsample' 1' &' 2'

estimates.' Where;' stands' for' summing' over' stratum' x' subC

stratum'level'variance,'covariance'at'the'domain'of'pooling.'

Note:)Results of these tests are given in Table-2A, 2B, 3A and 3B detailed below.

7.0 The' DPD,' NSSO' has' suggested' two' alternate' methods' for' pooling' of'central'and'state'sample'data.'These'methods,'detailed'below,'have'been'used'in'preparing'the'report:'

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a) Weighting'by'Matching'Ratio:'Building'aggregate'estimate'of'pooled'sample'in'proportion'matching'ratio'm:n'of'central'and'state'sample'aggregate' estimate;' where,' m' and' n' are' the' allotted' sample' for'central' and' state' sample' separately' for' rural' and' urban' sector.'Building' ratio' estimate' of' pooled' sample' as' ratio' of' aggregate'estimates.''

About&the&Method&(Text&adopted&from&DPD&Document):&

When'the'State’s'participation'is'equal'matching'of'central'samples,'the'simple'average'of'two'estimates'may'be'a'way'of'combining'the'estimates' considering' central' and' state' samples' as' independent'samples.'The'pooled'estimate'will'always'lie'between'the'estimates'based'on'central'and'state'sample'separately.''

b) Weighting' by' Inverse' of' Variance:' Ratio' estimates' are' built' by'weighting' the' ratio' estimate' of' central' and' state' sample' in'proportion' to' inverse' of' variance' of' ratio' of' the' central' and' state'sample.'

About&the&Method&(Text&adopted&from&DPD&Document):&

For' any' characteristic,' consider' the' state' sample' [s]' in' the' form'of'two'independent'subC'sample's1'and's2'and'the'central'sample'[c]'in'the'form'of'two'independent'subC'sample'c1'and'c2.'Based'on'this,'the'respective'estimates'for'state'and'central'can'be'computed'as:'

ts'=' '(ts1'+'ts2)/2'and'tc'=' '(tc1'+'tc2)/2'

Pooled' estimate' leading' to' optimum' combination' of' these' two'estimates' is' given' by' weighing' with' inverse' of' the' variance' of' the'estimate.'Thus,'the'pooled'estimate'is'given'by:'

Tp'=' 'with'V(Tp)'=' '

In'general' and' 'are'unknown'and'can'be'estimated'as'

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Where;' stands' for' summing' over' stratum' x' subCstratum' level'variance'at'the'domain'of'pooling.'

Thus,'pooled'estimate'and'estimate'of'pooled'variance'is'given'by'

tp'=' ','' =' '

By'virtue'of'weighing'the'two'estimates'at'the'domain'level'at'which'two' estimates' are' pooled,' the' pooled' estimate' will' always' lie'between'the'central'and'state'sample'estimates.)

Further,' let'rc'and'rs'be'the'estimate'of'Rc'and'Rs' i.e.' 'Y/X'based'on'central'and'state'sample'respectively'with'corresponding'estimated'Mean' Square' Error' MSE(rc)' and' MSE(rs).' The' pooled' estimate' and'estimate'of'variance'of'pooled'ratio'estimate'may'be'given'by:'

rp'=' ','' =' '

Where,' MSE(rc)' and' MSE(rs)' are' calculated' using' formula' given' in'para' 6.0' (b)' above.' Alternatively' one' can' generate' the' pooled'estimate' of' aggregate' by' inverse' weight' of' estimate' of' variance'obtained' from'central' and' state' sample'using' formula'given'earlier'for'the'characteristics'x'as'well'as'y'and'obtain'the'pooled'estimate'of' ratio' as' ratio' of' pooled' estimate' of' aggregate.' This' will' ensure'consistency'between'pooled'estimates'of'aggregate'and'the'pooled'estimate'of'ratio.'

Let'txp'and'typ'be'the'pooled'estimate'of'aggregate'for'the'parameter'X'and'Y.'The'pooled'estimate'of'R'(i.e'Y/X)'is'given'by'

rp=''typ'/'txp''where,'typ='atyc'+'btys'and'txp='ctxc'+'dtxs'and'(a,'b),'(c,'d)'are'the'estimated'inverse'variance'weight'pair'of'the'characteristic'x'and'y'respectively.'

The'estimated'mse'of'pooled'ratio'estimate'rp'is'given'by:'

mse(rp)'='( (typ)'–'2' rp' '(typ,'txp)'+'rp2' '(txp))/'txp2'

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where' =' ,' )=)) 'and'

' '(typ,'txp)='ac ()tyc','txc')+bd ()tys','txs').'

'(tyc,' txc)=' )based' on' subCsample' 1' &' 2'

estimates.''

Similarly,'' '(tys,'txs)=' )

Where,' stands' for' summing' over' stratum' x' subCstratum' level'

covariance'at'the'domain'of'pooling.'

Note:)Results from these methods are given in Table-4A & B to 11A & B and 12 detailed below.

8.0 Tables' prepared/' generated' for' central,' state' and' pooled' sample' along'with'Relative'Standard'Errors'(RSEs)'separately'for'rural'and'urban'sector'using'the'software'are'as'follows:'

a) TableC1A:'Total'Sample'Size'(First'State'Unit,'Household'&'Person)'–'Rural'

b) TableC1B:'Total'Sample'Size'(First'State'Unit,'Household'&'Person)'–'Urban'

c) TableC2A:' DistrictCwise' Results' of' Run' Test' of' MPCE' for' Pooled'Sample'–'Rural'(Schedule'1.0:'TypeC1'and'TypeC2)'

d) TableC2B:' DistrictCwise' Results' of' Run' Test' of' MPCE' for' Pooled'Sample'–'Urban'(Schedule'1.0:'TypeC1'and'TypeC2)'

e) TableC3A:' DistrictCwise' Results' of' Mean' Test' of' MPCE' for' Pooled'Sample'–'Rural'(Schedule'1.0:'TypeC1'and'TypeC2)'

f) TableC3B:' DistrictCwise' Results' of' Mean' Test' of' MPCE' for' Pooled'Sample'–'Urban'(Schedule'1.0:'TypeC1'and'TypeC2)'

g) TableC4A:' DistrictCwise' Estimated' Number' of' Households' (Pooling'Method:'Matching'Ratio)'and'Their'RSEs'–'Rural'(Schedule'1.0:'TypeC1)'

h) TableC4B:' DistrictCwise' Estimated' Number' of' Households' (Pooling'Method:' Matching' Ratio)' and' Their' RSEs' –' Urban' (Schedule' 1.0:'TypeC1)'

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8!

i) TableC5A:' DistrictCwise' Estimated' Number' of' Persons' &' Sex' Ratio'(Pooling'Method:'Matching'Ratio)'and'Their'RSEs'–'Rural' (Schedule'1.0:'TypeC1)'

j) TableC5B:' DistrictCwise' Estimated' Number' of' Persons' &' Sex' Ratio'(Pooling'Method:'Matching'Ratio)'and'Their'RSEs'–'Urban'(Schedule'1.0:'TypeC1)'

k) TableC6A:' DistrictCwise' Estimate' of' MPCE' (URP)' –' Rural' (Schedule'1.0:'TypeC1)'

l) TableC6B:' DistrictCwise' Estimate' of' MPCE' (URP)' –' Urban' (Schedule'1.0:'TypeC1)'

m) TableC7A:' DistrictCwise' Estimate' of' MPCE' (MRP)' –' Rural' (Schedule'1.0:'TypeC1)'

n) TableC7B:' DistrictCwise' Estimate' of'MPCE' (MRP)' –' Urban' (Schedule'1.0:'TypeC1)'

o) TableC8A:'DistrictCwise'Estimate'of'RSE'of'Total'MPCE'(URP'&'MRP)'–'Rural'(Schedule'1.0:'TypeC1)'

p) TableC8B:'DistrictCwise'Estimate'of'RSE'of'Total'MPCE'(URP'&'MRP)'–'Urban'(Schedule'1.0:'TypeC1)'

q) TableC9A:' DistrictCwise' Estimated' Number' of' Households' (Pooling'Method:'Matching'Ratio)'and'Their'RSEs'–'Rural'(Schedule'1.0:'TypeC2)'

r) TableC9B:' DistrictCwise' Estimated' Number' of' Households' (Pooling'Method:' Matching' Ratio)' and' Their' RSEs' –' Urban' (Schedule' 1.0:'TypeC2)'

s) TableC10A:' DistrictCwise' Estimated' Number' of' Persons' &' Sex' Ratio'(Pooling'Method:'Matching'Ratio)'and'Their'RSEs'–'Rural' (Schedule'1.0:'TypeC2)'

t) TableC10B:' DistrictCwise' Estimated' Number' of' Persons' &' Sex' Ratio'(Pooling'Method:'Matching'Ratio)'and'Their'RSEs'–'Urban'(Schedule'1.0:'TypeC2)'

u) TableC11A:'DistrictCwise'Estimate'of'MPCE'(MMRP)'–'Rural'(Schedule'1.0:'TypeC2)'

v) TableC11B:' DistrictCwise' Estimate' of' MPCE' (MMRP)' –' Urban'(Schedule'1.0:'TypeC2)'

w) TableC12:' DistrictCwise' Estimate' of' RSE' of' Total' MPCE' (MMRP)' –'Rural'&'Urban'(Schedule'1.0:'TypeC1'&'TypeC2)'

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9!

