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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F166]
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
177629
Downloads
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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_9a1

Overview

Valid: 8856
Invalid: 0
Type: Discrete
Decimal: 0
Start: 6
End: 7
Width: 2
Range: 1 - 76
Format: Numeric

Questions and instructions

Literal question
Name of District
Categories
Value Category Cases
1 Taplejung 102
1.2%
2 Panchthar 0
0%
3 Ilam 252
2.8%
4 Jhapa 169
1.9%
5 Morang 110
1.2%
6 Sunsari 92
1%
7 Dhankuta 136
1.5%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 181
2%
11 Solukhumbu 83
0.9%
12 Okhaldhunga 111
1.3%
13 Khotang 194
2.2%
14 Udayapur 197
2.2%
15 Saptari 171
1.9%
16 Siraha 0
0%
17 Dhanusha 178
2%
18 Mahottari 171
1.9%
19 Sarlahi 184
2.1%
20 Sindhuli 185
2.1%
21 Ramechhap 0
0%
22 Dolakha 145
1.6%
23 Sindhupalchok 439
5%
24 Kabhrepalanchok 326
3.7%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 216
2.4%
29 Rasuwa 0
0%
30 Dhading 290
3.3%
31 Makwanpur 225
2.5%
32 Rautahat 0
0%
33 Bara 154
1.7%
34 Parsa 172
1.9%
35 Chitwan 0
0%
36 Gorkha 115
1.3%
37 Lamjung 100
1.1%
38 Tanahun 144
1.6%
39 Syangja 108
1.2%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 85
1%
44 Parbat 0
0%
45 Baglung 155
1.8%
46 Gulmi 207
2.3%
47 Palpa 124
1.4%
48 Nawalparasi 289
3.3%
49 Rupandehi 229
2.6%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 258
2.9%
54 Rukum 336
3.8%
55 Salyan 0
0%
56 Dang 197
2.2%
57 Banke 139
1.6%
58 Bardiya 0
0%
59 Surkhet 229
2.6%
60 Dailekh 211
2.4%
61 Jajarkot 280
3.2%
62 Dolpa 0
0%
63 Jumla 89
1%
64 Kalikot 134
1.5%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 117
1.3%
68 Bajhang 100
1.1%
69 Achham 161
1.8%
70 Doti 104
1.2%
71 Kailali 263
3%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 108
1.2%
75 Darchula 91
1%
76 Other 0
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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