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    Home / Central Data Catalog / DDI-IND-NSSO-66-SCHEDULE-1.0T1 / variable [F22]
central

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

India, 2009 - 2010
Get Microdata
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
220377
Downloads
21340
  • Study Description
  • Data Dictionary
  • Downloads
  • Get Microdata
  • 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

FOD Sub-Region (FOD_Sub_Region)

Data file: Summary of comsumer expenditure

Overview

Valid: 3367655
Invalid: 0
Type: Discrete
Start: 27
End: 30
Width: 4
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
0000 253121
7.5%
0014 303
0%
0024 266
0%
0030 280
0%
0046 270
0%
0093 535
0%
0100 349
0%
0110 Jammu 27560
0.8%
0111 Udhampur 10618
0.3%
0120 Srinagar 20143
0.6%
0121 Anantnag 20605
0.6%
0122 Baramula 16345
0.5%
0130 287
0%
0170 261
0%
0182 284
0%
0210 Shimla 20404
0.6%
0211 Bilaspur 14856
0.4%
0212 Dharamshala 16907
0.5%
0213 Mandi 13928
0.4%
0231 899
0%
0282 277
0%
0310 Jalandhar 15966
0.5%
0311 Amritsar 10884
0.3%
0312 Firozpur 19427
0.6%
0313 Hoshiarpur 12121
0.4%
0320 Ludhiana 18179
0.5%
0321 Bathinda 8566
0.3%
0322 Patiala 16415
0.5%
0330 255
0%
0331 244
0%
0380 286
0%
0500 287
0%
0510 Dehradun 34583
1%
0511 Almora 25087
0.7%
0610 Chandigarh 13667
0.4%
0611 Ambala 15962
0.5%
0612 Bhiwani 11415
0.3%
0613 Hisar 13624
0.4%
0614 Karnal 20077
0.6%
0615 Rohtak 22633
0.7%
0706 302
0%
0710 Delhi 28339
0.8%
0810 Ajmer 14449
0.4%
0811 Jodhpur 21023
0.6%
0812 Udaipur 22506
0.7%
0820 Jaipur 32217
1%
0821 Alwar 11950
0.4%
0822 Ganganagar 11705
0.3%
0823 Kota 20954
0.6%
0910 Agra 30458
0.9%
0911 Aligarh 17385
0.5%
0912 Meerat 17100
0.5%
0920 Allahabad 14760
0.4%
0921 Azamgrah 19213
0.6%
0922 Faizabad 14715
0.4%
0923 Gorakgpur 25375
0.8%
0924 Varanasi 23048
0.7%
0930 Bareilly 18307
0.5%
0931 Moradabad 17636
0.5%
0932 Sitapur 12675
0.4%
0933 Sharanpur 15146
0.4%
0940 Lucknow 15396
0.5%
0941 Fatehpur 15046
0.4%
0942 Gonda 14857
0.4%
0943 Jhansi 17300
0.5%
0944 Kanpur 9795
0.3%
1010 Muzaffarpur 22734
0.7%
1011 Darbhanga 20236
0.6%
1012 Motihari 17897
0.5%
1013 Purnia 29380
0.9%
1020 Gaya 28902
0.9%
1021 Bhagalpur 14230
0.4%
1022 16778
0.5%
1050 244
0%
1110 Gangtok 25229
0.7%
1210 1076
0%
1211 532
0%
1281 291
0%
1310 Kohima 36781
1.1%
1311 Imphal 283
0%
1400 288
0%
1410 1714
0.1%
1420 257
0%
1513 242
0%
1710 Shillong 27121
0.8%
1711 Tura 18193
0.5%
1712 Agartala 0
0%
1810 Guwahati 40103
1.2%
1811 Silchar 20439
0.6%
1820 Dibrugarh 9791
0.3%
1821 Jorhat 24349
0.7%
1822 Tezpur 23988
0.7%
1900 292
0%
1910 Barddhaman 17784
0.5%
1911 Bankura 16135
0.5%
1912 Chinsura 28307
