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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F294]
agricultural-surveys

Household Risk and Vulnerability Survey 2016-2018

Nepal, 2016 - 2018
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Reference ID
NPL_2016-2018_HRVS_v01_EN_M_v01_A_OCS
Producer(s)
The World Bank
Collections
Agricultural Surveys
Metadata
Documentation in PDF DDI/XML JSON
Created on
Oct 06, 2020
Last modified
Nov 08, 2022
Page views
210043
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  • Study Description
  • Data Dictionary
  • Downloads
  • Get Microdata
  • Data files
  • Section_0
  • Section_1
  • Section_2
  • Section_3a
  • Section_3b
  • Section_4a
  • Section_4b
  • Section_5
  • Section_0
  • Section_1
  • Section_2
  • Section_3
  • Section_4
  • Section_5a
  • Section_5b
  • Section_6a
  • Section_6b
  • Section_6c
  • Section_6d
  • Section_7
  • Section_8
  • Section_9a1
  • Section_9a2
  • Section_9a3
  • Section_9a4
  • Section_9b1
  • Section_9b2
  • Section_9c
  • Section_9d
  • Section_9e
  • Section_9f
  • Section_10
  • Section_11
  • Section_12a
  • Section_12b
  • Section_12c
  • Section_12d
  • Section_13a
  • Section_13b
  • Section_13c
  • Section_13d
  • Section_14a
  • Section_14b
  • Section_14c
  • Section_15a
  • Section_15b
  • Section_17
  • Section_0
  • Section_1
  • Section_2
  • Section_3a
  • Section_3b
  • Section_5
  • Section_0
  • Section_2
  • Section_3
  • Section_4
  • Section_5a
  • Section_5b
  • Section_6a
  • Section_6b
  • Section_6c
  • Section_6d
  • Section_7
  • Section_8
  • Section_9a1
  • Section_9a2
  • Section_9a3
  • Section_9a4
  • Section_9b1
  • Section_9b2
  • Section_9c
  • Section_9d
  • Section_9e
  • Section_9f
  • Section_10
  • Section_11
  • Section_12a
  • Section_12b
  • Section_12c
  • Section_12d
  • Section_13a
  • Section_13b
  • Section_13c
  • Section_13d
  • Section_14a
  • Section_14b
  • Section_14c
  • Section_15a
  • Section_15b
  • Section_16
  • Section_17
  • Section_0
  • Section_1
  • Section_2
  • Section_3a
  • Section_3b
  • Section_4a
  • Section_4b
  • Section_5
  • Section_0
  • Section_1
  • Section_2
  • Section_3
  • Section_4
  • Section_5a
  • Section_5b
  • Section_6a
  • Section_6b
  • Section_6c
  • Section_6d
  • Section_7
  • Section_8
  • Section_9a1
  • Section_9a2
  • Section_9a3
  • Section_9a4
  • Section_9b1
  • Section_9b2
  • Section_9c
  • Section_9d
  • Section_9e
  • Section_9f
  • Section_10
  • Section_11
  • Section_12a
  • Section_12b
  • Section_12c
  • Section_12d
  • Section_13a
  • Section_13b
  • Section_13c
  • Section_13d
  • Section_14a
  • Section_14c
  • Section_15b
  • Section_16
  • Section_17
  • Section_16
  • Section_4a
  • Section_4b
  • Section_1
  • Section_11a
  • Section_14b
  • Section_15a

Name of District (district)

Data file: Section_11a

Overview

Valid: 15218
Invalid: 0
Type: Discrete
Decimal: 0
Start: 6
End: 7
Width: 2
Range: 1 - 75
Format: Numeric

Questions and instructions

Literal question
Name of District
Categories
Value Category Cases
1 Taplejung 133
0.9%
2 Panchthar 0
0%
3 Ilam 401
2.6%
4 Jhapa 557
3.7%
5 Morang 545
3.6%
6 Sunsari 397
2.6%
7 Dhankuta 193
1.3%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 223
1.5%
11 Solukhumbu 136
0.9%
12 Okhaldhunga 159
1%
13 Khotang 269
1.8%
14 Udayapur 261
1.7%
15 Saptari 410
2.7%
16 Siraha 0
0%
17 Dhanusha 383
2.5%
18 Mahottari 357
2.3%
19 Sarlahi 477
3.1%
20 Sindhuli 308
2%
21 Ramechhap 0
0%
22 Dolakha 168
1.1%
23 Sindhupalchok 340
2.2%
24 Kabhrepalanchok 334
2.2%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 265
1.7%
29 Rasuwa 0
0%
30 Dhading 411
2.7%
31 Makwanpur 378
2.5%
32 Rautahat 0
0%
33 Bara 401
2.6%
34 Parsa 355
2.3%
35 Chitwan 0
0%
36 Gorkha 175
1.1%
37 Lamjung 169
1.1%
38 Tanahun 294
1.9%
39 Syangja 236
1.6%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 100
0.7%
44 Parbat 0
0%
45 Baglung 213
1.4%
46 Gulmi 303
2%
47 Palpa 216
1.4%
48 Nawalparasi 740
4.9%
49 Rupandehi 849
5.6%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 190
1.2%
54 Rukum 222
1.5%
55 Salyan 0
0%
56 Dang 490
3.2%
57 Banke 425
2.8%
58 Bardiya 0
0%
59 Surkhet 316
2.1%
60 Dailekh 254
1.7%
61 Jajarkot 163
1.1%
62 Dolpa 0
0%
63 Jumla 76
0.5%
64 Kalikot 138
0.9%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 98
0.6%
68 Bajhang 200
1.3%
69 Achham 219
1.4%
70 Doti 179
1.2%
71 Kailali 713
4.7%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 211
1.4%
75 Darchula 168
1.1%
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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