9.0 There'are'certain'cautions'reported'by'the'DPD'as'follows:'

a) NonCadditivity' of' the' component' estimates' with' the' estimate' of'marginal'total'is'possible'e.g.'pooled'estimate'of'MPCE'of'‘Food’'and'‘NonCFood’'may'not'add'up'to' ‘Combined’'MPCE.'This'problem'can'be' obviated' generating' pooled' estimates' of' components' first' and'then'deriving'estimate'of'combined'as'sum.'

b) The' expected' agency' bias' in' the' two' sets' of' data' generated' by'different'agencies'cannot'be'denied.'As'such'they'cannot'be'merged'for'generating'pooled'estimate'without'testing'that'the'samples'are'realized'from'identical'distribution'function.'

10.0 The'poolability'tests'applied'reflect'that'in'most'of'the'cases'state'sample'and'central'sample'are'realized'from'the'identical'distribution'function.'''

However,'H0'are'not'accepted'in'following'cases'of'Run'Test:'

a) Rural' samples' from' Rudraprayag' (URP),' Dehradun' Hills' (MRP)' and'Udham'Singh'Nagar'(MMRP)'

b) Urban' samples' from'Haridwar' (URP,'MRP),'Uttarkashi' (MMRP)' and'Nainital'Hills'(MMRP)'

And,'H0'are'not'accepted'in'following'cases'of'Mean'Test:'

c) Rural' samples' from' Rudraprayag' (URP,' MMRP),' Dehradun' (URP),'Udham' Singh' Nagar' (URP,'MRP,'MMRP),' Nainital' Hills' (URP,'MRP,'MMRP),'Chamoli'(MRP)'and'Tehri'Garhwal'(MMRP)''

d) Urban' samples' from' Pauri' Garhwal' (URP),' Almora' (URP),' Udham'Singh' Nagar' (MRP),' Nainital' Hills' (MRP,' MMRP),' Chamoli' (MMRP),'Pithoragarh'(MMRP),'Champawat'(MMRP)'and'Nainital'(MMRP)''

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TABLES'

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Table'1A:!Total!Sample!Size!(First!Stage!Unit,!Household!&!Person)!–!Rural!

Schedule-1.0 Type

Central Sample State Sample

First Stage Unit

Surveyed Household Surveyed

Persons Surveyed

First Stage Unit


Persons Surveyed

Type-1 132 1048 5051 132 1048 5120

Type-2 132 1045 5021 132 1049 5015

Table'1B:!Total!Sample!Size!(First!Stage!Unit,!Household!&!Person)!–!Urban!

Schedule- 1.0 Type

Central Sample State Sample

First Stage Unit


Persons Surveyed

First Stage Unit


Persons Surveyed

Type-1 92 731 3326 92 734 3242

Type-2 92 730 3208 92 735 3303

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! 12!

Table'2A:!District'wise!Results!of!Run!Test!of!MPCE!for!Pooled!Sample!–!Rural!(Schedule!1.0:!Type'1!and!Type'2)!

District Code District Name

Uniform Reference Period (URP)

Mixed Reference Period (MRP)

Modified Mixed Reference Period

(MMRP)

Z-value Accept Z-value Accept Z-value Accept

1 Uttarkashi -1.59729 Y -0.88738 Y -1.59729 Y

2 Chamoli -1.77477 Y -1.41981 Y -2.12972 Y

3 Rudraprayag -2.66215 N -1.41981 Y -0.70991 Y

4 Tehri Garhwal -0.37648 Y -0.37648 Y 0.52810 Y

5 Dehradun -0.57887 Y 0.57887 Y -0.50747 Y

6 Pauri Garhwal -1.59189 Y 0.28943 Y -1.44717 Y

7 Pithoragarh 0.88738 Y 0.35495 Y -1.06486 Y

8 Bageshwar 0.17748 Y -1.24234 Y -1.24234 Y

9 Almora -0.14472 Y 0.57887 Y -0.28943 Y

10 Champawat -1.26004 Y -0.25201 Y -0.50402 Y

11 Nainital -0.62297 Y -2.04849 Y -0.89184 Y

12 Udham Singh Nagar -1.73660 Y -0.86830 Y -4.05207 N

13 Haridwar -0.43415 Y -1.88132 Y 0.72358 Y

14 Nainital Hills -1.76406 Y -2.26807 Y -2.26807 Y

15 Dehradun Hills -1.51205 Y -3.52811 N -2.26807 Y

Note: Z0.01 = -2.33 [One sided test]; Reject if Z-value < Z0.01 !

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! 13!

Table'2B:!District'wise!Results!of!Run!Test!of!MPCE!for!Pooled!Sample!–!Urban!(Schedule!1.0:!Type'1!and!Type'2)!


Uniform Reference Period (URP)

Mixed Reference Period (MRP)

Modified Mixed Reference Period

(MMRP)


1 Uttarkashi -0.76087 Y -1.78639 Y -3.02410 N

2 Chamoli 0.00000 Y 1.26004 Y 1.00803 Y

3 Rudraprayag -0.75602 Y 1.26004 Y 1.51205 Y

4 Tehri Garhwal -0.75602 Y -1.26004 Y 0.50402 Y

5 Dehradun 1.73660 Y 0.86830 Y -1.23298 Y

6 Pauri Garhwal -0.53243 Y -0.17748 Y -2.12972 Y

7 Pithoragarh -2.01606 Y -0.75602 Y -0.25201 Y

8 Bageshwar -1.26004 Y 0.75602 Y -0.25201 Y

9 Almora 1.26004 Y 1.26004 Y 2.01606 Y

10 Champawat 1.00803 Y 0.00000 Y -1.00803 Y

11 Nainital -0.26658 Y 0.44618 Y -0.70991 Y

12 Udham Singh Nagar 0.72358 Y 1.01302 Y -1.37808 Y

13 Haridwar -2.92024 N -2.62759 N -0.36319 Y

14 Nainital Hills -0.25201 Y 0.50402 Y -3.78012 N

15 Dehradun Hills -0.75602 Y -0.25201 Y 0.25201 Y

Note: Z0.01 = -2.33 [One sided test]; Reject if Z-value < Z0.01 !

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! 14!

Table'3A:!District'wise!Results!of!Mean!Test!of!MPCE!for!Pooled!Sample!–!Rural!(Schedule!1.0:!Type'1!and!Type'2)!


URP MRP MMRP


1 Uttarkashi 1.13018 Y 1.51655 Y 1.21716 Y

2 Chamoli 1.93568 Y 2.89834 N 1.60272 Y

3 Rudraprayag 3.05728 N 1.87331 Y 8.41584 N

4 Tehri Garhwal 2.12561 Y 0.15515 Y 7.49536 N

5 Dehradun 4.35666 N 1.08466 Y 1.42608 Y

6 Pauri Garhwal 2.13799 Y 2.33820 Y 0.25349 Y

7 Pithoragarh 1.55191 Y 1.60195 Y 1.29968 Y

8 Bageshwar 1.44111 Y 1.86922 Y 2.03150 Y

9 Almora 0.71911 Y 0.47277 Y 1.25798 Y

10 Champawat 0.80745 Y 0.06364 Y 0.51401 Y

11 Nainital 0.08123 Y 0.74471 Y 2.41421 Y

12 Udham Singh Nagar 29.07624 N 5.10481 N 4.16838 N

13 Haridwar 1.11916 Y 1.22859 Y 1.36430 Y

14 Nainital Hills 15.04925 N 33.44140 N 4.44864 N

15 Dehradun Hills 1.12104 Y 1.07460 Y 2.15748 Y

State Uttarakhand 0.72574 Y 0.76189 Y 0.74992 Y

Note: Z0.005 = 2.575 [One sided test]; Reject if absolute Z-value > Z0.005 !

! 15!

Table'3B:!District'wise!Results!of!Mean!Test!of!MPCE!for!Pooled!Sample!–!Urban!(Schedule!1.0:!Type'1!and!Type'2)!


URP MRP MMRP


1 Uttarkashi 1.21529 Y 1.25861 Y 2.45681 Y

2 Chamoli 0.33225 Y 0.47733 Y 3.80919 N

3 Rudraprayag 0.69597 Y 0.30126 Y 0.15073 Y

4 Tehri Garhwal 1.15670 Y 1.18080 Y 0.63193 Y

5 Dehradun 1.25492 Y 1.84317 Y 1.04979 Y

6 Pauri Garhwal 3.59496 N 2.49988 Y 2.40350 Y

7 Pithoragarh 0.96476 Y 2.02386 Y 5.18723 N

8 Bageshwar 1.02393 Y 1.60950 Y 0.94487 Y

9 Almora 3.74567 N 2.19789 Y 1.34378 Y

10 Champawat 2.10556 Y 1.73227 Y 2.83509 N

11 Nainital 0.52384 Y 0.44750 Y 3.05463 N

12 Udham Singh Nagar 2.22995 Y 10.81815 N 1.16444 Y

13 Haridwar 0.26624 Y 0.56434 Y 0.62445 Y

14 Nainital Hills 1.77569 Y 2.91393 N 4.22579 N

15 Dehradun Hills 0.56606 Y 0.54758 Y 1.20929 Y

State Uttarakhand 2.46749 Y 3.89547 N 1.70091 Y

Note: Z0.005 = 2.575 [One sided test]; Reject if absolute Z-value > Z0.005 !

