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    Home / Central Data Catalog / DDI-IND-NSSO-66-SCHEDULE-1.0T1 / variable [V563]
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
220746
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: Expenditure for purchase and construction (including repair and maintenance) of durable goods for domestic use

Overview

Valid: 3070178
Invalid: 0
Type: Discrete
Start: 27
End: 29
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Mountainous 23672
0.8%
012 Outer Hills 10307
0.3%
013 Jhelam Valley 51126
1.7%
014 Ladakh 0
0%
021 Central 30028
1%
022 Trans Himalayan & Southern 27367
0.9%
031 Northern 43422
1.4%
032 Southern 52070
1.7%
041 Chandigarh 8988
0.3%
051 Uttaranchal 58543
1.9%
061 Eastern 49547
1.6%
062 Western 26275
0.9%
071 Delhi 26200
0.9%
081 Western 28337
0.9%
082 North-Eastern 45279
1.5%
083 Southern 15532
0.5%
084 South-Eastern 18404
0.6%
085 Northern 27365
0.9%
091 Northern Upper Ganga Plains 46289
1.5%
092 Central 42831
1.4%
093 Eastern 110334
3.6%
094 Southern 24233
0.8%
095 Southern Upper Ganga Plains 73034
2.4%
101 Northern 73701
2.4%
102 Central 46263
1.5%
111 Sikkim 22199
0.7%
121 Arunachal Pradesh 37900
1.2%
131 Nagaland 32990
1.1%
141 Plains 58098
1.9%
142 Hills 20396
0.7%
151 Mizoram 36454
1.2%
161 Tripura 43397
1.4%
171 Meghalaya 38958
1.3%
181 Plains Eastern 23414
0.8%
182 Plains Western 30353
1%
183 Cachar Plain 12961
0.4%
184 Central Brahmputra Plains 20320
0.7%
191 Himalayan 19978
0.7%
192 Eastern Plains 49705
1.6%
193 Southern Plains 50618
1.6%
194 Central Plains 41733
1.4%
195 Western Plains 34466
1.1%
201 Rachi Plateau 34416
1.1%
202 Hazaribagh Plateau 45749
1.5%
211 Coastal 43127
1.4%
212 Southern 45407
1.5%
213 Northern 38818
1.3%
221 Northern Chattishgarh 8288
0.3%
222 Mahanadi Basin 49508
1.6%
223 Southern Chhattishgarh 10091
0.3%
231 Vindhya 26532
0.9%
232 Central 20847
0.7%
233 Malwa 34749
1.1%
234 South 28112
0.9%
235 South Western 18715
0.6%
236 Northern 21799
0.7%
241 South Eastern 41408
1.3%
242 Plains Northern 31587
1%
243 Dry areas 7034
0.2%
244 Kachchh 3093
0.1%
245 Saurashtra 32789
1.1%
251 Daman & Diu 4486
0.1%
261 Dadra & Nagar Haveli 6417
0.2%
271 Coastal 60143
2%
272 Inland Western 62693
2%
273 Inland Northern 30448
1%
274 Inland Central 41883
1.4%
275 Inland Eastern 44921
1.5%
276 Eastern 16647
0.5%
281 Costal Northern 46829
1.5%
282 Costal Southern 38315
1.2%
283 Inland North Western 56265
1.8%
284 Inland North Eastern 33150
1.1%
285 Inland Southern 38688
1.3%
291 Coastal & Ghats 11278
0.4%
292 Inland Eastern 13991
0.5%
293 Inland Southern 43073
1.4%
294 Inland Northern 55486
1.8%
301 Goa 14166
0.5%
311 Lakshadweep 5689
0.2%
321 Northern 56263
1.8%
322 Southern 79442
2.6%
331 Coastal Northern 60046
2%
332 Coastal 38335
1.2%
333 Southern 54152
1.8%
334 Inland 50523
1.6%
341 Pondicherry 17926
0.6%
351 Andaman & Nicobar Islands 13767
0.4%
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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