Users’ Manual for Handling Resampled Micro Data of
Nepal Living Standard Survey
NLSS Ⅲ 2010/11

Version 2.0

2020
The Institute of Statistical Mathematics (ISM)
andStatistical Information Institute for Consulting and Analysis (SINFONICA)

1

History of revision of the manualSecond version in March 2020
- Revised based on the discussions during the workshop in 2019First draft version 1.0 in November 2019

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2

CONTENTSNLSS Ⅲ 2010/11
Chapter 1. About this Manual

Page 4

Chapter 2. Outline of the survey

6

Chapter 3. Data and metadata provided

9

Chapter 4. Data import
4.1 Imported STATA data into R

15

4.2 List of data files and corresponding section of questionnaire

17

4.3 Structure of each data file20

4.4 Summary of each data file68

4.5 Codebook of data files

107

4.6 Distribution of categorical variables

224

Chapter 5. Data check
5.1 Household identifier

248

5.2 Weight248

5.3 Sample design249

5.4 Integrity check of household identifier

251

5.5 Personal identifier

253

Chapter 6. Household summary file

260

Chapter 7. Household consumption

281

Chapter 8. Household income

287

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3Chapter 9. Micro data to be provided

292

Attachments:
1. NLSS Ⅲ 2010/11 Household Questionnaire298

2. Data dictionary

378

3.

450

Consumption and income: excerpts from Statistical Report

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4Chapter 1. About this Manual
1. This manual was prepared for users to use the next 80% resampled micro data sets of Nepal
Living Standard Survey 2010/11 (NLSS Ⅲ).
Survey year

Data files

Contents

NLSS 2010/11

47 filespoverty, xh00 to xh44 and final_pref

CSV files[1] "poverty_80.csv""xh00_s00_80.csv""xh01_s01_80.csv"[4] "xh02_s02_80.csv""xh03_s03_80.csv""xh04_s04_80.csv"[7] "xh05_s05_80.csv""xh06_s06a_80.csv" "xh07_s06b_80.csv"[10] "xh08_s06c_80.csv" "xh09_s06d_80.csv" "xh10_s07_80.csv"[13] "xh11_s08_80.csv""xh12_s09a_80.csv" "xh13_s09b_80.csv"[16] "xh14_s09c_80.csv" "xh15_s09d_80.csv" "xh16_s10a_80.csv"[19] "xh17_s10b_80.csv" "xh18_s11_80.csv""xh19_s12_80.csv"[22] "xh20_s13a1_80.csv" "xh21_s13a2_80.csv" "xh22_s13b_80.csv"[25] "xh23_s13c1_80.csv" "xh24_s13c2_80.csv" "xh25_s13c3_80.csv"[28] "xh26_s13d1_80.csv" "xh27_s13d2_80.csv" "xh28_s13e1_80.csv"[31] "xh29_s13e2_80.csv" "xh30_s13e3_80.csv" "xh31_s13f_80.csv"[34] "xh32_s14_80.csv""xh33_s15a_80.csv" "xh34_s15b_80.csv"[37] "xh35_s15c_80.csv" "xh36_s15d_80.csv" "xh37_s16_80.csv"[40] "xh38_s17a_80.csv" "xh39_s17b_80.csv" "xh40_s18a_80.csv"[43] "xh41_s18b_80.csv" "xh42_s18c_80.csv" "xh43_s19_80.csv"[46] "xh44_s20_80.csv""final_pref_80.csv"

R data frames[1] "poverty.80""xh00_s00.80""xh01_s01.80""xh02_s02.80"[5] "xh03_s03.80""xh04_s04.80""xh05_s05.80""xh06_s06a.80"[9] "xh07_s06b.80" "xh08_s06c.80" "xh09_s06d.80" "xh10_s07.80"[13] "xh11_s08.80""xh12_s09a.80" "xh13_s09b.80" "xh14_s09c.80"[17] "xh15_s09d.80" "xh16_s10a.80" "xh17_s10b.80" "xh18_s11.80"[21] "xh19_s12.80""xh20_s13a1.80" "xh21_s13a2.80" "xh22_s13b.80"[25] "xh23_s13c1.80" "xh24_s13c2.80" "xh25_s13c3.80" "xh26_s13d1.80"[29] "xh27_s13d2.80" "xh28_s13e1.80" "xh29_s13e2.80" "xh30_s13e3.80"[33] "xh31_s13f.80" "xh32_s14.80""xh33_s15a.80" "xh34_s15b.80"[37] "xh35_s15c.80" "xh36_s15d.80" "xh37_s16.80""xh38_s17a.80"[41] "xh39_s17b.80" "xh40_s18a.80" "xh41_s18b.80" "xh42_s18c.80"

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5[45] "xh43_s19.80""xh44_s20.80""final_pref.80"

2. The original micro data sets composed of all the samples were provided by Central Bureau of
Statistics (CBS), Nepal based on the Charter for Experimental Laboratory for Research Purpose
Statistical Use of Micro Data, and resampled at the rate of 80% by Sinfonica.

3. The above resampled data sets are available through the Institute of Statistical Mathematics(ISM) both in R and CSV format.

4. This manual was first compiled in November 2019 by;
Hiroshige Furuta
Visiting Senior Research Fellow, Sinfonica

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6Chapter 2. Outline of NLSS Ⅲ 2010/11
Objective

To update data on the living standards of the people and assess the impact ofvarious government policies and programs on the socio-economic changes in the
country during the last 7 years, and track changes experienced by previously
enumerated households during the past fifteen and seven years.

Frequency

The first round of NLSS (NLSS I) was conducted in 1995/96. The second roundof NLSS (NLSS II) was originally scheduled for 2002/03 and conducted a year
later during 2003/04. The third round of NLSS (NLSS III) was conducted in
2010/11.

Topics covered

demography, housing, access to facilities, migration, consumer expenditure,
education, health, marriage and maternity history, work and time use,
employment and unemployment, wage and salary, agriculture, nonagriculturalactivities, credit and savings, absentees population, remittances and transfers,social assistances, adequacy of consumption and government services/facilities,
anthropometry

Questionnaire

A. Household questionnaire
80-paged household questionnaire with 21 sections and 9 appendices.
B. Community questionnaire
Rural community questionnaire
Urban community questionnaire

Reference period

Over a complete cycle of 12 months partitioned into three pre-scheduled phases.
Reference period of income and expenditure is the past 12 months.

Coverage

and

scope

Geographically, whole country.
75 Districts
3 Ecological belts; Mountains, Hills and Tatai
5 Development regions
Private households, excluding collective (institutional) households.
The household members were determined on the basis of the usual place of theirresidence.

Sample frame

The initial frame for the NLSS-III survey was based on the frame prepared forthe 2008 Nepal Labour Force Survey (NLFS-II).

Sample design

The NLSS-III followed the Living Standards Measurement Survey (LSMS)
methodology developed and promoted by the World Bank (WB).
Included two components; nationally representative random cross-section

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7

sample of 6,000 households (500 psu), and panel samples of 1,200 households(100 psu).
[CROSS-SECTION SAMPLE]
The cross-section sample was selected in three stages:
First stage (PSU): identical to the enumeration area unit of NLFS-Ⅱ. 800 PSUs.
Second stage: 500 of the 800 NLFS PSUs were selected.
Third stage: 12 households were selected with equal probability in each PSU. An
additional 6 households were selected in each PSU, to be used as replacement
household for non-response among the 12 originally selected households.

[PANEL SAMPLE]
The panel sample of the NLSS-III is composed of all households visited by the
NLSS-II in 100 of its primary sampling units (PSU).
Fifty of them were taken from the cross-sectional component of the NLSS-II,and the remaining fifty from its panel component.
In other words, one half of the NLSS-III panel households were households
visited for the first time during the NLSS-II, whereas the other half were thehouseholds visited during both the NLSS-I and the NLSS-II.
Sample size

Cross-section data: 5,988 households (499 PSUs) were enumerated.
Panel data: 1,032 households (100 PSUs).
In total 7,020 households.
Twelve households were selected for the enumeration from each of the selected
PSU.

Data collection

Face to face interview (paper and pencil method)
Data collection was carried out over a period of 12 months to cover a complete
cycle in agricultural activities and to capture seasonal variations in othervariables.
First phase: 69 PSUs on February 2010.
Second phase: 131 PSUs from April to June 2010.
Third phase: 200 PSUs from July to October 2010.
Fourth phase: from October to February 2011.

Field teams: altogether 20 teams. Each team covered 30 PSUs on average.
Each team consisted of one supervisor, three interviewers (including at least two
female interviewers) and one specially trained data entry operator.
Each team was equipped with a laptop computer, solar panel power box, GPS

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8device, kits for testing iodine in salt, instruments for measuring height andweight of children and other logistics required in the field.
Data processing

A distinctive feature of the NLSS-III was the use of personal computer in thefield. A data entry programme developed specifically for the survey was
installed on each computer provided to the field teams.
The process of real time entering, checking and correcting data in the field
helped to enhance the quality of data collected.
After the completion of the field work, the data diskettes were sent back to the
CBS from the field.
Data processing and analysis was done in the CBS using STATA statisticalsoftware package.

Final report

Nepal Living Standard Survey 2010/11 Statistical Report Volume One
Nepal Living Standard Survey 2010/11 Statistical Report Volume Two

Technical

The survey was carried out with the assistance from the World Bank.

support

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9Chapter 3. Data and metadata provided
The following micro data and metadata were provided to Sinfonica by CBS on the occasion of theworkshop in December 2015.

Type

Filename

Description

Included inthismanualData

46 STATA data files
Cross section data[1] "poverty.dta""xh00_s00.dta" "xh01_s01.dta" "xh02_s02.dta"[5] "xh03_s03.dta""xh04_s04.dta" "xh05_s05.dta" "xh06_s06a.dta"[9] "xh07_s06b.dta" "xh08_s06c.dta" "xh09_s06d.dta" "xh10_s07.dta"[13] "xh11_s08.dta""xh12_s09a.dta" "xh13_s09b.dta" "xh14_s09c.dta"[17] "xh15_s09d.dta" "xh16_s10a.dta" "xh17_s10b.dta" "xh18_s11.dta"[21] "xh19_s12.dta""xh20_s13a1.dta" "xh21_s13a2.dta" "xh22_s13b.dta"[25] "xh23_s13c1.dta" "xh24_s13c2.dta" "xh25_s13c3.dta" "xh26_s13d1.dta"[29] "xh27_s13d2.dta" "xh28_s13e1.dta" "xh29_s13e2.dta" "xh30_s13e3.dta"[33] "xh31_s13f.dta" "xh32_s14.dta" "xh33_s15a.dta" "xh34_s15b.dta"[37] "xh35_s15c.dta" "xh36_s15d.dta" "xh37_s16.dta" "xh38_s17a.dta"[41] "xh39_s17b.dta" "xh40_s18a.dta" "xh41_s18b.dta" "xh42_s18c.dta"[45] "xh43_s19.dta" "xh44_s20.dta"

Metadata
Questionnaire

NLSSⅢ Questionnaire.pdf

80 pages

Codebook

NLSSⅢ_Household_data_dictionary.doc

Provided upon request

ReportStatistical_Report_Vol1.pdf
Statistical_Report_Vol2.pdf

Documents

NLSS-Ⅲ_Pweights-1105.pdf

Survey design and implementation

Summary:
1. The data set provided to Sinfonica at first had the following limitations.
(Limitation)

The dataset of NLSS3 provided in 2015 included both cross section data and panel
data. We will use only cross section data.

The NLSSⅢ questionnaire forms provided in 2015 was only in local language.

Data dictionary was not included.

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

10

Taking into consideration of the difficulties of NLSSⅡ in the previous workshop,
the documents on household income and expenditure estimation method wasneeded.



The variables of total household income and household expenditure were not included in theprovided data set.

2. The additional document were provided to Sinfonica upon request.
Questionnaire form in English

Data dictionaryDocuments on estimation of hosehold income and expenditure. These are a part of Statistical
Report.

3. The summary file of household income and expenditure was provided during theworkshop.

During the discussion of comparative study in the workshop, the needs of a household-levelsummary file including variables of household income and expenditure were shared among
participants. As a result, the Delegates from Nepal kindly provided the summary file of“FINAL_PREF.dta” as well as the list of variable names and labels.

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11References


NADA (National Data Archive), Homepage of CBS, Nepal
https://nada.cbs.gov.np/index.php/catalog/37/(Accessed on 10 November 2019)

It provides survey reports, highlights and several documentation.

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12

Homepage of LSMS, World Bank

Microdata Catalog of NLSS Ⅲ
https://microdata.worldbank.org/index.php/catalog/1000/(Accessed on 10 December 2019)

It is almost same as the next IHSN website.

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13



IHSN

(International Household Survey Network)

http://catalog.ihsn.org/index.php/catalog/2138/study-description
Accessed on 10 November 2019

It provides the questionnaire, statistical report and technical document of sample design andimplementation.

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14“Nepal Living Standards Survey – III 2010 Sampling design and implementation”

http://microdata.worldbank.org/index.php/catalog/74/download/11546(Accessed on 10 December 2019)

This document provides a description of how the sample was designed and implemented.

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15Chapter 4. Data Import
4.1 Imported STATA data into R


45 data files and one weight file (poverty.dta) were provided.

> list.files()[1] "poverty.dta""xh00_s00.dta""xh01_s01.dta""xh02_s02.dta"[5] "xh03_s03.dta""xh04_s04.dta""xh05_s05.dta""xh06_s06a.dta"[9] "xh07_s06b.dta" "xh08_s06c.dta" "xh09_s06d.dta" "xh10_s07.dta"[13] "xh11_s08.dta""xh12_s09a.dta" "xh13_s09b.dta" "xh14_s09c.dta"[17] "xh15_s09d.dta" "xh16_s10a.dta" "xh17_s10b.dta" "xh18_s11.dta"[21] "xh19_s12.dta""xh20_s13a1.dta" "xh21_s13a2.dta" "xh22_s13b.dta"[25] "xh23_s13c1.dta" "xh24_s13c2.dta" "xh25_s13c3.dta" "xh26_s13d1.dta"[29] "xh27_s13d2.dta" "xh28_s13e1.dta" "xh29_s13e2.dta" "xh30_s13e3.dta"[33] "xh31_s13f.dta" "xh32_s14.dta""xh33_s15a.dta" "xh34_s15b.dta"[37] "xh35_s15c.dta" "xh36_s15d.dta" "xh37_s16.dta""xh38_s17a.dta"[41] "xh39_s17b.dta" "xh40_s18a.dta" "xh41_s18b.dta" "xh42_s18c.dta"[45] "xh43_s19.dta""xh44_s20.dta"> library(foreign)> STATA.files<-list.files()> outfiles<-list()> for(j in 1:46){+ outfiles<-c(outfiles,list(read.dta(STATA.files[j],convert.factors=F)))+ }> length(outfiles)[1] 46# Made list of data file names, number of records and variables> for(j in 1:46) cat(formatC(j,width=2,flag="0"),":",format(STATA.files[j],width=16),":",+ formatC(nrow(outfiles[[j]]),width=7),formatC(ncol(outfiles[[j]]),width=3),"\n")01 : poverty.dta
02 : xh00_s00.dta
03 : xh01_s01.dta
04 : xh02_s02.dta
05 : xh03_s03.dta
06 : xh04_s04.dta
07 : xh05_s05.dta
08 : xh06_s06a.dta
09 : xh07_s06b.dta
10 : xh08_s06c.dta
11 : xh09_s06d.dta
12 : xh10_s07.dta
13 : xh11_s08.dta
14 : xh12_s09a.dta
15 : xh13_s09b.dta

:
:
:
:
:
:
:
:
:
:
:
:
:
:
:

5988 165988 105
28670 225988 63
143712 1328670 27443112 201556888221556
7101796 1183832
828670 49
28670 6315483 191540 19

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16

16 : xh14_s09c.dta
17 : xh15_s09d.dta
18 : xh16_s10a.dta
19 : xh17_s10b.dta
20 : xh18_s11.dta
21 : xh19_s12.dta
22 : xh20_s13a1.dta
23 : xh21_s13a2.dta
24 : xh22_s13b.dta
25 : xh23_s13c1.dta
26 : xh24_s13c2.dta
27 : xh25_s13c3.dta
28 : xh26_s13d1.dta
29 : xh27_s13d2.dta
30 : xh28_s13e1.dta
31 : xh29_s13e2.dta
32 : xh30_s13e3.dta
33 : xh31_s13f.dta
34 : xh32_s14.dta
35 : xh33_s15a.dta
36 : xh34_s15b.dta
37 : xh35_s15c.dta
38 : xh36_s15d.dta
39 : xh37_s16.dta
40 : xh38_s17a.dta
41 : xh39_s17b.dta
42 : xh40_s18a.dta
43 : xh41_s18b.dta
44 : xh42_s18c.dta
45 : xh43_s19.dta
46 : xh44_s20.dta

:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:

28662
82815
28670
32611
28670
6552
13237
1906
42324
5956
17724
3430
28834
61800
37512
33344
16672
38961
2583
5711
920
5988
66285
6074
2244
2657
41916
5988
47904
5988
12795

17
8
18
31
11
37
30
24
15
9
11
17
6
6
14
6
6
12
41
20
19
15
8
26
15
18
14
9
9
33
19

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4.2 List of data file and corresponding section of questionnaireThe file of “poverty.dta” includes area information and weights.
The file of “xh00_s00.dta” includes the cover page and selected household questions.
The files of “xh01” to “xh44” correspond to respective sections of the questionnaire.

# Data files and the corresponding sections of the questionnaire are as follows.
File name

nrow

ncol

Section of the questionnaire

poverty.dta

5988

16

xh00_s00.dta

5988

105

xh01_s01.dta

28670

22

1

Household Roster

xh02_s02.dta

5988

63

2

Housing
Part A: Type of dwelling
Part B: Housing expenses
Part C: Utilities and amenities

xh03_s03.dta

143712

13

3

Access to facilities

xh04_s04.dta

28670

27

4

Migration

xh05_s05.dta

44311220

5

Food expenses and home production

6

Non-food expenditures and inventory of durable goods

xh06_s06a.dta

155688

8Part A: Frequent non-food expenditures

xh07_s06b.dta

221556

7

Part B: Infrequent non-food expenditures

xh08_s06c.dta

101796

11

Part C: Inventory of durable goods

xh09_s06d.dta

83832

8

Part D: Own account production of goods

xh10_s07.dta

28670

49

7

Education
Part A: Literacy
Part B: Past enrollment
Part C: Current enrollment

xh11_s08.dta

28670

63

8

HealthPart A: Chronic illnesses
Part B: Illnesses or injuries
Part C: HIV/AIDS Knowledge
Part D: Immunizations

9

Marriage and maternity history

xh12_s09a.dta

15483

19

Part A: Maternity history

xh13_s09b.dta

1540

19

Part B: Pre and post-natal care

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18xh14_s09c.dta

28662

17

Part C: Family planning

xh15_s09d.dta

82815

8

Part D: Household decisions (Part I)
10

Jobs and time use

xh16_s10a.dta

28670

18

Part A: Time use

xh17_s10b.dta

32611

31

Part B: Jobs during the past 12 months

xh18_s11.dta

28670

11

11

Unemployment and underemployment

xh19_s12.dta

6552

37

12

Wage jobs

13

Farming and livestock

xh20_s13a1.dta

13237

30

xh21_s13a2.dta

1906

24

Part A1: Landholding - land owned
Part A2: Landholding - land
sharecropped/rented/mortgaged -inPart A3: Landholding - increase-decrease in holdings

xh22_s13b.dta

42324

15

Part B: Production and uses

xh23_s13c1.dta

5956

9

Part C1: Expenditures on seeds and young plants

xh24_s13c2.dta

17724

11

Part C2: Expenditures on fertilizers and insecticides

xh25_s13c3.dta

3430

17

Part C3: Expenditures on agricultural inputs - hiring
labourxh26_s13d1.dta

28834

6

xh27_s13d2.dta

61800

6

xh28_s13e1.dta

37512

14

xh29_s13e2.dta

33344

6

xh30_s13e3.dta

16672

6

xh31_s13f.dta

38961

12

Part D: Agriculture-earnings/expenditures

Part E: Livestock – ownership and related expenses

Part F: Ownership of farming assets and extension
services
xh32_s14.dta

2583

41

14

Non-agriculture enterprises/activities
Part A: General characteristics
Part B: Income from enterprises

15

Credit and savings

xh33_s15a.dta

5711

20

Part A: Borrowing and outstanding loans

xh34_s15b.dta

920

19

Part B: Lending and outstanding loans

xh35_s15c.dta

5988

15

Part C: Other assets

xh36_s15d.dta

66285

8

Part D: Household decisions (Part II)

xh37_s16.dta

6074

26

xh38_s17a.dta

2244

15

16

Absentees information

17

Other remittancesPart A: Remittances and transfer-income sent

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19xh39_s17b.dta

2657

18

xh40_s18a.dta

41916

14

xh41_s18b.dta

5988

9

xh42_s18c.dta

47904

9

xh43_s19.dta

5988

xh44_s20.dta

12795

Part B: Remittances and transfer income received
18

Transfers, social assistance and Other Income

33

19Adequacy of consumption and government facilities

19

20

Anthropometrics

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4.3 Structure of each data file

Cross section data consists of 45 files; xh00 to xh44.
Displayed the variable names and variable types of each data frame as below.

Summary:
1) Top three variables; xppsu, xhnum and xstra are common.
xppsu: PSU
xhnum: Household number
xstra: Strata in the NLSS3’s sample> for(j in 2:46) cat(colnames(outfiles[[j]])[1:3],"\n")2) Naming rule of varible:
Variable name is set as the section number of the questionnaire plus the question number within
the section of the questionnaire.
Therefore there are many variables with the same name but the different definitions.

3) Almost all variables are numeric. But the next variables are character.
Data frame
xh00
xh01
xh03
xh05
xh06
xh07
xh08
xh09
xh12
xh15
xh17
xh19
xh20
xh21
xh22
xh23
xh24
xh25
xh26
xh27
xh28
xh29
xh30
xh31
xh32
xh32
xh33
xh34
xh37

Variable
v00_headn
v01_01
v03_txt
v05_itm
v06a_itm
v06b_itm
v06c_itm
v06d_itm
v09_03v09d_txt
v10_03_txt
v12_02_txt
v13_03v13_19v13_35nm
v13_40_txt
v13_44_txt
v13_51
v13d1_nm
v13d2_nm
v13_66nm
v13e2_nm
v13e3_nm
v13_75nm
v14_02av14_02B
v15_02
v15_15v16_nmeDefinition
Name of household head
Name, “Confidential”
Facility description
Food item name
Frq. non-food item
Infrq. non-food item
Durable goods item
Own account prod. goods item
Child name
Decision description
Occupation description, “Condifential”
Work activity description, “Confidential”
Name of plot, “Confidential”
Name of the plot, “Confidential”
Name of crop, “Confidential”
Crop description, “Confidential”
Fertilizer/insecticide description
Detail of work, “Confidential”
Source name
Expenditure nameLivestock name
Income item
Expenditure item
Expenditure name
“Confidential”
Detail of activities, “Confidential”
Loan description, “Confidential”
Loan description, “Confidential”
Name of absentees, “Confidential”

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

21

xh38
xh39
xh40
xh42

v17_03
v17_13v18a_txt
v18c_txt

Name of recipient, “Confidential”
Name of donor, “Confidential”
Source description
Other income description> for(j in 1:46){+ cat("\n",j,"###", STATA.files[j],"###\n")+ print(str(outfiles[[j]]))+ }1 ### poverty.dta ###'data.frame':
5988 obs. of 16 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 1 2 4 5 6 7 8 10 11 14 ...
$ hhsize: int 3 5 2 2 5 4 2 3 10 4 ...
$ belt: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbrur: int 1 1 1 1 1 1 1 1 1 1 ...
$ ad: int 1 1 1 1 1 1 1 1 1 1 ...
$ wt_ind: num 879 1465 586 586 1465 ...
$ wt_hh: num 293 293 293 293 293 ...
$ fpline_7: num 11929 11929 11929 11929 11929 ...
$ pline_7: num 19261 19261 19261 19261 19261 ...
$ pindex: num 1.03 1.03 1.03 1.03 1.03 ...
$ poor: int 0 0 0 0 0 0 0 0 0 0 ...
$ totcons_pc_7: num 71774 46656 69948 76446 41462 ...
$ food_pc_7: num 38480 23088 40014 37960 33415 ...
$ quintle_7: int 5 5 5 5 4 3 5 5 4 5 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr " 9 Nov 2011 15:27"- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 251 251 251 251 251 254 254 254 ...
- attr(*, "val.labels")= chr "" "" "" "belt" ...
- attr(*, "var.labels")= chr "psu" "household number" "household size" "" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 4..$ analytical_dom: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "mountains" "urban - kathmandu" "urban - hill" "urban -terai" ...
..$ urbrur: Named num 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ region: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "eastern" "centeral" "western" "mid-western" ...
..$ belt: Named num 1 2 3.. ..- attr(*, "names")= chr "mountain" "hill" "terai"
NULL
2 ### xh00_s00.dta ###'data.frame':
5988 obs. of 105 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 17 2 16 4 6 1 5 10 14 8 ...
$ xstra: int 100 100 100 100 100 100 100 100 100 100 ...
$ v00_dist: int 9 9 9 9 9 9 9 9 9 9 ...
$ v00_vdc: int 13 13 13 13 13 13 13 13 13 13 ...
$ v00_hno: int 17 2 16 4 13 1 5 10 14 8 ...
$ v00_a: int 1 1 1 1 2 1 1 1 1 1 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

22$ v00_b: int$ v00_c: int$ v00_d: int$ v00_e: int$ v00_f: int$ v00_g: int$ v00_team: int$ v00_int: int$ v00_sup: int$ v00_ent: int$ v00_int1_d: int$ v00_int1_m: int$ v00_int1_y: int$ v00_int2_d: int$ v00_int2_m: int$ v00_int2_y: int$ v00_int3_d: int$ v00_int3_m: int$ v00_int3_y: int$ v00_sup_d: int$ v00_sup_m: int$ v00_sup_y: int$ v00_headn: chr$ v13_02: int$ v13_18: int$ v13_29: int$ v13_30_1u: int$ v13_30_1r: int$ v13_30_1a: int$ v13_30_1p: int$ v13_30_2u: int$ v13_30_2b: int$ v13_30_2k: int$ v13_30_2d: int$ v13_31: int$ v13_32: int$ v13_33_1u: int$ v13_33_1r: int$ v13_33_1a: int$ v13_33_1p: int$ v13_33_2u: int$ v13_33_2b: int$ v13_33_2k: int$ v13_33_2d: int$ v13_34: int$ v13_38d_total: int$ v13_39: int$ v13_43: int$ v13_48a: int$ v13_48b: int$ v13_49: int$ v13_50: int$ v13_53_el_male : int$ v13_53_el_female: int$ v13_58: int$ v13_59: int

NA NA NA NA 2 NA NA NA NA NA ...
NA NA NA NA 6 NA NA NA NA NA ...
0 0 0 0 0 0 0 0 0 0 ...
1 1 1 1 1 1 1 1 5 1 ...
1 1 1 27 1 1 1 1 27 7 ...
2 2 2 2 2 2 2 2 2 2 ...
2 2 2 2 2 2 2 2 2 2 ...
24 24 25 22 25 23 23 25 23 23 ...
21 21 21 21 21 21 21 21 21 21 ...
22 22 22 22 22 22 22 22 22 22 ...
19 20 19 19 20 19 19 18 18 20 ...
9 9 9 9 9 9 9 9 9 9 ...
2067 2067 2067 2067 2067 2067 2067 2067 2067 2067 ...
20 21 20 20 21 20 20 21 21 22 ...
9 9 9 9 9 9 9 9 9 9 ...
2067 2067 2067 2067 2067 2067 2067 2067 2067 2067 ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
21 21 21 21 21 22 22 22 22 22 ...
9 9 9 9 9 9 9 9 9 9 ...
2067 2067 2067 2067 2067 2067 2067 2067 2067 2067 ...
"Confidential" "Confidential" "Confidential" "Confidential" ...
2 2 1 2 2 2 1 1 1 1 ...
2 2 2 2 1 2 2 2 1 2 ...
2 2 1 2 2 2 2 2 2 2 ...
NA NA 1 NA NA NA NA NA NA NA ...
NA NA 0 NA NA NA NA NA NA NA ...
NA NA 4 NA NA NA NA NA NA NA ...
NA NA 0 NA NA NA NA NA NA NA ...
NA NA 2 NA NA NA NA NA NA NA ...
NA NA 0 NA NA NA NA NA NA NA ...
NA NA 0 NA NA NA NA NA NA NA ...
NA NA 0 NA NA NA NA NA NA NA ...
NA NA 420000 NA NA NA NA NA NA NA ...
2 2 2 2 2 2 2 2 2 2 ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA 0 NA 0 NA 0 0 67500 0 ...
NA NA 1 NA 1 NA 1 1 2 1 ...
NA NA 1 NA 1 NA 1 2 1 1 ...
NA NA 3 NA 3 NA 3 NA 1 1 ...
NA NA 1 NA 1 NA 1 NA 1 1 ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA 1 NA 1 NA 2 2 1 1 ...
NA NA 8 NA 20 NA NA NA 0 0 ...
NA NA 20 NA 25 NA NA NA 5 0 ...
NA NA 25740 NA 7750 NA 0 0 9000 28610 ...
NA NA 2 NA 2 NA 2 2 2 2 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

23$ v13_60: int NA NA NA NA NA NA NA NA NA NA ...
$ v13_61: int NA NA NA NA NA NA NA NA NA NA ...
$ v13_62: int NA NA 25740 NA 7750 NA 0 0 9000 28610 ...
$ v13_65: int 2 2 1 2 1 1 1 1 2 1 ...
$ v13_73: int NA NA 2 NA 2 2 2 2 NA 2 ...
$ v13_74: int 2 1 1 2 1 1 1 1 1 1 ...
$ v13_82: int 2 2 2 2 2 2 2 2 2 2 ...
$ v13_83: int 3 3 3 3 3 3 3 3 3 3 ...
$ v13_84: int 2 2 2 2 2 1 2 1 2 2 ...
$ v13_85: int 3 3 3 2 3 NA 3 NA 3 3 ...
$ v14_01: int 1 2 1 1 1 1 2 2 2 2 ...
$ v15_01: int 2 1 1 2 1 1 1 1 1 2 ...
$ v15_14: int 2 2 2 2 2 2 2 2 2 2 ...
$ v16_00: int 2 1 2 1 2 1 1 1 2 2 ...
$ v17_01: int 2 1 1 2 1 1 1 2 2 1 ...
$ v17_11: int 1 1 1 2 1 2 1 2 1 1 ...
$ v01_00: int 0 0 0 0 0 0 0 0 0 0 ...
$ v02_a: int 1 1 1 1 1 2 1 2 4 1 ...
$ v03_a: int 1 1 1 1 1 2 1 1 4 1 ...
$ v05_00: int 1 1 2 1 2 2 1 2 2 1 ...
$ v06_00: int 1 1 1 1 2 2 1 2 1 1 ...
$ v09_36: int 1 1 2 1 2 2 1 2 2 2 ...
$ v13_01: int 1 1 1 1 1 1 1 1 1 1 ...
$ v13_17: int 1 1 1 1 1 1 1 1 1 1 ...
$ v15_38: int 0 0 1 0 1 1 0 1 1 1 ...
$ v19_01: int 1 1 1 1 2 2 1 1 1 1 ...
$ n_s01: int 1 5 6 2 5 3 2 3 4 2 ...
$ n_s9a: int 0 3 5 1 3 0 2 2 1 0 ...
$ n_s9b: int 0 0 0 0 0 0 0 0 0 0 ...
$ n_s10b: int 3 8 20 4 14 7 4 8 10 4 ...
$ n_s12: int 0 1 2 0 2 1 0 2 2 0 ...
$ n_s14: int 2 NA 2 1 1 3 NA NA NA NA ...
$ n_s15a: int NA 1 2 NA 1 1 1 2 1 NA ...
$ n_s15b: int NA NA NA NA NA NA NA NA NA NA ...
$ n_s16: int NA 2 NA 7 NA 2 2 2 NA NA ...
$ n_s17a: int NA 1 4 NA 1 2 2 NA NA 2 ...
[list output truncated]- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 252 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "V00_DIST" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "psu
geo-codes: district" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 46..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V00_DIST : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V00_A: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V00_B: Named num 1 2 3.. ..- attr(*, "names")= chr "dwelling not found" "household not found" "refusal"..$ V00_C: Named num 0

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

24.. ..- attr(*, "names")= chr "not a replacement"..$ V00_D: Named num 0.. ..- attr(*, "names")= chr "this household does not replace any other one"..$ V00_E: Named num 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "hindu" "bouddha" "islam" "kirant" ...
..$ V00_F: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "nepali" "maithili" "bhojpuri" "tharu (dagaura/rana)" ...
..$ V00_G: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V00_INT: Named num 12 13 14 15 22 23 24 25 32 33 ...
.. ..- attr(*, "names")= chr "laxman ban" "narayan karki" "indira khapangi magar" "anjanaoli" ...
..$ V00_SUP: Named num 11 21 31 41 51 61 71 81 91 101 ...
.. ..- attr(*, "names")= chr "nava raj shrestha" "suman kumar shrestha" "kedar prasad neupane""janak babu kafle" ...
..$ V00_ENT: Named num 13 22 33 43 52 64 74 84 93 102 ...
.. ..- attr(*, "names")= chr "narayan karki" "ramesh bahadur shrestha" "yam bahadur gurung""narayan bahadur thapa" ...
..$ V00_INT1_D: Named num 0.. ..- attr(*, "names")= chr "date not given"..$ V00_INT2_D: Named num 0.. ..- attr(*, "names")= chr "!end-of-list!"..$ V00_INT3_D: Named num 0.. ..- attr(*, "names")= chr "!end-of-list!"..$ V00_SUP_D : Named num 0.. ..- attr(*, "names")= chr "date not given"..$ V13_02: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_18: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_29: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_30_1U : Named num 1.. ..- attr(*, "names")= chr "ropani"..$ V13_30_2U : Named num 2.. ..- attr(*, "names")= chr "bigha"..$ V13_32: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_33_1U : Named num 1.. ..- attr(*, "names")= chr "ropani"..$ V13_33_2U : Named num 2.. ..- attr(*, "names")= chr "bigha"..$ V13_39: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_43: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_48A: Named num 1 2 3 4.. ..- attr(*, "names")= chr "government" "ngo" "private" "cooperative, community"..$ V13_48B: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_49: Named num 1 2 3.. ..- attr(*, "names")= chr "not available for purchase" "no money for purchase" "other"..$ V13_50: Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "exchange labour only"..$ V13_59: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

25..$ V13_65: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_73: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_74: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_82: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_83: Named num 1 2 3 4.. ..- attr(*, "names")= chr "service far away" "service is not good" "not necessary" "other"..$ V13_84: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_85: Named num 1 2 3 4.. ..- attr(*, "names")= chr "service far away" "service is not good" "not necessary" "other"..$ V14_01: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V15_01: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V15_14: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V16_00: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V17_01: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V17_11: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V01_00: Named num 0.. ..- attr(*, "names")= chr "!information not gathered at the field!"..$ V09_36: Named num 0.. ..- attr(*, "names")= chr "no competent respondent"
NULL
3 ### xh01_s01.dta ###'data.frame':
28670 obs. of 22 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 2 2 2 2 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v01_idc: int 1 1 2 3 4 5 1 2 3 4 ...
$ v01_01 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v01_02 : int 1 2 1 2 1 2 1 2 1 1 ...
$ v01_03 : int 49 29 10 4 25 19 50 44 25 17 ...
$ v01_04 : int 1 1 3 3 9 9 1 2 3 3 ...
$ v01_05a: int 10 9 9 9 9 9 9 81 9 9 ...
$ v01_05b: int 2 2 2 2 2 2 2 NA 1 1 ...
$ v01_06 : int 7 2 1 NA 1 1 2 2 1 1 ...
$ v01_07 : int NA 98 NA NA NA NA 2 1 NA NA ...
$ v01_08 : int 2 1 1 1 1 1 1 1 1 1 ...
$ v01_09 : int 12 12 12 12 12 12 12 12 12 12 ...
$ v01_10 : int 1 1 1 1 1 1 1 1 1 1 ...
$ v01_jdc: int 1 1 2 3 4 5 1 2 3 4 ...
$ v01_11 : int 2 3 2 2 2 2 3 3 1 1 ...
$ v01_12 : int NA NA NA NA NA NA NA NA 1 1 ...
$ v01_13 : int 8 17 10 10 16 16 16 16 NA NA ...
$ v01_14 : int 2 2 1 1 2 2 3 3 1 1 ...
$ v01_15 : int NA NA 1 1 NA NA NA NA 2 2 ...
$ v01_16 : int 17 17 NA NA 17 17 17 17 NA NA ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

26- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "id code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 12..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V01_02 : Named num 1 2.. ..- attr(*, "names")= chr "male" "female"..$ V01_04 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "head" "husband/wife" "son/daughter" "grandchild" ...
..$ V01_05A: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V01_05B: Named num 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ V01_06 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "never married" "single married" "poly married" "re-married" ...
..$ V01_08 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "chhetri" "brahman (hill)" "magar" "tharu" ...
..$ V01_10 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V01_11 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "died"..$ V01_13 : Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "pre-school/kindergarden" "class 1" "class 2" "class 3" ...
..$ V01_14 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "died"..$ V01_16 : Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "pre-school/kindergarden" "class 1" "class 2" "class 3" ...
NULL
4 ### xh02_s02.dta ###'data.frame':
5988 obs. of 63 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 16 4 6 1 5 10 14 8 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v02_01 : int 2 2 2 2 1 2 2 1 2 2 ...
$ v02_02a : int 2 6 11 4 3 9 6 11 4 4 ...
$ v02_02b : int 0 1 1 1 1 1 1 1 1 1 ...
$ v02_02c : int 1 1 2 1 0 1 1 2 1 1 ...
$ v02_02d : int 0 4 4 1 2 3 2 2 2 1 ...
$ v02_02e : int 0 0 0 0 0 1 1 0 0 0 ...
$ v02_02f : int 0 0 4 1 0 1 0 0 0 0 ...
$ v02_02g : int 1 0 0 0 0 2 1 0 0 0 ...
$ v02_02h : int 0 0 0 0 0 0 0 6 0 1 ...
$ v02_03 : int 2 1 1 1 1 1 1 1 1 1 ...
$ v02_04 : int 3 2 2 2 2 2 2 2 2 2 ...
$ v02_05 : int 4 3 3 4 3 3 3 3 3 3 ...
$ v02_06 : int 4 4 4 4 1 4 4 4 4 4 ...
$ v02_07 : int 2 2 2 1 1 2 2 2 2 2 ...
$ v02_08u : int 1 1 1 1 1 1 1 1 1 1 ...
$ v02_08rb: int 0 0 0 0 0 0 0 1 0 0 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

27$ v02_08ak: int 6 5 12 8 4 8 12 0 3 4 ...
$ v02_08pd: int 0 0 0 0 0 0 0 0 0 0 ...
$ v02_09 : int 132 412 976 225 320 860 620 1660 460 390 ...
$ v02_10 : int 2045 2040 2052 2065 2056 2050 2057 2065 2058 2040 ...
$ v02_11 : int 2 1 1 2 2 1 1 1 1 1 ...
$ v02_12 : int NA 1000000 5000000 NA NA 1950000 550000 7000000 550000 125000 ...
$ v02_13 : int NA 3500 12000 NA NA 5000 6500 8000 1500 1500 ...
$ v02_14 : int NA 1 1 NA NA 1 1 1 1 2 ...
$ v02_15 : int NA 500 1000 NA NA 600 700 6600 1000 NA ...
$ v02_16 : int 1 NA NA 2 4 NA NA NA NA NA ...
$ v02_17 : int NA NA NA 1000 100 NA NA NA NA NA ...
$ v02_18 : int 800 NA NA NA NA NA NA NA NA NA ...
$ v02_19 : int 5 1 1 5 5 1 1 1 1 5 ...
$ v02_20 : int NA 1 1 NA NA 1 1 1 1 NA ...
$ v02_21 : int NA 7 28 NA NA 28 42 28 42 NA ...
$ v02_22 : int 0 420 900 0 0 420 420 1200 600 0 ...
$ v02_23 : int 4 4 4 4 4 4 4 4 4 4 ...
$ v02_24 : int 3 3 3 3 3 4 4 3 3 5 ...
$ v02_25 : int NA NA NA NA NA NA NA NA NA NA ...
$ v02_26 : int 3 3 3 3 5 3 3 2 3 3 ...
$ v02_27 : int 1 1 1 1 4 1 1 1 1 1 ...
$ v02_28 : int 1 1 1 1 NA 1 1 1 1 1 ...
$ v02_29 : int 960 4200 2400 0 NA 6000 1800 3000 1800 2400 ...
$ v02_30 : int 35 21 42 42 NA 35 35 35 35 35 ...
$ v02_31a : int 2 2 1 2 2 1 2 1 1 1 ...
$ v02_31b : int 1 1 1 1 2 1 1 1 1 1 ...
$ v02_31c : int 1 1 1 2 2 1 1 1 1 2 ...
$ v02_31d : int 2 2 2 2 2 2 2 2 2 2 ...
$ v02_32 : int 5400 7800 9000 1800 NA 12200 6600 21600 6600 9000 ...
$ v02_33 : int 4 1 1 1 1 4 1 4 1 1 ...
$ v02_34 : int 5 2 2 2 2 5 1 5 2 2 ...
$ v02_35 : int 2 1 1 1 1 1 1 1 1 1 ...
$ v02_36 : int NA 2 1 2 1 2 1 1 2 1 ...
$ v02_37a : int NA NA 1 NA 1 NA 1 1 NA 1 ...
$ v02_37b : int NA NA 100 NA 48 NA 20 60 NA 30 ...
$ v02_37c : int NA NA 40 NA 30 NA 20 40 NA 30 ...
$ v02_38a : int NA NA 1 NA 4 NA 2 3 NA 2 ...
$ v02_38b : int NA NA 0 NA 0 NA 30 0 NA 0 ...
$ v02_39 : int NA NA 1 NA 1 NA 2 1 NA 1 ...
$ v02_40 : int NA NA NA NA NA NA 50 NA NA NA ...
$ v02_41 : int 2 2 1 2 1 2 2 1 2 1 ...
$ v02_42 : int NA NA 1 NA 1 NA NA 1 NA 1 ...
$ v02_43 : int 3 3 3 2 3 3 3 3 2 2 ...
$ v02_44 : int 3 2 3 3 2 3 3 3 3 3 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "V02_01" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "2.01 dwelling
unit occupied by own hhld only" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 33..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

28..$ V02_01 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_03 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_04 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "cement bonded bricks/stones" "mud bonded bricks/stones" "wood""bamboo/leaves" ...
..$ V02_05 : Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "pillar bonded" "cement bonded" "mod bonded" "wooden pillar" ...
..$ V02_06 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "straw/thatch" "earth/mud" "wood/planks" "galvanized iron" ...
..$ V02_07 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "no windows/no covering window" "shutters" "screens/glass" "other"..$ V02_08U: Named num 1 2.. ..- attr(*, "names")= chr "ropani" "bigha"..$ V02_11 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_14 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_16 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "renter" "providing free of cost" "squatting" "other"..$ V02_19 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "piped water" "covered well" "hand pump/tubewell" "open well" ...
..$ V02_20 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_21 : Named num 0 168.. ..- attr(*, "names")= chr "less than 1 hour a week" "all the week without interruption"..$ V02_23 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "underground drain" "open drains" "soak pit" "no"..$ V02_24 : Named num 1 2 3 4 5 6.. ..- attr(*, "names")= chr "collected by garbage truck" "private collector" "dumped""burned/buried" ...
..$ V02_26 : Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "flush - municipal sewer" "flush - septic tank" "non-flush""communal latrine" ...
..$ V02_27 : Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "electricity" "solar" "biogas" "kerosene" ...
..$ V02_28 : Named num 1 2 3.. ..- attr(*, "names")= chr "individual" "joint" "no meter"..$ V02_31A: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_31B: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_31C: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_31D: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_33 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "firewood" "dung" "leaves/rubbish/straw/thatch" "cylinder gas" ...
..$ V02_34 : Named num 1 2 3 4 5 6.. ..- attr(*, "names")= chr "open fireplace" "mud fireplace" "smokeless oven" "kerosene
stove" ...
..$ V02_35 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_36 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

29..$ V02_37A: Named num 1 2 3.. ..- attr(*, "names")= chr "bhari" "chart" "kg"..$ V02_39 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "own land" "community managed forest" "govt. forest" "other"..$ V02_41 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V02_42 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "own land" "community managed forest" "govt. forest" "other"..$ V02_43 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "large crystal salt" "crushed salt" "packet salt (two child logo)""other"..$ V02_44 : Named num 1 2 3.. ..- attr(*, "names")= chr "0 ppm" "below 15 ppm" "15 ppm or more"
NULL
5 ### xh03_s03.dta ###'data.frame':
143712 obs. of 13 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 17 17 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v03_idc: int 101 102 103 104 105 106 107 108 109 110 ...
$ v03_txt: chr "ECD center" "Primary School" "Secondary school" "Higher Secondary Sch" ...
$ v03_01 : int 6 1 1 1 7 1 1 1 4 6 ...
$ v03_02d: int NA 0 0 0 NA 0 0 0 0 NA ...
$ v03_02h: int NA 0 0 0 NA 0 0 0 4 NA ...
$ v03_02m: int NA 10 10 10 NA 30 10 10 0 NA ...
$ v03_03a: int NA 1 1 1 NA 3 2 2 40 NA ...
$ v03_03b: int NA 0 0 0 NA 0 0 0 0 NA ...
$ v03_04 : int 2 2 2 2 NA 1 1 1 1 1 ...
$ v03_05 : int NA NA NA NA NA 4 4 4 4 1 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 252 20 251 251 251 251 252 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "{3.00}
facility code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 4..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V03_01: Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "foot (without load)" "bicycle, rickshaw" "motorcycle, tamppo" "car,bus" ...
..$ V03_04: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V03_05: Named num 1 2 3 4.. ..- attr(*, "names")= chr "daily" "weekly" "monthly" "rarely"
NULL
6 ### xh04_s04.dta ###'data.frame':
28670 obs. of 27 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 2 2 2 2 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...

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30$ v04_idc : int 1 1 2 3 4 5 1 2 3 4 ...
$ v04_01 : int 1 1 1 NA 1 1 1 1 1 1 ...
$ v04_02 : int 2 2 2 NA 2 2 2 2 1 1 ...
$ v04_03a : int 9 9 9 NA 9 9 81 81 NA NA ...
$ v04_03b : int 2 2 2 NA 2 2 NA NA NA NA ...
$ v04_04 : int 2050 2063 2063 NA 2060 2064 2036 2036 NA NA ...
$ v04_05 : int 3 2 2 NA 4 4 3 2 NA NA ...
$ v04_06 : int 523 996 997 NA 997 997 621 996 NA NA ...
$ v04_07 : int 2 1 1 NA 2 1 2 2 1 2 ...
$ v04_08 : int NA 1 1 NA NA 1 NA NA 1 NA ...
$ v04_09_y: int NA 2062 2062 NA NA 2061 NA NA 2066 NA ...
$ v04_09_m: int NA 9 9 NA NA 8 NA NA 4 NA ...
$ v04_10_y: int NA 2063 2063 NA NA 2064 NA NA 2066 NA ...
$ v04_10_m: int NA 9 9 NA NA 8 NA NA 6 NA ...
$ v04_11a : int NA 9 9 NA NA 9 NA NA 27 NA ...
$ v04_11b : int NA 2 2 NA NA 2 NA NA 1 NA ...
$ v04_idc2: int NA 1 2 NA NA 5 NA NA 3 NA ...
$ v04_12 : int NA 2 4 NA NA 4 NA NA 6 NA ...
$ v04_13 : int NA 996 997 NA NA 997 NA NA 997 NA ...
$ v04_14 : int NA 2 2 NA NA 2 NA NA 2 NA ...
$ v04_15 : int NA NA NA NA NA NA NA NA NA NA ...
$ v04_16 : int NA NA NA NA NA NA NA NA NA NA ...
$ v04_17 : int NA NA NA NA NA NA NA NA NA NA ...
$ v04_18 : int NA 1 1 NA NA 9 NA NA 1 NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 252 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "id code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 18..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V04_02 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V04_03A : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V04_03B : Named num 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ V04_05 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "marriage" "follow the family" "other family reason" "education" ...
..$ V04_06 : Named num 11 111 112 114 121 122 123 131 211 212 ...
.. ..- attr(*, "names")= chr "armed forces" "legislators" "government officials" "officials of
special interest organizations" ...
..$ V04_07 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V04_10_Y: Named num 98.. ..- attr(*, "names")= chr "still away"..$ V04_10_M: Named num 98.. ..- attr(*, "names")= chr "still away"..$ V04_11A : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V04_11B : Named num 1 2.. ..- attr(*, "names")= chr "urban" "rural"

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31..$ V04_12 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "marriage" "follow the family" "other family reason" "education" ...
..$ V04_13 : Named num 11 111 112 114 121 122 123 131 211 212 ...
.. ..- attr(*, "names")= chr "armed forces" "legislators" "government officials" "officials of
special interest organizations" ...
..$ V04_14 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V04_15 : Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "relatives" "friends,neighbours" "other" "nobody helped" ...
..$ V04_16 : Named num 1 2.. ..- attr(*, "names")= chr "wage job" "self employment"..$ V04_17 : Named num 11 111 112 114 121 122 123 131 211 212 ...
.. ..- attr(*, "names")= chr "armed forces" "legislators" "government officials" "officials of
special interest organizations" ...
..$ V04_18 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "family reasons" "work finished" "dissatisfied with work" "start
new job" ...
NULL
7 ### xh05_s05.dta ###'data.frame':
443112 obs. of 20 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 17 17 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v05_idc: int 11 12 13 14 15 16 17 18 21 22 ...
$ v05_itm: chr "Fine rice" "Coarse rice" "Beaten, flattened rice" "Maize" ...
$ v05_01 : int 1 2 1 2 2 1 2 2 2 2 ...
$ v05_02 : int 0 NA 0 NA NA 0 NA NA NA NA ...
$ v05_03a: num NA NA NA NA NA NA NA NA NA NA ...
$ v05_03b: int NA NA NA NA NA NA NA NA NA NA ...
$ v05_04 : int NA NA NA NA NA NA NA NA NA NA ...
$ v05_05 : int 12 NA 12 NA NA 12 NA NA NA NA ...
$ v05_06a: num 10 NA 4 NA NA 8 NA NA NA NA ...
$ v05_06b: int 1 NA 8 NA NA 1 NA NA NA NA ...
$ v05_07 : int 600 NA 60 NA NA 280 NA NA NA NA ...
$ v05_08 : int 0 NA 0 NA NA 0 NA NA NA NA ...
$ v05_09 : int 7 NA 4 NA NA 7 NA NA NA NA ...
$ v05_10a: num 2.5 NA 1 NA NA 2 NA NA NA NA ...
$ v05_10b: int 1 NA 8 NA NA 1 NA NA NA NA ...
$ v05_11 : int 163 NA 15 NA NA 70 NA NA NA NA ...
$ v05_12 : int 2 NA 2 NA NA 2 NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 252 25 251 251 254 251 253 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "foodcode" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 6..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V05_01 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V05_03B: Named num 1 2 3 4 5 6 7 8 9 10 ...

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32.. ..- attr(*, "names")= chr "kg" "gram" "maund" "liter" ...
..$ V05_06B: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "kg" "gram" "maund" "liter" ...
..$ V05_10B: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "kg" "gram" "maund" "liter" ...
..$ V05_12 : Named num 1 2 3.. ..- attr(*, "names")= chr "home production" "purchase" "in-kind"
NULL
8 ### xh06_s06a.dta ###'data.frame':
155688 obs. of 8 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 17 17 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v06a_idc: int 211 212 213 214 215 221 222 223 224 225 ...
$ v06a_itm: chr "Wood (bundle wood, logwoo" "Kerosene oil" "Coal, charcoal" "Cylinder gas(LPG)" ...
$ v06_01 : int 2 1 1 1 1 1 1 1 1 1 ...
$ v06_02a : int NA 80 0 0 10 0 0 0 130 25 ...
$ v06_02b : int NA 800 500 6200 120 500 3000 500 600 300 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 252 25 251 253 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "frq. non-foodcode" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 2..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V06_01: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
9 ### xh07_s06b.dta ###'data.frame':
221556 obs. of 7 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 17 17 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v06b_idc: int 311 312 313 314 315 316 317 318 319 321 ...
$ v06b_itm: chr "Legal expenses and insura" "Income taxes, land taxes," "Repair and otherexpenses" "Postal expenses, telegram" ...
$ v06_03 : int 2 2 2 2 2 2 2 2 2 2 ...
$ v06_04 : int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 252 25 251 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "infrq.
non-food code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 2..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...

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33.. ..- attr(*, "names")= chr"221/rural-hill-eastern" ...
..$ V06_03: Named num 1 2.. ..- attr(*, "names")= chr
NULL"100/mountain" "218/urban-kathmandu valley" "219/urban-hill""yes" "no"10 ### xh08_s06c.dta ###'data.frame':
101796 obs. of 11 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 17 17 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v06c_idc: int 501 502 503 504 505 506 507 508 509 510 ...
$ v06c_itm: chr "Radio/cassette/CD player" "Camera (still/movie)" "Bicycle""Motorcycle/scooter" ...
$ v06_05 : int 1 2 2 2 2 2 2 1 2 1 ...
$ v06_06 : int 1 NA NA NA NA NA NA 1 NA 1 ...
$ v06_07 : int 3 NA NA NA NA NA NA 0 NA 10 ...
$ v06_08 : int 1 NA NA NA NA NA NA 1 NA 1 ...
$ v06_09 : int 600 NA NA NA NA NA NA 1000 NA 10000 ...
$ v06_10 : int 300 NA NA NA NA NA NA 800 NA 6000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 252 25 251 252 251 251 253 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "code ofdurable goods" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 3..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V06_05: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V06_08: Named num 1 2 3.. ..- attr(*, "names")= chr "purchased" "gift/payment" "dowry/inheritance"
NULL
11 ### xh09_s06d.dta ###'data.frame':
83832 obs. of 8 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 17 17 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v06d_idc: int 600 601 602 603 604 605 606 607 608 609 ...
$ v06d_itm: chr "Expenditures on self prod" "Dalo, Nanglo, Doko, Namlo" "Mandro, Mat, Sukul,
Bhaka" "Radi, Pakhi, Homespun clo" ...
$ v06_11 : int NA 2 2 1 2 2 2 2 2 1 ...
$ v06_12a : int NA NA NA 0 NA NA NA NA NA 300 ...
$ v06_12b : int NA NA NA 500 NA NA NA NA NA 3600 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 252 25 251 252 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "own accountprod. goods code" ...

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34- attr(*, "version")= int 8- attr(*, "label.table")=List of 2..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V06_11: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
12 ### xh10_s07.dta ###'data.frame':
28670 obs. of 49 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 2 2 2 2 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v07_idc : int 1 1 2 3 4 5 1 2 3 4 ...
$ v07_01 : int 1 1 1 1 1 1 1 1 1 1 ...
$ v07_02 : int 1 1 1 2 1 1 1 2 1 1 ...
$ v07_03 : int 1 1 1 NA 1 1 1 NA 1 1 ...
$ v07_04 : int 1 1 1 NA 1 1 1 NA 1 1 ...
$ v07_05 : int 2 2 2 2 2 2 2 2 2 2 ...
$ v07_06 : int NA NA NA NA NA NA NA NA NA NA ...
$ v07_07 : int NA NA NA NA NA NA NA NA NA NA ...
$ v07_08 : int 2 2 3 3 3 3 2 1 2 3 ...
$ v07_09 : int NA NA NA NA NA NA NA 6 NA NA ...
$ v07_idc2: int 1 1 NA NA NA NA 1 NA 3 NA ...
$ v07_10 : int 1 1 NA NA NA NA 1 NA 1 NA ...
$ v07_11 : int 3 8 NA NA NA NA 3 NA 11 NA ...
$ v07_12 : int 4 5 NA NA NA NA 4 NA 5 NA ...
$ v07_13a : int NA 9 NA NA NA NA NA NA 11 NA ...
$ v07_13b : int NA 0 NA NA NA NA NA NA 1 NA ...
$ v07_14 : int 12 16 NA NA NA NA 16 NA 22 NA ...
$ v07_15 : int 4 9 NA NA NA NA 4 NA 12 NA ...
$ v07_16 : int 5 7 NA NA NA NA 4 NA 15 NA ...
$ v07_idc3: int NA NA 2 3 4 5 NA NA NA 4 ...
$ v07_17 : int NA NA 1 2 6 6 NA NA NA 2 ...
$ v07_18 : int NA NA 5 0 13 13 NA NA NA 8 ...
$ v07_19 : int NA NA 5 5 0 0 NA NA NA 5 ...
$ v07_20 : int NA NA 5 5 0 0 NA NA NA 5 ...
$ v07_21 : int NA NA NA NA NA NA NA NA NA NA ...
$ v07_22 : int NA NA 1 1 1 1 NA NA NA 1 ...
$ v07_23a : int NA NA 0 1 0 0 NA NA NA 0 ...
$ v07_23b : int NA NA 20 0 20 20 NA NA NA 20 ...
$ v07_24 : int NA NA 2 2 2 2 NA NA NA 2 ...
$ v07_25 : int NA NA NA NA NA NA NA NA NA NA ...
$ v07_idc4: int NA NA 2 3 4 5 NA NA NA 4 ...
$ v07_26a : int NA NA 0 3500 3600 3500 NA NA NA 13500 ...
$ v07_26b : int NA NA 600 3500 4500 4500 NA NA NA 1500 ...
$ v07_26c : int NA NA 3000 800 1500 1500 NA NA NA 1500 ...
$ v07_26d : int NA NA 500 300 4000 4000 NA NA NA 1500 ...
$ v07_26e : int NA NA 0 0 0 0 NA NA NA 0 ...
$ v07_26f : int NA NA 0 0 0 0 NA NA NA 0 ...
$ v07_26g : int NA NA 300 0 600 600 NA NA NA 600 ...
$ v07_27 : int NA NA 2 2 2 2 NA NA NA 2 ...
$ v07_28 : int NA NA NA NA NA NA NA NA NA NA ...
$ v07_29 : int NA NA NA NA NA NA NA NA NA NA ...
$ v07_30 : int NA NA NA NA NA NA NA NA NA NA ...

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35$ v07_31 : int NA NA 4 98 12 12 NA NA NA 7 ...
$ v07_32 : int NA NA 5 0 5 5 NA NA NA 5 ...
$ v07_33a : int NA NA NA NA 10 10 NA NA NA 8 ...
$ v07_33b : int NA NA NA NA 2 1 NA NA NA 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "i dco d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 25..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V07_02: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V07_03: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V07_04: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "formal schooling" "taught at home" "govt. literacy course" "ngoliteracy course" ...
..$ V07_05: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V07_06: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "poor academic" "couldn't pay" "caste/ethnicity" "religion" ...
..$ V07_07: Named num 1 2 3 4.. ..- attr(*, "names")= chr "community/government" "institutional/private""gurukul/madarsha/gumba" "other"..$ V07_08: Named num 1 2 3.. ..- attr(*, "names")= chr "never attended school" "attended school/college in past""currently attending school/college"..$ V07_09: Named num 1 2 3 4 5 6 7 8 9 10.. ..- attr(*, "names")= chr "too young" "too expensive" "too far away" "had to help at home" ...
..$ V07_10: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "community/government" "institutional/private""technical/vocational" "gurukul/madarsha/gumba" ...
..$ V07_11: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "nursery/kg/pre-school" "class 01" "class 02" "class 03" ...
..$ V07_12: Named num 98.. ..- attr(*, "names")= chr "complete without attending school"..$ V07_15: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "nursery/kg/pre-school" "class 01" "class 02" "class 03" ...
..$ V07_16: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "further schooling not available" "too expensive" "too far away""had to help at home" ...
..$ V07_17: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "community/government" "institutional/private""technical/vocational" "gurukul/madarsha/gumba" ...
..$ V07_18: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "nursery/kg/pre-school" "class 01" "class 02" "class 03" ...
..$ V07_21: Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "illness/injury" "had to help with hhld chores" "had to work" "family
members sick or died" ...
..$ V07_22: Named num 1 2 3 4 5

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

36.. ..- attr(*, "names")= chr "walk" "bus/motor" "bicycle/rickshaw" "mixed (walk+vehicle)" ...
..$ V07_24: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V07_25: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V07_27: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V07_29: Named num 1 2 3 4.. ..- attr(*, "names")= chr "tuition fees" "uniforms/books" "hostel" "other"..$ V07_30: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "poor and talented" "girls" "dalits" "conflict affected" ...
..$ V07_31: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "nursery/kg/pre-school" "class 01" "class 02" "class 03" ...
..$ V07_32: Named num 98.. ..- attr(*, "names")= chr "complete primary level without attending school"
NULL
13 ### xh11_s08.dta ###'data.frame':
28670 obs. of 63 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 17 2 2 2 2 2 16 16 16 16 ...
$ xstra: int 100 100 100 100 100 100 100 100 100 100 ...
$ v08a_idc : int 1 1 2 3 4 5 1 2 3 4 ...
$ v08_01 : int 1 1 1 1 1 1 3 3 3 3 ...
$ v08_02 : int 8 8 8 8 8 8 8 8 8 8 ...
$ v08_03 : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_04 : int 1 1 2 2 2 2 2 2 2 2 ...
$ v08_05 : int 12 11 NA NA NA NA NA NA NA NA ...
$ v08_06 : int 13 4 NA NA NA NA NA NA NA NA ...
$ v08_07a : int 8890 25000 NA NA NA NA NA NA NA NA ...
$ v08_07b : int 200 10000 NA NA NA NA NA NA NA NA ...
$ v08_08 : int 2 2 NA NA NA NA NA NA NA NA ...
$ v08_09 : int 0 0 NA NA NA NA NA NA NA NA ...
$ v08_10 : int 2 2 1 1 1 1 1 1 1 1 ...
$ v08b_idc : int 1 1 2 3 4 5 1 2 3 4 ...
$ v08_11 : int 2 2 2 1 2 2 2 2 2 2 ...
$ v08_12 : int NA NA NA 14 NA NA NA NA NA NA ...
$ v08_13 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08_14 : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_15 : int NA NA NA 4 NA NA NA NA NA NA ...
$ v08_16 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08b_idc2: int NA NA NA 3 NA NA NA NA NA NA ...
$ v08_17a : int NA NA NA 0 NA NA NA NA NA NA ...
$ v08_17b : int NA NA NA 150 NA NA NA NA NA NA ...
$ v08_17c : int NA NA NA 0 NA NA NA NA NA NA ...
$ v08_18 : int NA NA NA 150 NA NA NA NA NA NA ...
$ v08_19 : int NA NA NA 2 NA NA NA NA NA NA ...
$ v08_20 : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_21 : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_22 : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_23 : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_24 : int NA NA NA NA NA NA NA NA NA NA ...
$ v08c_idc : int 1 1 2 3 4 5 1 2 3 4 ...
$ v08_25 : int 1 1 1 NA 3 1 1 1 1 1 ...
$ v08_26 : int 1 1 1 NA NA 3 1 1 6 6 ...
$ v08_27a : int 2 3 16 NA NA 1 2 16 2 2 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

37$ v08_27b : int 1 5 NA NA NA 3 3 NA 3 3 ...
$ v08_27c : int 4 6 NA NA NA 2 4 NA 5 4 ...
$ v08_27d : int 3 4 NA NA NA 6 5 NA 8 5 ...
$ v08d_idc : int 1 1 2 3 4 5 1 2 3 4 ...
$ v08_28 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08_29 : int NA NA NA 2 NA NA NA NA NA NA ...
$ v08_30a : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30b : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30c : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30d : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30e : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30f : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30g : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30h : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30i : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30j : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_30k : int NA NA NA NA NA NA NA NA NA NA ...
$ v08_31 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08_32 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08_33 : int NA NA NA 2 NA NA NA NA NA NA ...
$ v08_34 : int NA NA NA 4 NA NA NA NA NA NA ...
$ v08_35 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08_36 : int NA NA NA 2 NA NA NA NA NA NA ...
$ v08_37 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08_38 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v08_39 : int NA NA NA 4 NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "id code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 45..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V08_02 : Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "physical" "visual" "hearing" "visual and hearing" ...
..$ V08_03 : Named num 98.. ..- attr(*, "names")= chr " by birth"..$ V08_04 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_05 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "heart related" "respiratory" "asthma" "epilepsy" ...
..$ V08_08 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_10 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "excellent" "good" "fair" "poor"..$ V08_11 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_12 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "diarrhoea" "dysentry" "respiratory problems" "malaria" ...
..$ V08_13 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_14 : Named num 1 2 3 4 5 6 7 8 9

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

38.. ..- attr(*, "names")= chr "illness/injury not serious" "health facilty too far" "no transportfacility" "health care too expensive" ...
..$ V08_15 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "govt/shp" "govt/hp" "govt/phc" "govt/hospital" ...
..$ V08_16 : Named num 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "govt. doctor" "govt. paramedic" "govt. kaviraj/vaidya" "pvt.
doctor" ...
..$ V08_19 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_20 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_22 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_23 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "ors" "allopathic medicine" "traditional medicine" "other"..$ V08_24 : Named num 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "shp" "hp" "phc" "hospital" ...
..$ V08_25 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "person not contacted"..$ V08_26 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "radio" "television" "newspapers/pamphlets/posters""friends/relatives" ...
..$ V08_27A: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "abstain from sex" "use condoms" "limit sex to one partner" "limit
number of sexual partners" ...
..$ V08_27B: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "abstain from sex" "use condoms" "limit sex to one
partner" ...
..$ V08_27C: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "abstain from sex" "use condoms" "limit sex to one
partner" ...
..$ V08_27D: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "abstain from sex" "use condoms" "limit sex to one
partner" ...
..$ V08_28 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "don't know"..$ V08_29 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes, seen" "yes, not available" "no"..$ V08_30A: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30B: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30C: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30D: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30E: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30F: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30G: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30H: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30I: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

39..$ V08_30J: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_30K: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V08_31 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "don't know"..$ V08_32 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "don't know"..$ V08_33 : Named num 1 2 3.. ..- attr(*, "names")= chr "just after birth" "later" "don't know"..$ V08_34 : Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "once" "twice" "thrice" "more than 3 times" ...
..$ V08_35 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "don't know"..$ V08_37 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "don't know"..$ V08_38 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "don't know"..$ V08_39 : Named num 1 2 3 4 5 6.. ..- attr(*, "names")= chr "shp" "hp" "phc" "hospital" ...
NULL
14 ### xh12_s09a.dta ###'data.frame':
15483 obs. of 19 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 2 2 2 16 16 16 16 16 4 6 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ id_s9a : int 1 2 3 1 2 3 4 5 1 1 ...
$ v09a_sn : int 1 1 1 1 1 1 1 1 1 1 ...
$ v09a_idc: int 1 1 1 2 2 2 2 2 1 2 ...
$ v09a_rsp: int 1 1 1 2 2 2 2 2 1 2 ...
$ v09a_bo : int 1 2 3 1 2 3 4 5 1 1 ...
$ v09_03 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v09_04a : int 8 8 5 7 12 11 10 3 7 7 ...
$ v09_04b : int 2057 2059 2063 2039 2041 2043 2054 2057 2060 2045 ...
$ v09_05 : int 1 2 2 2 1 2 1 1 2 1 ...
$ v09_06 : int 1 1 1 1 1 1 1 1 1 1 ...
$ v09_07 : int 1 2 1 2 1 2 1 1 1 1 ...
$ v09_08 : int 2 NA 3 NA 3 NA 4 5 2 3 ...
$ v09_09a : int NA NA NA NA NA NA NA NA NA NA ...
$ v09_09b : int NA NA NA NA NA NA NA NA NA NA ...
$ v09_09c : int NA NA NA NA NA NA NA NA NA NA ...
$ v09_10 : int NA 2 NA 8 NA 12 NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 12 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "uniqueid" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V09_05: Named num 1 2

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

40.. ..- attr(*, "names")= chr "male" "female"..$ V09_06: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_07: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_10: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "nursery/kg/pre-school" "class 01" "class 02" "class 03" ...
NULL
15 ### xh13_s09b.dta ###'data.frame':
1540 obs. of 19 variables:$ xhpsu : int 1001 1002 1003 1003 1003 1005 1007 1007 1007 1009 ...
$ xhnum : int 11 10 8 17 13 17 10 5 11 7 ...
$ xstra : int 100 100 100 100 100 219 219 219 219 219 ...
$ v09b_ln1: int 1 1 1 1 1 1 1 1 1 1 ...
$ v09b_idc: int 6 2 1 2 2 4 3 4 4 2 ...
$ v09_12 : int 1 1 1 1 1 1 1 1 1 2 ...
$ v09_13 : int 4 4 4 4 4 4 4 4 4 NA ...
$ v09_14 : int 1 1 2 2 1 2 2 2 2 NA ...
$ v09_15 : int 3 4 3 3 7 3 6 3 4 NA ...
$ v09_16 : int 1 1 1 1 1 1 1 1 1 NA ...
$ v09_17 : int 2 2 2 2 1 3 1 3 1 NA ...
$ v09_18 : int NA NA NA NA NA NA NA NA NA 1 ...
$ v09b_ln2: int 1 1 1 1 1 1 1 1 1 1 ...
$ v09_19 : int 5 1 5 5 5 1 1 5 5 1 ...
$ v09_20 : int 6 1 6 6 6 5 4 6 6 1 ...
$ v09_21 : int 2 2 1 2 2 2 1 1 1 1 ...
$ v09_22 : int NA NA 4 NA NA NA 4 4 4 4 ...
$ v09_23 : int NA NA 2 NA NA NA 2 2 2 2 ...
$ v09_24 : int 1 1 NA 1 1 1 NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 13..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V09_12: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_13: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "shp" "hp" "phc" "hospital" ...
..$ V09_14: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "doctor" "nurse/anm ha/sahw /ahw/mchw/ vhw" "tba" "sba" ...
..$ V09_16: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_17: Named num 1 2 3.. ..- attr(*, "names")= chr "once" "twice" "more than twice"..$ V09_18: Named num 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "don't think is necessary" "health facilty too far" "no transport""health care too expensive" ...
..$ V09_19: Named num 1 2 3 4 5 6 7

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

41.. ..- attr(*, "names")= chr "home" "shp" "hp" "phc" ...
..$ V09_20: Named num 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "family member or relatives" "neighbours" "tba" "sba" ...
..$ V09_21: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_22: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "shp" "hp" "phc" "hospital" ...
..$ V09_23: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "doctor" "nurse/amn/ha/sahw/ahw/mchw/vhw" "tba" "sba" ...
..$ V09_24: Named num 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "don't think necessary" "health facilty too far" "no transport""health care too expensive" ...
NULL
16 ### xh14_s09c.dta ###'data.frame':
28662 obs. of 17 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 2 2 2 2 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v09c_idc: int 1 1 2 3 4 5 1 2 3 4 ...
$ v09_25 : int 2 1 2 2 2 2 2 1 2 2 ...
$ v09_26 : int NA 17 NA NA NA NA NA 14 NA NA ...
$ v09_27 : int NA 1 NA NA NA NA NA 1 NA NA ...
$ v09_28 : int NA 1 NA NA NA NA NA 1 NA NA ...
$ v09_29 : int NA 2 NA NA NA NA NA 1 NA NA ...
$ v09_30 : int NA NA NA NA NA NA NA 1 NA NA ...
$ v09_31 : int NA NA NA NA NA NA NA 8 NA NA ...
$ v09_32 : int NA NA NA NA NA NA NA NA NA NA ...
$ v09_33 : int NA 3 NA NA NA NA NA NA NA NA ...
$ v09_34 : int NA 2 NA NA NA NA NA 2 NA NA ...
$ v09_35a : int NA 2 NA NA NA NA NA 3 NA NA ...
$ v09_35b : int NA 1 NA NA NA NA NA 2 NA NA ...
$ v09_35c : int NA 1 NA NA NA NA NA 1 NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "i dco d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 10..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V09_25: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_27: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_28: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "radio" "television" "newspapers/ pamphlets/ posters" "friends/
relatives" ...
..$ V09_29: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_30: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

42..$ V09_31: Named num 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "pill" "iud" "injectables" "implants" ...
..$ V09_32: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "public health inst." "private health inst." "pharmacy" "vsc" ...
..$ V09_33: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "not available" "too expensive" "husband away" "want morechildren" ...
..$ V09_34: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
17 ### xh15_s09d.dta ###'data.frame':
82815 obs. of 8 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 17 17 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v09d_idc: int 1 2 3 4 5 6 7 8 9 10 ...
$ v09d_txt: chr "Up to what grade should t" "Which school do the child" "Obtaining health carefor" "Obtaining health care dur" ...
$ v09_37 : int 2 2 1 2 2 2 2 1 1 2 ...
$ v09_38 : int NA NA 1 NA NA NA NA 1 1 NA ...
$ v09_39 : int NA NA 1 NA NA NA NA 1 1 NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 251 251 251- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "decision
number" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 4..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V09_37: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V09_38: Named num 1 2 3.. ..- attr(*, "names")= chr "a lot" "a little" "not involved"..$ V09_39: Named num 1 2 3 4.. ..- attr(*, "names")= chr "me" "my spouse" "both" "other"
NULL
18 ### xh16_s10a.dta ###'data.frame':
28670 obs. of 18 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 2 2 2 2 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v10_idc: int 1 1 2 3 4 5 1 2 3 4 ...
$ v10_01a: int 3 0 0 NA 0 0 0 2 1 1 ...
$ v10_01b: int 0 0 0 NA 0 0 0 0 0 0 ...
$ v10_01c: int 0 0 0 NA 0 0 6 2 0 0 ...
$ v10_01d: int 0 0 0 NA 0 0 10 3 2 0 ...
$ v10_01e: int 0 0 0 NA 0 0 0 0 0 0 ...
$ v10_01f: int 0 0 0 NA 0 0 0 0 0 0 ...
$ v10_01g: int 3 0 0 NA 0 0 16 7 3 1 ...
$ v10_01h: int 0 0 0 NA 0 0 0 0 0 0 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

43$ v10_01i: int 10 10 0 NA 0 0 0 14 3 0 ...
$ v10_01j: int 10 10 0 NA 0 0 0 16 4 0 ...
$ v10_01k: int 0 1 0 NA 0 0 2 0 4 0 ...
$ v10_01l: int 0 0 0 NA 0 0 0 0 0 0 ...
$ v10_01m: int 0 10 0 NA 0 0 0 0 0 0 ...
$ v10_01n: int 0 0 0 NA 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "id code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 1..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
NULL
19 ### xh17_s10b.dta ###'data.frame':
32611 obs. of 31 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 17 17 2 2 2 2 16 16 16 16 ...
$ xstra: int 100 100 100 100 100 100 100 100 100 100 ...
$ v10b_ln: int 1 2 1 3 5 7 1 2 3 5 ...
$ v10_02: int 1 1 1 2 4 5 1 1 1 2 ...
$ v10_02_job: int 1 2 1 1 1 1 1 2 3 1 ...
$ v10_03_txt: chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v10_03: int 522 733 734 997 997 997 614 621 522 621 ...
$ v10_04a: int 1 2 1 NA NA NA 1 1 1 1 ...
$ v10_04b: int 1 2 1 NA NA NA 1 1 1 1 ...
$ v10_04c: int 1 2 1 NA NA NA 1 1 1 1 ...
$ v10_04d: int 1 2 1 NA NA NA 1 1 1 1 ...
$ v10_04e: int 1 2 1 NA NA NA 1 1 2 1 ...
$ v10_04f: int 1 2 1 NA NA NA 2 1 2 1 ...
$ v10_04g: int 1 1 1 NA NA NA 2 1 2 1 ...
$ v10_04h: int 1 1 1 NA NA NA 2 1 2 1 ...
$ v10_04i: int 1 1 1 NA NA NA 2 1 2 1 ...
$ v10_04j: int 1 1 2 NA NA NA 2 1 1 1 ...
$ v10_04k: int 1 2 2 NA NA NA 2 1 1 1 ...
$ v10_04l: int 1 2 1 NA NA NA 2 1 1 1 ...
$ v10_05a: int 30 15 26 NA NA NA 30 10 30 6 ...
$ v10_05b: int 10 4 3 NA NA NA 8 6 8 2 ...
$ v10_06a: int 9 4 8 NA NA NA 0 0 0 0 ...
$ v10_06b: int 10 4 8 NA NA NA 0 12 0 0 ...
$ v10_06c: int 10 4 8 NA NA NA 0 12 0 0 ...
$ v10_06d: int 10 4 8 NA NA NA 0 12 0 0 ...
$ v10_06e: int 10 4 8 NA NA NA 0 12 0 0 ...
$ v10_06f: int 10 0 8 NA NA NA 0 0 0 0 ...
$ v10_06g: int 10 0 0 NA NA NA 0 0 0 0 ...
$ v10_06h: int 69 20 48 NA NA NA 0 48 0 0 ...
$ v10_07: int 4 4 2 NA NA NA 2 3 4 3 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 12 252 251 251 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

44- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "person-job
seqno" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 16..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V10_02_JOB: Named num 1 2 3 4 5 99.. ..- attr(*, "names")= chr "1st job" "2nd job" "3rd job" "4th job" ...
..$ V10_03: Named num 11 111 112 114 121 122 123 131 211 212 ...
.. ..- attr(*, "names")= chr "armed forces" "legislators" "government officials" "officials of
special interest organizations" ...
..$ V10_04A: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04B: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04C: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04D: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04E: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04F: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04G: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04H: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04I: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04J: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04K: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_04L: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V10_07: Named num 1 2 3 4.. ..- attr(*, "names")= chr "wage employment in agri." "wage employment in non-agri.""self-employment in agri." "self-employment in non-agri."NULL
20 ### xh18_s11.dta ###'data.frame':
28670 obs. of 11 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 2 2 2 2 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v11_idc: int 1 1 2 3 4 5 1 2 3 4 ...
$ v11_01 : int 92 48 0 NA 0 0 64 56 87 22 ...
$ v11_02 : int NA NA 2 NA 2 2 NA NA NA NA ...
$ v11_03 : int NA NA NA NA NA NA NA NA NA NA ...
$ v11_04 : int NA NA 1 NA 1 1 NA NA NA NA ...
$ v11_05 : int NA NA NA NA NA NA NA NA NA 2 ...
$ v11_06 : int NA NA NA NA NA NA NA NA NA NA ...
$ v11_07 : int NA NA NA NA NA NA NA NA NA 9 ...
- attr(*, "datalabel")= chr ""Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

45- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 252 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "i dco d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 7..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V11_02: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V11_03: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V11_04: Named num 1 2 3 4 5 6 7 8 9 10.. ..- attr(*, "names")= chr "attending school" "household duties" "old/sick" "disabled" ...
..$ V11_05: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V11_06: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V11_07: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "could not find more work/lack of business" "lack of finance, rawmaterials" "machinery, electrical, other breakdow" "off season inactivity" ...
NULL
21 ### xh19_s12.dta ###'data.frame':
6552 obs. of 37 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 2 16 16 6 6 1 10 10 14 14 ...
$ xstra: int 100 100 100 100 100 100 100 100 100 100 ...
$ v12_ln1: int 1 1 2 1 2 1 1 2 1 2 ...
$ v12_01: int 1 1 3 1 3 3 1 2 1 2 ...
$ v12_01_job: int 1 1 3 2 2 1 1 1 2 2 ...
$ v12_02_txt: chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v12_02: int 22 2 75 45 45 65 75 80 1 1 ...
$ v12_03: int 2 3 2 1 1 2 2 2 1 1 ...
$ v12_04: int NA NA NA 400 300 NA NA NA 200 150 ...
$ v12_05a: int NA NA NA 7 7 NA NA NA 4 4 ...
$ v12_05b: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_06a: int NA NA NA NA NA NA NA NA 100 100 ...
$ v12_06b: int NA NA NA NA NA NA NA NA 0 0 ...
$ v12_ln2: int 1 NA 2 NA NA 1 1 2 NA NA ...
$ v12_07: int 2 NA 2 NA NA 2 2 2 NA NA ...
$ v12_08: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_09a: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_09b: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_10a: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_10b: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_11: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_12: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_13: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_14: int NA NA NA NA NA NA NA NA NA NA ...
$ v12_ln3: int 1 NA 2 NA NA 1 1 2 NA NA ...
$ v12_15a: int 3500 NA 3500 NA NA 7000 11500 11000 NA NA ...
$ v12_15b: int 0 NA 0 NA NA 0 0 0 NA NA ...

