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    Home / Central Data Catalog / DDI-IND-NSSO-66-SCHEDULE-1.0T1 / variable [V606]
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
220721
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 on miscellaneous goods and services including medical (non-institutional), rents and taxes during the last 30 days

Overview

Valid: 2163878
Invalid: 0
Type: Discrete
Start: 27
End: 29
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Mountainous 17284
0.8%
012 Outer Hills 6421
0.3%
013 Jhelam Valley 36285
1.7%
014 Ladakh 0
0%
021 Central 22902
1.1%
022 Trans Himalayan & Southern 18748
0.9%
031 Northern 30339
1.4%
032 Southern 36039
1.7%
041 Chandigarh 7299
0.3%
051 Uttaranchal 34539
1.6%
061 Eastern 36592
1.7%
062 Western 19956
0.9%
071 Delhi 22707
1%
081 Western 17574
0.8%
082 North-Eastern 27673
1.3%
083 Southern 9491
0.4%
084 South-Eastern 11759
0.5%
085 Northern 16671
0.8%
091 Northern Upper Ganga Plains 28758
1.3%
092 Central 26814
1.2%
093 Eastern 64926
3%
094 Southern 15723
0.7%
095 Southern Upper Ganga Plains 47074
2.2%
101 Northern 52828
2.4%
102 Central 34234
1.6%
111 Sikkim 14244
0.7%
121 Arunachal Pradesh 24568
1.1%
131 Nagaland 21699
1%
141 Plains 42232
2%
142 Hills 11725
0.5%
151 Mizoram 32432
1.5%
161 Tripura 35881
1.7%
171 Meghalaya 25854
1.2%
181 Plains Eastern 19914
0.9%
182 Plains Western 22772
1.1%
183 Cachar Plain 12880
0.6%
184 Central Brahmputra Plains 16367
0.8%
191 Himalayan 13425
0.6%
192 Eastern Plains 35088
1.6%
193 Southern Plains 37687
1.7%
194 Central Plains 32041
1.5%
195 Western Plains 22294
1%
201 Rachi Plateau 21583
1%
202 Hazaribagh Plateau 30139
1.4%
211 Coastal 26576
1.2%
212 Southern 23796
1.1%
213 Northern 20956
1%
221 Northern Chattishgarh 4870
0.2%
222 Mahanadi Basin 30572
1.4%
223 Southern Chhattishgarh 5998
0.3%
231 Vindhya 16290
0.8%
232 Central 13533
0.6%
233 Malwa 25435
1.2%
234 South 18057
0.8%
235 South Western 12661
0.6%
236 Northern 14228
0.7%
241 South Eastern 28629
1.3%
242 Plains Northern 21937
1%
243 Dry areas 4315
0.2%
244 Kachchh 2129
0.1%
245 Saurashtra 23320
1.1%
251 Daman & Diu 3979
0.2%
261 Dadra & Nagar Haveli 3985
0.2%
271 Coastal 48883
2.3%
272 Inland Western 46497
2.1%
273 Inland Northern 22741
1.1%
274 Inland Central 29967
1.4%
275 Inland Eastern 32013
1.5%
276 Eastern 11403
0.5%
281 Costal Northern 32481
1.5%
282 Costal Southern 29290
1.4%
283 Inland North Western 41600
1.9%
284 Inland North Eastern 23228
1.1%
285 Inland Southern 28540
1.3%
291 Coastal & Ghats 8931
0.4%
292 Inland Eastern 10953
0.5%
293 Inland Southern 36343
1.7%
294 Inland Northern 38853
1.8%
301 Goa 11810
0.5%
311 Lakshadweep 3389
0.2%
321 Northern 42636
2%
322 Southern 59044
2.7%
331 Coastal Northern 49701
2.3%
332 Coastal 29454
1.4%
333 Southern 44128
2%
334 Inland 36583
1.7%
341 Pondicherry 16066
0.7%
351 Andaman & Nicobar Islands 12617
0.6%
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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