! 16!

Table'4A:!District'wise!Estimated!Number!of!Households!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Rural!(Schedule!1.0:!Type'1)!

District Code

Estimated No. of Households (in Hundred)

RSE of Estimated Households Sample Households

Central State Pooled Central State Pooled Central State Pooled

1 655 501 578 1.74 12.25 5.40 64 64 128

2 760 720 740 11.68 11.81 8.31 64 64 128

3 525 468 497 6.50 18.30 9.27 64 64 128

4 1290 1125 1208 12.34 1.26 6.61 88 89 177

5 1162 1147 1154 11.51 2.91 5.97 96 96 192

6 1325 1478 1402 4.82 6.34 4.04 96 96 192

7 909 1034 971 2.98 13.28 7.21 64 64 128

8 590 579 585 6.45 7.15 4.81 64 64 128

9 1529 1192 1361 28.45 1.10 15.99 96 96 192

10 357 420 389 4.81 21.09 11.60 32 32 64

11 921 1001 961 1.80 48.63 25.34 64 63 127

12 1608 2411 2009 2.42 14.92 9.00 96 96 192

13 5537 2416 3977 62.84 0.05 43.74 96 96 192

14 431 386 409 4.39 7.27 4.14 32 32 64

15 159 176 167 20.12 26.18 16.79 32 32 64

State 17759 15054 16406 19.80 4.34 10.90 1048 1048 2096

District Codes: 1-Uttarkashi, 2-Chamoli, 3-Rudraprayag, 4-Tehri Garhwal, 5-Dehradun, 6-Pauri Garhwal, 7-Pithoragarh, 8-Bageshwar, 9-Almora, 10-Champawat, 11-Nainital, 12-Udham Singh Nagar, 13-Haridwar, 14-Nainital Hills, 15-Dehradun Hills !

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! 17!

Table'4B:!District'wise!Estimated!Number!of!Households!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Urban!(Schedule!1.0:!Type'1)!

District Code

Estimated No. of Households (in Hundred)

RSE of Estimated Households Sample Households


1 52 65 58 28.84 12.70 14.76 32 30 62

2 122 173 148 3.59 12.81 7.63 32 32 64

3 29 18 24 4.12 51.63 19.52 32 32 64

4 212 81 146 37.13 6.92 27.03 32 32 64

5 1743 1145 1444 21.39 9.21 13.42 96 96 192

6 152 182 167 2.82 7.34 4.20 64 64 128

7 131 135 133 27.74 13.04 15.18 32 32 64

8 17 13 15 15.96 15.79 11.34 32 32 64

9 152 110 131 19.94 22.39 14.91 32 32 64

10 48 99 74 7.47 22.05 14.95 32 32 64

11 440 443 442 1.36 4.14 2.18 63 64 127

12 1037 821 929 2.39 11.24 5.14 96 96 192

13 786 993 890 19.27 34.78 21.19 92 96 188

14 84 127 105 16.85 5.90 7.63 32 32 64

15 6 4 5 3.26 8.92 4.07 32 32 64

State 5012 4410 4711 8.26 8.53 5.94 731 734 1465


! 18!

Table'5A:!District'wise!Estimated!Number!of!Persons!&!Sex!Ratio!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Rural!(Schedule!1.0:!Type'1)!

Dist

rict C

ode Estimated No. of Persons (in Hundred)

RSE of Estimated Persons Sex Ratio RSE of Sex Ratio

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

1 2745 2267 2506 21.54 7.09 12.23 943 912 928 2.74 6.01 3.26

2 2833 3304 3068 18.90 3.32 8.91 1096 878 973 10.35 7.67 6.78

3 2344 2356 2350 14.10 1.24 7.06 1266 972 1109 2.27 1.63 1.48

4 5392 5673 5533 15.35 1.81 7.54 1098 991 1042 14.14 1.62 7.49

5 5557 5828 5692 7.03 2.05 3.59 891 927 909 2.35 1.66 1.43

6 5675 5056 5366 6.85 7.58 5.09 1044 1189 1110 4.79 37.84 20.39

7 3979 4188 4084 10.78 4.67 5.77 939 1182 1057 9.16 6.58 5.49

8 2379 2469 2424 1.19 12.62 6.45 1070 1174 1121 13.26 4.17 6.69

9 6643 5142 5893 32.09 1.82 18.10 977 1295 1105 22.37 1.81 9.95

10 1843 1620 1731 19.88 6.42 11.00 1015 1244 1116 34.65 7.43 16.29

11 3850 5117 4483 8.81 46.57 26.85 987 659 785 39.68 4.70 25.02

12 6928 12840 9884 14.43 12.90 9.79 876 1048 985 19.97 16.82 12.61

13 13979 13290 13635 16.68 6.68 9.15 497 960 692 50.15 26.55 25.76

14 1892 1965 1929 8.32 9.23 6.22 1175 1022 1094 0.73 25.35 11.85

15 1240 1165 1202 0.76 0.11 0.40 736 917 820 30.75 3.78 13.96

State 67279 72281 69780 5.40 4.29 3.42 863 1005 934 11.84 6.63 6.53


! 19!

Table'5B:!District'wise!Estimated!Number!of!Persons!&!Sex!Ratio!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Urban!(Schedule!1.0:!Type'1)!

Dist

rict C

ode Estimated No. of Persons (in Hundred)

RSE of Estimated Persons Sex Ratio RSE of Sex Ratio

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

1 235 267 251 39.67 16.82 20.61 906 861 882 17.46 16.54 12.07

2 450 696 573 14.76 24.52 15.98 753 624 672 4.80 8.59 4.81

3 108 61 85 11.90 59.49 22.65 909 652 807 12.70 14.99 9.37

4 1087 370 729 57.02 21.32 42.85 1121 775 1021 18.59 1.53 10.22

5 7333 4284 5809 23.69 3.53 15.01 982 952 971 11.29 3.31 5.94

6 780 635 708 15.23 3.70 8.55 835 915 870 2.06 3.89 2.27

7 517 531 524 32.88 9.53 16.92 1038 763 889 7.12 9.84 5.93

8 71 53 62 4.78 2.49 2.94 817 967 878 9.48 5.54 5.36

9 645 388 517 10.20 21.17 10.18 827 1077 913 20.15 30.94 20.40

10 199 449 324 8.53 34.59 24.11 931 842 868 4.83 0.91 2.63

11 2016 2270 2143 3.94 11.19 6.21 1040 921 975 3.76 9.22 4.79

12 5463 4217 4840 4.94 18.91 8.70 854 931 887 9.35 4.61 5.11

13 3831 4096 3963 17.80 19.85 13.39 743 1043 886 8.78 9.41 6.65

14 347 565 456 24.31 23.40 17.20 891 772 815 20.04 11.40 12.21

15 24 14 19 5.41 10.52 5.17 884 810 856 19.63 22.57 14.72

State 23106 18896 21001 8.67 6.43 5.58 901 928 913 5.46 3.21 3.15

District Codes: 1-Uttarkashi, 2-Chamoli, 3-Rudraprayag, 4-Tehri Garhwal, 5-Dehradun, 6-Pauri Garhwal, 7-Pithoragarh, 8-Bageshwar, 9-Almora, 10-Champawat, 11-Nainital, 12-Udham Singh Nagar, 13-Haridwar, 14-Nainital Hills, 15-Dehradun Hills

! 20!

Table'6A:!District'wise!Estimate!of!MPCE!(URP)–!Rural!(Schedule!1.0:!Type'1)!

Dist

rict C

ode Central Sample State Sample

Pooled by Matching Ratio

Pooled by Inverse Variance

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

1 547.9 393.2 941.1 657.0 592.1 1249.1 597.3 483.2 1080.4 563.9 394.7 941.2

2 673.9 454.1 1128.0 688.8 590.3 1279.1 681.9 527.4 1209.3 677.7 454.8 1138.3

3 621.7 414.1 1035.8 427.3 328.3 755.6 524.2 371.1 895.3 608.5 350.1 1014.4

4 598.1 439.3 1037.5 640.6 496.5 1137.1 619.9 468.6 1088.5 632.1 477.9 1115.2

5 625.7 654.7 1280.3 680.1 1174.5 1854.7 653.6 920.8 1574.3 628.5 656.8 1281.6

6 559.7 486.4 1046.1 762.5 513.8 1276.3 655.3 499.3 1154.6 579.0 487.0 1048.6

7 591.2 1473.8 2064.9 533.1 556.5 1089.5 561.4 1003.4 1564.7 578.3 617.3 1128.7

8 505.6 381.2 886.8 510.1 197.1 707.3 507.9 287.4 795.4 510.1 322.3 750.9

9 602.2 604.8 1207.0 503.6 439.9 943.4 559.2 532.8 1092.0 560.6 504.5 1060.6

10 460.6 232.2 692.8 428.0 388.8 816.9 445.4 305.5 750.8 432.9 388.7 810.8

11 559.7 574.5 1134.3 505.9 607.4 1113.2 529.0 593.3 1122.3 557.2 574.9 1133.9

12 576.7 508.1 1084.9 686.5 2618.0 3304.5 648.0 1878.5 2526.6 599.6 2615.9 3031.6

13 1152.2 1032.1 2184.3 554.4 545.1 1099.6 860.9 794.8 1655.6 555.2 553.6 1106.5

14 392.4 332.3 724.7 528.4 376.3 904.7 461.7 354.7 816.4 523.4 371.4 823.1

15 582.4 432.5 1014.9 675.6 570.3 1245.9 627.6 499.3 1126.8 606.8 527.7 1132.8

State 696.7 663.6 1360.3 603.2 932.6 1535.7 648.3 802.9 1451.2 604.2 881.9 1525.6

NOTE: Above figures are in Rupees. District Codes: 1-Uttarkashi, 2-Chamoli, 3-Rudraprayag, 4-Tehri Garhwal, 5-Dehradun, 6-Pauri Garhwal, 7-Pithoragarh, 8-Bageshwar, 9-Almora, 10-Champawat, 11-Nainital, 12-Udham Singh Nagar, 13-Haridwar, 14-Nainital Hills, 15-Dehradun Hills !