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46$ v12_15c: int 0 NA 0 NA NA 7000 11500 11000 NA NA ...
$ v12_15d: int 0 NA 0 NA NA 2500 0 0 NA NA ...
$ v12_15e: int 0 NA 0 NA NA 0 35000 0 NA NA ...
$ v12_16: int 2 NA 2 NA NA 1 1 1 NA NA ...
$ v12_17: int 2 NA 2 NA NA 2 1 1 NA NA ...
$ v12_18: int 2 NA 2 NA NA 2 1 1 NA NA ...
$ v12_19: int 2 NA 2 NA NA 2 2 2 NA NA ...
$ v12_20: int 2 NA 3 NA NA 3 3 3 NA NA ...
$ v12_21: int NA 45000 NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 12 251 251 252 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 17..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V12_02 : Named num 1 2 5 10 11 12 13 14 15 16 ...
.. ..- attr(*, "names")= chr "agriculture and related service activities" "forestry, logging
and related service activities" "fishing, operation of fish hatcheries and fish farms; service
activities incidental to fishing" "mining of coal and lignite; extraction of peat" ...
..$ V12_03 : Named num 1 2 3.. ..- attr(*, "names")= chr "daily basis" "long term basis" "contract/ piece-rate"..$ V12_05A: Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "paddy/rice" "wheat" "maize" "meals" ...
..$ V12_05B: Named num 0 1 2 3 4 5 6.. ..- attr(*, "names")= chr "!end-of-list!" "paddy/rice" "wheat" "maize" ...
..$ V12_07 : Named num 1 2.. ..- attr(*, "names")= chr "wage job in agriculture" "wage job in non-agri."..$ V12_09A: Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "paddy/rice" "wheat" "maize" "meals" ...
..$ V12_09B: Named num 0 1 2 3 4 5 6.. ..- attr(*, "names")= chr "!end-of-list!" "paddy/rice" "wheat" "maize" ...
..$ V12_11 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_12 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_13 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_14 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_16 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_17 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_18 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_19 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V12_20 : Named num 1 2 3.. ..- attr(*, "names")= chr "one" "two to nine" "ten or more"
NULL

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47

22 ### xh20_s13a1.dta ###'data.frame':
13237 obs. of 30 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 5 5 10 10 14 8 8 11 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13_plt1: int 1 2 1 2 1 2 1 1 2 1 ...
$ v13_03 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v13_04u : int 1 1 1 1 1 1 1 1 1 1 ...
$ v13_04rb: int 2 22 0 8 2 50 4 10 13 45 ...
$ v13_04ak: int 8 0 12 0 0 0 0 0 0 0 ...
$ v13_04pd: int 0 0 0 0 0 0 0 0 0 0 ...
$ v13_05 : int 9 9 9 9 9 9 9 9 9 9 ...
$ v13_06 : int 1 2 1 1 1 2 1 2 2 2 ...
$ v13_07 : int 2 1 2 1 2 1 2 1 1 2 ...
$ v13_08 : int NA 2 NA 2 NA 2 NA 2 2 NA ...
$ v13_09 : int NA 4 NA 2 NA 4 NA 2 2 NA ...
$ v13_10 : int 1500000 1600000 300000 400000 800000 1000000 250000 350000 455000 2250000 ...
$ v13_plt2: int 1 2 1 2 1 2 1 1 2 1 ...
$ v13_11 : int 1 1 1 2 1 3 1 1 1 1 ...
$ v13_12c : int NA NA NA 0 NA 0 NA NA NA NA ...
$ v13_12k : int NA NA NA 15000 NA 1600 NA NA NA NA ...
$ v13_13a : int 22 1 22 NA 22 NA 0 3 3 1 ...
$ v13_13b : int 37 NA 37 NA 37 NA NA NA 40 NA ...
$ v13_13c : int 44 NA 38 NA 44 NA NA NA 39 NA ...
$ v13_13d : int NA NA 44 NA NA NA NA NA 38 NA ...
$ v13_14 : int 1 1 1 2 1 2 1 1 1 1 ...
$ v13_15c : int NA NA NA 0 NA 0 NA NA NA NA ...
$ v13_15k : int NA NA NA 0 NA 0 NA NA NA NA ...
$ v13_16a : int 45 2 45 NA 45 NA 41 2 2 2 ...
$ v13_16b : int 55 12 36 NA NA NA NA 6 12 12 ...
$ v13_16c : int 52 NA NA NA NA NA NA 19 36 NA ...
$ v13_16d : int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 252 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "p l o tnu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 17..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_04U: Named num 1 2.. ..- attr(*, "names")= chr "ropani" "bigha"..$ V13_05 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V13_06 : Named num 1 2.. ..- attr(*, "names")= chr "up land" "low land"..$ V13_07 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_08 : Named num 1 2.. ..- attr(*, "names")= chr "seasonal" "year round"..$ V13_09 : Named num 1 2 3 4 5

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

48.. ..- attr(*, "names")= chr "tubewell/boring" "canal" "pond/tank" "other natural sources" ...
..$ V13_11 : Named num 1 2 3 4 5 6.. ..- attr(*, "names")= chr "croped yourself" "sharedcroped out" "fixed rent out" "mortgaged
out" ...
..$ V13_13A: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "no cultivate in dry season" "early paddy" "main paddy" "upland
paddy" ...
..$ V13_13B: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_13C: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_13D: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_14 : Named num 1 2 3 4 5 6.. ..- attr(*, "names")= chr "croped yourself" "sharedcroped out" "fixed rent out" "mortgaged
out" ...
..$ V13_16A: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "no cultivate in wet season" "early paddy" "main paddy" "upland
paddy" ...
..$ V13_16B: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_16C: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_16D: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
NULL
23 ### xh21_s13a2.dta ###'data.frame':
1906 obs. of 24 variables:$ xhpsu : int 1001 1001 1001 1002 1002 1002 1002 1002 1002 1002 ...
$ xhnum : int 6 14 11 9 9 7 6 13 13 12 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13_plt3: int 1 1 1 1 2 1 1 1 2 1 ...
$ v13_19 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v13_20 : int 1 1 1 1 1 1 1 3 3 1 ...
$ v13_21 : int NA NA NA NA NA NA NA NA NA NA ...
$ v13_22u : int 1 1 1 1 1 1 1 1 1 1 ...
$ v13_22rb: int 30 10 30 1 0 12 4 4 3 2 ...
$ v13_22ak: int 0 0 0 0 8 0 0 0 8 0 ...
$ v13_22pd: int 0 0 0 0 0 0 0 0 0 0 ...
$ v13_23 : int 2 2 2 2 1 2 2 1 2 2 ...
$ v13_24 : int 1 1 1 2 2 2 1 2 2 1 ...
$ v13_25 : int 2 2 2 NA NA NA 1 NA NA 2 ...
$ v13_26 : int 4 2 2 NA NA NA 3 NA NA 4 ...
$ v13_plt4: int 1 1 1 1 2 1 1 1 2 1 ...
$ v13_27a : int 1 0 1 0 0 0 27 0 0 0 ...
$ v13_27b : int 22 NA NA NA NA NA 46 NA NA NA ...
$ v13_27c : int 44 NA NA NA NA NA NA NA NA NA ...
$ v13_27d : int 5 NA NA NA NA NA NA NA NA NA ...
$ v13_28a : int 2 2 2 2 6 2 2 6 2 2 ...
$ v13_28b : int 12 NA NA NA 7 NA 6 7 12 12 ...
$ v13_28c : int 45 NA NA NA NA NA NA NA NA NA ...
$ v13_28d : int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

49- attr(*, "types")= int 252 251 252 251 12 251 253 251 252 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "p l o tnu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 15..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_20 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "sharedcroped out" "rent-in" "mortgaged-in" "other"..$ V13_22U: Named num 1 2.. ..- attr(*, "names")= chr "ropani" "bigha"..$ V13_23 : Named num 1 2.. ..- attr(*, "names")= chr "up land" "low land"..$ V13_24 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_25 : Named num 1 2.. ..- attr(*, "names")= chr "seasonal" "year round"..$ V13_26 : Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "tubewell" "canal" "pond/tank" "other natural sources" ...
..$ V13_27A: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "no cultivate in dry season" "early paddy" "main paddy" "upland
paddy" ...
..$ V13_27B: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_27C: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_27D: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_28A: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "no cultivate in wet season" "early paddy" "main paddy" "upland
paddy" ...
..$ V13_28B: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_28C: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_28D: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
NULL
24 ### xh22_s13b.dta ###'data.frame':
42324 obs. of 15 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 16 16 16 16 16 6 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13b_ln : int 1 2 3 4 5 6 7 8 9 1 ...
$ v13_35nm: chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v13_35cc: int 1 2 22 37 44 45 12 55 52 1 ...
$ v13_36 : int 2 2 1 1 1 1 2 2 2 2 ...
$ v13_37a : int 5 5 1 1 1 1 6 9 10 5 ...
$ v13_37b : int 20 40 100 80 200 110 10 30 70 16 ...
$ v13_37c : int 0 0 0 0 0 0 0 0 0 8 ...
$ v13_37d : int 0 0 0 0 0 0 0 0 0 0 ...
$ v13_38a : int NA NA NA NA NA NA NA NA NA NA ...
$ v13_38b : int NA NA NA NA NA NA NA NA NA NA ...

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50$ v13_38c : num NA NA NA NA NA NA NA NA NA NA ...
$ v13_38d : int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 251 251 253 252 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_35CC: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "early paddy" "main paddy" "upland paddy" "wheat" ...
..$ V13_36 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_37A : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "kg" "gram" "mund" "liter" ...
..$ V13_38A : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "kg" "gram" "mund" "liter" ...
NULL
25 ### xh23_s13c1.dta ###'data.frame':
5956 obs. of 9 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 16 16 16 16 6 5 5 5 5 5 ...
$ xstra: int 100 100 100 100 100 100 100 100 100 100 ...
$ v13c1_ln : int 1 2 3 4 1 1 2 3 4 5 ...
$ v13_40_txt: chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v13_40: int 22 37 44 45 22 22 37 44 45 36 ...
$ v13_41a: int 4 4 4 4 4 4 4 4 4 4 ...
$ v13_41b: int NA NA NA NA NA NA NA NA NA NA ...
$ v13_42: int 525 70 80 60 450 40 35 30 30 30 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 251 251 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 4..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_40 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "early paddy" "main paddy" "upland paddy" "wheat" ...
..$ V13_41A: Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "agri. dev. office" "seeds company/serv.centre" "other farmer""private dealer" ...
..$ V13_41B: Named num 0 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "!end-of-list!" "agri. dev. office" "seeds company/serv.centre""other farmer" ...
NULL

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51

26 ### xh24_s13c2.dta ###'data.frame':
17724 obs. of 11 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 16 16 16 16 16 16 6 6 6 6 ...
$ xstra: int 100 100 100 100 100 100 100 100 100 100 ...
$ v13c2_ln : int 1 2 3 4 5 6 1 2 3 4 ...
$ v13_44_txt: chr "Urea:Kg" "Complex
:Kg"
"DAP
:Kg" "Organic/Compost fert. :Kg" ...
$ v13_45yn : int 1 2 1 2 1 2 1 2 2 2 ...
$ v13_46a: int 1 NA 1 NA 1 NA 22 NA NA NA ...
$ v13_46b: int 22 NA 22 NA 22 NA 1 NA NA NA ...
$ v13_46c: int NA NA NA NA NA NA 2 NA NA NA ...
$ v13_47a: int 10 NA 10 NA 5 NA 30 NA NA NA ...
$ v13_47b: int 300 NA 450 NA 200 NA 900 NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 251 251 251 251 252 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_45YN: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V13_46A : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "early paddy" "main paddy" "upland paddy" "wheat" ...
..$ V13_46B : Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
..$ V13_46C : Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "early paddy" "main paddy" "upland paddy" ...
NULL
27 ### xh25_s13c3.dta ###'data.frame':
3430 obs. of 17 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 6 14 14 14 8 8 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13c3_ln: int 1 2 3 4 1 1 2 3 1 2 ...
$ v13_51 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v13_52 : int 2 2 2 2 2 2 2 2 2 2 ...
$ v13_53m : int 0 9 14 20 25 6 0 0 21 0 ...
$ v13_53f : int 60 6 0 0 10 0 6 10 60 20 ...
$ v13_54m : int 0 150 300 300 200 600 0 0 500 0 ...
$ v13_54f : int 150 150 0 0 100 0 200 200 150 100 ...
$ v13_55m : int 0 30 60 60 50 100 0 0 60 0 ...
$ v13_55f : int 30 30 0 0 50 0 100 100 60 60 ...
$ v13_56m : int 0 180 360 360 250 700 0 0 560 0 ...
$ v13_56f : int 180 180 0 0 150 0 300 300 210 160 ...
$ v13_57m : int 0 1620 5040 7200 6250 4200 0 0 11760 0 ...
$ v13_57f : int 10800 1080 0 0 1500 0 1800 3000 12600 3200 ...
$ v13_57t : int 10800 2700 5040 7200 7750 4200 1800 3000 24360 3200 ...

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52- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 252 252 252 252 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 2..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_52: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
28 ### xh26_s13d1.dta ###'data.frame':
28834 obs. of 6 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 16 16 16 6 6 6 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13d1_sn: int 1 2 3 4 5 6 7 1 2 3 ...
$ v13d1_nm: chr "TOTAL CROP SALES" "Sale of crop by-products" "Draft animals" "Tractor" ...
$ v13_63 : int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 24 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "s o u r cen u m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 1..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
NULL
29 ### xh27_s13d2.dta ###'data.frame':
61800 obs. of 6 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 16 16 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13d2_en: int 1 2 3 4 5 6 7 8 9 11 ...
$ v13d2_nm: chr "TOTAL EXPENDITURE ON SEED" "TOTAL EXPENDITURE ON FERT" "TOTAL EXPENDITURE ON
HIRE" "Irrigation charges/mainte" ...
$ v13_64 : int 735 950 25740 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "e x p e n
d i t u r en u m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 1

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53..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
NULL
30 ### xh28_s13e1.dta ###'data.frame':
37512 obs. of 14 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 16 16 16 16 16 6 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13e1_lc: int 1 2 3 4 5 6 7 8 9 1 ...
$ v13_66nm: chr "Bullocks/Cows" "He/She Buffaloes" "Goats/Castrated goat" "He/She Sheep" ...
$ v13_66yn: int 1 2 1 2 2 2 2 1 2 1 ...
$ v13_67a : int 4 NA 1 NA NA NA NA 2 NA 3 ...
$ v13_67b : int 40000 NA 7000 NA NA NA NA 1800 NA 20000 ...
$ v13_68a : int 5 NA 3 NA NA NA NA 3 NA 5 ...
$ v13_68b : int 50000 NA 18000 NA NA NA NA 1500 NA 36500 ...
$ v13_69a : int 2 NA 0 NA NA NA NA 1 NA 2 ...
$ v13_69b : int 20000 NA 0 NA NA NA NA 1200 NA 25000 ...
$ v13_70a : int 0 NA 0 NA NA NA NA 0 NA 0 ...
$ v13_70b : int 0 NA 0 NA NA NA NA 0 NA 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 22 251 252 253 252 253 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i v e s
t o c k c o d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 2..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_66YN: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
31 ### xh29_s13e2.dta ###'data.frame':
33344 obs. of 6 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 16 16 16 16 6 6 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13e2_id: int 1 2 3 4 5 6 7 8 1 2 ...
$ v13e2_nm: chr "Milk" "Ghee" "Eggs" "Curd" ...
$ v13_71 : int 0 0 0 0 6000 0 0 6000 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "i n c o me c o d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 1..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill"

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54"221/rural-hill-eastern" ...
NULL
32 ### xh30_s13e3.dta ###'data.frame':
16672 obs. of 6 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 6 6 6 6 1 1 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13e3_id: int 1 2 3 4 1 2 3 4 1 2 ...
$ v13e3_nm: chr "Fodder" "Transportation of animal" "Veterinary services, inoc" "Other
expenditures(Reprod" ...
$ v13_72 : int 0 0 0 400 0 0 200 800 5000 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "e x p e n
d i t u r e
c o d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 1..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
NULL
33 ### xh31_s13f.dta ###'data.frame':
38961 obs. of 12 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 2 2 2 2 2 2 2 2 2 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v13f_ec : int 1 2 3 4 5 6 7 8 9 1 ...
$ v13_75nm: chr "Tractor" "Plough" "Cart" "Thresher" ...
$ v13_75yn: int 2 2 2 2 2 2 2 2 1 2 ...
$ v13_76 : int NA NA NA NA NA NA NA NA 1 NA ...
$ v13_77 : int NA NA NA NA NA NA NA NA 300 NA ...
$ v13_78 : int NA NA NA NA NA NA NA NA 0 NA ...
$ v13_79 : int NA NA NA NA NA NA NA NA NA NA ...
$ v13_80 : int NA NA NA NA NA NA NA NA 0 NA ...
$ v13_81 : int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 23 251 251 253 252 253 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "e q u i p
m e n t c o d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 2..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V13_75YN: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL

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55

34 ### xh32_s14.dta ###'data.frame':
2583 obs. of 41 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 16 16 4 6 1 1 1 11 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v14_ec1: int 1 2 1 2 1 1 1 2 3 1 ...
$ v14_02a: chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v14_02b: chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v14_02c: int 52 17 52 55 17 1 74 52 55 52 ...
$ v14_03a: int 1 1 1 2 1 3 1 2 2 6 ...
$ v14_03b: int NA NA 2 3 NA NA NA 1 NA 9 ...
$ v14_03c: int NA NA NA 4 NA NA NA NA NA NA ...
$ v14_03d: int NA NA NA 5 NA NA NA NA NA NA ...
$ v14_03e: int NA NA NA NA NA NA NA NA NA NA ...
$ v14_04 : int 1 1 1 1 1 1 1 2 2 1 ...
$ v14_05a: int 17 5 3 0 7 1 0 5 10 10 ...
$ v14_05b: int 0 2 10 5 0 5 10 0 0 5 ...
$ v14_06 : int 2 2 1 1 1 3 2 1 1 3 ...
$ v14_07 : int 12 4 12 5 12 12 10 12 12 4 ...
$ v14_08 : int 1 1 1 1 1 1 2 1 1 1 ...
$ v14_09 : int NA NA NA NA NA NA 500 NA NA NA ...
$ v14_ec2: int 1 2 1 2 1 1 1 2 3 1 ...
$ v14_10a: int 1 1 1 1 1 1 1 1 2 1 ...
$ v14_10b: int NA NA NA NA 2 NA 2 NA NA NA ...
$ v14_11 : int 2 2 2 1 2 2 1 2 2 2 ...
$ v14_12 : int 2 2 2 4 1 2 1 5 2 1 ...
$ v14_13 : int 3 3 3 1 3 1 3 1 3 3 ...
$ v14_14 : int NA NA NA 2 NA 1 NA 3 NA NA ...
$ v14_15 : int 2 2 2 1 2 2 2 2 2 2 ...
$ v14_16 : int NA NA NA 1 NA NA NA NA NA NA ...
$ v14_17 : int 1 1 1 13 1 1 1 1 1 10 ...
$ v14_ec3: int 1 2 1 2 1 1 1 2 3 1 ...
$ v14_18 : int 523700 9630 525000 900000 40000 576000 70000 1600000 72000 4500 ...
$ v14_19 : int 0 0 0 25000 0 0 0 0 0 0 ...
$ v14_20 : int 1200 0 700 12500 0 6000 0 0 5000 0 ...
$ v14_21a: int 0 0 0 0 22000 0 0 0 0 0 ...
$ v14_21b: int 480000 3600 471800 781500 0 500500 0 1559000 40000 0 ...
$ v14_22 : int 500 30 0 6000 0 9500 1000 5000 3000 500 ...
$ v14_23 : int 42000 6000 52500 75000 18000 60000 69000 36000 24000 4000 ...
$ v14_24 : int 0 0 0 17500 0 0 0 0 0 0 ...
$ v14_25 : int 0 0 0 0 0 0 0 0 0 0 ...
$ v14_26 : int 60000 30 150000 175000 10000 5000 0 30000 6000 500 ...
$ v14_27 : int 50000 30 100000 0 5000 4000 0 31000 7000 500 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 12 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "e n t e r
p r i s ec o d e" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 16..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V14_02C: Named num 1 2 5 10 11 12 13 14 15 16 ...

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56.. ..- attr(*, "names")= chr "agriculture and related service activities" "forestry, logging
and related service activities" "fishing, operation of fish hatcheries and fish farms; service
activities incidental to fishing" "mining of coal and lignite; extraction of peat" ...
..$ V14_03B: Named num 0.. ..- attr(*, "names")= chr "!end-of-list!"..$ V14_03C: Named num 0.. ..- attr(*, "names")= chr "!end-of-list!"..$ V14_03D: Named num 0.. ..- attr(*, "names")= chr "!end-of-list!"..$ V14_03E: Named num 0.. ..- attr(*, "names")= chr "!end-of-list!"..$ V14_06 : Named num 1 2 3.. ..- attr(*, "names")= chr "home" "other fixed location" "other changing location"..$ V14_08 : Named num 1 2.. ..- attr(*, "names")= chr "owned by household only" "partnership/shared with other"..$ V14_10A: Named num 1 2 3 4 5 6 7 8 9 10.. ..- attr(*, "names")= chr "other households or individuals" "small enterprises" "large
private enterprises" "govt. or other public firm" ...
..$ V14_10B: Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "!end-of-list!" "other households or individuals" "small
enterprises" "large private enterprises" ...
..$ V14_11 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V14_12 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "didn't need any money" "own savings" "relatives/friends" "agri.
dev. bank" ...
..$ V14_13 : Named num 1 2 3.. ..- attr(*, "names")= chr "yes, successfully" "yes, but unsuccessfully" "no"..$ V14_14 : Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "relatives/friends" "agri. dev. bank" "commercial bank" "rural dev.
bank" ...
..$ V14_15 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V14_17 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "no major problem" "capital or credit problems" "lack of technicalknow-how" "supply of power" ...
NULL
35 ### xh33_s15a.dta ###'data.frame':
5711 obs. of 20 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 2 16 16 6 1 5 10 10 14 11 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v15a_ln1: int 1 1 2 1 1 1 1 2 1 1 ...
$ v15_02 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v15_03 : int 1 1 3 1 2 1 1 1 1 1 ...
$ v15_04a : int 11 8 5 5 5 3 2 3 4 2 ...
$ v15_04b : int 2066 2066 2067 2067 2065 2066 2065 2067 2065 2067 ...
$ v15_05 : int 1 2 5 5 5 1 2 1 1 5 ...
$ v15_06 : int 11 6 6 4 6 7 8 12 12 4 ...
$ v15_07 : int 100000 100000 25000 3000 500000 4000 150000 400000 500000 5000 ...
$ v15a_ln2: int 1 1 2 1 1 1 1 2 1 1 ...
$ v15_08a : int 0 0 0 0 0 0 0 0 0 0 ...
$ v15_08b : num 24 11 10 12 15 24 12 20 36 18 ...
$ v15_09a : int 11 8 9 11 5 2 2 5 3 1 ...
$ v15_09b : int 2068 2068 2067 2067 2068 2068 2067 2069 2070 2068 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

57$ v15_10 : int 2 2 1 3 2 2 1 3 3 2 ...
$ v15_11 : int 15000 15600 25833 NA 4500 1000 186000 NA NA 375 ...
$ v15_12 : int 2 2 8 8 2 8 1 8 8 8 ...
$ v15_13 : int 2 7 2 6 10 3 12 4 20 1 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 251 252 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "{15.02} loan
number" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V15_05: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "relatives/friends" "agri. dev. bank" "commercial bank" "rural dev.
bank" ...
..$ V15_06: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "purchase of inputs" "purchase of equipment" "purchase of land""purchase of livestock" ...
..$ V15_10: Named num 1 2 3.. ..- attr(*, "names")= chr "fully paid" "partly paid" "not paid at all"..$ V15_12: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "agricultural land" "buildings or other property" "gold/silver""property documents" ...
NULL
36 ### xh34_s15b.dta ###'data.frame':
920 obs. of 19 variables:$ xhpsu : int 1003 1005 1005 1007 1007 1007 1009 1012 1012 1012 ...
$ xhnum : int 14 17 15 16 17 17 5 11 16 3 ...
$ xstra : int 100 219 219 219 219 219 219 219 219 219 ...
$ v15b_ln1: int 1 1 1 1 1 2 1 1 1 1 ...
$ v15_15 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v15_16 : int 1 1 1 1 3 3 2 1 1 1 ...
$ v15_17a : int 5 2 2 1 3 4 2 1 8 1 ...
$ v15_17b : int 2067 2066 2067 2067 2063 2064 2065 2067 2059 2067 ...
$ v15_18 : int 4 5 4 4 4 5 4 5 4 5 ...
$ v15_19 : int 7 8 7 7 11 6 11 6 7 11 ...
$ v15_20 : int 25000 100000 20000 100000 50000 150000 60000 150000 10000 200000 ...
$ v15b_ln2: int 1 1 1 1 1 2 1 1 1 1 ...
$ v15_21a : int 0 0 0 0 0 0 0 0 0 0 ...
$ v15_21b : num 0 24 36 18 36 36 0 0 36 24 ...
$ v15_22a : int 6 2 8 1 3 4 12 6 7 7 ...
$ v15_22b : int 2067 2067 2067 2068 2065 2066 2066 2067 2067 2067 ...
$ v15_23 : int 3 3 3 3 3 2 1 3 3 3 ...
$ v15_24 : int NA NA NA NA NA 9000 80000 NA NA NA ...
$ v15_25 : int 5 8 8 8 8 8 7 8 4 8 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 251 252 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "{15.15} loan

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

58

number" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V15_18: Named num 1 2 3 4 5 6.. ..- attr(*, "names")= chr "employee or tenant farmer" "business customer" "other businessassociate" "friend/neighbour" ...
..$ V15_19: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "purchase of agri.inputs" "purchase of equipment" "purchase of land""purchase of livestock" ...
..$ V15_23: Named num 1 2 3.. ..- attr(*, "names")= chr "fully paid" "partly paid" "not paid at all"..$ V15_25: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "agricultural land" "buildings or other property" "gold/silver""property documents" ...
NULL
37 ### xh35_s15c.dta ###'data.frame':
5988 obs. of 15 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 16 4 6 1 5 10 14 8 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v15_26: int 2 2 2 2 2 2 2 2 2 2 ...
$ v15_27: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_28: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_29: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_30: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_31: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_32: int 2 2 2 2 2 2 2 2 2 2 ...
$ v15_33: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_34: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_35: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_36: int NA NA NA NA NA NA NA NA NA NA ...
$ v15_37: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 253 253 253 253 253 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "V15_26" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "15.26 own
other land or property?" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 3..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V15_26: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V15_32: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
38 ### xh36_s15d.dta ###'data.frame':
66285 obs. of

8 variables:

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

59$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 16 16 16 16 16 16 16 16 16 16 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v15d_idc: int 1 2 3 4 5 6 7 8 9 10 ...
$ v15d_txt: chr "Up to what grade should t" "Which school do the child" "Obtaining health carefor" "Obtaining health care dur" ...
$ v15_39 : int 1 1 1 2 2 2 1 1 1 1 ...
$ v15_40 : int 1 1 1 NA NA NA 1 1 1 1 ...
$ v15_41 : int 1 1 1 NA NA NA 1 1 1 1 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 251 251 251- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "{15.39}
decision number" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 4..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V15_39: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V15_40: Named num 1 2 3.. ..- attr(*, "names")= chr "a lot" "a little" "not involved"..$ V15_41: Named num 1 2 3 4.. ..- attr(*, "names")= chr "me" "my spouse/female head" "both" "other"
NULL
39 ### xh37_s16.dta ###'data.frame':
6074 obs. of 26 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 2 2 4 4 4 4 4 4 4 1 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v16a_ln1: int 1 2 1 2 3 4 5 6 7 1 ...
$ v16_nme : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v16_02 : int 1 2 2 1 1 1 2 2 1 2 ...
$ v16_03 : int 2 3 10 2 9 9 9 9 7 3 ...
$ v16_04 : int 35 8 71 35 33 26 30 30 5 23 ...
$ v16_05 : int 10 2 97 8 97 6 12 97 1 12 ...
$ v16_06 : int 2 NA 5 2 2 6 1 2 NA 1 ...
$ v16_07 : int 1 0 0 0 0 0 0 0 0 1 ...
$ v16_08a : int 96 9 9 9 9 9 9 9 9 5 ...
$ v16_08b : int NA 1 2 2 2 2 2 2 2 2 ...
$ v16_09 : int 6 3 2 2 2 2 2 2 2 4 ...
$ v16a_ln2: int 1 2 1 2 3 4 5 6 7 1 ...
$ v16_10 : int 816 997 621 621 621 621 621 621 997 997 ...
$ v16_11 : int 1 NA 2 2 2 2 2 2 NA NA ...
$ v16_12 : int 2 NA 5 5 5 5 5 5 NA NA ...
$ v16_13 : int 1 2 2 2 2 2 2 2 2 2 ...
$ v16_14 : int 1 NA NA NA NA NA NA NA NA NA ...
$ v16_15 : int 1 NA NA NA NA NA NA NA NA NA ...
$ v16_16 : int 60000 NA NA NA NA NA NA NA NA NA ...
$ v16_17 : int 0 NA NA NA NA NA NA NA NA NA ...
$ v16_18a : int 1 NA NA NA NA NA NA NA NA NA ...
$ v16_18b : int NA NA NA NA NA NA NA NA NA NA ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

60$ v16_19 : int 0 5000 0 0 0 0 0 0 0 36000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 12 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 16..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V16_02 : Named num 1 2.. ..- attr(*, "names")= chr "male" "female"..$ V16_03 : Named num 2 3 4 5 6 7 8 9 10 11 ...
.. ..- attr(*, "names")= chr "husband/wife" "son/daughter" "grandchild" "father/mother" ...
..$ V16_05 : Named num 0 1 2 3 4 5 6 7 8 9 ...
.. ..- attr(*, "names")= chr "nursery/kg/pre-school" "class 01" "class 02" "class 03" ...
..$ V16_06 : Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "never married" "single married" "poly married" "re-married" ...
..$ V16_07 : Named num 99.. ..- attr(*, "names")= chr "birth at there"..$ V16_08A: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V16_08B: Named num 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ V16_09 : Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "together family/relatives" "other family reasons" "education""training" ...
..$ V16_10 : Named num 11 111 112 114 121 122 123 131 211 212 ...
.. ..- attr(*, "names")= chr "armed forces" "legislators" "government officials" "officials of
special interest organizations" ...
..$ V16_11 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "wage job" "self-employed" "ext. economic work" "don't know"..$ V16_12 : Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "relatives" "friends/ neighbors" "employment agency" "other" ...
..$ V16_13 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V16_15 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "formal financial institutions" "hundi" "self" "throughfriends" ...
..$ V16_18A: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "daily consumption" "education" "capital formation" "business orinvestment" ...
..$ V16_18B: Named num 0 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "!end-of-list!" "daily consumption" "education" "capitalformation" ...
NULL
40 ### xh38_s17a.dta ###'data.frame':
2244 obs. of 15 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 2 16 16 16 16 6 1 1 5 5 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v17a_ln: int 1 1 2 3 4 1 1 2 1 2 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

61$ v17_02 : int 1 1 1 1 1 1 2 2 1 1 ...
$ v17_03 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v17_04 : int 1 1 1 1 1 2 2 2 1 1 ...
$ v17_05 : int 10 4 3 3 6 5 6 5 6 6 ...
$ v17_06 : int 2 1 2 2 2 2 2 1 2 2 ...
$ v17_07a: int 9 5 5 6 5 9 10 10 9 9 ...
$ v17_07b: int 2 2 2 2 2 2 2 2 2 2 ...
$ v17_08 : int 3 5 6 5 3 3 3 3 3 3 ...
$ v17_09a: int 0 0 0 2000 0 0 0 0 3000 2500 ...
$ v17_09b: int 1200 650 1500 1600 1200 300 1200 2000 0 0 ...
$ v17_10 : int 3 3 3 3 3 3 3 3 3 3 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 12 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 7..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V17_05 : Named num 2 3 4 5 6 7 8 9 10 11 ...
.. ..- attr(*, "names")= chr "husband/wife" "son/daughter" "grandchild" "father/mother" ...
..$ V17_06 : Named num 1 2.. ..- attr(*, "names")= chr "male" "female"..$ V17_07A: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V17_07B: Named num 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ V17_08 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "wage employee in agri." "wage employee in non-agri." "self employee
in agri." "self employee in non-agri." ...
..$ V17_10 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "formal financial institutions" "hundi" "self" "throughfriends" ...
NULL
41 ### xh39_s17b.dta ###'data.frame':
2657 obs. of 18 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 2 16 16 16 16 16 6 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v17b_ln: int 1 2 3 1 1 2 3 4 5 1 ...
$ v17_12 : int 1 1 1 1 1 1 1 1 1 2 ...
$ v17_13 : chr "Confidential" "Confidential" "Confidential" "Confidential" ...
$ v17_14 : int 1 1 1 1 4 4 5 5 6 4 ...
$ v17_15 : int 11 5 6 6 11 11 11 11 3 11 ...
$ v17_16 : int 1 2 2 2 1 1 1 1 2 2 ...
$ v17_17 : int 64 67 37 26 42 47 42 47 42 60 ...
$ v17_18a: int 4 10 81 5 9 9 9 9 5 9 ...
$ v17_18b: int 2 2 NA 2 2 2 2 2 2 2 ...
$ v17_19 : int 3 3 4 4 2 4 2 4 3 3 ...
$ v17_20a: int 2500 300 0 0 0 0 0 0 2500 150 ...
$ v17_20b: int 500 500 900 25000 800 1000 800 1000 1200 0 ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

62$ v17_21 : int 3 3 6 3 3 3 3 3 3 3 ...
$ v17_22a: int 1 1 1 1 1 1 1 1 1 1 ...
$ v17_22b: int NA NA NA NA NA NA 3 NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 12 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "l i n enu m b e r" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 9..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V17_15 : Named num 2 3 4 5 6 7 8 9 10 11 ...
.. ..- attr(*, "names")= chr "husband/wife" "son/daughter" "grandchild" "father/mother" ...
..$ V17_16 : Named num 1 2.. ..- attr(*, "names")= chr "male" "female"..$ V17_18A: Named num 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "taplejung" "panchthar" "ilam" "jhapa" ...
..$ V17_18B: Named num 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ V17_19 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "wage employee in agri." "wage employee in non-agri." "self employee
in agri." "self employee in non-agri." ...
..$ V17_21 : Named num 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "formal financial institutions" "hundi" "self" "throughfriends" ...
..$ V17_22A: Named num 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "daily consumption" "education" "capital formation" "business orinvestment" ...
..$ V17_22B: Named num 0 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "!end-of-list!" "daily consumption" "education" "capitalformation" ...
NULL
42 ### xh40_s18a.dta ###'data.frame':
41916 obs. of 14 variables:$ xhpsu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum: int 17 17 17 17 17 17 17 2 2 2 ...
$ xstra: int 100 100 100 100 100 100 100 100 100 100 ...
$ v18a_sn : int 1 2 3 4 5 6 7 1 2 3 ...
$ v18a_txt: chr "Old age pension" "Widow pension" "Disability allowance" "Endangered ethnicitiespe" ...
$ v18_01 : int 3 3 3 3 3 3 3 3 3 3 ...
$ v18_02 : int NA NA NA NA NA NA NA NA NA NA ...
$ v18_03 : int NA NA NA NA NA NA NA NA NA NA ...
$ v18_04 : int NA NA NA NA NA NA NA NA NA NA ...
$ v18_05 : int NA NA NA NA NA NA NA NA NA NA ...
$ v18_06 : int NA NA NA NA NA NA NA NA NA NA ...
$ v18_07 : int NA NA NA NA NA NA NA NA NA NA ...
$ v18_08 : int NA NA NA NA NA NA NA NA NA NA ...
$ v18_09 : int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

63- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 251 251 251 252 252 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "{18.00}
source serial number" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V18_01: Named num 1 2 3.. ..- attr(*, "names")= chr "yes" "no" "not applicable"..$ V18_03: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "monthly" "quarterly" "half yearly" "yearly" ...
..$ V18_04: Named num 9999.. ..- attr(*, "names")= chr " don't know"..$ V18_08: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
43 ### xh41_s18b.dta ###'data.frame':
5988 obs. of 9 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 16 4 6 1 5 10 14 8 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v18_101: int 2 2 2 2 2 2 2 2 2 2 ...
$ v18_102: int 2 2 2 2 2 2 2 2 2 2 ...
$ v18_103: int 2 2 2 2 2 2 2 2 2 2 ...
$ v18_104: int 2 2 2 2 2 2 2 2 2 2 ...
$ v18_105: int 2 2 2 2 2 2 2 2 2 2 ...
$ v18_106: int 2 2 2 2 2 2 2 2 2 2 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251- attr(*, "val.labels")= chr "" "" "XSTRA" "V18_101" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "18.10 1)
receive public food distrb. system" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 7..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V18_101: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V18_102: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V18_103: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V18_104: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V18_105: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V18_106: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL

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64

44 ### xh42_s18c.dta ###'data.frame':
47904 obs. of 9 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 17 17 17 17 17 17 17 2 2 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v18c_ic : int 1 2 3 4 5 6 7 8 1 2 ...
$ v18c_txt: chr "Current/savings account" "Fixed deposit" "Stocks, shares, treasury" "Empolyee
Provident Fund/C" ...
$ v18_11 : int 1 2 2 2 2 2 2 1 2 2 ...
$ v18_12 : int NA NA NA NA NA NA NA 2000 NA NA ...
$ v18_13 : int NA NA NA NA NA NA NA 2000 NA NA ...
$ v18_14 : int 3600 NA NA NA NA NA NA 0 NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 25 251 253 253 253- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "{18.11} otherincome code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 2..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V18_11: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"NULL
45 ### xh43_s19.dta ###'data.frame':
5988 obs. of 33 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 17 2 16 4 6 1 5 10 14 8 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v19_02 : int 2 2 2 2 2 2 2 2 1 2 ...
$ v19_03 : int 2 2 2 2 1 2 2 2 2 2 ...
$ v19_04 : int 2 2 2 2 1 2 2 2 2 2 ...
$ v19_05 : int 2 2 2 2 2 2 2 2 2 2 ...
$ v19_06 : int 2 2 2 2 2 2 2 2 2 2 ...
$ v19_07 : int 2 2 2 2 1 1 2 1 1 2 ...
$ v19_08 : int NA NA NA NA NA NA NA NA 1 NA ...
$ v19_09 : int 2 2 2 2 2 2 2 2 2 2 ...
$ v19_10 : int 2 2 2 2 2 2 2 2 2 2 ...
$ v19_11 : int 2 2 3 2 4 2 3 2 3 2 ...
$ v19_12 : int 3 3 3 3 4 3 3 3 3 3 ...
$ v19_13 : int 3 3 3 3 3 2 3 3 3 3 ...
$ v19_14 : int 2 2 4 2 2 2 2 2 3 2 ...
$ v19_15 : int 2 2 2 2 4 2 2 2 2 2 ...
$ v19_16 : int 2 2 2 2 2 2 2 2 1 2 ...
$ v19_17 : int NA NA NA NA NA NA NA NA 7 NA ...
$ v19_1801: int NA NA NA NA NA NA NA NA 1 NA ...
$ v19_1802: int NA NA NA NA NA NA NA NA 1 NA ...
$ v19_1803: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1804: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1805: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1806: int NA NA NA NA NA NA NA NA 2 NA ...

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

65$ v19_1807: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1808: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1809: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1810: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1811: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1812: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1813: int NA NA NA NA NA NA NA NA 2 NA ...
$ v19_1814: int NA NA NA NA NA NA NA NA 2 NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "V19_02" ...
- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "19.02
opinion on food consumption" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 30..$ XSTRA: Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V19_02 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "less than adequate" "just adequate" "more than adequate" "notapplicable"..$ V19_03 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "less than adequate" "just adequate" "more than adequate" "notapplicable"..$ V19_04 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "less than adequate" "just adequate" "more than adequate" "notapplicable"..$ V19_05 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "less than adequate" "just adequate" "more than adequate" "notapplicable"..$ V19_06 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "less than adequate" "just adequate" "more than adequate" "notapplicable"..$ V19_07 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "less than adequate" "just adequate" "more than adequate" "notapplicable"..$ V19_08 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V19_09 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "good" "fair" "bad" "not applicable"..$ V19_10 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "good" "fair" "bad" "not applicable"..$ V19_11 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "good" "fair" "bad" "not applicable"..$ V19_12 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "good" "fair" "bad" "not applicable"..$ V19_13 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "good" "fair" "bad" "not applicable"..$ V19_14 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "good" "fair" "bad" "not applicable"..$ V19_15 : Named num 1 2 3 4.. ..- attr(*, "names")= chr "good" "fair" "bad" "not applicable"..$ V19_16 : Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

66..$ V19_1801: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1802: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1803: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1804: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1805: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1806: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1807: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1808: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1809: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1810: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1811: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1812: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1813: Named num 1 2.. ..- attr(*, "names")= chr..$ V19_1814: Named num 1 2.. ..- attr(*, "names")= chr
NULL"yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no""yes" "no"46 ### xh44_s20.dta ###'data.frame':
12795 obs. of 19 variables:$ xhpsu : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ xhnum : int 2 2 2 2 2 11 11 11 11 11 ...
$ xstra : int 100 100 100 100 100 100 100 100 100 100 ...
$ v20_idc: int 1 2 3 4 5 1 2 3 4 5 ...
$ v20_01 : int NA NA 1 NA NA NA NA NA 1 NA ...
$ v20_02d: int NA NA 6 NA NA NA NA NA 29 NA ...
$ v20_02m: int NA NA 5 NA NA NA NA NA 7 NA ...
$ v20_02y: int NA NA 2063 NA NA NA NA NA 2064 NA ...
$ v20_03 : int NA NA 52 NA NA NA NA NA 37 NA ...
$ v20_04 : int NA NA 1 NA NA NA NA NA 1 NA ...
$ v20_05 : num NA NA 95.8 NA NA ...
$ v20_06 : int NA NA 1 NA NA NA NA NA 1 NA ...
$ v20_07 : num NA NA 14.3 NA NA ...
$ v20_08d: int NA NA 21 NA NA NA NA NA 22 NA ...
$ v20_08m: int NA NA 9 NA NA NA NA NA 9 NA ...
$ v20_08y: int NA NA 2067 NA NA NA NA NA 2067 NA ...
$ v20_09 : int NA NA 2 NA NA NA NA NA 2 NA ...
$ v20_10 : int NA NA 36 NA NA NA NA NA 36 NA ...
$ v20_11 : int NA NA 6 NA NA NA NA NA 4 NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%8.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 252 251 252 251 251 251 251 252 251 251 ...
- attr(*, "val.labels")= chr "" "" "XSTRA" "" ...

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67- attr(*, "var.labels")= chr "psu" "household number" "strata in the nlss3' sample" "{20-id}
id-code" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 7..$ XSTRA : Named num 100 218 219 221 222 223 224 225 310 321 ...
.. ..- attr(*, "names")= chr "100/mountain" "218/urban-kathmandu valley" "219/urban-hill""221/rural-hill-eastern" ...
..$ V20_01: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V20_04: Named num 1 2 3 4 5.. ..- attr(*, "names")= chr "yes" "could not meet the child after multiple visits" "too ill
or disabled" "refused" ...
..$ V20_06: Named num 1 2.. ..- attr(*, "names")= chr "standing" "lying"..$ V20_09: Named num 1 2.. ..- attr(*, "names")= chr "yes" "no"..$ V20_10: Named num 0 90.. ..- attr(*, "names")= chr "less than one month (or age less than 1 month)" "did not breastfeed"..$ V20_11: Named num 0 90.. ..- attr(*, "names")= chr "less than one month" "not yet"
NULL

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68

4.4 Summary of each data file
> for(j in 1:46){+ cat("\n",j,"###", STATA.files[j],"###\n")+ print(summary(outfiles[[j]]))+ }1 ### poverty.dta ###
xhpsu
xhnum
hhsize
belt
Min.
:1001
Min.
: 1.000Min.
: 1.000
Min.
:1.000
1st Qu.:1277
1st Qu.: 5.000
1st Qu.: 3.000
1st Qu.:2.000
Median :1495
Median :10.000
Median : 4.000
Median :2.000
Mean
:1469
Mean
: 9.281
Mean: 4.755
Mean:2.329
3rd Qu.:1670
3rd Qu.:14.000
3rd Qu.: 6.000
3rd Qu.:3.000
Max.
:1800
Max.
:34.000Max.
:20.000
Max.
:3.000regionurbrurad
wt_indMin.
:1.000Min.
:1.000Min.
: 1.000
Min.
: 280.3
1st Qu.:2.000
1st Qu.:1.000
1st Qu.: 3.000
1st Qu.: 2378.7
Median :2.000
Median :2.000
Median : 6.000
Median : 3956.6
Mean:2.497Mean:1.651
Mean: 6.054
Mean: 4680.0
3rd Qu.:3.000
3rd Qu.:2.000
3rd Qu.: 9.000
3rd Qu.: 6308.4
Max.
:5.000Max.
:2.000Max.
:12.000
Max.
:29247.3
wt_hh
fpline_7
pline_7
pindexMin.
: 280.3
Min.
:11929Min.
:19261Min.
:0.8306
1st Qu.: 678.6
1st Qu.:11929
1st Qu.:19261
1st Qu.:0.8752
Median : 963.7
Median :11929
Median :19261
Median :0.9567
Mean: 964.6
Mean:11929Mean:19261Mean:1.1151
3rd Qu.:1185.8
3rd Qu.:11929
3rd Qu.:19261
3rd Qu.:1.0972
Max.
:1609.1
Max.
:11929Max.
:19261Max.
:2.1251
poor
totcons_pc_7
food_pc_7
quintle_7
Min.
:0.0000
Min.
: 4541
Min.
:
0
Min.
:1.00
1st Qu.:0.0000
1st Qu.: 20745
1st Qu.: 13494
1st Qu.:2.00
Median :0.0000
Median : 32623
Median : 19071
Median :4.00
Mean:0.1852
Mean: 45739
Mean: 22297
Mean:3.44
3rd Qu.:0.0000
3rd Qu.: 55101
3rd Qu.: 27154
3rd Qu.:5.00
Max.
:1.0000
Max.
:510733
Max.
:208728Max.
:5.00
2 ### xh00_s00.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000v00_vdc
Min.
: 1.00
1st Qu.: 13.00
Median : 28.00
Mean: 29.05
3rd Qu.: 38.00
Max.
:112.00
v00_c: 3.00

Min.

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v00_hnoMin.
: 0.000
1st Qu.: 4.000
Median : 8.000
Mean: 8.969
3rd Qu.:13.000
Max.
:42.000
NA's
:1
v00_d
Min.
: 0.0000

v00_a
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.1343rd Qu.:1.000
Max.
:2.000v00_e:1.000Min.

v00_distMin.
: 1.00
1st Qu.:19.00
Median :28.00
Mean:33.77
3rd Qu.:49.00
Max.
:75.00v00_b
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.831
3rd Qu.:2.000
Max.
:3.000
NA's
:5187
v00_f
Min.
: 1.000

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69

1st Qu.: 6.00
Median : 9.00
Mean
:10.59
3rd Qu.:15.00
Max.
:32.00
NA's
:5187
v00_g
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.977
3rd Qu.:2.000
Max.
:2.000

1st Qu.: 0.0000
Median : 0.0000
Mean: 0.3669
3rd Qu.: 0.0000
Max.
:31.0000

1st Qu.:1.000
Median :1.000
Mean:1.296
3rd Qu.:1.000
Max.
:9.000

1st Qu.: 1.000
Median : 1.000
Mean: 3.179
3rd Qu.: 3.000
Max.
:93.000

v00_teamMin.
: 1.00
1st Qu.: 6.00
Median :11.00
Mean:10.55
3rd Qu.:15.00
Max.
:20.00v00_int
Min.
: 12.0
1st Qu.: 62.0
Median :112.0
Mean:109.2
3rd Qu.:155.0
Max.
:205.0v00_supMin.
: 11.0
1st Qu.: 61.0
Median :111.0
Mean:106.5
3rd Qu.:151.0
Max.
:201.0v00_ent
Min.
: 13.0
1st Qu.: 64.0
Median :113.0
Mean:108.2
3rd Qu.:152.0
Max.
:202.0v00_int1_d
Min.
: 1.00
1st Qu.: 8.00
Median :15.00
Mean:15.633rd Qu.:23.00
Max.
:32.00v00_int1_m
Min.
: 1.000
1st Qu.: 4.000
Median : 6.000
Mean: 6.353
3rd Qu.: 9.000
Max.
:12.000

v00_int1_y
Min.
:2066
1st Qu.:2067
Median :2067
Mean
:2067
3rd Qu.:2067
Max.
:2067

v00_int2_d
v00_int2_m
v00_int2_y
v00_int3_d
Min.
: 1.00Min.
: 1.000
Min.
:2066
Min.
: 1.00
1st Qu.: 8.00
1st Qu.: 4.000
1st Qu.:2067
1st Qu.: 7.00
Median :15.00
Median : 6.000
Median :2067
Median :15.00
Mean:15.59Mean: 6.313
Mean
:2067
Mean
:15.443rd Qu.:23.00
3rd Qu.: 9.000
3rd Qu.:2067
3rd Qu.:23.00
Max.
:32.00Max.
:12.000
Max.
:2067
Max.
:32.00
NA's
:11
NA's
:11
NA's
:11
NA's
:4261
v00_int3_m
v00_int3_y
v00_sup_dv00_sup_mMin.
: 1.000
Min.
:2066
Min.
: 0.00Min.
: 1.0
1st Qu.: 4.000
1st Qu.:2067
1st Qu.: 8.00
1st Qu.: 4.0
Median : 6.000
Median :2067
Median :15.00
Median : 6.0
Mean: 6.155
Mean
:2067
Mean
:15.49Mean: 6.3
3rd Qu.: 9.000
3rd Qu.:2067
3rd Qu.:23.00
3rd Qu.: 9.0
Max.
:12.000
Max.
:2067
Max.
:32.00
Max.
:12.0
NA's
:4261
NA's
:4261
NA's
:2
v00_sup_y
v00_headn
v13_02
v13_18
Min.
:2066
Length:5988
Min.
:1.000Min.
:1.000
1st Qu.:2067
Class :character
1st Qu.:1.000
1st Qu.:2.000
Median :2067
Mode :character
Median :1.000
Median :2.000
Mean
:2067
Mean
:1.288Mean:1.789
3rd Qu.:2067
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:2067
Max.
:2.000Max.
:2.000NA's:2
v13_29v13_30_1uv13_30_1r
v13_30_1aMin.
:1.000Min.
:1Min.
: 0.00Min.
: 0.000
1st Qu.:2.000
1st Qu.:1
1st Qu.: 0.00
1st Qu.: 0.000
Median :2.000
Median :1
Median : 0.00
Median : 0.000
Mean:1.964Mean:1
Mean: 1.09Mean: 1.283
3rd Qu.:2.000
3rd Qu.:1
3rd Qu.: 1.00
3rd Qu.: 0.000
Max.
:2.000Max.
:1
Max.
:26.00Max.
:12.000
NA's
:5776
NA's
:5776
NA's
:5776
v13_30_1pv13_30_2uv13_30_2bv13_30_2k
Min.
:0.000Min.
:2Min.
:0.000Min.
: 0.000

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70

1st Qu.:0.000
1st Qu.:2
1st Qu.:0.000
1st Qu.: 0.000
Median :0.000
Median :2
Median :0.000
Median : 1.000
Mean:0.057
Mean:2
Mean:0.093Mean: 2.533
3rd Qu.:0.000
3rd Qu.:2
3rd Qu.:0.000
3rd Qu.: 3.000
Max.
:3.000Max.
:2Max.
:2.000Max.
:19.000
NA's
:5776
NA's
:5774
NA's
:5774
NA's
:5774
v13_30_2d
v13_31
v13_32
v13_33_1u
Min.
: 0.000
Min.
:
0
Min.
:1.000Min.
:1
1st Qu.: 0.000
1st Qu.: 100000
1st Qu.:2.000
1st Qu.:1
Median : 0.000
Median : 250000
Median :2.000
Median :1
Mean: 1.178
Mean: 703109
Mean:1.956Mean:1
3rd Qu.: 0.000
3rd Qu.: 637500
3rd Qu.:2.000
3rd Qu.:1
Max.
:15.000
Max.
:15000000
Max.
:2.000
Max.
:1
NA's
:5774
NA's
:5774
NA's
:5723
v13_33_1r
v13_33_1av13_33_1pv13_33_2uMin.
: 0.000Min.
: 0.000
Min.
:0.000Min.
:2
1st Qu.: 0.000
1st Qu.: 0.000
1st Qu.:0.000
1st Qu.:2
Median : 0.000
Median : 0.000
Median :0.000
Median :2
Mean: 0.974
Mean: 1.313Mean:0.068Mean:2
3rd Qu.: 0.000
3rd Qu.: 0.000
3rd Qu.:0.000
3rd Qu.:2
Max.
:23.000Max.
:15.000
Max.
:3.000
Max.
:2
NA's
:5723
NA's
:5723
NA's
:5723
NA's
:5725
v13_33_2bv13_33_2kv13_33_2dv13_34Min.
:0.000Min.
: 0.000Min.
: 0.000
Min.
:0
1st Qu.:0.000
1st Qu.: 0.000
1st Qu.: 0.000
1st Qu.:
99000
Median :0.000
Median : 0.000
Median : 0.000
Median : 300000
Mean:0.061Mean: 2.019Mean: 2.582Mean: 591820
3rd Qu.:0.000
3rd Qu.: 2.000
3rd Qu.: 4.000
3rd Qu.: 700000
Max.
:6.000Max.
:19.000Max.
:18.000
Max.
:10000000
NA's
:5725
NA's
:5725
NA's
:5725
NA's
:5723
v13_38d_total
v13_39v13_43v13_48a
Min.
:
0
Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:0
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:3.000
Median :0Median :1.000
Median :1.000
Median :3.000
Mean: 11823
Mean:1.395Mean:1.283Mean:2.9433rd Qu.: 6611
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:3.000
Max.
:2400000
Max.
:2.000Max.
:2.000Max.
:4.000
NA's
:1870
NA's
:1869
NA's
:1868
NA's
:3034
v13_48bv13_49v13_50v13_53_el_maleMin.
:1.000Min.
:1.000Min.
:1.000Min.
: 0.00
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.: 0.00
Median :1.000
Median :1.000
Median :2.000
Median : 2.00
Mean
:1.099Mean:1.567Mean:1.837Mean: 6.08
3rd Qu.:1.000
3rd Qu.:2.000
3rd Qu.:3.000
3rd Qu.: 8.75
Max.
:2.000Max.
:3.000Max.
:3.000Max.
:150.00
NA's
:3034
NA's
:5695
NA's
:1869
NA's
:2662
v13_53_el_female
v13_58v13_59v13_60Min.
: 0.00
Min.
:
0
Min.
:1.00
Min.
:
0.00
1st Qu.: 0.00
1st Qu.:0
1st Qu.:2.00
1st Qu.:1.00
Median : 6.00
Median :0Median :2.00
Median :1.00
Mean: 11.14Mean: 3524
Mean
:1.99
Mean
: 52.33
3rd Qu.: 15.00
3rd Qu.: 3500
3rd Qu.:2.00
3rd Qu.:1.00
Max.
:165.00
Max.
:408000
Max.
:2.00
Max.
:2000.00
NA's
:2662
NA's
:1869
NA's
:1869
NA's
:5948
v13_61
v13_62
v13_65v13_73
Min.
:
0
Min.
:
0
Min.
:1.000Min.
:1.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

71

1st Qu.: 6750
Median : 15000
Mean: 24308
3rd Qu.: 31500
Max.
:210900
NA's:5948
v13_74Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.277
3rd Qu.:2.000
Max.
:2.000v13_85Min.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.849
3rd Qu.:3.000
Max.
:4.000NA's:889
v16_00Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.498
3rd Qu.:2.000
Max.
:2.000

1st Qu.:0
1st Qu.:1.000
1st Qu.:2.000
Median :0Median :1.000
Median :2.000
Mean: 3760Mean:1.304Mean:1.996
3rd Qu.: 3515
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:458000
Max.
:2.000Max.
:9.000
NA's
:1869
NA's
:1820
v13_82v13_83v13_84Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:2.000
1st Qu.:3.000
1st Qu.:2.000
Median :2.000
Median :3.000
Median :2.000
Mean:1.932Mean:2.825Mean:1.852
3rd Qu.:2.000
3rd Qu.:3.000
3rd Qu.:2.000
Max.
:2.000Max.
:4.000Max.
:2.000NA's:409
v14_01
v15_01
v15_14Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.:1.000
1st Qu.:1.00
1st Qu.:2.000
Median :2.000
Median :1.00
Median :2.000
Mean:1.654
Mean
:1.38
Mean
:1.882
3rd Qu.:2.000
3rd Qu.:2.00
3rd Qu.:2.000
Max.
:2.000
Max.
:2.00
Max.
:2.000
v17_01Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.7643rd Qu.:2.000
Max.
:2.000v17_11Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.688
3rd Qu.:2.000
Max.
:2.000v03_aMin.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.434
3rd Qu.: 2.000
Max.
:18.000

v05_00Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.537
3rd Qu.: 2.000
Max.
:16.000

v13_01Min.
: 0.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.399
3rd Qu.: 1.000
Max.
:19.000

v13_17Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.403
3rd Qu.: 2.000
Max.
:16.000

n_s01
Min.
: 1.000
1st Qu.: 3.000
Median : 4.000
Mean: 4.788
3rd Qu.: 6.000
Max.
:21.000

n_s9a
Min.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.587
3rd Qu.: 4.000
Max.
:16.000
NA's
:2
n_s14
Min.
:1.000n_s12: 0.000Min.

v01_00
v02_a
Min.
:0
Min.
: 1.000
1st Qu.:0
1st Qu.: 1.000
Median :0
Median : 1.000
Mean
:0
Mean
: 1.436
3rd Qu.:0
3rd Qu.: 2.000
Max.
:0
Max.
:18.000
NA's:2
v06_00v09_36Min.
: 1.000
Min.
:0.000
1st Qu.: 1.000
1st Qu.:1.000
Median : 1.000
Median :2.000
Mean: 1.474
Mean:1.581
3rd Qu.: 2.000
3rd Qu.:2.000
Max.
:16.000
Max.
:4.000NA's:1v15_38v19_01Min.
:0.000Min.
: 1.000
1st Qu.:0.000
1st Qu.: 1.000
Median :1.000
Median : 1.000
Mean:0.771Mean: 1.413
3rd Qu.:1.000
3rd Qu.: 2.000
Max.
:5.000Max.
:19.000
NA's
:1
n_s9b
n_s10bMin.
:0.0000
Min.
: 2.000
1st Qu.:0.0000
1st Qu.: 6.000
Median :0.0000
Median : 9.000
Mean:0.2576
Mean: 9.713
3rd Qu.:0.0000
3rd Qu.:12.000
Max.
:3.0000Max.
:50.000NA's:2n_s15an_s15bMin.
: 1.000
Min.
:1.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

72

1st Qu.: 0.000
Median : 1.000
Mean: 1.094
3rd Qu.: 2.000
Max.
:12.000
n_s16
Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 2.022
3rd Qu.: 2.000
Max.
:15.000
NA's:2984
n_s13a2Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.511
3rd Qu.:2.000
Max.
:8.000NA's:4727

1st Qu.:1.000
Median :1.000
Mean:1.245
3rd Qu.:1.000
Max.
:5.000NA's:3914
n_s17aMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.585
3rd Qu.:2.000
Max.
:8.000NA's:4572
n_s13bMin.
: 0.000
1st Qu.: 5.000
Median : 9.000
Mean: 9.368
3rd Qu.:14.000
Max.
:32.000
NA's:1470

1st Qu.:1.000
Median :1.000
Mean:1.305
3rd Qu.:1.000
Max.
:6.000NA's:5283
n_s13a1Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 3.104
3rd Qu.: 4.000
Max.
:22.000
NA's:1723
n_s13c3Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.716
3rd Qu.: 2.000
Max.
:11.000
NA's
:3989

xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:254.7
3rd Qu.:321.0
Max.
:325.0v01_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.462
3rd Qu.: 5.000
Max.
:21.000

3 ### xh01_s01.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1299
1st Qu.: 5.000
Median :1514
Median :10.000
Mean
:1481
Mean
: 9.288
3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

1st Qu.: 1.000
Median : 1.000
Mean: 1.537
3rd Qu.: 2.000
Max.
:10.000
NA's:2273
n_s17bMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.422
3rd Qu.:2.000
Max.
:8.000NA's:4120
n_s13c1Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.388
3rd Qu.: 3.000
Max.
:10.000
NA's:3494

v01_01
Length:28670
Class :character
Mode :character

v01_02Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.535
3rd Qu.:2.000
Max.
:2.000v01_03Min.
: 0.00
1st Qu.:11.00
Median :22.00
Mean:26.933rd Qu.:40.00
Max.
:99.00v01_04Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.217
3rd Qu.: 3.000
Max.
:14.000

v01_05aMin.
: 1.0
1st Qu.:18.0
Median :32.0
Mean
:36.2
3rd Qu.:53.0
Max.
:96.0

v01_05bv01_06v01_07Min.
:1.000Min.
:1.000Min.
: 1.00
1st Qu.:2.000
1st Qu.:1.000
1st Qu.: 1.00
Median :2.000
Median :2.000
Median : 2.00
Mean
:1.828Mean:1.923Mean:16.45
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.: 4.00
Max.
:2.000Max.
:7.000Max.
:98.00
NA's
:1168
NA's
:6310
NA's
:15802
v01_08v01_09v01_10v01_jdc
Min.
: 1.00Min.
: 0.00Min.
:1.000Min.
: 1.000
1st Qu.: 2.00
1st Qu.:12.00
1st Qu.:1.000
1st Qu.: 2.000
Median : 6.00
Median :12.00
Median :1.000
Median : 3.000
Mean: 11.52
Mean:11.63Mean:1.007Mean: 3.452
3rd Qu.: 13.00
3rd Qu.:12.00
3rd Qu.:1.000
3rd Qu.: 5.000
Max.
:103.00Max.
:12.00Max.
:2.000Max.
:20.000
NA's:196

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

73

v01_11Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.929
3rd Qu.:3.000
Max.
:3.000NA's:196v01_15Min.
: 1.00
1st Qu.: 2.00
Median : 2.00
Mean
: 2.46
3rd Qu.: 2.00
Max.
:19.00NA's:14204v01_12Min.
: 0.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.586
3rd Qu.: 1.000
Max.
:18.000
NA's:17897v01_16Min.
: 0.00
1st Qu.:17.00
Median :17.00
Mean:18.68
3rd Qu.:17.00
Max.
:98.00NA's:14662

v01_14Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.738
3rd Qu.:2.000
Max.
:3.000
NA's
:196

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v02_01Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.255
3rd Qu.:2.000
Max.
:2.0004 ### xh02_s02.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

v01_13Min.
: 1.00
1st Qu.:12.00
Median :17.00
Mean:17.233rd Qu.:17.00
Max.
:98.00NA's:10969v02_02aMin.
: 1.000
1st Qu.: 3.000
Median : 4.000
Mean: 4.669
3rd Qu.: 6.000
Max.
:43.000

v02_02bMin.
:0.0000
1st Qu.:0.0000
Median :1.0000
Mean:0.6854
3rd Qu.:1.0000
Max.
:3.0000

v02_02cMin.
: 0.0000
1st Qu.: 0.0000
Median : 1.0000
Mean: 0.6593
3rd Qu.: 1.0000
Max.
:17.0000v02_02dMin.
: 0.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.667
3rd Qu.: 2.000
Max.
:14.000

v02_02eMin.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean:0.2056
3rd Qu.:0.0000
Max.
:4.0000

v02_02fMin.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.1162
3rd Qu.: 0.0000
Max.
:21.0000v02_02gMin.
: 0.0000
1st Qu.: 0.0000
Median : 1.0000
Mean: 0.8985
3rd Qu.: 1.0000
Max.
:10.0000v02_02hMin.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean:0.4372
3rd Qu.:1.0000
Max.
:9.0000

v02_03Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.417
3rd Qu.:2.000
Max.
:2.000v02_04Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:2.099
3rd Qu.:2.000
Max.
:7.000v02_05Min.
:1.000
1st Qu.:2.000
Median :3.000
Mean:2.729
3rd Qu.:3.000
Max.
:5.000v02_07Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.9343rd Qu.:2.000

v02_08uMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.437
3rd Qu.:2.000

v02_08rb
Min.
: 0.00000
1st Qu.: 0.00000
Median : 0.00000
Mean: 0.05327
3rd Qu.: 0.00000

v02_06Min.
:1.00
1st Qu.:4.00
Median :5.00
Mean
:4.28
3rd Qu.:6.00
Max.
:7.00
v02_08akMin.
: 0.000
1st Qu.: 0.000
Median : 2.000
Mean: 3.051
3rd Qu.: 5.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

74

Max.

:4.000v02_08pdMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 3.022
3rd Qu.: 5.250
Max.
:19.000

Max.

:2.000Max.

v02_09Min.
: 12.0
1st Qu.: 274.0
Median : 473.0
Mean: 596.8
3rd Qu.: 756.0
Max.
:8932.0:30.00000

v02_10Min.
:1700
1st Qu.:2042
Median :2052
Mean
:2048
3rd Qu.:2060
Max.
:2090

Max.

:16.000

v02_11Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.15
3rd Qu.:1.00
Max.
:2.00

v02_12
v02_13
v02_14v02_15
Min.
:
0
Min.
:
0
Min.
:1.000Min.
:
0
1st Qu.: 100000
1st Qu.:300
1st Qu.:2.000
1st Qu.: 1500
Median : 250000
Median :800
Median :2.000
Median : 4000
Mean
: 1521869
Mean
:
5531
Mean
:1.904Mean: 7048
3rd Qu.: 800000
3rd Qu.:2000
3rd Qu.:2.000
3rd Qu.: 7000
Max.
:150000000
Max.
:4000000
Max.
:2.000Max.
:750000
NA's
:898
NA's
:898
NA's
:898
NA's
:5499
v02_16v02_17v02_18v02_19Min.
:1.000
Min.
:
0
Min.
:
1
Min.
:1.000
1st Qu.:1.000
1st Qu.: 400
1st Qu.: 1200
1st Qu.:1.000
Median :1.000
Median : 1000
Median : 2000
Median :2.000
Mean:1.235
Mean: 1986Mean: 2864Mean:2.329
3rd Qu.:1.000
3rd Qu.: 2000
3rd Qu.: 3400
3rd Qu.:3.000
Max.
:4.000Max.
:32000Max.
:46500Max.
:7.000
NA's
:5090
NA's
:5819
NA's
:5259
v02_20v02_21
v02_22
v02_23Min.
:1.000Min.
: 0.00
Min.
:
0.0
Min.
:1.000
1st Qu.:1.000
1st Qu.: 7.00
1st Qu.:0.0
1st Qu.:3.000
Median :1.000
Median : 36.00
Median :0.0Median :4.000
Mean:1.429Mean: 74.81Mean: 546.4
Mean:3.352
3rd Qu.:2.000
3rd Qu.:168.00
3rd Qu.: 300.0
3rd Qu.:4.000
Max.
:2.000Max.
:168.00
Max.
:36000.0
Max.
:4.000
NA's
:3047
NA's
:3047
v02_24v02_25v02_26v02_27Min.
:1.000
Min.
:
0.0
Min.
:1.000Min.
:1.000
1st Qu.:3.000
1st Qu.:0.0
1st Qu.:2.000
1st Qu.:1.000
Median :4.000
Median :0.0Median :3.000
Median :1.000
Mean:3.733Mean: 509.9
Mean:3.192Mean:1.781
3rd Qu.:5.000
3rd Qu.: 600.0
3rd Qu.:5.000
3rd Qu.:2.000
Max.
:6.000Max.
:8400.0
Max.
:5.000Max.
:5.000NA's:4778
v02_28v02_29v02_30v02_31aMin.
:1.000
Min.
:
0
Min.
: 0.0
Min.
:1.000
1st Qu.:1.000
1st Qu.: 960
1st Qu.: 14.0
1st Qu.:2.000
Median :1.000
Median : 1200
Median : 21.0
Median :2.000
Mean:1.308Mean: 2870Mean: 35.2Mean:1.831
3rd Qu.:1.000
3rd Qu.: 3315
3rd Qu.: 56.0
3rd Qu.:2.000
Max.
:3.000Max.
:96000Max.
:168.0Max.
:2.000
NA's
:1588
NA's
:1588
NA's
:1588
v02_31bv02_31cv02_31dv02_32Min.
:1.000Min.
:1.000Min.
:1.000Min.
:
0
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.: 2200
Median :1.000
Median :2.000
Median :2.000
Median : 4000
Mean
:1.353Mean:1.714Mean:1.963Mean: 7320
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.: 8400

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

75

Max.

:2.000v02_33Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:2.152
3rd Qu.:4.000
Max.
:7.000Max.

:2.000Max.

:400000:1940
v02_35v02_36Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Mean:1.237Mean:1.242
3rd Qu.:1.000
3rd Qu.:1.000
Max.
:2.000Max.
:2.000NA's:1421
v02_37cv02_38aMin.
: 10.00Min.
: 0.000
1st Qu.: 25.00
1st Qu.: 2.000
Median : 30.00
Median : 4.000
Mean: 54.27
Mean: 3.917
3rd Qu.: 40.00
3rd Qu.: 5.000
Max.
:1500.00
Max.
:27.000
NA's
:2599
NA's
:2528
v02_40v02_41
Min.
:
0.00
Min.
:1.000
1st Qu.:0.00
1st Qu.:1.000
Median :0.00
Median :1.000
Mean: 30.15
Mean:1.382
3rd Qu.:3.00
3rd Qu.:2.000
Max.
:6000.00
Max.
:2.000NA's:3353
v02_44Min.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.682
3rd Qu.:3.000
Max.
:3.000v02_34Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.792
3rd Qu.:5.000
Max.
:6.000v02_37av02_37b
Min.
:1.00
Min.
:
0.00
1st Qu.:1.00
1st Qu.: 30.00
Median :1.00
Median : 60.00
Mean
:1.08
Mean
: 89.63
3rd Qu.:1.00
3rd Qu.: 100.00
Max.
:3.00
Max.
:8400.00
NA's
:2528
NA's
:2528
v02_38bv02_39Min.
: 0.000
Min.
:1.000
1st Qu.: 0.000
1st Qu.:2.000
Median : 0.000
Median :2.000
Mean: 4.709Mean:2.171
3rd Qu.: 0.000
3rd Qu.:3.000
Max.
:50.000
Max.
:4.000
NA's
:2528
NA's
:2528
v02_42
v02_43Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:2.000
Median :1.000
Median :3.000
Mean:1.831Mean:2.431
3rd Qu.:2.000
3rd Qu.:3.000
Max.
:4.000Max.
:4.000NA's:2285
5 ### xh03_s03.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v03_txt
Length:143712
Class :character
Mode :character

v03_02mMin.
: 0.00
1st Qu.: 5.00
Median :15.00:2.000xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v03_01Min.
:1.000
1st Qu.:1.000
Median :3.000
Mean:3.4933rd Qu.:6.000
Max.
:7.000v03_03aMin.
:
0.0
1st Qu.:1.0
Median :
3.0

Max.
NA's

v03_idc
Min.
:101.0
1st Qu.:106.8
Median :112.5
Mean:112.5
3rd Qu.:118.2
Max.
:124.0v03_02dv03_02h
Min.
:0.00
Min.
: 0.0
1st Qu.:0.00
1st Qu.: 0.0
Median :0.00
Median : 0.0
Mean
:0.04
Mean
: 0.8
3rd Qu.:0.00
3rd Qu.: 1.0
Max.
:7.00
Max.
:21.0
NA's
:52463NA's:52463v03_03bv03_04
Min.
: 0.0
Min.
:1.000
1st Qu.: 0.0
1st Qu.:1.000
Median : 0.0
Median :1.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

76

Mean
:16.72
3rd Qu.:30.00
Max.
:55.00NA's:52463v03_05Min.
:1.00
1st Qu.:1.00
Median :2.00
Mean
:2.46
3rd Qu.:4.00
Max.
:4.00
NA's
:74046Mean:
7.8
3rd Qu.:8.0
Max.
:3500.0NA's:52463Mean:143.33rd Qu.:200.0
Max.
:990.0NA's:524636 ### xh04_s04.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1299
1st Qu.: 5.000
Median :1514
Median :10.000
Mean
:1481
Mean
: 9.288
3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:254.7
3rd Qu.:321.0
Max.
:325.0v04_01Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.644
3rd Qu.: 2.000
Max.
:16.000
NA's:3053
v04_04Min.
:1981
1st Qu.:2040
Median :2052
Mean
:2049
3rd Qu.:2060
Max.
:2067
NA's
:18736v04_08Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 2.055
3rd Qu.: 2.000
Max.
:22.000
NA's:26909v04_10_m
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 9.179
3rd Qu.:10.000
Max.
:98.000
NA's:26909v04_12Min.
: 1.000

v04_02Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.388
3rd Qu.:2.000
Max.
:2.000NA's:3053
v04_05Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 3.048
3rd Qu.: 3.000
Max.
:13.000
NA's:18736v04_09_y
Min.
:2044
1st Qu.:2063
Median :2065
Mean
:2064
3rd Qu.:2066
Max.
:2067
NA's
:26909v04_11aMin.
: 1.00
1st Qu.:27.00
Median :81.00
Mean:60.47
3rd Qu.:81.00
Max.
:96.00NA's:26909v04_13Min.
: 11.0Mean:1.475
3rd Qu.:2.000
Max.
:2.000NA's:10889v04_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.462
3rd Qu.: 5.000
Max.
:21.000
NA's:1
v04_03av04_03b
Min.
: 1.0
Min.
:1.000
1st Qu.:18.0
1st Qu.:2.000
Median :34.0
Median :2.000
Mean
:37.9
Mean
:1.896
3rd Qu.:56.0
3rd Qu.:2.000
Max.
:96.0
Max.
:2.000NA's:18736NA's:19805v04_06v04_07
Min.
: 11.0Min.
:1.000
1st Qu.:621.0
1st Qu.:2.000
Median :996.0
Median :2.000
Mean:832.9Mean:1.931
3rd Qu.:997.0
3rd Qu.:2.000
Max.
:999.0Max.
:2.000NA's:18736NA's:3053
v04_09_mv04_10_yMin.
: 1.000
Min.
: 98
1st Qu.: 3.000
1st Qu.:2065
Median : 6.000
Median :2066
Mean: 6.209
Mean:2009
3rd Qu.: 9.000
3rd Qu.:2066
Max.
:12.000
Max.
:2067
NA's
:26909NA's:26909v04_11bv04_idc2Min.
:1.000Min.
: 1.000
1st Qu.:1.000
1st Qu.: 1.000
Median :2.000
Median : 2.000
Mean:1.515Mean: 2.655
3rd Qu.:2.000
3rd Qu.: 3.000
Max.
:2.000Max.
:19.000
NA's:27893NA's:26909v04_14v04_15Min.
:1.000Min.
:1.000

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77

1st Qu.: 3.000
Median : 6.000
Mean: 5.979
3rd Qu.: 7.000
Max.
:13.000
NA's:26909v04_16Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.106
3rd Qu.:1.000
Max.
:2.000NA's:27633

1st Qu.:621.0
Median :621.0
Mean:754.5
3rd Qu.:996.0
Max.
:999.0NA's:26909v04_17
Min.
: 11.0
1st Qu.:711.0
Median :832.0
Mean:766.7
3rd Qu.:931.0
Max.
:999.0NA's:27633

1st Qu.:1.000
Median :1.000
Mean:1.411
3rd Qu.:2.000
Max.
:2.000NA's:26909v04_18Min.
: 1.00
1st Qu.: 1.00
Median : 1.00
Mean
: 4.41
3rd Qu.: 3.00
Max.
:99.00NA's:26909

1st Qu.:2.000
Median :2.000
Mean:2.389
3rd Qu.:3.000
Max.
:5.000NA's:276337 ### xh05_s05.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v05_idc
Min.
: 11.00
1st Qu.: 34.00
Median : 63.50
Mean: 77.41
3rd Qu.: 92.00
Max.
:990.00

v05_itm
Length:443112
Class :character
Mode :character

v05_03bMin.
: 1.0
1st Qu.: 1.0
Median : 1.0
Mean
: 2.8
3rd Qu.: 4.0
Max.
:11.0
NA's
:396591v05_06bMin.
: 1.00
1st Qu.: 1.00
Median : 1.00
Mean
: 2.21
3rd Qu.: 1.00
Max.
:11.00NA's:309628
v05_10aMin.
: 0.0
1st Qu.: 1.0
Median : 3.0
Mean: 92.7
3rd Qu.: 50.0
Max.
:5800.0
NA's:357863v05_01
v05_02
v05_03aMin.
:1.000Min.
: 0.00Min.
:
0.0
1st Qu.:1.000
1st Qu.: 0.00
1st Qu.:2.0
Median :1.000
Median : 0.00
Median :5.0
Mean:1.365Mean: 1.85Mean: 20.2
3rd Qu.:2.000
3rd Qu.: 2.00
3rd Qu.: 12.0
Max.
:2.000Max.
:12.00Max.
:8500.0
NA's:11976NA's:210737NA's:396591v05_04v05_05v05_06a
Min.
:
0.0
Min.
: 0.00Min.
: -5.00
1st Qu.:70.0
1st Qu.: 1.00
1st Qu.:1.00
Median :160.0
Median : 4.00
Median :
2.00
Mean
:
567.3
Mean
: 5.76Mean: 32.17
3rd Qu.:400.0
3rd Qu.:12.00
3rd Qu.:5.00
Max.
:100002.0
Max.
:12.00Max.
:7500.00
NA's:366308
NA's:169416NA's:309628
v05_07v05_08v05_09
Min.
:
0.0
Min.
:
0.0
Min.
:0.00
1st Qu.:50.0
1st Qu.:0.0
1st Qu.:0.00
Median : 100.0
Median :0.0Median :1.00
Mean: 382.1
Mean
:
87.8
Mean
:2.77
3rd Qu.: 240.0
3rd Qu.:0.0
3rd Qu.:7.00
Max.
:60000.0Max.
:1501500.0
Max.
:7.00
NA's
:222295
NA's:163428
NA's:169416v05_10bv05_11
v05_12
Min.
: 1.0
Min.
:
0.0
Min.
:1.00
1st Qu.: 1.0
1st Qu.:15.0
1st Qu.:2.00
Median : 2.0
Median :35.0
Median :2.00
Mean
: 2.6
Mean
: 137.2Mean:1.82
3rd Qu.: 4.0
3rd Qu.:90.0
3rd Qu.:2.00
Max.
:10.0
Max.
:72000.0
Max.
:3.00
NA's
:357863NA's:290215NA's:296203

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78

8 ### xh06_s06a.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v06a_itm
Length:155688
Class :character
Mode :character

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v06_01Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.39
3rd Qu.:2.00
Max.
:2.00
NA's
:5988

v06_02aMin.
:
0.0
1st Qu.:0.0
Median :
35.0
Mean
:
511.8
3rd Qu.:200.0
Max.
:300000.0
NA's:583849 ### xh07_s06b.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v06b_itm
Length:221556
Class :character
Mode :character

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v06_03Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.817
3rd Qu.:2.000
Max.
:2.000v06_08
:1.00

Min.

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v06_05Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.648
3rd Qu.:2.000
Max.
:2.000v06_09:Min.