0.8%
1913 Medinipur 21669
0.6%
1919 259
0%
1920 Kolkata 55967
1.7%
1921 Howrah 13137
0.4%
1930 Maldah 20764
0.6%
1931 Behrampur 21363
0.6%
1932 Siliguri 21674
0.6%
2010 Ranchi 27130
0.8%
2011 Dumka 22210
0.7%
2012 Hazaribag 27879
0.8%
2013 Jamshedpur 11839
0.4%
2019 262
0%
2021 255
0%
2110 Bhubaneswar 14169
0.4%
2111 Baripada 15269
0.5%
2112 Behrampur 15314
0.5%
2113 Cuttak 35159
1%
2120 Sambalpur 27192
0.8%
2121 Bhawanipatn 20918
0.6%
2210 Raipur 23918
0.7%
2211 Ambikapur 17225
0.5%
2212 Bilaspur 14714
0.4%
2213 Durg 15577
0.5%
2222 235
0%
2224 272
0%
2310 Bhopal 16332
0.5%
2311 Chhindwara 14033
0.4%
2312 Khandwa 9452
0.3%
2313 Indore 11232
0.3%
2320 Gwalior 13519
0.4%
2321 Ratlam 12800
0.4%
2322 Shivpuri 15901
0.5%
2323 Ujjain 15265
0.5%
2330 Jabalpur 13070
0.4%
2331 Rewa 21426
0.6%
2332 Sagar 14305
0.4%
2410 Ahmedabad 16109
0.5%
2411 Bhavnagar 10032
0.3%
2412 Jamnagar 7885
0.2%
2413 Rajkot 13153
0.4%
2414 Surendranaga 6108
0.2%
2420 Baroda 21957
0.7%
2421 Mahesana 10248
0.3%
2422 Nadiad 11352
0.3%
2423 Surat 12304
0.4%
2424 Valsad 16827
0.5%
2620 283
0%
2710 Aurangabad 19622
0.6%
2711 Jalgaon 18982
0.6%
2712 Nandel 21686
0.6%
2713 Nashik 13429
0.4%
2720 Mumbai 30875
0.9%
2721 Thane 25142
0.7%
2722 303
0%
2730 Nagpur 33767
1%
2731 Akola 14336
0.4%
2732 Amravati 15871
0.5%
2740 Pune 44159
1.3%
2741 Kolhapur 21683
0.6%
2742 Solapur 15319
0.5%
2790 277
0%
2810 Cuddapah 19507
0.6%
2811 Anantapur 10976
0.3%
2812 Guntur 12252
0.4%
2813 Kurnool 10348
0.3%
2814 Nellore 16929
0.5%
2820 Hyderabad 46624
1.4%
2821 Karimnagar 17418
0.5%
2822 Nizamabad 13430
0.4%
2823 Warangal 16472
0.5%
2830 Vijayawada 22507
0.7%
2831 Kakinada 13722
0.4%
2832 Visakhapatnam 24827
0.7%
2833 Guwahati 0
0%
2874 272
0%
2910 Bangalore 31688
0.9%
2911 Mangalore 11428
0.3%
2912 Mysore 14152
0.4%
2913 Shimoga 12006
0.4%
2920 Hubli 20605
0.6%
2921 Belgaum 14635
0.4%
2922 Bellary 15858
0.5%
2923 Gulbarga 10488
0.3%
2924 Panaji 267
0%
3010 15694
0.5%
3210 Kozhikode 32694
1%
3211 Kannur 19722
0.6%
3212 Palakkad 11547
0.3%
3213 Thrissur 15489
0.5%
3220 Thiruvanathapuram 16907
0.5%
3221 Kochi 20902
0.6%
3222 Kollam 27608
0.8%
3223 Kottayam 14070
0.4%
3310 Coimbatore 27503
0.8%
3311 Dharmapuri 9099
0.3%
3312 Salem 15155
0.5%
3313 Tiruchirappali 23403
0.7%
3320 Chennai 31204
0.9%
3321 Cuddalore 15684
0.5%
3322 Vellore 18294
0.5%
3323 Pondicherry 16560
0.5%
3330 Madurai 19587
0.6%
3331 Thanjavur 16436
0.5%
3332 Tirunelveli 21419
0.6%
3333 Virudhunagar 16276
0.5%
3510 Port Blair 18363
0.5%
7710 272
0%
8713 281
0%
9101 232
0%
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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