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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F175]
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
177723
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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_9f

Overview

Valid: 3944
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 17
0.4%
2 Panchthar 0
0%
3 Ilam 47
1.2%
4 Jhapa 221
5.6%
5 Morang 39
1%
6 Sunsari 43
1.1%
7 Dhankuta 128
3.2%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 156
4%
11 Solukhumbu 34
0.9%
12 Okhaldhunga 24
0.6%
13 Khotang 126
3.2%
14 Udayapur 122
3.1%
15 Saptari 42
1.1%
16 Siraha 0
0%
17 Dhanusha 67
1.7%
18 Mahottari 43
1.1%
19 Sarlahi 64
1.6%
20 Sindhuli 84
2.1%
21 Ramechhap 0
0%
22 Dolakha 46
1.2%
23 Sindhupalchok 60
1.5%
24 Kabhrepalanchok 105
2.7%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 29
0.7%
29 Rasuwa 0
0%
30 Dhading 62
1.6%
31 Makwanpur 117
3%
32 Rautahat 0
0%
33 Bara 35
0.9%
34 Parsa 56
1.4%
35 Chitwan 0
0%
36 Gorkha 27
0.7%
37 Lamjung 21
0.5%
38 Tanahun 34
0.9%
39 Syangja 37
0.9%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 38
1%
44 Parbat 0
0%
45 Baglung 116
2.9%
46 Gulmi 162
4.1%
47 Palpa 82
2.1%
48 Nawalparasi 59
1.5%
49 Rupandehi 50
1.3%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 65
1.6%
54 Rukum 87
2.2%
55 Salyan 0
0%
56 Dang 195
4.9%
57 Banke 120
3%
58 Bardiya 0
0%
59 Surkhet 120
3%
60 Dailekh 77
2%
61 Jajarkot 94
2.4%
62 Dolpa 0
0%
63 Jumla 18
0.5%
64 Kalikot 43
1.1%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 36
0.9%
68 Bajhang 65
1.6%
69 Achham 71
1.8%
70 Doti 64
1.6%
71 Kailali 370
9.4%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 61
1.5%
75 Darchula 65
1.6%
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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