0v06_02bMin.
:
2
1st Qu.:360
Median :
1000
Mean
:
5743
3rd Qu.:3000
Max.
:3500000NA's:58384v06b_idc
Min.
:311.0
1st Qu.:321.0
Median :412.0
Mean:374.43rd Qu.:422.0
Max.
:432.0v06_04Min.
:
0
1st Qu.:400
Median :1000
Mean: 10573
3rd Qu.:4000
Max.
:6500000NA's:180991

10 ### xh08_s06c.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v06c_itm
Length:101796
Class :character
Mode :character

v06a_idc
Min.
:211.0
1st Qu.:222.0
Median :228.5
Mean:228.8
3rd Qu.:236.0
Max.
:260.0v06c_idc
Min.
:501
1st Qu.:505
Median :509
Mean
:509
3rd Qu.:513
Max.
:517

v06_06Min.
: 1.00
1st Qu.: 1.00
Median : 1.00
Mean
: 1.49
3rd Qu.: 2.00
Max.
:111.00
NA's:82480v06_10Min.
:0v06_07Min.
: 0.00
1st Qu.: 1.00
Median : 2.00
Mean
: 3.67
3rd Qu.: 5.00
Max.
:89.00NA's:82480

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79

1st Qu.:1.00
Median :1.00
Mean
:1.13
3rd Qu.:1.00
Max.
:3.00
NA's
:82480

1st Qu.:1600
Median :4000
Mean: 16492
3rd Qu.: 10000
Max.
:3500000NA's:82480

11 ### xh09_s06d.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

1st Qu.:1500
Median :5000
Mean: 21667
3rd Qu.: 15000
Max.
:9500000NA's:65921

v06d_itm
Length:83832
Class :character
Mode :character

v06_11Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.828
3rd Qu.:2.000
Max.
:2.000NA's:5988

12 ### xh10_s07.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1299
1st Qu.: 5.000
Median :1514
Median :10.000
Mean
:1481
Mean
: 9.288
3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v07_01Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.718
3rd Qu.: 2.000
Max.
:19.000
NA's:1862
v07_05Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.998
3rd Qu.:2.000
Max.
:2.000NA's:1862
v07_09Min.
: 1.000
1st Qu.: 4.000
Median : 4.000
Mean: 4.854
3rd Qu.: 6.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v06d_idc
Min.
:600.0
1st Qu.:603.0
Median :606.5
Mean:606.5
3rd Qu.:610.0
Max.
:613.0v06_12aMin.
:
0.0
1st Qu.:0.0
Median :0.0
Mean: 191.6
3rd Qu.: 200.0
Max.
:30000.0NA's:70431xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:254.7
3rd Qu.:321.0
Max.
:325.0v06_12bMin.
:
6
1st Qu.:400
Median : 1000
Mean
: 2390
3rd Qu.: 3000
Max.
:100000
NA's:70431v07_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.462
3rd Qu.: 5.000
Max.
:21.000

v07_02
v07_03v07_04Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median :1.000
Mean:1.367Mean:1.034Mean:1.117
3rd Qu.:2.000
3rd Qu.:1.000
3rd Qu.:1.000
Max.
:2.000Max.
:2.000Max.
:5.000
NA's
:1862
NA's
:11706NA's:12287v07_06v07_07v07_08Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :2.000
Median :1.000
Median :2.000
Mean:2.619Mean:1.429Mean:2.068
3rd Qu.:5.000
3rd Qu.:2.000
3rd Qu.:3.000
Max.
:5.000Max.
:4.000Max.
:3.000NA's:28628NA's:28628NA's:1862
v07_idc2
v07_10v07_11Min.
: 1.000
Min.
:1.000Min.
: 0.000
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.: 5.000
Median : 2.000
Median :1.000
Median : 8.000
Mean: 2.496
Mean:1.809Mean: 7.536
3rd Qu.: 3.000
3rd Qu.:1.000
3rd Qu.:11.000

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80

Max.
NA's

:10.000Max.
:18.000:19969NA's:21091
v07_12
v07_13aMin.
: 0.000Min.
: 0.000
1st Qu.: 5.000
1st Qu.: 7.000
Median : 5.000
Median :10.000
Mean: 4.951
Mean: 8.185
3rd Qu.: 5.000
3rd Qu.:10.000
Max.
:98.000Max.
:19.000
NA's:21092NA's:22966v07_15v07_16Min.
: 0.000Min.
: 1.000
1st Qu.: 5.000
1st Qu.: 4.000
Median : 8.000
Median : 7.000
Mean: 8.059
Mean: 6.945
3rd Qu.:11.000
3rd Qu.: 9.000
Max.
:16.000Max.
:15.000
NA's:21092NA's:21092
v07_18v07_19Min.
: 0.000
Min.
:0.000
1st Qu.: 2.000
1st Qu.:0.000
Median : 5.000
Median :5.000
Mean: 5.619Mean:3.886
3rd Qu.: 9.000
3rd Qu.:6.000
Max.
:16.000
Max.
:7.000NA's:18142
NA's:18263v07_22
v07_23a
Min.
:1.00
Min.
: 0.000
1st Qu.:1.00
1st Qu.: 0.000
Median :1.00
Median : 0.000
Mean
:1.28
Mean
: 0.443
3rd Qu.:1.00
3rd Qu.: 1.000
Max.
:5.00
Max.
:32.000
NA's:18263
NA's:18263v07_25v07_idc4Min.
:1.000Min.
: 1.000
1st Qu.:1.000
1st Qu.: 3.000
Median :1.000
Median : 4.000
Mean:1.318Mean: 4.257
3rd Qu.:2.000
3rd Qu.: 5.000
Max.
:2.000Max.
:19.000
NA's:26293
NA's:18261
v07_26cv07_26d
Min.
:
0.0
Min.
:
0
1st Qu.:0.0
1st Qu.: 250
Median : 500.0
Median : 700
Mean: 628.8
Mean: 1172
3rd Qu.: 900.0
3rd Qu.: 1500
Max.
:15000.0Max.
:60000NA's:18261
NA's:18261
v07_26gv07_27
Min.
:
0
Min.
:1.000
1st Qu.:0
1st Qu.:2.000
Median : 200
Median :2.000
Mean: 1037Mean:1.852
3rd Qu.: 1200
3rd Qu.:2.000

Max.
:8.000Max.
:16.000
NA's:21091NA's:21092
v07_13bv07_14Min.
: 0.000
Min.
: 4.00
1st Qu.: 0.000
1st Qu.:13.00
Median : 0.000
Median :16.00
Mean: 0.646
Mean:16.69
3rd Qu.: 1.000
3rd Qu.:19.00
Max.
:15.000Max.
:55.00NA's:22966NA's:21092v07_idc3
v07_17Min.
: 1.000
Min.
:1.000
1st Qu.: 3.000
1st Qu.:1.000
Median : 4.000
Median :1.000
Mean: 4.266
Mean:1.807
3rd Qu.: 5.000
3rd Qu.:2.000
Max.
:19.000
Max.
:8.000NA's:18142
NA's:18142
v07_20v07_21Min.
:0.000Min.
:1.000
1st Qu.:2.000
1st Qu.:2.000
Median :6.000
Median :4.000
Mean:4.249Mean:4.401
3rd Qu.:6.000
3rd Qu.:7.000
Max.
:7.000Max.
:7.000NA's:18263NA's:27609v07_23bv07_24Min.
: 0.00Min.
:1.000
1st Qu.: 5.00
1st Qu.:2.000
Median :15.00
Median :2.000
Mean:16.72Mean:1.772
3rd Qu.:30.00
3rd Qu.:2.000
Max.
:55.00Max.
:2.000NA's:18263
NA's:18263v07_26av07_26b
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:60
Median :0Median :300
Mean: 3052Mean: 1771
3rd Qu.: 3600
3rd Qu.: 1300
Max.
:750000
Max.
:600000
NA's:18261
NA's:18261
v07_26ev07_26f
Min.
:
0
Min.
:
0.0
1st Qu.:0
1st Qu.:0.0
Median :0Median :
0.0
Mean
:
288
Mean
: 412.8
3rd Qu.:0
3rd Qu.:0.0
Max.
:197000
Max.
:60000.0NA's:18261
NA's:18261
v07_28v07_29
Min.
:
0
Min.
:1.000
1st Qu.:250
1st Qu.:1.000
Median :300
Median :2.000
Mean: 1417Mean:1.835
3rd Qu.:500
3rd Qu.:2.000

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Max.
NA's

:162000:18261
v07_30Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:2.467
3rd Qu.:3.000
Max.
:8.000NA's:27129v07_33bMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 0.459
3rd Qu.: 1.000
Max.
:10.000
NA's:23768Max.
:2.000NA's:18261
v07_31Min.
: 0.00
1st Qu.: 2.00
Median : 5.00
Mean
:11.543rd Qu.: 9.00
Max.
:98.00NA's:18261Max.
:125000
NA's:27129v07_32Min.
: 0.000
1st Qu.: 2.000
Median : 5.000
Mean: 4.101
3rd Qu.: 5.000
Max.
:98.000
NA's:18261Max.
:4.000NA's:27129v07_33aMin.
: 0.00
1st Qu.: 6.00
Median : 9.00
Mean
: 8.02
3rd Qu.:10.00
Max.
:18.00NA's:2376913 ### xh11_s08.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1299
1st Qu.: 5.000
Median :1514
Median :10.000
Mean
:1481
Mean
: 9.288
3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:254.7
3rd Qu.:321.0
Max.
:325.0v08a_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.462
3rd Qu.: 5.000
Max.
:21.000

v08_01Min.
: 0.00
1st Qu.: 1.00
Median : 1.00
Mean
: 1.75
3rd Qu.: 2.00
Max.
:85.00NA's:196v08_05Min.
: 1.000
1st Qu.: 6.000
Median :11.000
Mean: 8.903
3rd Qu.:12.000
Max.
:13.000
NA's:25402v08_08Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.907
3rd Qu.:2.000
Max.
:2.000NA's:25402
v08_11Min.
:1.000
1st Qu.:2.000
Median :2.000

v08_03v08_04Min.
: 0.00Min.
:1.000
1st Qu.: 5.00
1st Qu.:2.000
Median :13.00
Median :2.000
Mean:35.07Mean:1.885
3rd Qu.:98.00
3rd Qu.:2.000
Max.
:98.00Max.
:2.000NA's:27704NA's:196v08_07av08_07b
Min.
:
0
Min.
:
0
1st Qu.:500
1st Qu.:0
Median :1910
Median :
200
Mean
:
7104
Mean
: 26543rd Qu.:5000
3rd Qu.: 1000
Max.
:1250000
Max.
:400000
NA's:25402NA's:25402
v08_10v08b_idc
Min.
:1.000Min.
: 1.000
1st Qu.:1.000
1st Qu.: 2.000
Median :1.000
Median : 3.000
Mean:1.448Mean: 3.462
3rd Qu.:2.000
3rd Qu.: 5.000
Max.
:4.000Max.
:21.000
NA's:196
v08_13v08_14Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000

v08_02Min.
:1.000
1st Qu.:8.000
Median :8.000
Mean:7.826
3rd Qu.:8.000
Max.
:8.000NA's:196v08_06Min.
: 0.00
1st Qu.: 3.00
Median : 5.00
Mean
: 7.833rd Qu.:10.00
Max.
:79.00NA's:25402v08_09Min.
: 0.00
1st Qu.: 0.00
Median : 2.00
Mean
: 17.04
3rd Qu.: 12.00
Max.
:720.00
NA's:25402
v08_12Min.
: 1.000
1st Qu.: 5.000
Median : 5.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

82

Mean
:1.806Mean: 6.478Mean:1.307Mean:1.946
3rd Qu.:2.000
3rd Qu.: 7.000
3rd Qu.:2.000
3rd Qu.:1.000
Max.
:2.000Max.
:14.000
Max.
:2.000Max.
:9.000
NA's
:196
NA's
:23152NA's:23152NA's:26975v08_15v08_16v08b_idc2
v08_17aMin.
: 1.00Min.
:1.000Min.
: 1.000
Min.
:
0.0
1st Qu.: 4.00
1st Qu.:2.000
1st Qu.: 2.000
1st Qu.:0.0
Median : 7.00
Median :4.000
Median : 3.000
Median :0.0
Mean: 5.99Mean:3.484Mean: 3.243
Mean
:
266.2
3rd Qu.: 8.00
3rd Qu.:5.000
3rd Qu.: 4.000
3rd Qu.:70.0
Max.
:11.00Max.
:9.000Max.
:19.000
Max.
:100000.0
NA's:24847NA's:24847NA's:23152NA's:24847v08_17bv08_17cv08_18v08_19
Min.
:
0.0
Min.
:
0.00
Min.
:
0.0
Min.
:1.000
1st Qu.: 100.0
1st Qu.:0.00
1st Qu.:20.0
1st Qu.:2.000
Median : 300.0
Median :0.00
Median :200.0
Median :2.000
Mean: 757.6
Mean
:
94.83
Mean
:
799.5
Mean
:1.875
3rd Qu.: 600.0
3rd Qu.:0.00
3rd Qu.:500.0
3rd Qu.:2.000
Max.
:80000.0
Max.
:20000.00
Max.
:170000.0
Max.
:2.000NA's:24847NA's:24847NA's:23152NA's:23152
v08_20v08_21
v08_22
v08_23Min.
:1.000Min.
: 0.000
Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.: 2.000
1st Qu.:1.000
1st Qu.:1.000
Median :2.000
Median : 4.000
Median :1.000
Median :2.000
Mean:1.522Mean: 6.678
Mean:1.484Mean:1.796
3rd Qu.:2.000
3rd Qu.: 7.000
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:2.000Max.
:360.000
Max.
:2.000Max.
:4.000NA's:24091NA's:26479NA's:28480NA's:28572
v08_24v08c_idc
v08_25v08_26Min.
:1.000Min.
: 1.000
Min.
:1.000Min.
:1.000
1st Qu.:3.500
1st Qu.: 2.000
1st Qu.:1.000
1st Qu.:1.000
Median :8.000
Median : 3.000
Median :1.000
Median :2.000
Mean:5.694Mean: 3.462
Mean:1.489Mean:3.218
3rd Qu.:8.000
3rd Qu.: 5.000
3rd Qu.:2.000
3rd Qu.:6.000
Max.
:9.000Max.
:21.000
Max.
:3.000Max.
:7.000NA's:28634
NA's
:6486
NA's
:15760v08_27av08_27bv08_27cv08_27dMin.
: 1.000Min.
: 1.000Min.
: 1.000Min.
: 1.000
1st Qu.: 2.000
1st Qu.: 3.000
1st Qu.: 4.000
1st Qu.: 5.000
Median : 3.000
Median : 5.000
Median : 8.000
Median : 9.000
Mean: 5.898
Mean: 5.787
Mean: 7.355
Mean: 7.854
3rd Qu.:10.000
3rd Qu.: 9.000
3rd Qu.:10.000
3rd Qu.:10.000
Max.
:16.000Max.
:15.000Max.
:15.000Max.
:15.000
NA's:15760NA's:19231NA's:21789NA's:23956v08d_idc
v08_28v08_29v08_30aMin.
: 1.000
Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.: 2.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:1.000
Median : 3.000
Median :1.000
Median :2.000
Median :1.000
Mean: 3.462Mean:1.052Mean:2.208Mean:1.009
3rd Qu.: 5.000
3rd Qu.:1.000
3rd Qu.:3.000
3rd Qu.:1.000
Max.
:21.000
Max.
:3.000Max.
:3.000Max.
:2.000NA's:25812NA's:25936NA's:28000v08_30bv08_30cv08_30dv08_30eMin.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median :1.000
Median :1.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

83

Mean
:1.046
3rd Qu.:1.000
Max.
:2.000NA's:28000v08_30fMin.
:1.0
1st Qu.:1.0
Median :1.0
Mean
:1.1
3rd Qu.:1.0
Max.
:2.0
NA's
:28000v08_30jMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.321
3rd Qu.:2.000
Max.
:2.000NA's:28000v08_33Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.855
3rd Qu.:2.000
Max.
:3.000NA's:26657v08_37Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.2233rd Qu.:1.000
Max.
:3.000NA's:26606Mean:1.099
3rd Qu.:1.000
Max.
:2.000NA's:28000v08_30g
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.158
3rd Qu.:1.000
Max.
:2.000NA's:28000v08_30kMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.348
3rd Qu.:2.000
Max.
:2.000NA's:28000v08_34Min.
:1.000
1st Qu.:3.000
Median :4.000
Mean:3.495
3rd Qu.:4.000
Max.
:5.000NA's:26657v08_38Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.6133rd Qu.:2.000
Max.
:3.000NA's:26606Mean:1.146
3rd Qu.:1.000
Max.
:2.000NA's:28000v08_30hMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.272
3rd Qu.:2.000
Max.
:2.000NA's:28000v08_31Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0533rd Qu.:1.000
Max.
:3.000NA's:26606v08_35Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.152
3rd Qu.:1.000
Max.
:3.000NA's:26606v08_39Min.
:1.000
1st Qu.:1.000
Median :4.000
Mean:3.228
3rd Qu.:5.000
Max.
:6.000NA's:25936Mean:1.057
3rd Qu.:1.000
Max.
:2.000NA's:28000v08_30iMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.2843rd Qu.:2.000
Max.
:2.000NA's:28000v08_32Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.035
3rd Qu.:1.000
Max.
:3.000NA's:26606v08_36Min.
: 0.000
1st Qu.: 2.000
Median : 3.000
Mean: 2.831
3rd Qu.: 3.000
Max.
:31.000
NA's:26813

14 ### xh12_s09a.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.00
1st Qu.:1366
1st Qu.: 5.00
Median :1543
Median :10.00
Mean
:1503
Mean
: 9.29
3rd Qu.:1698
3rd Qu.:14.00
Max.
:1800
Max.
:34.00xstra
Min.
:100.0
1st Qu.:221.0
Median :224.0
Mean:256.8
3rd Qu.:322.0
Max.
:325.0

id_s9aMin.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.731
3rd Qu.: 4.000
Max.
:16.000

v09a_snMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.078
3rd Qu.:1.000
Max.
:5.000v09_03
Length:15483

v09a_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 2.000
Mean: 2.247
3rd Qu.: 2.000
Max.
:18.000
v09_04aMin.
: 1.000

v09a_rsp
Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.158
3rd Qu.: 2.000
Max.
:19.000
v09_04bMin.
:2026

v09a_boMin.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.478
3rd Qu.: 3.000
Max.
:11.000
v09_05:1.000Min.

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

84Class :character
Mode :character

v09_06Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.078
3rd Qu.:1.000
Max.
:2.000v09_09bMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 1.809
3rd Qu.: 3.000
Max.
:11.000
NA's:14273

1st Qu.: 4.000
Median : 6.000
Mean: 6.373
3rd Qu.: 9.000
Max.
:12.000

1st Qu.:2049
Median :2055
Mean
:2054
3rd Qu.:2060
Max.
:2067

1st Qu.:1.000
Median :1.000
Mean:1.481
3rd Qu.:2.000
Max.
:2.000v09_07v09_08v09_09a
Min.
:1.00
Min.
: 1.000Min.
: 0.000
1st Qu.:1.00
1st Qu.: 3.000
1st Qu.: 0.000
Median :1.00
Median : 4.000
Median : 0.000
Mean
:1.18
Mean
: 4.482
Mean: 1.631
3rd Qu.:1.00
3rd Qu.: 5.000
3rd Qu.: 1.000
Max.
:2.00
Max.
:20.000Max.
:28.000
NA's
:1210
NA's
:3777
NA's
:14273v09_09cv09_10Min.
: 0.000Min.
: 0.00
1st Qu.: 0.000
1st Qu.: 7.00
Median : 1.000
Median :11.00
Mean: 4.849Mean:40.62
3rd Qu.: 8.000
3rd Qu.:98.00
Max.
:30.000Max.
:98.00NA's:14273NA's:1170615 ### xh13_s09b.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1403
1st Qu.: 5.000
Median :1548
Median :10.000
Mean
:1517
Mean
: 9.298
3rd Qu.:1710
3rd Qu.:14.000
Max.
:1800
Max.
:29.000

xstra
Min.
:100.0
1st Qu.:221.0
Median :224.0
Mean:256.1
3rd Qu.:322.0
Max.
:325.0v09b_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 2.000
Mean: 3.219
3rd Qu.: 4.000
Max.
:18.000

v09_12Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.201
3rd Qu.:1.000
Max.
:2.000v09_15Min.
:1.000
1st Qu.:3.000
Median :4.000
Mean:3.796
3rd Qu.:5.000
Max.
:9.000NA's:310v09b_ln2Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.062
3rd Qu.:1.000
Max.
:3.000v09_16Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.051
3rd Qu.:1.000
Max.
:2.000NA's:310v09_19Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:2.482
3rd Qu.:5.000
Max.
:7.000v09b_ln1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.062
3rd Qu.:1.000
Max.
:3.000v09_13
v09_14Min.
: 1.000
Min.
:1.00
1st Qu.: 1.000
1st Qu.:1.00
Median : 4.000
Median :2.00
Mean: 3.589
Mean:1.68
3rd Qu.: 4.000
3rd Qu.:2.00
Max.
:11.000
Max.
:5.00
NA's
:310
NA's
:310
v09_17v09_18Min.
:1.000Min.
:1.000
1st Qu.:2.000
1st Qu.:1.000
Median :2.000
Median :1.000
Mean:2.057Mean:2.481
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:3.000Max.
:9.000
NA's
:373
NA's
:1230
v09_20v09_21Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:2.000
Median :2.000
Median :2.000
Mean:3.106Mean:1.781
3rd Qu.:6.000
3rd Qu.:2.000
Max.
:9.000Max.
:2.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

85v09_22Min.
: 1.000
1st Qu.: 2.000
Median : 4.000
Mean: 4.169
3rd Qu.: 4.000
Max.
:11.000
NA's:1202

v09_23Min.
:1.0
1st Qu.:1.0
Median :1.0
Mean
:1.5
3rd Qu.:2.0
Max.
:5.0
NA's
:1202

v09_24Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.976
3rd Qu.:1.000
Max.
:9.000NA's:338

16 ### xh14_s09c.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1299
1st Qu.: 5.000
Median :1514
Median :10.000
Mean
:1481
Mean
: 9.287
3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:254.7
3rd Qu.:321.0
Max.
:325.0v09c_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.462
3rd Qu.: 5.000
Max.
:21.000

v09_26Min.
: 6.00
1st Qu.:16.00
Median :17.00
Mean:17.69
3rd Qu.:20.00
Max.
:44.00NA's:23071
v09_30Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.282
3rd Qu.:2.000
Max.
:2.000NA's:25488v09_34Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.859
3rd Qu.:2.000
Max.
:2.000NA's:23904v09_27Min.
:0.000
1st Qu.:1.000
Median :1.000
Mean:1.148
3rd Qu.:1.000
Max.
:2.000NA's:23076v09_31Min.
:1.000
1st Qu.:3.000
Median :5.000
Mean:4.975
3rd Qu.:7.000
Max.
:9.000NA's:26382
v09_35aMin.
: 0.000
1st Qu.: 2.000
Median : 2.000
Mean: 3.933
3rd Qu.: 3.000
Max.
:98.000
NA's:23076v09_28Min.
:1.00
1st Qu.:1.00
Median :4.00
Mean
:3.41
3rd Qu.:5.00
Max.
:8.00
NA's
:23904
v09_32Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:2.121
3rd Qu.:3.000
Max.
:5.000NA's:27388v09_35bMin.
:0.000
1st Qu.:1.000
Median :1.000
Mean:1.372
3rd Qu.:2.000
Max.
:7.000NA's:2316317 ### xh15_s09d.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1282
1st Qu.: 5.000
Median :1501
Median :10.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0

v09d_idc
Min.
: 1
1st Qu.: 4Median : 8

v09_25Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.8043rd Qu.:2.000
Max.
:2.000v09_29Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.3333rd Qu.:2.000
Max.
:2.000NA's:23904
v09_33Min.
:1.000
1st Qu.:3.000
Median :4.000
Mean:4.846
3rd Qu.:6.000
Max.
:8.000NA's:26184
v09_35cMin.
:0.000
1st Qu.:1.000
Median :1.000
Mean:1.0733rd Qu.:1.000
Max.
:6.000NA's:23163

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

86

Mean
:1472
3rd Qu.:1672
Max.
:1800

Mean
: 9.308
3rd Qu.:14.000
Max.
:34.000

v09d_txt
Length:82815
Class :character
Mode :character

v09_37Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.548
3rd Qu.:2.000
Max.
:2.00018 ### xh16_s10a.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1299
1st Qu.: 5.000
Median :1514
Median :10.000
Mean
:1481
Mean
: 9.288
3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v10_01aMin.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.7987
3rd Qu.: 0.0000
Max.
:42.0000NA's:3053
v10_01eMin.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.1171
3rd Qu.: 0.0000
Max.
:56.0000NA's:3053
v10_01iMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 4.182
3rd Qu.: 7.000
Max.
:56.000
NA's:3053
v10_01mMin.
: 0.00
1st Qu.: 0.00
Median : 0.00
Mean
: 2.41
3rd Qu.: 0.00
Max.
:105.00
NA's
:3053

Mean
:252.5
3rd Qu.:321.0
Max.
:325.0Mean: 8
3rd Qu.:12
Max.
:15

v09_38Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.26
3rd Qu.:2.00
Max.
:3.00
NA's
:45347xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:254.7
3rd Qu.:321.0
Max.
:325.0v09_39Min.
:1.00
1st Qu.:2.00
Median :3.00
Mean
:2.55
3rd Qu.:3.00
Max.
:4.00
NA's
:45347v10_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.462
3rd Qu.: 5.000
Max.
:21.000

v10_01bv10_01cMin.
: 0.000Min.
: 0.000
1st Qu.: 0.000
1st Qu.: 0.000
Median : 0.000
Median : 0.000
Mean: 1.026
Mean: 3.008
3rd Qu.: 0.000
3rd Qu.: 4.000
Max.
:47.000Max.
:56.000
NA's
:3053
NA's
:3053
v10_01fv10_01g
Min.
: 0.0000Min.
: 0.000
1st Qu.: 0.0000
1st Qu.: 0.000
Median : 0.0000
Median : 2.000
Mean: 0.3225
Mean: 8.736
3rd Qu.: 0.0000
3rd Qu.:14.000
Max.
:40.0000Max.
:94.000
NA's
:3053
NA's
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v10_01jv10_01kMin.
: 0.000Min.
: 0.0000
1st Qu.: 0.000
1st Qu.: 0.0000
Median : 0.000
Median : 0.0000
Mean: 3.099Mean: 0.9071
3rd Qu.: 5.000
3rd Qu.: 1.0000
Max.
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NA's
:3053
NA's
:3053
v10_01nMin.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.2434
3rd Qu.: 0.0000
Max.
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NA's
:3053

v10_01dMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 3.463
3rd Qu.: 5.000
Max.
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NA's:3054
v10_01hMin.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.2599
3rd Qu.: 0.0000
Max.
:42.0000NA's:3053
v10_01lMin.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.2348
3rd Qu.: 0.0000
Max.
:60.0000
NA's
:3053

19 ### xh17_s10b.dta ###Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

87

xhpsu
Min.
:1001
1st Qu.:1328
Median :1526
Mean
:1490
3rd Qu.:1687
Max.
:1800
v10_02Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.985
3rd Qu.: 4.000
Max.
:19.000
v10_04aMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.258
3rd Qu.:2.000
Max.
:2.000NA's:10356v10_04eMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.277
3rd Qu.:2.000
Max.
:2.000NA's:10356v10_04iMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.367
3rd Qu.:2.000
Max.
:2.000NA's:10356v10_05aMin.
: 1.00
1st Qu.:10.00
Median :15.00
Mean:16.27
3rd Qu.:25.00
Max.
:30.00NA's:10357v10_06cMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 2.698
3rd Qu.: 6.000
Max.
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NA's:10356xhnum
Min.
: 1.000
1st Qu.: 5.000
Median :10.000
Mean: 9.283
3rd Qu.:14.000
Max.
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xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:253.1
3rd Qu.:321.0
Max.
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Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.269
3rd Qu.:1.000
Max.
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1st Qu.: 2.000
Median : 5.000
Mean: 5.863
3rd Qu.: 8.000
Max.
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v10_03_txt
Length:32611
Class :character
Mode :character

v10_03Min.
: 11.0
1st Qu.:621.0
Median :712.0
Mean:757.5
3rd Qu.:997.0
Max.
:998.0NA's:1v10_04bv10_04cv10_04dMin.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median :1.000
Mean:1.236Mean:1.158Mean:1.195
3rd Qu.:1.000
3rd Qu.:1.000
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Max.
:2.000Max.
:2.000Max.
:2.000NA's:10356NA's:10356NA's:10356v10_04fv10_04gv10_04hMin.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median :1.000
Mean:1.294Mean:1.233Mean:1.228
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3rd Qu.:1.000
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Max.
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:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median :1.000
Mean:1.369Mean:1.302Mean:1.238
3rd Qu.:2.000
3rd Qu.:2.000
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Max.
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:2.000NA's:10356NA's:10356NA's:10356v10_05bv10_06av10_06bMin.
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1st Qu.: 5.000
1st Qu.: 0.000
1st Qu.: 0.000
Median : 6.000
Median : 0.000
Median : 0.000
Mean: 6.412Mean: 2.651
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3rd Qu.: 8.000
3rd Qu.: 6.000
3rd Qu.: 6.000
Max.
:18.000Max.
:18.000Max.
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NA's:10357NA's:10356NA's:10356v10_06dv10_06ev10_06fMin.
: 0.000Min.
: 0.000Min.
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1st Qu.: 0.000
1st Qu.: 0.000
1st Qu.: 0.000
Median : 0.000
Median : 0.000
Median : 0.000
Mean: 2.642Mean: 2.604
Mean: 2.515
3rd Qu.: 6.000
3rd Qu.: 6.000
3rd Qu.: 5.000
Max.
:18.000Max.
:18.000Max.
:18.000
NA's:10356NA's:10356NA's:10356

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

88v10_06gMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 2.052
3rd Qu.: 4.000
Max.
:16.000
NA's:10356v10_06hMin.
: 0.00
1st Qu.: 0.00
Median : 8.00
Mean
: 17.86
3rd Qu.: 32.00
Max.
:117.00
NA's:10355v10_07Min.
:1.000
1st Qu.:2.000
Median :3.000
Mean:2.806
3rd Qu.:3.000
Max.
:4.000NA's:1035620 ### xh18_s11.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1299
1st Qu.: 5.000
Median :1514
Median :10.000
Mean
:1481
Mean
: 9.288
3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :224.0
Mean:254.7
3rd Qu.:321.0
Max.
:325.0v11_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.462
3rd Qu.: 5.000
Max.
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v11_01Min.
: 0.00
1st Qu.: 0.00
Median : 14.00
Mean: 24.25
3rd Qu.: 44.00
Max.
:150.00
NA's:3053
v11_05Min.
:1.0
1st Qu.:2.0
Median :2.0
Mean
:1.8
3rd Qu.:2.0
Max.
:2.0
NA's
:19140

v11_03Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.675
3rd Qu.:2.000
Max.
:2.000NA's:28026v11_07Min.
: 1.000
1st Qu.: 8.000
Median : 9.000
Mean: 7.932
3rd Qu.: 9.000
Max.
:13.000
NA's:19794v11_04Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 2.328
3rd Qu.: 2.000
Max.
:10.000
NA's:20772v11_02Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.921
3rd Qu.:2.000
Max.
:2.000NA's:20563v11_06Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.656
3rd Qu.:2.000
Max.
:2.000NA's:2676721 ### xh19_s12.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.00
1st Qu.:1275
1st Qu.: 5.00
Median :1516
Median :10.00
Mean
:1474
Mean
: 9.37
3rd Qu.:1680
3rd Qu.:14.00
Max.
:1800
Max.
:34.00xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:252.1
3rd Qu.:321.0
Max.
:325.0v12_ln1Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.832
3rd Qu.: 2.000
Max.
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v12_01Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.199
3rd Qu.: 3.000
Max.
:17.000

v12_01_job
Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.675
3rd Qu.:2.000
Max.
:6.000v12_02_txt
Length:6552
Class :character
Mode :character

v12_03Min.
:1.000
1st Qu.:1.000
Median :1.000

v12_04Min.
:
0.0
1st Qu.: 100.0
Median : 150.0

v12_05aMin.
:1.000
1st Qu.:4.000
Median :4.000

v12_02Min.
: 1.00
1st Qu.: 1.00
Median :45.00
Mean:35.943rd Qu.:60.00
Max.
:99.00NA's:3v12_05bMin.
:1.000
1st Qu.:4.000
Median :4.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

89

Mean
:1.489
3rd Qu.:2.000
Max.
:3.000Mean: 175.7
Mean:4.949Mean:4.575
3rd Qu.: 200.0
3rd Qu.:7.000
3rd Qu.:6.000
Max.
:8000.0
Max.
:7.000Max.
:6.000
NA's
:2676
NA's
:2676
NA's
:6117
v12_06av12_06bv12_ln2v12_07Min.
: 0.00
Min.
:
0.0
Min.
: 1.000
Min.
:1.000
1st Qu.: 35.00
1st Qu.:0.0
1st Qu.: 1.000
1st Qu.:2.000
Median : 50.00
Median :0.0Median : 1.000
Median :2.000
Mean: 76.58
Mean: 130.3
Mean: 1.598
Mean:1.992
3rd Qu.: 100.00
3rd Qu.:50.0
3rd Qu.: 2.000
3rd Qu.:2.000
Max.
:4500.00
Max.
:35000.0
Max.
:12.000
Max.
:2.000
NA's
:4221
NA's
:4221
NA's
:4401
NA's
:4401
v12_08v12_09av12_09bv12_10a
Min.
:
0
Min.
:3.000
Min.
:4.00
Min.
: 0.0
1st Qu.: 4000
1st Qu.:4.000
1st Qu.:4.75
1st Qu.: 30.0
Median :18000
Median :4.000
Median :5.00
Median :100.0
Mean:27729Mean:5.118
Mean
:5.00
Mean
:114.5
3rd Qu.:57600
3rd Qu.:7.000
3rd Qu.:5.25
3rd Qu.:120.0Max.
:72000Max.
:7.000
Max.
:6.00
Max.
:480.0
NA's
:6535
NA's
:6535
NA's
:6548
NA's
:6541
v12_10bv12_11
v12_12
v12_13
Min.
:
0
Min.
:1.000Min.
:1.000Min.
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1st Qu.:5
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:2
Median : 100
Median :2.000
Median :2.000
Median :2
Mean: 1142Mean:1.824Mean:1.824Mean:2
3rd Qu.: 148
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2
Max.
:11000Max.
:2.000Max.
:2.000
Max.
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NA's
:6541
NA's
:6535
NA's
:6535
NA's
:6535
v12_14v12_ln3v12_15av12_15bMin.
:1.000Min.
: 1.000
Min.
:
0
Min.
:
0.0
1st Qu.:1.000
1st Qu.: 1.000
1st Qu.: 4000
1st Qu.:0.0
Median :1.000
Median : 1.000
Median : 7000
Median :0.0
Mean:1.471Mean: 1.598
Mean: 9307
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3rd Qu.:2.000
3rd Qu.: 2.000
3rd Qu.: 11000
3rd Qu.:0.0
Max.
:2.000Max.
:12.000
Max.
:180000
Max.
:36000.0
NA's
:6535
NA's
:4418
NA's
:4418
NA's
:4418
v12_15cv12_15dv12_15ev12_16
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:1.000
1st Qu.:0
1st Qu.:0
1st Qu.:0
1st Qu.:1.000
Median : 1000
Median :0Median :0Median :2.000
Mean: 5896
Mean
: 590
Mean
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3rd Qu.: 9900
3rd Qu.:0
3rd Qu.: 3000
3rd Qu.:2.000Max.
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Max.
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Max.
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NA's
:4418
NA's
:4418
NA's
:4418
NA's
:4418
v12_17v12_18v12_19v12_20Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:2.000
Median :2.000
Median :2.000
Median :2.000
Median :3.000
Mean:1.732Mean:1.832Mean:1.761Mean:2.5233rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:3.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
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NA's
:4418
NA's
:4418
NA's
:4418
NA's
:4418
v12_21Min.
:
0
1st Qu.: 9000
Median : 20000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

90

Mean
: 66506
3rd Qu.: 50000
Max.
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NA's:6027
22 ### xh20_s13a1.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1446
1st Qu.: 5.000
Median :1616
Median : 9.000
Mean
:1567
Mean
: 9.097
3rd Qu.:1715
3rd Qu.:13.000
Max.
:1800
Max.
:32.000
v13_03
Length:13237
Class :character
Mode :character

v13_04pdMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 1.058
3rd Qu.: 0.000
Max.
:19.000

xstra
Min.
:100.0
1st Qu.:221.0
Median :223.0
Mean:240.6
3rd Qu.:321.0
Max.
:325.0

v13_04uMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.386
3rd Qu.:2.000
Max.
:2.000
v13_05Min.
: 1.00
1st Qu.:20.00
Median :38.00
Mean:38.12
3rd Qu.:55.00
Max.
:75.00v13_plt1
Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.982
3rd Qu.: 4.000
Max.
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v13_04rbMin.
: 0.000
1st Qu.: 0.000
Median : 1.000
Mean: 2.074
3rd Qu.: 3.000
Max.
:140.000v13_06Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.496
3rd Qu.:2.000
Max.
:2.000

v13_04akMin.
: 0.000
1st Qu.: 0.000
Median : 1.000
Mean: 3.197
3rd Qu.: 6.000
Max.
:19.000

v13_07Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.585
3rd Qu.:2.000
Max.
:2.000

v13_08Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.5533rd Qu.:2.000
Max.
:2.000NA's:7744
v13_11Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:2.026
3rd Qu.:2.000
Max.
:6.000

v13_09v13_10v13_plt2
Min.
:1.000
Min.
:
0
Min.
: 1.000
1st Qu.:1.000
1st Qu.:33000
1st Qu.: 1.000
Median :2.000
Median :100000Median : 2.000
Mean:1.941
Mean:
644346Mean: 2.982
3rd Qu.:2.000
3rd Qu.:300000
3rd Qu.: 4.000
Max.
:5.000Max.
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Max.
:22.000
NA's
:7744
NA's
:1
v13_12cv13_12kv13_13a
Min.
:
0.0
Min.
:
0
Min.
: 0.00
1st Qu.:0.0
1st Qu.:0
1st Qu.: 4.00
Median :0.0Median :500
Median : 8.00
Mean
: 805.2
Mean: 2899Mean:17.16
3rd Qu.:0.0
3rd Qu.: 3000
3rd Qu.:27.00
Max.
:60000.0Max.
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Max.
:67.00NA's:12077NA's:12077NA's:4082
v13_13bv13_13cv13_13dv13_14Min.
: 1.00Min.
: 1.00
Min.
: 1.0
Min.
:1.000
1st Qu.:15.00
1st Qu.:19.00
1st Qu.:22.0
1st Qu.:1.000
Median :27.00
Median :29.00
Median :37.0
Median :1.000
Mean:26.02Mean:30.24
Mean
:33.1
Mean
:1.4533rd Qu.:38.00
3rd Qu.:39.00
3rd Qu.:44.0
3rd Qu.:1.000
Max.
:67.00Max.
:67.00
Max.
:67.0
Max.
:6.000
NA's
:7390
NA's
:9519
NA's
:10888v13_15cv13_15kv13_16av13_16b
Min.
:
0.0
Min.
:
0
Min.
: 0.00Min.
: 1.00

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

91

1st Qu.:0.0
1st Qu.:0
Median :0.0Median : 2000
Mean
: 964.8
Mean: 5842
3rd Qu.:0.0
3rd Qu.: 7000
Max.
:125000.0
Max.
:480000
NA's:11867NA's:11867v13_16cv13_16dMin.
: 1.00Min.
: 1.00
1st Qu.:12.00
1st Qu.:19.00
Median :21.00
Median :36.00
Mean:27.32Mean:31.91
3rd Qu.:45.00
3rd Qu.:45.00
Max.
:67.00Max.
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NA's
:9323
NA's
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xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1419
1st Qu.: 5.000
Median :1553
Median :10.000
Mean
:1527
Mean
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3rd Qu.:1685
3rd Qu.:14.000
Max.
:1800
Max.
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xstra
Min.
:100.0
1st Qu.:221.0
Median :224.0
Mean:252.43rd Qu.:322.0
Max.
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Class :character
Mode :character

v13_20Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.749
3rd Qu.:3.000
Max.
:4.000v13_22rb
Min.
: 0.000
1st Qu.: 0.000
Median : 1.000
Mean: 2.233
3rd Qu.: 3.000
Max.
:150.000v13_22akMin.
: 0.000
1st Qu.: 0.000
Median : 1.000
Mean: 3.772
3rd Qu.: 8.000
Max.
:19.000

v13_24Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.448
3rd Qu.:2.000
Max.
:2.000v13_27aMin.
: 0.000
1st Qu.: 0.000
Median : 4.000
Mean: 9.436
3rd Qu.:15.000
Max.
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1st Qu.: 2.00
Median : 6.00
Mean
:10.033rd Qu.: 6.00
Max.
:67.00NA's:2320

v13_25Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.541
3rd Qu.:2.000
Max.
:2.000NA's:854v13_27bMin.
: 1.00
1st Qu.:15.00
Median :22.00
Mean:21.72
3rd Qu.:27.00
Max.
:65.00NA's:1244

1st Qu.: 7.00
Median :11.00
Mean:18.71
3rd Qu.:25.00
Max.
:67.00NA's:6535

v13_plt3
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.539
3rd Qu.:2.000
Max.
:8.000v13_21
v13_22u
Min.
:
0
Min.
:1.000
1st Qu.:0
1st Qu.:1.000
Median :0Median :1.000
Mean: 2869Mean:1.467
3rd Qu.: 2000
3rd Qu.:2.000
Max.
:90000Max.
:2.000NA's:1463
v13_22pd
v13_23Min.
: 0.0000
Min.
:1.000
1st Qu.: 0.0000
1st Qu.:1.000
Median : 0.0000
Median :2.000
Mean: 0.4187
Mean:1.669
3rd Qu.: 0.0000
3rd Qu.:2.000
Max.
:17.0000
Max.
:2.000v13_26Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.008
3rd Qu.:2.000
Max.
:5.000NA's:854v13_27cMin.
: 1.00
1st Qu.:15.00
Median :25.00
Mean:24.95
3rd Qu.:37.00
Max.
:65.00NA's:1600

v13_plt4
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.539
3rd Qu.:2.000
Max.
:8.000v13_27dMin.
: 4.00
1st Qu.:15.00
Median :22.00
Mean:26.05
3rd Qu.:38.00
Max.
:64.00NA's:1751

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

92v13_28aMin.
: 0.000
1st Qu.: 2.000
Median : 2.000
Mean: 5.625
3rd Qu.: 6.000
Max.
:65.000

v13_28bMin.
: 1.00
1st Qu.: 6.00
Median :11.00
Mean:12.08
3rd Qu.:12.00
Max.
:65.00NA's:1044

v13_28cMin.
: 1.00
1st Qu.:11.00
Median :12.00
Mean:19.96
3rd Qu.:23.00
Max.
:67.00NA's:1579

v13_28dMin.
: 6.0
1st Qu.:12.0
Median :21.0
Mean
:23.9
3rd Qu.:39.0
Max.
:60.0
NA's
:1791

24 ### xh22_s13b.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1437
1st Qu.: 5.000
Median :1615
Median :10.000
Mean
:1571
Mean
: 9.121
3rd Qu.:1727
3rd Qu.:14.000
Max.
:1800
Max.
:26.000

xstra
Min.
:100.0
1st Qu.:221.0
Median :224.0
Mean:244.33rd Qu.:321.0
Max.
:325.0v13b_lnMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.965
3rd Qu.:10.000
Max.
:32.000

v13_35nm
Length:42324
Class :character
Mode :character

v13_35ccMin.
: 1.00
1st Qu.: 8.00
Median :25.00
Mean:27.07
3rd Qu.:44.00
Max.
:67.00v13_37bMin.
:
0.0
1st Qu.:5.0
Median :
14.0
Mean
:
89.6
3rd Qu.:40.0
Max.
:300000.0

v13_37c
Min.
:
0.000
1st Qu.:0.000
Median :
0.000
Mean
:
3.136
3rd Qu.:0.000
Max.
:20000.000

v13_38bMin.
:
0.0
1st Qu.:5.0
Median :20.0
Mean: 265.8
3rd Qu.:80.0
Max.
:300000.0
NA's:37995v13_38cMin.
:
0.0
1st Qu.:20.0
Median : 100.0
Mean: 712.2
3rd Qu.: 1000.0
Max.
:40000.0NA's:3799525 ### xh23_s13c1.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1431
1st Qu.: 4.000
Median :1565
Median : 9.000
Mean
:1534
Mean
: 8.963
3rd Qu.:1698
3rd Qu.:13.000
Max.
:1800
Max.
:26.000

v13_36Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.899
3rd Qu.:2.000
Max.
:2.000v13_40_txt
Length:5956
Class :character
Mode :character

v13_37aMin.
: 1.00
1st Qu.: 1.00
Median : 1.00
Mean
: 3.12
3rd Qu.: 5.00
Max.
:11.00v13_37dMin.
:
0.00
1st Qu.:0.00
Median :
0.00
Mean
:
28.02
3rd Qu.:0.00
Max.
:300000.00
v13_38dMin.
:0
1st Qu.:0
Median :
0
Mean
:
1150
3rd Qu.:0
Max.
:2400000

xstra
Min.
:100.0
1st Qu.:222.0
Median :310.0
Mean:268.33rd Qu.:322.0
Max.
:325.0v13_40Min.
: 1.00
1st Qu.:15.00
Median :24.00

v13_38aMin.
: 0.00
1st Qu.: 1.00
Median : 3.00
Mean
: 4.36
3rd Qu.: 6.00
Max.
:11.00NA's:38007v13_41aMin.
:1.000
1st Qu.:3.000
Median :4.000

v13c1_lnMin.
: 1.00
1st Qu.: 1.00
Median : 2.00
Mean
: 2.15
3rd Qu.: 3.00
Max.
:10.00v13_41bMin.
:1.000
1st Qu.:2.000
Median :3.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

93

Mean
:27.36
3rd Qu.:44.00
Max.
:67.00Mean:3.609
3rd Qu.:4.000
Max.
:7.000Mean:3.529
3rd Qu.:4.000
Max.
:7.000NA's:5803

v13_42Min.
:
0.0
1st Qu.: 50.0
Median : 200.0
Mean: 582.3
3rd Qu.: 500.0
Max.
:38500.0

26 ### xh24_s13c2.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1428
1st Qu.: 5.000
Median :1564
Median :10.000
Mean
:1529
Mean
: 9.156
3rd Qu.:1683
3rd Qu.:14.000
Max.
:1800
Max.
:26.000
v13_44_txt
Length:17724
Class :character
Mode :character

xstra
Min.
:100.0
1st Qu.:222.0
Median :225.0
Mean:261.9
3rd Qu.:322.0
Max.
:325.0v13_46cMin.
: 1.00
1st Qu.: 6.00
Median :22.00
Mean:19.643rd Qu.:27.00
Max.
:65.00NA's:15300v13_45ynMin.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.629
3rd Qu.:2.000
Max.
:2.000v13_47aMin.
:
0.0
1st Qu.: 10.0
Median : 34.0
Mean
: 122.2
3rd Qu.: 100.0
Max.
:9999.0
NA's:1216127 ### xh25_s13c3.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1413
1st Qu.: 4.000
Median :1497
Median : 9.000
Mean
:1487
Mean
: 9.015
3rd Qu.:1622
3rd Qu.:13.000
Max.
:1800
Max.
:26.000
v13_51
Length:3430
Class :character
Mode :character

v13_46aMin.
: 1.000
1st Qu.: 2.000
Median : 2.000
Mean: 8.638
3rd Qu.: 6.000
Max.
:66.000
NA's:11141
v13_47bMin.
:0
1st Qu.:225
Median :700
Mean: 1568
3rd Qu.: 1700
Max.
:213750NA's:11141xstra
Min.
:100.0
1st Qu.:221.0
Median :224.0
Mean:260.33rd Qu.:321.0
Max.
:325.0v13_52Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:1.92
3rd Qu.:2.00
Max.
:2.00
NA's
:1

v13c2_lnMin.
:1.0
1st Qu.:2.0
Median :3.5
Mean
:3.5
3rd Qu.:5.0
Max.
:6.0
v13_46bMin.
: 1.00
1st Qu.: 4.00
Median : 4.00
Mean
:11.26
3rd Qu.:15.00
Max.
:66.00NA's:12988v13c3_lnMin.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.769
3rd Qu.: 2.000
Max.
:11.000

v13_53m
Min.
:
0.000
1st Qu.:0.000
Median :
4.000
Mean
:
8.847
3rd Qu.: 10.000
Max.
:3000.000
NA's
:275

v13_53fMin.
:
0.00
1st Qu.:2.00
Median :8.00
Mean: 15.59
3rd Qu.: 18.00
Max.
:2000.00
NA's:275

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

94v13_54mMin.
: 0.0
1st Qu.: 0.0
Median : 100.0
Mean: 123.4
3rd Qu.: 200.0Max.
:1125.0NA's:275
v13_56mMin.
: 0.0
1st Qu.: 0.0
Median : 150.0
Mean: 166.6
3rd Qu.: 250.0Max.
:2480.0NA's:275
v13_57t
Min.
:0
1st Qu.: 1000
Median : 2100
Mean
: 4231
3rd Qu.: 4572
Max.
:408000

v13_54fMin.
:
0.00
1st Qu.:0.00
Median : 100.00
Mean: 85.23
3rd Qu.: 130.00
Max.
:1080.00
NA's:275
v13_56fMin.
:
0.0
1st Qu.: 75.0
Median : 130.0
Mean: 126.4
3rd Qu.: 180.0
Max.
:2080.0NA's:275

v13_55mv13_55f
Min.
:
0.00
Min.
:
0.00
1st Qu.:0.00
1st Qu.:0.00
Median : 38.00
Median : 30.00
Mean: 43.22
Mean: 41.14
3rd Qu.: 50.00
3rd Qu.: 50.00
Max.
:2400.00
Max.
:2000.00
NA's
:275
NA's
:275
v13_57mv13_57f
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:250
Median :780
Median : 1100
Mean
: 1838Mean: 23533rd Qu.: 2000
3rd Qu.: 2640
Max.
:360000
Max.
:220000
NA's
:275
NA's
:275

28 ### xh26_s13d1.dta ###
xhpsu
xhnum
xstra
Min.
:1001
Min.
: 1.000
Min.
:100.0
1st Qu.:1430
1st Qu.: 5.000
1st Qu.:221.0
Median :1582
Median :10.000
Median :224.0
Mean
:1546
Mean
: 9.183
Mean:251.1
3rd Qu.:1704
3rd Qu.:14.000
3rd Qu.:321.0
Max.
:1800
Max.
:26.000
Max.
:325.0v13d1_nm
v13_63
Length:28834
Min.
:
0Class :character
1st Qu.:0Mode :character
Median :
0
Mean
:
1965
3rd Qu.:0
Max.
:2400000
29 ### xh27_s13d2.dta ###
xhpsu
xhnum
xstra
Min.
:1001
Min.
: 1.000
Min.
:100.0
1st Qu.:1430
1st Qu.: 5.000
1st Qu.:221.0
Median :1582
Median :10.000
Median :224.0
Mean
:1546
Mean
: 9.182
Mean:251.1
3rd Qu.:1704
3rd Qu.:14.000
3rd Qu.:321.0
Max.
:1800
Max.
:26.000
Max.
:325.0v13d2_nm
v13_64
Length:61800
Min.
:
0Class :character
1st Qu.:0Mode :character
Median :
0
Mean
:
1299
3rd Qu.:225
Max.
:1020825v13d1_sn
Min.
:1
1st Qu.:2
Median :4Mean:4
3rd Qu.:6
Max.
:7

v13d2_enMin.
: 1.0
1st Qu.: 4.0
Median : 8.0
Mean
: 8.4
3rd Qu.:13.0
Max.
:16.0

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

95

30 ### xh28_s13e1.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1430
1st Qu.: 5.000
Median :1574
Median :10.000
Mean
:1539
Mean
: 9.189
3rd Qu.:1701
3rd Qu.:14.000
Max.
:1800
Max.
:26.000
v13_66ynMin.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.719
3rd Qu.:2.000
Max.
:2.000

xstra
Min.
:100
1st Qu.:221
Median :224
Mean
:254
3rd Qu.:321
Max.
:325

v13_67aMin.
: 0.000
1st Qu.: 2.000
Median : 2.000
Mean: 4.072
3rd Qu.: 5.000
Max.
:117.000NA's:26956v13_68bv13_69a
Min.
:
0
Min.
: 0.00
1st Qu.: 2000
1st Qu.: 0.00
Median : 8000
Median : 0.00
Mean
: 14504
Mean: 1.15
3rd Qu.: 20000
3rd Qu.: 1.00
Max.
:1150000
Max.
:500.00
NA's:26956NA's:26956v13_70bMin.
:0
1st Qu.:0
Median :0
Mean: 1578
3rd Qu.:0
Max.
:150000
NA's:26956v13_67bMin.
:0
1st Qu.: 2100
Median : 9000
Mean
: 15771
3rd Qu.: 21000
Max.
:420000
NA's:26956v13_69bMin.
:0
1st Qu.:0
Median :0
Mean: 2830
3rd Qu.: 1500
Max.
:300000
NA's:2695631 ### xh29_s13e2.dta ###
xhpsu
xhnum
xstra
Min.
:1001
Min.
: 1.000
Min.
:100
1st Qu.:1430
1st Qu.: 5.000
1st Qu.:221
Median :1574
Median :10.000
Median :224
Mean
:1539
Mean
: 9.189
Mean:254
3rd Qu.:1701
3rd Qu.:14.000
3rd Qu.:321
Max.
:1800
Max.
:26.000
Max.
:325
v13e2_nm
v13_71
Length:33344
Min.
:
0Class :character
1st Qu.:0Mode :character
Median :0
Mean: 1086
3rd Qu.:0Max.
:720000
32 ### xh30_s13e3.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1430
1st Qu.: 5.000
Median :1574
Median :10.000

v13e1_lc
v13_66nm
Min.
:1Length:37512
1st Qu.:3
Class :character
Median :5
Mode :characterMean:5
3rd Qu.:7
Max.
:9

xstra
Min.
:100
1st Qu.:221
Median :224

v13_68aMin.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 4.089
3rd Qu.: 5.000
Max.
:136.000NA's:26956v13_70aMin.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 0.506
3rd Qu.: 0.000Max.
:400.000NA's:26956v13e2_idMin.
:1.00
1st Qu.:2.75
Median :4.50
Mean
:4.50
3rd Qu.:6.25
Max.
:8.00

v13e3_idMin.
:1.00
1st Qu.:1.75
Median :2.50

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

96

Mean
:1539
Mean
: 9.189
Mean:254
3rd Qu.:1701
3rd Qu.:14.000
3rd Qu.:321
Max.
:1800
Max.
:26.000
Max.
:325
v13e3_nm
v13_72
Length:16672
Min.
:
0Class :character
1st Qu.:0Mode :character
Median :0
Mean: 1216
3rd Qu.:985
Max.
:223000
33 ### xh31_s13f.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1427
1st Qu.: 5.000
Median :1573
Median :10.000
Mean
:1537
Mean
: 9.185
3rd Qu.:1701
3rd Qu.:14.000
Max.
:1800
Max.
:31.000

xstra
Min.
:100.0
1st Qu.:221.0
Median :224.0
Mean:254.43rd Qu.:322.0
Max.
:325.0v13_75nm
Length:38961
Class :character
Mode :character

v13_75ynMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.788
3rd Qu.:2.000
Max.
:2.000v13_78Min.
: 0.00
1st Qu.: 0.00
Median : 0.00
Mean
: 0.133rd Qu.: 0.00
Max.
:600.00
NA's:30686v13_79Min.
:
0.0
1st Qu.:357.5
Median :650.0
Mean: 39387.2
3rd Qu.: 2525.0
Max.
:850000.0
NA's:3889734 ### xh32_s14.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1228
1st Qu.: 5.000
Median :1453
Median :10.000
Mean
:1432
Mean
: 9.278
3rd Qu.:1641
3rd Qu.:14.000
Max.
:1800
Max.
:28.000
v14_02aLength:2583
Class :character
Mode :character

v14_03bMin.
: 1.000

Mean:2.50
3rd Qu.:3.25
Max.
:4.00

v13f_ecMin.
:1
1st Qu.:3Median :5Mean:5
3rd Qu.:7Max.
:9

v13_76v13_77
Min.
: 0.0
Min.
:
10
1st Qu.: 1.0
1st Qu.:600
Median : 3.0
Median :
1200
Mean
: 4.8
Mean
:
5315
3rd Qu.: 7.0
3rd Qu.:2200
Max.
:100.0Max.
:1500000NA's:30686NA's:30695v13_80v13_81Min.
: 0.000
Min.
:0
1st Qu.: 0.000
1st Qu.:200
Median : 0.000
Median :400
Mean: 0.266
Mean:
4848
3rd Qu.: 0.000
3rd Qu.:750
Max.
:12.000
Max.
:1253000NA's:30686NA's:37740xstra
Min.
:100.0
1st Qu.:218.0
Median :223.0
Mean:251.5
3rd Qu.:321.0
Max.
:325.0

v14_02b
Length:2583
Class :character
Mode :character

v14_03cMin.
: 1.00

v14_02cMin.
: 1
1st Qu.:17
Median :52
Mean:41
3rd Qu.:52
Max.
:95
NA's
:2
v14_03dMin.
: 2.000

v14_ec1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.238
3rd Qu.:1.000
Max.
:5.000
v14_03aMin.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.613
3rd Qu.: 2.000
Max.
:18.000
v14_03eMin.
: 5.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

97

1st Qu.: 2.000
Median : 2.000
Mean: 2.793
3rd Qu.: 3.000
Max.
:17.000
NA's:1504
v14_04Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.657
3rd Qu.: 2.000
Max.
:19.000

1st Qu.: 3.00
Median : 3.00
Mean
: 4.043rd Qu.: 4.00
Max.
:18.00NA's:2207
v14_05aMin.
:
0.0
1st Qu.:3.0
Median :8.0
Mean: 221.9
3rd Qu.: 19.0
Max.
:2067.0

1st Qu.: 4.000
Median : 4.000
Mean: 4.993
3rd Qu.: 5.000
Max.
:16.000
NA's:2444
v14_05bMin.
: 0.000
1st Qu.: 0.000
Median : 2.000
Mean: 3.144
3rd Qu.: 6.000
Max.
:12.000

1st Qu.: 5.000
Median : 6.000
Mean: 6.196
3rd Qu.: 7.000
Max.
:12.000
NA's:2537
v14_06Min.
:1.0
1st Qu.:1.0
Median :2.0
Mean
:1.7
3rd Qu.:2.0
Max.
:3.0

v14_07Min.
: 1.000
1st Qu.: 8.000
Median :12.000
Mean: 9.841
3rd Qu.:12.000
Max.
:12.000

v14_08Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.059
3rd Qu.:1.000
Max.
:2.000

v14_09Min.
: 40.0
1st Qu.: 250.0
Median : 500.0
Mean: 375.7
3rd Qu.: 500.0
Max.
:1000.0
NA's:2431
v14_11Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.748
3rd Qu.:2.000
Max.
:2.000v14_ec2Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.238
3rd Qu.:1.000
Max.
:5.000v14_10aMin.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.583
3rd Qu.: 1.000
Max.
:10.000

v14_10bv14_12Min.
: 1.000Min.
: 1.000
1st Qu.: 2.000
1st Qu.: 1.000
Median : 5.000
Median : 2.000
Mean: 5.159
Mean: 2.575
3rd Qu.:10.000
3rd Qu.: 3.000
Max.
:10.000
Max.
:11.000
NA's:1562
v14_13v14_14v14_15v14_16Min.
:1.000Min.
:1.000Min.
:1.000Min.
: 0.00
1st Qu.:3.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.: 1.00
Median :3.000
Median :3.000
Median :2.000
Median : 2.00
Mean
:2.549Mean:3.353Mean:1.813Mean: 10.37
3rd Qu.:3.000
3rd Qu.:5.000
3rd Qu.:2.000
3rd Qu.: 5.00
Max.
:3.000Max.
:8.000Max.
:2.000Max.
:500.00
NA's
:1960
NA's
:2100
v14_17v14_ec3
v14_18v14_19Min.
: 1.000
Min.
:1.000
Min.
:
0
Min.
:
0
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.:20000
1st Qu.:0
Median : 2.000
Median :1.000
Median :90000
Median :0
Mean: 4.299
Mean:1.238Mean: 623480
Mean: 44228
3rd Qu.: 9.000
3rd Qu.:1.000
3rd Qu.: 400000
3rd Qu.:0
Max.
:13.000
Max.
:5.000Max.
:81400000
Max.
:6148144
NA's
:1
NA's
:1
v14_20v14_21av14_21bv14_22
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
1st Qu.:0
1st Qu.:0
Median :300
Median :3000
Median :0Median :1000
Mean: 15881Mean: 314322
Mean
:
49310
Mean
:
37868
3rd Qu.: 5000
3rd Qu.: 108000
3rd Qu.:0
3rd Qu.:10000
Max.
:1958000
Max.
:60000000
Max.
:12000000
Max.
:16500000
NA's
:1
NA's
:1
NA's
:1
NA's
:1
v14_23v14_24v14_25v14_26
Min.
:-6411600
Min.
:
0
Min.
:
0
Min.
:
0

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

98

1st Qu.: 10800
Median : 42000
Mean: 162248
3rd Qu.: 130000
Max.
:39748800
NA's:1
v14_27Min.
:
0
1st Qu.:500
Median :10000
Mean: 366687
3rd Qu.: 100000
Max.
:140000000
NA's:1

1st Qu.:0
Median :0
Mean: 19791
3rd Qu.:0
Max.
:7000000NA's:1

35 ### xh33_s15a.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1392
1st Qu.: 4.000
Median :1539
Median : 9.000
Mean
:1505
Mean
: 9.124
3rd Qu.:1684
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v15_02
Length:5711
Class :character
Mode :character

v15_05Min.
: 1.00
1st Qu.: 1.00
Median : 1.00
Mean
: 3.81
3rd Qu.: 6.00
Max.
:11.00

1st Qu.:0
Median :
0
Mean
:
28649
3rd Qu.:0
Max.
:50000000
NA's
:1

xstra
Min.
:100
1st Qu.:221
Median :224
Mean
:251
3rd Qu.:321
Max.
:325

v15_03Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.252
3rd Qu.: 1.000
Max.
:17.000
v15_06Min.
: 1.000
1st Qu.: 6.000
Median : 7.000
Mean: 7.728
3rd Qu.:10.000
Max.
:12.000

v15_08av15_08b
Min.
:
0.0
Min.
: 0.0
1st Qu.:0.0
1st Qu.: 13.0
Median :0.0Median : 24.0
Mean
: 350.9
Mean: 22.1
3rd Qu.:0.0
3rd Qu.: 36.0
Max.
:500000.0
Max.
:120.0
NA's
:126
NA's
:7
v15_10v15_11Min.
:1.000Min.
:
0
1st Qu.:2.000
1st Qu.:4610
Median :3.000
Median :13500
Mean:2.479Mean:74622
3rd Qu.:3.000
3rd Qu.:47200
Max.
:3.000Max.
:18000000
NA's
:3111

1st Qu.:1000
Median :
15000
Mean
:
488138
3rd Qu.:132250Max.
:340000000
NA's:1

v15a_ln1Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.608
3rd Qu.: 2.000
Max.
:10.000

v15_04aMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.008
3rd Qu.: 9.000
Max.
:12.000
v15_07Min.
:
0
1st Qu.:
10000
Median :
25000
Mean
:
157769
3rd Qu.:80000
Max.
:180000000

v15_04bMin.
:
0
1st Qu.:2065
Median :2066
Mean
:2061
3rd Qu.:2067
Max.
:2069
v15a_ln2Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.608
3rd Qu.: 2.000
Max.
:10.000

v15_09aMin.
: 0.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.557
3rd Qu.:10.000
Max.
:12.000

v15_09bMin.
:0
1st Qu.:2067
Median :2067
Mean
:2060
3rd Qu.:2068
Max.
:6067

v15_12Min.
:1.000
1st Qu.:7.000
Median :8.000
Mean:6.718
3rd Qu.:8.000
Max.
:8.000v15_13Min.
:
0.000
1st Qu.:1.000
Median :
2.000
Mean
:
6.776
3rd Qu.:5.000
Max.
:2000.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

99

36 ### xh34_s15b.dta ###
xhpsu
xhnum
Min.
:1003
Min.
: 1.000
1st Qu.:1345
1st Qu.: 4.000
Median :1523
Median : 9.000
Mean
:1501
Mean
: 8.923
3rd Qu.:1705
3rd Qu.:13.000
Max.
:1800
Max.
:34.000
v15_15
Length:920
Class :character
Mode :character

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:242.9
3rd Qu.:321.0
Max.
:325.0v15_16Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.225
3rd Qu.:1.000
Max.
:6.000v15_17aMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.221
3rd Qu.: 9.000
Max.
:12.000

v15_17bMin.
:0
1st Qu.:2065
Median :2066
Mean
:2056
3rd Qu.:2067
Max.
:2069

v15_20Min.
:
10
1st Qu.:8000
Median :
20000
Mean
:
91959
3rd Qu.:60000
Max.
:16500000

v15b_ln2Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.366
3rd Qu.:1.000
Max.
:6.000v15_18Min.
:1.000
1st Qu.:4.000
Median :4.000
Mean:4.236
3rd Qu.:5.000
Max.
:6.000v15_19Min.
: 1.000
1st Qu.: 7.000
Median : 7.000
Mean: 8.029
3rd Qu.:11.000
Max.
:12.000

v15_21aMin.
:
0
1st Qu.:0
Median :
0
Mean
: 443
3rd Qu.:0
Max.
:120000NA's:9v15_23Min.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.748
3rd Qu.:3.000
Max.
:3.000v15_21bv15_22a
Min.
: 0.0
Min.
: 0.000
1st Qu.: 0.0
1st Qu.: 3.000
Median :24.0
Median : 7.000
Mean
:19.4
Mean
: 6.553
3rd Qu.:36.0
3rd Qu.:10.000
Max.
:72.0
Max.
:12.000NA's:2
v15_24
v15_25
Min.
:
0
Min.
:1.000
1st Qu.:4300
1st Qu.:8.000
Median : 12600
Median :8.000
Mean: 78513Mean:7.236
3rd Qu.: 36000
3rd Qu.:8.000
Max.
:5500000
Max.
:8.000NA's:709

37 ### xh35_s15c.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v15_27Min.
:
0
1st Qu.: 180000
Median : 600000

v15b_ln1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.366
3rd Qu.:1.000
Max.
:6.000xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v15_28Min.
:
0
1st Qu.:100000Median :400000v15_22bMin.
:0
1st Qu.:2067
Median :2067
Mean
:2060
3rd Qu.:2067
Max.
:2074

v15_26Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.782
3rd Qu.:2.000
Max.
:2.000v15_29Min.
:
1st Qu.:Median :000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

100

Mean
: 2370770
3rd Qu.: 2000000
Max.
:150000000
NA's
:4683
v15_30Min.
:
0
1st Qu.:0
Median :0
Mean: 53368
3rd Qu.:0
Max.
:10000000
NA's
:4683
v15_34Min.
:
0
1st Qu.:25000
Median : 122500
Mean: 2206323
3rd Qu.: 785000
Max.
:250000000
NA's
:5772

Mean
: 1952495
Mean
:
94432
3rd Qu.: 1500000
3rd Qu.:0Max.
:140000000
Max.
:12000000
NA's
:4683
NA's
:4683
v15_31
v15_32
v15_33
Min.
:
0
Min.
:1.000Min.
:
0
1st Qu.:0
1st Qu.:2.000
1st Qu.:
30000
Median :0Median :2.000
Median : 150000
Mean: 8896Mean:1.964Mean: 1192754
3rd Qu.:0
3rd Qu.:2.000
3rd Qu.: 862500
Max.
:500000
Max.
:2.000Max.
:30000000
NA's
:4683
NA's
:5772
v15_35v15_36v15_37
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
1st Qu.:0
Median :0Median :0Median :0
Mean: 42966
Mean: 21956
Mean: 20622
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0Max.
:2250000
Max.
:2500000
Max.
:600000
NA's
:5772
NA's
:5772
NA's
:5772

38 ### xh36_s15d.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1270
1st Qu.: 5.000
Median :1492
Median :10.000
Mean
:1464
Mean
: 9.301
3rd Qu.:1664
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v15d_txt
Length:66285
Class :character
Mode :character

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:252.7
3rd Qu.:321.0
Max.
:325.0v15_39Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.555
3rd Qu.:2.000
Max.
:2.000v15_40Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.18
3rd Qu.:1.00
Max.
:3.00
NA's
:3679439 ### xh37_s16.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1396
1st Qu.: 4.000
Median :1568
Median : 9.000
Mean
:1514
Mean
: 9.108
3rd Qu.:1689
3rd Qu.:13.000
Max.
:1800
Max.
:32.000
v16_nmeLength:6074
Class :character
Mode :character

v15d_idc
Min.
: 1
1st Qu.: 4Median : 8
Mean: 8
3rd Qu.:12
Max.
:15

xstra
Min.
:100.0
1st Qu.:221.0
Median :223.0
Mean:248.8
3rd Qu.:321.0
Max.
:325.0

v16_02Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.277
3rd Qu.:2.000
Max.
:2.000v15_41Min.
:1.00
1st Qu.:2.00
Median :3.00
Mean
:2.57
3rd Qu.:3.00
Max.
:4.00
NA's
:36794v16a_ln1Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.259
3rd Qu.: 3.000
Max.
:15.000

v16_03Min.
: 2.000
1st Qu.: 3.000
Median : 3.000
Mean: 3.952
3rd Qu.: 4.000
Max.
:14.000

v16_04Min.
: 0.00
1st Qu.:18.00
Median :24.00
Mean:25.15
3rd Qu.:32.00
Max.
:83.00

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

101v16_05Min.
: 0.00
1st Qu.: 5.00
Median :10.00
Mean:12.39
3rd Qu.:12.00
Max.
:97.00NA's:189
v16_08bMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.376
3rd Qu.:2.000
Max.
:2.000NA's:2720
v16_11Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.3033rd Qu.:1.000
Max.
:4.000NA's:2439
v16_15Min.
:1.000
1st Qu.:1.000
Median :3.000
Mean:2.506
3rd Qu.:3.000
Max.
:7.000NA's:3580
v16_18bMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:3.5043rd Qu.:5.000
Max.
:8.000NA's:4552

v16_06Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.7043rd Qu.:2.000
Max.
:8.000NA's:701
v16_09Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 4.381
3rd Qu.: 6.000
Max.
:12.000

v16_08aMin.
: 1.0
1st Qu.:27.0
Median :59.0
Mean
:56.7
3rd Qu.:81.0
Max.
:99.0

v16a_ln2Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.259
3rd Qu.: 3.000
Max.
:15.000

v16_10Min.
: 11.0
1st Qu.:713.0
Median :995.0
Mean:817.8
3rd Qu.:997.0
Max.
:999.0v16_12
v16_13
v16_14Min.
:1.000Min.
:1.000Min.
: 0.000
1st Qu.:2.000
1st Qu.:1.000
1st Qu.: 1.000
Median :3.000
Median :2.000
Median : 2.000
Mean:2.612Mean:1.589Mean: 3.181
3rd Qu.:3.000
3rd Qu.:2.000
3rd Qu.: 4.000
Max.
:5.000Max.
:2.000Max.
:80.000
NA's
:2439
NA's
:3580
v16_16v16_17v16_18a
Min.
:
0
Min.
:
0
Min.
:1.000
1st Qu.:10000
1st Qu.:0
1st Qu.:1.000
Median :40000
Median :200
Median :1.000
Mean: 109061
Mean
:
6309
Mean
:2.318
3rd Qu.: 100000
3rd Qu.:5000
3rd Qu.:2.000
Max.
:50000000
Max.
:1000000
Max.
:8.000
NA's
:3580
NA's
:3580
NA's
:3580
v16_19Min.
:
0
1st Qu.:0
Median :
0
Mean
:
28011
3rd Qu.:500
Max.
:111111111

40 ### xh38_s17a.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1335
1st Qu.: 5.000
Median :1507
Median :10.000
Mean
:1471
Mean
: 9.539
3rd Qu.:1666
3rd Qu.:14.000
Max.
:1799
Max.
:34.000
v17_02Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.529
3rd Qu.: 2.000

v16_07Min.
:
0.000
1st Qu.:1.000
Median :
2.000
Mean
:
7.024
3rd Qu.:6.000
Max.
:2064.000

xstra
Min.
:100.0
1st Qu.:223.0
Median :310.0
Mean:278.1
3rd Qu.:322.0
Max.
:325.0

v17_03
Length:2244
Class :character
Mode :character

v17a_lnMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.607
3rd Qu.:2.000
Max.
:8.000

v17_04Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.475
3rd Qu.: 2.000

v17_05Min.
: 2.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.492
3rd Qu.: 7.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

102

Max.

:14.000

v17_06Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.819
3rd Qu.:2.000
Max.
:2.000
v17_09aMin.
:
0
1st Qu.:0
Median :0
Mean: 4110
3rd Qu.: 1000
Max.
:700000

Max.

v17_16Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.41
3rd Qu.:2.00
Max.
:2.00
NA's
:3
v17_19Min.
:1.000
1st Qu.:2.000
Median :3.000
Mean:3.2043rd Qu.:4.000
Max.
:7.000NA's:1
v17_22aMin.
:1.000
1st Qu.:1.000
Median :1.000

Max.

:14.000

v17_07aMin.
: 1.00
1st Qu.:18.00
Median :33.00
Mean:38.68
3rd Qu.:57.00
Max.
:96.00

v17_07bv17_08
Min.
:1.0
Min.
:1.000
1st Qu.:2.0
1st Qu.:3.000
Median :2.0
Median :3.000
Mean
:1.8
Mean
:3.631
3rd Qu.:2.0
3rd Qu.:4.000
Max.
:2.0
Max.
:7.000NA's:313
v17_09bv17_10
Min.
:
0
Min.
:1.000
1st Qu.:500
1st Qu.:3.000
Median : 1000
Median :3.000
Mean: 2071Mean:3.246
3rd Qu.: 2000
3rd Qu.:3.000
Max.
:120002Max.
:7.000

41 ### xh39_s17b.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1335
1st Qu.: 5.000
Median :1513
Median :10.000
Mean
:1484
Mean
: 9.277
3rd Qu.:1681
3rd Qu.:14.000
Max.
:1797
Max.
:34.000
v17_12Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.677
3rd Qu.: 2.000
Max.
:18.000:11.000

xstra
Min.
:100.0
1st Qu.:221.0
Median :310.0
Mean:265.1
3rd Qu.:322.0
Max.
:325.0v17_13
Length:2657
Class :character
Mode :character

v17b_lnMin.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.46
3rd Qu.:2.00
Max.
:8.00

v17_14Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.054
3rd Qu.: 2.000
Max.
:18.000

v17_15Min.
: 2.000
1st Qu.: 5.000
Median : 6.000
Mean: 5.994
3rd Qu.: 6.000
Max.
:14.000
NA's:2
v17_17v17_18av17_18bMin.
: 0.00Min.
: 1.00Min.
:1.000
1st Qu.:30.00
1st Qu.:19.00
1st Qu.:2.000
Median :40.00
Median :40.00
Median :2.000
Mean:42.23Mean:44.33Mean:1.769
3rd Qu.:52.00
3rd Qu.:71.00
3rd Qu.:2.000
Max.
:92.00Max.
:96.00Max.
:2.000
NA's
:3
NA's
:584
v17_20av17_20bv17_21
Min.
:
0
Min.
:
0
Min.
:1.000
1st Qu.:0
1st Qu.:500
1st Qu.:3.000
Median :0Median : 1000
Median :3.000
Mean: 13271
Mean: 3293Mean:3.202
3rd Qu.:2000
3rd Qu.: 2500
3rd Qu.:3.000
Max.
:7000000
Max.
:500000
Max.
:7.000v17_22bMin.
:1.000
1st Qu.:2.000
Median :2.000

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103

Mean
:1.445
3rd Qu.:1.000
Max.
:8.000Mean:3.265
3rd Qu.:5.000
Max.
:8.000NA's:2109

42 ### xh40_s18a.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v18a_txt
Length:41916
Class :character
Mode :character

v18_04Min.
: 100
1st Qu.: 1000
Median : 2000
Mean
: 1951
3rd Qu.: 2000
Max.
:12000NA's:40959v18_08Min.
:1.00
1st Qu.:1.00
Median :2.00
Mean
:1.65
3rd Qu.:2.00
Max.
:2.00
NA's
:40959xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0

v18_01Min.
:1.00
1st Qu.:3.00
Median :3.00
Mean
:2.89
3rd Qu.:3.00
Max.
:3.00
v18_05Min.
: 0.00
1st Qu.: 1.00
Median : 3.00
Mean
: 3.533rd Qu.: 4.00
Max.
:200.00
NA's:40959v18_09Min.
:
2
1st Qu.: 1200
Median : 2000
Mean
: 2232
3rd Qu.: 2500
Max.
:20000NA's:4157943 ### xh41_s18b.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v18_102v18_103Min.
:1.000Min.
:1.000
1st Qu.:2.000
1st Qu.:2.000
Median :2.000
Median :2.000
Mean:1.984Mean:1.998
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:2.000Max.
:2.000v18_106Min.
:1.000

v18_02Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.11
3rd Qu.:1.00
Max.
:7.00
NA's
:40959v18_06Min.
:
0
1st Qu.: 2000
Median : 2000
Mean
: 2355
3rd Qu.: 2000
Max.
:125000
NA's:40959xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v18_104Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.979
3rd Qu.:2.000
Max.
:2.000v18a_snMin.
:1
1st Qu.:2
Median :4Mean:4
3rd Qu.:6
Max.
:7
v18_03Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:2.34
3rd Qu.:2.00
Max.
:5.00
NA's
:40959v18_07Min.
: 0.00
1st Qu.: 4.00
Median : 4.00
Mean
: 4.41
3rd Qu.: 4.00
Max.
:14.00NA's:41069v18_101
Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:1.99
3rd Qu.:2.00
Max.
:2.00
v18_105Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.989
3rd Qu.:2.000
Max.
:2.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

104

1st Qu.:2.000
Median :2.000
Mean:1.971
3rd Qu.:2.000
Max.
:2.00044 ### xh42_s18c.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000
v18c_txt
Length:47904
Class :character
Mode :character

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0v18_11Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.877
3rd Qu.:2.000
Max.
:2.000v18c_ic
Min.
:1.00
1st Qu.:2.75
Median :4.50
Mean
:4.50
3rd Qu.:6.25
Max.
:8.00

v18_12Min.
:
0
1st Qu.:1300
Median :
5000
Mean
:
51714
3rd Qu.:17000
Max.
:15000000
NA's:44394

v18_13Min.
:
0
1st Qu.:1000
Median :
4000
Mean
:
64299
3rd Qu.:16750
Max.
:23000000
NA's:44394

v18_14Min.
:
0
1st Qu.:0
Median :0
Mean: 12122
3rd Qu.: 2000
Max.
:2400000NA's:4199045 ### xh43_s19.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1277
1st Qu.: 5.000
Median :1495
Median :10.000
Mean
:1469
Mean
: 9.281
3rd Qu.:1670
3rd Qu.:14.000
Max.
:1800
Max.
:34.000

xstra
Min.
:100.0
1st Qu.:219.0
Median :223.0
Mean:251.6
3rd Qu.:321.0
Max.
:325.0

v19_02Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.875
3rd Qu.:2.000
Max.
:4.000

v19_03Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.806
3rd Qu.:2.000
Max.
:4.000

v19_04Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.856
3rd Qu.:2.000
Max.
:4.000

v19_05Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.8533rd Qu.:2.000
Max.
:4.000

v19_06Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.097
3rd Qu.:2.000
Max.
:4.000

v19_07Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.562
3rd Qu.:2.000
Max.
:4.000

v19_08Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.211
3rd Qu.:1.000
Max.
:2.000

v19_09Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.106
3rd Qu.:2.000
Max.
:4.000v19_10Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.299
3rd Qu.:3.000
Max.
:4.000

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

105v19_11Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.3243rd Qu.:3.000
Max.
:4.000NA's:5107
v19_12Min.
:1.000
1st Qu.:2.000
Median :3.000
Mean:2.706
3rd Qu.:3.000
Max.
:4.000v19_15Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.065
3rd Qu.:2.000
Max.
:4.000v19_16Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.931
3rd Qu.:2.000
Max.
:2.000v19_1802Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.3343rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1806
Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:1.91
3rd Qu.:2.00
Max.
:2.00
NA's
:5575
v19_1810
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.925
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1814Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.939
3rd Qu.:2.000
Max.
:2.000NA's:5575

v19_13Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.268
3rd Qu.:3.000
Max.
:4.000v19_14Min.
:1.000
1st Qu.:2.000
Median :3.000
Mean:2.909
3rd Qu.:4.000
Max.
:4.000v19_1803
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.431
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1807Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.961
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1811
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.961
3rd Qu.:2.000
Max.
:2.000NA's:5575

v19_17Min.
: 1.00
1st Qu.: 3.00
Median : 4.00
Mean
: 5.86
3rd Qu.: 7.00
Max.
:30.00NA's:5575
v19_1804Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.939
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1808Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.588
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1812Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.6633rd Qu.:2.000
Max.
:2.000NA's:5575

v19_1801
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.477
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1805Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.872
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1809
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.835
3rd Qu.:2.000
Max.
:2.000NA's:5575
v19_1813
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.889
3rd Qu.:2.000
Max.
:2.000NA's:5575

46 ### xh44_s20.dta ###
xhpsu
xhnum
Min.
:1001
Min.
: 1.000
1st Qu.:1398
1st Qu.: 5.000
Median :1548
Median :10.000
Mean
:1515
Mean
: 9.316xstra
Min.
:100.0
1st Qu.:221.0
Median :225.0
Mean:257.8v20_idc
Min.
: 1.000
1st Qu.: 2.000
Median : 4.000
Mean: 4.099

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

106

3rd Qu.:1707
Max.
:1800

3rd Qu.:14.000
Max.
:29.000

3rd Qu.:322.0
Max.
:325.0

3rd Qu.: 5.000
Max.
:20.000

v20_01Min.
:1.00
1st Qu.:1.00
Median :2.00
Mean
:1.58
3rd Qu.:2.00
Max.
:2.00
NA's
:9948
v20_03Min.
: 0.00
1st Qu.:15.00
Median :30.00
Mean:29.86
3rd Qu.:44.00
Max.
:59.00NA's:9949
v20_07Min.
: 2.30
1st Qu.: 8.50
Median :10.80
Mean:10.76
3rd Qu.:13.00
Max.
:27.20NA's:10278v20_09Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.258
3rd Qu.:2.000
Max.
:2.000NA's:9949

v20_02dMin.
: 1.00
1st Qu.: 8.00
Median :14.00
Mean:14.45
3rd Qu.:21.00
Max.
:32.00NA's:9954
v20_04Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.178
3rd Qu.:1.000
Max.
:5.000NA's:9949
v20_08dMin.
: 1.00
1st Qu.: 8.00
Median :14.00
Mean:15.08
3rd Qu.:23.00
Max.
:32.00NA's:10278v20_10Min.
: 0.00
1st Qu.: 6.00
Median :12.00
Mean:16.48
3rd Qu.:24.00
Max.
:90.00NA's:9949

v20_02m
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.518
3rd Qu.: 9.000
Max.
:12.000
NA's:9954
v20_05Min.
: 8.50
1st Qu.: 74.25
Median : 84.50
Mean: 83.36
3rd Qu.: 93.10
Max.
:114.20NA's:10280v20_08m
Min.
: 1.000
1st Qu.: 4.000
Median : 6.000
Mean: 6.234
3rd Qu.: 9.000
Max.
:12.000
NA's:10278v20_11Min.
: 0.00
1st Qu.: 6.00
Median : 7.00
Mean
:15.08
3rd Qu.: 9.00
Max.
:90.00NA's:9949

v20_02yMin.
:2061
1st Qu.:2063
Median :2064
Mean
:2064
3rd Qu.:2066
Max.
:2067
NA's
:9954
v20_06Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.357
3rd Qu.:2.000
Max.
:2.000NA's:10280v20_08yMin.
:2066
1st Qu.:2067
Median :2067
Mean
:2067
3rd Qu.:2067
Max.
:2067
NA's
:10278>>

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107

4.5 Codebook of data files


Generated codebook of data files using information of STATA data files.