! 21!

Table'6B:!District'wise!Estimate!of!MPCE!(URP)!–!Urban!(Schedule!1.0:!Type'1)!

Dist

rict C




Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

1 612.8 716.1 1328.9 846.7 1709.6 2556.3 737.3 1244.7 1982.0 613.2 716.8 1329.5

2 855.5 1036.0 1891.5 849.4 1140.9 1990.3 851.8 1099.7 1951.5 849.4 1136.8 1987.7

3 737.8 738.1 1475.8 668.3 1051.1 1719.5 712.7 851.2 1563.9 668.7 1043.8 1716.2

4 617.0 695.4 1312.5 845.3 1503.9 2349.1 675.0 900.9 1575.9 630.5 739.6 1369.9

5 788.3 1221.2 2009.5 862.4 1462.3 2324.6 815.6 1310.1 2125.7 792.1 1233.1 2025.1

6 625.2 719.3 1344.5 1052.7 1683.2 2735.9 817.0 1151.8 1968.8 996.5 1374.3 2375.4

7 702.1 896.6 1598.7 681.7 1046.1 1727.8 691.8 972.3 1664.1 683.7 916.3 1606.8

8 665.6 680.8 1346.4 576.8 403.1 979.8 627.7 562.3 1190.0 585.9 405.0 981.5

9 767.4 936.2 1703.6 826.7 1978.3 2804.9 789.6 1327.5 2117.1 807.6 1661.6 2471.2

10 719.6 797.3 1516.9 573.4 557.3 1130.7 618.3 631.0 1249.3 679.7 585.2 1379.9

11 591.4 575.6 1166.9 596.8 627.5 1224.3 594.2 603.1 1197.3 596.1 613.6 1210.9

12 528.2 433.3 961.5 980.8 989.0 1969.7 725.4 675.4 1400.8 532.2 442.0 964.4

13 835.7 954.1 1789.8 756.0 972.7 1728.7 794.5 963.7 1758.2 770.1 963.9 1739.0

14 802.5 2019.2 2821.7 672.5 629.6 1302.1 722.0 1158.7 1880.7 725.1 674.2 1362.5

15 860.7 806.1 1666.7 751.8 670.9 1422.6 820.5 756.2 1576.7 755.1 705.5 1456.9

State 700.0 872.7 1572.7 819.4 1097.8 1917.2 753.7 974.0 1727.7 712.5 936.2 1650.2

NOTE: Above figures are in Rupees. District Codes: 1-Uttarkashi, 2-Chamoli, 3-Rudraprayag, 4-Tehri Garhwal, 5-Dehradun, 6-Pauri Garhwal, 7-Pithoragarh, 8-Bageshwar, 9-Almora, 10-Champawat, 11-Nainital, 12-Udham Singh Nagar, 13-Haridwar, 14-Nainital Hills, 15-Dehradun Hills

! 22!

Table'7A:!District'wise!Estimate!of!MPCE!(MRP)!–!Rural!(Schedule!1.0:!Type'1)!

Dist

rict C




Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

1 547.9 440.6 988.6 657.0 594.2 1251.2 597.3 510.1 1107.3 563.9 442.5 988.6

2 673.9 562.3 1236.2 688.8 778.5 1467.3 681.9 678.7 1360.6 677.7 659.5 1236.6

3 621.7 438.2 1059.9 427.3 409.4 836.7 524.2 423.8 948.0 608.5 438.1 1054.3

4 598.1 490.3 1088.4 640.6 452.8 1093.4 619.9 471.1 1091.0 632.1 461.7 1093.3

5 625.7 702.5 1328.1 680.1 779.3 1459.5 653.6 741.8 1395.4 628.5 760.0 1336.7

6 559.7 471.8 1031.6 762.5 448.2 1210.7 655.3 460.7 1116.0 579.0 471.7 1032.6

7 591.2 758.1 1349.2 533.1 500.4 1033.5 561.4 625.9 1187.3 578.3 652.9 1164.3

8 505.6 459.9 965.5 510.1 176.6 686.7 507.9 315.6 823.5 510.1 252.2 716.1

9 602.2 534.3 1136.5 503.6 497.6 1001.1 559.2 518.3 1077.4 560.6 521.3 1085.2

10 460.6 385.7 846.3 428.0 439.5 867.6 445.4 410.9 856.2 432.9 418.8 860.7

11 559.7 919.9 1479.6 505.9 722.2 1228.0 529.0 807.0 1336.0 557.2 855.5 1411.0

12 576.7 551.1 1127.8 686.5 1306.5 1993.0 648.0 1041.8 1689.8 599.6 585.7 1138.8

13 1152.2 2661.7 3813.9 554.4 537.6 1092.0 860.9 1626.5 2487.3 555.2 543.5 1098.5

14 392.4 318.7 711.2 528.4 582.0 1110.3 461.7 452.9 914.6 523.4 319.7 950.6

15 582.4 439.9 1022.3 675.6 501.9 1177.5 627.6 469.9 1097.5 606.8 472.2 1078.0

State 696.7 998.0 1694.7 603.2 683.5 1286.7 648.3 835.1 1483.4 604.2 685.4 1289.2

NOTE: Above figures are in Rupees. District Codes: 1-Uttarkashi, 2-Chamoli, 3-Rudraprayag, 4-Tehri Garhwal, 5-Dehradun, 6-Pauri Garhwal, 7-Pithoragarh, 8-Bageshwar, 9-Almora, 10-Champawat, 11-Nainital, 12-Udham Singh Nagar, 13-Haridwar, 14-Nainital Hills, 15-Dehradun Hills !

! 23!

Table'7B:!District'wise!Estimate!of!MPCE!(MRP)!–!Urban!(Schedule!1.0:!Type'1)!

Dist

rict C




Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

1 612.8 865.1 1477.9 846.7 1156.3 2003.0 737.3 1020.0 1757.3 613.2 866.7 1479.4

2 855.5 1169.1 2024.7 849.4 1011.2 1860.6 851.8 1073.2 1925.0 849.4 1012.3 1861.1

3 737.8 878.2 1615.9 668.3 1064.8 1733.1 712.7 945.6 1658.3 668.7 1063.7 1732.4

4 617.0 772.6 1389.7 845.3 1191.3 2036.6 675.0 879.0 1554.1 630.5 827.6 1459.4

5 788.3 1300.3 2088.6 862.4 1407.1 2269.5 815.6 1339.7 2155.3 792.1 1384.4 2147.5

6 625.2 792.1 1417.3 1052.7 1563.8 2616.4 817.0 1138.4 1955.4 996.5 1467.8 2569.2

7 702.1 835.4 1537.6 681.7 788.7 1470.4 691.8 811.7 1503.5 683.7 793.3 1477.0

8 665.6 630.7 1296.3 576.8 323.3 900.1 627.7 499.5 1127.2 585.9 341.3 901.3

9 767.4 892.6 1659.9 826.7 1887.0 2713.6 789.6 1265.9 2055.5 807.6 1065.8 1829.9

10 719.6 794.3 1513.9 573.4 496.0 1069.4 618.3 587.6 1205.9 679.7 502.0 1163.5

11 591.4 811.0 1402.4 596.8 705.9 1302.7 594.2 755.3 1349.6 596.1 728.6 1323.0

12 528.2 496.7 1024.9 980.8 1256.0 2236.7 725.4 827.5 1552.8 532.2 522.8 1137.4

13 835.7 1041.7 1877.3 756.0 1299.6 2055.6 794.5 1174.9 1969.4 770.1 1066.2 1929.4

14 802.5 1163.7 1966.2 672.5 779.7 1452.2 722.0 925.9 1647.9 725.1 783.3 1475.0

15 860.7 884.5 1745.2 751.8 762.9 1514.7 820.5 839.6 1660.1 755.1 803.7 1555.2

State 700.0 943.1 1643.2 819.4 1196.2 2015.6 753.7 1057.0 1810.7 712.5 1004.5 1813.9


! 24!

Table'8A:!District'wise!Estimate!of!RSE!of!Total!MPCE!(URP!&!MRP)!–!Rural!(Schedule!1.0:!Type'1)!

