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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F225]
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
213742
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  • Study Description
  • Data Dictionary
  • Downloads
  • Get Microdata
  • Data files
  • Section_0
  • Section_1
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  • Section_3a
  • Section_3b
  • Section_4a
  • Section_4b
  • Section_5
  • Section_0
  • Section_1
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  • 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
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  • Section_13d
  • Section_14a
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  • 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
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  • 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_11

Overview

Valid: 5291
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 36
0.7%
2 Panchthar 0
0%
3 Ilam 119
2.2%
4 Jhapa 119
2.2%
5 Morang 125
2.4%
6 Sunsari 78
1.5%
7 Dhankuta 56
1.1%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 95
1.8%
11 Solukhumbu 52
1%
12 Okhaldhunga 57
1.1%
13 Khotang 112
2.1%
14 Udayapur 122
2.3%
15 Saptari 87
1.6%
16 Siraha 0
0%
17 Dhanusha 125
2.4%
18 Mahottari 79
1.5%
19 Sarlahi 68
1.3%
20 Sindhuli 100
1.9%
21 Ramechhap 0
0%
22 Dolakha 70
1.3%
23 Sindhupalchok 125
2.4%
24 Kabhrepalanchok 158
3%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 87
1.6%
29 Rasuwa 0
0%
30 Dhading 161
3%
31 Makwanpur 116
2.2%
32 Rautahat 0
0%
33 Bara 43
0.8%
34 Parsa 43
0.8%
35 Chitwan 0
0%
36 Gorkha 90
1.7%
37 Lamjung 101
1.9%
38 Tanahun 137
2.6%
39 Syangja 152
2.9%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 68
1.3%
44 Parbat 0
0%
45 Baglung 147
2.8%
46 Gulmi 220
4.2%
47 Palpa 141
2.7%
48 Nawalparasi 217
4.1%
49 Rupandehi 173
3.3%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 74
1.4%
54 Rukum 123
2.3%
55 Salyan 0
0%
56 Dang 115
2.2%
57 Banke 139
2.6%
58 Bardiya 0
0%
59 Surkhet 156
2.9%
60 Dailekh 140
2.6%
61 Jajarkot 53
1%
62 Dolpa 0
0%
63 Jumla 17
0.3%
64 Kalikot 63
1.2%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 53
1%
68 Bajhang 68
1.3%
69 Achham 115
2.2%
70 Doti 64
1.2%
71 Kailali 238
4.5%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 98
1.9%
75 Darchula 96
1.8%
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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