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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F170]
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
176585
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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_9b1

Overview

Valid: 10365
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 91
0.9%
2 Panchthar 0
0%
3 Ilam 335
3.2%
4 Jhapa 202
1.9%
5 Morang 114
1.1%
6 Sunsari 104
1%
7 Dhankuta 209
2%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 219
2.1%
11 Solukhumbu 118
1.1%
12 Okhaldhunga 120
1.2%
13 Khotang 256
2.5%
14 Udayapur 209
2%
15 Saptari 103
1%
16 Siraha 0
0%
17 Dhanusha 104
1%
18 Mahottari 93
0.9%
19 Sarlahi 127
1.2%
20 Sindhuli 196
1.9%
21 Ramechhap 0
0%
22 Dolakha 157
1.5%
23 Sindhupalchok 307
3%
24 Kabhrepalanchok 275
2.7%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 204
2%
29 Rasuwa 0
0%
30 Dhading 355
3.4%
31 Makwanpur 290
2.8%
32 Rautahat 0
0%
33 Bara 96
0.9%
34 Parsa 86
0.8%
35 Chitwan 0
0%
36 Gorkha 147
1.4%
37 Lamjung 199
1.9%
38 Tanahun 286
2.8%
39 Syangja 221
2.1%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 117
1.1%
44 Parbat 0
0%
45 Baglung 351
3.4%
46 Gulmi 463
4.5%
47 Palpa 237
2.3%
48 Nawalparasi 235
2.3%
49 Rupandehi 203
2%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 256
2.5%
54 Rukum 361
3.5%
55 Salyan 0
0%
56 Dang 175
1.7%
57 Banke 116
1.1%
58 Bardiya 0
0%
59 Surkhet 297
2.9%
60 Dailekh 345
3.3%
61 Jajarkot 333
3.2%
62 Dolpa 0
0%
63 Jumla 121
1.2%
64 Kalikot 227
2.2%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 182
1.8%
68 Bajhang 173
1.7%
69 Achham 243
2.3%
70 Doti 183
1.8%
71 Kailali 193
1.9%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 200
1.9%
75 Darchula 131
1.3%
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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