District Code

URP MRP

Central State Pooled by Matching

Ratio

Pooled by

Inverse Variance


Ratio

Pooled by

Inverse Variance

1 0.46 21.81 12.61 0.46 0.16 13.84 7.82 0.16

2 1.81 5.89 3.23 1.73 0.28 5.43 2.93 0.28

3 2.45 11.66 5.12 2.40 1.78 14.06 6.28 1.77

4 3.99 1.93 2.15 1.74 2.90 0.50 1.47 0.49

5 0.49 7.10 4.19 0.49 2.33 8.02 4.34 2.24

6 1.06 8.39 4.66 1.05 0.56 6.31 3.43 0.56

7 29.82 11.56 20.08 10.93 11.18 12.28 8.30 8.34

8 12.22 8.68 7.83 7.11 14.61 7.05 9.05 6.39

9 22.63 25.90 16.78 17.17 15.51 22.54 13.29 12.80

10 21.63 4.17 10.23 4.10 32.62 21.85 19.55 18.15

11 3.02 23.04 11.53 2.99 11.92 23.46 12.64 10.66

12 6.59 0.81 1.51 0.83 1.69 8.45 5.01 1.66

13 44.23 7.07 29.27 7.00 58.02 9.92 44.54 9.85

14 1.22 0.89 0.73 0.72 1.30 0.68 0.65 0.62

15 14.50 11.57 9.14 9.09 8.47 9.82 6.58 6.43

State 17.25 3.78 8.33 3.69 31.50 3.25 18.05 3.23


! 25!

Table'8B:!District'wise!Estimate!of!RSE!of!Total!MPCE!(URP!&!MRP)!–!Urban!(Schedule!1.0:!Type'1)!

District Code

URP MRP


Ratio

Pooled by

Inverse Variance


Ratio

Pooled by

Inverse Variance

1 1.66 39.50 25.48 1.66 1.55 20.80 11.87 1.55

2 15.51 2.45 7.62 2.42 16.95 1.06 8.93 1.06

3 23.56 2.34 11.19 2.33 23.99 1.71 11.72 1.71

4 16.07 37.08 28.44 14.96 12.95 25.41 17.63 11.65

5 2.78 10.53 5.91 2.69 2.68 3.55 2.28 2.14

6 24.78 7.20 9.83 7.14 33.17 3.64 12.27 3.63

7 2.09 7.50 4.02 2.01 2.05 0.71 1.10 0.67

8 26.53 2.47 15.04 2.46 18.96 1.54 10.92 1.54

9 14.41 5.77 6.94 5.47 11.60 16.18 11.66 9.64

10 7.20 13.03 7.34 6.36 15.05 11.04 10.64 9.01

11 8.22 4.32 4.57 3.82 14.17 7.73 8.25 6.79

12 2.52 22.92 16.14 2.51 3.33 4.77 3.61 2.86

13 11.69 5.46 6.53 4.95 9.09 12.93 8.02 7.44

14 29.72 13.11 22.75 12.28 8.77 2.56 5.35 2.46

15 23.99 11.35 13.67 10.28 21.90 11.65 12.68 10.30

State 4.21 6.41 4.04 3.53 3.94 3.49 2.64 2.63


!

! 26!

Table'9A:!District'wise!Estimated!Number!of!Households!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Rural!(Schedule!1.0:!Type'2)!

District Code

Estimated Number of Households (in Hundred))

RSE of Estimated No. of Households Sample Households


1 655 501 578 1.74 12.25 5.40 64 64 128

2 760 720 740 11.68 11.81 8.31 64 64 128

3 525 494 505 6.50 22.53 11.53 64 64 128

4 1290 1190 1240 12.34 4.47 6.77 88 89 177

5 1162 1147 1154 11.51 2.91 5.97 95 96 191

6 1325 1478 1402 4.82 6.34 4.04 96 96 192

7 909 1034 971 2.98 13.28 7.21 64 64 128

8 590 579 585 6.45 7.15 4.81 64 64 128

9 1529 1192 1361 28.45 1.10 15.99 96 96 192

10 357 420 389 4.81 21.09 11.60 32 32 64

11 921 1142 1032 1.80 51.00 28.23 62 64 126

12 1661 2411 2035 5.58 14.92 9.13 96 96 192

13 5537 2437 3998 62.84 0.89 43.52 96 96 192

14 431 386 409 4.39 7.27 4.14 32 32 64

15 159 176 167 20.12 26.18 16.79 32 32 64

State 17813 15306 16566 19.74 4.78 10.84 1045 1049 2094


!

! 27!

Table'9B:!District'wise!Estimated!Number!of!Households!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Urban!(Schedule!1.0:!Type'2)!

District Code

Estimated Number of Households (in Hundred))

RSE of Estimated No. of Households Sample Households


1 52 65 58 28.84 12.70 14.76 32 32 64

2 122 173 148 3.59 12.81 7.63 32 32 64

3 29 19 24 4.12 51.59 20.57 32 32 64

4 212 81 146 37.13 6.92 27.03 32 32 64

5 1743 1145 1444 21.39 9.21 13.42 95 96 191

6 152 182 167 2.82 7.34 4.20 64 64 128

7 131 135 133 27.74 13.04 15.18 32 32 64

8 17 13 15 15.96 15.79 11.34 32 32 64

9 152 110 131 19.94 22.39 14.91 32 32 64

10 48 99 74 7.47 22.05 14.95 32 32 64

11 440 443 442 1.36 4.14 2.18 64 64 128

12 1037 838 938 2.39 9.06 4.26 95 96 191

13 786 993 890 19.27 34.78 21.19 92 95 187

14 84 127 105 16.85 5.90 7.63 32 32 64

15 6 4 5 3.26 8.92 4.07 32 32 64

State 5012 4427 4719 8.26 8.42 5.90 730 735 1465


! 28!

Table'10A:!District'wise!Estimated!Number!of!Persons!!&!Rex!Ratio!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Rural!(Schedule!1.0:!Type'2)!

Dist

rict C

ode

Estimated No. of Persons (in Hundred)

RSE of Estimated No. of Persons Sex Ratio RSE of Sex Ratio

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

1 2980 2413 2697 14.27 0.09 7.88 866 1120 972 5.67 5.08 3.87

2 3312 3720 3516 1.62 6.83 3.69 1091 1118 1105 12.81 11.33 8.53

3 2016 2081 2036 10.46 19.01 11.01 1309 987 1132 11.18 12.23 8.38

4 5463 5092 5285 13 17.64 10.83 955 1025 987 11.84 14.14 9.31

5 5689 5503 5596 16.35 5.47 8.74 899 1004 949 6.88 6.34 4.68

6 5671 5965 5818 5.17 9.98 5.70 1222 1190 1206 4.92 11.48 6.19

7 3506 3822 3664 5.5 2.22 2.87 1105 1093 1098 9.41 4.53 5.24

8 2352 2354 2353 4.53 11.56 6.21 1123 1233 1177 13.84 3.79 6.89

9 5803 5140 5471 18.6 3.96 10.04 1118 1314 1206 18.83 3.54 8.94

10 1772 1722 1747 4.57 31.31 15.60 1061 1330 1186 21.88 7.59 10.67

11 3783 6128 4956 12.4 51.83 32.39 862 872 868 17.69 2.45 8.87

12 9594 11731 10646 6.93 27.94 15.71 943 838 886 17.89 4.92 9.80

13 13359 13274 13404 18.1 5.68 9.45 469 901 658 54.93 4.76 19.85

14 2442 1865 2153 4.93 9.82 5.09 1084 953 1025 17.69 29.47 16.59

15 985 1137 1061 10.11 22.35 12.86 898 995 948 6.56 3.76 3.68

State 68727 71947 70403 4.47 6.72 4.07 882 1003 942 11.94 2.63 5.76


! 29!

Table'10B:!District'wise!Estimated!Number!of!Persons!&!Sex!Ratio!(Pooling!Method:!Matching!Ratio)!and!Their!RSEs!–!Urban!(Schedule!1.0:!Type'2)!

Dist

rict C

ode

Estimated No. of Persons (in Hundred)

RSE of Estimated No. of Persons Sex Ratio RSE of Sex Ratio

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

Cent

ral

Stat

e

Pool

ed

1 202 248 225 22.23 6.63 10.63 806 894 854 6.20 3.38 3.42

2 397 779 588 8.25 6.14 4.93 772 1081 965 5.25 3.62 2.92

3 96 73 85 6.41 42.83 18.74 1019 587 806 3.76 15.56 6.14

4 781 352 567 48.46 20.03 33.95 561 671 593 6.95 41.16 23.52

5 7574 4351 5962 32.44 14.62 21.28 890 771 845 10.97 3.96 6.05

6 700 760 730 13.30 11.12 8.61 1150 934 1031 13.74 2.71 7.76

7 538 562 550 34.74 3.03 17.06 929 834 880 1.60 21.91 10.42

8 73 67 70 3.81 0.05 1.99 1104 1085 1095 2.38 13.10 6.60

9 591 427 509 17.65 32.65 17.10 1113 1207 1151 9.01 17.14 9.99

10 187 463 325 12.91 32.24 23.26 910 752 795 6.35 17.42 9.00

11 2216 2284 2250 11.93 11.01 8.11 937 823 877 2.76 2.16 1.79

12 5283 4771 5032 11.89 13.58 8.97 814 985 893 3.47 1.63 1.82

13 3416 3800 3608 12.78 15.68 10.24 858 1021 940 8.57 14.26 8.68

14 277 585 431 21.02 4.03 7.29 844 826 832 2.16 0.48 1.12

15 20 13 16 3.63 10.16 4.71 797 689 753 14.84 13.58 10.01

State 22350 19535 20948 11.75 5.84 6.83 868 901 884 4.32 3.41 2.74


! 30!

