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    Home / Central Data Catalog / DDI-IND-NSSO-66-SCHEDULE-1.0T1 / variable [F21]
central

Household Consumer Expenditure Type-1, July 2009 - June 2010
NSS 66th Round

India, 2009 - 2010
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Reference ID
DDI-IND-NSSO-66-SCHEDULE-1.0T1
Producer(s)
National Sample Survey Office,NSSO
Collections
Household Consumption Expenditure
Metadata
DDI/XML JSON
Created on
Jan 18, 2018
Last modified
Mar 27, 2019
Page views
220656
Downloads
21366
  • Study Description
  • Data Dictionary
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  • Data files
  • Identification
    of Sample
    Household
  • Household
    Characteristics
  • Demographic and
    other
    particulars of
    household
    members
  • Consumption of
    cereals,
    pulses, milk
    and milk
    products, sugar
    and salt during
    the last 30
    days
  • Summary of
    comsumer
    expenditure
  • Consumption of
    clothing,
    bedding and
    footwear during
    last 30 and
    365 days
  • Expenditure on
    education and
    medical
    (institutional)
    goods and
    services
  • Expenditure for
    purchase and
    construction
    (including
    repair and
    maintenance) of
    durable goods
    for domestic
    use
  • Expenditure on
    miscellaneous
    goods and
    services
    including
    medical
    (non-institutional),
    rents and taxes
    during the last
    30 days

Region (Region)

Data file: Consumption of cereals, pulses, milk and milk products, sugar and salt during the last 30 days

Overview

Valid: 5310709
Invalid: 0
Type: Discrete
Start: 27
End: 29
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Mountainous 42591
0.8%
012 Outer Hills 17134
0.3%
013 Jhelam Valley 71208
1.3%
014 Ladakh 0
0%
021 Central 55281
1%
022 Trans Himalayan & Southern 47249
0.9%
031 Northern 67944
1.3%
032 Southern 82430
1.6%
041 Chandigarh 16179
0.3%
051 Uttaranchal 93398
1.8%
061 Eastern 84016
1.6%
062 Western 43789
0.8%
071 Delhi 41873
0.8%
081 Western 39255
0.7%
082 North-Eastern 63859
1.2%
083 Southern 21985
0.4%
084 South-Eastern 26918
0.5%
085 Northern 37934
0.7%
091 Northern Upper Ganga Plains 70029
1.3%
092 Central 66320
1.2%
093 Eastern 170034
3.2%
094 Southern 37153
0.7%
095 Southern Upper Ganga Plains 111976
2.1%
101 Northern 133574
2.5%
102 Central 87876
1.7%
111 Sikkim 39374
0.7%
121 Arunachal Pradesh 69394
1.3%
131 Nagaland 49727
0.9%
141 Plains 89873
1.7%
142 Hills 28899
0.5%
151 Mizoram 73059
1.4%
161 Tripura 100355
1.9%
171 Meghalaya 67658
1.3%
181 Plains Eastern 54050
1%
182 Plains Western 65841
1.2%
183 Cachar Plain 31261
0.6%
184 Central Brahmputra Plains 47312
0.9%
191 Himalayan 32168
0.6%
192 Eastern Plains 89151
1.7%
193 Southern Plains 91925
1.7%
194 Central Plains 77726
1.5%
195 Western Plains 59177
1.1%
201 Rachi Plateau 57854
1.1%
202 Hazaribagh Plateau 77451
1.5%
211 Coastal 78052
1.5%
212 Southern 70088
1.3%
213 Northern 59410
1.1%
221 Northern Chattishgarh 13147
0.2%
222 Mahanadi Basin 76000
1.4%
223 Southern Chhattishgarh 16210
0.3%
231 Vindhya 39475
0.7%
232 Central 33790
0.6%
233 Malwa 57418
1.1%
234 South 42171
0.8%
235 South Western 29029
0.5%
236 Northern 33361
0.6%
241 South Eastern 66215
1.2%
242 Plains Northern 49131
0.9%
243 Dry areas 10580
0.2%
244 Kachchh 4788
0.1%
245 Saurashtra 52190
1%
251 Daman & Diu 7786
0.1%
261 Dadra & Nagar Haveli 11046
0.2%
271 Coastal 108154
2%
272 Inland Western 115205
2.2%
273 Inland Northern 55080
1%
274 Inland Central 69974
1.3%
275 Inland Eastern 77868
1.5%
276 Eastern 30072
0.6%
281 Costal Northern 83434
1.6%
282 Costal Southern 72267
1.4%
283 Inland North Western 98753
1.9%
284 Inland North Eastern 54030
1%
285 Inland Southern 70034
1.3%
291 Coastal & Ghats 22813
0.4%
292 Inland Eastern 28450
0.5%
293 Inland Southern 90084
1.7%
294 Inland Northern 95861
1.8%
301 Goa 25242
0.5%
311 Lakshadweep 9794
0.2%
321 Northern 108090
2%
322 Southern 152598
2.9%
331 Coastal Northern 114923
2.2%
332 Coastal 73630
1.4%
333 Southern 109125
2.1%
334 Inland 93056
1.8%
341 Pondicherry 34683
0.7%
351 Andaman & Nicobar Islands 35342
0.7%
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.
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