Nepal Fishery Survey 2015
Nepal, 2015 - 2016
Central Bureau of Statistics
Created on July 05, 2018
Last modified July 05, 2018
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CASTE CODE (CCODE)
Data file: F_PROBLEMS
Overview
Valid:
7332
Invalid:
462
Minimum:
0
Maximum:
110
Type:
Discrete
Decimal:
0
Width:
3
Range:
0 - 110
Format:
Numeric
Questions and instructions
Categories
| Value | Category | Cases | |
|---|---|---|---|
| 0 | 26 |
0.4%
|
|
| 1 | CHHETRI | 966 |
13.2%
|
| 2 | BRAHMAN (HILL) | 1028 |
14%
|
| 3 | MAGAR | 293 |
4%
|
| 4 | TAMANG | 232 |
3.2%
|
| 5 | THARU | 1166 |
15.9%
|
| 6 | NEWAR | 280 |
3.8%
|
| 7 | MUSLIM | 192 |
2.6%
|
| 8 | KAMI | 58 |
0.8%
|
| 9 | YADAV | 477 |
6.5%
|
| 10 | RAI | 228 |
3.1%
|
| 11 | GURUNG | 104 |
1.4%
|
| 12 | DAMAIN/DHOLI | 15 |
0.2%
|
| 13 | LIMBU | 123 |
1.7%
|
| 14 | THAKURI | 102 |
1.4%
|
| 15 | SARKI | 39 |
0.5%
|
| 16 | TELI | 180 |
2.5%
|
| 17 | CHAMAR/HARIJAN/RAM | 12 |
0.2%
|
| 18 | KOIRI | 129 |
1.8%
|
| 19 | KURMI | 102 |
1.4%
|
| 20 | SANYASI | 42 |
0.6%
|
| 21 | DHANUK | 138 |
1.9%
|
| 22 | MUSAHAR | 3 |
0%
|
| 23 | DUSADH/PASWAN/PASI | 27 |
0.4%
|
| 24 | SHERPA | 0 |
0%
|
| 25 | SONAR | 3 |
0%
|
| 26 | KEWAT | 84 |
1.1%
|
| 27 | BRAHMAN (TARAI) | 63 |
0.9%
|
| 28 | BANIYA | 18 |
0.2%
|
| 29 | GHARTI/BHUJEL | 15 |
0.2%
|
| 30 | MALLAH | 403 |
5.5%
|
| 31 | KALWAR | 48 |
0.7%
|
| 32 | KUMAL | 24 |
0.3%
|
| 33 | HAJAM/THAKUR | 21 |
0.3%
|
| 34 | KANU | 30 |
0.4%
|
| 35 | RAJBANSI | 132 |
1.8%
|
| 36 | SUNUWAR | 0 |
0%
|
| 37 | SUDHI | 33 |
0.5%
|
| 38 | LOHAR | 3 |
0%
|
| 39 | TATMA | 9 |
0.1%
|
| 40 | KHATWE | 3 |
0%
|
| 41 | DHOBI | 9 |
0.1%
|
| 42 | MAJHI | 49 |
0.7%
|
| 43 | NUNIYA | 24 |
0.3%
|
| 44 | KUMHAR | 9 |
0.1%
|
| 45 | DANUWAR | 18 |
0.2%
|
| 46 | CHEPANG/PRAJA | 0 |
0%
|
| 47 | HALUWAI | 12 |
0.2%
|
| 48 | RAJPUT | 30 |
0.4%
|
| 49 | KAYASTHA | 12 |
0.2%
|
| 50 | BADHAE | 15 |
0.2%
|
| 51 | MARWADI | 0 |
0%
|
| 52 | SANTHAL/SATAR | 6 |
0.1%
|
| 53 | DHAGAR/JHAGAR | 6 |
0.1%
|
| 54 | BANTAR | 0 |
0%
|
| 55 | BARAE | 6 |
0.1%
|
| 56 | KAHAR | 9 |
0.1%
|
| 57 | GANGAI | 54 |
0.7%
|
| 58 | LODH | 0 |
0%
|
| 59 | RAJBHAR | 0 |
0%
|
| 60 | THAMI | 3 |
0%
|
| 61 | DHIMAL | 0 |
0%
|
| 62 | BHOTE | 3 |
0%
|
| 63 | BING/BINDA | 0 |
0%
|
| 64 | BHEDIYAR/GADERI | 0 |
0%
|
| 65 | NURANG | 0 |
0%
|
| 66 | YAKKHA | 0 |
0%
|
| 67 | DARAI | 3 |
0%
|
| 68 | TAJPURIYA | 21 |
0.3%
|
| 69 | THAKALI | 0 |
0%
|
| 70 | CHIDIMAR | 0 |
0%
|
| 71 | PAHARI | 0 |
0%
|
| 72 | MALI | 0 |
0%
|
| 73 | BANGALI | 3 |
0%
|
| 74 | CHHANTAL | 9 |
0.1%
|
| 75 | DOM | 0 |
0%
|
| 76 | KAMAR | 0 |
0%
|
| 77 | BOTE | 0 |
0%
|
| 78 | BRAHMU/BARAMU | 0 |
0%
|
| 79 | GAINE | 3 |
0%
|
| 80 | JIREL | 0 |
0%
|
| 81 | ADIBASI/JANAJATI | 0 |
0%
|
| 82 | CHURAUTE | 0 |
0%
|
| 83 | BADI | 0 |
0%
|
| 84 | MECHE | 0 |
0%
|
| 85 | LEPCHA | 0 |
0%
|
| 86 | HALKHOR | 0 |
0%
|
| 87 | PUNJABI/SIKH | 9 |
0.1%
|
| 88 | KISAN | 0 |
0%
|
| 89 | RAJI | 3 |
0%
|
| 90 | BYANGSI | 0 |
0%
|
| 91 | HAYU | 0 |
0%
|
| 92 | KOCHE | 0 |
0%
|
| 93 | DHUNIA | 0 |
0%
|
| 94 | WALUNG | 0 |
0%
|
| 95 | JAIN | 0 |
0%
|
| 96 | MUNDA | 0 |
0%
|
| 97 | RAUTE | 0 |
0%
|
| 98 | YEHLMO | 0 |
0%
|
| 99 | PATHARKATA/KUSWADIYA | 0 |
0%
|
| 100 | KUSUNDA | 0 |
0%
|
| 101 | Bhumihar | 69 |
0.9%
|
| 102 | Karmarong | 0 |
0%
|
| 103 | Sakhariya | 0 |
0%
|
| 110 | 96 |
1.3%
|
|
| Sysmiss | 462 |
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.