Table'11A:!District'wise!Estimate!of!MPCE!(MMRP)!–!Rural!(Schedule!1.0:!Type'2)!

Dist

rict C




Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

1 671.5 417.8 1089.3 779.8 576.8 1356.5 719.9 488.9 1208.9 676.6 417.8 1090.4

2 758.6 489.8 1248.4 855.6 860.6 1716.2 809.9 686.0 1495.9 821.4 497.7 1364.3

3 693.2 507.7 1201.0 521.0 447.7 968.6 603.4 476.2 1079.6 603.0 459.6 1133.3

4 703.5 481.4 1184.9 838.3 565.5 1403.9 769.3 523.2 1292.5 720.7 483.2 1191.3

5 717.3 761.0 1478.3 786.3 861.3 1647.5 751.2 810.3 1561.5 774.4 797.9 1587.2

6 635.4 563.9 1199.3 830.1 333.5 1163.6 735.2 445.8 1181.0 642.6 359.1 1179.3

7 727.1 528.6 1255.8 646.8 412.3 1059.1 685.2 468.0 1153.2 727.0 415.1 1077.6

8 611.7 523.3 1135.0 664.9 181.9 846.8 638.3 352.5 990.8 612.6 357.9 1075.7

9 700.2 484.2 1184.4 482.0 384.8 866.8 597.7 437.5 1035.2 526.6 438.0 989.2

10 573.4 426.3 999.7 475.9 382.7 858.6 525.3 404.8 930.1 498.6 388.8 883.4

11 703.0 831.2 1534.2 593.0 480.8 1073.8 635.0 614.5 1249.5 643.5 829.5 1480.9

12 548.6 467.9 1016.4 1054.8 1134.1 2188.9 828.9 835.6 1664.5 597.3 841.2 1228.5

13 1334.9 2732.6 4067.5 648.1 558.8 1206.9 989.3 1640.6 2630.0 649.1 568.1 1216.8

14 471.6 264.4 736.0 682.0 548.3 1230.3 562.7 387.4 950.1 625.8 265.3 889.4

15 787.5 430.7 1218.2 691.6 618.7 1310.3 736.1 531.4 1267.5 707.1 606.8 1262.8

State 788.4 959.0 1747.4 746.4 627.8 1374.2 766.9 789.3 1556.2 752.9 632.1 1386.5


! 31!

Table'11B:!District'wise!Estimate!of!MPCE!(MMRP)!–!Urban!(Schedule!1.0:!Type'2)!

Dist

rict C




Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

Food

Non-

Food

Tota

l

1 793.0 796.5 1589.5 1217.0 1287.0 2504.0 1027.0 1067.3 2094.3 820.1 916.7 1595.1

2 954.7 1017.8 1972.5 1243.5 1561.4 2804.9 1146.1 1378.1 2524.1 958.4 1483.2 2209.3

3 820.4 981.8 1802.2 815.2 1064.8 1880.0 818.1 1017.8 1835.9 818.4 1037.4 1848.2

4 852.0 1064.3 1916.3 1280.0 1205.4 2485.5 984.9 1108.1 2093.0 858.6 1065.1 1916.6

5 984.7 1126.0 2110.6 1039.9 1524.5 2564.4 1004.8 1271.4 2276.2 993.3 1161.2 2159.6

6 750.7 799.8 1550.5 1361.8 1535.5 2897.3 1068.8 1182.8 2251.5 1133.5 1395.4 2563.4

7 875.9 1108.3 1984.2 871.6 1025.2 1896.8 873.7 1065.8 1939.6 871.7 1029.2 1982.5

8 712.4 649.6 1362.0 756.1 365.7 1121.8 733.3 514.1 1247.3 754.4 633.8 1212.0

9 1092.2 1092.0 2184.2 826.8 1655.7 2482.5 980.8 1328.5 2309.3 1090.1 1621.7 2198.8

10 800.6 638.8 1439.4 721.3 427.0 1148.4 744.2 488.1 1232.3 788.5 540.1 1364.8

11 658.3 685.2 1343.5 801.5 816.8 1618.3 730.9 752.0 1483.0 762.3 727.6 1391.5

12 590.9 492.2 1083.1 983.8 554.8 1538.6 777.1 521.0 1298.1 591.0 492.7 1083.3

13 1004.7 1062.8 2067.5 861.9 1447.0 2308.9 929.5 1265.1 2194.6 1004.1 1066.9 2069.9

14 1132.9 1095.3 2228.2 885.1 788.5 1673.6 964.7 887.0 1851.7 1118.8 980.2 2114.3

15 1115.3 1420.6 2535.9 927.4 913.8 1841.2 1040.7 1219.3 2260.0 950.6 913.8 1846.9

State 847.1 897.8 1744.9 967.4 1117.4 2084.7 903.1 999.9 1903.0 861.5 943.7 1816.1


! 32!

Table'12:!District'wise!Estimate!of!RSE!of!Total!MPCE!(MMRP)!–!Rural!&!Urban!(Schedule!1.0:!Type'1!&!Type'2)!

District Code

RURAL URBAN

Central State Pooled

by Matching

Ratio

Pooled by

Inverse Variance

Central State Pooled

by Matching

Ratio

Pooled by

Inverse Variance

1 1.30 16.15 9.08 1.30 1.84 14.82 8.89 1.83

2 11.64 14.75 9.76 9.24 5.91 6.59 4.33 4.46

3 1.24 2.40 1.28 1.11 22.04 17.55 14.06 13.73

4 0.42 2.05 1.13 0.41 1.18 36.23 21.52 1.18

5 6.44 4.30 3.80 3.58 6.73 15.92 9.50 6.21

6 8.80 8.05 5.97 5.94 31.34 9.63 12.44 9.44

7 11.47 4.38 6.56 4.10 0.12 0.88 0.43 0.12

8 5.67 14.93 7.16 5.33 14.75 13.89 10.19 10.16

9 16.71 18.08 12.19 12.42 2.25 8.72 4.81 2.18

10 24.93 13.43 14.76 11.85 3.61 7.71 4.17 3.28

11 4.23 16.70 7.63 4.12 2.80 5.05 3.03 2.46

12 11.77 11.63 8.45 8.81 0.82 25.42 15.07 0.82

13 51.46 10.20 39.86 10.10 1.86 16.66 8.81 1.85

14 8.41 7.50 5.85 5.78 2.67 6.99 3.54 2.51

15 2.44 2.34 1.68 1.69 22.56 2.84 12.71 2.82

State 28.01 6.58 15.99 6.41 5.24 8.52 5.25 4.48


! 33!

!

!

!

!

!

!

!

!

!

!

ANNEXUREE'

RURAL * CENTRAL * URBAN STATE GOVERNMENT OF INDIA NATIONAL SAMPLE SURVEY ORGANISATION SOCIO-ECONOMIC SURVEY SIXTY-SIXTH ROUND: JULY 2009 - JUNE 2010

SCHEDULE 1.0: CONSUMER EXPENDITURE Schedule Type 1

[0] descriptive identification of sample household

1. state/u.t.: 5. hamlet name:

2. district: 6. ward/inv. unit/block:

3. tehsil/town: 7. name of head of household:

4. village name: 8. name of informant:

[1] identification of sample household

item no.

item code item no. item code

1. srl. no. of sample village/ block 11. sub-sample

2. round number 6 6 12. FOD sub-region

3. schedule number 0 1 0 13. sample hamlet-group/sub-block number

4. sample (central-1, state-2) 14. second stage stratum

5. sector (rural-1, urban-2) 15. sample household number

6. NSS region 16. srl. no. of informant (as in col.1, block 4)

7. district 17. response code

8. stratum 18. survey code

9. sub-stratum 19. reason for substitution of original household (code)

10. sub-round 20. schedule type 1

CODES FOR BLOCK 1 item 17: response code : informant: co-operative and capable -1, co-operative but not capable -2,

busy -3, reluctant - 4, others - 9

item 18: survey code : original – 1, substitute – 2, casualty – 3

item 19: reason for substitution of original household : informant busy -1, members away from home -2, informant non-cooperative -3, others - 9

* tick mark ( ) may be put in the appropriate place.

