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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F292]
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
179143
Downloads
494
  • 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

District name (district)

Data file: Section_4b

Overview

Valid: 633
Invalid: 0
Type: Discrete
Decimal: 0
Start: 4
End: 5
Width: 2
Range: 1 - 75
Format: Numeric

Questions and instructions

Literal question
District name
Categories
Value Category Cases
1 Taplejung 6
0.9%
2 Panchthar 0
0%
3 Ilam 13
2.1%
4 Jhapa 34
5.4%
5 Morang 15
2.4%
6 Sunsari 10
1.6%
7 Dhankuta 5
0.8%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 6
0.9%
11 Solukhumbu 7
1.1%
12 Okhaldhunga 5
0.8%
13 Khotang 7
1.1%
14 Udayapur 8
1.3%
15 Saptari 10
1.6%
16 Siraha 0
0%
17 Dhanusha 12
1.9%
18 Mahottari 9
1.4%
19 Sarlahi 13
2.1%
20 Sindhuli 12
1.9%
21 Ramechhap 0
0%
22 Dolakha 8
1.3%
23 Sindhupalchok 10
1.6%
24 Kabhrepalanchok 10
1.6%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 37
5.8%
29 Rasuwa 0
0%
30 Dhading 46
7.3%
31 Makwanpur 10
1.6%
32 Rautahat 0
0%
33 Bara 9
1.4%
34 Parsa 7
1.1%
35 Chitwan 0
0%
36 Gorkha 7
1.1%
37 Lamjung 5
0.8%
38 Tanahun 8
1.3%
39 Syangja 6
0.9%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 3
0.5%
44 Parbat 0
0%
45 Baglung 6
0.9%
46 Gulmi 16
2.5%
47 Palpa 6
0.9%
48 Nawalparasi 19
3%
49 Rupandehi 19
3%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 33
5.2%
54 Rukum 30
4.7%
55 Salyan 0
0%
56 Dang 12
1.9%
57 Banke 12
1.9%
58 Bardiya 0
0%
59 Surkhet 26
4.1%
60 Dailekh 16
2.5%
61 Jajarkot 30
4.7%
62 Dolpa 0
0%
63 Jumla 4
0.6%
64 Kalikot 13
2.1%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 3
0.5%
68 Bajhang 7
1.1%
69 Achham 7
1.1%
70 Doti 7
1.1%
71 Kailali 19
3%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 6
0.9%
75 Darchula 4
0.6%
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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