> for(n in 1:46){+ df<-outfiles[[n]]+ cat("\n\n","###",n,":",STATA.files[n],"###\n")+ cb<-vector(length=ncol(df),mode="list")+ for(j in 1:length(cb)){+ cb[[j]]$varname<-names(df)[j]+ cb[[j]]$varlabel<-attributes(df)$var.labels[j]+ cb[[j]]$vallabel<-(attributes(df)$label.table[attributes(df)$val.labels])[[j]]+ }+ for(j in 1:length(cb)){+ cat(format(j,width=2),format(cb[[j]]$varname,width=10),+ format(cb[[j]]$varlabel,width=30),"\n")+ if(length(cb[[j]])==3){+ t<-cb[[j]]$vallabel+ for(k in 1:length(t)){+ cat(rep(" ",10),format(t[k],width=3),format(names(t)[k],width=30),"\n")
+ }
+ }
+ }
+ }
### 1 : poverty.dta ###1 xhpsupsu
2 xhnumhousehold number
3 hhsizehousehold size
4 belt1 mountain
2 hill3 terai
5 region
1 eastern
2 centeral
3 western
4 mid-western
5 far-western
6 urbrur1 urban2 rural
7 adanalytical domains
1 mountains
2 urban - kathmandu
3 urban - hill
4 urban -terai
5 rural hills - eastern
6 rural hills -central
7 rural hills - western
8 rural hills - mid and far west
9 rural terai - eastern
10 rural terai - central
11 rural terai - western
12 rural terai - mid and far west
8 wt_indindividual weights

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108

9 wt_hhhousehold weights
10 fpline_7
pc annual food poverty line, temporally adjusted national prices: 7d
11 pline_7
pc annual total poverty line, temporally adjusted national prices: 7d
12 pindex
spatial price index to convert temporally adj overall prices to national prices
13 poorpoor household
14 totcons_pc_7 annual per capita nomial hh consumption, 7d
15 food_pc_7 annual nominal pc food consumption, 7d
16 quintle_7 nominal quintiles of annual pc consumption, 7d### 2 : xh00_s00.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v00_dist
psu geo-codes: district
1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang
6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga
13 khotang
14 udayapur
15 saptari
16 siraha
17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu

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109

5 v00_vdc
6 v00_hno
7 v00_a

8 v00_b

28 nuwakot
29 rasuwa
30 dhading
31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung
38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot
62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang
69 achham
70 doti
71 kailali
72 kanchanpur
73 dadeldhura
74 baitadi
75 darchulapsu geo-codes: vdc/municipality
a. household no.
a. household was interviewed
1 yes
2 no
b. reason household not interviewed
1 dwelling not found
2 household not foundNepal NLSS Ⅲ 2010/11 Manual Version 2.0

110

9 v00_c
10 v00_d
11 v00_e

12 v00_f

3 refusal
c. replaced by household number
0 not a replacementd. this household replaces household number
0 this household does not replace any other one
e. religion of hhld head
1 hindu2 bouddha
3 islam
4 kirant
5 jain6 christian
7 shikh
8 bahai
9 other religion
f. language used in household
1 nepali
2 maithili
3 bhojpuri
4 tharu (dagaura/rana)
5 tamang
6 newar
7 magar
8 awadhi
9 bantawa
10 gurung11 limbu
12 bajjika
13 urdu14 rajbansi
15 sherpa
16 hindi
17 chamling
18 santhali
19 chepang
20 danuwar
21 jhangar/dhangar
22 sunuwar
23 bangla
24 marwari (rajsthani)
25 manjhi
26 thami
27 kulung28 dhimal
29 angika
30 yakkha
31 thulung
32 sangpang
33 bhujel/khawas
34 darai
35 khaling
36 kumal37 thakali
38 chhantyal/chhantel
39 nepali sign language
40 tibbetan

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111

13 v00_g

41 dumi42 jirel
43 wambule/ umbule
44 puma
45 yholmo
46 nachhiring
47 dura48 meche49 pahari
50 lepcha/ lapche
51 bote52 bahing
53 koi/ koyu
54 raji
55 hayu
56 byangshi
57 yamphu/ yamphe
58 ghale
59 khariya
60 chhiling
61 lohorung
62 punjabi
63 chinese
64 english
65 mewahang
66 sanskrit
67 kaike
68 raute
69 kisan70 churauti
71 baram/ maramu
72 tilung73 jero/ jerung
74 dungmali
75 oriya
76 lingkhim
77 kusunda
78 sindhi
79 koche80 hariyanwi
81 magahi
82 sam
83 kurmali
84 kagate
85 dzonkha
86 kuki
87 chhintang
88 mizo
89 nagamese
90 lhomi
91 assamise
92 sadhani
93 other language
g. interpreter used
1 yes
2 no

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112

14 v00_team
15 v00_int

team number
interviewer
12 laxman ban
13 narayan karki
14 indira khapangi magar
15 anjana oli
22 ramesh bahadur shrestha
23 ishwor lal mandal
24 khadka kumari lamichhane (sapkota)
25 pratima gyawali (adhikari)
32 suresh kumar shrestha
33 yam bahadur gurung
34 ganga bhujel
35 bebi deji gupta
42 loknath lamichhane
43 narayan bahadur thapa
44 ramita poudel
45 usha kumari choudhari
52 santosh chandra thapa
53 santosh karki
54 gyani dhakal
55 gita rimal
62 kashi mandal
63 bij kumar yadav
64 shushila kumari shah65 uma kumari
72 ramprit yadav
73 jaya kishan prasad sonar
74 madhu mallik
75 kalyani sah
82 mahantha prasad yadav
83 rajendra prasad choudhari
84 nargis khatoon
85 pinki kumari ojha
92 gajendra lal shrestha
93 pushkar prasad guragai
94 sunita dhakal (pokhrel)
95 sanu maiya shrestha
102 krishna prasad choulagai
103 kalkika shrestha
104 indira birbal
105 rama kumari thapa
106 prativa soti
112 kiran bista
113 dipak bhattarai
114 dambar kumari ghale (pariyar)
115 binu shrestha
122 renu kumari ghimire
123 rajendra parajuli
124 renuka lamichhane
125 kaushila adhikari (bohara)
132 rabi lal shrestha
133 surendra raj poudel
134 samjhana basnet
135 bindu pande
142 tilak prasad acharya

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113

16 v00_sup

17 v00_ent

143 ghanashyam sapkota
144 anjana kumari shrestha
145 sharada pande
152 yubraj khadka
153 ramchandra gautam
154 kalpana bhandari (thapa)
155 sakuntala dhakal
162 surya giri
163 kamal prasad poudel
164 shila g.c.
165 shushila kumal
172 saroj pokhrel
173 prem kumari sharma
174 shira sharma
175 shushma adhikari
182 megh nath regmi
183 lanka bahadur bam
184 shanta kumari pokhrel
185 laxmi gupta
192 tej narayan adhikari
193 hari bahadur gharti
194 prativa shoti
195 pooja thapa
196 prathana bhatta
202 ram datta bhatta
203 shanbhu narayan panta
204 pushpa joshi
205 sapana bhattasupervisor
11 nava raj shrestha
21 suman kumar shrestha
31 kedar prasad neupane
41 janak babu kafle
51 balaram dulal
61 pawan kumar thakur
71 raj kishor sah
81 sanjaya kumar yadav
91 damodar shrestha
101 deepak koirala
111 urmila shahi
121 ram prasad wagle
131 kanta lal choudhari
141 bishnu raj regmi
151 jeevnath acharya
161 damodar neupane
171 damodar gautam
181 ram bahadur thapa
191 bishwo darshan dhungana
201 mohan dev bhatta
entry operator
13 narayan karki
22 ramesh bahadur shrestha
33 yam bahadur gurung
43 narayan bahadur thapa
52 santosh chandra thapa
64 shushila kumari shah

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114

74 madhu mallik
84 nargis khatoon
93 pushkar prasad guragai
102 krishna prasad choulagai
106 prativa soti
113 dipak bhattarai
122 renu kumari ghimire
133 surendra raj poudel
142 tilak prasad acharya
152 yubraj khadka
162 surya giri
172 saroj pokhrel
183 lanka bahadur bam
193 hari bahadur gharti
194 prativa shoti
202 ram datta bhatta
18 v00_int1_d 1st interview/day
0 date not given
19 v00_int1_m 1st interview/month
20 v00_int1_y 1st interview/year
21 v00_int2_d 2nd interview/day
0 !end-of-list!
22 v00_int2_m 2nd interview/month
23 v00_int2_y 2nd interview/year
24 v00_int3_d 3rd interview/day
0 !end-of-list!
25 v00_int3_m 3rd interview/month
26 v00_int3_y 3rd interview/year
27 v00_sup_d supervision/day
0 date not given
28 v00_sup_m supervision/month
29 v00_sup_y supervision/year
30 v00_headn name of household head
31 v13_02{13.02} household has own agricultural land
1 yes
2 no
32 v13_18{13.18} household has land sharecropped/rented/mortgaged-in
1 yes
2 no
33 v13_29{13.29} household did sell, transfer land
1 yes
2 no
34 v13_30_1u {13.30} 1st area sold, transfered/unit (in ropani)1 ropani
35 v13_30_1r {13.30} 1st area sold, transfered/ropani
36 v13_30_1a {13.30} 1st area sold, transfered/aana
37 v13_30_1p {13.30} 1st area sold, transfered/paisa
38 v13_30_2u {13.30} 2nd area sold, transfered/unit (in bigha)
2 bigha39 v13_30_2b {13.30} 2nd area sold, transfered/bigha
40 v13_30_2k {13.30} 2nd area sold, transfered/kattha
41 v13_30_2d {13.30} 2nd area sold, transfered/dhur
42 v13_31{13.31} 1st area sold, transfered/amount received (rs)43 v13_32{13.32} household did acquired land
1 yes
2 no

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44 v13_33_1u{13.33} 1st area acquired/unit (in ropani)1 ropani
45 v13_33_1r {13.33} 1st area acquired/ropani
46 v13_33_1a {13.33} 1st area acquired/aana
47 v13_33_1p {13.33} 1st area acquired/paisa
48 v13_33_2u {13.33} 2nd area acquired/unit (in bigha)
2 bigha49 v13_33_2b {13.33} 2nd area acquired/bigha
50 v13_33_2k {13.33} 2nd area acquired/kattha
51 v13_33_2d {13.33} 2nd area acquired/dhur
52 v13_34{13.34} area acquired/amount paid (rs)53 v13_38d_total {13.38} 98-control total of the 'amount of total sales'
54 v13_39{13.39} household purchased/received seeds, young plants
1 yes
2 no
55 v13_43{13.43} household purchased fertilizers, insecticides
1 yes
2 no
56 v13_48a{13.48a} fertilizer/place where it was obtained
1 government
2 ngo
3 private
4 cooperative, community
57 v13_48b{13.48b} fertilizer/could obtain as needed
1 yes
2 no
58 v13_49{13.49} reason for unable to obtain all needed
1 not available for purchase
2 no money for purchase
3 other
59 v13_50
13.50 hire any casual farm workers
1 yes
2 no
3 exchange labour only
60 v13_53_el_male {13.53) exchange labor man-days, male
61 v13_53_el_female {13_53} exchange labor man-days, female
62 v13_58{13.58} total expenditure for hiring casual farm workers
63 v13_59{13.59} hired any permanent farm workers
1 yes
2 no
64 v13_60{13.60} permanent workers/number hired
65 v13_61{13.61} permanent workers/paid in cash and in-kind to
66 v13_62{13.62} total expenditure on hired workers
67 v13_65{13.65} household owns livestock
1 yes
2 no
68 v13_73{13.73} hired labor for tending livestock
1 yes
2 no
69 v13_74{13.74} household owned any equipment
1 yes
2 no
70 v13_82{13.82} advice taken from govt. agriculture technician
1 yes
2 no
71 v13_83{13.83} reason for not taking advice from govt. agriculture

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72 v13_84

73 v13_85

74 v14_01

75 v15_01

76 v15_14

77 v16_00

78 v17_01

79 v17_11

80 v01_00
81 v02_a82 v03_a
83 v05_00
84 v06_00
85 v09_36
86 v13_01
87 v13_17
88 v15_38
89 v19_01
90 n_s01
91 n_s9a92 n_s9b
93 n_s10b
94 n_s12
95 n_s14
96 n_s15a
97 n_s15b
98 n_s16
99 n_s17a
100 n_s17b
101 n_s13a1
102 n_s13a2
103 n_s13b

1 service far away
2 service is not good
3 not necessary
4 other{13.84} advice taken from govt. livestock service technician
1 yes
2 no
{13.85} reason for not taking advice from govt. livestock se
1 service far away
2 service is not good
3 not necessary
4 other{14.01} any self-employment activities
1 yes
2 no
{15.01} any outstanding loans
1 yes
2 no
{15.14} any loans owed to the household
1 yes
2 no
{16.00} any absentees
1 yes
2 no
{17.01} any remittances sent out
1 yes
2 no
{17.11} any remittances received
1 yes
2 no
{1.00} respondent id-code
0 !information not gathered at the field!
{2.a} respondent id-code{3.00} respondent id-code{5.00} respondent id-code{6.00} respondent id-code{9.36} respondent id-code
0 no competent respondent{13.01} respondent id-code{13.17} respondent id-code{15.38} respondent id-code{19.01} respondent id-code{n-01} number of lines in section 1{n-09a} number of lines in section 9a{n-09b} number of lines in section 9b{n-10b} number of lines in section 10b{n-12} number of lines in section 12{n-14} number of lines in section 14{n-15a} number of lines in section 15-a{n-15b} number of lines in section 15-b
{n-16} number of lines in section 16{n-17a} number of lines in section 17a{n-17b} number of lines in section 17b{n-13a1} number of plots 13-a1{n-13a2} number of plots 13-a2{n-13b} number of lines 13-b

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104 n_s13c1
105 n_s13c3{n-13c1} number of plots 13-c1{n-13c3} number of plots 13-c3### 3 : xh01_s01.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v01_idc
id code5 v01_01
1.01 name
6 v01_02
1.02 sex
1 male2 female
7 v01_03
1.03 age
8 v01_04{1.04} relationship to head
1 head2 husband/wife
3 son/daughter
4 grandchild
5 father/mother6 brother/sister
7 nephew/niece
8 son/daughter-in-law
9 brother/sister-in-law
10 father/mother-in-law
11 other family relative
12 servant/servant's relatives
13 tenant/tenant's relatives
14 other non related
9 v01_05a{1.05} birth place: district/foreign
1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang
6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga

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13 khotang
14 udayapur
15 saptari
16 siraha
17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu
28 nuwakot
29 rasuwa
30 dhading
31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung
38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot
62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang

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10 v01_05b

11 v01_06

12 v01_07
13 v01_08

69 achham
70 doti
71 kailali
72 kanchanpur
73 dadeldhura
74 baitadi
75 darchula
81 india
82 bhutan
83 china
84 bangladesh
85 hongkong
86 malayasia
87 japan
88 saudi arabia
89 qatar
90 united arab emirates
91 united kingdom
92 united states of america
93 south korea
94 australia
95 israel
96 other country{1.05} birth place: urban/rural
1 urban2 rural1.06 marital status
1 never married
2 single married
3 poly married
4 re-married
5 widow/widower
6 divorced
7 separated{1.07} spouse id code
{1.08} caste/ethnic
1 chhetri
2 brahman (hill)
3 magar
4 tharu
5 tamang
6 newar7 muslim
8 kami9 yadav
10 rai11 gurung12 damain/dholi
13 limbu
14 thakuri
15 sarki
16 teli
17 chamar/harijan/ram
18 koiri
19 kurmi
20 sanyasi

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21 dhanuk
22 musahar
23 dusadh/paswan/pasi
24 sherpa
25 sonar
26 kewat
27 brahman (terai)
28 baniya
29 gharti/bhujel
30 mallah
31 kalwar
32 kumal
33 hajam/thakur
34 kanu
35 rajbansi
36 sunuwar
37 sudhi
38 lohar
39 tatma
40 khatwe
41 dhobi
42 majhi
43 nuniya
44 kumhar
45 danuwar
46 chepang/praja
47 haluwai
48 rajpur
49 kayastha
50 badhae
51 marwadi
52 santhal/satar
53 dhagar/jhagar
54 bantar
55 barae
56 kahar
57 gangai
58 lodh59 rajbhar
60 thami
61 dhimal
62 bhote
63 bing/binda
64 bhediyar/gaderi
65 nurang
66 yakkha
67 darai
68 tajpuriya
69 thakali
70 chidimar
71 pahari
72 mali
73 bangali
74 chhantal
75 dom
76 kamar

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14 v01_09
15 v01_10

16 v01_jdc
17 v01_11

18 v01_12
19 v01_13

77 bote78 brahmu/baramu
79 gaine
80 jirel81 adibasi/janajati
82 dura
83 churaute84 badi85 meche86 lepcha
87 halkhor
88 punjabi/sikh
89 kisan90 raji
91 byangsi
92 hayu
93 koche
94 dhunia
95 walung96 jain
97 munda
98 raute
99 yehlmo
100 patharkata/kuswadiya
101 kusunda
102 other dalit
103 other caste
1.09 months at home
1.10 member or not?
1 yes
2 no
i d
c o d e1.11 father living in home?
1 yes
2 no
3 died
{1.12} father id code
{1.13} father: highest degree completed
0 pre-school/kindergarden
1 class 1
2 class 2
3 class 3
4 class 4
5 class 5
6 class 6
7 class 7
8 class 8
9 class 9
10 class 10
11 slc12 intermediate/class 12
13 bachelor level
14 master level or higher
15 professional degree
16 literate (levelless)
17 illiterate

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20 v01_14

21 v01_15
22 v01_16

98 dk
1.14 mother living in home?
1 yes
2 no
3 died
1.15 id code of mother{1.16} mother: highest degree completed
0 pre-school/kindergarden
1 class 1
2 class 2
3 class 3
4 class 4
5 class 5
6 class 6
7 class 7
8 class 8
9 class 9
10 class 10
11 slc12 intermediate/class 12
13 bachelor level
14 master level or higher
15 professional degree
16 literate (levelless)
17 illiterate
98 dk### 4 : xh02_s02.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v02_01
2.01 dwelling unit occupied by own hhld only
1 yes
2 no
5 v02_02a2.02 a) total room
6 v02_02b.
b) kitchen
7 v02_02c.
c) toilet/bathroom
8 v02_02d.
d) bedrooms
9 v02_02e.
e) living/dining rooms
10 v02_02f.
f) business rooms
11 v02_02g.
g) mixed used rooms

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12 v02_02h
13 v02_03

14 v02_04

15 v02_05

16 v02_06

17 v02_07

18 v02_08u

19 v02_08rb
20 v02_08ak
21 v02_08pd
22 v02_09
23 v02_10
24 v02_11

25 v02_12
26 v02_13
27 v02_14

28 v02_15
29 v02_16

30 v02_17
31 v02_18
32 v02_19.
h) other rooms
2.03 having kitchen garden
1 yes
2 no
2.04 material of outside wall
1 cement bonded bricks/stones
2 mud bonded bricks/stones
3 wood4 bamboo/leaves
5 unbaked bricks
6 other material
7 no outside walls
2.05 foundation of dwelling
1 pillar bonded
2 cement bonded
3 mod bonded
4 wooden pillar
5 other
2.06 main roofing material
1 straw/thatch
2 earth/mud
3 wood/planks
4 galvanized iron
5 concrete/cement
6 tiles/slate
7 other
2.07 types of window
1 no windows/no covering window
2 shutters3 screens/glass
4 other
2.08 size of housing plot - unit
1 ropani
2 bigha.
- ropani/bigha.
- aana/kattha.
- paisa/dhur
2.09 area inside of the dwelling (sq.ft.)
2.10 year built
2.11 dwelling owned by you?
1 yes
2 no
2.12 sale value(rs.)
2.13 rental value(rs.)
2.14 rent part of dwelling?
1 yes
2 no
2.15 rent received(rs.)
2.16 present occupancy status
1 renter2 providing free of cost
3 squatting
4 other
2.17 rental value(rs.)
2.18 rent paid
(rs.)
2.19 source of drinking water

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33 v02_20

34 v02_21

35 v02_22
36 v02_23

37 v02_24

38 v02_25
39 v02_26

40 v02_27

41 v02_28

42 v02_29
43 v02_30
44 v02_31a

45 v02_31b

46 v02_31c

47 v02_31d

1 piped water
2 covered well
3 hand pump/tubewell
4 open well
5 spring water
6 river
7 other source
2.20 piped water connected to house
1 yes
2 no
2.21 water comes hours/week
0 less than 1 hour a week
168 all the week without interruption
2.22 amount paid for water(rs.)
2.23 sanitation system for liquid waste
1 underground drain
2 open drains
3 soak pit
4 no2.24 garbage disposal method
1 collected by garbage truck
2 private collector
3 dumped
4 burned/buried
5 dumped and used for fertilizer
6 other
2.25 amount paid for garbage(rs.)
2.26 type of toilet used
1 flush - municipal sewer
2 flush - septic tank
3 non-flush
4 communal latrine
5 no toilet
2.27 source of light
1 electricity
2 solar
3 biogas
4 kerosene
5 other
2.28 types of meter
1 individual
2 joint
3 no meter
2.29 amount paid for electricity(rs.)
2.30 how many hrs/week not have eletricity
2.31 a) having telephone
1 yes
2 no
.
b) having mobile phone
1 yes
2 no
.
c) having cable tv
1 yes
2 no
.
d) having email/internet
1 yes

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48 v02_32
49 v02_33

50 v02_34

51 v02_35

52 v02_36

53 v02_37a

54 v02_37b
55 v02_37c
56 v02_38a
57 v02_38b
58 v02_39

59 v02_40
60 v02_41

61 v02_42

62 v02_43

63 v02_44

2 no2.32 amount paid for facilities (rs)2.33 main cooking fuel1 firewood
2 dung3 leaves/rubbish/straw/thatch
4 cylinder gas
5 kerosene
6 bio-gas
7 other
2.34 stove for cooking
1 open fireplace
2 mud fireplace
3 smokeless oven4 kerosene stove
5 gas stove
6 other
2.35 use firewood during past 12 months
1 yes
2 no
2.36 collect firewood during past 12 months?
1 yes
2 no
2.37 avg. no. of firewood collection - unit
1 bhari
2 chart3 kg.
- number.
1 bhari/chart equal to kg
2.38 time to collect firewood - hour.
- minute
2.39 place of collecting firewood
1 own land
2 community managed forest
3 govt. forest
4 other
2.40 price per unit of firewood
2.41 collect fodder
1 yes
2 no
2.42 place of collecting fodder
1 own land
2 community managed forest
3 govt. forest
4 other
2.43 type of salt used for cooking
1 large crystal salt
2 crushed salt
3 packet salt (two child logo)
4 other
2.44 iodine level in salt used for cooking
1 0 ppm
2 below 15 ppm
3 15 ppm or more

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2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v03_idc{3.00} facility code
5 v03_txt{3.00} facility description
6 v03_01{3.01} way to travel to the closest facility
1 foot (without load)
2 bicycle, rickshaw
3 motorcycle, tamppo
4 car, bus
5 mixed (foot and vehicle)
6 facility is next to the household
7 not applicable (no facility around)
7 v03_02d{3.02} time to go to/days
8 v03_02h{3.02} time to go to/hours
9 v03_02m{3.02} time to go to/minutes
10 v03_03a{3.03} distance to closest facility/kms
11 v03_03b{3.03} distance to closest facility/mts
12 v03_04{3.04} was the facility used past 12 months
1 yes
2 no
13 v03_05{3.05} how often was used
1 daily
2 weekly
3 monthly
4 rarely### 6 : xh04_s04.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai

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4 v04_idc
5 v04_01
6 v04_02

7 v04_03a

321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
id code{4.01} respondent id-code
4.02 ever migrated?
1 yes
2 no
4.03 migrated from - district/country
1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang
6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga
13 khotang
14 udayapur
15 saptari
16 siraha
17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu
28 nuwakot
29 rasuwa
30 dhading
31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung
38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung

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8 v04_03b

4.03

9 v04_04
10 v04_05

4.04
4.05

46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot
62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang
69 achham
70 doti
71 kailali
72 kanchanpur
73 dadeldhura
74 baitadi
75 darchula
81 india
82 bhutan
83 china
84 bangladesh
85 hongkong
86 malaysia
87 japan
88 saudi arabia
89 qatar
90 united arab emirates
91 united kingdom
92 united states of america
93 south korea
94 australia
95 israel
96 other country- urban/rural
1 urban2 ruralwhen move to this placereason for migration
1 marriage
2 follow the family
3 other family reason
4 education
5 training

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11 v04_06

4.06

6 looking for work
7 start new job
8 start new business9 job transfer
10 conflict
11 natural disaster12 easier life style
13 otherprimary activity [nsco]
11 armed forces
111 legislators
112 government officials
114 officials of special interest organizations
121 directors and chief executives
122 production and operations department managers
123 other department managers
131 general managers/managing proprietors
211 physicists, chemists and related professionals
212 mathematicians, statisticians and related professionals
213 computing professionals
214 architects, engineers and related professionals
221 life science professionals
222 health professionals, except nursing
223 nursing and midwifery professionals
231 college, university and higher education teaching professionals
232 secondary education teaching professionals
233 primary and pre-primary education teaching professionals
234 special education teaching professionals
235 other teaching professionals
241 business professionals
242 legal professionals
243 archivists, librarians and related information professionals
244 social science and related professionals
245 writers and creative or performing artists
246 religious professionals
311 physical and engineering science technicians
312 computer associate professionals
313 optical and electronic equipment operators
314 aircraft controllers and technicians
315 safety and quality inspectors
321 life science technicians and related associate professionals
322 modern health associate professional, except nursing
323 nursing and midwifery associate professionals
324 traditional medicine practitioners and faith healers
331 primary education teaching associate professionals
332 pre-primary education teaching associate professionals
333 special education teaching associate professionals
334 other teaching associate professionals
341 finance and sales associate professionals
342 business services agent and trade brokers
343 administrative associate professionals
344 customs, tax and related government associate professionals
345 police inspectors and detectives
346 social work associate professionals
347 artistic, entertainment and soprts associate professionals
348 religious associate professionals

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412 numerical clerks/office assistants
413 material-recording and transport clerks/office assistants
414 library, mail and related clerks/office assistants
419 other office clerks/assistants
421 cashiers, tellers and related clerks/office assistants
422 client information clerks/office assistants
511 travel attendants and related workers
512 housekeeping and restaurant services workers
513 personal care and related workers
514 other professional services workers
515 astrologers, fortune-tellers and related workers
516 protective service workers
521 fashion and other models
522 shop salespersons and demonstratotrs
523 stall and market salespersons
611 market-oriented gardeners and crop growers
612 market-oriented animal producers and related workers
613 market-oriented crop and animal producers
614 forestry and related workers
615 fishery workers
621 subsistence agricultural and fishery workers
711 miners, shoftirers, stone cutters and carvers
712 building frame and related trades workers
713 building finishers and related trades workers
714 painters, building structure cleaners and related trades workers
721 metal moulders, welders, sheet-metal workers, structural-metal preparer
722 blacksmiths, tool-makers and related trades workers
723 machinery mechanics and fitters
724 electrical and electronic equipment mechanics and fitters
731 precision workers in metal and related materials
732 potters, glass-makers and related trades workers
733 handicraft workers in wood, textile, leather and related materials
734 printing and related trades workers
741 food processing and related trades workers
742 wood treaters, cabinet-makers and related traders workers
743 textile, garment and related trades workers
744 pelt, leather and shoe making trades workers
811 mining and mineral-processing plant operators
812 metal-processing-plant operators
813 glass, ceramics and relative plant operators
814 wood-processing and papermaking-plant operators
815 chemical-processing-plant operators
816 power-production and related plant operators
817 automated-assembly-line and industrial-robot operators
821 metal and mineral products machine operators
822 chemical-products machine operators
823 rubber and plastic products machine operators
824 wood-products machine operators
825 printing, binding and paper products machine operators
826 textile, fur and leather-products machine operators
827 food and related products machine operators
828 assemblers
829 other machine operators and assemblers
831 locomotive-engine drivers and related workers
832 motor vehicle drivers

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12 v04_07

13 v04_08
14 v04_09_y
15 v04_09_m
16 v04_10_y
17 v04_10_m
18 v04_11a

833 agricultural and other mobile-plant operators
911 street vendors and related workers
912 shoe cleaning and other street services elementary occupations
913 domestic and related helpers, cleaners and launderers
914 building caretakers, windows and related cleaners
915 messengers, porters, doorkeepers and related workers
916 garbage collectors and related labourers
921 agricultural, fishery and related labourers
931 mining and construction labourers
932 manufacturing labourers
933 transport labourers and freight handlers
996 household work
997 student
998 not working
999 not reported
4.07 live outside more than 2 cont. month?
1 yes
2 no
4.08 no.of times lives outside{4.09} last time left to live outside, year
{4.09} last time left to live outside, month{4.10} last time came back, year
98 still away
{4.10} last time came back, month
98 still away
4.11 place of living last time - distric/country
1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang
6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga
13 khotang
14 udayapur
15 saptari
16 siraha
17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu
28 nuwakot
29 rasuwa
30 dhading

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31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung
38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot
62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang
69 achham
70 doti
71 kailali
72 kanchanpur
73 dadeldhura
74 baitadi
75 darchula
81 india
82 bhutan
83 china
84 bangladesh
85 hongkong
86 malaysia
87 japan
88 saudi arabia
89 qatar
90 united arab emirates
91 united kingdom

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92 united states of america
93 south korea
94 australia
95 israel
96 other country- urban/rural
1 urban2 rural

19 v04_11b.

20 v04_idc2
21 v04_12

id code4.12 reason for migration
1 marriage
2 follow the family
3 other family reason
4 education
5 training
6 looking for work
7 start new job
8 start new business9 job transfer
10 conflict
11 natural disaster12 easier life style
13 other
4.13 primary activity [nsco] before living
11 armed forces
111 legislators
112 government officials
114 officials of special interest organizations
121 directors and chief executives
122 production and operations department managers
123 other department managers
131 general managers/managing proprietors
211 physicists, chemists and related professionals
212 mathematicians, statisticians and related professionals
213 computing professionals
214 architects, engineers and related professionals
221 life science professionals
222 health professionals, except nursing
223 nursing and midwifery professionals
231 college, university and higher education teaching professionals
232 secondary education teaching professionals
233 primary and pre-primary education teaching professionals
234 special education teaching professionals
235 other teaching professionals
241 business professionals
242 legal professionals
243 archivists, librarians and related information professionals
244 social science and related professionals
245 writers and creative or performing artists
246 religious professionals
311 physical and engineering science technicians
312 computer associate professionals
313 optical and electronic equipment operators
314 aircraft controllers and technicians
315 safety and quality inspectors
321 life science technicians and related associate professionals

22 v04_13

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322 modern health associate professional, except nursing
323 nursing and midwifery associate professionals
324 traditional medicine practitioners and faith healers
331 primary education teaching associate professionals
332 pre-primary education teaching associate professionals
333 special education teaching associate professionals
334 other teaching associate professionals
341 finance and sales associate professionals
342 business services agent and trade brokers
343 administrative associate professionals
344 customs, tax and related government associate professionals
345 police inspectors and detectives
346 social work associate professionals
347 artistic, entertainment and soprts associate professionals
348 religious associate professionals
411 secretaries and keyboard-operating clerks/assistants
412 numerical clerks/office assistants
413 material-recording and transport clerks/office assistants
414 library, mail and related clerks/office assistants
419 other office clerks/assistants
421 cashiers, tellers and related clerks/office assistants
422 client information clerks/office assistants
511 travel attendants and related workers
512 housekeeping and restaurant services workers
513 personal care and related workers
514 other professional services workers
515 astrologers, fortune-tellers and related workers
516 protective service workers
521 fashion and other models
522 shop salespersons and demonstratotrs
523 stall and market salespersons
611 market-oriented gardeners and crop growers
612 market-oriented animal producers and related workers
613 market-oriented crop and animal producers
614 forestry and related workers
615 fishery workers
621 subsistence agricultural and fishery workers
711 miners, shoftirers, stone cutters and carvers
712 building frame and related trades workers
713 building finishers and related trades workers
714 painters, building structure cleaners and related trades workers
721 metal moulders, welders, sheet-metal workers, structural-metal preparer
722 blacksmiths, tool-makers and related trades workers
723 machinery mechanics and fitters
724 electrical and electronic equipment mechanics and fitters
731 precision workers in metal and related materials
732 potters, glass-makers and related trades workers
733 handicraft workers in wood, textile, leather and related materials
734 printing and related trades workers
741 food processing and related trades workers
742 wood treaters, cabinet-makers and related traders workers
743 textile, garment and related trades workers
744 pelt, leather and shoe making trades workers
811 mining and mineral-processing plant operators
812 metal-processing-plant operators
813 glass, ceramics and relative plant operators

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23 v04_14

4.14

24 v04_15

4.15

25 v04_16

4.16

26 v04_17

4.17

814 wood-processing and papermaking-plant operators
815 chemical-processing-plant operators
816 power-production and related plant operators
817 automated-assembly-line and industrial-robot operators
821 metal and mineral products machine operators
822 chemical-products machine operators
823 rubber and plastic products machine operators
824 wood-products machine operators
825 printing, binding and paper products machine operators
826 textile, fur and leather-products machine operators
827 food and related products machine operators
828 assemblers
829 other machine operators and assemblers
831 locomotive-engine drivers and related workers
832 motor vehicle drivers
833 agricultural and other mobile-plant operators
911 street vendors and related workers
912 shoe cleaning and other street services elementary occupations
913 domestic and related helpers, cleaners and launderers
914 building caretakers, windows and related cleaners
915 messengers, porters, doorkeepers and related workers
916 garbage collectors and related labourers
921 agricultural, fishery and related labourers
931 mining and construction labourers
932 manufacturing labourers
933 transport labourers and freight handlers
996 household work
997 student
998 not working
999 not reported
have any job there?
1 yes
2 no
who help to find job/start business?
1 relatives
2 friends,neighbours
3 other
4 nobody helped
5 not applicabletypes of work done
1 wage job
2 self employment
primary activity [nsco] there
11 armed forces
111 legislators
112 government officials
114 officials of special interest organizations
121 directors and chief executives
122 production and operations department managers
123 other department managers
131 general managers/managing proprietors
211 physicists, chemists and related professionals
212 mathematicians, statisticians and related professionals
213 computing professionals
214 architects, engineers and related professionals
221 life science professionals

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222 health professionals, except nursing
223 nursing and midwifery professionals
231 college, university and higher education teaching professionals
232 secondary education teaching professionals
233 primary and pre-primary education teaching professionals
234 special education teaching professionals
235 other teaching professionals
241 business professionals
242 legal professionals
243 archivists, librarians and related information professionals
244 social science and related professionals
245 writers and creative or performing artists
246 religious professionals
311 physical and engineering science technicians
312 computer associate professionals
313 optical and electronic equipment operators
314 aircraft controllers and technicians
315 safety and quality inspectors
321 life science technicians and related associate professionals
322 modern health associate professional, except nursing
323 nursing and midwifery associate professionals
324 traditional medicine practitioners and faith healers
331 primary education teaching associate professionals
332 pre-primary education teaching associate professionals
333 special education teaching associate professionals
334 other teaching associate professionals
341 finance and sales associate professionals
342 business services agent and trade brokers
343 administrative associate professionals
344 customs, tax and related government associate professionals
345 police inspectors and detectives
346 social work associate professionals
347 artistic, entertainment and soprts associate professionals
348 religious associate professionals
411 secretaries and keyboard-operating clerks/assistants
412 numerical clerks/office assistants
413 material-recording and transport clerks/office assistants
414 library, mail and related clerks/office assistants
419 other office clerks/assistants
421 cashiers, tellers and related clerks/office assistants
422 client information clerks/office assistants
511 travel attendants and related workers
512 housekeeping and restaurant services workers
513 personal care and related workers
514 other professional services workers
515 astrologers, fortune-tellers and related workers
516 protective service workers
521 fashion and other models
522 shop salespersons and demonstratotrs
523 stall and market salespersons
611 market-oriented gardeners and crop growers
612 market-oriented animal producers and related workers
613 market-oriented crop and animal producers
614 forestry and related workers
615 fishery workers
621 subsistence agricultural and fishery workers

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27 v04_18

4.18

711 miners, shoftirers, stone cutters and carvers
712 building frame and related trades workers
713 building finishers and related trades workers
714 painters, building structure cleaners and related trades workers
721 metal moulders, welders, sheet-metal workers, structural-metal preparer
722 blacksmiths, tool-makers and related trades workers
723 machinery mechanics and fitters
724 electrical and electronic equipment mechanics and fitters
731 precision workers in metal and related materials
732 potters, glass-makers and related trades workers
733 handicraft workers in wood, textile, leather and related materials
734 printing and related trades workers
741 food processing and related trades workers
742 wood treaters, cabinet-makers and related traders workers
743 textile, garment and related trades workers
744 pelt, leather and shoe making trades workers
811 mining and mineral-processing plant operators
812 metal-processing-plant operators
813 glass, ceramics and relative plant operators
814 wood-processing and papermaking-plant operators
815 chemical-processing-plant operators
816 power-production and related plant operators
817 automated-assembly-line and industrial-robot operators
821 metal and mineral products machine operators
822 chemical-products machine operators
823 rubber and plastic products machine operators
824 wood-products machine operators
825 printing, binding and paper products machine operators
826 textile, fur and leather-products machine operators
827 food and related products machine operators
828 assemblers
829 other machine operators and assemblers
831 locomotive-engine drivers and related workers
832 motor vehicle drivers
833 agricultural and other mobile-plant operators
911 street vendors and related workers
912 shoe cleaning and other street services elementary occupations
913 domestic and related helpers, cleaners and launderers
914 building caretakers, windows and related cleaners
915 messengers, porters, doorkeepers and related workers
916 garbage collectors and related labourers
921 agricultural, fishery and related labourers
931 mining and construction labourers
932 manufacturing labourers
933 transport labourers and freight handlers
996 household work
997 student
998 not working
999 not reported
main reason to come back1 family reasons
2 work finished3 dissatisfied with work
4 start new job
5 start new business6 job transfer

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7 conflict
8 natural disaster9 easier life style
10 other
99 not returned### 7 : xh05_s05.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v05_idc
food code
5 v05_itm
food item name
6 v05_01
5.01 consumed (yes/no)
1 yes
2 no
7 v05_02{05.02} home-prod-12-mo/number of months
8 v05_03a{05.03} a: home-prod-12-mo/quantity
9 v05_03b{05.03} b: home-prod-12-mo/measurement unit
1 kg
2 gram3 maund
4 liter
5 muri6 pathi
7 manna
8 kuruwa
9 piece10 dozen
11 quintal
10 v05_04{05.04} home-prod-12-mo/value
11 v05_05{05.05} purchases-12-mo/number of months
12 v05_06a{05.06} a: purchases-12-mo/quantity
13 v05_06b{05.06} b: purchases-12-mo/measurement unit
1 kg
2 gram3 maund
4 liter
5 muri6 pathi
7 manna
8 kuruwa

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14 v05_07
15 v05_08
16 v05_09
17 v05_10a18 v05_10b

19 v05_11
20 v05_12

9 piece10 dozen
11 quintal{05.07} purchases-12-mo/value{05.08} in-kind-12-mo/value{05.09} consumed-7-days/number of days
{05.10} a: consumed-7-days/quantity{05.10} b: consumed-7-days/measurement unit
1 kg
2 gram3 maund
4 liter
5 muri6 pathi
7 manna
8 kuruwa
9 piece10 dozen
11 quintal{05.11} consumed-7-days/value{05.12} consumed-7-days/source
1 home production
2 purchase
3 in-kind### 8 : xh06_s06a.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v06a_idc
frq. non-food code
5 v06a_itm
frq. non-food item
6 v06_01
6.01 consumed (yes/no)
1 yes
2 no
7 v06_02a6.02 amount of past 30 days (rs.)
8 v06_02b6.02 amount of past 12 months (rs.)### 9 : xh07_s06b.dta ###1 xhpsupsu
2 xhnumhousehold number

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3 xstra

4 v06b_idc
5 v06b_itm
6 v06_03

7 v06_04

strata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
infrq. non-food code
infrq. non-food item
6.03 purchased/received (yes/no)
1 yes
2 no
6.04 amount of past 12 months (rs.)### 10 : xh08_s06c.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v06c_idc
code of durable goods
5 v06c_itm
durable goods item
6 v06_05
6.05 having durable goods (yes/no)
1 yes
2 no
7 v06_06
6.06 no. of durable goods having now
8 v06_07
6.07 years before bring
9 v06_08
6.08 how received?
1 purchased
2 gift/payment
3 dowry/inheritance
10 v06_09
6.09 price at that period
11 v06_10
6.10 current sale value

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2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v06d_idc
own account prod. goods code
5 v06d_itm
own account prod. goods item
6 v06_11
6.11 produced (yes/no)
1 yes
2 no
7 v06_12a6.12 amount of past 30 days (rs.)
8 v06_12b6.12 amount of past 12 months (rs.)### 12 : xh10_s07.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v07_idc
i d
c o d e5 v07_01{7.01} respondent id-code
6 v07_02
7.02 can read a letter?
1 yes
2 no
7 v07_03
7.03 can write a letter?
1 yes
2 no
8 v07_04
7.04 where did person learn?
1 formal schooling
2 taught at home

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9 v07_05

7.05

10 v07_06

7.06

11 v07_07

7.07

12 v07_08

7.08

13 v07_09

7.09

14 v07_idc2
15 v07_10

i d
7.10

16 v07_11

7.11

3 govt. literacy course
4 ngo literacy course
5 other
ever been refused admission
1 yes
2 no
reason to refuse admission
1 poor academic
2 couldn't pay
3 caste/ethnicity
4 religion
5 others
types of school/college refused
1 community/government
2 institutional/private
3 gurukul/madarsha/gumba
4 other
educational background
1 never attended school
2 attended school/college in past
3 currently attending school/college
reason for never attending school
1 too young
2 too expensive
3 too far away
4 had to help at home
5 education not useful
6 parents did not want
7 not willing to attend
8 disable
9 not allowed admission
10 other reasonsc o d etype of school/college
1 community/government
2 institutional/private
3 technical/vocational
4 gurukul/madarsha/gumba
5 community (public) campus
6 private campus
7 constituent campus
8 otherhighest level completed
0 nursery/kg/pre-school
1 class 01
2 class 02
3 class 03
4 class 04
5 class 05
6 class 06
7 class 07
8 class 08
9 class 09
10 class 10
11 slc12 intermediate

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17 v07_12
18 v07_13a
19 v07_13b
20 v07_14
21 v07_15

22 v07_16

23 v07_idc3
24 v07_17

25 v07_18

13 bachelor
14 master
15 profesional degree
16 levelless
7.12 years to complete primary level (1-5)
98 complete without attending school{7.13} a: years takes to complete class 10{7.13} b: times the slc exam was given
7.14 age when left school
7.15 last attending before leaving school
0 nursery/kg/pre-school
1 class 01
2 class 02
3 class 03
4 class 04
5 class 05
6 class 06
7 class 07
8 class 08
9 class 09
10 class 10
11 slc12 intermediate
13 bachelor
14 master
15 profesional degree
16 levelless{7.16} reason for leaving school
1 further schooling not available
2 too expensive
3 too far away
4 had to help at home
5 parents did not want
6 completed desired schooling
7 marriage
8 moved away
9 poor academic progress
10 started working/got a job
11 lack of toilet facility
12 language problem
13 not allowed admission
14 mistreatment at school
15 other reasonsi d
c o d e7.17 type of school/college
1 community/government
2 institutional/private
3 technical/vocational
4 gurukul/madarsha/gumba
5 community (public) campus
6 private campus
7 constituent campus
8 other
7.18 class currently attending
0 nursery/kg/pre-school
1 class 01

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26 v07_19
27 v07_20
28 v07_21

29 v07_22

30 v07_23a
31 v07_23b
32 v07_24

33 v07_25

34 v07_idc4
35 v07_26a
36 v07_26b
37 v07_26c
38 v07_26d
39 v07_26e
40 v07_26f
41 v07_26g
42 v07_27

43 v07_28
44 v07_29

45 v07_30

2 class 02
3 class 03
4 class 04
5 class 05
6 class 06
7 class 07
8 class 08
9 class 09
10 class 10
11 slc12 intermediate
13 bachelor
14 master
15 profesional degree
7.19 no. days attending school
7.20 no. days supposed to go school{7.21} reason for absenting from school
1 illness/injury
2 had to help with hhld chores
3 had to work
4 family members sick or died
5 family function
6 quit school
7 others
7.22 mode of transport
1 walk2 bus/motor
3 bicycle/rickshaw
4 mixed (walk+vehicle)
5 other
7.23 time taken: hrs.
: minute
7.24 take any private tuition
1 yes
2 no
7.25 tuition teacher from own school?
1 yes
2 no
i d
c o d e7.26 expenditure on : monthly tuition fee (rs).
: on other fee (rs).
: on uniform (rs).
: on textbook/supplies (rs).
: on transportation (rs).
: on private tuition (rs).
: on others (rs)7.27 receive scholarship?
1 yes
2 no
7.28 value of scholarship (rs)7.29 form of scholarship
1 tuition fees
2 uniforms/books
3 hostel4 other
7.30 types of scholarship

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46 v07_31

47 v07_32
48 v07_33a
49 v07_33b>

1 poor and talented
2 girls
3 dalits
4 conflict affected
5 disabled
6 hiv/aids
7 karnali zone
8 other
7.31 class attending in last academic year
0 nursery/kg/pre-school
1 class 01
2 class 02
3 class 03
4 class 04
5 class 05
6 class 06
7 class 07
8 class 08
9 class 09
10 class 10
11 slc12 intermediate
13 bachelor
14 master
15 profesional degree
98 did not attend last year
7.32 years to complete pri. level excl. ecd
98 complete primary level without attending school{7.33} a: years took completing class 10{7.33} b: times the slc exam was given### 13 : xh11_s08.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v08a_idc
id code5 v08_01{8.01} respondent id-code
6 v08_02{8.02} type of disability
1 physical2 visual

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7 v08_03

8.03

8 v08_04

8.04

9 v08_05

8.05

10 v08_06
11 v08_07a
12 v08_07b
13 v08_08

8.06
8.07
.
8.08

14 v08_09
15 v08_10

8.09
8.10

16 v08b_idc
17 v08_11

i d
8.11

18 v08_12

8.12

3 hearing
4 visual and hearing
5 speech
6 mental
7 multiple
8 nonehow long suffered from disability?
98 by birthsuffer from chronic illness?
1 yes
2 no
primary chronic illness
1 heart related
2 respiratory
3 asthma
4 epilepsy
5 cancer
6 diabetes
7 kidney/liver deseases
8 rheumatism related
9 gynecological problems
10 occupational illnesses
11 high/low blood pressure
12 gastrointestinal deseases
13 otheryears of illness start
amount spent : medicine (rs): other expenses (rs)received in-kind medication
1 yes
2 no
days of work missed
present health status
1 excellent
2 good3 fair4 poorc o d eany health problem & injured
1 yes
2 no
types of illness or injury
1 diarrhoea
2 dysentry
3 respiratory problems
4 malaria
5 cold/fever/flu
6 other fever
7 skin disease
8 tb
9 measles
10 jaundice
11 parasites
12 injury
13 dental problems
14 other

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19 v08_13

20 v08_14

21 v08_15

22 v08_16

23 v08b_idc2
24 v08_17a
25 v08_17b
26 v08_17c
27 v08_18
28 v08_19

29 v08_20

30 v08_21
31 v08_22

32 v08_23

33 v08_24

8.13

consult with anyone?
1 yes
2 no
8.14 main reason for not consulting
1 illness/injury not serious
2 health facilty too far
3 no transport facility
4 health care too expensive
5 transport too expensive
6 health workers unfriendly
7 health workers not present
8 health care not good quality
9 other{8.15} place of last consultation
1 govt/shp
2 govt/hp
3 govt/phc
4 govt/hospital
5 govt/mobile clinic
6 govt/ayurved centre
7 priv/pharmacy
8 priv/clinic
9 priv/hospital
10 priv/health worker's home
11 other{8.16} consulted with
1 govt. doctor
2 govt. paramedic
3 govt. kaviraj/vaidya
4 pvt. doctor
5 pvt. pharmacist
6 pvt. paramedic
7 pvt. kaviraj/vaidya
8 traditional healer
9 otheri d
c o d e8.17 cost of consultation :fees(rs).
:medicine (rs).
:travel(rs)
8.18 total cost spent (rs)8.19 received in-kind medication
1 yes
2 no
8.20 stopped work?
1 yes
2 no
8.21 days of work missed
8.22 anything to treat the diarrhoea?
1 yes
2 no
8.23 treatment given
1 ors
2 allopathic medicine
3 traditional medicine
4 other
8.24 obtain the ors from

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34 v08c_idc
35 v08_25

i d
8.25

36 v08_26

8.26

37 v08_27a

8.27

38 v08_27b

.

1 shp
2 hp
3 phc
4 hospital
5 mobile clinic
6 made at home
7 non-govt. inst.
8 medicine shop
9 otherc o d eheard about hiv/aids
1 yes
2 no
3 person not contacted
first media to know about hiv/aids
1 radio
2 television
3 newspapers/pamphlets/posters
4 friends/relatives
5 health workers
6 textbook
7 other
methods of prevention - first
1 abstain from sex
2 use condoms
3 limit sex to one partner
4 limit number of sexual partners
5 avoid sex with prostitutes
6 avoid sex with persons who have many partners
7 avoid sex with homosexuals
8 avoid sex with persons who inject drugs intravenously
9 avoid blood transfusions
10 avoid injections
11 avoid sharing razors/blades
12 avoid kissing
13 avoid mosquito bites
14 seek protection from traditional practitioner
15 other
16 don't know
- second
0 !end-of-list!
1 abstain from sex
2 use condoms
3 limit sex to one partner
4 limit number of sexual partners
5 avoid sex with prostitutes
6 avoid sex with persons who have many partners
7 avoid sex with homosexuals
8 avoid sex with persons who inject drugs intravenously
9 avoid blood transfusions
10 avoid injections
11 avoid sharing razors/blades
12 avoid kissing
13 avoid mosquito bites
14 seek protection from traditional practitioner
15 other

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39 v08_27c

40 v08_27d

41 v08d_idc
42 v08_28

43 v08_29

44 v08_30a

45 v08_30b

46 v08_30c

47 v08_30d

48 v08_30e.

- third
0 !end-of-list!
1 abstain from sex
2 use condoms
3 limit sex to one partner
4 limit number of sexual partners
5 avoid sex with prostitutes
6 avoid sex with persons who have many partners
7 avoid sex with homosexuals
8 avoid sex with persons who inject drugs intravenously
9 avoid blood transfusions
10 avoid injections
11 avoid sharing razors/blades
12 avoid kissing
13 avoid mosquito bites
14 seek protection from traditional practitioner
15 other.
- fourth
0 !end-of-list!
1 abstain from sex
2 use condoms
3 limit sex to one partner
4 limit number of sexual partners
5 avoid sex with prostitutes
6 avoid sex with persons who have many partners
7 avoid sex with homosexuals
8 avoid sex with persons who inject drugs intravenously
9 avoid blood transfusions
10 avoid injections
11 avoid sharing razors/blades
12 avoid kissing
13 avoid mosquito bites
14 seek protection from traditional practitioner
15 otheri d
c o d e8.28 ever been immunized?
1 yes
2 no
3 don't know
8.29 have immunization card?
1 yes, seen
2 yes, not available
3 no8.30 received vaccine : bcg
1 yes
2 no
.
: dpt1
1 yes
2 no.
: dpt21 yes
2 no.
: dpt31 yes
2 no.
: polio 1

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1 yes
2 no
49 v08_30f.

: polio 2
1 yes
2 no

50 v08_30g.

: polio 3
1 yes
2 no

51 v08_30h.

: measles
1 yes
2 no

52 v08_30i.

: hp-b 1
1 yes
2 no

53 v08_30j.

: hp-b 2
1 yes
2 no

54 v08_30k.

55 v08_31

8.31

56 v08_32

8.32

57 v08_33

8.33

58 v08_34

8.34

59 v08_35

8.35

60 v08_36
61 v08_37

8.36
8.37

62 v08_38

8.38

63 v08_39

8.39: hp-b 3
1 yes
2 noreceived bcg vaccine
1 yes
2 no
3 don't know
received polio vaccine
1 yes
2 no
3 don't know
when first polio vaccine received
1 just after birth
2 later
3 don't know
no. of time polio vaccine received
1 once2 twice
3 thrice
4 more than 3 times
5 don't know
received dpt vaccine
1 yes
2 no
3 don't know
no. of time dpt vaccine received
received measles vaccine
1 yes
2 no
3 don't know
received hp-b vaccine
1 yes
2 no
3 don't know
place of immunization
1 shp
2 hp
3 phc
4 hospital

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5 outreach clinic
6 other health inst.

### 14 : xh12_s09a.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 id_s9a
unique id
5 v09a_sns e r i a ln o.
o f
w o m e n
6 v09a_idc
i d
c o d eo f
w o m e n
7 v09a_rsp
i d
c o d eo fr e s p o n d e n t
8 v09a_bo
b i r t ho r d e r
9 v09_03{9.03} child name
10 v09_04a{9.04} date of birth, month
11 v09_04b{9.04} date of birth, year
12 v09_05
9.05 sex of child
1 male2 female
13 v09_06
9.06 still alive
1 yes
2 no
14 v09_07
9.07 currently live with you?
1 yes
2 no
15 v09_08
9.08 id code of child
16 v09_09a
9.09 lived for: year
17 v09_09b.
: month
18 v09_09c.
: day
19 v09_10
9.10 highest level completed
0 nursery/kg/pre-school
1 class 01
2 class 02
3 class 03
4 class 04
5 class 05
6 class 06
7 class 07
8 class 08
9 class 09
10 class 10
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12 intermediate
13 bachelor
14 master
15 profesional degree
98 never attending school### 15 : xh13_s09b.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v09b_ln1
l i n en u m b e r
5 v09b_idc
9.11 id code of women
6 v09_12
9.12 went for pre-natal consultation
1 yes
2 no
7 v09_13
9.13 place where first care received
1 shp
2 hp
3 phc
4 hospital
5 mobile clinic
6 ayurved centre
7 pharmacy
8 clinic
9 pvt. hospital
10 health worker's home
11 other
8 v09_14
9.14 care provider
1 doctor
2 nurse/anm ha/sahw /ahw/mchw/ vhw
3 tba
4 sba
5 other
9 v09_15
9.15 at what month
10 v09_16
9.16 given tetanus injection after birth
1 yes
2 no
11 v09_17
9.17 no. of times
1 once2 twice
3 more than twice

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12 v09_18

13 v09b_ln2
14 v09_19

15 v09_20

16 v09_21

17 v09_22

18 v09_23

19 v09_24

9.18

reason for not pre-natal consultation
1 don't think is necessary
2 health facilty too far
3 no transport
4 health care too expensive
5 transport too expensive
6 health workers unfriendly
7 health workers not present
8 health care not good quality
9 otherl i n en u m b e r
9.19 where given birth
1 home
2 shp
3 hp
4 phc
5 hospital
6 pvt. hospital
7 other
9.20 who assisted
1 family member or relatives
2 neighbours
3 tba
4 sba
5 ha/sahw/ahw/mchw/chw/vhw
6 anm/nurse/doctor
7 other
8 no one
9 don't know
9.21 post-natal checkup
1 yes
2 no
9.22 place of post-natal check-up
1 shp
2 hp
3 phc
4 hospital
5 mobile clinic
6 ayurved centre
7 pharmacy
8 clinic
9 pvt. hospital
10 health worker's home
11 other
9.23 care provider
1 doctor
2 nurse/amn/ha/sahw/ahw/mchw/vhw
3 tba
4 sba
5 other
9.24 reason for not post-natal consultation
1 don't think necessary
2 health facilty too far
3 no transport
4 health care too expensive
5 transport too expensive

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6 health workers unfriendly
7 health workers not present
8 health care not good quality
9 other### 16 : xh14_s09c.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v09c_idc
i d
c o d e5 v09_25
9.25 is this 15-49 married women?
1 yes
2 no
6 v09_26
9.26 age at first marriage
7 v09_27
9.27 know any method to prevent pregnancy
1 yes
2 no
8 v09_28
9.28 medium to learn
1 radio
2 television
3 newspapers/ pamphlets/ posters
4 friends/ relatives
5 health worker
6 husband7 textbook
8 other
9 v09_29
9.29 ever used
1 yes
2 no
10 v09_30
9.30 currently using
1 yes
2 no
11 v09_31
9.31 which method currently used
1 pill2 iud
3 injectables
4 implants
5 condom
6 trad. method
7 female sterilization
8 male sterilization

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12 v09_32

9.32

13 v09_33

9.33

14 v09_34

9.34

15 v09_35a
16 v09_35b
17 v09_35c

9.35
.
.

9 other
where obtained
1 public health inst.
2 private health inst.
3 pharmacy
4 vsc
5 other
why not?
1 not available
2 too expensive
3 husband away
4 want more children
5 religious reasons
6 scared of side-effects
7 husband does not want
8 otherhealth worker visit?
1 yes
2 no
child like to have: total: boys: girl### 17 : xh15_s09d.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v09d_idc
decision number
5 v09d_txt
decision description
6 v09_37
9.37 have to make decision ?
1 yes
2 no
7 v09_38
9.38 involvement in decision making
1 a lot
2 a little
3 not involved
8 v09_39
9.39 who made final decision
1 me
2 my spouse3 both4 other

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2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v10_idc
id code5 v10_01a10.01 hour spent on fetching water
6 v10_01b.
firewood collecting
7 v10_01c.
fodder collecting
8 v10_01d.
care animal
9 v10_01e.
knitting/weaving/tailoring
10 v10_01f.
processing preserved food
11 v10_01g.
total [a-f]12 v10_01h.
hhld repairing
13 v10_01i.
cooking/serving food
14 v10_01j.
clearing house
15 v10_01k.
shopping
16 v10_01l.
caring for elderly/sick/disable
17 v10_01m.
baby sitting/child caring
18 v10_01n.
other volunteer/comm. service### 19 : xh17_s10b.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v10b_ln
person-job seqno

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5 v10_02{10.02} person id-code
6 v10_02_job {10.02} job-id
1 1st job
2 2nd job
3 3rd job
4 4th job
5 5th job
99 total number of hours worked during the past 7 days
7 v10_03_txt {10.03} occupation description
8 v10_03{10.03} occupation code (nsco)
11 armed forces
111 legislators
112 government officials
114 officials of special interest organizations
121 directors and chief executives
122 production and operations department managers
123 other department managers
131 general managers/managing proprietors
211 physicists, chemists and related professionals
212 mathematicians, statisticians and related professionals
213 computing professionals
214 architects, engineers and related professionals
221 life science professionals
222 health professionals, except nursing
223 nursing and midwifery professionals
231 college, university and higher education teaching professionals
232 secondary education teaching professionals
233 primary and pre-primary education teaching professionals
234 special education teaching professionals
235 other teaching professionals
241 business professionals
242 legal professionals
243 archivists, librarians and related information professionals
244 social science and related professionals
245 writers and creative or performing artists
246 religious professionals
311 physical and engineering science technicians
312 computer associate professionals
313 optical and electronic equipment operators
314 aircraft controllers and technicians
315 safety and quality inspectors
321 life science technicians and related associate professionals
322 modern health associate professional, except nursing
323 nursing and midwifery associate professionals
324 traditional medicine practitioners and faith healers
331 primary education teaching associate professionals
332 pre-primary education teaching associate professionals
333 special education teaching associate professionals
334 other teaching associate professionals
341 finance and sales associate professionals
342 business services agent and trade brokers
343 administrative associate professionals
344 customs, tax and related government associate professionals
345 police inspectors and detectives
346 social work associate professionals
347 artistic, entertainment and soprts associate professionals

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348 religious associate professionals
411 secretaries and keyboard-operating clerks/assistants
412 numerical clerks/office assistants
413 material-recording and transport clerks/office assistants
414 library, mail and related clerks/office assistants
419 other office clerks/assistants
421 cashiers, tellers and related clerks/office assistants
422 client information clerks/office assistants
511 travel attendants and related workers
512 housekeeping and restaurant services workers
513 personal care and related workers
514 other professional services workers
515 astrologers, fortune-tellers and related workers
516 protective service workers
521 fashion and other models
522 shop salespersons and demonstratotrs
523 stall and market salespersons
611 market-oriented gardeners and crop growers
612 market-oriented animal producers and related workers
613 market-oriented crop and animal producers
614 forestry and related workers
615 fishery workers
621 subsistence agricultural and fishery workers
711 miners, shoftirers, stone cutters and carvers
712 building frame and related trades workers
713 building finishers and related trades workers
714 painters, building structure cleaners and related trades workers
721 metal moulders, welders, sheet-metal workers, structural-metal preparer
722 blacksmiths, tool-makers and related trades workers
723 machinery mechanics and fitters
724 electrical and electronic equipment mechanics and fitters
731 precision workers in metal and related materials
732 potters, glass-makers and related trades workers
733 handicraft workers in wood, textile, leather and related materials
734 printing and related trades workers
741 food processing and related trades workers
742 wood treaters, cabinet-makers and related traders workers
743 textile, garment and related trades workers
744 pelt, leather and shoe making trades workers
811 mining and mineral-processing plant operators
812 metal-processing-plant operators
813 glass, ceramics and relative plant operators
814 wood-processing and papermaking-plant operators
815 chemical-processing-plant operators
816 power-production and related plant operators
817 automated-assembly-line and industrial-robot operators
821 metal and mineral products machine operators
822 chemical-products machine operators
823 rubber and plastic products machine operators
824 wood-products machine operators
825 printing, binding and paper products machine operators
826 textile, fur and leather-products machine operators
827 food and related products machine operators
828 assemblers
829 other machine operators and assemblers
831 locomotive-engine drivers and related workers

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9 v10_04a

10 v10_04b

11 v10_04c

12 v10_04d

13 v10_04e

14 v10_04f

15 v10_04g

16 v10_04h

17 v10_04i

18 v10_04j

19 v10_04k

20 v10_04l

21 v10_05a
22 v10_05b
23 v10_06a
24 v10_06b

832 motor vehicle drivers
833 agricultural and other mobile-plant operators
911 street vendors and related workers
912 shoe cleaning and other street services elementary occupations
913 domestic and related helpers, cleaners and launderers
914 building caretakers, windows and related cleaners
915 messengers, porters, doorkeepers and related workers
916 garbage collectors and related labourers
921 agricultural, fishery and related labourers
931 mining and construction labourers
932 manufacturing labourers
933 transport labourers and freight handlers
996 household work
997 student
998 not working
999 not reported
10.04 work done in baishakh
1 yes
2 no
.
jestha1 yes
2 no.
aashadh
1 yes
2 no
.
shravan
1 yes
2 no
.
bhadra1 yes
2 no.
ashwin
1 yes
2 no.
kartik
1 yes
2 no.
mangsir
1 yes
2 no
.
poush
1 yes
2 no
.
magh
1 yes
2 no
.
falgun
1 yes
2 no.
chaitra1 yes
2 no10.05 on average no.of days/month work
10.05 on average no.of hours/day work
(10.06a) hours worked past 7 days/sunday(10.06b) hours worked past 7 days/monday

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25 v10_06c
26 v10_06d
27 v10_06e
28 v10_06f
29 v10_06g
30 v10_06h
31 v10_07.
tuesday.
wednesday.
thursday.
friday.
saturday.
total{10.07} sector of employment
1 wage employment in agri.
2 wage employment in non-agri.
3 self-employment in agri.
4 self-employment in non-agri.

### 20 : xh18_s11.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v11_idc
i d
c o d e5 v11_01
11.01 total hours work [10.01(g)+10.06(h)]6 v11_02
11.02 available for work?
1 yes
2 no
7 v11_03
11.03 look for work
1 yes
2 no
8 v11_04
11.04 reason for not available/not look for work
1 attending school
2 household duties
3 old/sick
4 disabled
5 thought no work available
6 awaiting reply to earlier enquiries
7 waiting to start arranged job/business8 off season
9 not available
10 others
9 v11_05
11.05 available for additional work?
1 yes
2 no
10 v11_06
11.06 look for additional work
1 yes
2 no

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11 v11_07

11.07

reason for not available/not look for additional work
1 could not find more work/lack of business2 lack of finance, raw materials
3 machinery, electrical, other breakdow
4 off season inactivity
5 industrial dispute
6 other involuntary
7 have sufficient work
8 household duties
9 student, unpaid training
10 illness, disability
11 vacation, family reason
12 pregnant/delivery
13 other voluntary### 21 : xh19_s12.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v12_ln1
l i n en u m b e r
5 v12_01{12.01} person id-code
6 v12_01_job {12.01} job-id
7 v12_02_txt {12.02} work activity description
8 v12_02{12.02} work activity code [nsic]
1 agriculture and related service activities
2 forestry, logging and related service activities
5 fishing, operation of fish hatcheries and fish farms; service activities
incidental to fishing
10 mining of coal and lignite; extraction of peat
11 extraction of crude petrolium and natural gas; service activities
incidental to oil and gas extraction excluding surveying
12 mining of uranium and thorium ores
13 mining of metal ores
14 other mining and quarrying
15 manufacture of food products and beverages
16 manufacture of tobacco products
17 manufacture of textiles
18 manufacture of wearing apprel; dressing and dyeing of fur
19 tanning and dressing of leather; manufacture of luggage, handbags, saddlery
and harness
20 manufacture of wood and of products of wood and cork, except furniture;

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manufacture of articles of straw and plaiting materials
21 manufacture of paper and paper products
22 publishing, printing and reproduction of recorded media
23 manufacture of coke, refined petrolium products and nuclear fuel24 manufacture of chemicals and chemical products
25 manufacture of rubber and plasics products
26 manufacture of other non-metalic minaral products
27 manufacture of basic metals
28 manufacture of fabricated metal products, except machinery and equipment
29 manufacture of machinery and equipment n.e.c.
30 manufacture of office, accounting and computing machinery
31 manufacture of electrical machinery and apparatus n.e.c.
32 manufacture of radio, tv and communication equipment and apparatus
33 manufacture of medical, precision and optical instruments, watches andclocks34 manufacture of motor vehicles; trailers and semi-trailers
35 manufacture of other transport equipment
36 manufacture of furniture; manufacturing n.e.c.
37 recycling
40 electricity and gas supply
41 collections, purification and distribution of water
45 construction
50 sale, maintenance and repair of motor vehicles and motorcycles; retail saleof automotive fuel51 wholesale trade and commission trade, except of motor vehicles andmotorcycles
52 retail trade, except of motor vehicles and motorcycles; repair of personaland household goods
55 hotels and restaurants
60 land transport
61 water transport
62 air transport
63 supporting and auxiliary transport activities; activities of travel
agencies
64 post and telecommunications
65 financial intermediation, except insurance and pension funding
66 insurance and pension funding, except compulsory social security
67 activities auxiliary to financial intermediation
70 real estate activities
71 renting of machinery and equipment without operator and of personal andhousehold goods
72 computer and related activities
73 researches and development
74 other business activities
75 public administration and defence; compulsory social security
80 education
85 healths and social work
90 sewage and refuse disposal, sanitation and similar activities
91 activities of membership organizations n.e.c.
92 recreational, cultural and sporting activities
93 other service activities
95 private households with employed persons
99 extra-territorial organization and bodies
9 v12_03
12.03 basis of payment receive
1 daily basis
2 long term basis

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10 v12_04
11 v12_05a

12 v12_05b

13 v12_06a
14 v12_06b
15 v12_ln2
16 v12_07

17 v12_08
18 v12_09a

19 v12_09b

20 v12_10a
21 v12_10b
22 v12_11

23 v12_12

24 v12_13

25 v12_14

26 v12_ln3

3 contract/ piece-ratecash per day (rs.)
in-kind payment- first1 paddy/rice
2 wheat
3 maize
4 meals
5 clothing
6 other
7 received nothing in kind.
- second
0 !end-of-list!
1 paddy/rice
2 wheat
3 maize
4 meals
5 clothing
6 other
12.06 value of in kind
- rs./day.
- other (rs/whole period)l i n en u m b e r
12.07 sector of long term worker
1 wage job in agriculture
2 wage job in non-agri.
12.08 cash received(rs.)
12.09 in-kind received : first
1 paddy/rice
2 wheat
3 maize
4 meals
5 clothing
6 other
7 nothing in-kind.
: second
0 !end-of-list!
1 paddy/rice
2 wheat
3 maize
4 meals
5 clothing
6 other
12.10 value of in-kind- rs/day.
- rs/whole period
12.11 takin loan from employer
1 yes
2 no
12.12 other member also work same employer
1 yes
2 no
12.13 share crop with employer
1 yes
2 no
12.14 tent livestock for emploer
1 yes
2 no
l i n en u m b e r
12.04
12.05

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27 v12_15a
28 v12_15b
29 v12_15c
30 v12_15d
31 v12_15e
32 v12_16

12.15
.
.
.
.
12.16

33 v12_17

12.17

34 v12_18

12.18

35 v12_19

12.19

36 v12_20

12.20

37 v12_21

12.21

received as salary/monthtranspor/month
bonus/year
uniform/year
other/year
tax already deducted
1 yes
2 no
provision of provident fund
1 yes
2 no
provision of pension
1 yes
2 no
provision of subsidized medical care
1 yes
2 no
no. of employee
1 one
2 two to nine
3 ten or more
how much receive in-kind and cash### 22 : xh20_s13a1.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v13_plt1
p l o tn u m b e r
5 v13_03
13.03 name of plot
6 v13_04u13.04 area
- unit1 ropani
2 bigha
7 v13_04rb.
- ropani/bigha
8 v13_04ak.
- aana/kattha
9 v13_04pd.
- paisa/dhur
10 v13_05
13.05 district where plot located1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang

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6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga
13 khotang
14 udayapur
15 saptari
16 siraha
17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu
28 nuwakot
29 rasuwa
30 dhading
31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung
38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot

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11 v13_06

12 v13_07

13 v13_08

14 v13_09

15 v13_10
16 v13_plt2
17 v13_11

18 v13_12c
19 v13_12k
20 v13_13a62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang
69 achham
70 doti
71 kailali
72 kanchanpur
73 dadeldhura
74 baitadi
75 darchula
13.06 types of land
1 up land
2 low land
13.07 plot irrigated?
1 yes
2 no
13.08 seasonal or year round irrigation
1 seasonal
2 year round13.09 mode of irrigation
1 tubewell/boring
2 canal3 pond/tank
4 other natural sources
5 mixed13.10 value of this plot (rs.)
p l o tn u m b e r{13.11} dry season: use of plot
1 croped yourself
2 sharedcroped out
3 fixed rent out
4 mortgaged out
5 left fallow
6 other{13.12} dry season: net rent received/cash (rs){13.12} dry season: net rent received/in-kind (rs){13.13} dry season: 1st crop
0 no cultivate in dry season
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea

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21 v13_13b

15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees{13.13} dry season: 2nd crop
0 !end-of-list!
1 early paddy

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2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear

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22 v13_13c

58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees{13.13} dry season: 3rd crop
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables

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23 v13_13d

45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees{13.13} dry season: 4th crop
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed

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24 v13_14

25 v13_15c
26 v13_15k
27 v13_16a

32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees{13.14} wet season: use of plot
1 croped yourself
2 sharedcroped out
3 fixed rent out
4 mortgaged out
5 left fallow
6 other{13.15} wet season: net rent received/cash (rs){13.15} wet season: net rent received/in-kind (rs){13.16} wet season: 1st crop
0 no cultivate in wet season
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat

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10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees

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28 v13_16b

66 bamboo
67 other trees{13.16} wet season: 2nd crop
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana

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29 v13_16c

53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees{13.16} wet season: 3rd crop
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger

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30 v13_16d

40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees{13.16} wet season: 4th crop
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers

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27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees### 23 : xh21_s13a2.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai

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4 v13_plt3
5 v13_19
6 v13_20

7 v13_21
8 v13_22u

9 v13_22rb
10 v13_22ak
11 v13_22pd
12 v13_23

13 v13_24

14 v13_25

15 v13_26

16 v13_plt4
17 v13_27a

321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-westernp l o tn u m b e r
13.19 name of the plot
13.20 contractual arrangement
1 sharedcroped out
2 rent-in
3 mortgaged-in
4 other
13.21 rent paid (cash only)
13.22 area of other's land
- unit1 ropani
2 bigha.
- ropani/bigha.
- aana/kattha.
- paisa/dhur
13.23 types of land
1 up land
2 low land
13.24 plot irrigated?
1 yes
2 no
13.25 seasonal or year round irrigation
1 seasonal
2 year round13.26 mode of irrigation
1 tubewell
2 canal3 pond/tank
4 other natural sources
5 mixedp l o tn u m b e r
13.27 dry season : crop 1
0 no cultivate in dry season
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram

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18 v13_27b.

20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees: crop 2
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize

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7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit

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19 v13_27c.

63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees: crop 3
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime

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20 v13_27d.

50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees: crop 4
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies

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21 v13_28a

13.28

37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees
wet season : crop 1
0 no cultivate in wet season
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato

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22 v13_28b.

24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees: crop 2
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals

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11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo

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23 v13_28c.

67 other trees: crop 3
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava

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24 v13_28d.

54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees: crop 4
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric

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41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees### 24 : xh22_s13b.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v13b_ln
l i n en u m b e r
5 v13_35nm
13.35 n a m e o f c r o p
6 v13_35cc{13.35} crop code
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize

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7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit

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7 v13_36

13.36

8 v13_37a

13.37

9 v13_37b
10 v13_37c
11 v13_37d
12 v13_38a

.
.
.
13.38

13 v13_38b
14 v13_38c
15 v13_38d.
.
.

63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees
used improved seed?
1 yes
2 no
a. production unit
1 kg
2 gram3 mund4 liter
5 muri6 pathi
7 mana8 kuruwa
9 piece10 dozen
11 quintalb. total qty. harvestedc. given to landlord
d. amount sold/to be solda. sold unit
1 kg
2 gram3 mund4 liter
5 muri6 pathi
7 mana8 kuruwa
9 piece10 dozen
11 quintalb. total qty. soldc. price per unit (rs.)
d. total sales amount (rs)### 25 : xh23_s13c1.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western

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324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v13c1_ln
l i n en u m b e r
5 v13_40_txt {13.40} crop description
6 v13_40{13.40} crop code
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango

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7 v13_41a8 v13_41b

9 v13_42

52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees
97 transportation cost
98 expenditure on seeds/plant
13.41 where did you obtain : first
1 agri. dev. office
2 seeds company/serv.centre
3 other farmer
4 private dealer
5 from india
6 landlord
7 other.
: second
0 !end-of-list!
1 agri. dev. office
2 seeds company/serv.centre
3 other farmer
4 private dealer
5 from india
6 landlord
7 other
13.42 amount spent on seed/young plant### 26 : xh24_s13c2.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western

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4 v13c2_ln
l i n en u m b e r
5 v13_44_txt {13.44} fertilizer/insecticide description
6 v13_45yn
13.45 fertilizer used
1 yes
2 no
7 v13_46a13.46 fertilizer used : crop 1
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus

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51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees
8 v13_46b.

: crop 2
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato
25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions

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9 v13_46c.

38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees: crop 3
0 !end-of-list!
1 early paddy
2 main paddy
3 upland paddy
4 wheat
5 spring/winter maize
6 summer maize
7 millet8 barley
9 buckwheat
10 other cereals
11 soybeans
12 black gram
13 red gram
14 grass pea
15 lentil
16 gram
17 pea18 green gram
19 coarse gram
20 cow pea
21 other legumes
22 winter potato
23 summer potato
24 sweet potato

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10 v13_47a
11 v13_47b

13.47
.

