Login
Login
|
Microdata at FAO
    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F164]
agricultural-surveys

Household Risk and Vulnerability Survey 2016-2018

Nepal, 2016 - 2018
Get Microdata
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
177449
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

Name of District (district)

Data file: Section_7

Overview

Valid: 14624
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 168
1.1%
2 Panchthar 0
0%
3 Ilam 386
2.6%
4 Jhapa 491
3.4%
5 Morang 473
3.2%
6 Sunsari 358
2.4%
7 Dhankuta 212
1.4%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 276
1.9%
11 Solukhumbu 169
1.2%
12 Okhaldhunga 164
1.1%
13 Khotang 325
2.2%
14 Udayapur 304
2.1%
15 Saptari 328
2.2%
16 Siraha 0
0%
17 Dhanusha 353
2.4%
18 Mahottari 273
1.9%
19 Sarlahi 372
2.5%
20 Sindhuli 421
2.9%
21 Ramechhap 0
0%
22 Dolakha 221
1.5%
23 Sindhupalchok 342
2.3%
24 Kabhrepalanchok 370
2.5%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 210
1.4%
29 Rasuwa 0
0%
30 Dhading 299
2%
31 Makwanpur 389
2.7%
32 Rautahat 0
0%
33 Bara 262
1.8%
34 Parsa 207
1.4%
35 Chitwan 0
0%
36 Gorkha 132
0.9%
37 Lamjung 133
0.9%
38 Tanahun 229
1.6%
39 Syangja 193
1.3%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 83
0.6%
44 Parbat 0
0%
45 Baglung 190
1.3%
46 Gulmi 301
2.1%
47 Palpa 189
1.3%
48 Nawalparasi 469
3.2%
49 Rupandehi 599
4.1%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 256
1.8%
54 Rukum 281
1.9%
55 Salyan 0
0%
56 Dang 593
4.1%
57 Banke 425
2.9%
58 Bardiya 0
0%
59 Surkhet 363
2.5%
60 Dailekh 353
2.4%
61 Jajarkot 193
1.3%
62 Dolpa 0
0%
63 Jumla 114
0.8%
64 Kalikot 242
1.7%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 181
1.2%
68 Bajhang 178
1.2%
69 Achham 241
1.6%
70 Doti 177
1.2%
71 Kailali 778
5.3%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 200
1.4%
75 Darchula 158
1.1%
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.
Back to Catalog
Food and Agriculture Organization of the United Nations

FOLLOW US ON

  • icon-facebook
  • icon-flickr
  • icon-instagram
  • icon-linkedin
  • icon-rss
  • icon-slideshare
  • icon-soundcloud
  • icon-tiktok
  • icon-tuotiao
  • icon-twitter
  • icon-wechat
  • icon-weibo
  • icon-youtube
  • FAO Organizational Chart
  • Regional Office for AfricaRegional Office for Asia and the PacificRegional Office for Europe and Central AsiaRegional Office for Latin America and the CaribbeanRegional Office for the Near East and North AfricaCountry Offices
  • Jobs
  • |
  • Contact us
  • |
  • Terms and Conditions
  • |
  • Scam Alert
  • |
  • Report Misconduct

Download our App

© FAO 2025