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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / NPL_2016-2018_HRVS_V01_EN_M_V01_A_OCS / variable [F206]
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
178650
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
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  • 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_5a

Overview

Valid: 85659
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 574
0.7%
2 Panchthar 0
0%
3 Ilam 1924
2.2%
4 Jhapa 3030
3.5%
5 Morang 3014
3.5%
6 Sunsari 1929
2.3%
7 Dhankuta 1012
1.2%
8 Tehrathum 0
0%
9 Sankhuwasabha 0
0%
10 Bhojpur 1247
1.5%
11 Solukhumbu 942
1.1%
12 Okhaldhunga 1000
1.2%
13 Khotang 1426
1.7%
14 Udayapur 1783
2.1%
15 Saptari 1914
2.2%
16 Siraha 0
0%
17 Dhanusha 2068
2.4%
18 Mahottari 2145
2.5%
19 Sarlahi 2776
3.2%
20 Sindhuli 2025
2.4%
21 Ramechhap 0
0%
22 Dolakha 1214
1.4%
23 Sindhupalchok 1985
2.3%
24 Kabhrepalanchok 2034
2.4%
25 Lalitpur 0
0%
26 Bhaktapur 0
0%
27 Kathmandu 0
0%
28 Nuwakot 1833
2.1%
29 Rasuwa 0
0%
30 Dhading 2787
3.3%
31 Makwanpur 1907
2.2%
32 Rautahat 0
0%
33 Bara 1889
2.2%
34 Parsa 1375
1.6%
35 Chitwan 0
0%
36 Gorkha 1213
1.4%
37 Lamjung 1238
1.4%
38 Tanahun 1983
2.3%
39 Syangja 1430
1.7%
40 Kaski 0
0%
41 Manang 0
0%
42 Mustang 0
0%
43 Myagdi 799
0.9%
44 Parbat 0
0%
45 Baglung 1563
1.8%
46 Gulmi 2277
2.7%
47 Palpa 1454
1.7%
48 Nawalparasi 3634
4.2%
49 Rupandehi 3935
4.6%
50 Kapilbastu 0
0%
51 Arghakhanchi 0
0%
52 Pyuthan 0
0%
53 Rolpa 1235
1.4%
54 Rukum 1362
1.6%
55 Salyan 0
0%
56 Dang 2534
3%
57 Banke 2285
2.7%
58 Bardiya 0
0%
59 Surkhet 1936
2.3%
60 Dailekh 1158
1.4%
61 Jajarkot 1033
1.2%
62 Dolpa 0
0%
63 Jumla 443
0.5%
64 Kalikot 779
0.9%
65 Mugu 0
0%
66 Humla 0
0%
67 Bajura 607
0.7%
68 Bajhang 1000
1.2%
69 Achham 1338
1.6%
70 Doti 913
1.1%
71 Kailali 3550
4.1%
72 Kanchanpur 0
0%
73 Dadeldhura 0
0%
74 Baitadi 1247
1.5%
75 Darchula 880
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.
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