25 colocasia
26 other tubers
27 mustard
28 ground nut
29 linseed
30 sesame
31 other oilseed
32 sugarcane
33 jute34 tobacco
35 other cash crops
36 chilies
37 onions
38 garlic
39 ginger
40 turmeric
41 cardamom
42 coriander seed
43 other spices
44 winter vegetables
45 summer vegetables
46 orange
47 lemon48 lime49 sweet lime
50 other citrus
51 mango
52 banana
53 guava
54 jack fruit
55 pineapple
56 lichee
57 pear58 apple
59 plum
60 papaya
61 pomegranate
62 other fruit
63 tea64 thatch
65 fodder trees
66 bamboo
67 other trees
qty. purchasedamount paid (rs.)### 27 : xh25_s13c3.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central

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4 v13c3_ln
5 v13_51
6 v13_52

7 v13_53m
8 v13_53f
9 v13_54m
10 v13_54f
11 v13_55m
12 v13_55f
13 v13_56m
14 v13_56f
15 v13_57m
16 v13_57f
17 v13_57t

223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-westernl i n en u m b e r
13.51 detail of work
13.52 hire worker on piece-rate basis
1 yes
2 no
13.53 working days : male (rs/day/worker)
13.53: female (rs/day/worker)
13.54 cash rate
: male(rs/day/worker)
13.54: female (rs/day/worker)
13.55 in-kind rate : male(rs/day/worker)
13.55: female (rs/day/worker)
13.56 total rate
: male [b+c] (rs/day/worker)
13.56: female [b+c] (rs/day/worker)
13.57 total exp.
: male [a*d] (rs.)
13.57: female [a*d] (rs.)
13.57: total (rs.)### 28 : xh26_s13d1.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v13d1_sn
s o u r c e
n u m b e r
5 v13d1_nm
s o u r c e
n a m e6 v13_63
13.63
yearly income### 29 : xh27_s13d2.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley

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4 v13d2_en
5 v13d2_nm
6 v13_64

219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-westerne x p e n d i t u r en u m b e r
e x p e n d i t u r e
n a m e13.64
yearly expenditure### 30 : xh28_s13e1.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v13e1_lc
l i v e s t o c k c o d e
5 v13_66nm
13.66 l i v e s t o c k n a m e
6 v13_66yn
13.66 yes/no
1 yes
2 no
7 v13_67a
13.67 owned now(no.)
8 v13_67b
13.67 selling value(rs.)
9 v13_68a13.68 owned 12 months ago (no.)
10 v13_68b13.68 selling value(rs.)
11 v13_69a13.69 sold past 12 months (no.)
12 v13_69b
13.69 amount receive(rs.)
13 v13_70a13.70 bought past 12 month (no.)
14 v13_70b
13.70 amount paid(rs.)### 31 : xh29_s13e2.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley

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4 v13e2_id
5 v13e2_nm
6 v13_71

219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-westerni n c o m e c o d e
i n c o m e
i t e m13.71 total income### 32 : xh30_s13e3.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v13e3_id
e x p e n d i t u r e
c o d e5 v13e3_nm
e x p e n d i t u r e
i t e m
6 v13_72
13.72 total expenditure### 33 : xh31_s13f.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western

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4 v13f_ec
5 v13_75nm
6 v13_75yn

7 v13_76
8 v13_77
9 v13_78
10 v13_79
11 v13_80
12 v13_81

324 324/rural-terai-mid-western
325 325/rural-terai-far-westerne q u i p m e n t
c o d e13.75 e q u i p m e n t n a m e
13.75 yes/no
1 yes
2 no
13.76 owned now(no.)
13.77 selling value(rs.)
13.78 sold(no.)
13.79 amount received(rs.)
13.80 bought(no.)
13.81 amount paid(rs.)### 34 : xh32_s14.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v14_ec1
e n t e r p r i s e
c o d e5 v14_02a
14.02 detail of activities
6 v14_02b
14.02 produced goods and services
7 v14_02c
14.02 produced goods and services(nsic)
1 agriculture and related service activities
2 forestry, logging and related service activities
5 fishing, operation of fish hatcheries and fish farms; service activities
incidental to fishing
10 mining of coal and lignite; extraction of peat
11 extraction of crude petrolium and natural gas; service activities
incidental to oil and gas extraction excluding surveying
12 mining of uranium and thorium ores
13 mining of metal ores
14 other mining and quarrying
15 manufacture of food products and beverages
16 manufacture of tobacco products
17 manufacture of textiles
18 manufacture of wearing apprel; dressing and dyeing of fur
19 tanning and dressing of leather; manufacture of luggage, handbags, saddlery
and harness
20 manufacture of wood and of products of wood and cork, except furniture;
manufacture of articles of straw and plaiting materials
21 manufacture of paper and paper products

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22 publishing, printing and reproduction of recorded media
23 manufacture of coke, refined petrolium products and nuclear fuel24 manufacture of chemicals and chemical products
25 manufacture of rubber and plasics products
26 manufacture of other non-metalic minaral products
27 manufacture of basic metals
28 manufacture of fabricated metal products, except machinery and equipment
29 manufacture of machinery and equipment n.e.c.
30 manufacture of office, accounting and computing machinery
31 manufacture of electrical machinery and apparatus n.e.c.
32 manufacture of radio, tv and communication equipment and apparatus
33 manufacture of medical, precision and optical instruments, watches andclocks34 manufacture of motor vehicles; trailers and semi-trailers
35 manufacture of other transport equipment
36 manufacture of furniture; manufacturing n.e.c.
37 recycling
40 electricity and gas supply
41 collections, purification and distribution of water
45 construction
50 sale, maintenance and repair of motor vehicles and motorcycles; retail saleof automotive fuel51 wholesale trade and commission trade, except of motor vehicles andmotorcycles
52 retail trade, except of motor vehicles and motorcycles; repair of personaland household goods
55 hotels and restaurants
60 land transport
61 water transport
62 air transport
63 supporting and auxiliary transport activities; activities of travel
agencies
64 post and telecommunications
65 financial intermediation, except insurance and pension funding
66 insurance and pension funding, except compulsory social security
67 activities auxiliary to financial intermediation
70 real estate activities
71 renting of machinery and equipment without operator and of personal andhousehold goods

8 v14_03a9 v14_03b
10 v14_03c

72 computer and related activities
73 researches and development
74 other business activities
75 public administration and defence; compulsory social security
80 education
85 healths and social work
90 sewage and refuse disposal, sanitation and similar activities
91 activities of membership organizations n.e.c.
92 recreational, cultural and sporting activities
93 other service activities
95 private households with employed persons
99 extra-territorial organization and bodies{14.03} a-pid of 1st person working in this activity{14.03} b-pid of 2nd person working in this activity
0 !end-of-list!
{14.03} c-pid of 3rd person working in this activity
0 !end-of-list!

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11 v14_03d
12 v14_03e
13 v14_04
14 v14_05a
15 v14_05b
16 v14_06

17 v14_07
18 v14_08

19 v14_09
20 v14_ec2
21 v14_10a

22 v14_10b

23 v14_11

24 v14_12

25 v14_13{14.03}d-pid of 4th person working in this activity
0 !end-of-list!
{14.03} e-pid of 5th person working in this activity
0 !end-of-list!
{14.04} respondent id-code
14.05
operating since - years.
- months
14.06 place operated
1 home2 other fixed location
3 other changing location
14.07 months operating
14.08 owned by
1 owned by household only
2 partnership/shared with other14.09 profit (in %)e n t e r p r i s e
c o d e{14.10} 1st customer
1 other households or individuals
2 small enterprises
3 large private enterprises
4 govt. or other public firm
5 local retail traders
6 exporters or foreign purchasers
7 contractor
8 tourists
9 ngo/internat. organization
10 other{14.10} 2nd customer
0 !end-of-list!
1 other households or individuals
2 small enterprises
3 large private enterprises
4 govt. or other public firm
5 local retail traders
6 exporters or foreign purchasers
7 contractor
8 tourists
9 ngo/internat. organization
10 other
14.11 registered with govt. agency
1 yes
2 no
14.12 main source of money
1 didn't need any money
2 own savings
3 relatives/friends
4 agri. dev. bank
5 commercial bank6 rural dev. bank7 other financial institution
8 local group (dhukuti)
9 ngo or relief agency
10 sale of assets
11 other
14.13 tried to borrow

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26 v14_14

27 v14_15

28 v14_16
29 v14_17

30 v14_ec3
31 v14_18
32 v14_19
33 v14_20
34 v14_21a
35 v14_21b
36 v14_22
37 v14_23
38 v14_24
39 v14_25
40 v14_26
41 v14_27

1 yes, successfully
2 yes, but unsuccessfully
3 no14.14 borrowed from1 relatives/friends
2 agri. dev. bank
3 commercial bank4 rural dev. bank5 other financial institution
6 local group (dhukuti)
7 ngo or relief agency
8 other
14.15 hired anyone
1 yes
2 no
14.16 no. of workers{14.17} problems for running business
1 no major problem
2 capital or credit problems
3 lack of technical know-how
4 supply of power
5 problems with equipment or spare parts
6 lack of adequate labor
7 government regulations
8 lack of raw materials
9 lack of customers
10 transport problems
11 intimidation and labor unrest
12 supply of water
13 othere n t e r p r i s e
c o d e14.18 gross revenues(rs.)
14.19 exp. on wages(rs.)
14.20 exp. in fuel, elect, etc.(rs.)
14.21 exp. on raw materials : cash(rs.).
: in-kind (rs.)
14.22 other operating exp.
(rs.)
14.23 net revenues(rs.)
14.24 exp. on capital goods(rs.)
14.25 sale of assets(rs.)
14.26 value of enterprise(rs.)
14.27 value one year ago(rs.)### 35 : xh33_s15a.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western

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4 v15a_ln1
5 v15_02
6 v15_03
7 v15_04a
8 v15_04b
9 v15_05

10 v15_06

11 v15_07
12 v15a_ln2
13 v15_08a
14 v15_08b
15 v15_09a
16 v15_09b
17 v15_10

18 v15_11
19 v15_12

310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western{15.02} loan number{15.02} loan description
15.03 id code of borrower
15.04 loan obtained : month.
: year
15.05 obtained from1 relatives/friends
2 agri. dev. bank
3 commercial bank4 rural dev. bank5 other financial institution
6 ngo or relief agency
7 landlord/employer.
8 shopkeeper
9 money lender
10 co-operative
11 other{15.06} purpose of borrowing
1 purchase of inputs
2 purchase of equipment
3 purchase of land
4 purchase of livestock
5 building improvements for business6 other business or farm use
7 household consumption needs
8 purchase/improvement of dwelling
9 marriage/family events
10 consumer durables
11 to travel abroad
12 other personal use
15.07 amount borrowed(rs.)
l o a nn u m b e r
15.08 amount of interest (rs.).
interest rate (%)
15.09 repayment time : month.
: year
15.10 finished repaying loan
1 fully paid
2 partly paid
3 not paid at all
15.11 amount repaid (rs.)
15.12 collateral
1 agricultural land2 buildings or other property
3 gold/silver
4 property documents
5 personal guarantee
6 past borrowing record
7 other
8 no collateral

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20 v15_13

15.13

days taken### 36 : xh34_s15b.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v15b_ln1{15.15} loan number
5 v15_15{15.15} loan description
6 v15_16
15.16 id code of lender
7 v15_17a15.17 loan given : month
8 v15_17b.
: year9 v15_18
15.18 relationship to lender
1 employee or tenant farmer
2 business customer
3 other business associate
4 friend/neighbour
5 relative
6 other
10 v15_19
15.19 purpose of lending
1 purchase of agri.inputs
2 purchase of equipment
3 purchase of land
4 purchase of livestock
5 building improvements for business6 other business or farm use
7 household consumption needs
8 purchase/improvement of dwelling
9 marriage/family events
10 consumer durables
11 to travel abroad
12 other personal use
11 v15_20
15.20 amount lent(rs.)
12 v15b_ln2
l o a nn u m b e r
13 v15_21a
15.21 amount of interest
14 v15_21b.
interest rate (%)
15 v15_22a
15.22 repayment time : month
16 v15_22b.
: year17 v15_23
15.23 finished repaying loan
1 fully paid
2 partly paid
3 not paid at all

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18 v15_24
19 v15_25

15.24
15.25

amount repaid (rs.)
collateral
1 agricultural land2 buildings or other property
3 gold/silver
4 property documents
5 personal guarantee
6 past borrowing record
7 other
8 no collateral### 37 : xh35_s15c.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v15_26
15.26 own other land or property?
1 yes
2 no
5 v15_27
15.27 value of other property(rs.)
6 v15_28
15.28 value a year ago(rs.)
7 v15_29
15.29 value of assets purchased (rs.)
8 v15_30
15.30 value of sold assets(rs.)
9 v15_31
15.31 rent received 12 months(rs.)
10 v15_32
15.32 own other assets?
1 yes
2 no
11 v15_33
15.33 value of other assets(rs.)
12 v15_34
15.34 value a year ago(rs.)
13 v15_35
15.35 value of assets purchased (rs.)
14 v15_36
15.36 value of sold assets(rs)
15 v15_37
15.37 rent received in 12 months(rs.)### 38 : xh36_s15d.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern

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4 v15d_idc
5 v15d_txt
6 v15_39

7 v15_40

8 v15_41

222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western{15.39} decision number{15.39} decision description{15.39} have to make decision
1 yes
2 no
{15.40} involvement in decision making
1 a lot
2 a little
3 not involved{15.41} who made final decision
1 me
2 my spouse/female head
3 both4 other### 39 : xh37_s16.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v16a_ln1
l i n en u m b e r
5 v16_nme
n a m eo f
a b s e n t e e s
6 v16_02
16.02 sex of absentees
1 male2 female
7 v16_03
16.03 relations to hhld head
2 husband/wife
3 son/daughter
4 grandchild
5 father/mother6 brother/sister
7 nephew/niece

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8 v16_04
9 v16_05

16.04
16.05

10 v16_06

16.06

11 v16_07

16.07

12 v16_08a

16.08

8 son/daughter-in-law
9 brother/sister-in-law
10 father/mother-in-law
11 other family relative
12 servant, servant's relatives
13 tenat, tenant's relatives
14 other person not relatedage
highest grate completed ?
0 nursery/kg/pre-school
1 class 01
2 class 02
3 class 03
4 class 04
5 class 05
6 class 06
7 class 07
8 class 08
9 class 09
10 class 10
11 slc12 intermediate
13 bachelor
14 master
15 profesional degree
16 literate (levelless)
17 illiterate
97 don't know
marital status
1 never married
2 single married
3 poly married
4 re-married
5 widow, widower
6 divorced
7 separated
8 don't know
when leave this hhld ?
99 birth at there
current place of living - district/foreign
1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang
6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga
13 khotang
14 udayapur
15 saptari
16 siraha

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17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu
28 nuwakot
29 rasuwa
30 dhading
31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung
38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot
62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang
69 achham
70 doti
71 kailali
72 kanchanpur

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73 dadeldhura
74 baitadi
75 darchula
81 india
82 bhutan
83 china
84 bangladesh
85 hongkong
86 malaysia
87 japan
88 saudi arabia
89 qatar
90 united arab emirates
91 united kingdom
92 united states of america
93 south korea
94 australia
95 israel
96 other country
13 v16_08b

14 v16_09

15 v16a_ln2
16 v16_10.

- urban/rural
1 urban2 rural16.09 reason for leaving
1 together family/relatives
2 other family reasons
3 education
4 training
5 looking for work
6 start new job
7 start new business8 job transfer
9 conflict
10 natural disaster11 easier life style
12 otherl i n en u m b e r
16.10 primary activity [nsco]
11 armed forces
111 legislators
112 government officials
114 officials of special interest organizations
121 directors and chief executives
122 production and operations department managers
123 other department managers
131 general managers/managing proprietors
211 physicists, chemists and related professionals
212 mathematicians, statisticians and related professionals
213 computing professionals
214 architects, engineers and related professionals
221 life science professionals
222 health professionals, except nursing
223 nursing and midwifery professionals
231 college, university and higher education teaching professionals
232 secondary education teaching professionals
233 primary and pre-primary education teaching professionals
234 special education teaching professionals

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235 other teaching professionals
241 business professionals
242 legal professionals
243 archivists, librarians and related information professionals
244 social science and related professionals
245 writers and creative or performing artists
246 religious professionals
311 physical and engineering science technicians
312 computer associate professionals
313 optical and electronic equipment operators
314 aircraft controllers and technicians
315 safety and quality inspectors
321 life science technicians and related associate professionals
322 modern health associate professional, except nursing
323 nursing and midwifery associate professionals
324 traditional medicine practitioners and faith healers
331 primary education teaching associate professionals
332 pre-primary education teaching associate professionals
333 special education teaching associate professionals
334 other teaching associate professionals
341 finance and sales associate professionals
342 business services agent and trade brokers
343 administrative associate professionals
344 customs, tax and related government associate professionals
345 police inspectors and detectives
346 social work associate professionals
347 artistic, entertainment and soprts associate professionals
348 religious associate professionals
411 secretaries and keyboard-operating clerks/assistants
412 numerical clerks/office assistants
413 material-recording and transport clerks/office assistants
414 library, mail and related clerks/office assistants
419 other office clerks/assistants
421 cashiers, tellers and related clerks/office assistants
422 client information clerks/office assistants
511 travel attendants and related workers
512 housekeeping and restaurant services workers
513 personal care and related workers
514 other professional services workers
515 astrologers, fortune-tellers and related workers
516 protective service workers
521 fashion and other models
522 shop salespersons and demonstratotrs
523 stall and market salespersons
611 market-oriented gardeners and crop growers
612 market-oriented animal producers and related workers
613 market-oriented crop and animal producers
614 forestry and related workers
615 fishery workers
621 subsistence agricultural and fishery workers
711 miners, shoftirers, stone cutters and carvers
712 building frame and related trades workers
713 building finishers and related trades workers
714 painters, building structure cleaners and related trades workers
721 metal moulders, welders, sheet-metal workers, structural-metal preparer
722 blacksmiths, tool-makers and related trades workers

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17 v16_11

18 v16_12

19 v16_13

723 machinery mechanics and fitters
724 electrical and electronic equipment mechanics and fitters
731 precision workers in metal and related materials
732 potters, glass-makers and related trades workers
733 handicraft workers in wood, textile, leather and related materials
734 printing and related trades workers
741 food processing and related trades workers
742 wood treaters, cabinet-makers and related traders workers
743 textile, garment and related trades workers
744 pelt, leather and shoe making trades workers
811 mining and mineral-processing plant operators
812 metal-processing-plant operators
813 glass, ceramics and relative plant operators
814 wood-processing and papermaking-plant operators
815 chemical-processing-plant operators
816 power-production and related plant operators
817 automated-assembly-line and industrial-robot operators
821 metal and mineral products machine operators
822 chemical-products machine operators
823 rubber and plastic products machine operators
824 wood-products machine operators
825 printing, binding and paper products machine operators
826 textile, fur and leather-products machine operators
827 food and related products machine operators
828 assemblers
829 other machine operators and assemblers
831 locomotive-engine drivers and related workers
832 motor vehicle drivers
833 agricultural and other mobile-plant operators
911 street vendors and related workers
912 shoe cleaning and other street services elementary occupations
913 domestic and related helpers, cleaners and launderers
914 building caretakers, windows and related cleaners
915 messengers, porters, doorkeepers and related workers
916 garbage collectors and related labourers
921 agricultural, fishery and related labourers
931 mining and construction labourers
932 manufacturing labourers
933 transport labourers and freight handlers
995 don't know
996 household work
997 student
998 not working
999 not reported
16.11 types of job
1 wage job
2 self-employed
3 ext. economic work
4 don't know
16.12 how find current job?
1 relatives
2 friends/ neighbors
3 employment agency
4 other
5 don't know
16.13 received money or goods

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20 v16_14
21 v16_15

16.14
16.15

22 v16_16
23 v16_17
24 v16_18a

16.16
16.17
16.18

25 v16_18b.

26 v16_19

16.19

1 yes
2 no
no. of times received
methods of sending
1 formal financial institutions
2 hundi3 self
4 through friends
5 through other family members/relatives
6 through acquaintances
7 others
cash receive(rs.)
in-kind receive(rs.)
major use of remittance : first
1 daily consumption
2 education
3 capital formation
4 business or investment
5 household assets
6 savings
7 repay loan
8 others: second
0 !end-of-list!
1 daily consumption
2 education
3 capital formation
4 business or investment
5 household assets
6 savings
7 repay loan
8 others
total amount sent (rs.)### 40 : xh38_s17a.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v17a_ln
l i n en u m b e r
5 v17_02{17.02} respondent id-code
6 v17_03
17.03 name of recipient

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7 v17_04
8 v17_05

17.04
17.05

9 v17_06

17.06

10 v17_07a

17.07

id code of remit. sender
relationship to the recipient
2 husband/wife
3 son/daughter
4 grandchild
5 father/mother6 brother/sister
7 nephew/niece
8 son/daughter-in-law
9 brother/sister-in-law
10 father/mother-in-law
11 other family relative
12 servant, servant's relative
13 tenant, tenant's relatives
14 other person not related
sex of receipant
1 male2 femaleplace of living- district/country
1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang
6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga
13 khotang
14 udayapur
15 saptari
16 siraha
17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu
28 nuwakot
29 rasuwa
30 dhading
31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung

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11 v17_07b.

38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot
62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang
69 achham
70 doti
71 kailali
72 kanchanpur
73 dadeldhura
74 baitadi
75 darchula
81 india
82 bhutan
83 china
84 bangladesh
85 hongkong
86 malaysia
87 japan
88 saudi arabia
89 qatar
90 united arab emirates
91 united kingdom
92 united states of america
93 south korea
94 australia
95 israel
96 other country- urban/rural
1 urban

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12 v17_08

17.08

13 v17_09a
14 v17_09b
15 v17_10

17.09
.
17.10

2 ruralprimary work activity of recipient
1 wage employee in agri.
2 wage employee in non-agri.
3 self employee in agri.
4 self employee in non-agri.
5 study
6 other
7 don't know
cash sent(rs.)
in-kind sent (rs.)
how usually send the amount?
1 formal financial institutions
2 hundi3 self
4 through friends
5 through other family members/relatives
6 through acquaintances
7 others### 41 : xh39_s17b.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v17b_ln
l i n en u m b e r
5 v17_12{17.12} respondent id-code
6 v17_13
17.13 name of donor
7 v17_14
17.14 id code of remit. receiver
8 v17_15
17.15 relationship to the recipient?
2 husband/wife
3 son/daughter
4 grandchild
5 father/mother6 brother/sister
7 nephew/niece
8 son/daughter-in-law
9 brother/sister-in-law
10 father/mother-in-law
11 other family relative
12 servant, servant's relative
13 tenant, tenant's relatives

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9 v17_16

10 v17_17
11 v17_18a

14 other person not related
sex of donor
1 male2 female17.17 age of donor{17.18} place of residence - district/country
1 taplejung
2 panchthar
3 ilam4 jhapa
5 morang
6 sunsari
7 dhankuta
8 terhathum
9 sankhuwasabha
10 bhojpur
11 solukhumbu
12 okhaldhunga
13 khotang
14 udayapur
15 saptari
16 siraha
17 dhanusa
18 mahottari
19 sarlahi
20 sindhuli
21 ramechhap
22 dolakha
23 sindhupalchok
24 kavre
25 lalitpur
26 bhaktapur
27 kathmandu
28 nuwakot
29 rasuwa
30 dhading
31 makwanpur
32 rautahat
33 bara34 parsa
35 chitawan
36 gorkha
37 lamjung
38 tanahu
39 syangja
40 kaski
41 manang
42 mustang
43 myagdi
44 parbat
45 baglung46 gulmi
47 palpa
48 nawalparasi
49 rupandehi
50 kapilbastu
17.16

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12 v17_18b

13 v17_19

14 v17_20a
15 v17_20b
16 v17_21

51 arghakhanchi
52 pyuthan
53 rolpa
54 rukum
55 salyan
56 dang
57 banke
58 bardiya
59 surkhet
60 dailekh
61 jajarkot
62 dolpa
63 jumla
64 kalikot
65 mugu
66 humla
67 bajura
68 bajhang
69 achham
70 doti
71 kailali
72 kanchanpur
73 dadeldhura
74 baitadi
75 darchula
81 india
82 bhutan
83 china
84 bangladesh
85 hongkong
86 malaysia
87 japan
88 saudi arabia
89 qatar
90 united arab emirates
91 united kingdom
92 united states of america
93 south korea
94 australia
95 israel
96 other country{17.18} place of residence- urban/rural
1 urban2 rural{17.19} primary work activity
1 wage employee in agri.
2 wage employee in non-agri.
3 self employee in agri.
4 self employee in non-agri.
5 study
6 other
7 don't know
17.20 cash received(rs.).
in-kind received (rs.)
17.21 how usually send the amount?
1 formal financial institutions

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17 v17_22a

17.22

18 v17_22b.

2 hundi3 self
4 through friends
5 through other family members/relatives
6 through acquaintances
7 others
major use of remittance : first
1 daily consumption
2 education
3 capital formation
4 business or investment
5 household assets
6 savings
7 repay loan
8 others: second
0 !end-of-list!
1 daily consumption
2 education
3 capital formation
4 business or investment
5 household assets
6 savings
7 repay loan
8 others### 42 : xh40_s18a.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v18a_sn{18.00} source serial number
5 v18a_txt{18.00} source description
6 v18_01
18.01 hhld member receive payment
1 yes
2 no
3 not applicable
7 v18_02
18.02 no. of person receiving
8 v18_03
18.03 how frequently
1 monthly
2 quarterly
3 half yearly

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9 v18_04
10 v18_05
11 v18_06
12 v18_07
13 v18_08

14 v18_09

4 yearly
5 once only
{18.04} amount is supposed to receive (rs)9999 don't know
18.05 month ago last payment received
18.06 received amount (rs.)
18.07 no.of month due
18.08 anyone currently payment
1 yes
2 no
18.09 total amount owed?

### 43 : xh41_s18b.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v18_101
18.10 1) receive public food distrb. system
1 yes
2 no
5 v18_102
18.10 2) receive nutritional program for children
1 yes
2 no
6 v18_103
18.10 3) receive nutritional program for mother
1 yes
2 no
7 v18_104
18.10 4) receive food for work
1 yes
2 no
8 v18_105
18.10 5) receive cash for work
1 yes
2 no
9 v18_106
18.10 6) receive rciwp programme
1 yes
2 no

### 44 : xh42_s18c.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain

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4 v18c_ic
5 v18c_txt
6 v18_11

7 v18_12
8 v18_13
9 v18_14

218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western{18.11} other income code
{18.11} other income description
18.11 having income
1 yes
2 no
18.12 current amount/value(rs.)
18.13 amount/value a year ago
18.14 amount received (rs.)### 45 : xh43_s19.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western
225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western
4 v19_02
19.02 opinion on food consumption
1 less than adequate
2 just adequate
3 more than adequate
4 not applicable
5 v19_03
19.03 opinion on housing
1 less than adequate
2 just adequate
3 more than adequate
4 not applicable
6 v19_04
19.04 opinion on clothing
1 less than adequate
2 just adequate
3 more than adequate
4 not applicable

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7 v19_05

19.05

8 v19_06

19.06

9 v19_07

19.07

10 v19_08

19.08

11 v19_09

19.09

12 v19_10

19.10

13 v19_11

19.11

14 v19_12

19.12

15 v19_13

19.13

16 v19_14

19.14

17 v19_15

19.15

18 v19_16

19.16

opinion on health care getting
1 less than adequate
2 just adequate
3 more than adequate
4 not applicable
opinion on children's schooling
1 less than adequate
2 just adequate
3 more than adequate
4 not applicable
opinion on family's total income
1 less than adequate
2 just adequate
3 more than adequate
4 not applicableeats too little food to live
1 yes
2 no
status of health facility
1 good2 fair3 bad
4 not applicablestatus of education facility
1 good2 fair3 bad
4 not applicablestatus of drinking water facility
1 good2 fair3 bad
4 not applicablestatus of electricity facility
1 good2 fair3 bad
4 not applicablestatus of road facility
1 good2 fair3 bad
4 not applicablestatus of postal facility
1 good2 fair3 bad
4 not applicablestatus of telephone facility
1 good2 fair3 bad
4 not applicableenough food or money to buy food
1 yes
2 no

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19 v19_17
20 v19_1801

19.17
19.18

21 v19_1802

19.18

22 v19_1803

19.18

23 v19_1804

19.18

24 v19_1805

19.18

25 v19_1806

19.18

26 v19_1807

19.18

27 v19_1808

19.18

28 v19_1809

19.18

29 v19_1810

19.18

30 v19_1811

19.18

31 v19_1812

19.18

32 v19_1813

19.18

33 v19_1814

19.18

no.of days facing scarcity1) rely on less preferred/expensive foods
1 yes
2 no
2) borrow food or money
1 yes
2 no
3) purchase food on credit
1 yes
2 no
4) gather wild food/eat unmature crops
1 yes
2 no
5) consume seed stock
1 yes
2 no
6) send hhld member to eat elsewhere
1 yes
2 no
7) send hhld member to beg
1 yes
2 no
8) limit portion sizes at mealtime
1 yes
2 no
9) restrict consumption of adults
1 yes
2 no
10) feed working members of hhld
1 yes
2 no
11) ration the money
1 yes
2 no
12) reduced the meals eaten in a day
1 yes
2 no
13) skip entire days without eating
1 yes
2 no
14) sell assets, jewelry
1 yes
2 no

### 46 : xh44_s20.dta ###1 xhpsupsu
2 xhnumhousehold number
3 xstrastrata in the nlss3' sample
100 100/mountain218 218/urban-kathmandu valley
219 219/urban-hill
221 221/rural-hill-eastern
222 222/rural-hill-central
223 223/rural-hill-western
224 224/rural-hill-mid-western

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4 v20_idc
5 v20_01

6 v20_02d
7 v20_02m
8 v20_02y
9 v20_03
10 v20_04

11 v20_05
12 v20_06

13 v20_07
14 v20_08d
15 v20_08m
16 v20_08y
17 v20_09

18 v20_10

19 v20_11

225 225/rural-hill-far-western
310 310/urban-terai
321 321/rural-terai-eastern
322 322/rural-terai-central
323 323/rural-terai-western
324 324/rural-terai-mid-western
325 325/rural-terai-far-western{20-id} id-code{20.01} has birth certificate
1 yes
2 no
{20.02} date of birth/day{20.02} date of birth/month{20.02} date of birth/year{20.03} age in months{20.04} measurement finished
1 yes
2 could not meet the child after multiple visits
3 too ill or disabled
4 refused
5 other{20.05} height (cms, 1 decimal){20.06} way was measured
1 standing
2 lying{20.07} weight (kgs, 1 decimal){20.08} date of measurement/day{20.08} date of measurement/month{20.08} date of measurement/year{20.09} did breastfeed
1 yes
2 no
{20.10} months breastfeeding
0 less than one month (or age less than 1 month)
90 did not breastfeed{20.11} months after birth was given complimentary food0 less than one month
90 not yet>

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4.6 Distribution of categorical variables# file.names: Rnames[j](j=1…46)> Rnames<-gsub(".dta","",STATA.files)# file.list: outfiles[[j]]> df<-outfiles[[3]] : Household roster> colnames(df)[6] : Variable names[1] "v01_02"> attributes(df)$var.labels[6] : Variable label[1] "1.02 sex"
# list of column numbers of categorical variables> ck<-list()> ck[[1]]<-c(4:7)> ck[[2]]<-c(4,7,8,11:13,31:33,43,54:59,63,67:79)> ck[[3]]<-c(6,8,10,11,15,17,19,20,22)> ck[[4]]<-c(4,14:18,24,27,29,32,33,36,37,39:41,44:47,49:53,58,60:63)> ck[[5]]<-c(6,12,13)> ck[[6]]<-c(6,8,10,12,19,21,23:25,27)> ck[[7]]<-c(6,9,13,18,20)> ck[[8]]<-c(6)> ck[[9]]<-c(6)> ck[[10]]<-c(6,9)> ck[[11]]<-c(6)> ck[[12]]<-c(6:13,15,16,21,22,24,25,28,29,32,33,42,44:46)> ck[[13]]<-c(6,8,9,13,15,17:22,28,29,31:33,35:40,42:59,61:63)> ck[[14]]<-c(12:14,19)> ck[[15]]<-c(6:8,10:12,14:19)> ck[[16]]<-c(5,7:14)> ck[[17]]<-c(6:8)> ck[[18]]<-c()> ck[[19]]<-c(6,9:20,31)> ck[[20]]<-c(6:11)> ck[[21]]<-c(8,9,11,12,16,18,19,22:25,32:36)> ck[[22]]<-c(6,11:14,17,24)> ck[[23]]<-c(6,8,12:15)> ck[[24]]<-c(7,8,12)> ck[[25]]<-c(7,8)> ck[[26]]<-c(6)> ck[[27]]<-c(6)> ck[[28]]<-c()> ck[[29]]<-c()> ck[[30]]<-c(6)> ck[[31]]<-c()> ck[[32]]<-c()> ck[[33]]<-c(6)> ck[[34]]<-c(7,18,21:27,29)> ck[[35]]<-c(9,10,17,19)> ck[[36]]<-c(9,10,17,19)> ck[[37]]<-c(4,10)> ck[[38]]<-c(6:8)> ck[[39]]<-c(6,7,9,10,13,14,17:19,21,24,25)> ck[[40]]<-c(8,9,11,12,15)> ck[[41]]<-c(8,9,12,13,17:18)> ck[[42]]<-c(6,8,13)> ck[[43]]<-c(4:9)

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

225> ck[[44]]<-c(6)> ck[[45]]<-c(4:18,20:33)> ck[[46]]<-c(5,10,12,17)> for(j in 1:length(ck)){+ if(length(ck[[j]])==0) {next}+ cat("\n\n#### FREQUENCY OF VARIABLES IN", j, ":", Rnames[j],+ "#########################################\n\n")+ for(k in ck[[j]]){+ var.name<-colnames(outfiles[[j]])[k]+ var.desc<-attributes(outfiles[[j]])$var.labels[k]+ cat("-----", k, ":", var.name,"-----",var.desc,"-----------------------------")+ print(addmargins(table(outfiles[[j]][k],useNA="ifany")))+ }}#### FREQUENCY OF VARIABLES IN 1 : poverty #########################################----- 4 : belt ----- ----------------------------1
2
3 Sum
408 3204 2376 5988----- 5 : region ----- ----------------------------1
2
3
4
5 Sum
1272 2280 1152 756 528 5988----- 6 : urbrur ----- ----------------------------1
2 Sum
2088 3900 5988----- 7 : ad ----- analytical domains ----------------------------1
2
3
4
5
6
7
8
9
10
11
12 Sum408 864 480 672 384 480 480 516 480 480 348 396 5988#### FREQUENCY OF VARIABLES IN 2 : xh00_s00 #########################################----- 4 : v00_dist ----- psu geo-codes: district ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
12
48 84 144 204 168
48
24
72
60
24
36
60
72 108 108 108
18
19 20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
96
96 72
48
84
60
96 180 108 72060
12
72 10884
60 108
35
36 37
38
39
40
43
44
45
46
47
48
49
50
51
52
53
108
60 36
84
84 18036
24
60
60
48 144 180 108
48
48
60
54
55 56
57
58
59
60
61
63
64
65
67
68
69
70
71
72
36
48 120 108
96
96
60
24
24
24
12
24
36
60
60 13296
73
74 75 Sum36
60 24 5988----- 7 : v00_a ----- a. household was interviewed ----------------------------1
2 Sum
5187 801 5988----- 8 : v00_b ----- b. reason household not interviewed ----------------------------1
2
3 <NA> Sum
194 548 59 5187 5988----- 11 : v00_e ----- e. religion of hhld head ----------------------------1
2
3
4
5
7
8
9 Sum
5048 501 175 146 114
2
1
1 5988----- 12 : v00_f ----- f. language used in household ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

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89 136
48
49
59
15
12
23
20
31
9
18
19 20
21
22
23
24
25
26
27
30
31
32
33
34
35
40
1
16 17
1
4
5
6
4
2
14
7
2
5
1
1
2
2
41
43 52
58
61
67
83
91
93 Sum
3
1
1
1
11
1
4
1
5 5988----- 13 : v00_g ----- g. interpreter used ----------------------------1
2 Sum
137 5851 5988----- 31 : v13_02 ----- {13.02} household has own agricultural land ----------------------------1
2 Sum
4265 1723 5988----- 32 : v13_18 ----- {13.18} household has land sharecropped/rented/mortgaged-in----------------------------1
2 Sum
1261 4727 5988----- 33 : v13_29 ----- {13.29} household did sell, transfer land ----------------------------1
2 Sum
214 5774 5988----- 43 : v13_32 ----- {13.32} household did acquired land ----------------------------1
2 Sum
265 5723 5988----- 54 : v13_39 ----- {13.39} household purchased/received seeds, young plants----------------------------1
2 <NA> Sum
2494 1625 1869 5988----- 55 : v13_43 ----- {13.43} household purchased fertilizers, insecticides----------------------------1
2 <NA> Sum
2954 1166 1868 5988----56:v13_48a----{13.48a}
fertilizer/place
where
it
was
obtained----------------------------1
2
34 <NA> Sum
101 151 2517 185 3034 5988----- 57 : v13_48b ----- {13.48b} fertilizer/could obtain as needed ----------------------------1
2 <NA> Sum
2661 293 3034 5988----- 58 : v13_49 ----- {13.49} reason for unable to obtain all needed ----------------------------1
2
3 <NA> Sum
151 118
24 5695 5988----- 59 : v13_50 ----- 13.50 hire any casual farm workers ----------------------------1
2
3 <NA> Sum
1999 793 1327 1869 5988----- 63 : v13_59 ----- {13.59} hired any permanent farm workers ----------------------------1
2 <NA> Sum
40 4079 1869 5988----- 67 : v13_65 ----- {13.65} household owns livestock ----------------------------1
2 Sum
4168 1820 5988----- 68 : v13_73 ----- {13.73} hired labor for tending livestock ----------------------------1
2
9 <NA> Sum
25 4142
1 1820 5988----- 69 : v13_74 ----- {13.74} household owned any equipment ----------------------------1
2 Sum
4329 1659 5988----- 70 : v13_82 ----- {13.82} advice taken from govt. agriculture technician-----------------------------

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1
2 Sum
409 5579 5988----- 71 : v13_83 ----- {13.83} reason for not taking advice from govt. agriculture----------------------------1
2
34 <NA> Sum
511 127 4770 171 409 5988----- 72 : v13_84 ----- {13.84} advice taken from govt. livestock service technician----------------------------1
2 Sum
889 5099 5988----- 73 : v13_85 ----- {13.85} reason for not taking advice from govt. livestock se----------------------------1
2
34 <NA> Sum401
93 4481 124 889 5988----- 74 : v14_01 ----- {14.01} any self-employment activities ----------------------------1
2 Sum
2074 3914 5988----- 75 : v15_01 ----- {15.01} any outstanding loans ----------------------------1
2 Sum
3715 2273 5988----- 76 : v15_14 ----- {15.14} any loans owed to the household ----------------------------1
2 Sum
705 5283 5988----- 77 : v16_00 ----- {16.00} any absentees ----------------------------1
2 Sum
3004 2984 5988----- 78 : v17_01 ----- {17.01} any remittances sent out ----------------------------1
2 Sum
1416 4572 5988----- 79 : v17_11 ----- {17.11} any remittances received ----------------------------1
2 Sum
1868 4120 5988#### FREQUENCY OF VARIABLES IN 3 : xh01_s01 #########################################----- 6 : v01_02 ----- 1.02 sex ----------------------------1
2
Sum13337 15333 28670----- 8 : v01_04 ----- {1.04} relationship to head ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
5988 4268 11591 2618
753
484
406 1593
238
220
385
68
5
53
Sum
28670
----- 10 : v01_05b ----- {1.05} birth place: urban/rural ----------------------------1
2 <NA>
Sum
4732 22770 1168 28670----- 11 : v01_06 ----- 1.06 marital status ----------------------------1
2
3
4
5
6
7 <NA>
Sum
8050 11927
203
738 125855
129 6310 28670----- 15 : v01_10 ----- 1.10 member or not? ----------------------------1
2
Sum
28474
196 28670----- 17 : v01_11 ----- 1.11 father living in home? ----------------------------1
2
3 <NA>
Sum
10773 8954 8747
196 28670

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2
3
4
5
6
7
8
9
10
11
12
13
14
29
154
258
323
694
266
426
524
288
577
826
313
209
104
15
16
17
98 <NA>Sum
7 3085 8976
642 10969 28670----- 20 : v01_14 ----- 1.14 mother living in home? ----------------------------1
2
3 <NA>
Sum
14466 7014 6994
196 28670----- 22 : v01_16 ----- {1.16} mother: highest degree completed ----------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
1
32
27
70
64
119
58
59
99
33
73
113
39
18
14
15
16
17
98 <NA>
Sum
5
1 1117 11676
404 14662 28670#### FREQUENCY OF VARIABLES IN 4 : xh02_s02 #########################################----- 4 : v02_01 ----- 2.01 dwelling unit occupied by own hhld only ----------------------------1
2 Sum
4461 1527 5988----- 14 : v02_04 ----- 2.04 material of outside wall ----------------------------1
2
3
4
5
6
7 Sum
1945 2756 329 797
31 1291 5988----- 15 : v02_05 ----- 2.05 foundation of dwelling ----------------------------1
2
3
4
5 Sum
1032 944 2794 1048 170 5988----- 16 : v02_06 ----- 2.06 main roofing material ----------------------------1
2
3
4
5
6
7 Sum
952
91 63 1689 1631 1537
25 5988----- 17 : v02_07 ----- 2.07 types of window ----------------------------1
2
3
4 Sum
1765 2948 1182
93 5988----- 18 : v02_08u ----- 2.08 size of housing plot - unit ----------------------------1
2 Sum
3370 2618 5988----- 24 : v02_11 ----- 2.11 dwelling owned by you? ----------------------------1
2 Sum5090 898 5988----- 27 : v02_14 ----- 2.14 rent part of dwelling? ----------------------------1
2 <NA> Sum
489 4601 898 5988----- 29 : v02_16 ----- 2.16 present occupancy status ----------------------------1
2
34 <NA> Sum
729 147
2
20 5090 5988----- 32 : v02_19 ----- 2.19 source of drinking water ----------------------------1
2
3
4
5
6
7 Sum
2941
92 1932 391 452
58 122 5988----- 33 : v02_20 ----- 2.20 piped water connected to house ----------------------------1
2 <NA> Sum
1679 1262 3047 5988----- 36 : v02_23 ----- 2.23 sanitation system for liquid waste ----------------------------1
2
3
4 Sum
911 424 302 4351 5988----- 37 : v02_24 ----- 2.24 garbage disposal method ----------------------------1
2
3
4
5
6 Sum

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775 312 1308 1058 2412 123 5988----- 39 : v02_26 ----- 2.26 type of toilet used ----------------------------1
2
3
4
5 Sum
734 1838 1169
39 2208 5988----- 40 : v02_27 ----- 2.27 source of light ----------------------------1
2
3
4
5 Sum
4400 303
4 756 525 5988----- 41 : v02_28 ----- 2.28 types of meter ----------------------------1
2
3 <NA> Sum
3584 276 540 1588 5988----- 44 : v02_31a ----- 2.31 a) having telephone ----------------------------1
2 Sum
1014 4974 5988----- 45 : v02_31b ----- .
b) having mobile phone ----------------------------1
2 Sum
3873 2115 5988----- 46 : v02_31c ----- .
c) having cable tv ----------------------------1
2 Sum
1714 4274 5988----- 47 : v02_31d ----- .
d) having email/internet ----------------------------1
2 Sum
219 5769 5988----- 49 : v02_33 ----- 2.33 main cooking fuel ----------------------------1
2
3
4
5
6
7 Sum
3513 452 166 1585
48 17945 5988----- 50 : v02_34 ----- 2.34 stove for cooking ----------------------------1
2
3
4
5
6 Sum
1163 2746 184
48 1764
83 5988----- 51 : v02_35 ----- 2.35 use firewood during past 12 months ----------------------------1
2 Sum
4567 1421 5988----- 52 : v02_36 ----- 2.36 collect firewood during past 12 months? ----------------------------1
2 <NA> Sum
3460 1107 1421 5988----- 53 : v02_37a ----- 2.37 avg. no. of firewood collection - unit ----------------------------1
2
3 <NA> Sum
3255 134
71 2528 5988----- 58 : v02_39 ----- 2.39 place of collecting firewood ----------------------------1
2
34 <NA> Sum
825 1569 714 352 2528 5988----- 60 : v02_41 ----- 2.41 collect fodder ----------------------------1
2 Sum
3703 2285 5988----- 61 : v02_42 ----- 2.42 place of collecting fodder ----------------------------1
2
34 <NA> Sum
2173 631 252 647 2285 5988----- 62 : v02_43 ----- 2.43 type of salt used for cooking ----------------------------1
2
3
4 Sum
1264 894 3815
15 5988----- 63 : v02_44 ----- 2.44 iodine level in salt used for cooking ----------------------------1
2
3 Sum
426 1053 4509 5988#### FREQUENCY OF VARIABLES IN 5 : xh03_s03 #########################################

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230----- 6 : v03_01 ----- {3.01} way to travel to the closest facility ----------------------------1
2
3
4
5
6
7
Sum
58921 12240
1643
4201 14244 41574 10889 143712----- 12 : v03_04 ----- {3.04} was the facility used past 12 months ----------------------------1
2
<NA>
Sum
69666 63157 10889 143712----- 13 : v03_05 ----- {3.05} how often was used ----------------------------1
2
3
4
<NA>
Sum
30670
5939
3297 29760 74046 143712#### FREQUENCY OF VARIABLES IN 6 : xh04_s04 #########################################----- 6 : v04_02 ----- 4.02 ever migrated? ----------------------------1
2 <NA>
Sum
15683 9934 3053 28670----- 8 : v04_03b ----- 4.03 - urban/rural ----------------------------1
2 <NA>
Sum
920 7945 19805 28670----- 10 : v04_05 ----- 4.05 reason for migration ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 <NA>
4613 2307
555
686
4
487
135
186
77
47
50
676
111 18736
Sum
28670----- 12 : v04_07 ----- 4.07 live outside more than 2 cont. month? ----------------------------1
2 <NA>
Sum
1761 23856 3053 28670----- 19 : v04_11b ----- - urban/rural ----------------------------1
2 <NA>
Sum
377
400 27893 28670----- 21 : v04_12 ----- 4.12 reason for migration ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 <NA>
41
152
259
127
30
612
272
33
25
9
3
13
185 26909
Sum
28670----- 23 : v04_14 ----- 4.14 have any job there? ----------------------------1
2 <NA>
Sum
1037
724 26909 28670----- 24 : v04_15 ----- 4.15 who help to find job/start business? ----------------------------12
3
45 <NA> Sum
218
452
155
170
42 27633 28670----- 25 : v04_16 ----- 4.16 types of work done ----------------------------1
2 <NA>
Sum927
110 27633 28670----- 27 : v04_18 ----- 4.18 main reason to come back ----------------------------1
2
3
4
5
6
7
8
9
10
99 <NA>
Sum
910
375
167
11
11
20
7
1
20
208
31 26909 28670#### FREQUENCY OF VARIABLES IN 7 : xh05_s05 #########################################----- 6 : v05_01 ----- 5.01 consumed (yes/no) ----------------------------1
2
<NA>
Sum
273696 157440 11976 443112----- 9 : v05_03b ----- {05.03} b: home-prod-12-mo/measurement unit ----------------------------1
2
3
4
5
6
7
8
9
10
11
<NA>

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231

31198
629
11
3571
72
4834
1518
55
3489
1106
38 396591
Sum
443112----- 13 : v05_06b ----- {05.06} b: purchases-12-mo/measurement unit ----------------------------1
2
3
4
5
6
7
8
9
10
11
<NA>
1005916329
10 10659
116
1523
1235
64
9655
3173
129 309628
Sum
443112----- 18 : v05_10b ----- {05.10} b: consumed-7-days/measurement unit ----------------------------1
2
4
5
6
7
8
9
10
<NA>
Sum
41957 21095 10237
8
1458
2443
62
6918
1071 357863 443112----- 20 : v05_12 ----- {05.12} consumed-7-days/source ----------------------------1
2
3
<NA>
Sum
31766 109806
5337 296203 443112#### FREQUENCY OF VARIABLES IN 8 : xh06_s06a #########################################----- 6 : v06_01 ----- 6.01
1
2
<NA>
Sum
91316 58384
5988 155688

consumed (yes/no) -----------------------------#### FREQUENCY OF VARIABLES IN 9 : xh07_s06b #########################################----- 6 : v06_03 ----- 6.03
1
2
Sum40565 180991 221556

purchased/received (yes/no) -----------------------------#### FREQUENCY OF VARIABLES IN 10 : xh08_s06c #########################################----- 6 : v06_05 ----- 6.05 having durable goods (yes/no) ----------------------------1
2
Sum35875 65921 101796----- 9 : v06_08 ----- 6.08 how received? ----------------------------1
2
3
<NA>
Sum
17239
1607
470 82480 101796#### FREQUENCY OF VARIABLES IN 11 : xh09_s06d #########################################----- 6 : v06_11 ----- 6.11
1
2 <NA>
Sum
13401 64443 5988 83832

produced (yes/no) -----------------------------#### FREQUENCY OF VARIABLES IN 12 : xh10_s07 #########################################----- 6 : v07_02 ----- 7.02
12 <NA>
Sum
16964 9844 1862 28670----- 7 : v07_03 ----- 7.03
12 <NA>
Sum

can read a letter? -----------------------------

can write a letter? -----------------------------

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232

16383
581 11706 28670----- 8 : v07_04 ----- 7.04 where did person learn? ----------------------------12
3
45 <NA>
Sum
15438
417
236
147
145 12287 28670----- 9 : v07_05 ----- 7.05 ever been refused admission ----------------------------1
2 <NA>
Sum
42 26766 1862 28670----- 10 : v07_06 ----- 7.06 reason to refuse admission ----------------------------12
3
45 <NA>
Sum16
94
1
12 28628 28670----- 11 : v07_07 ----- 7.07 types of school/college refused ----------------------------1
2
34 <NA>
Sum28
11
2
1 28628 28670----- 12 : v07_08 ----- 7.08 educational background ----------------------------1
2
3 <NA>
Sum
8701 7579 10528 1862 28670----- 13 : v07_09 ----- 7.09 reason for never attending school ----------------------------1
2
3
4
5
6
7
8
9
10 <NA>Sum
748
409
650 2708
213 2866417
92
6
592 19969 28670----- 15 : v07_10 ----- 7.10 type of school/college ----------------------------1
2
3
4
5
6
7
8 <NA>
Sum
6272
199
13
82
163
208
526
116 21091 28670----- 16 : v07_11 ----- 7.11 highest level completed ----------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
60
156
416
579
655
847
417
642
598
378
666 1024
618
333
14
15
16 <NA>
Sum
168
13
8 21092 28670----- 21 : v07_15 ----- 7.15 last attending before leaving school ----------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
34
73
237
500
595
831
577
476
690
468
688
852
819
483
14
15
16 <NA>
Sum228
19
8 21092 28670----- 22 : v07_16 ----- {7.16} reason for leaving school ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
60
450
238 2253
496
243
938
52 1300
767
3
3
7
25
15 <NA>Sum
743 21092 28670----- 24 : v07_17 ----- 7.17 type of school/college ----------------------------1
2
3
4
5
6
7
8 <NA>
Sum
6500 2882
28
80
235
464
319
20 18142 28670----- 25 : v07_18 ----- 7.18 class currently attending ----------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
1231 1052
948
811
789
787
744
599
620
585
558
275
840
541
14
15
16 <NA>
Sum
139
7
2 18142 28670----- 28 : v07_21 ----- {7.21} reason for absenting from school ----------------------------1
2
3
4
5
6
7 <NA>
Sum
175
146
191
20
58
38
433 27609 28670----- 29 : v07_22 ----- 7.22 mode of transport ----------------------------12
3
45 <NA>
Sum
8705
800
644
202
56 18263 28670----- 32 : v07_24 ----- 7.24 take any private tuition ----------------------------1
2 <NA>
Sum
2377 8030 18263 28670----- 33 : v07_25 ----- 7.25 tuition teacher from own school? ----------------------------1
2 <NA>
Sum

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233

1621
756 26293 28670----- 42 : v07_27 ----- 7.27 receive scholarship? ----------------------------1
2 <NA>
Sum
1541 8868 18261 28670----- 44 : v07_29 ----- 7.29 form of scholarship ----------------------------1
2
34 <NA>
Sum
616
743
2
180 27129 28670----- 45 : v07_30 ----- 7.30 types of scholarship ----------------------------1
2
3
4
5
6
7
8 <NA>
Sum
504
441
458
9
6
2
5
116 27129 28670----- 46 : v07_31 ----- 7.31 class attending in last academic year ----------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
1340 1026
826
787
796
754
607
608
586
548
300
478
598
374
14
15
98 <NA>Sum
713
707 18261 28670#### FREQUENCY OF VARIABLES IN 13 : xh11_s08 #########################################----- 6 : v08_02 ----- {8.02} type of disability ----------------------------1
2
3
4
5
6
7
8 <NA>
Sum
289
214
219
23
80
71
70 27508
196 28670----- 8 : v08_04 ----- 8.04 suffer from chronic illness? ----------------------------1
2 <NA>
Sum
3268 25206
196 28670----- 9 : v08_05 ----- 8.05 primary chronic illness ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 <NA>
187
99
330
42
7
202
46
467
140
14
400
894
440 25402
Sum
28670----- 13 : v08_08 ----- 8.08 received in-kind medication ----------------------------1
2 <NA>
Sum
305 2963 25402 28670----- 15 : v08_10 ----- 8.10 present health status ----------------------------1
2
34 <NA>
Sum
16359 11492
595
28
196 28670----- 17 : v08_11 ----- 8.11 any health problem & injured ----------------------------1
2 <NA>
Sum
5518 22956
196 28670----- 18 : v08_12 ----- 8.12 types of illness or injury ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
865
93
137
71 1713 1234
109
6
10
30
19
281
49
901
<NA>
Sum
23152 28670----- 19 : v08_13 ----- 8.13 consult with anyone? ----------------------------1
2 <NA>
Sum
3823 1695 23152 28670----- 20 : v08_14 ----- 8.14 main reason for not consulting ----------------------------1
2
3
4
6
7
8
9 <NA>
Sum
1372
82
9
61
3
10
18
140 26975 28670----- 21 : v08_15 ----- {8.15} place of last consultation ----------------------------1
2
3
4
5
6
7
8
9
10
11 <NA>
Sum
450
282
93
486
63
19
924 1090
24150
125 24847 28670----- 22 : v08_16 ----- {8.16} consulted with ----------------------------1
2
3
4
5
6
7
8
9 <NA>
Sum

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234

717
952
8
775
811
483
13
46
18 24847 28670----- 28 : v08_19 ----- 8.19 received in-kind medication ----------------------------1
2 <NA>
Sum
687 4831 23152 28670----- 29 : v08_20 ----- 8.20 stopped work? ----------------------------1
2 <NA>
Sum
2191 2388 24091 28670----- 31 : v08_22 ----- 8.22 anything to treat the diarrhoea? ----------------------------1
2 <NA>
Sum98
92 28480 28670----- 32 : v08_23 ----- 8.23 treatment given ----------------------------1
2
34 <NA>
Sum
36
49
10
3 28572 28670----- 33 : v08_24 ----- 8.24 obtain the ors from ----------------------------1
2
4
5
6
8
9 <NA>
Sum
5
4
3
4
1
17
2 28634 28670----- 35 : v08_25 ----- 8.25 heard about hiv/aids ----------------------------1
2
3 <NA>
Sum
12910 7697 1577 6486 28670----- 36 : v08_26 ----- 8.26 first media to know about hiv/aids ----------------------------1
2
3
4
5
6
7 <NA>
Sum
4788 1717
518 1562
600 3561
164 15760 28670----- 37 : v08_27a ----- 8.27 methods of prevention - first ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
15
1629 3943 2022
247
520
502
9
239
565
603
168
15
14
278
16 <NA>Sum
2156 15760 28670----- 38 : v08_27b ----- .
- second ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
411 1847 1716
457
824
668
26
421 1186 1035
433
29
29
5
15 <NA>Sum
352 19231 28670----- 39 : v08_27c ----- .
- third ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
223
619
616
317
599
511
34
608 1158 1189
557
50
81
7
15 <NA>Sum
312 21789 28670----- 40 : v08_27d ----- .
- fourth ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
147
341
333
158
306
392
53
467
669
830
633
81
107
25
15 <NA>Sum
172 23956 28670----- 42 : v08_28 ----- 8.28 ever been immunized? ----------------------------1
2
3 <NA>
Sum2734
100
24 25812 28670----- 43 : v08_29 ----- 8.29 have immunization card? ----------------------------1
2
3 <NA>
Sum
670
825 1239 25936 28670----- 44 : v08_30a ----- 8.30 received vaccine : bcg ----------------------------1
2 <NA>
Sum664
6 28000 28670----- 45 : v08_30b ----- .
: dpt1 ----------------------------1
2 <NA>
Sum639
31 28000 28670----- 46 : v08_30c ----- .
: dpt2 ----------------------------1
2 <NA>
Sum

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235

604
66 28000 28670----- 47 : v08_30d ----- .
: dpt3 ----------------------------1
2 <NA>
Sum572
98 28000 28670----- 48 : v08_30e ----- .
: polio 1 ----------------------------1
2 <NA>
Sum
632
38 28000 28670----- 49 : v08_30f ----- .
: polio 2 ----------------------------1
2 <NA>
Sum
603
67 28000 28670----- 50 : v08_30g ----- .
: polio 3 ----------------------------1
2 <NA>
Sum564
106 28000 28670----- 51 : v08_30h ----- .
: measles ----------------------------1
2 <NA>
Sum488
182 28000 28670----- 52 : v08_30i ----- .
: hp-b 1 ----------------------------1
2 <NA>
Sum480
190 28000 28670----- 53 : v08_30j ----- .
: hp-b 2 ----------------------------1
2 <NA>
Sum
455
215 28000 28670----- 54 : v08_30k ----- .
: hp-b 3 ----------------------------1
2 <NA>
Sum
437
233 28000 28670----- 55 : v08_31 ----- 8.31 received bcg vaccine ----------------------------1
2
3 <NA>
Sum
1980
59
25 26606 28670----- 56 : v08_32 ----- 8.32 received polio vaccine ----------------------------1
2
3 <NA>
Sum201329
22 26606 28670----- 57 : v08_33 ----- 8.33 when first polio vaccine received ----------------------------1
2
3 <NA>
Sum
328 1649
36 26657 28670----- 58 : v08_34 ----- 8.34 no. of time polio vaccine received ----------------------------12
3
45 <NA>
Sum
84
168
555 1079
127 26657 28670----- 59 : v08_35 ----- 8.35 received dpt vaccine ----------------------------1
2
3 <NA>
Sum
1857
100
107 26606 28670----- 61 : v08_37 ----- 8.37 received measles vaccine ----------------------------1
2
3 <NA>
Sum
1708
251
105 26606 28670----- 62 : v08_38 ----- 8.38 received hp-b vaccine ----------------------------1
2
3 <NA>
Sum
1205
452
407 26606 28670----- 63 : v08_39 ----- 8.39 place of immunization ----------------------------123456 <NA>
Sum
715
496
105
398
910
110 25936 28670#### FREQUENCY OF VARIABLES IN 14 : xh12_s09a #########################################----- 12 : v09_05 ----- 9.05
1
2
Sum8035 7448 15483

sex of child -----------------------------

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236----- 13 : v09_06 ----- 9.06 still alive ----------------------------1
2
Sum
14273 1210 15483----- 14 : v09_07 ----- 9.07 currently live with you? ----------------------------1
2 <NA>
Sum
11706 2567 1210 15483----- 19 : v09_10 ----- 9.10 highest level completed ----------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
136
55
115
136
138
230
127
173
201
135
122
347
332
124
14
15
98 <NA>
Sum
24
2 1380 11706 15483#### FREQUENCY OF VARIABLES IN 15 : xh13_s09b #########################################----- 6 : v09_12 ----- 9.12 went for pre-natal consultation ----------------------------1
2 Sum
1230 310 1540----- 7 : v09_13 ----- 9.13 place where first care received ----------------------------1
2
3
4
5
7
8
9
10
11 <NA> Sum
323 236 48 391
31
3
92
82
3
21 310 1540----- 8 : v09_14 ----- 9.14 care provider ----------------------------1
2
45 <NA> Sum
400 8272
1 310 1540----- 10 : v09_16 ----- 9.16 given tetanus injection after birth ----------------------------1
2 <NA> Sum1167
63 310 1540----- 11 : v09_17 ----- 9.17 no. of times ----------------------------1
2
3 <NA> Sum
169 762 236 373 1540----- 12 : v09_18 ----- 9.18 reason for not pre-natal consultation ----------------------------123479 <NA> Sum
211
42
2
5
1
49 1230 1540----- 14 : v09_19 ----- 9.19 where given birth ----------------------------1
2
3
4
5
6
7 Sum
951
22
36
28 42858
17 1540----- 15 : v09_20 ----- 9.20 who assisted ----------------------------1
2
3
4
5
6
7
8
9 Sum
725 12173
27 118 452
617
1 1540----- 16 : v09_21 ----- 9.21 post-natal checkup ----------------------------1
2 Sum
338 1202 1540----- 17 : v09_22 ----- 9.22 place of post-natal check-up ----------------------------1
2
3
4
5
7
8
9
10
11 <NA> Sum
63
42
12 1465420
37
3
6 1202 1540----- 18 : v09_23 ----- 9.23 care provider ----------------------------1
2
35 <NA> Sum
176 159
1
2 1202 1540----- 19 : v09_24 ----- 9.24 reason for not post-natal consultation ----------------------------1
2
3
4
6
7
8
9 <NA> Sum
982
71
4
11
2
2
1 129 338 1540#### FREQUENCY OF VARIABLES IN 16 : xh14_s09c #########################################

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237----- 5 : v09_25 ----- 9.25 is this 15-49 married women? ----------------------------1
2
Sum
5612 23050 28662----- 7 : v09_27 ----- 9.27 know any method to prevent pregnancy ----------------------------0
1
2 <NA>
Sum
1 4758827 23076 28662----- 8 : v09_28 ----- 9.28 medium to learn ----------------------------1
2
3
4
5
6
7
8 <NA>
Sum
1520
465
70 1158792
316
420
17 23904 28662----- 9 : v09_29 ----- 9.29 ever used ----------------------------1
2 <NA>
Sum
3174 1584 23904 28662----- 10 : v09_30 ----- 9.30 currently using ----------------------------1
2 <NA>
Sum
2280
894 25488 28662----- 11 : v09_31 ----- 9.31 which method currently used ----------------------------1
2
3
4
5
6
7
8
9 <NA>
Sum
238
44
633
54
297
21
660
325
8 26382 28662----- 12 : v09_32 ----- 9.32 where obtained ----------------------------12
3
45 <NA>
Sum
548
162
442
106
16 27388 28662----- 13 : v09_33 ----- 9.33 why not? ----------------------------1
2
3
4
5
6
7
8 <NA>
Sum
21
17
782
651
48
416
76
467 26184 28662----- 14 : v09_34 ----- 9.34 health worker visit? ----------------------------1
2 <NA>
Sum
673 4085 23904 28662#### FREQUENCY OF VARIABLES IN 17 : xh15_s09d #########################################----- 6 : v09_37 ----- 9.37 have to make decision ? ----------------------------1
2
Sum
37468 45347 82815----- 7 : v09_38 ----- 9.38 involvement in decision making ----------------------------1
2
3 <NA>
Sum
28081 9020
367 45347 82815----- 8 : v09_39 ----- 9.39 who made final decision ----------------------------1
2
34 <NA>
Sum
7836 5433 19811 4388 45347 82815#### FREQUENCY OF VARIABLES IN 19 : xh17_s10b #########################################----- 6 : v10_02_job ----- {10.02} job-id ----------------------------1
2
3
4
5
6
Sum
25547 5611 1211
220
20
2 32611----- 9 : v10_04a ----- 10.04 work done in baishakh ----------------------------1
2 <NA>
Sum
16523 5732 10356 32611----- 10 : v10_04b ----- .
jestha ----------------------------1
2 <NA>
Sum
16999 5256 10356 32611

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238----- 11 : v10_04c ----- .
aashadh ----------------------------1
2 <NA>
Sum
18731 3524 10356 32611----- 12 : v10_04d ----- .
shravan ----------------------------1
2 <NA>
Sum
17910 4345 10356 32611----- 13 : v10_04e ----- .
bhadra ----------------------------1
2 <NA>
Sum
16094 6161 10356 32611----- 14 : v10_04f ----- .
ashwin ----------------------------1
2 <NA>
Sum
15705 6550 10356 32611----- 15 : v10_04g ----- .
kartik ----------------------------1
2 <NA>
Sum
17073 5182 10356 32611----- 16 : v10_04h ----- .
mangsir ----------------------------1
2 <NA>
Sum
17186 5069 10356 32611----- 17 : v10_04i ----- .
poush ----------------------------1
2 <NA>
Sum
14096 8159 10356 32611----- 18 : v10_04j ----- .
magh ----------------------------1
2 <NA>
Sum
14044 8211 10356 32611----- 19 : v10_04k ----- .
falgun ----------------------------1
2 <NA>
Sum
15536 6719 10356 32611----- 20 : v10_04l ----- .
chaitra ----------------------------1
2 <NA>
Sum
16960 5295 10356 32611----- 31 : v10_07 ----- {10.07} sector of employment ----------------------------1
2
34 <NA>
Sum
1923 4629 11538 4165 10356 32611#### FREQUENCY OF VARIABLES IN 20 : xh18_s11 #########################################----- 6 : v11_02 ----- 11.02 available for work? ----------------------------1
2 <NA>
Sum
644 7463 20563 28670----- 7 : v11_03 ----- 11.03 look for work ----------------------------1
2 <NA>
Sum209
435 28026 28670----- 8 : v11_04 ----- 11.04 reason for not available/not look for work----------------------------1
2
3
4
5
6
7
8
9
10 <NA>
Sum
5014
991
868
118
70
28
12
93
75
629 20772 28670----- 9 : v11_05 ----- 11.05 available for additional work? ----------------------------1
2 <NA>
Sum
1903 7627 19140 28670----- 10 : v11_06 ----- 11.06 look for additional work ----------------------------1
2 <NA>
Sum
654 1249 26767 28670----- 11 : v11_07 ----- 11.07 reason for not available/not look for additional work----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 <NA>

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239

353
Sum
28670

108

39

898

37

59

461

2321

3585

495

55

77

388 19794#### FREQUENCY OF VARIABLES IN 21 : xh19_s12 #########################################----- 8 : v12_02 ----- {12.02} work activity code [nsic] ----------------------------1
2
5
10
11
12
13
14
15
16
17
18
19
20
21
22
23
2157
83
4
13
12
1
8
29
89
8 110
81
12 142
12
26
1
24
25 26
27
28
29
30
31
32
33
34
35
36
37
40
41
45
12
28 48
10
46
17
24
15
9
4
4
6
41
3
53
22 1355
50
51 52
55
60
61
62
63
64
65
66
67
70
71
72
73
74
30
20 161 111 243
2
11
52
47
81
10
43
6
2
19
11
32
75
80 85
90
91
92
93
95
99 <NA> Sum
160 507 17933
36
61 10396
8
3 6552----- 9 : v12_03 ----- 12.03 basis of payment receive ----------------------------1
2
3 Sum
3876 2151 525 6552----- 11 : v12_05a ----- 12.05 in-kind payment- first ----------------------------1
2
3
4
5
6
7 <NA> Sum
412
13 24 1715
6 161 1545 2676 6552----- 12 : v12_05b ----- .
- second ----------------------------123456 <NA> Sum
17
34
6 186
9 183 6117 6552----- 16 : v12_07 ----- 12.07 sector of long term worker ----------------------------1
2 <NA> Sum
17 2134 4401 6552----- 18 : v12_09a ----- 12.09 in-kind received : first ----------------------------3
457 <NA> Sum18
2
6 6535 6552----- 19 : v12_09b ----- .
: second ----------------------------4
5
6 <NA> Sum12
1 6548 6552----- 22 : v12_11 ----- 12.11 takin loan from employer ----------------------------1
2 <NA> Sum
3
14 6535 6552----- 23 : v12_12 ----- 12.12 other member also work same employer ----------------------------1
2 <NA> Sum
3
14 6535 6552----- 24 : v12_13 ----- 12.13 share crop with employer ----------------------------2 <NA> Sum
17 6535 6552----- 25 : v12_14 ----- 12.14 tent livestock for emploer ----------------------------1
2 <NA> Sum
98 6535 6552----- 32 : v12_16 ----- 12.16 tax already deducted ----------------------------1
2 <NA> Sum
660 1474 4418 6552----- 33 : v12_17 ----- 12.17 provision of provident fund ----------------------------1
2 <NA> Sum
572 1562 4418 6552----- 34 : v12_18 ----- 12.18 provision of pension ----------------------------1
2 <NA> Sum
359 1775 4418 6552----- 35 : v12_19 ----- 12.19 provision of subsidized medical care -----------------------------

Nepal NLSS Ⅲ 2010/11 Manual Version 2.02401
2 <NA> Sum
509 1625 4418 6552----- 36 : v12_20 ----- 12.20
1
2
3 <NA> Sum
141 736 1257 4418 6552

no. of employee -----------------------------#### FREQUENCY OF VARIABLES IN 22 : xh20_s13a1 #########################################----- 6 : v13_04u ----- 13.04 area
- unit ----------------------------1
2
Sum
8131 5106 13237----- 11 : v13_06 ----- 13.06 types of land ----------------------------1
2
Sum6678 6559 13237----- 12 : v13_07 ----- 13.07 plot irrigated? ----------------------------1
2
Sum5493 7744 13237----- 13 : v13_08 ----- 13.08 seasonal or year round irrigation ----------------------------1
2 <NA>
Sum
2458 3035 7744 13237----- 14 : v13_09 ----- 13.09 mode of irrigation ----------------------------12
3
45 <NA>
Sum
1553 3306
109
453
72 7744 13237----- 17 : v13_11 ----- {13.11} dry season: use of plot ----------------------------1
2
3
4
5
6
Sum
9155
824
159
140 2782
177 13237----- 24 : v13_14 ----- {13.14} wet season: use of plot ----------------------------1
2
3
4
5
6
Sum
10917 1006
165
136
814
199 13237#### FREQUENCY OF VARIABLES IN 23 : xh21_s13a2 #########################################----- 6 : v13_20 ----- 13.20 contractual arrangement ----------------------------1
2
3
4 Sum
1166 248 297 195 1906----- 8 : v13_22u ----- 13.22 area of other's land
- unit ----------------------------1
2 Sum
1016 890 1906----- 12 : v13_23 ----- 13.23 types of land ----------------------------1
2 Sum
630 1276 1906----- 13 : v13_24 ----- 13.24 plot irrigated? ----------------------------1
2 Sum
1052 854 1906----- 14 : v13_25 ----- 13.25 seasonal or year round irrigation ----------------------------1
2 <NA> Sum
483 569 854 1906----- 15 : v13_26 ----- 13.26 mode of irrigation ----------------------------12
3
45 <NA> Sum
223 710 1890
11 854 1906

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241#### FREQUENCY OF VARIABLES IN 24 : xh22_s13b #########################################----- 7 : v13_36 ----- 13.36 used improved seed? ----------------------------1
2
Sum
4260 38064 42324----- 8 : v13_37a ----- 13.37
a. production unit ----------------------------1
2
3
4
5
6
7
8
9
10
11
Sum
25756
66 2177
21 4521 5053
506
6 1331
706 2181 42324----- 12 : v13_38a ----- 13.38
a. sold unit ----------------------------0
1
2
3
4
5
6
7
9
10
11 <NA>Sum
3 1810
3
704
6
473
290
10
187
108
723 38007 42324#### FREQUENCY OF VARIABLES IN 25 : xh23_s13c1 #########################################----- 7 : v13_41a ----- 13.41
1
2
3
4
5
6
324 480 1285 3259 43956----- 8 : v13_41b ----- .
1
2
3
4
5
6
17
26 38
45
5
2

where did you obtain
7 Sum
113 5956: first -----------------------------: second ----------------------------7 <NA> Sum
20 5803 5956#### FREQUENCY OF VARIABLES IN 26 : xh24_s13c2 #########################################----- 6 : v13_45yn ----- 13.45
1
2
Sum6583 11141 17724

fertilizer used -----------------------------#### FREQUENCY OF VARIABLES IN 27 : xh25_s13c3 #########################################----- 6 : v13_52 ----- 13.52
12 <NA> Sum
274 3155
1 3430hire worker on piece-rate basis -----------------------------#### FREQUENCY OF VARIABLES IN 30 : xh28_s13e1 #########################################----- 6 : v13_66yn ----- 13.66
1
2
Sum10556 26956 37512

yes/no -----------------------------#### FREQUENCY OF VARIABLES IN 33 : xh31_s13f #########################################----- 6 : v13_75yn ----- 13.75
1
2
Sum8275 30686 38961

yes/no -----------------------------#### FREQUENCY OF VARIABLES IN 34 : xh32_s14 #########################################----- 7 : v14_02c ----- 14.02
1
2
5
10
11
12
166
79 24
1
1
1

produced goods and services(nsic) ----------------------------14
15
16
17
18
19
20
21
22
23
24
11 316
2
96
99
7 116
3
9
1
8

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25
26 27
28
29
31
32
33
34
36
37
40
41
45
50
51
52
3
9
7
47
2
5
7
4
1
15
2
6
4
80
30
53 739
55
60 62
63
64
65
66
67
70
72
73
74
75
80
85
90
91
158
95
2
15
15
7
2
10
35
8
1
78
5
29
34
1
31
92
93 95 <NA> Sum2374
4
2 2583----- 18 : v14_08 ----- 14.08 owned by ----------------------------1
2 Sum
2431 152 2583----- 21 : v14_10a ----- {14.10} 1st customer ----------------------------1
2
3
4
5
6
7
8
9
10 Sum2109 213 44
24
83
9
41
14
2
44 2583----- 22 : v14_10b ----- {14.10} 2nd customer ----------------------------1
2
3
4
5
6
7
8
9
10 <NA> Sum
129 332 26
21 12373138
6 308 1562 2583----- 23 : v14_11 ----- 14.11 registered with govt. agency ----------------------------1
2 Sum
650 1933 2583----- 24 : v14_12 ----- 14.12 main source of money ----------------------------1
2
3
4
5
6
7
8
9
10
11 Sum
820 1042 334
66
45
20 1233527
28
43 2583----- 25 : v14_13 ----- 14.13 tried to borrow ----------------------------1
2
3 Sum
543
80 1960 2583----- 26 : v14_14 ----- 14.14 borrowed from ----------------------------1
2
3
4
5
6
7
8 <NA> Sum
220
62 42
31 1794130
18 1960 2583----- 27 : v14_15 ----- 14.15 hired anyone ----------------------------1
2 Sum
483 2100 2583----- 29 : v14_17 ----- {14.17} problems for running business ----------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 Sum1210 291 56 113
142461
86 43769
55
36 131 2583#### FREQUENCY OF VARIABLES IN 35 : xh33_s15a #########################################----- 9 : v15_05 ----- 15.05 obtained from ----------------------------1
2
3
4
5
6
7
8
9
10
11 Sum2943 247 102 127 750 242
48 135 538 322 257 5711----- 10 : v15_06 ----- {15.06} purpose of borrowing ----------------------------1
2
3
4
5
6
7
8
9
10
11
12 Sum120
23 301 311
45 752 1717 593 367
81 602 799 5711----- 17 : v15_10 ----- 15.10 finished repaying loan ----------------------------1
2
3 Sum
374 2226 3111 5711----- 19 : v15_12 ----- 15.12 collateral ----------------------------1
2
3
4
5
6
7
8 Sum
411 358 79 154 357
56 104 4192 5711#### FREQUENCY OF VARIABLES IN 36 : xh34_s15b #########################################----- 9 : v15_18 ----- 15.18

relationship to lender -----------------------------

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243

1
2 3
4
5
6 Sum
40 28 7 490 310 45 920----- 10 : v15_19 ----- 15.19 purpose of lending ----------------------------1
2 3
4
5
6
7
8
9 10 11 12 Sum
13
3 61 23
9 117 272 71 36 16 198 101 920----- 17 : v15_23 ----- 15.23 finished repaying loan ----------------------------1
2 3 Sum
21 190 709 920----- 19 : v15_25 ----- 15.25 collateral ----------------------------1
2 3
4
5
6
7
8 Sum
55 11 2 24 35 14 13 766 920#### FREQUENCY OF VARIABLES IN 37 : xh35_s15c #########################################----- 4 : v15_26 ----- 15.26 own other land or property? ----------------------------1
2 Sum
1305 4683 5988----- 10 : v15_32 ----- 15.32 own other assets? ----------------------------1
2 Sum
216 5772 5988#### FREQUENCY OF VARIABLES IN 38 : xh36_s15d #########################################----- 6 : v15_39 ----- {15.39} have to make decision ----------------------------1
2
Sum29491 36794 66285----- 7 : v15_40 ----- {15.40} involvement in decision making ----------------------------1
2
3 <NA>
Sum
24408 4890
193 36794 66285----- 8 : v15_41 ----- {15.41} who made final decision ----------------------------1
2
34 <NA>
Sum
6563 2944 16580 3404 36794 66285#### FREQUENCY OF VARIABLES IN 39 : xh37_s16 #########################################----- 6 : v16_02 ----- 16.02 sex of absentees ----------------------------1
2 Sum
4390 1684 6074----- 7 : v16_03 ----- 16.03 relations to hhld head ----------------------------2
3
4
5
6
7
8
9
10
11
12
14 Sum912 3070 805 136 211 138 576 117
3272
3
2 6074----- 9 : v16_05 ----- 16.05 highest grate completed ? ----------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
270 136 203 196 266 423 222 345 402 300 364 833 700 303 144
12 137
17
97 <NA> Sum
395 234 189 6074----- 10 : v16_06 ----- 16.06 marital status ----------------------------1
2
3
4
5
6
7
8 <NA> Sum
2171 2952 76
96
39
14
13
12 701 6074----- 13 : v16_08b ----- .
- urban/rural----------------------------1
2 <NA> Sum
2094 1260 2720 6074

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244----- 14 : v16_09 ----- 16.09 reason for leaving ----------------------------1
2
3
4
5
6
7
8
9
11
12 Sum944 471 1261
21 1559 1158 205 164
7 104 180 6074----- 17 : v16_11 ----- 16.11 types of job ----------------------------1
2
34 <NA> Sum
2971 432 27 205 2439 6074----- 18 : v16_12 ----- 16.12 how find current job? ----------------------------12
3
45 <NA> Sum
891 838 1067 467 372 2439 6074----- 19 : v16_13 ----- 16.13 received money or goods ----------------------------1
2 Sum
2494 3580 6074----- 21 : v16_15 ----- 16.15 methods of sending ----------------------------1
2
3
4
5
6
7 <NA> Sum
939 102 916 376 117
38
6 3580 6074----- 24 : v16_18a ----- 16.18 major use of remittance : first ----------------------------1
2
3
4
5
6
7
8 <NA> Sum
1761 116 93
2074
26 32975 3580 6074----- 25 : v16_18b ----- .
: second ----------------------------1
2
3
4
5
6
7
8 <NA> Sum
335 505 49
28 23781 23849 4552 6074#### FREQUENCY OF VARIABLES IN 40 : xh38_s17a #########################################----- 8 : v17_05 ----- 17.05 relationship to the recipient ----------------------------2
3
4
5
6
7
8
9
10
11
12
13
14 Sum31 846 32 274 496
92
31 23473 1212
1
11 2244----- 9 : v17_06 ----- 17.06 sex of receipant ----------------------------1
2 Sum
406 1838 2244----- 11 : v17_07b ----- .
- urban/rural ----------------------------1
2 <NA> Sum
386 1545 313 2244----- 12 : v17_08 ----- 17.08 primary work activity of recipient ----------------------------1
2
3
4
5
6
7 Sum
95 206 1173 220 122 365
63 2244----- 15 : v17_10 ----- 17.10 how usually send the amount? ----------------------------1
2
3
4
5
6
7 Sum
20
1 191478 18832
11 2244#### FREQUENCY OF VARIABLES IN 41 : xh39_s17b #########################################----- 8 : v17_15 ----- 17.15 relationship to the recipient? ----------------------------2
3
4
5
6
7
8
9
10
11
14 <NA> Sum
21 548 12 703 749
52
49 18252 25433
2 2657----- 9 : v17_16 ----- 17.16 sex of donor ----------------------------1
2 <NA> Sum
1565 1089
3 2657----- 12 : v17_18b ----- {17.18} place of residence- urban/rural ----------------------------1
2 <NA> Sum
479 1594 584 2657----- 13 : v17_19 ----- {17.19} primary work activity -----------------------------

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1
2
3
4
5
6
80 708 1171 354
41 233----- 17 : v17_22a ----- 17.22
1
2
3
4
5
6
2320 100 23
9 128
14----- 18 : v17_22b ----- .
1
2
3
4
5
6
112 2308579
58

7 <NA> Sum69
1 2657
major use of remittance : first ----------------------------78 Sum
1350 2657: second ----------------------------7
8 <NA> Sum18
38 2109 2657#### FREQUENCY OF VARIABLES IN 42 : xh40_s18a #########################################----- 6 : v18_01 ----- 18.01 hhld member receive payment ----------------------------1
2
3
Sum
957 2708 38251 41916----- 8 : v18_03 ----- 18.03 how frequently ----------------------------12
3
45 <NA>
Sum
132
614
72
31
108 40959 41916----- 13 : v18_08 ----- 18.08 anyone currently payment ----------------------------1
2 <NA>
Sum
337
620 40959 41916#### FREQUENCY OF VARIABLES IN 43 : xh41_s18b #########################################----- 4 : v18_101 ----- 18.10 1) receive public food distrb. system ----------------------------1
2 Sum
58 5930 5988----- 5 : v18_102 ----- 18.10 2) receive nutritional program for children----------------------------1
2 Sum
94 5894 5988----- 6 : v18_103 ----- 18.10 3) receive nutritional program for mother----------------------------1
2 Sum
12 5976 5988----- 7 : v18_104 ----- 18.10 4) receive food for work ----------------------------1
2 Sum
128 5860 5988----- 8 : v18_105 ----- 18.10 5) receive cash for work ----------------------------1
2 Sum
63 5925 5988----- 9 : v18_106 ----- 18.10 6) receive rciwp programme ----------------------------1
2 Sum
175 5813 5988#### FREQUENCY OF VARIABLES IN 44 : xh42_s18c #########################################----- 6 : v18_11 ----- 18.11
1
2
Sum5914 41990 47904

having income -----------------------------#### FREQUENCY OF VARIABLES IN 45 : xh43_s19 #########################################

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246----- 4 : v19_02 ----- 19.02 opinion on food consumption ----------------------------1
2
3
4 Sum
881 4975 131
1 5988----- 5 : v19_03 ----- 19.03 opinion on housing ----------------------------1
2
3
4 Sum
1258 4640 84
6 5988----- 6 : v19_04 ----- 19.04 opinion on clothing ----------------------------1
2
3
4 Sum
944 4962 81
1 5988----- 7 : v19_05 ----- 19.05 opinion on health care getting ----------------------------1
2
3
4 Sum
1045 4820 80
43 5988----- 8 : v19_06 ----- 19.06 opinion on children's schooling ----------------------------1
2
3
4 Sum
932 4266 65 725 5988----- 9 : v19_07 ----- 19.07 opinion on family's total income ----------------------------1
2
3
4 Sum
2784 3086 77
41 5988----- 10 : v19_08 ----- 19.08 eats too little food to live ----------------------------1
2 <NA> Sum
695 186 5107 5988----- 11 : v19_09 ----- 19.09 status of health facility ----------------------------1
2
3
4 Sum
711 4117 974 186 5988----- 12 : v19_10 ----- 19.10 status of education facility ----------------------------1
2
3
4 Sum
781 3638 565 1004 5988----- 13 : v19_11 ----- 19.11 status of drinking water facility ----------------------------1
2
3
4 Sum
989 2763 1541 695 5988----- 14 : v19_12 ----- 19.12 status of electricity facility ----------------------------1
2
3
4 Sum
523 2017 2148 1300 5988----- 15 : v19_13 ----- 19.13 status of road facility ----------------------------1
2
3
4 Sum
705 3185 1889 209 5988----- 16 : v19_14 ----- 19.14 status of postal facility ----------------------------1
2
3
4 Sum
216 2767 348 2657 5988----- 17 : v19_15 ----- 19.15 status of telephone facility ----------------------------1
2
3
4 Sum
955 4131 459 443 5988----- 18 : v19_16 ----- 19.16 enough food or money to buy food ----------------------------1
2 Sum
413 5575 5988----- 20 : v19_1801 ----- 19.18 1) rely on less preferred/expensive foods----------------------------1
2 <NA> Sum
216 197 5575 5988----- 21 : v19_1802 ----- 19.18 2) borrow food or money ----------------------------1
2 <NA> Sum
275 138 5575 5988----- 22 : v19_1803 ----- 19.18 3) purchase food on credit ----------------------------1
2 <NA> Sum
235 178 5575 5988----- 23 : v19_1804 ----- 19.18 4) gather wild food/eat unmature crops

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247----------------------------1
2 <NA> Sum
25 388 5575 5988----- 24 : v19_1805 ----- 19.18 5) consume seed stock ----------------------------1
2 <NA> Sum
53 360 5575 5988----- 25 : v19_1806 ----- 19.18 6) send hhld member to eat elsewhere ----------------------------1
2 <NA> Sum
37 376 5575 5988----- 26 : v19_1807 ----- 19.18 7) send hhld member to beg ----------------------------1
2 <NA> Sum
16 397 5575 5988----- 27 : v19_1808 ----- 19.18 8) limit portion sizes at mealtime ----------------------------1
2 <NA> Sum
170 243 5575 5988----- 28 : v19_1809 ----- 19.18 9) restrict consumption of adults ----------------------------1
2 <NA> Sum
68 345 5575 5988----- 29 : v19_1810 ----- 19.18 10) feed working members of hhld ----------------------------1
2 <NA> Sum
31 382 5575 5988----- 30 : v19_1811 ----- 19.18 11) ration the money ----------------------------1
2 <NA> Sum
16 397 5575 5988----- 31 : v19_1812 ----- 19.18 12) reduced the meals eaten in a day ----------------------------1
2 <NA> Sum
139 274 5575 5988----- 32 : v19_1813 ----- 19.18 13) skip entire days without eating ----------------------------1
2 <NA> Sum
46 367 5575 5988----- 33 : v19_1814 ----- 19.18 14) sell assets, jewelry ----------------------------1
2 <NA> Sum
25 388 5575 5988#### FREQUENCY OF VARIABLES IN 46 : xh44_s20 #########################################----- 5 : v20_01 ----- {20.01} has birth certificate ----------------------------1
2 <NA>
Sum
1195 1652 9948 12795----- 10 : v20_04 ----- {20.04} measurement finished ----------------------------12
3
45 <NA>
Sum
2515
239
13
73
6 9949 12795----- 12 : v20_06 ----- {20.06} way was measured ----------------------------1
2 <NA>
Sum
1617
898 10280 12795----- 17 : v20_09 ----- {20.09} did breastfeed ----------------------------1
2 <NA>
Sum
2111
735 9949 12795

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Chapter 5. Data Check
5.1 Household identifier: poverty.dta> d<-outfiles[[1]]> dim(d)[1] 5988
16> colnames(d)[1] "xhpsu"[5] "region"[9] "wt_hh"[13] "poor""xhnum""hhsize""urbrur""ad""fpline_7""pline_7""totcons_pc_7" "food_pc_7""belt""wt_ind""pindex""quintle_7"

The number of psu is 499. The median of household number is 10. A combination of xhpau(psu) and xhnum (household number) is a unique household identifier.