Schedule Type 1 Schedule 1.0:2

[2] particulars of field operations srl. no. item investigator supervisory officer

(1) (2) (3) (4) 1. i) name

(block letters)

ii) code 2. date(s) of : DD MM YY DD MM YY (i) survey/inspection (ii) receipt (iii) scrutiny (iv) despatch 3. number of additional sheets

attached

4. total time taken to canvass schedule 1.0 (in minutes)

5. whether schedule contains remarks (yes-1, no-2)

in block 13/14 elsewhere in the schedule

6. signature


[3] household characteristics

1. household size during July 08 to June

09

14. land cultivated (0.000 ha)

2. principal industry (NIC-2004)

description: 15. land irrigated

(0.000 ha)

code (5-digit) 3. principal occupation (NCO-

2004)

description: primary source of energy for

16. cooking (code)

code (3-digit) 17. lighting (code)

4. household type (code) 18. dwelling unit code (owned-1, hired-2, no dwelling unit-3, others-9)

5. religion (code)

6. social group (code) 19. is any member of the household a regular salary earner? (yes-1, no -2)

7. whether owns any land (yes-1, no -2)

8. if yes in item 7, type of land owned (homestead only – 1, homestead and other land – 2, other land only – 3)

20. did the household perform any ceremony during the last 30 days? (yes – 1, no – 2)

land as on the date of survey (in 0.000 hectares) 21. no. of meals served to non-household members during the last 30 days

9. owned

10. leased-in 22. whether the household has access to internet

11. otherwise possessed (neither owned at home on the date of survey ( yes-1, no-2) nor leased-in)

12. leased-out

13. total possessed [items (9+10+11-12)]

CODES FOR BLOCK 3

item 4: household type : for rural areas: self-employed in non-agriculture-1, agricultural labour-2, other labour-3, self-employed in agriculture-4, others-9

for urban areas: self-employed-1, regular wage/salary earning-2, casual labour-3, others-9

item 5: religion : Hinduism-1, Islam-2, Christianity -3, Sikhism-4, Jainism-5, Buddhism-6, Zoroastrianism-7, others-9

item 6: social group : Scheduled Tribes-1, Scheduled Castes-2, Other Backward Classes-3, others-9 item 16: primary source of energy for cooking : coke, coal-01, firewood and chips-02, LPG-03, gobar gas-04,

dung cake-05, charcoal-06, kerosene-07, electricity-08, others-09, no cooking arrangement-10 item 17: primary source of energy for lighting : kerosene-1, other oil -2, gas-3, candle-4, electricity-5, others-9, no

lighting arrangement-6 Note: 1 acre = 0.4047 hectare


[4] demographic and other particulars of household members

srl. no. name of member

rela

tion

to h

ead

(cod

e)

sex

(mal

e-1,

fem

ale-

2)

age

(yea

rs)

mar

ital s

tatu

s (co

de)

gene

ral e

duca

tiona

l lev

el

(cod

e)

no. of days

stayed away from home during last 30 days

no. of meals

usually taken in

a day

no. of meals taken during last 30 days away from home at free of cost

on p

aym

ent

home

from

scho

ol,

balw

adi,

etc.

from

em

ploy

er

as

perq

uisi

tes o

r pa

rt of

wag

e

othe

rs

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

CODES FOR BLOCK 4

Col. (3): relation to head: self-1, spouse of head-2, married child-3, spouse of married child-4, unmarried child-5,

grandchild-6, father/mother/father-in-law/mother-in-law-7, brother/sister/brother-in-law/sister-in-law/other relatives-8, servants/employees/other non-relatives-9

Col.(6): marital status: never married – 1, currently married – 2, widowed – 3, divorced/separated – 4 Col. (7): general educational level: not literate -01,

literate without formal schooling: through EGS/NFEC/AEC - 02, through TLC -03, others- 04; literate with formal schooling: below primary -05, primary -06, middle -07, secondary -08, higher secondary -10, diploma/certificate course -11, graduate -12, postgraduate and above -13


[5.1] consumption of cereals, pulses, milk and milk products, sugar and salt during the last 30 days ended

on ...................

item code

consumption out of home produce

total consumption source code

quantity@ (0.000)

value (Rs.)

quantity@ (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) (7) rice – PDS 101 1 rice – other sources 102 chira 103 khoi, lawa 104 muri 105 *other rice products 106 *wheat/ atta – PDS 107 1 wheat/ atta – other sources 108 maida 110 suji, rawa 111 *sewai, noodles 112 *bread (bakery) 113 *other wheat products 114 *jowar & its products 115 bajra & its products 116 maize & products 117 barley & its products 118 small millets & their products 120 ragi & its products 121 other cereals 122 cereal: sub-total (101-122) 129

cereal substitutes: tapioca, etc. 139

arhar, tur 140 gram: split 141 gram: whole 142 moong 143 masur 144 urd 145 peas 146 khesari 147 other pulses 148 gram products 150 besan 151 other pulse products 152 pulses & pulse products: s.t. (140-152) 159

@Unit is kg unless otherwise specified in col(1). $Source code: only purchase –1, only home-grown stock –2, both purchase and home-grown stock –3, only free collection –4, only exchange

of goods and services –5, only gifts / charities – 6, others –9 *Source code cannot be 2, 3 or 4 for these items. For home-processed items such as muri, consumption should be recorded against ingredients

(e.g. home-produced muri: record against rice).


[5.1] consumption of cereals, pulses, milk and milk products, sugar and salt during the last 30 days ended on ...................

item code


total consumption source code

quantity@ (0.000)

value (Rs.)

quantity@ (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) (7) milk: liquid (litre) 160 baby food 161 *milk: condensed/ powder 162 *curd 163 *ghee 164 *butter 165 *ice-cream 166 *other milk products 167 *milk & milk products: s.t.(160-167) 169

sugar - PDS 170 1

sugar - other sources 171 *

gur 172 *

candy, misri 173 *

honey 174

sugar: s.t. (170-174) 179

salt 189


of goods and services –5, only gifts / charities – 6, others –9 *Source code cannot be 2, 3 or 4 for these items. For home-processed items such as muri, consumption should be recorded against ingredients

(e.g. home-produced muri: record against rice).


[5.2] consumption of edible oil, egg, fish and meat, vegetables, fruits, spices, beverages and processed

food and pan, tobacco and intoxicants during the last 30 days ended on …...............

item code consumption out of home produce

total consumption source$

quantity@ (0.000)

value (Rs.)

quantity@ (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) (7) vanaspati, margarine 190 *mustard oil 191 groundnut oil 192 coconut oil 193 edible oil: others 194 edible oil: s.t. (190-194) 199

eggs (no.) 200 fish, prawn 201 goat meat/mutton 202 beef/ buffalo meat 203 pork 204 chicken 205 others: birds, crab, oyster, tortoise, etc.

206

egg, fish & meat: s.t. (200-206)

209

@Unit is kg unless otherwise specified in col(1). $Source code: only purchase –1, only home-grown stock –2, both purchase and home-grown stock –3, only free collection –4, only

exchange of goods and services –5, only gifts / charities – 6, others –9 *Source code cannot be 2, 3 or 4 for these items. For home-processed items such as muri, consumption should be recorded against

ingredients (e.g. home-produced muri: record against rice).


[5.2] consumption of edible oil, egg, fish and meat, vegetables, fruits, spices, beverages and processed food and pan, tobacco and intoxicants during the last 30 days ended on …...............

item code consumption out of home produce


quantity@ (0.000)

value (Rs.)

quantity@ (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) (7) potato 210

onion 211

radish 212

carrot 213

turnip 214

beet 215

sweet potato 216

arum 217

pumpkin 218

gourd 220

bitter gourd 221

cucumber 222

parwal, patal 223

jhinga, torai 224

snake gourd 225

papaya: green 226

cauliflower 227

cabbage 228

brinjal 230

lady's finger 231 palak/other leafy vegetables

232

french beans, barbati 233

tomato 234

peas 235

chillis: green 236

capsicum 237

plantain: green 238

jackfruit: green 240

lemon (no.) 241

other vegetables 242 vegetables: s.t. (210-242) 249

@Unit is kg unless otherwise specified in col(1). $Source code: only purchase –1, only home-grown stock –2, both purchase and home-grown stock –3, only free collection –4, only

exchange of goods and services –5, only gifts / charities – 6, others –9 *Source code cannot be 2, 3 or 4 for these items. For home-processed items consumption should be recorded against ingredients.





item code quantity@ (0.000)

value (Rs.)

quantity@ (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) (7)banana (no.) 250 jackfruit 251 watermelon 252 pineapple (no.) 253 coconut (no.) 254 coconut green (no.) 255 guava 256 singara 257 orange, mausami (no.) 258 papaya 260 mango 261 kharbooza 262 pears/naspati 263 berries 264 leechi 265 apple 266 grapes 267 other fresh fruits 268 fruits (fresh): s.t.(250-268) 269

coconut: copra 270 groundnut 271 dates 272 cashewnut 273 walnut 274 other nuts 275 raisin, kishmish, monacca, etc. 276 other dry fruits 277 fruits (dry): s.t. (270-277) 279

garlic (gm) 280 ginger (gm) 281 turmeric (gm) 282 black pepper (gm) 283 dry chillies (gm) 284 tamarind (gm) 285 curry powder (gm) 286 oilseeds (gm) 287 other spices (gm) 288 spices: s.t. (280-288) 289






value (Rs.)

quantity@ (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) (7)tea : cups (no.) 290 tea : leaf (gm) 291 coffee : cups (no.) 292 coffee: powder (gm) 293 mineral water (litre) 294 cold beverages: bottled/canned (litre)

295 *

fruit juice and shake (litre) 296 *other beverages: cocoa, chocolate, etc.

297 *

biscuits 298 *cake, pastry 300 *prepared sweets 301 *cooked meals received as assistance or payment** (no.)

