Nepal Fishery Survey 2015

Nepal, 2015 - 2016
Agriculture, Forestry and Fisheries

Central Bureau of Statistics

CSV JSON

DIST (DIST)

Data file: EMPLOYEE

Overview

Valid: 3124

Invalid: 0

Minimum: 1

Maximum: 73

Type: Discrete

Decimal: 0

Width: 2

Range: 1 - 75

Format: Numeric

Questions and instructions

Categories

Value	Category	Cases
1	Taplejung	5	0.2%
2	Panchthar	11	0.4%
3	Ilam	28	0.9%
4	Jhapa	101	3.2%
5	Morang	175	5.6%
6	Sunsari	116	3.7%
7	Dhankuta	27	0.9%
8	Terhathum	23	0.7%
9	Sankhuwasabha	21	0.7%
10	Bhojpur	25	0.8%
11	Solukhumbu	2	0.1%
12	Okhaldhunga	2	0.1%
13	Khotang	22	0.7%
14	Udayapur	49	1.6%
15	Saptari	103	3.3%
16	Siraha	137	4.4%
17	Dhanusa	132	4.2%
18	Mahottari	70	2.2%
19	Sarlahi	76	2.4%
20	Sindhuli	11	0.4%
21	Ramechhap	15	0.5%
22	Dolakha	8	0.3%
23	Sindhupalchok	20	0.6%
24	Kavre	24	0.8%
25	Lalitpur	51	1.6%
26	Bhaktapur	23	0.7%
27	Kathmandu	27	0.9%
28	Nuwakot	42	1.3%
29	Rasuwa	4	0.1%
30	Dhading	40	1.3%
31	Makwanpur	46	1.5%
32	Rautahat	116	3.7%
33	Bara	177	5.7%
34	Parsa	115	3.7%
35	Chitawan	176	5.6%
36	Gorkha	26	0.8%
37	Lamjung	9	0.3%
38	Tanahu	44	1.4%
39	Syangja	44	1.4%
40	Kaski	52	1.7%
41	Manang	0	0%
42	Mustang	0	0%
43	Myagdi	22	0.7%
44	Parbat	25	0.8%
45	Baglung	27	0.9%
46	Gulmi	9	0.3%
47	Palpa	20	0.6%
48	Nawalparasi	125	4%
49	Rupandehi	131	4.2%
50	Kapilbastu	72	2.3%
51	Arghakhanchi	16	0.5%
52	Pyuthan	21	0.7%
53	Rolpa	10	0.3%
54	Rukum	9	0.3%
55	Salyan	13	0.4%
56	Dang	57	1.8%
57	Banke	59	1.9%
58	Bardiya	113	3.6%
59	Surkhet	19	0.6%
60	Dailekh	12	0.4%
61	Jajarkot	3	0.1%
62	Dolpa	0	0%
63	Jumla	4	0.1%
64	Kalikot	0	0%
65	Mugu	0	0%
66	Humla	0	0%
67	Bajura	3	0.1%
68	Bajhang	0	0%
69	Achham	3	0.1%
70	Doti	0	0%
71	Kailali	102	3.3%
72	Kanchanpur	52	1.7%
73	Dadeldhura	2	0.1%
74	Baitadi	0	0%
75	Darchula	0	0%

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.