> length(unique(d$xhpsu))[1] 499> summary(d$xhnum)
Min. 1st Qu. Median
Mean 3rd Qu.
1.000 5.000 10.000 9.281 14.000> length(unique(d$xhpsu*100+d$xhnum))[1] 5988

Max.
34.0005.2 Weight: poverty.dta
The variable of wt_hh is a household weight. The number of unique wt_hh is 104. The
estimated number of households is 5,776,252.

> length(unique(d$wt_hh))[1] 104> sum(d$wt_hh)[1] 5776252


The variable of wt_ind is a individual weight, defined as a production of wt_hh and hhsize. The
estimated number of household members is 28,023,715.

> table(abs(d$wt_hh*d$hhsize-d$wt_ind)<0.01)
TRUE
5988
> sum(d$wt_hh*d$hhsize)[1] 28023715

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249

5.3 Sample design# Number of sample households by belt and development region> t<-table(d$region,d$belt)> belt.names<-c("mountain", "hill", "terai")> region.names<-c("eastern", "centeral", "western", "mid-western", "far-western")> colnames(t)<-belt.names> rownames(t)<-region.names> addmargins(t)eastern
centeralwestern
mid-western
far-western
Sum

mountain hill terai Sum
108 432
732 1272
156 1464
660 22800 720
432 1152
60 372324 756
84 216
228 528
408 3204 2376 5988

# Number of sample households by strata and urban/rural> t<-table(d$ad,d$urbrur)> urbrur.names<-c("urban","rural")> ad.names<-c("mountains","urban - kathmandu","urban - hill","urban -terai","rural hills eastern",+ "rural hills -central","rural hills - western","rural hills - mid and far west",+ "rural terai - eastern","rural terai - central","rural terai - western",+ "rural terai - mid and far west")> colnames(t)<-urbrur.names> rownames(t)<-ad.names> addmargins(t)mountainsurban - kathmandu
urban - hillurban -terairural hills - easternrural hills -central
rural hills - westernrural hills - mid and far west
rural terai - easternrural terai - central
rural terai - westernrural terai - mid and far westSum

urban rural Sum
72
336 408
864
0 864
480
0 480
672
0 672
0
384 3840
480 4800480 4800516 5160
480 4800480 4800348 3480
396 3962088 3900 5988

Each analytical domain belongs to urban or rural, except mountains.

# Number of sample households by strata, region and urban/rural> t<-table(d$ad,d$region,d$urbrur)> dimnames(t)<-list(ad.names,region.names,urbrur.names)

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250

> t
, , urban

mountainsurban - kathmandu
urban - hillurban -terairural hills - easternrural hills -central
rural hills - westernrural hills - mid and far west
rural terai - easternrural terai - central
rural terai - westernrural terai - mid and far west

eastern centeral western mid-western far-western
36
36
0
0
0
0
864
0
0
0
48
120
240
36
36
252
180
84
84
72
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

, , rural

mountainsurban - kathmandu
urban - hillurban -terairural hills - easternrural hills -central
rural hills - westernrural hills - mid and far west
rural terai - easternrural terai - central
rural terai - westernrural terai - mid and far westeastern centeral western mid-western far-western
72
120
0
60
84
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
384
0
0
0
0
0
480
0
0
0
0
0
480
0
0
0
0
0
336
180
480
0
0
0
0
0
480
0
0
0
0
0
348
0
0
0
0
0
240
156

Clarify the definition of urban and rural.
When and how determined?

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5.4 Integrity check of household identifier
StrategyGenerate variable of household identifier hhid for each data file.

Confirm that the variable of hhid in each data file is consistent.

>> outfiles.old<-outfiles> for(j in 1:46){+ d<-outfiles[[j]]+ d$hhid<-d$xhpsu*100 + d$xhnum+ outfiles[[j]]<-d+ }> HHID<-outfiles[[1]]$hhid> for(j in 1:46){+ d<-outfiles[[j]]+ cat(formatC(j, width=2,flag="0"),format(STATA.files[j],width=14),+ ":",sum(!is.element(d$hhid,HHID)),"\n")+ }The number of hhid which does not belong to HHID
05 xh03_s03.dta: 006 xh04_s04.dta: 0
07 xh05_s05.dta: 0
08 xh06_s06a.dta : 0
09 xh07_s06b.dta : 0
10 xh08_s06c.dta : 0
11 xh09_s06d.dta : 0
12 xh10_s07.dta: 0
13 xh11_s08.dta: 0
14 xh12_s09a.dta : 0
15 xh13_s09b.dta : 0
16 xh14_s09c.dta : 0
17 xh15_s09d.dta : 0
18 xh16_s10a.dta : 0
19 xh17_s10b.dta : 0
20 xh18_s11.dta: 0
21 xh19_s12.dta: 0
22 xh20_s13a1.dta : 0
23 xh21_s13a2.dta : 0
24 xh22_s13b.dta : 0
25 xh23_s13c1.dta : 0
26 xh24_s13c2.dta : 0
27 xh25_s13c3.dta : 0
28 xh26_s13d1.dta : 0
29 xh27_s13d2.dta : 0
30 xh28_s13e1.dta : 0
31 xh29_s13e2.dta : 0
32 xh30_s13e3.dta : 0
33 xh31_s13f.dta : 0
34 xh32_s14.dta: 0
35 xh33_s15a.dta : 0
36 xh34_s15b.dta : 0

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37 xh35_s15c.dta
38 xh36_s15d.dta
39 xh37_s16.dta
40 xh38_s17a.dta
41 xh39_s17b.dta
42 xh40_s18a.dta
43 xh41_s18b.dta
44 xh42_s18c.dta
45 xh43_s19.dta
46 xh44_s20.dta
>

: 0
: 0
: 0
: 0
: 0
: 0
: 0
: 0
: 0
: 0

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5.5 Personal identifier
03 xh01_s01: Household roster
v01_idc: id code, serial number of household members within the household> d<-outfiles[[3]]> colnames(d)[1] "xhpsu""xhnum""xstra""v01_idc" "v01_01"[8] "v01_04" "v01_05a" "v01_05b" "v01_06" "v01_07"[15] "v01_10" "v01_jdc" "v01_11" "v01_12" "v01_13"[22] "v01_16" "hhid"> range(d$xhnum)[1] 1 34
> range(d$v01_idc)[1] 1 21

"v01_02""v01_08""v01_14""v01_03""v01_09""v01_15"

Defined a personal identifier pid as follows;> d$pid<-d$hhid*100+d$v01_idc> dim(d)[1] 28670
24> length(unique(d$pid))[1] 28670> head(d[c("xhpsu","xhnum","v01_idc","hhid","pid")])xhpsu xhnum v01_idc
hhid
pid
1 100117
1 100117 10011701
2 10012
1 100102 10010201
3 10012
2 100102 10010202
4 1001
2
3 100102 10010203
5 1001
2
4 100102 10010204
6 1001
2
5 100102 10010205



Problems faced
Indiviual-level files have different names of personal identification number, which is acombination of section number and “idc”.

Ex: 06 xh04_s04.dta: Migration> colnames(outfiles[[6]])[1] "xhpsu""xhnum""xstra""v04_idc" "v04_01""v04_02"[7] "v04_03a" "v04_03b" "v04_04""v04_05""v04_06""v04_07"[13] "v04_08""v04_09_y" "v04_09_m" "v04_10_y" "v04_10_m" "v04_11a"[19] "v04_11b" "v04_idc2" "v04_12""v04_13""v04_14""v04_15"[25] "v04_16""v04_17""v04_18""hhid"
Ex: 18 xh16_s10a.dta: Jobs and time use> colnames(outfiles[[18]])[1] "xhpsu""xhnum""xstra""v10_idc" "v10_01a" "v10_01b" "v10_01c"[8] "v10_01d" "v10_01e" "v10_01f" "v10_01g" "v10_01h" "v10_01i" "v10_01j"[15] "v10_01k" "v10_01l" "v10_01m" "v10_01n" "hhid"

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List of data files with personal identification code
No Data file
Personal identification Remarks
variable3

Xh01_s01

4 v01_idc

Household roster

6

Xh04_s04

4 v04_idc

Migration

13

Xh11_S08

4 v08a_idc

Health14

Xh12_s09a

6 v09a_idc

Maternity history (ID code of woman)

15

Xh13_s09b

5 v09b_idc

Pre and post-natal care (ID code of woman)

16

Xh14_s09c

4 v09c_idc

Family planning (ID code of woman)

18

Xh16_s10a

4 v10_idc

Time use

19

Xh17_s10b

5 v10_02

Jobs

20

Xh18_s11

4 v11_idc

Unemployment

21

Xh19_s12

4 v12_01

Wage jobs> save(outfiles,STATA.files,ad.names,belt.names,region.names,+ file="outfiles.rev.RData")
Generated variable of pid: personal identifier> outfiles.old2<-outfiles> PID.file.no<-c(3,6,13,14,15,16,18,19,20,21)> PID.variable.no<-c(4,4,4,6,5,4,4,5,4,4)> for(j in 1:10){+ n<-PID.file.no[j]+ d<-outfiles[[n]]+ k<-PID.variable.no[j]+ d$pid<-d$hhid*100 + d[,k]+ outfiles[[n]]<-d+ }# Example> head(outfiles[[3]][,c(4,23,24)])
v01_idc hhidpid
1
1 100117 10011701
2
1 100102 10010201
32 100102 10010202
43 100102 10010203
54 100102 10010204
65 100102 10010205



Confirmed that the generated pid is consistent with that of household roster, xh01_s01.

> PID<-outfiles[[3]]$pid> for(j in 1:10){# Roster

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255+ n<-PID.file.no[j]+ d<-outfiles[[n]]+ k<-PID.variable.no[j]+ cat(formatC(n, width=2,flag="0"),format(STATA.files[n],width=14),+ ":",format(colnames(d)[k],width=10),":",colnames(d)[ncol(d)],+ ":",sum(!is.element(d$pid,PID)),"\n")+ }# list of data file with personal identification, personal identification variable, generated
personal identifier and the number of pid which does not belong to PID of household roster
03 xh01_s01.dta: v01_idc: pid : 006 xh04_s04.dta: v04_idc: pid : 1
13 xh11_s08.dta: v08a_idc: pid : 0
14 xh12_s09a.dta : v09a_idc: pid : 0
15 xh13_s09b.dta : v09b_idc: pid : 0
16 xh14_s09c.dta : v09c_idc: pid : 0
18 xh16_s10a.dta : v10_idc: pid : 019 xh17_s10b.dta : v10_02: pid : 0
20 xh18_s11.dta: v11_idc: pid : 0
21 xh19_s12.dta: v12_ln1: pid : 98


ERRORS# xh04_s04: Migration> d<-outfiles[[6]]> d[!is.element(d$pid,PID),c(1:4,28,29)]
xhpsu xhnum xstra v04_idc
11084

1436

7

221

hhid pid

NA 143607

NA> d[d$hhid==143607,c(1:4,28,29)]
xhpsu xhnum xstra v04_idc

hhid

pid

11081

1436

7

221

1 143607 14360701

11082

1436

7

221

2 143607 14360702

11083

1436

7

221

3 143607 14360703

11084

1436

7

221

NA 143607

NA> d<-outfiles[[3]]> d[d$hhid==143607,c(1:8,23,24)]
xhpsu xhnum xstra v01_idc

v01_01 v01_02 v01_03 v01_04

hhid

pid

11081

1436

7

221

1 Confidential

230

1 143607 14360701

11082

1436

7

221

2 Confidential

1

10

3 143607 14360702

11083

1436

7

221

3 Confidential

2

6

3 143607 14360703

11084

1436

7

221

4 Confidential

1

2

3 143607 14360704# The record in line 11084 of xh04_s04 should be dropped, because the age is two years old in thehousehold roster.

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256# xh19_s12: wage jobs
> d<-outfiles[[21]]> d[!is.element(d$pid,PID),c(1:6,38,39)]
xhpsu xhnum xstra v12_ln1 v12_01 v12_01_job

hhid

pid

246

1058

4

310

3

2

1 105804 10580403

258

1068

11

310

2

1

3 106811 10681102

259

1068

11

310

3

1

4 106811 10681103

344

1092

13

310

3

2

1 109213 10921303

430

1113

4

310

5

3

2 111304 11130405

431

1113

4

310

6

3

3 111304 11130406

435

1113

11

310

4

3

3 111311 11131104

495

1128

4

219

4

3

2 112804 11280404

521

1130

14

219

6

3

2 113014 11301406

522

1130

14

219

7

3

4 113014 11301407

539

1132

10

219

3

2

2 113210 11321003

540

1132

10

219

4

2

3 113210 11321004

602

1141

17

218

4

3

2 114117 11411704

640

1145

8

218

3

2

1 114508 11450803

655

1145

5

218

3

2

1 114505 11450503

788

1161

11

218

2

1

2 116111 11611102

850

1166

10

218

3

2

1 116610 11661003

920

1173

13

218

2

1

2 117313 11731302

1089

1189

18

218

3

2

3 118918 11891803

1175

1203

2

218

3

2

1 120302 12030203

1257

1217

18

218

3

2

1 121718 12171803

1381

1238

19

218

6

5

1 123819 12381906

1403

1242

16

218

2

1

2 124216 12421602

1917

1320

2

219

2

1

2 132002 13200202

1918

1320

2

219

3

1

3 132002 13200203

1971

1333

13

310

8

5

1 133313 13331308

1972

1333

13

310

9

7

1 133313 13331309

2044

1353

5

310

6

3

1 135305 13530506

2045

1353

5

310

7

4

2 135305 13530507

2167

1383

13

310

6

3

3 138313 13831306

2455

1423

16

221

7

5

3 142316 14231607

2534

1433

14

221

4

3

2 143314 14331404

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2678

1444

2

321

2

1

3 144402 14440202

2709

1445

1

321

7

3

2 144501 14450107

2717

1446

13

321

3

1

4 144613 14461303

2718

1446

13

321

4

2

2 144613 14461304

2719

1446

13

321

5

2

3 144613 14461305

2979

1473

8

321

6

4

1 147308 14730806

2980

1473

8

321

7

5

2 147308 14730807

3075

1486

8

321

5

3

2 148608 14860805

3076

1486

8

321

6

4

1 148608 14860806

3077

1486

8

321

7

4

2 148608 14860807

3087

1486

5

321

3

2

1 148605 14860503

3098

1489

13

321

2

1

2 148913 14891302

3152

1498

5

321

3

1

3 149805 14980503

3153

1498

5

321

4

2

1 149805 14980504

3216

1507

7

322

3

2

1 150707 15070703

3262

1514

14

322

4

3

1 151414 15141404

3263

1514

14

322

5

3

2 151414 15141405

3415

1532

7

322

4

2

2 153207 15320704

3416

1532

7

322

5

3

1 153207 15320705

3417

1532

7

322

6

3

2 153207 15320706

3436

1534

1

322

4

3

1 153401 15340104

3758

1562

17

322

3

2

2 156217 15621703

3858

1570

13

222

3

1

3 157013 15701303

3878

1573

13

222

2

1

3 157313 15731302

3927

1577

13

222

3

1

3 157713 15771303

3928

1577

13

222

4

2

3 157713 15771304

4111

1595

17

222

4

2

1 159517 15951704

4195

1605

17

222

6

4

2 160517 16051706

4378

1616

5

100

7

5

2 161605 16160507

4379

1616

5

100

8

6

2 161605 16160508

4437

1620

15

100

6

5

1 162015 16201506

4438

1620

15

100

7

5

2 162015 16201507

4463

1622

4

100

4

1

3 162204 16220404

4464

1622

4

100

5

2

2 162204 16220405

4465

1622

4

100

6

2

3 162204 16220406

4483

1623

1

100

2

1

4 162301 16230102

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4530

1631

11

223

4

2

2 163111 16311104

4601

1640

7

223

2

1

2 164007 16400702

4678

1651

17

223

7

4

3 165117 16511707

4679

1651

17

223

8

4

4 165117 16511708

4684

1651

1

223

3

1

3 165101 16510103

4695

1653

10

223

3

2

2 165310 16531003

4796

1662

10

223

7

6

2 166210 16621007

4946

1681

1

323

6

5

5 168101 16810106

4993

1685

2

323

3

1

4 168502 16850203

4994

1685

2

323

4

2

2 168502 16850204

5079

1689

1

323

2

1

2 168901 16890102

5101

1691

14

323

5

3

3 169114 16911405

5102

1691

14

323

6

3

4 169114 16911406

5127

1692

16

323

5

3

3 169216 16921605

5221

1697

8

323

7

5

2 169708 16970807

5232

1698

10

323

4

3

1 169810 16981004

5346

1707

13

323

4

2

1 170713 17071304

5463

1716

4

324

3

1

3 171604 17160403

5469

1716

10

324

5

2

4 171610 17161005

5936

1753

5

224

3

2

2 175305 17530503

6049

1759

10

100

9

2

3 175910 17591009

6210

1768

14

100

3

1

3 176814 17681403

6361

1787

11

325

8

4

4 178711 17871108

6362

1787

11

325

9

5

2 178711 17871109

6384

1787

7

325

6

3

1 178707 17870706

6385

1787

7

325

7

3

2 178707 17870707

6471

1792

3

325

6

1

3 179203 17920306

6490

1793

14

325

5

4

4 179314 17931405

6499

1794

1

325

5

1

6 179401 17940105

6500

1794

1

325

6

2

1 179401 17940106



Because the above persons are not included in the household roster, these records should be
dropped.

For instance,> d<-outfiles[[3]]Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

259> d[d$hhid==105804,]xhpsu xhnum xstra v01_idc

v01_01 v01_02 v01_03 v01_04 v01_05a v01_05b v01_06

1209

1058

4

310

1 Confidential

1

40

1

6

1

2

1210

1058

4

310

2 Confidential

2

26

2

6

1

2

v01_07 v01_08 v01_09 v01_10 v01_jdc v01_11 v01_12 v01_13 v01_14 v01_15 v01_16

hhid

1209

2

13

12

1

1

3

NA

17

2

NA

17 105804

1210

1

13

12

1

2

3

NA

17

2

NA

17 105804

pid1209 10580401
1210 10580402> d[d$hhid==106811,]
xhpsu xhnum xstra v01_idc
1313

1068

11

310

v01_01 v01_02 v01_03 v01_04 v01_05a v01_05b v01_06

1 Confidential

1

37

1

86

NA

2

v01_07 v01_08 v01_09 v01_10 v01_jdc v01_11 v01_12 v01_13 v01_14 v01_15 v01_16
1313

98

10

12

1

1

2

NA

17

pid
1313 10681101(20191108)

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3

NA

hhid

17 106811

260

Chapter 6

Household Summary File

Summary file of household income and consumption was provided at the Workshop 2019 upon request.
> library(readstata13)> d<-read.dta13("FINAL_PREF.dta")> dim(d)[1] 5988 264> colnames(d)[1] "xhpsu"[4] "district_name"[7] "belt"[10] "urbrur"[13] "PCBS"[16] "wt_hh"[19] "Date"[22] "Central"[25] "Farwestern"[28] "Terai"[31] "MTS"[34] "UT"[37] "RHW"[40] "RTC"[43] "Season1"[46] "Nepal"[49] "hhsizeT"[52] "hhsize_3"[55] "hhsize_6"[58] "nkid715"[61] "nelderly"[64] "s_adultm"[67] "nkid06T"[70] "nkid06_3"[73] "nemp_male"[76] "nemp20_male"[79] "depratio2"[82] "has_dommig"[85] "head_idcode"[88] "head_type"[91] "head_type_3"[94] "head_ethgroup_1"[97] "head_ethgroup_4"[100] "head_ethgroup_7"[103] "head_ethgroup_10"[106] "head_educ"[109] "head_educ_3"[112] "head_occup"[115] "head_occup_3""xhnum""vdc""region""stratum""PCBS_urbrur""xstra""___BASIC___""Western""Mountain""Urban""UK""RHE""RHMF""RTW""Season2""___DEMOGRAPHICS___""hhsize_1""hhsize_4""hhsize_7""nadultm""s_kid06""s_adultf""nkid06_1""nkid06_4""nemp_fem""nemp20_fem""depratio3""has_formig""head_age""head_type_1""head_type_4""head_ethgroup_2""head_ethgroup_5""head_ethgroup_8""head_ethgroup_11""head_educ_1""head_educ_4""head_occup_1""head_occup_4""district""vdc_name""season""group""wt_ind""AD""Eastern""Midwestern""Hill""Rural"
"UH"
"RHC"
"RTE"
"RTMF""Season3""hhsize""hhsize_2""hhsize_5""nkid06""nadultf""s_kid715""s_elderly""nkid06_2""nemp""nemp20""depratio1""no_absent""___HEAD___""head_fem""head_type_2""head_ethgroup""head_ethgroup_3""head_ethgroup_6""head_ethgroup_9""head_eth_comp""head_educ_2""head_educ_5""head_occup_2""head_occup_5"

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

261[118] "head_occup_6""head_occup_7""head_occup_8"[121] "head_occup_9""head_occup_10""max_educ_fem"[124] "max_educ_fem_1""max_educ_fem_2""max_educ_fem_3"[127] "max_educ_fem_4""max_educ_fem_5""land"[130] "land_group""land_group_1""land_group_2"[133] "land_group_3""land_group_4""land_group_5"[136] "___ACCESS___""wi30_pri_sch""wi30_sec_sch"[139] "wi30_hsec_sch""wi30_hpost""wi30_hosp"[142] "wi30_bus_stop""wi30_proad""wi30_droad"[145] "wi30_haat""wi30_market""wi30_ag_cent"[148] "wi30_coop""wi30_bank""___EXPENDITURE___"[151] "hprod_30""purchase_30""inkind_30"[154] "nfooditm_30""food_30""nonfood_30"[157] "tobacco_30""fuel""nfood"[160] "garbage""electric""telephon"[163] "educatn""consdur""rental_income_30"[166] "totcons_30""totcons_pc_30""food_pc_30"[169] "nonfood_pc_30""sh_food_30""sh_tobacco_30"[172] "sh_fuel_30""sh_nfood_30""sh_garbage_30"[175] "sh_electric_30""sh_telephon_30""sh_educatn_30"[178] "sh_consdur_30""sh_rental_income_30" "sh_nonfood_30"[181] "hprod_7""purchase_7""inkind_7"[184] "nfooditm_7""food_7""nonfood_7"[187] "tobacco_7""rental_income_7""totcons_7"[190] "totcons_pc_7""food_pc_7""nonfood_pc_7"[193] "sh_food_7""sh_tobacco_7""sh_fuel_7"[196] "sh_nfood_7""sh_garbage_7""sh_electric_7"[199] "sh_telephon_7""sh_educatn_7""sh_consdur_7"[202] "sh_rental_income_7" "sh_nonfood_7""___INCOME___"[205] "remit_family""remit_sent_family" "remit_sent_other"[208] "remit_other""remitinc""remit_sent"[211] "propinc""othinc""wageinc"[214] "agwageinc""nagwageinc""entrpinc"[217] "farminc""housinc""totinc"[220] "sh_farminc""sh_wageinc""sh_entrpinc"[223] "sh_propinc""sh_remitinc""sh_housinc"[226] "sh_othinc""totinc_pc""___POVERTY_LINES___"[229] "nomfpline_7""nomnfpline_7""nompline_7"[232] "fpline_7""nfpline_7""pline_7"[235] "___PRICE_INDICES___" "tfpindex""pindex"[238] "fpindex""nfpindex""pindex_95"[241] "___REAL_VALUES___""food_pc_7_tadj""nonfood_pc_7_tadj"[244] "totcons_pc_7_tadj""rpcfexp""rpcnfexp"[247] "rpcexp""rpcinc""rpcfexp_95"[250] "rpcnfexp_95""rpcexp_95""rpcinc_95"[253] "___OUTLIERS___""incoutlier""expoutlier"[256] "___QUINTILES___""nexpquint""nexpdec"[259] "rexpquint""rexpdec""poor"[262] "gap""gapsq""food_poor"

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

262> str(d)'data.frame':
5988 obs. of 264 variables:$ xhpsu: num 1546 1237 1130 1156 1183 ...
$ xhnum: num 17 11 14 2 16 15 11 14 13 5 ...
$ district: num 33 27 24 25 27 74 20 74 47 47 ...
$ district_name: chr "Bara" "Kathmandu" "Kavrepalanchowk" "Lalitpur" ...
::

List of variable and label in the summary file

The columns of variable and label in the next list were provided by the Delegates from Nepal. The
column of remark was generated by the author.
Nepal NLSSⅢ 2010
FINAL_PREF.dta
VARIABLE

LABEL

1

xhpsu

PSU

2

xhnum

Household number

3

district

District4

district_name

5

vdc

6

vdc_name7

belt{bind:belt

8

region{bind:region

9season{bind:

10

urbrur{bind:urbrur

11

stratum{bind:

12group{bind:group

13

PCBS

CBS Population estimate, 2010: National

14

PCBS_urbrur

CBS Population estimate, 2010: Urban/Rural

15

wt_indIndividual weights

16

wt_hh

Household weights

17

xstra

Strata in the NLSS3' sample

18

AD

Remark

VDC{space 44

Analytical domains

19

Date

Date of Interview

20___BASIC___

21

Eastern

22

Central23Western

24

Midwestern

25

Farwestern

26

Mountain

27

Hill

1-3

15=16*46(hhsize)

21+22+23+24+25=1

26+27+28=1

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

263

28

Terai

29

Urban

30

Rural

31

MTS

Mountains

32

UK

Urban – Kathmandu

33

UH

Urban – Hill

34

UT

Urban – Terai

35

RHE

Rural Hills – Eastern

36

RHC

Rural Hills – Central42

37

RHW

Rural Hills – Western

38

RHMF

Rural Hills – Mid and Far West

39

RTE

Rural Terai – Eastern

40

RTC

Rural Terai – Central

41

RTW

Rural Terai – Western

42

RTMF

Rural Terai – Mid and Far Western

43

Season144

Season245

Season3

46

Nepal

47

___DEMOGRAPHI~_

48

hhsizeHousehold size

49

hhsizeT

HH size, capped at 7

50

hhsize_1

hhsizeT==1.000051

hhsize_2

hhsizeT==

2.000052

hhsize_3

hhsizeT==

3.000053

hhsize_4

hhsizeT==

4.000054

hhsize_5

hhsizeT==

5.000055

hhsize_6

hhsizeT==

6.000056

hhsize_7

hhsizeT==

7.000057

nkid06# kids 0-6

58

nkid715# kids 7-15

59

nadultm# adult men 16-64

60

nadultf# adult female 16-64

61

nelderly# elderly 65+

62s_kid06

Share of nkid06 in household

63s_kid715

Share of nkid715 in household64s_adultm

Share of nadultm in household

65s_adultf

Share of nadultf in household

66

s_elderly

Share of nelderly in household

67

nkid06T

RECODE of nkid06 (# kids 0-6)

68

nkid06_1

nkid06T==

0.000069

nkid06_2

nkid06T==

1.000070

nkid06_3

nkid06T==

2.000071

nkid06_4

nkid06T==

3.000072

nemp# employed73

nemp_male# employed male

29+30=1
Sum of 31 to 42 = 1

43+44+45=1

=1
1-20

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

50+51+52+53+54+
55+56=1

68+69+70+71=1

72=73+74

264

74

nemp_fem# employed fem

75

nemp20# employed, 20+ hrs

76

nemp20_male# employed male, 20+ hrs

77

nemp20_fem# employed female, 20+ hrs

78

depratio1#work pers/hhsize, 20hrs+

79

depratio2#wrk men/hhsize, 20hrs+

80

depratio3#dep age ![16 - 64]/# of age [16-64]

81

no_absent

Has no migrants

82

has_dommig

Has domestic migrant83

has_formig

Has foreign migrant84___HEAD___85

head_idcode

ID code of the head86

head_ageAge of the head87

head_fem

Head is female

88

head_type{space 44

Household head type89

head_type_1

head_type==M, <=25

90

head_type_2

head_type==M, [26, 45]91

head_type_3

head_type==M, 46+

92

head_type_4

head_type==F

93

head_ethgroup

75=76+77

89+90+91+92=1{space 44

Head's ethnicity: 11 groups

94

head_ethgroup_1

head_ethgroup==Hill Brahman

95

head_ethgroup_2

head_ethgroup==Hill Chhetri

96

head_ethgroup_3

head_ethgroup==Terai Brahman

97

head_ethgroup_4

head_ethgroup==Terai middle caste

98

head_ethgroup_5

head_ethgroup==Hill Dalit

99

head_ethgroup_6

head_ethgroup==Terai Dalit

100

head_ethgroup_7

head_ethgroup==Newar101

head_ethgroup_8

head_ethgroup==Hill Janajati

102

head_ethgroup_9

head_ethgroup==Terai janajati

103

head_ethgrou~10

head_ethgroup==Muslim

104

head_ethgrou~11

head_ethgroup==Other105

head_eth_comp

Head's ethnicity: Comparable to PA06

106

head_educ{space 44

Head's education level

107

head_educ_1

head_educ==<=0 years of schooling108

head_educ_2

head_educ==Literate, or109

head_educ_3

head_educ==completed [5-7]110

head_educ_4

head_educ==completed [8-10]

111

head_educ_5

head_educ==completed 11+112

head_occup

HH head's occupation113

head_occup_1

head_occup==SE: Ag

114

head_occup_2

head_occup==SE: Manu

115

head_occup_3

head_occup==SE: Trade116

head_occup_4

head_occup==SE: Services

Sum of 94 to 104=1

Sum of 107 to 111=1<5

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

Sum of 113 to 122=1

265

117

head_occup_5

head_occup==Wage: Ag118

head_occup_6

head_occup==Wage: Professoional

119

head_occup_7

head_occup==Wage: Other120

head_occup_8

head_occup==Extended economic activity

121

head_occup_9

head_occup==Unemployed122

head_occup_10

head_occup==OLF

123

max_educ_fem

head_educ{space 44

Maximum female education

124

max_educ_fem_1

max_educ_fem==<=0 years of schooling125

max_educ_fem_2

max_educ_fem==Literate, or126

max_educ_fem_3

max_educ_fem==completed [5-7]127

max_educ_fem_4

max_educ_fem==completed [8-10]

128

max_educ_fem_5

max_educ_fem==completed 11+

129

land

Total area of land owned (ha)130

land_groupland_group{space 44

Land ownership group131

land_group_1

land_group==No Land132

land_group_2

land_group==<0.2 ha.

133

land_group_3

land_group==0.2-1 ha.

134

land_group_4

land_group==1-2 ha.

135

land_group_5

land_group==2+ Ha

136___ACCESS___137

wi30_pri_sch

Within 30min of pri_sch

138

wi30_sec_sch

Within 30min of sec_sch

139

wi30_hsec_sch

Within 30min of hsec_sch

140

wi30_hpost

Within 30min of hpost

141

wi30_hosp

Within 30min of hosp

142

wi30_bus_stop

Within 30min of bus_stop

143

wi30_proad

Within 30min of proad

144

wi30_droad

Within 30min of droad

145

wi30_haat

Within 30min of haat146

wi30_market

Within 30min of market

147

wi30_ag_cent

Within 30min of ag_cent

148

wi30_coop

Within 30min of coop149

wi30_bankWithin 30min of bank

150___EXPENDITUR~_

151

hprod_30

Annual consumption of home produced food, 1m

152

purchase_30

Annual food purchases for HH consumption, 1m

153

inkind_30

Anuual in-kind food consumption, 1m

154

nfooditm_30

Number of food items consumed, 1m

155

food_30

Annual food consumption, 1m

156

nonfood_30

Total annual nonfood consumption, 1m

157

tobacco_30

Tobacco consumption, 1m

158

fuel

Fuel consumption

159

nfood

Non food consumption (ex fuel)Sum of 124 to 128=1<5

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

Sum of 131 to 135=1

266

160

garbageAmount paid for garbage disposal

161

electric

Amount paid for electricity

162

telephonAmount paid for Telephone

163

educatn

Expenditures in education

164

consdur

Consumption of durable items

165

rental_incom~30

Annual rental income from housing: COMP

166

totcons_30

Total annual household consumption, 1m

167

totcons_pc_30

Annual Per capita nominal hh consumption, 1m

168

food_pc_30

Annual nominal PC Food consumption, 1m

169

nonfood_pc_30

Annual nominal PC non-food consumption, 1m

170

sh_food_30

Share of food_30 in total consumption

171

sh_tobacco_30

Share of tobacco_30 in total consumption

172

sh_fuel_30

Share of fuel_30 in total consumption

173

sh_nfood_30

Share of nfood_30 in total consumption

174

sh_garbage_30

Share of garbage_30 in total consumption

175

sh_electric_30

Share of electric_30 in total consumption

176

sh_telephon_30

Share of telephon_30 in total consumption

177

sh_educatn_30

Share of educatn_30 in total consumption

178

sh_consdur_30

Share of consdur_30 in total consumption

179

sh_rental_in~30

Share of rental_income_30 in total consumption

180

sh_nonfood_30

Share of total non-food expenditures, 1m

181

hprod_7

Consumption of home produced food, 7d

182

purchase_7

Food purchases for HH consumption, 7d

183

inkind_7

In-kind food consumption, 7d

184

nfooditm_7

Number of food items consumed, 7d

185

food_7

Food consumption, 7d

186

nonfood_7

Total annual nonfood consumption, 7d

187

tobacco_7

Tobacco consumption, 7d

188

rental_income_7

Annual rental income from housing: PREF

189

totcons_7

Total annual household consumption, 7d

190

totcons_pc_7

Annual Per capita nomial hh consumption, 7d= 185(food_7) +186(nonfood_7)=189/48(hhsize)

191

food_pc_7

Annual nominal PC food consumption, 7d=185/48

192

nonfood_pc_7

Annual nominal PC non-food consumption, 7d=186/48

193

sh_food_7

Share of food_7 in total consumption=185/189

194

sh_tobacco_7

Share of tobacco_7 in total consumption=187/189

195

sh_fuel_7

Share of fuel_7 in total consumption

196

sh_nfood_7

Share of nfood_7 in total consumption

197

sh_garbage_7

Share of garbage_7 in total consumption

198

sh_electric_7

Share of electric_7 in total consumption

199

sh_telephon_7

Share of telephon_7 in total consumption

200

sh_educatn_7

Share of educatn_7 in total consumption

201

sh_consdur_7

Share of consdur_7 in total consumption

202

sh_rental_inc~7

Share of rental_income_7 in total consumption=188/189203

sh_nonfood_7

Share of total non-food expenditures, 7d=186/189=sum of 194 to 202

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0=181+182+183

267

204

___INCOME___205

remit_family

Remittance received from absentees

206

remit_sent_fa~yRemittance sent to absentees

207

remit_sent_ot~rRemittance sent to others

208

remit_other

Remittance received from others

209

remitincTotal remittance received=205+208

210

remit_sent

Total remittance sent

=206+207

211

propinc

Non agricultural rental income

212

othincOther income

213

wageinc

Wage income

214

agwageincWage income from agriculture

215

nagwageinc

Wage income from non-agriculture

216

entrpincNon-ag enterprise income

217

farminc

Net income from farming

218

housinc

Rental income from housing: PREF

219

totincTotal household income

220

sh_farmincShare of farm income in total income

221

sh_wageincShare of wage income in total income

222

sh_entrpinc

Share of non-farm enterprise income in total income223

sh_propinc

Share of non-ag rental income in total income

224

sh_remitincShare of transfer income in total income

225

sh_housinc

Share of housing income in total income

226

sh_othincShare of other income in total income

227

totinc_pc

Total annual PC income

228___POVERTY_LI~_229

nomfpline_7

Per capita annual food poverty lines, prices by AD

230

nomnfpline_7

231

nompline_7

232

fpline_7

233

nfpline_7234pline_7

PC annual non-food poverty line, temporally adjusted
prices by AD
PC annual poverty line, temporally adjusted prices byAD
PC annual food poverty line, temporally adjusted
national prices: 7d
PC annual non-food poverty line, temporally adjusted
national prices: 7d
PC annual total poverty line, temporally adjusted
national prices: 7d

235___PRICE_INDI~_

236

tfpindex

Temporal food price index

0.957-1.030

237

pindex0.831-2.125

238

fpindex239

nfpindex

240

pindex_95

Spatial price index to convert temporally adj overall
prices to national prices
Spatial price index to convert temporally adj food prices
to national prices
Spatial price index to conver non-food prices tonational prices
Index to convert all Nepal 2010 prices to all Nepal
1995/96 prices

241___REAL_VALUE~_

242

food_pc_7_tadj

Temporally adjusted PC food consumption=191/236

243

nonfood_pc_7_~j

Temporally adjusted PC nonfood consumption=192/236

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0=214+212=209+211+212+213+216+217+218=219/48(hhsize)

0.889-1.225
0.585-3.590
2.571 (const.)

268

244

totcons_pc_7_~j

Temporally adjusted PC consumption=242+243

245

rpcfexp

Real PC food consumption, 2010 prices=242/238

246

rpcnfexp

Real PC nonfood consumption, 2010 prices=243/239

247

rpcexpReal PC consumption, 2010 prices

248

rpcincReal PC income, 2010 prices=244/237≠245+246=227/237

249

rpcfexp_95

Real PC food consumption, 1995/96 prices=245/240250

rpcnfexp_95

Real PC nonfood consumption, 1995/96 prices=246/240251

rpcexp_95

Real PC consumption, 1995/96 prices=247/240

252

rpcinc_95

Real PC income, 1995/96 prices=248/240253___OUTLIERS___

254

incoutlier

Real PC inc outlier, 1% removed from each tail1: 122

255

expoutlier

Real PC exp outlier, 1% removed from each tail1: 160256___QUINTILES___257

nexpquint

5 quantiles of totcons_pc_7

258

nexpdec

10 quantiles of totcons_pc_7

259

rexpquint

5 quantiles of rpcexp

260

rexpdec

10 quantiles of rpcexp

261

poor

Poor household

262

gap

Poverty gap, FGT(1) x100

263

gapsq

Poverty gap squared, FGT(2) x100

264

food_poor

PC food exp < food poverty line

# Outliers:> table(d$incoutlier)0
1
5866 122> table(d$expoutlier)015828 160############################################################################



INCOME related variables# Mean of variables
t<-round(apply(d[,c(205:219,227)],2,function(x) weighted.mean(x,d$wt_hh)),1)
data.frame(Mean=t)remit_family
remit_sent_family
remit_sent_otherremit_otherremitincremit_sent
propincothincwageinc
agwageinc

Mean
47808.4
32581.9
1951.95912.953721.3
34533.7
1919.78975.251885.05463.6

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

269

nagwageinc
entrpincfarminc
housinc
totinctotinc_pc

46421.546296.2
247557.420219.3
430574.1
98349.2(for 5988 hh)t<-round(apply(d[,c(220:226)],2,function(x) weighted.mean(x,d$wt_hh)),3)
data.frame(Share=t)
Sharesh_farminc 0.277
sh_wageinc 0.262
sh_entrpinc 0.106
sh_propinc 0.003
sh_remitinc 0.172
sh_housinc 0.155
sh_othinc 0.024
sum(t)[1] 0.999

Remarks: The above values of shares are consistent with Table 11.2 and Table 11.3 in the official
report. The value 37.2 of non-farm income in Table 11.2 corresponds to the sum of wage income(sh_wageinc), enterprise income (sh_entrpinc) and non-agricultural rental income (sh_propinc).
The value 38.3 of self in Table 11.3 corresponds to the sum of farm income (sh_farminc) and enterpriseincome (sh_entrpinc). The value 35.5 of others in Table 11.3 corresponds to the sum of sh_propinc,
sh_remitinc, sh_housinc and sh_othinc.

# Relationship between variables> table(d$totinc_pc == d$totinc/d$hhsize)
TRUE
5988
> table(abs(d$totinc - (d$remitinc+d$propinc+d$othinc+d$wageinc+d$entrpinc++ d$farminc+d$housinc))<1)
TRUE
5988
> table(d$remit_sent == (d$remit_sent_family + d$remit_sent_other))
FALSE TRUE
1 5987> table(abs(d$remit_sent - (d$remit_sent_family + d$remit_sent_other))<=4)
TRUE
5988
> table(d$wageinc == (d$agwageinc + d$nagwageinc))
TRUE

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

270

5988
# Share> table(abs(d$sh_farminc - (d$farminc / d$totinc))<=0.001)
TRUE
5988
> table(abs(d$sh_wageinc - (d$wageinc / d$totinc))<=0.001)
TRUE
5988
> table(abs(d$sh_entrpinc - (d$entrpinc / d$totinc))<=0.001)
TRUE
5988
> table(abs(d$sh_propinc - (d$propinc / d$totinc))<=0.001)
TRUE
5988
> table(abs(d$sh_remitinc - (d$remitinc / d$totinc))<=0.001)
TRUE
5988
> table(abs(d$sh_housinc - (d$housinc / d$totinc))<=0.001)
TRUE
5988
> table(abs(d$sh_othinc - (d$othinc / d$totinc))<=0.001)
TRUE
5988



Outliers

Mean values are easily affected by outliers. The values in Table 11.1 of the report excluded outliers
from the analysis. Generally, the reported mean value depends on the definition of outlier. The report
describes that one percent each of both ends of the per capita income distribution are defined as outliersand excluded from the analysis. The outliers are 120 households in total, 2 percent of all households.
The mean and median per capita incomes are Rs. 41,659 and Rs. 25,093 respectively.
On the other, the summary file included the flag － the variable of “incoutlier”, real pc exp outlier,
1% removed from each tail. In the file, the outliers are 122 households.

# Mean of per capita income for all samples is 88,750, almost double of the official value.
Note: Individual weight wt_ind is defined as wt_hh multiplied by hhsize.

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

271> weighted.mean(d$totinc_pc,d$wt_ind)[1] 88749.99(for 5988 hh)# Mean of per capita income for households excluding 122 outlier households is 41,890, almost thesame as the official value.
> dd<-subset(d,d$incoutlier==0)> dim(dd)[1] 5866 264> weighted.mean(dd$totinc_pc,dd$wt_ind)[1] 41889.71(for 5866 hh)# According to per capita income distribution ignoring weights, the median is 32,622 (for 5988 hh).
> summary(d$totinc_pc)
Min. 1st Qu.
Median-2861300
17083
32622

Mean
107007

3rd Qu.
Max.
67330 47222380# Lower tail of the distribution> df<-d[order(d$totinc_pc),]> df$sn<-1:5988> df[c(1:3,50:63),c("totinc_pc","incoutlier","sn")]totinc_pc incoutlier sn
3086 -2861300.000
1 12260 -291210.580
1 25809 -233560.600
1 31228
2306.357
0 50
2659
2337.764
1 51
1073
2337.7690 52
9982433.420
0 53
5830
2468.601
0 54
1987
2477.794
0 55
4059
2530.771
1 56
3312
2544.874
0 57
4822
2616.068
1 58
5552
2670.3870 59
768
2772.6460 60
5402807.317
0 61
1691
2840.361
0 62
1864
2867.888
0 63# Upper tail of the distribution> df<-d[order(-d$totinc_pc),]> df$sn<-1:5988> df[c(1:3,50:63),c("totinc_pc","incoutlier","sn")]totinc_pc incoutlier sn
4285 47222379.9
1 1
244 27544317.4
1 22579 26580374.7
1 3
2389
708602.4
0 50
5409
683540.4
1 51
4076
679500.0
0 522750
672934.7
1 53

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4557
5542
1149
5275
886
3899
390
802
2707
1330

672639.7
667256.6643483.9
627389.6
619965.3
616300.0596851.0
593666.7
584562.7
570660.4

1 54
1 55
1 56
0 57
1 58
0 59
1 60
0 61
1 62
0 63

Remarks:
The flag variable of incoutlier in the summary file is not compatible with the order of per capita
household income, totinc_pc, because the incoutlier is defined based on real per capita income.

==== Weighted median of all households====# All housheolds> x<-d$totinc_pc> w<-d$wt_ind> weighted.mean(x,w)[1] 88749.99>> od<-order(x)> x<-x[od]> w<-w[od]> p<-cumsum(w)/sum(w)> sn<-1:length(x)> f<-data.frame(sn,x,w,p)> max(x[p<0.5])[1] 25091.26> min(x[p>0.5])[1] 25093.22> (max(x[p<0.5])+min(x[p>0.5]))/2[1] 25092.24

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The weighted median for all households is 25,093, the same as the official value.
# Households excluding 122 outliers> x<-d$totinc_pc[d$incoutlier==0]> w<-d$wt_ind[d$incoutlier==0]> weighted.mean(x,w)[1] 41889.71> od<-order(x)> x<-x[od]> w<-w[od]> p<-cumsum(w)/sum(w)> sn<-1:length(x)> f<-data.frame(sn,x,w,p)> dim(f)[1] 58664> (max(x[p<0.5])+min(x[p>0.5]))/2[1] 25092.24

The value of the weighted median for households excluding outliers is equal to that of all households.
Because the distribution is based on individual weights, and one percent of individual weights are
removed both from the lower tail and the upper tail, resulting in that the position the center of the
distribution is unchanged.

# Outliers based on nominal per capita income> df<-d[order(d$totinc_pc), c("totinc_pc","wt_ind","incoutlier")]> df$sn<-1:5988> df$flag<-ifelse(df$sn<=60 | df$sn>(5988-60),1,0)> sum(df$flag)[1] 120> x<-df$totinc_pc[df$flag==0]> w<-df$wt_ind[df$flag==0]> weighted.mean(x,w)[1] 42675.77> od<-order(x)> x<-x[od]> w<-w[od]> p<-cumsum(w)/sum(w)> sn2<-1:length(x)> f<-data.frame(sn2,x,w,p)> dim(f)[1] 58684> (max(x[p<0.5])+min(x[p>0.5]))/2[1] 25197.23

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274# Outliers of real per capita income> df<-d[order(d$totinc_pc), c("totinc_pc","rpcinc","wt_ind","incoutlier")]> df$sn<-1:5988> df$flag<-ifelse(df$sn<=60 | df$sn>(5988-60),1,0)> df2<-df[order(df$rpcinc),]

Remarks: The outlier should not be defined as the number of sample households.

> sum(df2$wt_ind[1:53])/sum(df2$wt_ind)[1] 0.01016654> sum(df2$wt_ind[5920:5988])/sum(df2$wt_ind)[1] 0.009923228

Remarks:
The number of households omitted is not 1% of the total sample households. The sum of weights
which belonged to the omitted households should be 1% of the total weights.

Conclusions:
The outlier in the provided summary file was based on the distribution of real per capita income. One

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percent of individual weights were removed from both side ends. In total, 122 households were definedas outliers; 53 households for the lower tail and 69 households for the upper tail. The number ofoutliers (122) in the summary file was different from that (120) described in the official report.

=== Nominal and real income ===Concept of real income in the summary file is unclear at the moment.



rpcinc (Real PC income at 2010 prices) is defined as totinc_pc (Total annual PC income) divided
by pindex (Spatial price index convert temporally adj overall prices to national prices).
Income value evaluated at local prices might be adjusted to income value evaluated at national
prices by using spatial price index (national=1)..

> head(d[,c(227,236:240,248,252)])
totinc_pc tfpindex
pindexfpindex nfpindex pindex_95
rpcinc rpcinc_95
1 227309.18 0.9571238 0.9106311 0.9436656 0.8568816
2.571 249617.20 97089.539
2 63720.49 0.9571238 2.1251400 1.2247049 3.5902073
2.571 29984.14 11662.441
3 25383.68 0.9571238 1.0163805 0.9895859 1.0599773
2.571 24974.58 9713.956
4 144000.00 0.9571238 2.1251400 1.2247049 3.5902073
2.571 67760.24 26355.598
5 29410.36 0.9571238 2.1251400 1.2247049 3.5902073
2.571 13839.26 5382.830
6 22716.77 0.9571238 0.8491027 0.9868198 0.6250279
2.571 26753.86 10406.012> table(abs(d[,248]-d[,227]/d[,237])<1)
TRUE
5988



rpcinc_95 (Real PC income at 1995/96 prices) is defined as rpcinc (Real PC income at 2010
prices) divided by pindex_95 (Index to convert all Nepal 2010 prices to all Nepal 1995/96 prices).
Real PC income value at 2010 national prices might be converted to real PC income value at
1995/96 prices by using temporal 2010 Nepal price index=2.571 (1995/96 Nepal price index=1).

> head(d[,c(227,240,248,252)])
totinc_pc pindex_95
rpcinc rpcinc_95
1 227309.18
2.571 249617.20 97089.539
2 63720.49
2.571 29984.14 11662.441
3 25383.68
2.571 24974.58 9713.956
4 144000.00
2.571 67760.24 26355.598
5 29410.36
2.571 13839.26 5382.830
6 22716.77
2.571 26753.86 10406.012> table(abs(d[,252]-d[,248]/d[,240])<1)
TRUE
5988

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LOCAL

NATIONAL

totinc_pc

rpcincat 2010 Nepal prices

Spatially adjusted
Temporally adjusted
rpcinc_95

at 1995/96 Nepal prices=== Nominal and real consumption ===Process for adjustment of consumption is almost the same as income, but it was conducted
separately for food consumption and non-food consumption. In addition, temporal pricedifferences by survey month is adjusted.

LOCAL

NATIONAL

food_pc
nonfood_pc
Temporally adjusted
food_pc_tadj
nonfood_pc_tadj
ttcons_pc_tadj

rpcfexp
rpcnfexp
rpcexpat 2010 Nepal prices

Spatially adjusted
Temporally adjusted
rpcfexp_95
rpcnfexp_95
rpcexp_95

at 1995/96 Nepal prices==== Price index ====
Two types of price indexes are included in the summary file; spatially adjusting index and temporally
adjusting index.

1) Spatially adjusting indexes
Spatial index converts food consumption value, nonfood consumption value and overall consumption
value evaluated at local prices to those evaluated at average national prices. Spatial index is definedby 12 analytical region.

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277> length(unique(d$AD))[1] 12> length(unique(d$pindex))[1] 12> length(unique(d$fpindex))[1] 12> length(unique(d$nfpindex))[1] 12> table(tapply(d$pindex,d$AD,function(x) length(unique(x)))==1)
TRUE
12> table(tapply(d$fpindex,d$AD,function(x) length(unique(x)))==1)
TRUE
12> table(tapply(d$nfpindex,d$AD,function(x) length(unique(x)))==1)
TRUE
12> t<-tapply(d$pindex,d$AD,function(x) unique(x))> Over.all<-round(as.vector(t),3)> Analytical.domain<-names(t)> Food<-round(as.vector(tapply(d$fpindex,d$AD,function(x) unique(x))),3)> Nonfood<-round(as.vector(tapply(d$nfpindex,d$AD,function(x) unique(x))),3)> data.frame(Analytical.domain, Over.all, Food, Nonfood)# Spatially adjusting price index by analytical region
Analytical.domain Over.all Food Nonfood
1
Mountains
1.031 1.114
0.895
2Urban - Kathmandu
2.125 1.225
3.590
3Urban - Hill
1.016 0.990
1.060
4
Urban -Terai
1.097 0.984
1.281
5Rural Hills - Eastern
0.859 1.031
0.580
6Rural Hills -Central
0.970 1.026
0.880
7Rural Hills - Western
0.957 1.051
0.804
8 Rural Hills - Mid and Far west
0.849 0.987
0.625
9Rural Terai - Eastern
0.875 0.950
0.753
10Rural Terai - Central
0.911 0.944
0.857
11Rural Terai - Western
0.831 0.889
0.736
12 Rural Terai - Mid and Far west
0.899 0.922
0.8622) Temporal food price index based on 2010 average priceTemporal food price index is defined by 3 season.
> length(unique(d$tfpindex))[1] 3> length(unique(d$season))[1] 3> table(tapply(d$tfpindex,d$season,function(x) length(unique(x)))==1)
TRUE
3

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278# Relationship between interview date and season> table(substr(d$Date,1,7),d$season)
1
2
3
2010-02 137
25
2010-03 496 46 43
2010-04 454
002010-05 561 1202010-06 199
002010-07 24 563
02010-08 7 719
02010-09 41 597 57
2010-10 0 88 80
2010-11 21 13 567
2010-12 15
0 619
2011-01 0
0 621
2011-02 1
00# Food price index by season> round(tapply(d$tfpindex,d$season,function(x) unique(x)),3)1
2
30.957 1.030 1.013# Note: tfpindex is unique also by psu.
> table(tapply(d$tfpindex,d$xhpsu,function(x) length(unique(x)))==1)
TRUE
499

3) Temporal price index to convert all Nepal 2010 prices to all Nepal 1995/96 prices
The variable of pindex_95 is constant with the value of 2.571.

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#####

Consumption related variables ###### Mean of variables
t<-round(apply(d[,c(181:192)],2,function(x) weighted.mean(x,d$wt_hh)),1)
data.frame(Mean=t)
Mean
hprod_7
30507.9
purchase_7
57990.7
inkind_7
3087.5nfooditm_7
23.2
food_791586.0
nonfood_7
77389.2
tobacco_7
1760.3
rental_income_7 20219.3
totcons_7
168975.2
totcons_pc_7
39775.3
food_pc_7
21068.8
nonfood_pc_7
18706.5t<-round(apply(d[,c(193:203)],2,function(x) weighted.mean(x,d$wt_hh)),3)
data.frame(Share=t)
Sharesh_food_7
0.614
sh_tobacco_7
0.013
sh_fuel_7
0.015
sh_nfood_7
0.138
sh_garbage_7
0.000
sh_electric_7
0.008
sh_telephon_7
0.017
sh_educatn_7
0.058
sh_consdur_7
0.033
sh_rental_income_7 0.104
sh_nonfood_7
0.386> sum(t)[1] 1.386> sum(t[-11])[1] 1> t[11]==sum(t[2:10])
sh_nonfood_7
TRUE

Remarks:
The above values of shares are consistent with Table 10.4 of the official report. The value 0.614 of
sh_food_7 is the same as “share of food” (61.5%) in the table, the value 0.104 of sh_rental.income_7
is the same as “share of housing rent” (11.0%), and the value 0.058 of sh_education_7 is almost thesame as “share of education” (5.3%).

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280# Relationship among variables> table(d[,189]==(d[,185]+d[,186]))
TRUE
5988
> table(d[,185]==(d[,181]+d[,182]+d[,183]))
TRUE
5988
> table(d[,190]==(d[,189]/d[,48]))
TRUE
5988
> table(d[,191]==(d[,185]/d[,48]))
TRUE
5988
> table(d[,192]==(d[,186]/d[,48]))
TRUE
5988
# nominal values and shares> table(abs(d[,193]-d[,185]/d[,189])<0.001)
TRUE
5988
> table(abs(d[,194]-d[,187]/d[,189])<0.001)
TRUE
5988
> table(abs(d[,202]-d[,188]/d[,189])<0.001)
TRUE
5988
> table(abs(d[,203]-d[,186]/d[,189])<0.001)
TRUE
5988

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Chapter 7 Household Consumption
The estimation method of household consumption is written in detail at Chapter X: Consumption in
NSLLⅢ Statistical Report Vol. One (See Attachments).

The composition of household consumption is as the next;

The followings are summary of how to estimates household consumption.
Three main categories: food, nonfood and housing

Data in the past 7 days are used for estimation, while two types of food data asked in Foodquestionnaire: the typical month criterion and the past 7 days criterion.

Tobacco in food questionnaire is categorized into nonfood.

Part A: frequent items in the past 30 days and Part B: infrequent items in the past 12 months in
nonfood questionnaire. This approach is different from NLSS-II.



Excluded nonfood expenditure, as the next:
Firewood

Excluded

Education

Calculated separately

HealthExcluded. The incidence of health shocks itself
is a sign of reduction in welfare.

Repairandmaintenance

and

home

Excluded.

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282

construction and improvements
Marriages, dowries, funerals, charity andExcludedother social and religious functions
Durable goods (items 412, 414-432)

Excluded. The flow of services from durable
goods is estimated, as described below.

Expenditure of durable goods － the flow of services accrued to the household － is estimatedfrom the information;
1) Number of each durable item owned
2) Year of purchase3) Price of purchase4) Estimates of the current value of the itemFor expenditure on utilities, electricity, garbage collection and telephone charges are included,
but expenditure on water is excluded.



Expenditure on education defined as the maximum of the next two estimates;
1) The household estimate provided in Section 6 (item 236).
2) The total expenditure on education estimated from information collected in Section 7; theaggregation of household members currently going to school.

Consumption of housing
1) The actual monthly rent paid by households.
2) Imputed rent for households that owned their dwelling unit and households that live in housing
provided for free.
3) Hedonic approach is applied for 269 households whose reported rent is less than NRs 100 per
month and greater than NRs 30,000

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

Mean and median value of annual household consumption for households
excluding outliers# Exclude 160 households with expoutlier=1 (Real PC expenditure outlier, 1% removed from eachtail> d<-d.save> d<-subset(d,expoutlier==0)> dim(d)[1] 5828 264# Mean of household consumption excluding outliers> t<-round(apply(d[,c(181:189)],2,+ function(x) weighted.mean(x,d$wt_hh)),1)> data.frame(Mean=t)
Mean
hprod_7
30824.0
purchase_7
56907.1
inkind_7
3083.8nfooditm_7
23.1
food_790814.9
nonfood_7
71960.2
tobacco_7
1750.1
rental_income_7 18908.7
totcons_7
162775.1# Mean of per capita household consumption excluding outliers> t<-round(apply(d[,c(190:192)],2,+ function(x) weighted.mean(x,d$wt_ind)),1)> data.frame(Mean=t)
Mean
totcons_pc_7 33429.0
food_pc_7
18650.6
nonfood_pc_7 14778.4# Shares> t<-round(apply(d[,c(193:203)],2,function(x) weighted.mean(x,d$wt_hh)),3)> data.frame(Share=t)
Sharesh_food_7
0.618
sh_tobacco_7
0.013
sh_fuel_7
0.015
sh_nfood_7
0.138
sh_garbage_7
0.000
sh_electric_7
0.008
sh_telephon_7
0.017
sh_educatn_7
0.057
sh_consdur_7
0.031
sh_rental_income_7 0.102
sh_nonfood_7
0.382

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284> sum(t)[1] 1.381> sum(t[-11])[1] 0.999> abs(t[11]-sum(t[2:10]))<=0.0011
sh_nonfood_7
TRUE

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285### Weighted median #### Weighted quantile# param x Vector of values# param w Vector of weights# param probs Vector of probabilities# param na.rm Ignore missing data#
weighted.quantile <- function(x, w, probs=seq(0,1,0.25), na.rm=TRUE) {x <- as.numeric(as.vector(x))
w <- as.numeric(as.vector(w))
if(anyNA(x) || anyNA(w)) {ok <- !(is.na(x) | is.na(w))
x <- x[ok]w <- w[ok]}stopifnot(all(w >= 0))if(all(w == 0)) stop("All weights are zero", call.=FALSE)
oo <- order(x)x <- x[oo]w <- w[oo]Fx <- cumsum(w)/sum(w)result <- numeric(length(probs))
for(i in seq_along(result)) {p <- probs[i]lefties <- which(Fx <= p)if(length(lefties) == 0) {result[i] <- x[1]} else {left <- max(lefties)
result[i] <- x[left]
if(Fx[left] < p && left < length(x)) {right <- left+1
y <- x[left] + (x[right]-x[left]) * (p-Fx[left])/(Fx[right]-Fx[left])if(is.finite(y)) result[i] <- y}}}names(result) <- paste0(format(100 * probs, trim = TRUE), "%")
return(result)}# Weighted median# param x Vector of values# param w Vector of weights# param na.rm Ignore missing data#
weighted.median <- function(x, w, na.rm=TRUE) {unname(weighted.quantile(x, probs=0.5, w=w, na.rm=na.rm))}

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286> dim(d)[1] 5828

264# Median of household consumption variables excluding outliers> t<-round(apply(d[,c(181:189)],2,+ function(x) weighted.median(x,d$wt_hh)),1)> data.frame(Median=t)
Medianhprod_7
23140.0
purchase_7
45656.0
inkind_7
0.0
nfooditm_7
23.0
food_779467.3
nonfood_744390.4
tobacco_7
728.0
rental_income_7
8903.8totcons_7
129543.3# Median of per capita household consumption excluding outliers> t<-round(apply(d[,c(190:192)],2,+ function(x) weighted.mean(x,d$wt_ind)),1)> data.frame(Mean=t)
Mean
totcons_pc_7 33429.0
food_pc_7
18650.6
nonfood_pc_7 14778.4

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Chapter 8Household income

Estimation method of household income is written in detail in Chapter XI: Income in NSLL III
Statistical Report Vol. One (See Attachments).

Main components of household income are:
Crop income
Non-crop farm income
Valuation of housing consumption of own dwelling
Wage income
Non-farm enterprise income
Transfer income (Remittance income)Non-agriculture rental income
Other income.

The below list shows the components of total household income.

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Box 11.1: Components of household income
Main Component

Items to Add

Items to Deduct

Farm Income+ Value of total crop production (net of share paid - Cultivation costs (seeds, fertilizers, hired labour,to landlord)
irrigation etc.)

Corresponding questionnaire and data file+ Value of by-product production+ Net income from renting farm assets (draft - maintenance expenditures on farm machinery andanimal, tractor, thresher etc.)
buildings+ Value of sales from non-crop farm production - Fodder and other livestock expenditure (veterinary(milk, ghee, eggs etc.)
services)+ Earning from the sale of livestock- Expenditure for the purchase of livestock+ Value of home -produced non-crop consumption+ Total cash and in-kind received from tenants on - Cash rent paid to landlord on land leased-inland leased-out
Wage Income

Section 12, xh31_s13f+ Value of cash and in-kind earning per year in
agriculture (includes daily, piece-rate andpermanent labour)[[33]]+ Value of cash and in-kind earning per year
outside agriculture (includes daily, piece-rate
and permanent labour)+Grossincomefrom
non-agriculture - Wage paid both cash and in-kind
Non-farm
Enterprises Income enterprises/activities during past 12 months- Energy expenditure- Expenditure on raw material- Other operating expenditure- Share of net revenues paid to partners (or kept bypartners)

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Section 14, Part B Income from enterprise
xh32_s14[[34]]

289

Non-agriculture
Rental-income

Transfers+ Income from renting out non agriculture
property+ Income from renting out non agriculture assets

Section 14, xh35_s15c[[37]]+ Remittances (cash and in-kind)

Section 17, Part B Remittances received,
xh39?s17b [[41]]

Owner-occupied
housing+ Imputed rental value of housing which would
had to be paid to purchase housing services

Section 2 Housing, xh02_s02

Other Income+ Interest, dividends, profit earning from sharesand savings/deposit accounts

Section 18 Other income, xh40_s18a+ Social security payment+ Pension income (domestic and foreign)+ Commission fees androyalties, other incomes

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290



Mean and median value of annual household income for households excluding outliers# Exclude 122 households with incoutlier=1 (Real PC income outlier, 1% removed from eachtail> d.save<-d> d<-subset(d,incoutlier==0)> dim(d)[1] 5866 264# Mean of household income variables excluding outliers> t<-round(apply(d[,c(217,213,216,211,209,218,212,219)],2,+ function(x) weighted.mean(x,d$wt_hh)),1)> data.frame(Mean=t)Meanfarminc 45629.9
wageinc 50715.7
entrpinc 36058.0
propinc
1787.0remitinc 40935.6
housinc 20099.7
othinc8278.8totinc203504.7# Mean per capita income excluding outliers> weighted.mean(d$totinc_pc,d$wt_ind)[1] 41889.71# Shares> t<-round(apply(d[,c(220:226)],2,function(x) weighted.mean(x,d$wt_hh)),3)> data.frame(Share=t)
Sharesh_farminc 0.287
sh_wageinc 0.264
sh_entrpinc 0.117
sh_propinc 0.004
sh_remitinc 0.181
sh_housinc 0.123
sh_othinc 0.024> sum(t)[1] 1Remarks:
Average share of each income source to total income is calculated as the mean of the household shares.

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291### Weighted median ###Weighted quantile function of weighted.quantile() and weighted
weighted.median() were defined in Chapter 7: Household Consumption.
> dim(d)[1] 5866

264# Median of household income variables excluding outliers> t<-round(apply(d[,c(217,213,216,211,209,218,212,219)],2,+ function(x) weighted.median(x,d$wt_hh)),1)> data.frame(Median=t)
Medianfarminc 21839.7
wageinc 10800.0
entrpinc0.0
propinc
0.0
remitinc 1200.0
housinc
8903.8othinc0.0
totinc127307.6# Median of per capita income excluding outliers> weighted.median(d$totinc_pc,d$wt_ind)[1] 25092.91

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medianfunctionof292

Chapter 9. Micro data to be provided
Strategy0.

To store household summary file of FINAL_PREF in outfiles[[47]]1. Household data and personal data
The data files will be resampled as follows;
1.1

To order household identifier of “hhid”.

1.2

To select 80% of hhid by systematic sampling method.

1.3

To select records which hhid belongs to the above selected hhid from the data files.

2. Weight
The weight of the file “poverty” will be adjusted by dividing by 0.8.

3.

Resampled data files will be provided in CSV and R format.

Data files# Household summary file> library(readstata13)> d<-read.dta13("FINAL_PREF.dta")> dim(d)[1] 5988 264> d$hhid<-d$xhpsu*100+d$xhnum> outfiles[[47]]<-d> Rnames[47]<-"final_pref"# Number of records and variables of each file
> for(j in 1:47) cat(format(Rnames[j],width=12),":",format(nrow(outfiles[[j]]),width=10),":",+ format(ncol(outfiles[[j]]),width=5),"\n")povertyxh00_s00
xh01_s01
xh02_s02
xh03_s03
xh04_s04
xh05_s05
xh06_s06a
xh07_s06b
xh08_s06c
xh09_s06d
xh10_s07
xh11_s08
xh12_s09a

:
:
:
:
:
:
:
:
:
:
:
:
:
:

5988 :
5988 :
28670 :
5988 :
143712 :28670 :
443112 :155688 :221556 :101796 :83832 :
28670 :
28670 :
15483 :

17
106
24
64
14
29
21
9
8
12
9
50
65
21

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xh13_s09b
xh14_s09c
xh15_s09d
xh16_s10axh17_s10bxh18_s11xh19_s12xh20_s13a1
xh21_s13a2
xh22_s13b
xh23_s13c1
xh24_s13c2
xh25_s13c3
xh26_s13d1
xh27_s13d2
xh28_s13e1
xh29_s13e2
xh30_s13e3
xh31_s13f
xh32_s14xh33_s15axh34_s15bxh35_s15cxh36_s15d
xh37_s16xh38_s17axh39_s17bxh40_s18axh41_s18b
xh42_s18c
xh43_s19
xh44_s20
final_pref

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:
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:

1540 :
28662 :
82815 :
28670 :
32611 :
28670 :
6552 :
13237 :
1906 :
42324 :
5956 :
17724 :
3430 :
28834 :
61800 :
37512 :
33344 :
16672 :
38961 :
2583 :
5711 :
920 :
5988 :
66285 :
6074 :
2244 :
2657 :
41916 :
5988 :
47904 :
5988 :
12795 :
5988 :

21
19
9
20
33
13
39
31
25
16
10
12
18
7
7
15
7
7
13
42
21
20
16
9
27
16
19
15
10
10
34
20
265

Resampling# Select 80% of hhid
> HHID<-outfiles[[1]]$hhid> length(HHID)[1] 5988> HHID<-HHID[order(HHID)]> head(HHID)[1] 100101 100102 100104 100105 100106 100107> Int<-5> (St<-sample(1:5,1))[1] 3> HHID.selected<-HHID[(1:length(HHID))%%Int!=(St-1)]> length(HHID.selected)/length(HHID)[1] 0.7999332> head(HHID.selected)[1] 100101 100104 100105 100106 100107 100110# Resampled at the rate of 80%# outfiles[[1]] to outfiles[[47]]Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

294> Rnames.80<-paste(Rnames,".80",sep="")> Rnames.80[1] "poverty.80""xh00_s00.80""xh01_s01.80""xh02_s02.80""xh03_s03.80"[6] "xh04_s04.80""xh05_s05.80""xh06_s06a.80" "xh07_s06b.80" "xh08_s06c.80"[11] "xh09_s06d.80" "xh10_s07.80""xh11_s08.80""xh12_s09a.80" "xh13_s09b.80"[16] "xh14_s09c.80" "xh15_s09d.80" "xh16_s10a.80" "xh17_s10b.80" "xh18_s11.80"[21] "xh19_s12.80""xh20_s13a1.80" "xh21_s13a2.80" "xh22_s13b.80" "xh23_s13c1.80"[26] "xh24_s13c2.80" "xh25_s13c3.80" "xh26_s13d1.80" "xh27_s13d2.80" "xh28_s13e1.80"[31] "xh29_s13e2.80" "xh30_s13e3.80" "xh31_s13f.80" "xh32_s14.80""xh33_s15a.80"[36] "xh34_s15b.80" "xh35_s15c.80" "xh36_s15d.80" "xh37_s16.80""xh38_s17a.80"[41] "xh39_s17b.80" "xh40_s18a.80" "xh41_s18b.80" "xh42_s18c.80" "xh43_s19.80"[46] "xh44_s20.80""final_pref.80"> outfiles.80<-list()> for(j in 1:47){+ d<-outfiles[[j]]+ outfiles.80[[j]]<-subset(d,is.element(d$hhid,HHID.selected))+ }> length(outfiles.80)[1] 47> for(j in 1:47){+ cat(format(Rnames.80[j],width=14),":",+ format(nrow(outfiles.80[[j]]),width=6),":",+ format(ncol(outfiles.80[[j]]),width=3),":",+ round(nrow(outfiles.80[[j]])/nrow(outfiles[[j]]),3),"\n")+ }# list of resampled file name, number of records, number of variables and# resampling rate
poverty.80
xh00_s00.80
xh01_s01.80
xh02_s02.80
xh03_s03.80
xh04_s04.80
xh05_s05.80
xh06_s06a.80
xh07_s06b.80
xh08_s06c.80
xh09_s06d.80
xh10_s07.80
xh11_s08.80
xh12_s09a.80
xh13_s09b.80
xh14_s09c.80
xh15_s09d.80
xh16_s10a.80
xh17_s10b.80
xh18_s11.80
xh19_s12.80
xh20_s13a1.80
xh21_s13a2.80
xh22_s13b.80:
4790 : 17 : 0.8:4790 : 106 : 0.8: 22927 : 24 : 0.8:4790 : 64 : 0.8: 114960 : 14 : 0.8: 22927 : 29 : 0.8: 354460 : 21 : 0.8: 124540 :9 : 0.8: 177230 :8 : 0.8: 81430 : 12 : 0.8: 67060 :9 : 0.8: 22927 : 50 : 0.8: 22927 : 65 : 0.8: 12304 : 21 : 0.795:
1227 : 21 : 0.797: 22919 : 19 : 0.8: 66045 :9 : 0.798: 22927 : 20 : 0.8: 26069 : 33 : 0.799: 22927 : 13 : 0.8:
5219 : 39 : 0.797: 10569 : 31 : 0.798:
1533 : 25 : 0.804: 33993 : 16 : 0.803

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xh23_s13c1.80
xh24_s13c2.80
xh25_s13c3.80
xh26_s13d1.80
xh27_s13d2.80
xh28_s13e1.80
xh29_s13e2.80xh30_s13e3.80
xh31_s13f.80
xh32_s14.80
xh33_s15a.80
xh34_s15b.80
xh35_s15c.80
xh36_s15d.80
xh37_s16.80
xh38_s17a.80
xh39_s17b.80
xh40_s18a.80
xh41_s18b.80
xh42_s18c.80
xh43_s19.80
xh44_s20.80
final_pref.80

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4743 : 10 : 0.796
14202 : 12 : 0.801
2748 : 18 : 0.801
23093 :
7 : 0.801
49485 :
7 : 0.801
30168 : 15 : 0.804
26816 :
7 : 0.804
13408 :
7 : 0.804
31239 : 13 : 0.8022078 : 42 : 0.804
4570 : 21 : 0.8
764 : 20 : 0.83
4790 : 16 : 0.8
52980 :
9 : 0.799
4846 : 27 : 0.798
1750 : 16 : 0.78
2090 : 19 : 0.787
33530 : 15 : 0.84790 : 10 : 0.838320 : 10 : 0.84790 : 34 : 0.8
10288 : 20 : 0.804
4790 : 265 : 0.8# Adjusted weight in poverty.80> d<-outfiles.80[[1]]> colnames(d)[1] "xhpsu""xhnum""hhsize""belt""region"[7] "ad""wt_ind""wt_hh""fpline_7""pline_7"[13] "poor""totcons_pc_7" "food_pc_7""quintle_7""hhid"> d$HWT<-d$wt_hh/0.8> d$PWT<-d$wt_ind/0.8> colnames(d)[1] "xhpsu""xhnum""hhsize""belt""region"[7] "ad""wt_ind""wt_hh""fpline_7""pline_7"[13] "poor""totcons_pc_7" "food_pc_7""quintle_7""hhid"[19] "PWT"> head(d[c(1:3,8,9,17,18,19)])
xhpsu xhnum hhsize
wt_ind
wt_hh
hhid
HWT
PWT
1 1001
1
3 879.1722 293.0574 100101 366.3218 1098.9653
3 10014
2 586.1149 293.0574 100104 366.3218 732.6436
4 10015
2 586.1149 293.0574 100105 366.3218 732.6436
5 100165 1465.2871 293.0574 100106 366.3218 1831.6089
6 10017
4 1172.2297 293.0574 100107 366.3218 1465.2872
8 100110
3 879.1722 293.0574 100110 366.3218 1098.9653> sum(d$HWT)[1] 5777067> sum(outfiles[[1]]$wt_hh)[1] 5776252> outfiles.80[[1]]<-d

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"HWT"

296

> d<-outfiles.80[[47]]> d$HWT<-d$wt_hh/0.8> d$PWT<-d$wt_ind/0.8> outfiles.80[[47]]<-d> dim(d)[1] 4790 267> save(outfiles.80,file="Resampled_80%.RData")# Resampled data set in R format> Rnames.80[1] "poverty.80""xh00_s00.80""xh01_s01.80""xh02_s02.80""xh03_s03.80"[6] "xh04_s04.80""xh05_s05.80""xh06_s06a.80" "xh07_s06b.80" "xh08_s06c.80"[11] "xh09_s06d.80" "xh10_s07.80""xh11_s08.80""xh12_s09a.80" "xh13_s09b.80"[16] "xh14_s09c.80" "xh15_s09d.80" "xh16_s10a.80" "xh17_s10b.80" "xh18_s11.80"[21] "xh19_s12.80""xh20_s13a1.80" "xh21_s13a2.80" "xh22_s13b.80" "xh23_s13c1.80"[26] "xh24_s13c2.80" "xh25_s13c3.80" "xh26_s13d1.80" "xh27_s13d2.80" "xh28_s13e1.80"[31] "xh29_s13e2.80" "xh30_s13e3.80" "xh31_s13f.80" "xh32_s14.80""xh33_s15a.80"[36] "xh34_s15b.80" "xh35_s15c.80" "xh36_s15d.80" "xh37_s16.80""xh38_s17a.80"[41] "xh39_s17b.80" "xh40_s18a.80" "xh41_s18b.80" "xh42_s18c.80" "xh43_s19.80"[46] "xh44_s20.80""final_pref.80"# Converted to CSV> CSVnames<-gsub("\\.","_",Rnames.80)> CSVnames<-paste(CSVnames,".csv",sep="")> CSVnames[1] "poverty_80.csv""xh00_s00_80.csv""xh01_s01_80.csv""xh02_s02_80.csv"[5] "xh03_s03_80.csv""xh04_s04_80.csv""xh05_s05_80.csv""xh06_s06a_80.csv"[9] "xh07_s06b_80.csv" "xh08_s06c_80.csv" "xh09_s06d_80.csv" "xh10_s07_80.csv"[13] "xh11_s08_80.csv""xh12_s09a_80.csv" "xh13_s09b_80.csv" "xh14_s09c_80.csv"[17] "xh15_s09d_80.csv" "xh16_s10a_80.csv" "xh17_s10b_80.csv" "xh18_s11_80.csv"[21] "xh19_s12_80.csv""xh20_s13a1_80.csv" "xh21_s13a2_80.csv" "xh22_s13b_80.csv"[25] "xh23_s13c1_80.csv" "xh24_s13c2_80.csv" "xh25_s13c3_80.csv" "xh26_s13d1_80.csv"[29] "xh27_s13d2_80.csv" "xh28_s13e1_80.csv" "xh29_s13e2_80.csv" "xh30_s13e3_80.csv"[33] "xh31_s13f_80.csv" "xh32_s14_80.csv""xh33_s15a_80.csv" "xh34_s15b_80.csv"[37] "xh35_s15c_80.csv" "xh36_s15d_80.csv" "xh37_s16_80.csv""xh38_s17a_80.csv"[41] "xh39_s17b_80.csv" "xh40_s18a_80.csv" "xh41_s18b_80.csv" "xh42_s18c_80.csv"[45] "xh43_s19_80.csv""xh44_s20_80.csv""final_pref_80.csv"> for(j in 1:47){+ cmd<-paste("write.csv(outfiles.80[[",j,"]],'",CSVnames[j],"',row.names=F)",sep="")+ eval(parse(text=cmd))+ }> list.files()[1] "final_pref_80.csv" "poverty_80.csv""xh00_s00_80.csv"[4] "xh01_s01_80.csv""xh02_s02_80.csv""xh03_s03_80.csv"[7] "xh04_s04_80.csv""xh05_s05_80.csv""xh06_s06a_80.csv"[10] "xh07_s06b_80.csv" "xh08_s06c_80.csv" "xh09_s06d_80.csv"[13] "xh10_s07_80.csv""xh11_s08_80.csv""xh12_s09a_80.csv"[16] "xh13_s09b_80.csv" "xh14_s09c_80.csv" "xh15_s09d_80.csv"[19] "xh16_s10a_80.csv" "xh17_s10b_80.csv" "xh18_s11_80.csv"[22] "xh19_s12_80.csv""xh20_s13a1_80.csv" "xh21_s13a2_80.csv"[25] "xh22_s13b_80.csv" "xh23_s13c1_80.csv" "xh24_s13c2_80.csv"

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297[28] "xh25_s13c3_80.csv" "xh26_s13d1_80.csv" "xh27_s13d2_80.csv"[31] "xh28_s13e1_80.csv" "xh29_s13e2_80.csv" "xh30_s13e3_80.csv"[34] "xh31_s13f_80.csv" "xh32_s14_80.csv""xh33_s15a_80.csv"[37] "xh34_s15b_80.csv" "xh35_s15c_80.csv" "xh36_s15d_80.csv"[40] "xh37_s16_80.csv""xh38_s17a_80.csv" "xh39_s17b_80.csv"[43] "xh40_s18a_80.csv" "xh41_s18b_80.csv" "xh42_s18c_80.csv"[46] "xh43_s19_80.csv""xh44_s20_80.csv"

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NEPAL SURVEY OF LIVING CONDITIONS
Sir/Madam:
The Central Bureau of Statistics Office in cooperation with ………….. Is undertaking a household
survey to collect …

Central Bureau of Statistics (CBS)

Nepal Living Standards Survey - ROUND III
2010

Your cooperation is earnestly solicited
Very truly yours,

HOUSEHOLD QUESTIONNAIRE
HOUSEHOLD ID
PSU NUMBER

Mr. Uttam Narayan Malla

HH

Director General
Central Bureau of Statistics
Thapatali, Kathmandu, Nepal

All personal information asked within this questionnaire will be kept confidencial
according to Statistical Act. 2015. This information will be used only for statistical
purposes.
HEAD OF THE
HOUSEHOLD:

Certification:
I hereby certify that the data gathered in this questionnaire were obtained/reviewed by me personallyand in accordance with instructions

TELEPHONE:
Signature of Enumerator

Date accomplishedSignature of Supervisor

Date reviewed

ADDRESS:LOCALITY:

GPS COORDINATES\WARD/SUB-WARD:

VDC / MUNICIPALITY:

LATITUDE (NORTH).