302

cooked meals purchased (no.) 303 salted refreshments 304 pickles (gm) 305 *sauce (gm) 306 *jam, jelly (gm) 307 *other processed food 308 *beverages, etc.: sub-total (290-308)

309

pan: leaf (no.) 310 pan: finished (no.) 311 *ingredients for pan (gm) 312 pan: s.t. (310-312) 319


of goods and services –5, only gifts / charities – 6, others –9 *Source code cannot be 2, 3 or 4 for these items. For home-processed items consumption should be recorded against ingredients. ** Do not include cooked meals received from other households.






value (Rs.)

quantity@ (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) (7)bidi (no.) 320 cigarettes (no.) 321 leaf tobacco (gm) 322 snuff (gm) 323 hookah tobacco (gm) 324 cheroot (no.) 325 zarda, kimam, surti (gm) 326 other tobacco products 327 tobacco: s.t. (320-327) 329

ganja (gm) 330 toddy (litre) 331 country liquor (litre) 332 *beer (litre) 333 *foreign/refined liquor or wine (litre) 334 *other intoxicants 335 intoxicants: s.t. (330-335) 339

[6] consumption of energy (fuel, light & household appliances) during the last 30 days ended on

……………………… item code consumption out of home

produce total consumption source$

quantity@ (0.000)

value (Rs.)

quantity@ (0.000)

value (Rs.)

(2) (1) (3) (4) (5) (6) (7) coke 340 firewood and chips 341 electricity (std. unit) 342 dung cake 343 kerosene – PDS (litre) 344 1 kerosene – other sources (litre) 345 matches (box) 346 coal 347 LPG [excl. conveyance] 348 charcoal 350 candle (no.) 351 gobar gas 352 petrol (litre) [excl. conveyance] 353 diesel (litre) [excl. conveyance] 354 other fuel 355 fuel and light: s.t. (340-355) 359

@Unit is kg unless otherwise specified in col(1). $Source code: only purchase –1, only home-grown stock –2, both purchase and home-grown stock –3, only free collection –4, only exchange of

goods and services –5, only gifts / charities – 6, others –9. *Source code cannot be 2, 3 or 4 for these items.


[7] consumption of clothing, bedding, etc.

Item code during last 30 days during last 365 days

quantity (0.000)

value (Rs.)

quantity (0.000)

value (Rs.)

(1) (2) (3) (4) (5) (6) clothing: first-hand

dhoti (no.) 360 sari (no.) 361 cloth for shirt, pyjama, salwar, etc. (metre) 362 cloth for coat, trousers, overcoat, etc. (metre) 363 chaddar, dupatta, shawl, etc. (no.) 364 lungi (no.) 365 gamchha, towel, handkerchief (no.) 366 hosiery articles, stockings, undergarments, etc.(no.)

367

ready-made garments (no.) 368 headwear (no.), belts 370 sweater, muffler, scarf, etc. (no.) 371 knitting wool, cotton yarn (gm) 372 clothing (first-hand): other 373

clothing: second-hand 374 clothing: sub-total (360-374) 379

bed sheet, bed cover (no.) 380 rug, blanket (no.) 381 pillow, quilt, mattress (no.) 382 cloth for upholstery, curtain, table-cloth, etc. 383 mosquito net (no.) 384 mats and matting (no.) 385 cotton (gm) 386 bedding: others 387 bedding, etc.: s.t. (380-387) 389

[8] consumption of footwear

item code during last 30 days during last 365 days

no. of pairs

value (Rs.) no. of pairs

value (Rs.)

(1) (2) (3) (4) (5) (6) leather boots, shoes 390 leather sandals, chappals, etc. 391 other leather footwear 392 rubber / PVC footwear 393 other footwear 394 footwear: second-hand 395 footwear: sub-total (390-395) 399


[9] expenditure on education and medical (institutional) goods and services

item code

during last 30 days

during last 365 days

value (Rs.)

value (Rs.)

(1) (2) (3) (4)

books, journals: first hand 400

books, journals, etc.: second hand 401

newspapers, periodicals 402

library charges 403

stationery, photocopying charges 404 tuition and other fees (school, college, etc.) 405

private tutor/ coaching centre 406

educational CD 407

other educational expenses 408

education: s.t. (400-408) 409

medicine 410

X-ray, ECG, pathological test, etc. 411 doctor's/surgeon's fee 412 hospital & nursing home charges 413 other medical expenses 414

medical - institutional: s.t. (410-414) 419


[10] expenditure on miscellaneous goods and services including medical (non-institutional), rents and taxes

during the last 30 days ended on ….......................

Item code value (Rs.) item code value (Rs.)

(1) (2) (3) (1) (2) (3) medicine 420 toilet soap 450

X-ray, ECG, pathological test, etc. 421 toothpaste, toothbrush, comb, etc. 451

doctor’s/ surgeon’s fee 422 powder, snow, cream, lotion and 452

family planning appliances 423 perfume

other medical expenses 424 hair oil, shampoo, hair cream 453

medical – non-institutional: sub-total 429 shaving blades, shaving stick, razor 454

(420-424) shaving cream, aftershave lotion 455

sanitary napkins 456

cinema, theatre 430 other toilet articles 457

mela, fair, picnic 431 toilet articles: sub-total (450-457) 459

sports goods, toys, etc. 432

club fees 433 electric bulb, tubelight 460

goods for recreation and hobbies 434 electric batteries 461

photography 435 other non-durable electric goods 462

VCD/ DVD hire (incl. instrument) 436 earthenware 463

cable TV 437 glassware 464

other entertainment 438 bucket, water bottle/ feeding bottle 465

entertainment: sub-total (430-438) 439 & other plastic goods

coir, rope, etc. 466

spectacles 440 washing soap/soda/powder 467

torch 441 other washing requisites 468

lock 442 incense (agarbatti), room freshener 470

umbrella, raincoat 443 flower (fresh): all purposes 471

lighter (bidi/ cigarette/ gas stove) 444 mosquito repellent, insecticide, acid 472

other minor durable-type goods 445 etc.

minor durable-type goods: sub- 449 other petty articles 473

total (440-445) other household consumables: 479

sub-total (460-473)


[10] expenditure on miscellaneous goods and services including medical (non-institutional), rents and taxes

during the last 30 days ended on ….......................

Item code value (Rs.) item code value (Rs.)

(1) (2) (3) (1) (2) (3) domestic servant/cook 480 air fare 500

attendant 481 railway fare 501

sweeper 482 bus/tram fare 502

barber, beautician, etc. 483 taxi, auto-rickshaw fare 503

washerman, laundry, ironing 484 steamer, boat fare 504

tailor 485 rickshaw (hand drawn & cycle) fare 505

grinding charges 486 horse cart fare 506

telephone charges: landline* 487 porter charges 507

telephone charges: mobile 488 petrol for vehicle 508

postage & telegram 490 diesel for vehicle 510

miscellaneous expenses 491 lubricants & other fuels for vehicle 511

priest 492 school bus, van, etc. 512

legal expenses 493 other conveyance expenses 513

repair charges for non-durables 494 conveyance: sub-total (500-513) 519

pet animals (incl. birds, fish) 495

other consumer services excluding 496 house rent, garage rent (actual) 520*

conveyance hotel lodging charges 521

consumer services excluding 499 residential land rent 522* conveyance: sub-total (480-496) other consumer rent 523

rent: sub-total (520-523) 529

house rent, garage rent (imputed- 539

urban only)

water charges 540*

other consumer taxes & cesses 541*

consumer taxes and cesses: sub- 549

total (540-541)

*The value may be derived as the amount last paid divided by the number of months for which amount was paid.


[11] expenditure for purchase and construction (including repair and maintenance) of durable goods for domestic use description code

whe

ther

pos

sess

ed o

n th

e da

te o

f su

rvey

(yes

-1,n

o-2)

during the last 30 days during the last 365 days first-hand purchase cost of

raw materials and services for construc-tion and repair (Rs.)

second-hand pur-chase: value (Rs.)

total expenditure(Rs.) [(6)+(7)+ (8)]

first-hand purchase cost of raw materials and services for construc-tion and repair (Rs.)

second-hand purchase

total expenditure (Rs.) [(12)+(13)+ (15)]

num

ber p

urch

ased

whe

ther

hire

-pu

rcha

sed?

(yes

-1,n

o-2)

value (Rs)

num

ber p

urch

ased

whe

ther

hire

-pu

rcha

sed?

(yes

-1,n

o-2)

value (Rs.)

num

ber p

urch

ased

value (Rs.)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

bedstead 550 almirah, dressing table 551 chair, stool, bench, table 552 suitcase, trunk, box, handbag and other travel goods 553

foam, rubber cushion 554 carpet, daree & other floor mattings 555 paintings, drawings, engravings, etc. 556 other furniture & fixtures (couch, sofa, etc.) 557

furniture & fixtures: sub-total (550-557) 559

radio, 2-in-1 560 television 561 VCR/VCD/DVD player 562 camera & photographic equipment 563 CD, DVD, audio/video cassette, etc 564 musical instruments 565 other goods for recreation 566 goods for recreation: sub-total (560-566)

569


[11] expenditure for purchase and construction (including repair and maintenance) of

Household&Consumption&Expenditure&Survey · 2016. 1. 7. ·...

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