LONGITUDE (EAST).

DISTRICT:TEAM NUMBER:

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Survey Information
RESULT OF THE VISIT
INTERVIEWER:

No.

A.

1 ► HOUSEHOLD DATA
2

YES
NO

DATE OF INTERVIEWER'S VISITS
MONTH
YEAR
DAY

DWELLING NOT FOUND
HOUSEHOLD NOT FOUND
REFUSAL

2
3

CODE
SUPERVISORDATE OFSUPERVISION

DAY

MONTH

1
2
3

THIS HOUSEHOLD WILL BE REPLACED BY HOUSEHOLDNUMBER:D.

THIS HOUSEHOLD WILL BE REPLACED BY
HOUSEHOLD NUMBER:

YEAR

HOUSEHOLD DATADATA ENTRY
1ST ROUND OF DATA ENTRYDATE OF ENTRY
DATA ENTRY
OPERATOR'S CODE
DAY
MONTH
YEAR

E. RELIGION OF HEADC.

REASON NOT INTERVIEWED

B.

1

SUPERVISOR: PLEASE FILL IF HOUSEHOLD TO BE REPLACED, OR IF
THIS HOUSEHOLD IS A REPLACEMENT HOUSEHOLD:

HOUSEHOLD INTERVIEWED?

DATA ENTRY
OPERATOR'S CODE

CORRECTION OF DATA ENTRYDATE OF CORRECTION
DAY
MONTH
YEAR

F. LANGUAGE USED IN THE HOUSEHOLDREMARKS:REMARKS:G. INTERPRETER:
YES

1

NO

2

VERIFIED BY SUPERVISOR?
DATE OF REVIEWUSE RELIGION CODES PROVIDED OF HEAD: AT THE BACK OF
THE QUESTIONNAIRE

SUPERVISOR'S CODE

DAY

MONTH

YEAR

SIGNATURE:
SUPERVISOR'S NAME:

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

YES

1

NO

2

300

Table of Contents
Page number
SectionSectionSectionSectionSectionSectionSectionSectionSectionSectionSectionSectionSection1 Household Roster
2 HousingPart A: Type of dwelling
Part B: Housing expenses
Part C: Utilities and amenities
3 Access to facilities
4 Migration
5 Food expenses and home production
6 Non-food expenditures and inventory of durable goods
Part A: Frequent non-food expenditures
Part B: Infrequent non-food expenditures
Part C: Inventory of durable goods
Part D: Own account production of goods
7 Education
Part A: Literacy
Part B: Past enrollment
Part C: Current enrollment
8 Health
Part A: Chronic illnesses
Part B: Illnesses or injuries
Part C: HIV/AIDS Knowledge
Part D: Immunizations
9 Marriage and maternity history
Part A: Maternity history
Part B: Pre and post-natal carePart C: Family planningPart D: Household decisions (Part I)
10 Jobs and time use
Part A: Time use
Part B: Jobs during the past 12 months
11 Unemployment and underemployment
12 Wage jobs
13 Farming and livestock
Part A1: Landholding - land owned
Part A2: Landholding - land sharecropped/rented/mortgaged -inPart A3: Landholding - increase-decrease in holdings
Part B: Production and uses

4
6
7
7
9
10
12
17
18
19
20
21
22
23
25
26
28
29
30
33
35
36
37
38
40
41

Page number
SectionSectionSectionSectionSectionSectionSectionSection

13 Farming and livestock (cont.)
Part C1: Expenditures on seeds and young plants
Part C2: Expenditures on fertilizers and insecticides
Part C3: Expenditures on agricultural inputs - hiring labour
Part D: Agriculture-earnings/expenditures
Part E: Livestock – ownership and related expenses
Part F: Ownership of farming assets and extension services
14 Non-agriculture enterprises/activities
Part A: General characteristics
Part B: Income from enterprises
15 Credit and savings
Part A: Borrowing and outstanding loans
Part B: Lending and outstanding loans
Part C: Other assets
Part D: Household decisions (Part II)
16 Absentees information
17 Other remittancesPart A: Remittances and transfer-income sent
Part B: Remittances and transfer income received
18 Transfers, social assistance and Other Income
19 Adequacy of consumption and government facilities
20 Anthropometrics

49
49
50
51
52
53
54
56
57
59
61
62
63
65
66
67
69
71

TABLE A

DISTRICT CODES

72

TABLE BMONTH CODES74

TABLE CLANGUAGE CODES74

TABLE DRELIGION CODES74

TABLE EEDUCATION CODES

75

TABLE F

QUANTITY CODES

75

TABLE GETHNICITY CODES76

TABLE H

CROP CODES76

TABLE I

OCCUPATION NSCO CODES

77

TABLE J

INDUSTRY NSIC CODES

79

44
46
47
48

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Section 1

Household Roster(1.04)FOR INDIVIDUALS 10 YRS AND ABOVE(1.05)IDENTIFICATION CODE

What is the relationship of ..[NAME].. to the head of household? Where was ..[NAME].. born?

01

HEAD

01

HUSBAND/WIFE

02

SON/DAUGHTERGRANDCHILD

03 Was it then an urban or rural
04 area?

FATHER/MOTHER

05(1.06)(1.07)

What is the present marital status of ..[NAME]..?

COPY THE ID
CODE OF THE
SPOUSE

1

NEVER MARRIEDSINGLE MARRIED

2

BROTHER/SISTER

06

POLY MARRIED

3

07

URBAN

1

RE-MARRIEDSON/DAUGHTER-IN-LAW
BROTHER/SISTER-IN-LAW

08
09

RURAL

2

WIDOW / WIDOWER

45►(1.08)

DIVORCED

6►(1.08)FATHER/MOTHER-IN-LAW

107►(1.08)OTHER FAMILY RELATIVE

11

SEE DISTRICTS IN TABLE SEPARATED"A"SERVANT/SERVANT’S RELATIVES

12

TENANT/TENANT’S RELATIVE

13

OTHER PERSON NOT RELATED

14

SEE CASTE/ETHNICITYCODES IN TABLE "G"►(1.08)NEPHEW/NIECE(1.10)(1.09)(1.08)
What's ..[NAME].. Caste/Ethnic During the past ACCORDING TO
12 months, how CRITERIA, IS
group?
many months did ..[NAME].. A..[NAME].. live
MEMBER OF THE
here?
HOUSEHOLD?

IF NOT A
HOUSEHOLD
MEMBER WRITE"98"

WRITE 12 IF
ALWAYSPRESENT, OR IF
AWAY LESS YES
THAN A MONTHNO► NEXT PERSONSPOUSE'S
DISTRICTID CODE

U/R

12

DESCRIPTIONCODEMONTHS

01

02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section1 Household Roster
MOTHERFATHER(1.12)(1.11)IDENTIFICATION CODE

Does ..[NAME].. 's father COPY THE ID
live in this household? CODE OF THE
FATHER(1.13)
What was the highest classthat the father of ..[NAME]..
completed?

(1.14)(1.15)

Does ..[NAME].. 'smother live in thishousehold?

COPY THE ID
CODE OF THE
MOTHERWRITE "98" IF DON'T
KNOWWRITE "98" IF DON'T
KNOW

►
YES

1

NO

2
►

(1.13)

DIED►(1.14)►YES

SEE EDUCATION CODESNOIN TABLE "E"3(1.13)

1

FATHER'S►►EDUCATION CODE

NEXT PERSON

2(1.16)DIED
ID CODE(1.16)
What was the highest classthat the mother of…[NAME]… completed?

SEE EDUCATION CODESIN TABLE "E"3(1.16)

MOTHER'S
ID CODEEDUCATION CODE

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section 2 Housing
2A.- COPY THE ID OF RESPONDENT FROM SECTION 1:

Part A: Type of dwelling(2.01) Is this dwelling unit occupied by your household only?

(2.06)

MAIN MATERIAL ROOF IS MADE OF

YES

1

STRAW/ THATCH

1

NO

2

EARTH/MUD

2(2.02) How many rooms does your household occupy?

WOOD/ PLANKS

3GALVANIZED IRON

4

a)

TOTAL

e) Living / dining rooms

CONCRETE/CEMENT

5b)Kitchenf) Business

TILES/SLATE

6c)Toilet / bathroomg) Mixed use

OTHER

7

d)

Bedroomsh) Other(2.07)(2.03) Is there a kitchen garden?

THE WINDOWS ARE FITTED (CHECK THE FIRST THAT APPLIES)NO WINDOWS/ NO COVERING

1

YES

1

SHUTTERS

2NO

2SCREENS/GLASS

3

OTHER

4

(2.04) MAIN CONSTRUCTION MATERIAL OF OUTSIDE WALLSCEMENT BONDED BRICKS/STONES1

MUD BONDED BRICKS/STONES

2

WOOD

3

ROPANI

1

BAMBOO\LEAVES

4

BIGHA

2

UNBAKED BRICKS

5OTHER MATERIAL

6NO OUTSIDE WALLS

7(2.08)HOW BIG IS THE HOUSING PLOT?
CODE(2.09)

R/B

A/K

P/D

HOW BIG IS THE INSIDE OF THE DWELLING?
SQUARE FEET

(2.05) FOUNDATION OF DWELLINGPILLAR BONDED

1

CEMENT BOUNDED

2

MOD BONDED

3

WOODEN PILLAR

4

OTHER

5

(2.10)

Which year was the house that you are living built?
YEAR:

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Section2 HousingPart B: Housing expenses

Part C: Utilities and amenities(2.11) Is this dwelling yours?

(2.19) Where does your drinking water come from?

YES

1

NO

2

(2.16)►(2.12) If you wanted to buy a dwelling just like this today, how muchmoney would you have to pay?

RUPEESINCLUDE VALUE OF HOUSING PLOT
(2.13) If someone wanted to rent this dwelling today, how much money
would they have to pay each month?

1

NO

2

1

COVERED WELL

2►

(2.22)

HAND PUMP/ TUBEWELL

3►(2.22)

OPEN WELL

4►(2.22)SPRING WATER

5►(2.22)

RIVER

6►(2.22)OTHER SOURCE

7►(2.22)(2.20) Do you have water piped into your house?
RUPEES(2.14) Did you rent out part of this dwelling unit?
YES

PIPED WATER SUPPLY

YES(2.21)

1

NO
2
How many hours per day does your household have tap water?
HRS/WEEKPART C►(2.22) How much did you pay for water over the last 12 months?
(2.15) How much do you receive as rent per month?

RUPEESRUPEES► PART C(EXCLUDE WATER USED FOR IRRIGATION)WRITE ZERO IF NOTHING(2.23) Are you connected to a sanitary system for liquid wastes?

(2.16) What is your present occupancy status?
RENTERPROVIDED FREE OF CHARGEBY RELATIVES, LANDLORD OR
EMPLOYER

1►
2

SQUATTING
OTHER(2.18)UNDERGROUND DRAINS

1

SOAK PIT

3

OPEN DRAINS

2

NO

4

(2.24) How does your household dispose of its garbage mainly?
COLLECTED BY GARBAGE TRUCK1PRIVATE/COMMUNITY COLLECTOR

2

3

DUMPED3►(2.26)

4

BURNED/BURIED

4►(2.26)DUMPED AND USED FOR FERTILIZER

5►(2.26)(2.17) If someone wanted to rent this dwelling (only the unit occupied by
the household) today, how much money would they have to pay
each month?

RUPEES

OTHER
6(2.25) How much do you pay for garbage disposal over the last 12
months?

► PART C

RUPEES

WRITE ZERO IF NOTHING(2.26) What type of toilet is used by your household?

(2.18) What is the rent per month? (cash plus value of in-kindpayments)

HOUSEHOLD FLUSH (CONNECTED TO MUNICIPAL SEWER)RUPEES

1

HOUSEHOLD FLUSH (CONNECTED TO SEPTIC TANK)

2

HOUSEHOLD NON-FLUSH

3

COMMUNAL LATRINE

4NO TOILET

5

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Section2 HousingPart C: Utilities and amenities(2.27)Part C: Utilities and amenities

What is the main source of lighting for your dwelling?

(2.37) How many bharis/carts/kg of firewood did you collect during the past 12 months?

ELECTRICITY

1

BHARI

1

SOLAR

2►

(2.31)KEROSENE

4►(2.31)CART

2

BIOGAS3►(2.31)

OTHER

5►(2.31)KILOGRAM

3

UNIT

INTERVIEWER, COMPLETE:(2.28)(2.29)

Do you have a joint or individual electric meter?
INDIVIDUAL

1

JOINT

2

NUMBER
1 BHARI / CART =KG(2.38) How long does it take to collect one bhari/cart/kg of firewood?

NO METER

3

TIME TAKEN ROUND TRIPHOURS

MINUTESHow much did you spend on electricity over the past 12 months?
WRITE ZERO IF NOTHINGRUPEES(2.39) Where did you mailny collect the firewood?

(2.30)(2.31)In the past 7 days, how many hours (total) did your household
NOT have electricity?

HRS

Which of the following facilities are there in your dwelling unit?
YES1a) Telephonec) Cable T.V.

NO

2b) Mobile phoned) Email/Internet

OWN LAND1►

COMMUNITY MANAGED FOREST

2

GOVERNMENT FOREST

3

OTHER

4

(2.41)(2.40) How much did you pay for each bhari/cart/kg?
WRITE ZERO IF NOTHING

IF ALL ANSWERS ARE NO ►(2.32)(2.33)RUPEES(2.33)How much did you pay for using those facilities listed in (2.31)
over the last 12 months?

(2.41) Did you collect fodder for your livestock over the past 12 months?
RUPEES

What kind of fuel is most often used by your household for cooking?

YES

1

NO

2

►

OWN LAND

1

FIREWOOD

1CYLINDER GAS

4COMMUNITY MANAGED FOREST

2

DUNG
LEAVES/ RUBBISH/
STRAW/THATCH2

KEROSENE

5

GOVERNMENT FOREST

3

BIO-GAS

6

OTHER

7
(2.43)

What type of stove does your household mainly use for cooking?

1

1

KEROSENE STOVE

4

CRUSHED SALT

2

MUD

2

GAS STOVE

5PACKED SALT WITH TWO CHILD LOGO

3

SMOKELESS OVEN

3

OTHER

6

YES

1

NO

2

►

(2.41)(2.36) Did your household collect any firewood in the past 12 months?
YES

1

NO

2

►

4

What type of salt do you use for cooking?
LARGE CRYSTAL SALTOPEN FIREPLACE(2.35) Did your household use any firewood over the past 12 months?

OTHER

Part D: Iodized salt

3(2.34)

NEXT SECTION(2.42) Where did you mainly collect the fodder?

OTHERS (SPECIFY)

4(2.44) INTERVIEWER: USE YOUR SALT TEST KIT TO ASSES THE LEVEL OF IODINE
IN SALT USED IN THIS HOUSEHOLD
0 PPM

1

BELOW 15 PPM

2ABOVE 15 PPM

3(2.41)

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Section3A

3

Access to facilities

COPY THE ID OF RESPONDENT FROM SECTION 1:(3.01)

FACILITY CODE

How do you OR would you normally travel to the closest..[FACILITY]..?
FOOT (WITHOUT LOAD)

2

MOTORCYCLE/TAMPOO

3

CAR/BUS4

MIXED (FOOT+VEHICLE)

56 ► (3.04)
7 ► NEXTPRESENT NEXT TO HH
FACILITY DESCRIPTION101

ECD center

102

Primary School

103

Secondary school

104

Higher Secondary School

105

Health post/Sub-health post

106

Public Hospital/PHC

107

Clinic/Hospital Private

108

Bus Stop

109

Paved Road

110

Dirt Road, vehicle passable

111

Dirt Road, vehicle impassable

112

Local Shop/Shops (shopping centers)

113

Haat Bazaar

114

Market Center

115

Agriclture Center

116

Sajha (Cooperatives)

117

Bank

118

Source of Drinking Water in rainy season

119

Source of Drinking Water in dry season

120

Post Office121

Telephone Booth

122

Police station

123

Internet access

124

Community library

1

BICYCLE/RICKSHAWNOT APPLICABLE(3.02)(3.03)How long does it take to get from your How far is the closest..[FACILITY].. from thishouse to the closest ..[FACILITY]..?
household?

(3.04)Have the members of
this household used..[FACILITY].. during
the past 12 months?

YES

ONE WAY ONLY

NO

1
2

(3.05)How often do members of thishousehold use ..[FACILITY].. during thepast 12 months?
DAILY
WEEKLYMONTHLYRARELY1
2
3
4► NEXT FACILITY
DAYS

HOURS

MINUTESKM

METERS

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Section 4 Migration

ALL HOUSEHOLD MEMBERS 5 YEARS AND OLDERFIRST IN-MIGRATION TO THIS PLACEIDENTIFICATION CODE

(4.01)ID CODE OFRESPONDENT(4.02)(4.03)(4.04)
When didHas
I want to ask you about the first..[NAME]..
..[NAME]..
time ..[NAME].. moved to thismove to thisalways lived location. Where did ..[NAME]..
WRITE ID CODE in thisplace?
move from?
FROM
location sincebirth?
HOUSEHOLDROSTER OFPERSONPROVIDING THIS
Was it then an urban or rural area?
INFORMATION
YES
1
URBAN
1
►
RURAL
2
(4.07)NO

2
SEE DISTRICT CODES IN TABLE "A"RESPONDENT'S
ID CODE

DISTRICT / COUNTRY

U/R

YEAR

OUT-MIGRATION OVER THE PAST 5 YEARS(4.07)(4.06)(4.08)(4.09)(4.10)
What primary activity did ..[NAME].. Has ..[NAME].. How many times When was the last time When was the last time..[NAME].. left to live..[NAME].. came back to
do when arrived at this place for the lived outside has ..[NAME]..
this location for lived outside this outside this location for this household after
first time?
01
more than 2 location for more a period of 2 continuous living outside for a02
continuous
period of 2 continuousthan 2 continuous months or more?
03
months over months over themonths or more?
the past 5
past 5 years?
04
years ?
05

(4.05)Why did ..[NAME].. leave the previous
place and move to this place?
MARRIAGEFOLLOW THE FAMILYOTHER FAMILY REASONSEDUCATION
TRAININGLOOKING FOR WORKSTART NEW JOB

06
07

START NEW BUSINESS08

JOB TRANSFER

09

CONFLICT

10NATURAL DISASTER

11

EASIER LIFE STYLE

12

OTHER

13

SEE OCCUPATION CODES INTABLE "I"OCCUPATIONDESCRIPTION

NSCO
CODE

YES

1

NO

2
► NEXTPERSONIF STILL AWAY WRITE"98" IN MONTH AND
YEAR

N OF TIMES

YEAR

MONTH

YEAR

MONTH

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section4

Migration

ALL HOUSEHOLD MEMBERS 5 YEARS AND OLDER

OUT-MIGRATION OVER THE PAST 5 YEARSIDENTIFICATION CODE

(4.11)(4.12)
Where was the place where What was the main reason for ..[NAME]..
..[NAME].. lived this last
to go to that place?
time?
(LAST TIME)

Was it an urban or rural
area?

01

FOLLOW THE FAMILYOTHER FAMILY REASONS

02
03

EDUCATION

04

TRAINING

05

RELATIVES1FRIENDS,NEIGHBORS

2

1 START NEW JOB

06
07

RURAL

2 START NEW BUSINESS08

LOOKING FOR WORKJOB TRANSFERSEE DISTRICT CODES IN CONFLICT
DISTRICT /COUNTRY(4.15)
Who helped…[NAME]… to findthe last job or to starta business in thatplace where he/shelast moved?

MARRIAGE

URBANTABLE "A"

(4.13)(4.14)
What primary activity did …[NAME]… do just Did..[NAME]..
before leaving this household last time?
do any job
there?

09
10

NATURAL DISASTER

11

EASIER LIFE STYLE12

U/R OTHER

13

1 OTHERNO
2 NOBODY► (4.18) HELPEDSEE OCCUPATION CODES IN TABLE "I"
YES

N/AOCCUPATION DESCRIPTION

NSCO
CODE

(4.16)(4.17)
What type of work What primary activity did ..[NAME].. whenarrived to that last place?
did ..[NAME]..
when he/she
arrived to that lastplace?

WAGE JOB

1

3
4 SELF2EMPLOYMENT
5

SEE OCCUPATION CODES IN TABLE "I"(4.18)
What was the main reason why..[NAME].. came back to thishousehold last time?

FAMILY REASONS

1WORK FINISHEDDISSATISFIED WITH WORK

2
3

START NEW JOB

4START NEW BUSINESS

5

JOB TRANSFER
CONFLICT

67NATURAL DISASTER

89

EASIER LIFE STYLE10NSCO OTHER
OCCUPATION DESCRIPTION

CODE

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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5 Food expenses and home production
PART A: TYPICAL MONTH
FOOD PURCHASES FOR HH CONSUMPTION(5.05)(5.06)In a typical month
How many
during which you
months in thepast 12 months purchased
did you purchase ..[FOOD]. howmuch did you..[FOOD].. ?
purchase?

(3) ONLY ONCE YOU HAVE FINISHED WITHQUESTIONS (5.02) TO (5.08) FOR ALL ITEMSCONSUMED, ASK QUESTION (5.09) TO (5.12)

IF NONE, WRITE
ZERO AND ►IF NONE, WRITEQUANTITY CODESZERO AND ►IN TABLE "F"(5.08)ITEM DESCRIPTIONMONTHS

INSTRUCTIONS:(1) ASK QUESTION (5.01) ABOUT EACH ITEM IN THE
LIST FIRST. PUT A TICK "9" IN THE APPROPRIATE
BOX (YES OR NO)(2) IF THE ANSWER TO Q. (5.01) IS YES, ASK Q.
(5.02) TO (5-08)

010

HOME PRODUCTION(5.02)(5.03)(5.04)How much would
How many months In a typical monthin the past 12
during which you ate your household
months did you..[FOOD].., how much have to spend in
consume ..[FOOD].. did your household the market to buy
that you grew orconsume ..[FOOD]..? this quantity of..[FOOD].. (i.e. theproduced yourself?
amount consumed
in a typicalmonth)?

NO YES(5.05)QUANTITY CODESIN TABLE "F"
QUANTITYUNIT

RUPEESMONTHSQUANTITY UNIT

(5.07)How muchwould younormally
have tospend in total
to buy thisquantity?

IN-KIND

ASK (5.09) FOR ALL ITEMS FIRST(5.08)
What is the total
value of..[FOOD]..
consumed thatyou received inkind over the past
12 months (wages
for work, etc.)?

(5.12)(5.09)(5.10)(5.11)How many How much ..[FOOD].. What is theMAINdays has the did your household value of this SOURCE
household consume during the quantity of..[FOOD]..
consumed past 7 days?
consumed..[FOOD]..
during the pastduring thepast 7 days?
7 days?

IF NONE, WRITEZERO
RUPEESRUPEESIF NONE,
WRITE
ZERO AND QUANTITY CODESIN TABLE "F"
►NEXT
DAYS

QUANTITYUNIT

RUPEES

3 IN-KIND

CODE(5.01)Have you consumed ..[FOOD].. during the past 12
months?

PART B: PAST 7 DAYS

2 FOOD PURCHASES(5.00) ID CODE RESPONDENT:

1 HOME PRODUCTION

Section

1 GRAINS AND CEREALS:

011 Fine rice
012 Coarse rice
013 Beaten, flattened rice
014 Maize
015 Maize flour
016 Wheat flour
017 Millet
018 Other grains/cereals (Buckwheat, Barley,
Sorghum, etc.)020

2 PULSES AND LENTILS:

021 Black Gram (Mas )022 Lentil (Musuro )023 Red Gram
024 Horse Gram (Chana )025 Other pulses (Green Gram, Masyang , Vetch,
Khesari , etc.)
026 Beans
027 Other beans
900

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5 Food expenses and home production
PART A: TYPICAL MONTH(5.05)(5.06)In a typical month
How many
during which you
months in thepast 12 months purchased
did you purchase ..[FOOD]. howmuch did you..[FOOD].. ?
purchase?

(3) ONLY ONCE YOU HAVE FINISHED WITHQUESTIONS (5.02) TO (5.08) FOR ALL ITEMSCONSUMED, ASK QUESTION (5.09) TO (5.12)

IF NONE, WRITE
ZERO AND ►IF NONE, WRITEQUANTITY CODESZERO AND ►IN TABLE "F"(5.08)ITEM DESCRIPTIONMONTHS

INSTRUCTIONS:(1) ASK QUESTION (5.01) ABOUT EACH ITEM IN THE
LIST FIRST. PUT A TICK "9" IN THE APPROPRIATE
BOX (YES OR NO)(2) IF THE ANSWER TO Q. (5.01) IS YES, ASK Q.
(5.02) TO (5-08)030

FOOD PURCHASES FOR HH CONSUMPTIONHOME PRODUCTION(5.02)(5.04)(5.03)How much would
How many months In a typical monthin the past 12
during which you ate your household
months did you..[FOOD].., how much have to spend in
consume ..[FOOD].. did your household the market to buy
that you grew orconsume ..[FOOD]..? this quantity of..[FOOD].. (i.e. theproduced yourself?
amount consumed
in a typicalmonth)?

NO YES(5.05)QUANTITY CODESIN TABLE "F"
QUANTITYUNIT

RUPEESMONTHSQUANTITY UNIT

(5.07)How muchwould younormally
have tospend in total
to buy thisquantity?

IN-KIND

ASK (5.09) FOR ALL ITEMS FIRST(5.08)
What is the total
value of..[FOOD]..
consumed thatyou received inkind over the past
12 months (wages
for work, etc.)?

(5.10)(5.11)(5.12)(5.09)How many How much ..[FOOD].. What is theMAINdays has the did your household value of this SOURCE
household consume during the quantity of..[FOOD]..
consumed past 7 days?
consumed..[FOOD]..
during the pastduring thepast 7 days?
7 days?

IF NONE, WRITEZERO
RUPEESRUPEESIF NONE,
WRITE
ZERO AND QUANTITY CODESIN TABLE "F"
►NEXT
DAYS

QUANTITYUNIT

RUPEES

3 IN-KIND

CODE(5.01)Have you consumed ..[FOOD].. during the past 12
months?

PART B: PAST 7 DAYS

2 FOOD PURCHASES(5.00) ID CODE RESPONDENT:

1 HOME PRODUCTION

Section3 EGGS AND MILK PRODUCTS

031 Eggs
032 Milk
033 Condensed milk
034 Baby milk/Powder milk
035 Curd/Whey036 Other milk products (Cheese, Paneer , etc.)
040

4 COOKING OILS

041 Ghee042 Vegetable oil
043 Mustard oil
044 Other oil (Soya, Sunflower, Corn, etc.)
050

5 VEGETABLES:

051 Potatoes
059 Colocassia
052 Onions
053 Cauliflower/Cabbage
901

PAGE TOTAL

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5 Food expenses and home production
PART A: TYPICAL MONTH
FOOD PURCHASES FOR HH CONSUMPTIONHOME PRODUCTION(5.02)(5.04)(5.03)How much would
How many months In a typical monthin the past 12
during which you ate your household
months did you..[FOOD].., how much have to spend in
consume ..[FOOD].. did your household the market to buy
that you grew orconsume ..[FOOD]..? this quantity of..[FOOD].. (i.e. theproduced yourself?
amount consumed
in a typicalmonth)?

(5.05)(5.06)In a typical month
How many
during which you
months in thepast 12 months purchased
did you purchase ..[FOOD]. howmuch did you..[FOOD].. ?
purchase?

(3) ONLY ONCE YOU HAVE FINISHED WITHQUESTIONS (5.02) TO (5.08) FOR ALL ITEMSCONSUMED, ASK QUESTION (5.09) TO (5.12)

IF NONE, WRITE
ZERO AND ►IF NONE, WRITEQUANTITY CODESZERO AND ►IN TABLE "F"(5.08)ITEM DESCRIPTIONMONTHS

INSTRUCTIONS:(1) ASK QUESTION (5.01) ABOUT EACH ITEM IN THE
LIST FIRST. PUT A TICK "9" IN THE APPROPRIATE
BOX (YES OR NO)(2) IF THE ANSWER TO Q. (5.01) IS YES, ASK Q.
(5.02) TO (5-08)

NO YES(5.05)QUANTITY CODESIN TABLE "F"
QUANTITYUNIT

RUPEESMONTHSQUANTITY UNIT

(5.07)How muchwould younormally
have tospend in total
to buy thisquantity?

IN-KIND

ASK (5.09) FOR ALL ITEMS FIRST(5.08)
What is the total
value of..[FOOD]..
consumed thatyou received inkind over the past
12 months (wages
for work, etc.)?

(5.10)(5.11)(5.12)(5.09)How many How much ..[FOOD].. What is theMAINdays has the did your household value of this SOURCE
household consume during the quantity of..[FOOD]..
consumed past 7 days?
consumed..[FOOD]..
during the pastduring thepast 7 days?
7 days?

IF NONE, WRITEZERO
RUPEESRUPEESIF NONE,
WRITE
ZERO AND QUANTITY CODESIN TABLE "F"
►NEXT
DAYS

QUANTITYUNIT

RUPEES

3 IN-KIND

CODE(5.01)Have you consumed ..[FOOD].. during the past 12
months?

PART B: PAST 7 DAYS

2 FOOD PURCHASES(5.00) ID CODE RESPONDENT:

1 HOME PRODUCTION

Section054 Tomatoes
055 Green leafy vegetables
056 Pointed gourd
057 Bitter gourd
058 Other vegetables
060

6 FRUITS AND NUTS:

061 Bananas
062 Citrus fruits (Oranges, Lemon, Lime, Sweet
orange, Pummelo, etc.)
063 Mangoes
064 Apples
065 Pineapple
066 Papaya
067 Other fruits (Grape, Pomegranate, etc.)
068 Dried fruits (Walnut, Coconut, etc.)
070

7 FISH AND MEAT:

071 Fish072 Mutton
902

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5 Food expenses and home production
PART A: TYPICAL MONTH
FOOD PURCHASES FOR HH CONSUMPTIONHOME PRODUCTION(5.02)(5.04)(5.03)How much would
How many months In a typical monthin the past 12
during which you ate your household
months did you..[FOOD].., how much have to spend in
consume ..[FOOD].. did your household the market to buy
that you grew orconsume ..[FOOD]..? this quantity of..[FOOD].. (i.e. theproduced yourself?
amount consumed
in a typicalmonth)?

(5.05)(5.06)In a typical month
How many
during which you
months in thepast 12 months purchased
did you purchase ..[FOOD]. howmuch did you..[FOOD].. ?
purchase?

(3) ONLY ONCE YOU HAVE FINISHED WITHQUESTIONS (5.02) TO (5.08) FOR ALL ITEMSCONSUMED, ASK QUESTION (5.09) TO (5.12)

IF NONE, WRITE
ZERO AND ►IF NONE, WRITEQUANTITY CODESZERO AND ►IN TABLE "F"(5.08)ITEM DESCRIPTIONMONTHS

INSTRUCTIONS:(1) ASK QUESTION (5.01) ABOUT EACH ITEM IN THE
LIST FIRST. PUT A TICK "9" IN THE APPROPRIATE
BOX (YES OR NO)(2) IF THE ANSWER TO Q. (5.01) IS YES, ASK Q.
(5.02) TO (5-08)

NO YES(5.05)QUANTITY CODESIN TABLE "F"
QUANTITYUNIT

RUPEESMONTHSQUANTITY UNIT

(5.07)How muchwould younormally
have tospend in total
to buy thisquantity?

IN-KIND

ASK (5.09) FOR ALL ITEMS FIRST(5.08)
What is the total
value of..[FOOD]..
consumed thatyou received inkind over the past
12 months (wages
for work, etc.)?

(5.10)(5.11)(5.12)(5.09)How many How much ..[FOOD].. What is theMAINdays has the did your household value of this SOURCE
household consume during the quantity of..[FOOD]..
consumed past 7 days?
consumed..[FOOD]..
during the pastduring thepast 7 days?
7 days?

IF NONE, WRITEZERO
RUPEESRUPEESIF NONE,
WRITE
ZERO AND QUANTITY CODESIN TABLE "F"
►NEXT
DAYS

QUANTITYUNIT

RUPEES

3 IN-KIND

CODE(5.01)Have you consumed ..[FOOD].. during the past 12
months?

PART B: PAST 7 DAYS

2 FOOD PURCHASES(5.00) ID CODE RESPONDENT:

1 HOME PRODUCTION

Section073 Buffalo meat
074 Chicken
075 Other meats (Pig, Boar, Duck, etc.)
080

8 SPICES AND CONDIMENTS:

081 Salt
082 Cumin seed/Black pepper
083 Turmeric
084 Ginger/Garlic
085 Chilies
086 Other spices and condiments (Coriander,
Nutmeg, Clove, etc.)
090

9 SWEETS AND CONFECTIONERY:

091 Sugar
092 Gur (Sakhar )093 Sweets (Mithai )094 Other sweets (Sugar candy, Chocolate, etc.)
903

PAGE TOTAL

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5 Food expenses and home production
PART A: TYPICAL MONTH
FOOD PURCHASES FOR HH CONSUMPTIONHOME PRODUCTION(5.02)(5.04)(5.03)How much would
How many months In a typical monthin the past 12
during which you ate your household
months did you..[FOOD].., how much have to spend in
consume ..[FOOD].. did your household the market to buy
that you grew orconsume ..[FOOD]..? this quantity of..[FOOD].. (i.e. theproduced yourself?
amount consumed
in a typicalmonth)?

(5.05)(5.06)In a typical month
How many
during which you
months in thepast 12 months purchased
did you purchase ..[FOOD]. howmuch did you..[FOOD].. ?
purchase?

(3) ONLY ONCE YOU HAVE FINISHED WITHQUESTIONS (5.02) TO (5.08) FOR ALL ITEMSCONSUMED, ASK QUESTION (5.09) TO (5.12)

IF NONE, WRITE
ZERO AND ►IF NONE, WRITEQUANTITY CODESZERO AND ►IN TABLE "F"(5.08)ITEM DESCRIPTIONMONTHS

INSTRUCTIONS:(1) ASK QUESTION (5.01) ABOUT EACH ITEM IN THE
LIST FIRST. PUT A TICK "9" IN THE APPROPRIATE
BOX (YES OR NO)(2) IF THE ANSWER TO Q. (5.01) IS YES, ASK Q.
(5.02) TO (5-08)

NO YES(5.05)QUANTITY CODESIN TABLE "F"
QUANTITYUNIT

RUPEESMONTHSQUANTITY UNIT

(5.07)How muchwould younormally
have tospend in total
to buy thisquantity?

IN-KIND

ASK (5.09) FOR ALL ITEMS FIRST(5.08)
What is the total
value of..[FOOD]..
consumed thatyou received inkind over the past
12 months (wages
for work, etc.)?

(5.10)(5.11)(5.12)(5.09)How many How much ..[FOOD].. What is theMAINdays has the did your household value of this SOURCE
household consume during the quantity of..[FOOD]..
consumed past 7 days?
consumed..[FOOD]..
during the pastduring thepast 7 days?
7 days?

IF NONE, WRITEZERO
RUPEESRUPEESMONTHLY
PURCHASES
RUPEESMONTHLY
IN-KINDRUPEESIF NONE,
WRITE
ZERO AND QUANTITY CODESIN TABLE "F"
►NEXT
DAYS

QUANTITYUNIT

RUPEES

3 IN-KIND

CODE(5.01)Have you consumed ..[FOOD].. during the past 12
months?

PART B: PAST 7 DAYS

2 FOOD PURCHASES(5.00) ID CODE RESPONDENT:

1 HOME PRODUCTION

Section100

10 NON-ALCOHOLIC BEVERAGES
101 Tea (dried leaves)
102 Coffee (ground, instant)
103 Fruit juices/Carbonated drinks (Coca cola,
Pepsi cola, etc.)
104 Other non-alcoholic drinks (Mineral water,
Sarbat , etc.)

110

11

ALCOHOLIC BEVERAGES

111 Wine
112 Gin, whiskey
113 Beer/jandh
114 Other alcoholic drinks (Tadi etc)
120

12 TOBACCO & TOBACCO PRODUCTS:
121 Cigarettes
122 Bidis
123 Tobacco
124 Other (jarda, khaini, betel nut)

130

13 MISC. FOOD PRODUCTS:
131 Meals taken outside home
132 Bread/ biscuit / Noodles
133 Misc. other food expenditures
904

PAGE TOTAL

990

ASK RESPONDENT TO ESTIMATE AVERAGE
MONTHLY EXPENDITURE ON FOOD, VALUE OF
HOME PRODUCED FOOD, AND FOOD RECEIVED IN
KINDMONTHLY HOME PRODUCTIONRUPEESVALUE OF CONSUMPTION DURING THE PAST7 DAYS RUPEES

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6 Non-food expenditures and inventory of durable goods

Part A: Frequent non-food expenditures(6.00) ID CODE RESPONDENT:PUT A TICK "9" IN THE APPROPRIATE BOX FOR ALL ITEMS. IF
THE ANSWER IS YES, ASK Q (6.02)

ITEM DESCRIPTION
210

NO

YES

21 FUELS:(6.02)
What is the money value of the amountpurchased or received in-kind by yourhousehold:A.
…. during the past
30 days?

B….during the past
12 months?

RUPEESRUPEES(6.01)
Were any of the following items purchased or received in-kind over the past 12
months?

CODE

CODE

(6.01)
Were any of the following items purchased or received in-kind over the
past 12 months?

PUT A TICK "9" IN THE APPROPRIATE BOX FOR ALL ITEMS. IF THE
ANSWER IS YES, ASK Q (6.02)

ITEM DESCRIPTION
230

211 Wood (bundle wood, logwood, sawdust)
212 Kerosene oil

232 Petrol, diesel, motor oil (for personal vehicle only)

213 Coal, charcoal

233 Entertainment (cinema, CD/cassette rentals, etc.)

214 Cylinder gas (LPG)

234

215 Matches, candles, lighters, lanterns, etc.

235 Pocket money to children

220

236 Educational and professional services

221 Ready-made clothing and apparel
222

237

Cloth, wool, yarn, and thread for making clothes
and sweaters

YES

RUPEES

238 Traditional medicines and health services

224 Footwear (shoes, slippers, sandals, etc.)

241 Light bulbs, shades, batteries, etc.

225 Toilet soap

242

226 Toothpaste, tooth powder, toothbrush, etc.

906

PAGE TOTAL260

ASK RESPONDENT TO ESTIMATE AVE. MONTHLY & ANNUAL EXPENDITURE
ON FREQUENTLY PURCHASED NON-FOOD ITEMSHousehold cleaning articles (soap, bleach, washing powder,etc.)

Other personal care items (shampoo, combs,
cosmetics, etc.)

228 Dry cleaning and washing expenses

905

RUPEES

Modern medicines and health services (doctor fees, hospitalcharges etc.)

239 Wages paid to watchman, servant, gardener, driver, etc.

229

B….during the past
12 months?

Newspapers, books, stationery supplies(except educational
expenses)

223 Tailoring expenses

227

A.
…. during the past
30 days?

23 OTHER FREQUENT EXPENSES:

Public transportation (buses, taxis, rickshaws, train tickets,
231
etc.)

22 APPAREL AND PERSONAL CARE ITEMS:

NO(6.02)
What is the money value of the amountpurchased or received in-kind by yourhousehold:

Personal services (haircuts, shaving, shoeshine,
etc.)
PAGE TOTAL

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6 Non-food expenditures and inventory of durable goods

CODE(6.03)
Were any of the following items purchased or received in-kind over the past 12
months?
PUT A TICK "9" IN THE APPROPRIATE BOX FOR ALL ITEMS. IF THE
ANSWER IS YES, ASK Q 6.04

ITEM DESCRIPTION
310

NO

YES

31 INFREQUENT EXPENSES:(6.04)
What is the money value of theamount purchased or received inkind by your household during thepast 12 months:

Part B: Infrequent non-food expenditures(6.03)
Were any of the following items purchased or received in-kind over the past 12
months?
CODE

SectionPUT A TICK "9" IN THE APPROPRIATE BOX FOR ALL ITEMS. IF THE
ANSWER IS YES, ASK Q 6.04

ITEM DESCRIPTIONRUPEES410

411 Crockery, cutlery and kitchen utensils (household use)

312 Income taxes, land taxes, housing and property taxes

412

Repair and other expenses for personal vehicle(registration,fines)RUPEES

Kitchen appliances (refrigerator, cooking range, blenders,
etc.)

413 Pillows, mattresses, blankets, etc.

314 Postal expenses, telegrams, fax, telephone

414 Jewelry, watches

315 Excursion, holiday, (including travel and lodging)

415 Furniture and fixtures

316 Toys, sports goods

416 Electric fans

317 Repair and maintenance of the house

417 Heaters (electric, gas, kerosene)

318 Repair and servicing of household effects

418 Sewing machine

319 Home improvements and additions

419 Iron (electric or other)320

421 Television/VCR

32 MISCELLANEOUS EXPENSES:

321 Marriages, births, and other ceremonies

422 Washing machine

322 Dowry & bride price given

423 Cassette recorder or player, radio, etc.

323 Dowry & bride price received

424 Camera, camcorder, etc.

324 Funeral and death related expenses

425 Bicycle

325 Expenditure on religious ceremonies

426 Motorcycle

326 Charity

427 Motor car or other such vehicle

327 Cash losses

428 Other durable goods (bullock/he buffalo carts, etc.)

328 Gifts and donations
907

YES

41 DURABLE GOODS:

311 Legal expenses and insurance (life, car, etc.)

313

NO

(6.04)
What is the money value of theamount purchased or received inkind by your household during thepast 12 months:

429 Pressure lamps / petromax
TOTAL

431 Telephone sets / cordless/mobile phone/pager
432 Computer/Printer
908

TOTAL

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Section

6 Non-food expenditures and inventory of durable goods(6.06)How many ..[ITEM].. does
your household own?

CODE

(6.05)
Does your household own any of the following items?

Part C: Inventory of durable goods(6.07)(6.08)(6.09)How many years ago did you Did you purchase it, receive it as a gift How much was it worth when
acquire ..[ITEM]..?
or payment for services, or receive it as you acquired it?
dowry or inheritance?

(6.10)
if you wanted to sell this..[ITEM].. today, how muchmoney would you receive forit?

IF MORE THAN ONE ITEM OWNED, ASK ABOUT MOST RECENTLY ACQUIRED ITEM.

PUT A TICK "9" IN THE APPROPRIATE BOX FOR ALL ITEMS. IF
THE ANSWER IS YES, ASK Q. (6.06) TO (6.10)

ITEM DESCRIPTION

NO

YES

NUMBER

PURCHASE

1

GIFT/PAYMENT2DOWRY/INHERITANCE

3

YEARS

IF MORE THAN ONE ITEMOWNED, ASK ABOUT TOTALVALUE OF ALL ITEMSRUPEESRUPEES

501 Radio/cassette/CD player
502 Camera (still/movie)
503 Bicycle
504 Motorcycle/scooter
505 Motor car, etc.
506 Refrigerator or freezer
507 Washing machine
508 Fans
509 Heaters
510 Television/VCR/VCD Player
511 Pressure lamps / petromax
512 Telephone sets / cordless/mobile
513 Sewing machine
514 Furniture, rugs, clocks
515 Kitchen utensils
516 Jewelry (including watches)
517 Computer/Printer
909

TOTAL

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6 Non-food expenditures and inventory of durable goods

Section

CODE(6.11)
Were any of the following items produced and consumed by your household over the past 12 months?

Part D: Own account production of goods(6.12)
What is the monetary value in the local market of the items produced andconsumed yourself during the past:

PUT A TICK "9" IN THE APPROPRIATE BOX FOR ALL ITEMS. IF THE ANSWER IS YES, ASK Q.
(6.12)A.
…. during the past 30 days?

SELF PRODUCED AND CONSUMED ITEMS

NO

YES

B
….during the past 12 months?

RUPEESRUPEES

600 Expenditures on self produced and consumed items
601 Dalo, Nanglo, Doko, Namlo, Rope, Mudha, etc.
602 Mandro, Mat, Sukul, Bhakari, Ghum, Chitro, Broom, etc.
603 Radi, Pakhi, Homespun clothes, etc.
604 Firewood/Dung collection
605 Furniture and allied wooden materials
606 Sickle, Chulesi, Knife, etc.
607 Tailoring
608 Shoe making/repairing
609 Water fetching
610 Minor house repairing
611 Biogas
612 Pickle, Gundruk, Masyaura, Titaura, Jam, etc.
613 Other (Communal construction, Duna, Tapari, Batti, etc.)
910

TOTAL

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Section

7 Education

IDENTIFICATION CODE

(7.01)ID CODE OFRESPONDENT(7.02)(7.03)(7.04)Can ..[NAME].. Can ..[NAME].. Where did ..[NAME]..
read a letter? write a letter? learn to read and write?

WRITE ID CODE
FROM
HOUSEHOLDROSTER OFPERSONPROVIDING THISINFORMATION

YES
NO

1

YES

2

NO

► (7.05)RESPONDENT'S

Part A: Literacy

ALL PERSONS 3 YEARS AND OLDER

1
2

FORMALSCHOOLING

1

TAUGHT AT
HOME

2

GOVT.
LITERACY
COURSE

3

NGO LITERACY

4(7.06)(7.07)
What was the main What type of school / college did refuse
reason that..[NAME]..?
..[NAME].. wasrefused admission?

POOR
ACADEMICSCOMMUNITY/GOVERNMENT

1

INSTITUTIONAL/PRIVATE

2

1 GURUKUL / MADARSHA / GUMBA

COULDN'T PAY 2 OTHER
YES► (7.05) COURSE
OTHER(7.05)
Has..[NAME]..
ever beenrefusedadmission inany school,
upto grades
8?

NO
►

1 CASTE/
2 ETHNICITY

3(7.08) RELIGION
OTHER

4
5

5

3
4

(7.08)
INTERVIEWER:ASK EACH PERSON ABOUT THEIREDUCATIONAL BACKGROUND, AND
CODE THEIR EDUCATIONALBACKGROUND AS FOLLOWS:

NEVER ATTENDEDSCHOOL1

ATTENDEDSCHOOL/COLLEGE IN
THE PAST

2

CURRENTLYATTENDING SCHOOL /COLLEGE /PRESCHOOL

3►PART B►(7.09)Why didn’t ..[NAME].. ever attend
school?
TOO YOUNG

01

TOO EXPENSIVE

02

TOO FAR AWAYHAD TO HELP AT HOME

03
04

EDUCATION NOT USEFUL

05

PARENTS DID NOT WANT

06

NOT WILLING TO ATTEND07

DISABLED

08

NOT ALLOWED ADMISSION

09

OTHER REASONS

10PART C► NEXT PERSONID CODE

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section7

Education

Part B: Past enrollment

PERSONS WHO HAVE ATTENDED SCHOOL/COLLEGE IN THE PASTIDENTIFICATION CODE

INTERVIEWER: ASK ONLY OF THOSE PERSONS WHO HAVE ATTENDED SCHOOL/COLLEGE IN THE PAST(7.10)(7.11)
What type of school / college did ..[NAME].. What was thelast attend?
highest class that..[NAME]..
completed?
COMMUNITY/GOVERNMENT

1

INSTITUTIONAL/PRIVATE2TECHNICAL/VOCATIONAL3GURUKUL / MADARSHA / GUMBA

4COMMUNITY (PUBLIC) CAMPUS(7.12)How many years did it take..[NAME].. to complete primay
education (class 5) excluding timespent in ECD/Pre-school?

(7.13)B.
A.
How many How many
years did it times did..[NAME]..
take..[NAME].. appear forto complete SLC
class 10? examinations?
WRITE "98" IF PRIMARY LEVELIS COMPLETED WITHOUTATTENDING SCHOOL(7.14)How old was..[NAME].. when
he/she left
school?

(7.15)
What class was..[NAME].. lastattending before
leaving theschool?

5

PRIVATE CAMPUS

6

CONSTITUENT (ANGIK) CAMPUS

7

OTHER

8

EDUCATION
CODES IN TABLE"E"EDUCATION CODEEDUCATION
CODES IN
TABLE "E"IF PRIMARY SCHOOL NOTCOMPLETED WRITE NUMBER
OF YEARS SCHOOL ATTENDED► (7.14)
NUMBER OF YEARSN YEARSTIMES

AGE IN YEARSEDUCATION
CODE(7.16)Why did ..[NAME].. leave school/college?
FURTHER SCHOOLING NOT AVAILABLE

01

TOO EXPENSIVE

02

TOO FAR AWAY03

HAD TO HELP AT HOME

04

PARENTS DID NOT WANT

05

COMPLETED DESIRED SCHOOLING

06

MARRIAGE

07

MOVED AWAY

08

POOR ACADEMIC PROGRESS

09

STARTED WORKING/GOT A JOB

10LACK OF TOILET FACILITY

11

LANGUAGE

12NOT ALLOWED ADMISSION

13

MISTREATMENT AT SCHOOL

14OTHER REASONS

15

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section

7 Education

Part C: Current enrollment

PERSONS CURRENTLY ATTENDING SCHOOL/COLLEGE

INTERVIEWER: ASK ONLY THOSE PERSONS CURRENTLY ATTENDING SCHOOL/COLLEGE OR PRESCHOOLIDENTIFICATION CODE

(7.17)
What type of school/college is ..[NAME]..
currently attending?

COMMUNITY/GOVERNMENT

1

INSTITUTIONAL/PRIVATE

2(7.18)
What class is ..[NAME]..
currently attending?

(7.19)(7.20)(7.21)How many days How many days INTERVIEWER: IF (7.19) IS DIFFERENTTHAN (7.20) THEN ASK:
did ..[NAME].. go was
to school during ..[NAME]..suppos
the past 7 days? ed to go toschool during the Why was ..[NAME].. absent from school
some days?
past 7 days?

(7.22)How do ..[NAME]..go toschool/college?

(7.23)How much time does..[NAME].. spend
commuting every day?

TECHNICAL/VOCATIONAL3GURUKUL / MADARSHA / GUMBA

4ILLNESS / INJURY
HAD TO HELP WITH HOUSEHOLDCHORES

1
2
WALK

1

COMMUNITY (PUBLIC) CAMPUS

5

HAD TO WORK

3

BUS

FAMILY MEMBERS SICK OR DIED4

PRIVATE CAMPUS

6BICYCLE/RICKSHAW

2
3

CONSTITUENT (ANGIK) CAMPUS

7

OTHER

8

EDUCATION CODES INTABLE "E"EDUCATION CODEN DAYSFAMILY FUNCTIONS

5

MIXED

4

REMOVED OR QUIT SCHOOL

6

OTHER

5

OTHER

7

N DAYS(7.24)In the past 12
months, did..[NAME].. takeany private
tuition classes?

(7.25)
Was the private
tuition ..[NAME]..
received from ateacher in own
school?

GOING TO SCHOOL AND
RETURING HOME

HRS

YES

1

YES

1

NO

2

NO

2

►

(7.26)

MINS

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section7

EducationPERSONS CURRENTLY ATTENDING SCHOOL/COLLEGE

Part C: Current enrollment
INTERVIEWER: ASK ONLY OF THOSE PERSONS CURRENTLY ATTENDING SCHOOL/COLLEGEIDENTIFICATION CODE

(7.26)How much has your household spent during the past 12 months for ..[NAME's].. schooling?

IF NOTHING WAS SPENT, WRITE ZERO.
A

B

C

Tuition fee

Other fee
Uniform(exams,admission,
events, etc)

D

E

F

G

Textbook /Supplies

Transportatio Private
tuitionn

Others(snacks, tea,
etc)

(7.27)
Did..[NAME]..
receive ascholarshipto help pay
for youreducational
expenses?

(7.28)How much
did..[NAME]..
receive overthe past 12
months?

(7.30)(7.29)
What is the major What is the type of thescholarship?
form of thescholarship?

POOR ANDTALENTEDTUITION /
FEES

1

GIRLS

2

1 DALITS

3

CONFLICT
2 AFFECTED

4DISABLED

5
6

YES

1

UNIFORMS/
BOOKS

NO

2

HOSTEL3 HIV/AIDSOTHER

4 KARNALI REGION 7►(7.31)
What class was..[NAME]..
attending in thelast academic
year?

OTHER
RUPEESRUPEESRUPEESRUPEESRUPEESRUPEESRUPEES8(7.33)B.
A.
How many How many
years did it times did..[NAME]..
take..[NAME].. to appear for
SLC
complete
examinationsclass 10?
?

WRITE "98" IF PRIMARY LEVELIS COMPLETED WITHOUTATTENDING SCHOOLIF NOTAPPLICABLE
WRITE "98"(7.31)RUPEES(7.32)How many years did it take..[NAME].. to complete primay
education (class 5) excluding timespent in ECD/Pre-school?

EDUCATION
CODE

IF PRIMARY SCHOOL NOTCOMPLETED WRITE NUMBER
OF YEARS SCHOOL ATTENDEDAND► NEXT PERSONNUMBER OF YEARSN YEARS

TIMES

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section 8 HealthPart A: Chronic illnesses

ALL HOUSEHOLD MEMBERS
CHRONIC ILLNESSIDENTIFICATION CODE

(8.01)ID CODE OFRESPONDENTWRITE ID CODE
FROM
HOUSEHOLDROSTER OFPERSONPROVIDING THISINFORMATIONRESPONDENT'S
ID CODE(8.04)(8.06)(8.07)(8.05)(8.02)(8.03)
Do you suffer from any of For how long Do ..[NAME].. suffer What chronic illness do ..[NAME].. primarily suffer How many years How much has ..[NAME].. spent in thefrom?
past 12 months on the treatment of thisago did thefrom a chronic
the following disabilities? have you
illness in...
illness start?
suffered from illness?
INCLUDE COST OF CONSULTATIONS,
this
HEART CONDITIONS
01
DIAGNOSIS, MEDICINES AND TRAVEL
disability?
RESPIRATORY
02
Physical?

1

ASTHMA

03

Visual?

2

EPILEPSY

04

Hearing?
Visual and hearing?

3
4

CANCER05

DIABETES

06

KIDNEY/LIVER DESEASES

07

Speech?

5

Mental?

6

WRITE "98"
IF SINCE YES
BIRTH
NO

Multiple?

7

►

None

8
► (8.04)
YEARS(8.10)

1

RHEUMATISM RELATED

082

GYNECOLOGICAL PROBLEMS

09

OCCUPATIONAL ILLNESSES

10

HIGH/LOW BLOOD PRESSURE

11

GASTROINTESTINAL DESEASES

12

OTHER

13

A
… medicines?

NB. OF YEARSRUPEESB(8.08)(8.09)How many days
Has
did ..[NAME].. has..[NAME]..
received any to stop doing
his/her usual
in-kindmedication activity due to thisillness during thefor thetreatment of past 12 months?
this illness
during thepast 12months?

… other expenses
such asconsultations,diagnosis, transport,
etc.?

RUPEES

YES

1

NO

2

(8.10)
What is thepresent healthstatus of..[NAME]..?

EXCELLENT

1

GOOD

2

FAIR

3

POOR

4

NB. OF DAYS

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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IDENTIFICATION CODE

Section 8 HealthPart B: Illnesses or injuries

ALL HOUSEHOLD MEMBERS(8.13)
Was anyone
consulted (e.g. a
doctor, nurse,
pharmacist or otherhealer) for theillness or injury in
04 the last month?

(8.12)(8.11)
Has ..[NAME].. had any health problem or What type of illness or injury?
has been injured during the past 30 days, DIARRHOEA
01
for example diarrhoea, respiratory
DYSENTRY
02
problems, fever, burned, etc?
RESPIRATORY
03
PROBLEMS

1

NO

2

►

(8.25)(8.15)(8.16)
Where did ..[NAME].. go for the last Whom did ..[NAME].. consult with?
consultation?
GOVT.HEALTH INST.
SHP

ILLNESS/INJURY NOT SERIOUS ENOUGH

01 HP

HEALTH FACILTY TOO FAR

02 PHC

01 GOVT.HEALTH INST.

COLD/FEVER/FLU

05

NO TRANSPORT

03 HOSPITAL

02 DOCTOR
03 PARAMEDIC (HA, SAHW,
04 AHW, ANM)OTHER FEVER

06

HEALTH CARE TOO EXPENSIVE

04 MOBILE CLINIC

05 KAVIRAJ/VAIDYASKIN DISEASE

07

TRANSPORT TOO EXPENSIVE

05 AYURVED CENTRE

06 PVT. HEALTH INST.

TB

08

HEALTH WORKERS UNFRIENDLY

06 PVT. HEALTH INST.

MEASLES

09 YESHEALTH WORKERS NOT PRESENT

07 PHARMACY

07 PHARMACIST

5JAUNDICE

10HEALTH CARE NOT GOOD QUALITY

08 CLINIC

08 PARAMEDIC

6OTHER (SPECIFY___)

09 PVT. HOSPITAL

09 KAVIRAJ/VAIDYA7

MALARIA
YES(8.14)
What is the main reason why ..[NAME].. didn't consult anybody?

PARASITES

11 NO

INJURYDENTAL PROBLEMS

1213OTHER (SPECIFY___)

141► (8.15)2► (8.18)

DOCTOR

1
2
3
4

HEALTH WORKER'S HOME

10 TRADITIONAL HEALER

8

OTHER

11 OTHER

9

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section 8 HealthPart B: Illnesses or injuries

ALL HOUSEHOLD MEMBERS

FOR CHILDREN UNDER 5 YEARS WITH DIARRHOEAIDENTIFICATION CODE

5 YEARS AND OLDER(8.17)How much was spent for the past consultation of thisinjury and illness for service cost (cost of diagnostic
service consisting of lab fee and cost of other services
consisting of registration fee, consultation fee, surgery
fee, etc.) medicine cost and travel cost over the past 30
days?

A
DIAGNOSTIC &OTHER SERVICE
COST

B
MEDICINE
COST

RUPEESRUPEES(8.19)(8.20)(8.18)
Did ..[NAME]..
How much in Has
have to stop
total was spent ..[NAME]..
over thisreceived any doing his/her
illness or injury in-kind
usual activities
over the past medication because of this30 days?
illness or injury?
for thetreatment of
this illness or
injury during
the past 30
days?

(=A+B+C)CTRAVEL COST

RUPEESRUPEES(8.21)(8.22)How many days Did you give ..[NAME]..
anything to treat the
did ..[NAME]..
have to stop doing diarrhoea?
his/her usual
activities?

1

NO

2

ORS (PACKET OR
HOME-MADE)ASK ONLY FOR THOSE
WITH ANSWER "01" TO ALLOPATHIC(8.12) AND CHILDREN MEDICINE► (8.28)
UNDER 5 YEARS

YES

1

►

NO

2

(8.25)YES

1

NO► (8.25)
YES(8.23)
What did you give .[NAME].
to treat the diarrhoea?

TRADITIONALMEDICINE

12

3► (8.28)

2► (8.28)

OTHER(8.24)
Where did you obtain the ORS?

4

SHP

1

HP

2

PHC

3

HOSPITAL

4MOBILE CLINIC

5MADE AT HOME

6

NON-GOVT. INST.

7

PHARMACY

8OTHER.

9► (8.28)
DAYS

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section 8 HealthPart C: HIV/AIDS Knowledge

ALL PERSONS 10 YEARS AND OLDER
ALL PERSONS 10 YEARS AND OLDER(8.25)Have you heard about
HIV/AIDS?

(8.26)How did you first hear about
HIV/AIDS?

(8.27)
What can a person do to prevent it?

IDENTIFICATION CODE

ABSTAIN FROM SEX
USE CONDOMS

YES
NO

1

3

LIMIT SEX TO ONE PARTNER/STAY FAITHFUL TOONE PARTNER

03

RADIO

1

TELEVISION
NEWSPAPERS/PAMPHLE
TS/POSTERS

2
3

LIMIT NUMBER OF SEXUAL PARTNERS

04

4

AVOID SEX WITH PROSTITUTES

05

AVOID SEX WITH PERSONS WHO HAVE MANYPARTNERS

06

AVOID SEX WITH HOMOSEXUALS

07

2► NEXT PERSON FRIENDS/RELATIVESPERSON NOTCONTACTED

PROMPT: Anything else?
RECORD ALL WAYS MENTIONED. DO NOT READ OUT RESPONSES
01
AVOID SEX WITH PERSONS WHO INJECT DRUGSINTRAVENOUSLY
02

HEALTH WORKERS5TEXTBOOKS

6► NEXT PERSON OTHER

7

1st

2nd

08
09

AVOID BLOOD TRANSFUSIONSAVOID INJECTIONS

10AVOID SHARING RAZORS/BLADES11AVOID KISSING

12

AVOID MOSQUITO BITES

13

SEEK PROTECTION FROM TRADITIONAL PRACTITIONER14OTHER (SPECIFY___)

15

DON’T KNOW

16
3rd

4th

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section 8 HealthPart D: Immunizations

CHILDREN UNDER 5 YEARS

3 YES, SEEN

BCG

YES, NOT► NEXT AVAILABLE

Hp-B 3

Hp-B 2NO
Hp-B 1(8.31)POLIO 33►

(8.35)(8.36)MEASLES
Hp-B(8.37)(8.38)Has [NAME]
Has [NAME]
When was How many times Has [NAME] been How many Has [NAME] Has
received a BCG received a polio
the first polio was the polio
given a DPT
times did been given [NAME]
vaccination
vaccine, that is pink vaccine
vaccine given? vaccination, that is [NAME] get an injection been given
againstan injection
or white drops in the received, justan injection usually this shot? against
tuberculosis,
mouth?
after birth or
given in the thigh or
measles? against Hpthat is an
B?
later?
buttocks at the same
injection in thetime as polio drops?
upper arm thatleft a scar?

YES(8.31)

NO

DPT(8.34)ONCE

MEASLES►

POLIO(8.33)(8.32)(8.39)POLIO 2CHILD

(8.31)(8.39)
Where was the mostrecent immunization
given?

SHP

1

HP
PHC

2
3

HOSPITALOUTREACH
CLINIC

4
5

►

1
2

POLIO 1

DO NOTKNOW

ASK TO SEE THE
CARD

1
2

DPT3

► NEXT
CHILD

YES
NO

DPT2

1
2

DPT1

YES
NO

BCG

IDENTIFICATION CODECOPY FROM CARD
(8.28)(8.29)(8.30)
Has..[NAME]. Do you have a card CHECK FROM CARD WHETHER IMMUNIZATION HAS. ever been which shows which TAKEN PLACE
immunized? vaccinations [NAME]
has received?

DON’T
KNOW

1

YES

1

NO

2

JUST
AFTER
BIRTH

► (8.35)
2 DON’T 3 ► LATER
3 KNOW(8.35) DON’T

1 TWIICE
THRICE> 3 TIMES
2 DON’T
3 KNOW

KNOW

1
2 YES

1

3 NO

2

YES

4
► (8.37)
5 DON’T
3
KNOW► (8.37)

1 YES

1

NO

2 NO

2

DON’T
KNOW

3 DON’T
KNOW

3OTHER HEALTH 6
INST.

TIMES

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section9

Marriage and maternity history

ALL EVER MARRIED WOMEN AGED 15-49 YEARS WHO HAVE GIVEN LIVE BIRTHPart A: Maternity history

WOMAN

NAME OF WOMAN:ID CODE OF WOMAN(9.01)BIRTH ORDER(9.03)
What is the child’s name?

(9.04)
When was [ORDER] childborn?

IF NOT KNOWN,ESTIMATE USING
SUPPLEMENTARYCALENDAR

NAME

MTH

YR

(9.05)
What is the sexof [NAME]?

MALE
FEMALE(9.08)(9.07)COPY ID CODE OF
Does [NAME]
currently live with CHILD FROM
HOUSEHOLD ROSTERyou?

(9.06)Is [NAME] still
alive?

1 YES
2 NO

1

YES

1

2

NO

2

► (9.09)►(9.10)(9.02) ID CODE OF RESPONDENT(9.09)How long did the [NAME] live?

(9.10)
What was the highest
level of schooling that
[NAME] completed?

► NEXT CHILD

ID CODE

1

IF CHILD HAS NEVER
GONE TO SCHOOLWRITE "98"
YRS

MTHS

DAYS

EDUCATION CODE

01
02
03
04
05
06
07
08
09
10
11
12

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Section9

Marriage and maternity history

ALL EVER MARRIED WOMEN AGED 15-49 YEARS WHO HAVE GIVEN LIVE BIRTHPart A: Maternity history

WOMAN

NAME OF WOMAN:ID CODE OF WOMAN(9.01)BIRTH ORDER(9.03)
What is the child’s name?

(9.04)
When was [ORDER] childborn?

IF NOT KNOWN,ESTIMATE USING
SUPPLEMENTARYCALENDAR

NAME

MTH

YR

(9.05)
What is the sexof [NAME]?

MALE
FEMALE(9.06)Is [NAME] still
alive?

1 YES
2 NO(9.07)(9.08)
Does [NAME]
COPY ID CODE OF
currently live with CHILD FROMyou?
HOUSEHOLD ROSTER

1

YES

1

2

NO

2

► (9.09)►(9.10)(9.02) ID CODE OF RESPONDENT(9.09)How long did the [NAME] live?

(9.10)
What was the highest
level of schooling that
[NAME] completed?

► NEXT CHILD

ID CODE

2

IF CHILD HAS NEVER
GONE TO SCHOOLWRITE "99"
YRS

MTHS

DAYS

EDUCATION CODE

01
02
03
04
05
06
07
08
09
10
11
12

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Section9

Marriage and maternity history

ALL EVER MARRIED WOMEN AGED 15-49 YEARS WHO HAVE GIVEN LIVE BIRTHPart A: Maternity history

WOMAN

NAME OF WOMAN:(9.01)BIRTH ORDER(9.03)
What is the child’s name?

(9.04)
When was [ORDER] childborn?

IF NOT KNOWN,ESTIMATE USING
SUPPLEMENTARYCALENDAR

NAME

MTH

YR

(9.05)
What is the sexof [NAME]?

MALE
FEMALE(9.06)Is [NAME] still
alive?

1 YES
2 NO

ID CODE OF WOMAN(9.07)(9.08)
Does [NAME]
COPY ID CODE OF
currently live with CHILD FROMyou?
HOUSEHOLD ROSTER

1

YES

1

2

NO

2

► (9.09)►(9.10)(9.02) ID CODE OF RESPONDENT(9.09)How long did the [NAME] live?

(9.10)
What was the highest
level of schooling that
[NAME] completed?

► NEXT CHILD

ID CODE

3

IF CHILD HAS NEVER
GONE TO SCHOOLWRITE "99"
YRS

MTHS

DAYS

EDUCATION CODE

01
02
03
04
05
06
07
08
09
10
11
12

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9

SectionMarriage and maternity history

ALL WOMEN WHO HAVE GIVE LIFE BIRTH DURING PAST 36 MONTHSPart B: Pre and post-natal care

LAST PRE-NATAL CARE

LINE NUMBER(9.11)(9.12)(9.13)COPY ID CODE While you were
Where did you first receive this care?
pregnant with your
OF WOMEN
last child, did you go
WITH
GOVT.HEALTH INST.
for prenatal
CHILDREN
01
LESS THAN 3 consultations to a SHPYEARS FROMPART AQUESTION:(9.01)

health care facility? HP

02

PHC

03

HOSPITAL

04

MOBILE CLINIC

05

AYURVED CENTRE

06(9.14)
Who provided this care?

DOCTORNURSE/ANM
HA/SAHW/AHW/MCHW/ VHW(9.18)(9.17)(9.15)(9.16)How many times did What is the main reason why you didn't go for
At what month of During this pregnancy,
prenatal consultations the last time you were
were you given an injection you receive thispregnancy did
pregnant?
injection?
you go for your in the arm to prevent thebaby from getting tetanus
first visit?
that is convulsions after
birth?

1
2

PVT. HEALTH INST.

YES

1

NO

2

PHARMACY

07

TBA

3

CLINIC

08

SBA

4

PVT. HOSPITALHEALTH WORKER'S HOME

09
10

OTHER

5

OTHER

11

1

DON'T THINK IS NECESSARY

1

TWICE
MORE THAN
TWICE

2
3

HEALTH FACILTY TOO FARNO TRANSPORT

2
3

HEALTH CARE TOO EXPENSIVE

4TRANSPORT TOO EXPENSIVE

5

YES

1

HEALTH WORKERS UNFRIENDLY

6

NO

2

►

HEALTH WORKERS NOT PRESENT

7► (9.19)(9.19)HEALTH CARE NOT GOOD QUALITY

8

OTHER (SPECIFY___)

9► (9.18)
ID CODEONCEMONTHS

1
2
3
4
5
6

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9

SectionMarriage and maternity history

ALL WOMEN WHO HAVE GIVE LIFE BIRTH DURING PAST 36 MONTHSPart B: Pre and post-natal care

POST-NATAL CARE

LINE NUMBER(9.19)
Where did you give birth?

(9.20)
Who assisted you with this birth?

(9.21)(9.22)
After the birth, did you visit Where did you go for this visit?
a health care facility within
six weeks of delivery for a
GOVT.HEALTH INST.
post-natal checkup?
SHP

01

HP

02

PHC

HOME

1

SHP

1

HP

2 FAMILY MEMBER OR
3 RELATIVES

PHC

4 NEIGHBOURS2

HOSPITALHOSPITAL
PVT. HOSPITAL

5 TBA
6 SBA

3

MOBILE CLINIC

03 DOCTOR
04 NURSE/ANM HA/SAHW
05 /AHW/MCHW/ VHW

AYURVED CENTRE

06

PVT. HEALTH INST.

OTHER

HA/SAHW/AHW/
7 MCHW/CHW/VHW

45

ANM/NURSE/DOCTOR

6

OTHER

7

NO ONE

8

DON'T KNOW

9(9.24)
What is the main reason why you didn't go for postnatal consultations the last time you were pregnant?

(9.23)
Who provided this care?

1
2
1
2
3

YES

1

PHARMACY

07 SBA

4NO

2

CLINIC

08 OTHER

5HEALTH CARE TOO EXPENSIVE

4

PVT. HOSPITALHEALTH WORKER'S HOME

09
10

TRANSPORT TOO EXPENSIVE

5HEALTH WORKERS UNFRIENDLY

6HEALTH WORKERS NOT PRESENT

7HEALTH CARE NOT GOOD QUALITY

8

OTHER (SPECIFY___)9(9.24)

OTHER

11

3

DON'T THINK IS NECESSARYHEALTH FACILTY TOO FARNO TRANSPORT►TBA► NEXT PERSON

1
2
3
4
5
6

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Section 9 Marriage and maternity history

ALL CURRENTLY MARRIED WOMEN AGED 15-49 YEARSIDENTIFICATION CODE

Part C: Family planning(9.25)(9.26)How old
INTERVIEWER:
LOOK BACK AT SECTION were you
when you
1, QUESTIONS:
first got
(1.02)-SEX, (1.03)-AGE
married?
AND (1.06)-MARITALSTATUS :(9.27)(9.28)
Do you know By which medium did youof any method learn about family planning
to prevent
methods?
pregnancy orspace births?
RADIO
1
TELEVISION
NEWSPAPERS/PAMPHLETS/
POSTERSIS THE PERSON AWOMAN, AGED 15-49
YEARS AND MARRIED?

YES

1

NO

2
► NEXT PERSON
YEARS

methods?

2
3

INJECTABLES

4

YES
NO

2 HEALTH WORKER

5
6

TEXTBOOKS

7

OTHER

8

YES

1 YES
2 NO

► (9.33)

1
2 PUBLIC HEALTH
3 INSTITUTION
4CONDOM

5 PRIVATE
6 HEALTH
INSTITUTION► (9.34)
NO(9.32)
Where do you/ did you
get this method?

IMPLANTS
TRAD. METHODFRIENDS/
1 RELATIVES► (9.35) HUSBAND(9.31)(9.30)(9.29)Have you (or Are you(or Which method do you
your
your
currently use?
husband)husband)ever used currently
any of these using any of PILL
IUD
methods? these

1 FEMALE
2 STERILIZATION

7 PHARMACY► (9.34)► (9.33)

MALE STERILIZ.

3

OTHER

5

► (9.34)
9

1

TOO EXPENSIVE

2(9.34)(9.35)
During the last How many children would you
six months, did like to have?
any healthworker visit
your home to How many TOTAL?
How many boys?
talk aboutfamilyHow many girls?
planning?

2

4

8

NOT AVAILABLE

HUSBAND AWAY
3
WANT MORE CHILDREN 4

VSC► (9.34)
OTHER

1(9.33)
Why not?

RELIGIOUS REASONS

5SCARED OF SIDEEFFECTS

6

HUSBAND DOES NOT
WANT

7

OTHER

8

IF RESPONDENT REPLIES“UPTO GOD OR KARMA”
CODE AS “98”TO COLUMN
TOTAL
YES

1

NO

2
A

B

C

TOTAL

BOYS

GIRLS

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section 9 Marriage and maternity history

PART D: Household decisions (PART I)RESPONDENT: WOMAN WHO IS THE SPOUSE OF THE HOUSEHOLD HEAD OR FEMALE HEAD OF THE HOUSEHOLD(9.36)COPY THE ID CODE OF RESPONDENTDECISION NUMBER(9.38)(9.37)
During the last 12 months Were you involved in the most
did your household have to recent decision on ..[TYPE OF
DECISION]..?
make a decision on…[TYPE OF DECISION]..?

(9.39)
Who made the final decision on ..[TYPE
OF DECISION]..?

ME

1

YES

1

A LOT

1

MY SPOUSE

2

NO

2

A LITTLE

2

BOTH

3

NOT INVOLVED

3

OTHER

4

► NEXT DECISIONDECISION DESCRIPTION
01

Up to what grade should the children attend school

02

Which school do the children go to

03

Obtaining health care for self

04

Obtaining health care during pregnancy

05

How many children to have

06

Which contraceptive method use

07

Obtaining health care for children

08

Spending on food09

Spending on major household items

10

Selling household assets (including livestock)

11

Which crops to grow12

To take loans

13

How to use loans

14

To migrate for employment

15

How to use remittances

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Section10

Jobs and time use

Part A: Time use

FOR ALL HOUSEHOLD MEMBERS 5 YEARS AND OLDER(10.01)
How many hours has ..[NAME].. spent doing the following activities during the past 7 days?

IDENTIFICATION CODE

IN COMPLETED HOURS
A

B

C

D

Fetching water Collecting
Collecting
firewood and fodderdung

N° HRS

N° HRS

N° HRSEF

Taking care of Making mats,knitting,
animals
weaving,
tailoring

N° HRS

N° HRSG

WRITE ZERO IF NONE
H

Processing
TOTAL
preserved food ( A + B +C+ D+(pickle, jam,wine, etc) and E + F)
milling

N° HRS

N° HRS

Minorhouseholdrepairs

N° HRS

I

J

K

L

Cooking/
Cleaning
Shopping forserving food house,householdfor household laundry, dishes

N° HRS

N° HRS

N° HRSM

N

Babysitting /Caring for
elderly, sick or caring forchildrendisabled

N° HRS

N° HRS

Other
volunteer/com
munityservices

N° HRS

01
02
03
04
05
06
07
08
09
10
11
12
13
14
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B

C

D

E

F

G

H

TOTAL

SATURDAYIN AGRICULTURENOT INAGRICULTURE

1
2
3
4
99

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

1
2
3
4
99

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

1
2
3
4
99

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

1
2
3
4
99

1
2

SELF - EMPLOY-MENTFRIDAYHRS / DAYAWEDNESDAY

DAYS / MTH

IN AGRICULTURENOT IN AGRICULTURE

WRITE ZERO IF NONETUESDAYL

WAGE EMPLOYMENTWRITE THE NUMBER OF HOURS PER DAY

A
B...how many...how manydays per month hours per day
did you work on did you work on
this?
this?

MONDAYK

SECTOR OF EMPLOYMENT(10.07)
What was the type of work?

(10.05)(10.06)
On average, during the months How many hours per day did you work on this job during the past 7
when you did this job during the days?
past 12 months,

SUNDAYJ

PAST 7 DAYS

Chaitra

I

Magh

H

Falgun

G

Poush

F

KartikE

MangsirD

Bhadra

NSCO
CODE

C
Ashah

DESCRIPTION OF OCCUPATION

ID CODEB
JesthaEXCLUDE ACTIVITIES ALREADY ASKED IN
PART A

BiisharA

1
2

AshwinYESNOPROBE FOR ALL JOBS CONDUCTED DURING
THE PAST 12 MONTHS, EVEN IF THEY ARE NOT
BEING CONDUCTED AT PRESENTPart B: Jobs during the past 12 months

FOR ALL HOUSEHOLD MEMBERS 5 YEARS AND OLDER

PAST 12 MONTHS(10.04)In which month did you work on this job during thepast 12 months ?

THURSDAY

Jobs and time use(10.03)
Please describe all the jobs conducted during thelast 12 months.

JOB ID(10.02)
INTERVIEWER:
COPY THE ID
CODE OF ALL
HOUSEHOLD
MEMBERS 5YEARS AND
OLDER

10

ShrawanSectionTOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

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B

C

D

E

F

G

1
2

H

TOTAL

SATURDAYSELF - EMPLOY-MENTFRIDAYHRS / DAYAWEDNESDAY

DAYS / MTH

IN AGRICULTURENOT IN AGRICULTURE

WRITE ZERO IF NONETUESDAYL

WAGE EMPLOYMENTWRITE THE NUMBER OF HOURS PER DAY

A
B...how many...how manydays per month hours per day
did you work on did you work on
this?
this?

MONDAYK

SECTOR OF EMPLOYMENT(10.07)
What was the type of work?

(10.05)(10.06)
On average, during the months How many hours per day did you work on this job during the past 7
when you did this job during the days?
past 12 months,

SUNDAYJ

PAST 7 DAYS

Chaitra

I

Magh

H

Falgun

G

Poush

F

KartikE

MangsirD

Bhadra

NSCO
CODE

C
Ashah

DESCRIPTION OF OCCUPATION

ID CODEB
JesthaEXCLUDE ACTIVITIES ALREADY ASKED IN
PART A

BiisharA

1
2

AshwinYESNOPROBE FOR ALL JOBS CONDUCTED DURING
THE PAST 12 MONTHS, EVEN IF THEY ARE NOT
BEING CONDUCTED AT PRESENTPart B: Jobs during the past 12 months

FOR ALL HOUSEHOLD MEMBERS 5 YEARS AND OLDER

PAST 12 MONTHS(10.04)In which month did you work on this job during thepast 12 months ?

THURSDAY

Jobs and time use(10.03)
Please describe all the jobs conducted during thelast 12 months.

JOB ID(10.02)
INTERVIEWER:
COPY THE ID
CODE OF ALL
HOUSEHOLD
MEMBERS 5YEARS AND
OLDER

10

ShrawanSectionIN AGRICULTURENOT INAGRICULTURE

3
4

1
2
3
4

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

99
1
2
3
4

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

99
1
2
3
4

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

99
1
2
3
4

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

99

Section10

Jobs and time use

FOR ALL HOUSEHOLDYEARS AND
OLDER
Nepal MEMBERSNLSS Ⅲ 52010/11
ManualVersion 2.0

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PAST 12 MONTHS

PAST 7 DAYSSECTOR OF EMPLOYMENT(10.03)(10.04)(10.05)

Please describe all the jobs conducted during thelast 12 months.

In which month did you work on this job during thepast 12 months ?

On average, during the months How many hours per day did you work on this job during the past 7
when you did this job during the days?
past 12 months,

C

D

E

F

G

IN AGRICULTURE

1NOT IN AGRICULTURE

2

H

TOTAL

SELF - EMPLOY-MENT
SATURDAYHRS / DAYB

FRIDAYDAYS / MTH

A

THURSDAYLWEDNESDAYK

TUESDAYJ

WAGE EMPLOYMENTWRITE ZERO IF NONEMONDAY

I(10.07)
What was the type of work?

WRITE THE NUMBER OF HOURS PER DAY

A
B...how many...how manydays per month hours per day
did you work on did you work on
this?
this?

SUNDAY

H(10.06)

Chaitra

G

Magh

F

Falgun

E

Poush

D

KartikC

Mangsir2

Ashwin

NSCO
CODE

NO

BhadraDESCRIPTION OF OCCUPATION

ID CODE

B

1

Ashah

EXCLUDE ACTIVITIES ALREADY ASKED IN
PART A

BiisharA

YES

Shrawan

PROBE FOR ALL JOBS CONDUCTED DURING
THE PAST 12 MONTHS, EVEN IF THEY ARE NOT
BEING CONDUCTED AT PRESENT

JesthaJOB ID(10.02)
INTERVIEWER:
COPY THE ID
CODE OF ALL
HOUSEHOLD
MEMBERS 5YEARS ANDOLDER

IN AGRICULTURE

3NOT INAGRICULTURE

4

1
2
3
4
99

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

1
2
3
4
99

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

1
2
3
4
99

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

1
2
3
4
99

TOTAL NUMBER OF HOURS WORKED BY THE PERSON DURING THE PAST 7 DAYS:

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Section

11 Unemployment / Under-employment

FOR ALL HOUSEHOLD MEMBERS 5 YEARS AND OLDER

IDENTIFICATION CODEUNEMPLOYMENT (NOT WORKING)z

IF MORE THAN 40 HRS ► NEXTPERSONz

IF GREATER THAT ZERO BUTLESS THAN 40 HRS ► (11.05)

z

IF ZERO, CONTINUE WITHQUESTION (11.02)

UNDEREMPLOYMENT (WORKING <40 HOURS/WEEK)(11.04)(11.03)
Did ..[NAME].. Why was ..[NAME].. not available /did ..[NAME].. notlook for work look for work during the past 7 days?
during the past
ATTENDING SCHOOL
01
7 days?
HOUSEHOLD DUTIES
02(11.01)(11.02)
INTERVIEWER:
Was
LOOK BACK TO QUESTIONS (10.01)..[NAME]..
COLUMN "G" AND (10.06) COLUMN "H":available for
HOW MANY HOURS DID ..[NAME].. WORK IN work during
TOTAL DURING THE PAST 7 DAYS?
the past 7
days?

OLD/SICK

03

DISABLED

04

THOUGHT NO WORK AVAILABLE

0506(11.05)
Was..[NAME]..
available for
additional
work during
the past 7
days?

(11.06)(11.07)
Did ..[NAME].. Why was ..[NAME].. not available/ did ..[NAME].. not look for more work
look forduring the past 7 days?
additional work
COULD NOT FIND MORE WORK/LACK OF BUSINESS
01
during the past
LACK
OF
FINANCE,RAW
MATERIALS
02
7 days?
MACHINERY, ELECTRICAL , OTHER BREAKDOW
03
OFF SEASON INACTIVITY

AWAITING REPLY TO EARLIER ENQUIRIESWAITING TO START ARRANGED JOB/BUSINESS 07
YES
YES

1

NO

2 NO

1
► NEXT OFF SEASONPERSON NOT AVAILABLE
2 OTHERS (SPECIFY)► (11.04)

YES

08
09

YES

1

10

NO

2
► (11.07) NO► NEXT PERSON

104

INDUSTRIAL DISPUTE (STRIKE, LAID OFF)05

OTHER INVOLUNTARY (SPECIFY)

06

HAVE SUFFICIENT WORK

07

HOUSEHOLD DUTIES08

STUDENT, UNPAID TRAINING

09► NEXT ILLNESS, DISABILITYPERSON VACATION, FAMILY REASON

10

2

11

PREGNANT/DELIVERY12

OTHER VOLUNTARY (SPECIFY)13

NB. OF HRS

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section

12 Wage jobs

(12.01)(12.02)(12.03)
INTERVIEWER:
Which industrial sector do you work for?
How did you work to receive wages?
COPY THE ID CODE ANDJOB ID FROM SECTION 10
FOR ALL JOBS
CLASSIFIED WAGE JOB(QUESTION (10.07) CODES
1 AND 2)WRITE THE NAME OF GOODS AND SERVICES PRODUCED
1
AT THE INDUSTRIAL SECTOR WHERE THE RESPONDENT DAILY BASISWORKS
LONG TERM BASIS
2► (12.07)
CONTRACT/ PIECE3
RATE► (12.21)
ID CODEJOB ID

LINE NUMBER

PAID BY DAY

PRODUCED GOODS AND SERVICES

NSIC
CODE

(12.05)(12.04)
How much did you get What did you get in kind?
in cash per day for thisjob?

(12.06)
What was the value of what you received in kind?

PADDY/RICE
WHEAT
MAIZE
MEALS
CLOTHINGOTHER
RECEIVED NOTHING IN KIND

1
2
3
4
5
6
7

► NEXT JOBRUPEESFIRST

SECOND

RECORD VALUE OF DAILY IN-KIND
PAYMENTS AND VALUE OF IN-KIND
PAYMENTS FOR WHOLE PERIOD WORKED► NEXT JOBRUPEES PERDAYRUPEES/WHOLE PERIOD

1
2
3
4
5
6
7
8

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Section

12 Wage jobs
PAID ON A LONGER BASIS (IN AGRICULTURE)

PAID ON A LONGER BASIS(12.07)
INTERVIEWER:
IS THIS WAGE JOB INVOLVED IN
AGRICULTURE?

(12.08)(12.09)How much did you get What did you get in kind?
in cash for this job overthe past 12 months?

(12.10)What was the value of what you
received in kind?

LINE NUMBER

PADDY/RICE

1
WAGE JOB IN AGRICULTURE
WAGE JOB NOT IN
AGRICULTURE

1
2
3
4
5
6
7

WHEAT
MAIZE
MEALS
CLOTHINGOTHER
RECEIVED NOTHING IN KIND

2
► (12.15)(12.11)(12.14)(12.12)(12.13)
Did you at any Did any other
Did you shareDid you tend any
time take a loan member of your crop any land with livestock for yourfrom yourhousehold also
your employer?
employer?
employer?
work for the same
employer?

RECORD VALUE OF DAILY IN-KIND
PAYMENTS AND VALUE OF IN-KIND
PAYMENTS FOR WHOLE PERIOD
WORKED
YES

1 YES

1 YES

1 YES

1

NO

2 NO

2 NO

2 NO

2

► (12.12)

RUPEESFIRST

SECONDRUPEESPER DAYRUPEES/WHOLEPERIOD► NEXT
WORK

1
2
3
4
5
6
7
8

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Section

12

Wage jobs
PAID ON A LONGER BASIS (NOT IN AGRICULTURE)(12.15)How much did you get for this job?

(12.16)
Are taxes
already
deducted?

CONTRACT/PIECE-RATE(12.17)
Do you contributeto an Employee
Provident Fund?

(12.19)(12.18)Will you receive a Do you receive
pension when you subsidized
medical care?
retire?

(12.20)How many people
work for youremployer?

(12.21)
During the past 12 months, having
worked on a contract how much did
you receive in-kind and cash?

LINE NUMBER

WRITE ZERO IF NOTHINGPER MONTH
Take-home pay per Transport permonth?
month?

PAST 12 MONTHS
Bonuses, tips,
Uniform / clothing ? Any otherallowances (include.
payments?
Dasain)?

YES

1 YES

1 YES

1 YES

NO

2 NO

2 NO

2 NO

ONE
1 2-9
2 10 OR MORE

1
2
3

► NEXTWORKRUPEESRUPEESRUPEESRUPEESRUPEESRUPEES

1
2
3
4
5
6
7
8

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Section(13.01)13

Farming and livestock

PART A1: Landholding - land owned

ID CODE OF RESPONDENT:

PLOT NUMBER(13.03)
MAKE A LIST OF ALL THE PLOTS/GARDENSTHAT THE HOUSEHOLD OWNS:(13.02)

Does your household own any agricultural land?

(13.04)
What is the total area of this ..[PLOT]..?

(13.05)(13.06)
Where is this plot What type of land islocated?
the ..[PLOT]..?

YES
NO

1
2 ► PART A2(13.07)(13.08)Is the .[PLOT].
Is the irrigation on
irrigated or rainfed? the .[PLOT].
seasonal or year
round?

UNIT CODEROPANI

1

TUBEWELL/BORING
CANAL

1
2

BIGHA

2

POND/TANK3OTHER NATURAL
SOURCES

4

MIXED

5

UPLAND

1 IRRIGATED

1LOWLAND

2 RAINFED

2

AREA
PLOT DESCRIPTION OR NAME

(13.10)(13.09)What is the mode of irrigation on If you wanted to buy/sella plot exactly like this,the .[PLOT].?
how much would it
cost/fetch you?

UNIT

R/B

A/K

► (13.10)
P/D

DISTRICT CODESEASONAL
YEAR
ROUND

1
2

RUPEES

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20

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Section 13

Farming and livestock

PART A1: Landholding - land owned

DRY SEASONWET SEASON(13.11)
Over the past DRY SEASON what did you dowith the .[PLOT].?

SHARECROPPED OUT(13.12)(13.13)For the plots which you did For the plots which you cropped
not crop yourself during the yourself, what crops did you growlast dry season, what net during the dry season?
1 ► (13.13) rent did you receive fromthe tenant?
2

FIXED RENT OUT
MORTGAGED OUT

34 ► (13.14)

PLOT NUMBER

CROPPED YOURSELFCROP CODE► (13.14)

LEFT FALLOW

5 ► (13.14)

OTHER

6

NET RENT (Rs.)
CASH

IN-KIND

A

B

C(13.14)
Over the past WET SEASON what did you do with the.[PLOT].?

CROPPED YOURSELF(13.15)(13.16)
For the plots which you did not For the plots which you cropped
crop yourself during the last wet yourself, what crops did you growseason, , what net rent did you during the wet season?
receive from the tenant?

1 ► (13.16)

SHARECROPPED OUT

2

FIXED RENT OUT

3

CROP CODE

MORTGAGED OUT

4►NEXT PLOTLEFT FALLOW

5►NEXT PLOT

OTHER

6

D

► NEXT PLOT

NET RENT (Rs.)
CASH

IN-KIND

A

B

C

D

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20

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Section

PLOT NUMBER(13.17)

13

Farming and livestock

ID CODE OF RESPONDENT:PART A2: Landholding - land sharecropped/rented/mortgaged -in(13.18) Over the past AGRICULTURE YEAR did your household cultivate land owned bysomeone else (or that was mortgaged in)?

(13.20)(13.22)(13.19)(13.21)
MAKE A LIST OF ALL THE
What is the contractual arrangement on How much “rent” did What is the total area of this ..[PLOT]..?
you pay for this plot
PLOTS/GARDENS THAT THE
this .[PLOT].?
to the landlord?
HOUSEHOLD CULTIVATED
THROUGH SHARECROPPING-IN,
RENTING-IN OR MORTGAGING- SHARECROPPEDUNIT CODE
1
IN
ROPANI1► (13.22)
RENTED-IN

2

MORTGAGED-IN

3

OTHER
PLOT DESCRIPTION OR NAMEBIGHAINCLUDE ONLY
► (13.22) CASH PAYMENTS
IF NOTHINGWRITE ZERO
4
RUPEES

R/B

1
2 ► PART A3(13.23)(13.24)(13.25)What type of land is Is the .[PLOT].
Is the irrigation on thethe ..[PLOT]..?
irrigated or rainfed? .[PLOT]. seasonal or year
round?

2
UPLAND

1

IRRIGATED1

LOWLAND

2

RAINFED

2

AREA
UNIT

YES
NO

A/K

► (13.27)

SEASONAL
YEAR ROUND

1
2(13.26)
What is the mode of irrigationon the .[PLOT].?
TUBEWELL/ BORING

1

CANAL

2

POND/TANK3OTHER NATURAL
SOURCES

4

MIXED

5

P/D

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

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Section 13

Farming and livestock

PART A3: Landholding - increase-decrease in holdings

PART A2: Landholding - land sharecropped/rented/mortgaged -in(13.29) Did your household sell/transfer any land over the past 12 months?
YES
1NO
2 ► (13.32)(cont.)WET SEASONDRY SEASON

PLOT NUMBER(13.27)
What crops did you cultivate over the past DRY
SEASON?

(13.28)What crops did you cultivate over the past WET
SEASON?

CROP CODE

CROP CODE(13.30) How much land did your household sell/transfer?
R/B
1

ROPANI

2

BIGHA

P/D

A/K

(13.31) How much did your household receive from the sales?
WRITE ZERO IF FREERUPEES(13.32) Did your household buy/get any land over the past 12 months?
A

B

C

D

A

B

C

D

01

YES

1

02

NO

2

► PART B

03
04
05

IF THERE IS NO LAND ON THE OWNERSHIP (13.02) , NO LAND OPERATED (3.18), NO LAND SOLD/BOUGHT(13.29) AND NO LAND BOUGHT (13.32) DURING THE REFERENCE PERIOD► PART E

06
07(13.33) How much land did your household buy/get?

08

R/B

09

1

ROPANI

10

2

BIGHA

A/K

P/D

11
12

(13.34) How much did your household pay for this land?

13

WRITE ZERO IF FREERUPEES

14
15

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Section13

Farming and livestockLINE NUMBER(13.35)In the past AGRICULTURE YEAR, what crops
did you grow?
LIST ALL CROPS GROWN BY HOUSEHOLDFIRST BEFORE ASKING Q. 13.36 - 13.38.

(13.36)
Did you use an
improved variety
of seed of..[CROP]..?

PART B: Production and uses

(13.37)
Please provide the following information related to quantity of ..[CROP]..
produced by your household, as well as the various uses to which it was put:

(13.38)
Please report the total quantity and value of ...[crop]...sold

IF CROP NOT, LEAVE THE BLANK IN COLUMN "A" AND
WRITE "0" IN COLUMN B , C & D

USE QUANTITY CODES IN COLUMN "A"

CROP DESCRIPTION

YES

1

NO

2

A
UNIT

B
Total quantity
HarvestedC
Quantity given tolandlordD
Quantity sold (orexpected to sell)

CODE

A
UNIT

B
Total quantity soldC
Price per unit

D
TOTAL SALES(B*C)

RUPEES/UNIT

RUPPES

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

98 TOTAL

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Section13

Farming and livestock

PART C1: Expenditures on seeds and young plants
Did you purchase or receive any seeds or young plants over the past AGRICULTURE YEAR?
YES
NO

1
2 ► PART C2(13.40)
For which crops did you purchase orreceive any seeds or young plants overthe past AGRICULTURE YEAR??
LINE NUMBER

PART C2: Expenditures on fertilizers and insecticides(13.43) Did you purchase any fertilizers or insecticides over the past AGRICULTURE YEAR (or receive them from the landlord)?

WRITE NAME AND CODE OF CROP

CROP DESCRIPTION

CODE

YES
NO

(13.41)
Where did you obtain them?
AGRICULTURE. DEV. OFFICE/SER.CENTRE 1
SEEDS COMPANY/COOPERATIVE

2OTHER FARMERS

3PRIVATE DEALER

4FROM INDIA

5

LANDLORD

6

OTHER

7
FIRST

SECOND

01
02
03(13.42)
How much did
you spend on
buying them?

1
2 ► PART C3(13.44)TYPE OF FERTILIZER ORINSECTICIDE:(13.46)(13.45)
Was this fertilizer CROPS ON WHICH USED:or insecticide usedin the past AGRIC
YEAR?

LINE NUMBER(13.39)IF NOTHING WRITE ZEROIF NOTHINGWRITE ZERO

YES

1

NO

2
CROP CODE► NEXT TYPEDESCRIPTIONRUPEES(13.47)AMOUNT PURCHASEDUNIT

01 UREA

KG

02 COMPLEX

KG

03 DAP
04 ORGANIC/COMPOSTFERTILIZER

KG

A

B

C

QUANTITY

KG

05

05 OTHER CHEMICALFERTILIZER

06

06 INSECT/PESTICIDES

07

07

TRANSPORTATION COSTS:

08

08

TOTAL EXPENDITURE ON FERTILIZER AND INSECTICIDES:

04

RUPEES

KG

09(13.48)10B) Were you able to obtain all the fertilizer you
needed over the past AGRICULTURE YEAR?

A)
Where did you obtain most of the fertilizer?

11
12

GOVERNMENT

1

13

NGO

2

YES

1 ► PART C314PRIVATE

3

NO

2

15
16

97

TRANSPORTATION COSTS:

98

TOTAL EXPENDITURE ON SEEDS AND PLANTS:

COOPERATIVE / COMMUNITY

4(13.49) Why were you unable to get all the fertilizer you needed in the past AGRICULTURE YEAR?
NOT AVAILABLE FOR PURCHASE

1NO MONEY FOR PURCHASE

2

OTHER

3

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Section(13.50)13PART C3: Expenditures on agricultural inputs - hiring labour

Did you hire any casual farm workers over the past AGRICULTURE YEAR?

(13.51)
WORKERS HIRED ONA DAILY BASIS
LINE NUMBER

Farming and livestock

USE SEPARATE ROWS FOR
WORKERS HIRED ON A PIECERATE BASIS AND TIME BASIS

YES
1
NO
2 ► (13.58)
ONLY EXCHANGE LABOUR
LINE-13
PAID ON A DAILY BASIS(13.55)(13.52)(13.53)(13.54)
Did you hire these workers For how many days in total did How much did you pay in cash What was the value of what
you gave in kind to eachon a piece-rate basis?
you hire this type of workers per day to each worker?
worker? (meals, etc.)
over the past AGRICULTURE
YEAR?

YES 1
NO

► (13.57)

A

B

C

D

TOTAL MAN-DAYS

RUPEES PER DAY

RUPEES PER DAY

EXPENDITURE PER
WORKER

MALE

MALE

MALE

MALE

2

DESCRIPTION(13.56)INTERVIEWER:
ADD THE AMOUNTSREPORTED IN Q13. 53 AND
Q13.54(B + C)FEMALE

FEMALE

FEMALE

FEMALE(13.57)
INTERVIEWER:
MULTIPLY MAN-DAYS REPORTED IN "A"
BY THE AMOUNT IN "D"(A. x D)IF SKIPPED FROM Q13.52, WRITE THE
TOTAL ONLYRUPEES
EXPENDITURE PER WORKER
MALE

FEMALE

TOTAL

01
02
03
04
05
06
07
08
09
10
11
12
13

(13.59)EXCHANGE LABOR

Did you hire any permanent farm workers over the past AGRICULTURE
YEAR?
YES

1

NO

2 ► (13.62)(13.58) TOTAL EXPENDITURE ON HIRING CASUAL WORKERS (IF NOTHING WRITE ZERO)RUPEES(13.61) How much did you pay (cash and in-kind) the permanent workers over the
past AGRICULTURE YEAR? SUB-TOTAL - PERMANENT WORKERSRUPEES(13.62) TOTAL EXPENDITURE ON HIRING FARM LABOR(13.60)How many permanent workers did you hire?

(ADD ( Q 13.58 AND Q 13.61 ) AND WRITE IN BOX)RUPEESNUMBER

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Farming and livestock

PART D: Agriculture-earnings/expenditures

REVENUES

EXPENDITURES

SOURCE NUMBER

REVENUE SOURCERUPEES

1 TOTAL CROP SALES(COPY FROM PART B ROW 98)
2(13.63)TOTAL REVENUE OVER
AGRICULTURE YEAR

Sale of crop by-products (straw, husk, etc.)

INCOME FROM RENTING OUT:
3 Draft animals

EXPENDITURE ITEM
EXP. NUMBER

Section(13.64)TOTAL REVENUE OVER
AGRICULTURE YEARRUPEES

1 TOTAL EXPENDITURE ON SEEDS, ETC.
(COPY FROM PART C1 ROW 16)
2 TOTAL EXPENDITURE ON FERTILIZER(COPY FROM PART C2 ROW 8)3 TOTAL EXPENDITURE ON HIRED LABOR(COPY FROM PART C3 question [13.62] )4 Irrigation charges/maintenance of watercourse, etc.

4 Tractor
5 Transportation of crops to market
5 Thresher
6 Sacks, twine, or other containers
6 Other machinery
7 Storage facilities
7 Other income
8 Improvements on land or buildings
8 TOTAL REVENUES
9 Repair and maintenance of equipment
EXPENDITURE ON RENTING IN:
11 Draft animals
12 Tractor
13 Thresher
14 Other machinery
15 Other expenditures
16 TOTAL EXPENDITURES

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SectionLIVESTOCK CODE

(13.65)13

Farming and livestockHas your household owned any livestock over the past 12 months?

(13.66)(13.67)
Did you own any ..[ANIMALS].. over the past 12
How many do you own now?
months? PUT A TICK "9" IN THE APPROPRIATE BOX
For how much could you buy them all
FOR ANIMAL. IF THE ANSWER IS YES, ASK Q
today?
(13.67) TO (13.70)

NO

ANIMALYESNUMBER

RUPEESPART E: Livestock – ownership and related expenses
YES

1

NO

2 ► PART F(13.68)(13.69)How many did you have 12 months ago? How many did you sell over the past 12
months?
For how much could you have bought
How much did you sell them for?
them all then?

NUMBER

RUPEESNUMBER

RUPEES(13.70)How many did you buy over the past 12
months?
How much did you pay for them?

NUMBER

RUPEES

1 Bullocks/Cows
2 He/She Buffaloes
3 Goats/Castrated goat
4 He/She Sheep
5 Yaks/Naks
6 Pigs/Pork
7 Horses/Donkeys/Mules
8 Poultry/Ducks/ Pigeons
9 Other livestock
TOTAL

10

INCOME ITEM

EXPENDITURES(13.71)TOTAL REVENUE OVER PAST 12 MONTHSRUPEESNUMBERNUMBERINCOMEEXPENDITURE ITEMRUPEES

1 Milk

1 Fodder2 Ghee2 Transportation of animal feed

3 Eggs3 Veterinary services, inoculations, etc.
4 Other expenditures(Reproduction, Shade
improvement, Twine, etc.)

4 Curd
5 Meat(13.72)TOTAL EXPENDITURE OVER PAST 12 MONTHS

6 Animal hides
7 Other income (Reproduction, Manure, Wool, Bones, etc.)

8 TOTAL EXPENDITURES

8 TOTAL INCOME(13.73)

During the past 12 months, did you hire any labor for
tending livestock?

YES

1

NO

2

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Section(13.74)13

Farming and livestockHas your household owned any equipment over the past 12 months?

PART F: Ownership of farming assets and extension services
YES

1

NO

2 ► (13.82)

EQ. CODE(13.75)
Do you own a ..[EQUIPMENT]..?

(13.76)(13.78)(13.77)
How many
For how much could you How many ..[AGRICULTURAL..[AGRICULTURALEQUI sell them all today?
EQUIPMENT].. did your household sell
PMENT].. does yourover the past 12 months?
PUT A TICK "9" IN THE APPROPRIATE BOX FOR ALL
household presently
EQUIPMENT. IF THE ANSWER IS YES, ASK Q.
IF NONE WRITE
own?
(13.76) TO (13.81)
ZERO AND ► (13.80)

Tractor2

Plough3

Cart

4

Thresher

5

Trolley

6

Water Pump

7

Generator/Diesel Engine

8

Grain Storage Bin

9Other Machinery10(13.82)(13.83)NO

EQUIPMENT1YES

NUMBER

RUPEES(13.79)(13.80)How many ..[AGRICULTURAL
How much did you
receive from the sale OF EQUIPMENT].. did your household buy
over the past 12 months?
AGRICULTURAL
EQUIPMENT?
IF NONE WRITE
ZERO AND ►NEXT EQUIPMENTRUPEESNUMBERNUMBER(13.81)
How much did you pay for
AGRICULTURAL
EQUIPMENT?

RUPEESTOTAL

Have you or any member of your household taken technical advice from Government Agriculture
Technician over the past 12 months?

(13.84)

Have you or any member of your household taken technical advice from Government
Livestock Service Technician over the past 12 months?

YES

1 ► (13.84)

YES

1 ► NEXT SECTION

NO

2

NO

2

Why haven't you or any member of your household taken technical advice from Government Agriculture
Technician over the past 12 months?

(13.85)

Why haven't you or any member of your household taken technical advice from Government
Livestock Service Technician over the past 12 months?

SERVICE FAR AWAY

1SERVICE FAR AWAY

1SERVICE IS NOT GOOD

2

SERVICE IS NOT GOOD

2

NOT NECESSARY

3NOT NECESSARY

3

OTHER

4

OTHER

4

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Section(14.01)14

Non-agriculture enterprises/activities

PART A: General characteristics

CHECK SECTION 10 QUESTION (10.07) . SEE IF ANY SELF-EMPLOYMENT ACTIVITIES OUTSIDEAGRICULTURE REPORTED (CODE "4")(14.02)
What kind of enterprise did/do you operate?

(14.03)Which people in the household work in thisenterprise/activity?

PRODUCED, GOODS AND NSICSERVICESCODEENTERPRISE CODE

2 ► NEXT SECTION(14.06)
Where do you operate
the enterprise?

HOME

WRITE ID CODES OF MAIN PERSON IN
COLUMN "A" AND OF OTHERS IN OTHER
COLUMNS FROM HOUSEHOLD ROSTERTOTAL TIME IS SUM OF OTHER FIXEDYEARS AND MONTHS. LOCATIONOTHERCHANGING
LOCATIONADESCRIPTION OF THE ACTIVITY

1

NO(14.05)(14.04)
WRITE ID
For how long has the
CODE OFenterprise been
PERSON
operating?
INTERVIEWE
D

CROSS CHECK SELF-EMPLOYMENT ACTIVITIES REPORTED IN
SECTION 10 QUESTION (10.07), CODE 4

WRITE DESCRIPTION IN FULL THE KIND OF ACTIVITY, GOODS ANDSERVICES PRODUCED

YES

B

C

D

(14.07)In the past
12 months,
how manymonths didthe
1 enterprise
2 operate?

(14.08)(14.09)Who owns the business? What share of
the profits is
kept by yourhousehold?

3► (14.10)
PARTNERSHIP/
2
SHARED WITH
OTHER OWNERSOWNED BY
HOUSEHOLD
ONLY

1

E

ID CODE ID CODE ID CODE ID CODE ID CODE

ID CODEYEARSMONTHSMONTHSPERCENT

01
02
03
04
05
06
07
08
09
10

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Section 14

Non-agriculture enterprises/activities

LARGE PRIVATE ENTERPRISES(14.12)(14.11)(14.13)What was your main source of money for Have you tried to borrow
Is thesetting up the business?
money to operate or expand
enterprise
your business in the past 12
registered with01
months?
the01
government? DIDN'T NEED ANY MONEY02
OWN SAVINGS
02
03
RELATIVES/FRIENDS
03

GOVT. OR OTHER PUBLIC FIRM

04

AGRI. DEV. BANK

04

LOCAL RETAIL TRADERS

05

COMMERCIAL BANK

05 YES, SUCCESSFULLYEXPORTERS OR FOREIGN
PURCHASERS

06 YES(14.10)
Who are your customers?
OTHER HOUSEHOLDS OR
INDIVIDUALSSMALL ENTERPRISESENTERPRISE CODE

PART A: General characteristics

NO

1 GRAMEEN-TYPE BANK
06
2 OTHER FINANCIAL INSTITUTION 07 YES, BUT
UNSUCCESSFULLYCONTRACTOR

07

TOURISTS

08

LOCAL GROUP (DHUKUTI)

08 NO

NGO/INTERNAT. ORGANIZATION

09

NGO OR RELIEF AGENCY

09

OTHER

10

SALE OF ASSETS

10

OTHER

11

FIRST

►

(14.14)
Whom did you borrow, or try toborrow, from?

RELATIVES/FRIENDS

1

AGRI. DEV. BANK

2COMMERCIAL BANK

3

GRAMEEN-TYPE BANK
1 OTHER FINANCIAL
INSTITUTION

4
5(14.15)
Did you hire
anyone overthe past 12
months?

(14.16)
How many
workers do
you normallyhire, during amonth whenthe enterpriseis operating?

YES
6 NO

3 NGO OR RELIEF AGENCY

701

CAPITAL OR CREDIT PROBLEMS

02

LACK OF TECHNICAL KNOW-HOW
SUPPLY OF POWER

03
04

1

LACK OF ADEQUATE LABOR

06

2

GOVERNMENT REGULATIONS07

LACK OF RAW MATERIALS08► (14.17)(14.15)SECOND

NO MAJOR PROBLEM

PROBLEMS WITH EQUIPMENT OR SPARE 05
PARTS

2 LOCAL GROUP (DHUKUTI)

OTHER(14.17)
What problems, if any, do you have in running yourbusiness?

8
NUMBERLACK OF CUSTOMERS

09

TRANSPORT PROBLEMS10INTIMIDATION AND LABOR UNREST
SUPPLY OF WATER

12

OTHER

13

11

01
02
03
04
05
06
07
08
09
10

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Section 14

Non-agriculture enterprises/activities

INCOME

ENTERPRISE CODE

(14.18)GROSS REVENUES OVER
THE PAST 12 MONTHS(FROM SALES)

PART B: Income from enterprises

EXPENDITURES OVER PAST 12 MONTHS(14.19)EXPENDITURES ON
WAGESBOTH CASH
AND IN-KIND(14.20)EXPENDITURE ON
FUEL, KEROSENE,
ELECTRICITY, ETC.

(14.21)
EXPENDITURE ON RAW MATERIALS(14.22)OTHER OPERATING
EXPENSES(14.23)
NET REVENUES(14.25)(14.24)
EXPENDITURE ON SALE OF ASSETSCAPITAL GOODS OVER PAST 12
MONTHSOVER PAST 12
MONTHS(14.26)(14.27)
If someone wanted And how muchto buy thiswould he had toenterprise today,
pay one year ago?
how much would hehave to pay?

=A(B + C + D
+ E)

A

B

C

WRITE ZERO IFNOTHINGWRITE ZERO IFNOTHINGRUPEESRUPEESDE

WRITE ZERO IF NOTHINGWRITE ZERO IFNOTHINGRUPEESRUPEES

CASH

IN-KINDRUPEESRUPEESRUPEESRUPEESRUPEESRUPEES

01
02
03
04
05
06
07
08
09
10

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Section

LOAN NUMBER(15.01)15

Credit and savings

PART A: Borrowing and outstanding loans

Do you or any member of your household have loans outstanding, or have your household contracted any loans over the past 12 months
that you have already repaid (include both cash and in-kind loans)?

(15.02)(15.03) (15.04)
ASK THE RESPONDENT TO FIRST MENTION ALL Who was When did you get
the loan?
THE LOANS THAT NEEDS TO BE PAID (OR THAT the
primary
HAS ALREADY BEEN PAID IN CASE OF THE
borro-wer
REFERENCE PERIOD) BY THE HOUSEHOLD
in thehousehold?

DESCRIPTION OF LOAN

ID CODE MONTH

YEAR(15.05)
From whom did you obtain the loan?

YES

1

NO

2 ► PART B(15.07)
How much in total did
you borrow?

(15.06)
For what purpose did you obtain the loan?
BUSINESS OR FARM USE:
PURCHASE OF INPUTS (FERTILIZERS, SEEDS, INSECTICIDES,
ETC.)

01

RELATIVES/FRIENDS

01 PURCHASE OF EQUIPMENT

02

AGRI. DEV. BANK

02 PURCHASE OF LAND

03

COMMERCIAL BANK

03 PURCHASE OF LIVESTOCK

04

GRAMEEN-TYPE BANK

04 BUILDING IMPROVEMENTS FOR BUSINESS05

OTHER FINANCIAL INSTITUTION

05 OTHER BUSINESS OR FARM USE

06

NGO OR RELIEF AGENCY

06 PERSONAL USE:LANDLORD/EMPLOYER.

07 HOUSEHOLD CONSUMPTION NEEDSSHOPKEEPER

08 PURCHASE/IMPROVEMENT OF DWELLING

08

MONEY LENDER

09 MARRIAGE/FAMILY EVENTS

09

COOPERATIVE

10 CONSUMER DURABLES

10

OTHER

11 TO TRAVEL ABROAD

11OTHER PERSONAL USE

INCLUDE ONLY THE
PRINCIPAL

07

12

RUPEES

01
02
03
04
05
06
07
08
09
10

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Section 15

Credit and savings

PART A: Borrowing and outstanding loans(15.08)(15.09)What is/was the interest or interest rate on the Did/do you have to repay the loan by aparticular time?
loan?

(15.10)(15.11)(15.12)
Have you repaid the lone over the last 12 How much Principal What collateral did you use to secure the loan?
and interest in totalmonths?
have you repaid?

AGRICULTURAL LANDLOAN NUMBER

BUILDINGS OR OTHER PROPERTYFULLY PAID

1

PARTLY PAID

2

PROPERTY DOCUMENTS3► (15.12)

PERSONAL GUARANTEENOT PAID AT ALL

RUPEESPERCENT PER
YEAR

MONTH

GOLD/SILVER(15.13)How many days did it take toobtain the loan?

1 COUNT FROM THE TIME
2 YOU FORMALLY
REQUESTED OR APPLIED
3
FOR THE LOAN TO THE
4 TIME YOU RECEIVED THE
5 MONEY

PAST BORROWING RECORD

6

OTHER

7

NO COLLATERAL

8

DAYS

YEAR

01
02
03
04
05
06
07
08
09
10

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Section

LOAN NUMBER(15.14)15

Credit and savings

PART B: Lending and outstanding loans

Does any person outside your household have loans owed to you or any member of your household?
Have any loans been repaid to members of your household over the past 12 months?

(15.15)(15.16) (15.17)(15.18)ASK THE RESPONDENT TO FIRST MENTION ALL Who was When was the loan What is the relationship of the borrower to the
the
made?
primary lender?
THE LOANS THAT NEEDS TO BE OWED (OR
THAT HAS ALREADY BEEN RETURNED IN CASE primarylender in
OF THE REFERENCE PERIOD) BY THE
the
HOUSEHOLDEMPLOYEE OR TENANT FARMER
household?
BUSINESS CUSTOMERDESCRIPTION OF LOAN

ID CODE MONTH

YEAR

YES

1

NO

2 ► PART C(15.20)How much in total did
you lend?

(15.19)What was the primary purpose for which you lent the money?
BUSINESS OR FARM USE:
PURCHASE OF INPUTS (FERTILIZERS, SEEDS, INSECTICIDES,
ETC.)

01

1 PURCHASE OF EQUIPMENT

02

2 PURCHASE OF LAND

03

OTHER BUSINESS ASSOCIATE

3 PURCHASE OF LIVESTOCK04

FRIEND/NEIGHBOR

4 BUILDING IMPROVEMENTS FOR BUSINESS05

RELATIVE

5 OTHER BUSINESS OR FARM USE

06

OTHER

6 PERSONAL USE:
HOUSEHOLD CONSUMPTION NEEDS

07

PURCHASE/IMPROVEMENT OF DWELLING

08

MARRIAGE/FAMILY EVENTS

09

CONSUMER DURABLES

10

TO TRAVEL ABROAD

11OTHER PERSONAL USE

12INCLUDE ONLY THE
PRINCIPALRUPEES

01
02
03
04
05
06
07
08
09
10

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Section 15

Credit and savings

PART B: Lending and outstanding loans(15.21)(15.22)(15.23)What is/was the interest or interest rate on the When is/was the borrower scheduled to Has the borrower finished repaying thefinish repaying the loan?
loan?
loan?

(15.24)
How much in total has been
repaid on the loan?

(15.25)What collateral did you lend against?

LOAN NUMBER

AGRICULTURAL LANDFULLY PAIDPARTLY PAIDNOT PAID AT ALL

RUPEESPERCENT PER
YEAR

MONTH

YEAR

1

1

BUILDINGS OR OTHER PROPERTY

2

GOLD/SILVER

3

2

PROPERTY DOCUMENTS

43► (15.25)

PERSONAL GUARANTEE

5

PAST BORROWING RECORD

6

OTHER

7

NO COLLATERAL8RUPEES

01
02
03
04
05
06
07
08
09
10

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Section 15(15.26)(15.27)(15.28)(15.29)Credit and savings

PART C: Other assets

Does your household own any land or property (do not include property inwhich the household lives, or land or property already reported in Section
13)?

(15.32)

YES

1

YES

1

NO

2 ► (15.32)

NO

2

How much money would it cost to buy property likethat owned by your household?

RUPEESHow much money would it have cost a year ago to
buy the property that your household now owns?

RUPEESHow much did your household spend in total overthe past 12 months in purchasing this property?

RUPEES(15.33)(15.34)(15.35)

WRITE ZERO IF NOTHING(15.30)How much did your household receive in total overthe past 12 months from selling this property?

How much did your household receive in total overthe past 12 months from renting this property toothers?

►PART D

How much money would it cost to buy assets(as mentioned in Q 15.32)
like those owned by your household?

RUPEESHow much money would it have cost a year ago to buy the assets (asmentioned in Q 15.32) that your household now owns?

RUPEESHow much did your household spend in total over the past 12 months
in purchasing these assets?

RUPEES

WRITE ZERO IF NOTHING(15.36)
RUPEES

WRITE ZERO IF NOTHING(15.31)

Does your household own any other real assets (other than financial assets or those assets already beenreported in Section 12 or 13)?

How much did your household receive in total over the past 12 months
from selling these assets?

RUPEES

WRITE ZERO IF NOTHING(15.37)
RUPEESHow much did your household receive in total over the past 12 months
from renting these assets to others?

RUPEES

WRITE ZERO IF NOTHINGWRITE ZERO IF NOTHING

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Section15

Credit and savings

PART D: Household decisions (PART II)RESPONDENT: MALE HOUSEHOLD HEAD OR THE SENIOR MALE HOUSEHOLD MEMBER IF A FEMALE IS THE HEAD
(15.38)COPY THE ID CODE OF RESPONDENTDECISION NUMBER(15.40)(15.39)During the last 12 months Were you involved in the most
did your household have to recent decision on ..[TYPE OF
DECISION]..?
make a decision on…[TYPE OF DECISION]..?

(15.41)
Who made the final decision on ..[TYPE
OF DECISION]..?
ME

1MY SPOUSE/FEMALE HEAD

2

A LOT

1

BOTH

3

OTHER

4

YES

1

A LITTLE

2NO

2

NOT INVOLVED

3► NEXT DECISIONDECISION DESCRIPTION
01

Up to what grade should the children attend school

02

Which school do the children go to

03

Obtaining health care for self

04

Obtaining health care during pregnancy

05

How many children to have

06

Which contraceptive method use

07

Obtaining health care for children

08

Spending on food09

Spending on major household items

10

Selling household assets (including livestock)

11

Which crops to grow12

To take loans

13

How to use loans

14

To migrate for employment

15

How to use remittances

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Section 16

LINE NUMBER(16.00)Absentees information

PART A: General information on absentees

Are there any people that are not current household members, therefore are not in Section 1, but were household members sometime in the
YESpast and is expected to come back to this household?
NO(16.01)(16.02)
LIST ALL THE PEOPLE
GENDER
THAT ARE NOTCURRENT HOUSEHOLDMEMBERS BUT WERE
HOUSEHOLD MEMBERS
SOMETIME IN THE PASTAND ARE EXPECTED TO
COME BACK(16.04)(16.03)(16.05)What is ..[PERSON].. relationship to the head How old is .. What was theof this household?
[PERSON].. highest class
now?
that
..[PERSON]..
completed?
HUSBAND/WIFE
02
SON/DAUGHTER

03

GRANDCHILD

04

FATHER/MOTHER

2 ► NEXT SECTION(16.07)(16.08)How many years Where does
ago did..[PERSON].. live now?
..[PERSON]..
leave thishousehold?

(16.09)What is the main reason why ..[PERSON].. left
this household?

1

TOGETHER WITH FAMILY/RELATIVESOTHER FAMILY REASONS

01
02

05

NEVER MARRIEDSINGLE MARRIED2EDUCATION

BROTHER/SISTER

06

POLY MARRIED

3

TRAINING

03
04

NEPHEW/NIECE

07

RE-MARRIEDSON/DAUGHTER-IN-LAW
BROTHER/SISTER-IN-LAW

08
09

WIDOW / WIDOWER

45DIVORCED

6MALE

1 FATHER/MOTHER-IN-LAW

10SEPARATED7

FEMALE

2 OTHER FAMILY RELATIVE

11

DON'T KNOW

8SERVANT/SERVANT’S RELATIVES

NAME(16.06)
What's ..[PERSON]..'s marital
status?

1

AGE INCOMPLETED YEARS CODE "97" IF
DOES NOTKNOW

IF LESS THAN
1 WRITE "0"
Is it an urban orrural area?

12

LOOKING FOR WORK

05

START NEW JOB

06

START NEW BUSINESS

07
08

JOB TRANSFER

URBAN

1

CONFLICT

09

RURAL

2

NATURAL DISASTER

10

TENANT/TENANT’S RELATIVE

13

EASIER LIFE STYLE

11

OTHER PERSON NOT RELATED

14

OTHER

12

YEARS

EDUCATIONCODEYEARS AGODISTRICT /COUNTRY

U/R

01
02
03
04
05
06
07
08
09
10
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Section 16

Absentees information

LINE NUMBER(16.10)(16.11)
What is ..[PERSON]'s primary activity What´s
now?
..[PERSON]..
type of job?

1

SELFEMPLOYED

2

ECON.
WORK

IF DON’T KNOW WRITE"995"

OCCUPATIONDESCRIPTION

NSCO
CODE

(16.12)How did ..[PERSON]..
find this current job?

WAGE
JOB

IF HOUSEHOLD CHORES,STUDENTAND NO WORK AT ALL
THEN WRITE "998" AND ►(16.13) EXT.

DON'T
KNOW

PART B: Employment and remittances from absentees

RELATIVESFRIENDS/
3 NEIGHBORSEMPLOYMENTAGENCY
4 OTHER
DON'T KNOW

1

(16.13)(16.14)(16.15)(16.16)(16.17)
During the past How many
What is the
How did ..[PERSON].. send you How much
12 months, have times did the most of the money or goods during money did thevalue of all
the past 12 months?
the members of members ofgoods received
householdthis household this household
members receive by thereceived money receive money
householdfromor goods from or goods frommembers from..[PERSON]..
..[PERSON]..? ..[PERSON]..
during the past ..[PERSON]..
during the pastduring the past
12 months?
12 months?
12 months?
FORMAL FINANCIAL
1
INSTITUTIONS

2
3
4

YES

1

5

NO

2

DAILY CONSUMPTION

12

EDUCATION

2

SELF

3

CAPITAL FORMATION

3

THROUGH FRIENDS

4

BUSINESS OR INVESTMENT

4

THROUGH OTHER FAMILYMEMBERS/ RELATIVES

5

HOUSEHOLD ASSETS/DURABLES

5SAVINGS

6

THROUGHACQUAINTANCES6OTHERS

7

WRITE ZERO IF WRITE ZERO IF REPAY LOANNOTHING OTHERS (SPECIFY)NOTHINGRUPEESRUPEES(16.19)How much in total
has been sent by
the household
members to..[PERSON]..
during hte past 12
months?

THE TWO MOST IMPORTANT

HUNDI► (16.19)

TIMES(16.18)What was the use of the remittances
received from …[PERSON].. during the past
12 months?

FIRST

7
8
SECONDINCLUDE VALUEOF GOODSWRITE ZERO IFNOTHINGRUPEES

01
02
03
04
05
06
07
08
09
10
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Section 17

Other remittancesPART A: Remittances and transfer-income sent

(17.01) During the past 12 months, did you, or any member of your household send money or other payments (forexample, food or clothing) to someone who is not a member of your household?

YES

1

NO

2 ► PART B

NOTE FOR THE INTERVIEWER:NOT INCLUDE THOSE ABSENTEES ALREADY LISTED IN SECTION 16

LINE NUMBER(17.03)(17.02)
ID CODE OF What are the names of the people
RESPONDEN to whom members of yourThousehold have sent money orgoods during the past 12 months?

(17.04)
Who in yourhousehold isprimarily
responsible forsending thisassistance?ID
CODE OFRESPONDENT

LIST ALL NAMES BEFORE GOING
TO Q. 17.04-17.10
IF THE RESPONDENT DOES NOTWISH TO GIVE NAMES, LEAVE
BLANK(17.05)(17.06)(17.07)
What is the relationship of the ..[RECIPIENT] .. What is the Where does theto the donor?
sex of the ..[RECIPIENT] ..
.[RECIPIEN currently live?
T].?
HUSBAND/WIFE
02(17.08)What is the .[RECIPIENT].’s
primary work activity?

SON/DAUGHTER

03
04

WAGE EMPLOYEE IN
AGRICULTURE

1

GRANDCHILDFATHER/MOTHER

05
06 MALEWAGE EMPLOYEE IN
NON-AGRI.

2

BROTHER/SISTERNEPHEW/NIECE

07 FEMALE 2 Is it an urban or rural
area?
08

SELF EMPLOYEE IN
AGRICULTURE

3

SELF EMPLOYEE IN
1 NON-AGRI.

42 STUDY
OTHER

5SON/DAUGHTER-IN-LAW
BROTHER/SISTER-IN-LAW

09

WRITE ID FATHER/MOTHER-IN-LAW
CODE FROM OTHER FAMILY RELATIVESECTION SERVANT/SERVANT’S RELATIVESTENANT/TENANT’S RELATIVE
1A
OTHER PERSON NOT RELATEDID CODENAMESID CODE

1

10

URBAN

11

RURAL

12
13
14

DON'T KNOW
DISTRICT /COUNTRY

U/R(17.09)How much in total did
you send to.[RECIPIENT]. over the
past 12 months?

6
7
CASH

(17.10)How did the ..[DONOR]..
usually send the amount?

FORMAL FINANCIAL
INSTITUTIONS

1

HUNDI

2

SELF

3

THROUGH FRIENDS

4THROUGH OTHER
FAMILY MEMBERS/RELATIVES

5

THROUGHACQUAINTANCES

6

IN-KIND OTHERS

7

01
02
03
04
05
06
07
08

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Section 17 RemittancesPART B: Remittances and transfer income received(17.11) During the past 12 months, have you received any money or payments in kind, or gifts from any person who is not amember of your household?

YES

1

NO

2 ► NEXT SECTIONNOTE FOR THE INTERVIEWER:

LINE NUMBERNOT INCLUDE THOSE ABSENTEES ALREADY LISTED IN SECTION 16(17.12)(17.13)(17.15)(17.14)
ID CODE What are the names of all Who in your What is thethe people who sent you household is relationship of the
OF
RESPONDE money or goods during primarily..[DONOR].. to thethe past 12 months?
responsible for recipient?
NT
receiving this
LIST ALL NAMES
assistance?
BEFORE GOING TO Q.
17.14 - 17.22.

(17.17)(17.16)(17.18)What is the sex What is the Where does theage of the ..[DONOR].. currentlyof the..[DONOR].? ..[DONOR]. live?
?

USE CODES FROM MALE
QUESTION
FEMALEIF THE RESPONDENTDOES NOT WISH TO
GIVE NAMES, LEAVE
BLANK

NAMES

(17.21)(17.20)How did the
How much in total did you
receive from. ..[DONOR].. over ..[DONOR]..
the past 12 months?
usually send
the amount?

Is it an urban or rural
area?

2

YEARS

(17.22)
What was the use of the remittances
received from …[DONOR].. during thepast 12 months?

THE TWO MOST IMPORTANT

1(17.05)

ID CODE

What is the.[DONOR].’s
primary work
activity?

WRITE
COMPLETED YEARSWRITE ID
CODE FROMSECTION 1A

ID CODE(17.19)

URBAN

1

RURAL

2

DISTRICT /COUNTRYUSE CODESFROMQUESTION

USE CODESFROMQUESTION(17.08)(17.10)U/R

CASH

IN-KINDDAILY CONSUMPTION

1EDUCATION

2

CAPITAL FORMATION

3BUSINESS OR INVESTMENT

4HOUSEHOLD ASSETS

5SAVINGS

6

REPAY LOAN

7

OTHERS (SPECIFY)

8

FIRST

SECOND

01
02
03
04
05
06
07
08

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SERIAL NUMBER

Section 18 Transfers, social assistance and Other Income(18.01)(18.02)

Did any of the household
members receive
payment from..[SOURCE].. during thepast 12 months?

How frequently are theHow many
household members supposedhouseholdmembers are to receive the payments fromreceiving the ...[SOURCE]...?
payments from...[SOURCE]...?

(18.03)MONTHLY

SOURCE(18.04)(18.05)

Over that [PERIOD]
how much are thehousehold members
supposed to receivefrom ..[SOURCE]..?

How many months How muchago did the[SOURCE] wasactually received
householdmembers receive last time?
(Please provide
THE LAST
approximate
payment frommonetary value for..[SOURCE]..?
in-kind payments)

1(18.06)(18.07)(18.08)(18.09)How many Is anyone currently
months was owed payment?
thispayment
for?

YES

1

QUARTERLY

2

YES

NO
► NEXT SOURCE2

HALF YEARLY3YEARLYNOT APPL.
► NEXT SOURCE

3

4 WRITE "999" IF DON'T
KNOW
5

NO
2
► NEXT SOURCE

ONCE ONLYNUMBERPERIODRUPEESMONTHS AGORUPEESMONTHS

What is thetotal amount
owed?

1

RUPEESCash Transfer Programs
01 Old age pension
02 Widow pension
03 Disability Allowance
04 Endangered Ethnicities Pension
05 Maternal Incentive Scheme
06 Martyr’s Family benefits

SERIAL NUMBER

07 People’s movement victims Benefits(18.10)
Did any of the household members participate in or
receive any benefits from ..[PROGRAM].. during the past
12 months?
YES

1

NO

2

In-Kind Transfer programs
01 Public Food Distribution System
02 Nutritional Supplement program for children
03 Nutritional Supplement program for mothers

Public Works
04 Food for Work
05 Cash for Work
06 Rural Community Infrastructure Works Programme (RCIW)

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Section 18 Transfers, social assistance and Other Income

INCOME CODE

OTHER TYPE OF INCOME(18.11)(18.12)
What is the current
PUT A TICK "9" IN THE APPROPRIATE BOX FOR
ALL ITEMS. IF THE ANSWER IS YES, ASK Q. (18.12) value of the .[ITEM].
that the household
TO (18.14)
owns?

ITEM

NO

YES

RUPEES(18.13)
And what was thevalue of the.[ITEM]. one yearago?

(18.14)
How much has the household
received from ..[ITEM].. in thepast 12 months? (interest,
dividends, profit, payments,
etc.)

RUPEESRUPEES

1 Current/Savings account2 Fixed deposit
3 Stocks, shares, treasury bills, etc.
4

Employee Provident Fund/Citizen
Investment fund

5 Internal Pension
6 External Pension

7 Commission fee, royalties, etc.

8 Other
9 Total

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Section(19.01)19

Adequacy of consumption and government facilities

ID CODE OF RESPONDENT

I would like to ask your opinion of your family’s standard of living.

It was less than adequate for your family's needs

1

It was just adequate for your family's needs

2

It was more than adequate for your family's needs

3

Not applicable4(19.02) Concerning your family’s food consumption over the past one month, which of the following is true?
(19.03) Concerning your family’s housing, which of the following is true?
(19.04) Concerning your family’s clothing, which of the following is true?
(19.05) Concerning the health care your family gets, which of the following is true?
(19.06) Concerning your children’s schooling, which of the following is true?
(19.07) Concerning your family’s total income over the past one month, which of the following is true?
IF THE ANSWER TO Q. (19.02) IS “1”, ASK:
(19.08) Do you consider that you, or any member of your family eats too little food to live a healthy and active live?
YES

1

NO

2

Now, I would like to ask your opinion about the condition of government facilities yourhousehold consuming.

GOOD

1

FAIR

2

BAD

3

NOT APPLICABLE

4(19.09) How do you take the health facilities consuming by your household?
(19.10) How do you take the education facilities consuming by your household?
(19.11) How do you take the drinking water facilities consuming by your household?
(19.12) How do you take the electricity facilities consuming by your household?
(19.13) How do you take the road facilities consuming by your household?
(19.14) How do you take the postal facilities consuming by your household?
(19.15) How do you take the telephone facilities consuming by your household?

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Section(19.17)Adequacy of consumption and government facilities

In the past 30 days, have there been times when the household members didn't have enough foodor money to buy food?

YES

1

NO

2 ► NEXT SECTION

During the past 30 days, how many days has the household experienced this problem?

DAYS(19.18)In the past 30 days, when your household didn't have enough food ormoney to buy food, has your household had to ... [COPING
OPTION]...:SERIAL NUMBER(19.16)

19

YES

1

NO

2

COPING OPTION
01 …rely on less preferred and less expensive foods?
02 …borrow food or money, or rely on help from friends or relatives?
03 …purchase food on credit?
04 …gather wild food, hunt or harvest immature crops?
05 …consume seed stock held for next season?
06 ...send household members to eat elsewhere?
07 …send household members to beg?
08 …limit portion sizes at mealtimes?
09 …restrict consumption of adults so children can eat?
10 …feed working members of household at the expense of non-working household members?
11 …ration the money you had and buy prepared food?
12 …reduced the number of meals eaten in a day?
13 …skip entire days without eating?
14 …sell assets, jewelry to purchase food?

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Section

20 Anthropometrics

FOR CHILDREN 59 MONTHS AND LESSIDENTIFICATION CODE

EXACT AGE OF THE CHILD(20.02)(20.01)Is there a What is ..[NAME].. date ofbirth
birth?
certificate
of..[NAME]..?

YES

1

NO

2

RESULT(20.03)What's..[NAME]..
age inmonths?

ANTHROPOMETRIC MEASUREMENTS(20.04)(20.05)RESULT OF THE MEASUREMENT RECORD HEIGHT IN
CENTIMETERS
MEASURED

1

COULD NOT MEET THE
CHILD AFTERMULTIPLE VISITS

2(20.06)(20.07)
RECORD WEIGHT IN
RECORDKILOGRAMSMETHODFORMEASURINGHEIGHT

REFUSED

3

LYING

DAY

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

MONTH

YEAR

AGE INMONTHS(20.11)(20.10)How many After how
months was many months..[NAME].. did ..[NAME]..
breastfed? start
receiving
complimentary
food?

2

► (20.09)

WRITE "90"
IF NONE4► (20.09)

OTHER (SPECIFY)(20.09)Was ..[NAME]..
exclusively breastfed
until 6 months of age?
IF THE CHILD IS
LESS THAN 6
MONTHS ASK:
Has ..[NAME].. been
exclusively breastfed
until now?

STANDIN 1
G► (20.09)
TOO ILL OR
DISABLED

BREASTFEEDING PRACTICES(20.08)DATE OF MEASUREMENT

5

YES

1

NO

2

WRITE "90" IF
NOTRECEIVING
YET► (20.09)
CENTIMETERSKILOGRAMS

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

DAY MONTHYEARMONTHSMONTHS

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

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A

C

DISTRICT CODESTAPLEJUNG
PANCHTHAR
ILAM
JHAPA
MORANGSUNSARIDHANKUTATEHRATHUMSANKHUWASABHABHOJPURSOLUKHUMBUOKHALDHUNGAKHOTANG

01
02
03
04
05
06
07
08
09
10
11
12
13

LAMJUNG
TANAHUNSYANGJAKASKI
MANANGMUSTANG
MYAGDIPARBATBAGLUNGGULMI
PALPA
NAWALPARASIRUPANDEHI

37
DANDHELDHURA
38
BAITADI
39
DARCHULA
40 OTHER COUNTRIES:
41
INDIA
42
BHUTAN
43
CHINA
44BANGLADESH
45
HONG KONG
46
MALAYASIA
47
JAPAN
48
SAUDI ARABIA
49
QATAR

UDAYAPURSAPTARISIRAHADHANUSHAMAHOTTARISARLAHISINDHULIRAMECHHAPDOLAKHASINDHUPALCHOK

14
15
16
17
18
19
20
21
22
23

KAPILBASTU
ARGHAKHANCHIPYUTHANROLPARUKUM
SALYANDANG
BANKE
BARDIYASURKHET

50
51
52
53
54
55
56
57
58
59

KAVREPALANCHOK24DAILEKH

60

LALITPURBHAKTAPUR
KATHMANDUNUWAKOTRASUWADHADING
MAKWANPURRAUTAHATBARAPARSACHITWANGORKHA

25
26
27
28
29
30
31
32
33
34
35
36

JAJARKOT
DOLPA
JUMLA
KALIKOTMUGU
HUMLABAJURABAJHANG
ACHHAM
DOTI
KAILALIKANCHANPUR

61
62
63
64
65
66
67
68
69
70
71
72

73
74
75
81
82
83
84
85
86
87
88
89

UNITED ARAB EMIRATES

90

UNITED KINGDOMUNITED STATES OF
AMERICA91
92

SOUTH KOREA
AUSTRALIAISRAELOTHER COUNTRY

93
94
95
96

B MONTH CODESBAISAKH
JESTHAASADHSHRAWANBHADRA
ASHWINKARTIK
MARG
PAUSH
MAGH
FALGUNCHAITRA

01
02
03
04
05
06
07
08
09
10
11
12

C

LANGUAGE CODESLANGUAGE CODES (cont)NEPALIMAITHILIBHOJPURI
THARU (DAGAURA/RANA)TAMANG
NEWAR
MAGAR
AWADHIBANTAWAGURUNG
LIMBU
BAJJIKA
URDU

01
02
03
04
05
06
07
08
09
10
11
12
13

JIREL
WAMBULE/ UMBULEPUMAYHOLMONACHHIRING
DURA
MECHE
PAHARILEPCHA/ LAPCHEBOTE
BAHING
KOI/ KOYU
RAJI

42
43
44
45
46
47
48
49
50
51
52
53
54

RAJBANSISHERPAHINDI
CHAMLINGSANTHALICHEPANGDANUWARJHANGAR/ DHANGARSUNUWARBANGLA

14
15
16
17
18
19
20
21
22
23

HAYU
BYANGSHIYAMPHU/ YAMPHEGHALEKHARIYACHHILING
LOHORUNGPUNJABICHINESEENGLISH

55
56
57
58
59
60
61
62
63
64

MARWARI (RAJSTHANI)

24

MEWAHANG

65

MANJHITHAMI
KULUNGDHIMALANGIKAYAKKHATHULUNGSANGPANGBHUJEL/ KHAWASDARAIKHALINGKUMALTHAKALICHHANTYAL/ CHHANTELNEPALI SIGN LANGUAGE
TIBBETAN
DUMI

25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

SANSKRIT
KAIKE
RAUTE
KISAN
CHURAUTIBARAM/ MARAMU
TILUNG
JERO/ JERUNGDUNGMALIORIYALINGKHIM
KUSUNDASINDHIKOCHE
HARIYANWIMAGAHI
SAM

66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82

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LANGUAGE CODES (end)

KURMALIKAGATEDZONKHAKUKI
CHHINTANGMIZONAGAMESELHOMI
ASSAMISESADHANIOTHERS

D

83
84
85
86
87
88
89
90
91
92
93

RELIGION CODESHINDUBOUDDHAISLAMKIRANTJAIN
CHRISTIAN
SHIKH

01
02
03
04
05
06
07

BAHAI

08

OTHER RELIGION09

E EDUCATION CODESF QUANTITY CODES

PRE-SCHOOL/KINDERGARTEN

00

CLASS 1
CLASS 2
CLASS 3
CLASS 4
CLASS 5
CLASS 6
CLASS 7
CLASS 8
CLASS 9CLASS 10
SLC

01
02
03
04
05
06
07
08
09
10
11

INTERMEDIATE LEVEL OR +2
LEVEL

12

BACHELOR LEVELMASTER LEVEL OR HIGHERPROFESSIONAL DEGREE

13
14
15
16
17

LITERATE (LEVELLESS)ILLITERATEKILOGRAM
GRAM
MAUND
LITER
MURI
PATHI
MANNA
KURUWANUMBER/PIECES
DOZEN

01
02
03
04
05
06
07
08
09
10

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G ETHNICITY CODESCHHETRI
BRAHMAN (HILL)
MAGAR
THARU
TAMANGNEWARMUSLIM
KAMI
YADAV
RAI
GURUNGDAMAIN/DHOLILIMBU
THAKURISARKITELICHAMAR/HARIJAN/RAM
KOIRI
KURMI
SANYASIDHANUK
MUSAHAR
DUSADH/PASWAN/PASISHERPASONAR
KEWAT
BRAHMAN (TARAI)BANIYA
GHARTI/BHUJELMALLAHKALWARKUMAL
HAJAM/THAKUR
KANU

H CROP CODES
001
002
003
004
005
006
007
008
009
010
011
012
013
014
015
016
017
018
019
020
021
022
023
024
025
026
027
028
029
030
031
032
033
034

RAJBANSISUNUWAR
SUDHI
LOHAR
TATMA
KHATWEDHOBI
MAJHI
NUNIYAKUMHARDANUWARCHEPANG/PRAJAHALUWAIRAJPUTKAYASTHABADHAE
MARWADISANTHAL/SATARDHAGAR/JHAGARBANTARBARAE
KAHAR
GANGAILODH
RAJBHARTHAMI
DHIMAL
BHOTE
BING/BINDA
BHEDIYAR/GADERINURANGYAKKHADARAITAJPURIYA

035
036
037
038
039
040
041
042
043
044
045
046
047
048
049
050
051
052
053
054
055
056
057
058
059
060
061
062
063
064
065
066
067
068

THAKALICHIDIMARPAHARIMALI
BANGALICHHANTAL
DOM
KAMAR
BOTE
BRAHMU/BARAMU
GAINE
JIREL
ADIBASI/JANAJATIDURACHURAUTE
BADI
MECHE
LEPCHA
HALKHORPUNJABI/SIKH
KISAN
RAJI
BYANGSIHAYU
KOCHE
DHUNIAWALUNG
JAIN
MUNDA
RAUTE
YEHLMOPATHARKATA/KUSWADIYAKUSUNDAOTHER DALITOTHER CASTE

069
070
071
072
073
074
075
076
077
078
079
080
081
082
083
084
085
086
087
088
089
090
091
092
093
094
095
096
097
098
099
100
101
102
103

CEREALS:
EARLY PADDY
MAIN PADDY
UPLAND PADDYWHEATSPRING/WINTER MAIZE
SUMMER MAIZEMILLET
BARLEYBUCKWHEATOTHER CEREALSPULSES AND LEGUMES:
SOYBEANSBLACK GRAMRED GRAMGRASS PEALENTILGRAM
PEA
GREEN GRAM
COARSE GRAM
COW PEAOTHER LEGUMES
TUBER AND BULB CROPS:
WINTER POTATOSUMMER POTATOSWEET POTATOCOLOCASIA
OTHER TUBERSOILSEED CROPSMUSTARDGROUND NUT
LINSEEDSESAMEOTHER OILSEEDCASH CROPS:SUGARCANE
JUTE
TOBACCOOTHER CASH CROPS

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35

SPICES:CHILIESONIONSGARLICGINGERTURMERICCARDAMOM
CORIANDER SEEDOTHER SPICESVEGETABLES:WINTER VEGETABLES
SUMMER VEGETABLESCITRUS FRUITS:ORANGELEMONLIMESWEET LIMEOTHER CITRUS
NON-CITRUS FRUITS:MANGOBANANAGUAVAJACK FRUITPINEAPPLELICHEE
PEAR
APPLE
PLUM
PAPAYAPOMEGRANATEOTHER FRUIT
OTHER:TEA
THATCHFODDER TREESBAMBOOOTHER TREES

36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67

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OCCUPATION NSCO CODESOCCUPATION NSCO CODES (cont)

ARMED FORCES

011

SAFETY AND QUALITY INSPECTORS315

LEGISLATORS

111

LIFE SCIENCE TECHNICIANS AND RELATED ASSOCIATEPROFESSIONALS

321

GOVERNMENT OFFICIALS

112

MODERN HEALTH ASSOCIATE PROFESSIONAL, EXCEPT NURSING

322

OFFICIALS OF SPECIAL INTEREST ORGANIZATIONS114

NURSING AND MIDWIFERY ASSOCIATE PROFESSIONALS

323

DIRECTORS AND CHIEF EXECUTIVES

121

TRADITIONAL MEDICINE PRACTITIONERS AND FAITH HEALERS

324

PRODUCTION AND OPERATIONS DEPARTMENT MANAGERS122

PRIMARY EDUCATION TEACHING ASSOCIATE PROFESSIONALS331

OTHER DEPARTMENT MANAGERS

123

PRE-PRIMARY EDUCATION TEACHING ASSOCIATE PROFESSIONALS

332

GENERAL MANAGERS/MANAGING PROPRIETORS131

SPECIAL EDUCATION TEACHING ASSOCIATE PROFESSIONALS333

PHYSICISTS, CHEMISTS AND RELATED PROFESSIONALS211

OTHER TEACHING ASSOCIATE PROFESSIONALS

334

MATHEMATICIANS, STATISTICIANS AND RELATED PROFESSIONALS212

FINANCE AND SALES ASSOCIATE PROFESSIONALS

341

COMPUTING PROFESSIONALS213

BUSINESS SERVICES AGENT AND TRADE BROKERS

342

ARCHITECTS, ENGINEERS AND RELATED PROFESSIONALS214

ADMINISTRATIVE ASSOCIATE PROFESSIONALS

343

LIFE SCIENCE PROFESSIONALS221

CUSTOMS, TAX AND RELATED GOVERNMENT ASSOCIATEPROFESSIONALS

344

HEALTH PROFESSIONALS, EXCEPT NURSING222

POLICE INSPECTORS AND DETECTIVES

345

NURSING AND MIDWIFERY PROFESSIONALS

223

SOCIAL WORK ASSOCIATE PROFESSIONALS

346

COLLEGE, UNIVERSITY AND HIGHER EDUCATION TEACHINGPROFESSIONALS

231

ARTISTIC, ENTERTAINMENT AND SOPRTS ASSOCIATE PROFESSIONALS

347

SECONDARY EDUCATION TEACHING PROFESSIONALS232

RELIGIOUS ASSOCIATE PROFESSIONALS

348

PRIMARY AND PRE-PRIMARY EDUCATION TEACHING PROFESSIONALS

233

SECRETARIES AND KEYBOARD-OPERATING CLERKS/ASSISTANTS

411

SPECIAL EDUCATION TEACHING PROFESSIONALS

234

NUMERICAL CLERKS/OFFICE ASSISTANTS

412

OTHER TEACHING PROFESSIONALS

235

MATERIAL-RECORDING AND TRANSPORT CLERKS/OFFICE ASSISTANTS

413

BUSINESS PROFESSIONALS241

LIBRARY, MAIL AND RELATED CLERKS/OFFICE ASSISTANTS

414

LEGAL PROFESSIONALS

242

OTHER OFFICE CLERKS/ASSISTANTS

419

ARCHIVISTS, LIBRARIANS AND RELATED INFORMATIONPROFESSIONALS243

CASHIERS, TELLERS AND RELATED CLERKS/OFFICE ASSISTANTS

421

SOCIAL SCIENCE AND RELATED PROFESSIONALS

244

CLIENT INFORMATION CLERKS/OFFICE ASSISTANTS

422

WRITERS AND CREATIVE OR PERFORMING ARTISTS

245

TRAVEL ATTENDANTS AND RELATED WORKERS

511

RELIGIOUS PROFESSIONALS

246

HOUSEKEEPING AND RESTAURANT SERVICES WORKERS512

PHYSICAL AND ENGINEERING SCIENCE TECHNICIANS

311

PERSONAL CARE AND RELATED WORKERS

513

COMPUTER ASSOCIATE PROFESSIONALS312

OTHER PROFESSIONAL SERVICES WORKERS

514

OPTICAL AND ELECTRONIC EQUIPMENT OPERATORS

313

ASTROLOGERS, FORTUNE-TELLERS AND RELATED WORKERS

515

AIRCRAFT CONTROLLERS AND TECHNICIANS

314

PROTECTIVE SERVICE WORKERS

516

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OCCUPATION NSCO CODES (cont)

OCCUPATION NSCO CODES (end)

FASHION AND OTHER MODELS

521

CHEMICAL-PROCESSING-PLANT OPERATORS

815

SHOP SALESPERSONS AND DEMONSTRATOTRS

522

POWER-PRODUCTION AND RELATED PLANT OPERATORS

816

STALL AND MARKET SALESPERSONS

523

AUTOMATED-ASSEMBLY-LINE AND INDUSTRIAL-ROBOT OPERATORS

817

MARKET-ORIENTED GARDENERS AND CROP GROWERS

611

METAL AND MINERAL PRODUCTS MACHINE OPERATORS

821

MARKET-ORIENTED ANIMAL PRODUCERS AND RELATED WORKERS

612

CHEMICAL-PRODUCTS MACHINE OPERATORS

822

MARKET-ORIENTED CROP AND ANIMAL PRODUCERS

613

RUBBER AND PLASTIC PRODUCTS MACHINE OPERATORS

823

FORESTRY AND RELATED WORKERS

614

WOOD-PRODUCTS MACHINE OPERATORS

824

FISHERY WORKERS

615

PRINTING, BINDING AND PAPER PRODUCTS MACHINE OPERATORS

825

SUBSISTENCE AGRICULTURAL AND FISHERY WORKERS

621

TEXTILE, FUR AND LEATHER-PRODUCTS MACHINE OPERATORS

826

MINERS, SHOFTIRERS, STONE CUTTERS AND CARVERS

711

FOOD AND RELATED PRODUCTS MACHINE OPERATORS

827

BUILDING FRAME AND RELATED TRADES WORKERS

712

ASSEMBLERS

828

BUILDING FINISHERS AND RELATED TRADES WORKERS

713

OTHER MACHINE OPERATORS AND ASSEMBLERS

829

PAINTERS, BUILDING STRUCTURE CLEANERS AND RELATED TRADES
WORKERS

714

LOCOMOTIVE-ENGINE DRIVERS AND RELATED WORKERS

831

METAL MOULDERS, WELDERS, SHEET-METAL WORKERS, STRUCTURALMETAL PREPARER

721

MOTOR VEHICLE DRIVERS

832

BLACKSMITHS, TOOL-MAKERS AND RELATED TRADES WORKERS

722

AGRICULTURAL AND OTHER MOBILE-PLANT OPERATORS

833

MACHINERY MECHANICS AND FITTERS

723

STREET VENDORS AND RELATED WORKERS

911

ELECTRICAL AND ELECTRONIC EQUIPMENT MECHANICS AND FITTERS

724

SHOE CLEANING AND OTHER STREET SERVICES ELEMENTARYOCCUPATIONS

912

PRECISION WORKERS IN METAL AND RELATED MATERIALS

731

DOMESTIC AND RELATED HELPERS, CLEANERS AND LAUNDERERS

913

POTTERS, GLASS-MAKERS AND RELATED TRADES WORKERS

732

BUILDING CARETAKERS, WINDOWS AND RELATED CLEANERS914

HANDICRAFT WORKERS IN WOOD, TEXTILE, LEATHER AND RELATEDMATERIALS

733

MESSENGERS, PORTERS, DOORKEEPERS AND RELATED WORKERS

915

PRINTING AND RELATED TRADES WORKERS

734

GARBAGE COLLECTORS AND RELATED LABOURERS

916

FOOD PROCESSING AND RELATED TRADES WORKERS

741

AGRICULTURAL, FISHERY AND RELATED LABOURERS

921

WOOD TREATERS, CABINET-MAKERS AND RELATED TRADERS WORKERS

742

MINING AND CONSTRUCTION LABOURERS

931

TEXTILE, GARMENT AND RELATED TRADES WORKERS743

MANUFACTURING LABOURERS

932

PELT, LEATHER AND SHOE MAKING TRADES WORKERS

744

TRANSPORT LABOURERS AND FREIGHT HANDLERS

933

MINING AND MINERAL-PROCESSING PLANT OPERATORS

811

HOUSEHOLD WORK996

METAL-PROCESSING-PLANT OPERATORS812

STUDENT997

GLASS, CERAMICS AND RELATIVE PLANT OPERATORS

813

NOT WORKING

998

WOOD-PROCESSING AND PAPERMAKING-PLANT OPERATORS814

NOT REPORTED

999

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INDUSTRY NSIC CODES

INDUSTRY NSIC CODES (cont)

AGRICULTURE AND RELATED SERVICE ACTIVITIES01

MANUFACTURE OF OFFICE, ACCOUNTING AND COMPUTING MACHINERY30

FORESTRY, LOGGING AND RELATED SERVICE ACTIVITIES

02

MANUFACTURE OF ELECTRICAL MACHINERY AND APPARATUS N.E.C.

31

FISHING, OPERATION OF FISH HATCHERIES AND FISH FARMS;SERVICE ACTIVITIES INCIDENTAL TO FISHING

05

MANUFACTURE OF RADIO, TV AND COMMUNICATION EQUIPMENT AND
APPARATUS

32

MINING OF COAL AND LIGNITE; EXTRACTION OF PEAT10

MANUFACTURE OF MEDICAL, PRECISION AND OPTICAL INSTRUMENTS,
WATCHES AND CLOCKS33

EXTRACTION OF CRUDE PETROLIUM AND NATURAL GAS; SERVICEACTIVITIES INCIDENTAL TO OIL AND GAS EXTRACTION EXCLUDINGSURVEYING11

MANUFACTURE OF MOTOR VEHICLES; TRAILERS AND SEMI-TRAILERS

34

MINING OF URANIUM AND THORIUM ORES12

MANUFACTURE OF OTHER TRANSPORT EQUIPMENT35

MINING OF METAL ORES

13

MANUFACTURE OF FURNITURE; MANUFACTURING N.E.C.

36

OTHER MINING AND QUARRYING

14

RECYCLING

37

MANUFACTURE OF FOOD PRODUCTS AND BEVERAGES

15

ELECTRICITY AND GAS SUPPLY

40

MANUFACTURE OF TOBACCO PRODUCTS

16

COLLECTIONS, PURIFICATION AND DISTRIBUTION OF WATER

41

MANUFACTURE OF TEXTILES

17

CONSTRUCTION

45

MANUFACTURE OF WEARING APPREL; DRESSING AND DYEING OF FUR

18

SALE, MAINTENANCE AND REPAIR OF MOTOR VEHICLES AND
MOTORCYCLES; RETAIL SALE OF AUTOMOTIVE FUEL50TANNING AND DRESSING OF LEATHER; MANUFACTURE OF LUGGAGE,HANDBAGS, SADDLERY AND HARNESS

19WHOLESALE TRADE AND COMMISSION TRADE, EXCEPT OF MOTOR
VEHICLES AND MOTORCYCLES

51

MANUFACTURE OF WOOD AND OF PRODUCTS OF WOOD AND CORK,EXCEPT FURNITURE; MANUFACTURE OF ARTICLES OF STRAW AND
PLAITING MATERIALS20

RETAIL TRADE, EXCEPT OF MOTOR VEHICLES AND MOTORCYCLES;REPAIR OF PERSONAL AND HOUSEHOLD GOODS

52

MANUFACTURE OF PAPER AND PAPER PRODUCTS

21

HOTELS AND RESTAURANTS

55

PUBLISHING, PRINTING AND REPRODUCTION OF RECORDED MEDIA

22

LAND TRANSPORT60

MANUFACTURE OF COKE, REFINED PETROLIUM PRODUCTS AND
NUCLEAR FUEL

23

WATER TRANSPORT61

MANUFACTURE OF CHEMICALS AND CHEMICAL PRODUCTS

24

AIR TRANSPORT62

MANUFACTURE OF RUBBER AND PLASICS PRODUCTS

25

SUPPORTING AND AUXILIARY TRANSPORT ACTIVITIES; ACTIVITIESOF TRAVEL AGENCIES63

MANUFACTURE OF OTHER NON-METALIC MINARAL PRODUCTS

26

POST AND TELECOMMUNICATIONS

64

MANUFACTURE OF BASIC METALS

27

FINANCIAL INTERMEDIATION, EXCEPT INSURANCE AND PENSIONFUNDING

65

MANUFACTURE OF FABRICATED METAL PRODUCTS, EXCEPT MACHINERYAND EQUIPMENT

28

INSURANCE AND PENSION FUNDING, EXCEPT COMPULSORY SOCIALSECURITY66

MANUFACTURE OF MACHINERY AND EQUIPMENT N.E.C.

29

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1/11/2011

J

INDUSTRY NSIC CODES (end)

ACTIVITIES AUXILIARY TO FINANCIAL INTERMEDIATION

67

REAL ESTATE ACTIVITIES70

RENTING OF MACHINERY AND EQUIPMENT WITHOUT OPERATOR AND OFPERSONAL AND HOUSEHOLD GOODS

71

COMPUTER AND RELATED ACTIVITIES72

RESEARCHES AND DEVELOPMENT

73

OTHER BUSINESS ACTIVITIES74

PUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY SOCIALSECURITY75

EDUCATION

80

HEALTHS AND SOCIAL WORK85

SEWAGE AND REFUSE DISPOSAL, SANITATION AND SIMILAR
ACTIVITIES90

ACTIVITIES OF MEMBERSHIP ORGANIZATIONS N.E.C.

91

RECREATIONAL, CULTURAL AND SPORTING ACTIVITIES92

OTHER SERVICE ACTIVITIES93PRIVATE HOUSEHOLDS WITH EMPLOYED PERSONS95

EXTRA-TERRITORIAL ORGANIZATION AND BODIES99

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

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377
1/11/2011

IDENTIFICATION CODEA
TICK (9)ONLY THE
HOUSEHOLD
MEMBERS(1.01)
MAKE A COMPLETE LIST OF ALL CONCERNEDBEFORE GOING TO Q.2 – 10. FIRST OF ALL WRITE
THE NAME OF HOUSEHOLD HEAD, THEN HEAD’SSPOUSE, SON/DAUGHTER, GRANDSON/DAUGHTER, PARENTS, ETC. RESPECTIVELY.

(1.02)(1.03)How old is..[NAME]..?

GENDERMALE
1
FEMALEAGE INCOMPLETED
YEARS

2
NAME

IDENTIFICATION CODE

flap

YEARS

01

01

02

02

03

03

04

04

05

05

06

06

07

07

08

08

09

09

10

10

11

11

12

12

13

13

14

14

15

15

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

80 / 80

378

Nepal Living Standard Survey – III
2010

DATA DICTIONARY
for Raw Data User

Central Bureau of Statistics
Thapathali, Kathmandu
Nepal
2011

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

379
NLSS-III: Household questionnaire-------------------------------------------------------------------------------------------------------------Level Label

Level Name

Record Label

Record Name

Type
Value

Rec
Req

Max

Len

-------------------------------------------------------------------------------------------------------------NLSS-III questionnaire

NLSS_III_QUEST

Cover, Survey Information & Single Record

S00

s00__

Yes

1

216

Section 1: Household Roster

S01

s01__

No

35

67

Section 2: Housing

S02

s02__

No

1

167

Section 3: Access to Facilities

S03

s03__

No

24

55

Section 4: Migration

S04

s04__

No

35

61

Section 5: Food consumption

S05

s05__

No

91

105

Section 6A: Frequent non-food expenditures

S06A

s06A_

No

31

54

Section 6B: Infrequent non-food expenditures

S06B

s06B_

No

42

47

Section 6C: Inventory of durable goods

S06C

s06C_

No

18

60

Section 6D: Own account production of goods

S06D

s06D_

No

15

54

Section 7: Education

S07

s07__

No

35

125

Section 8: Health

S08

s08__

No

35

135

Section 9A: Maternity history

S09A

s09A_

No

20

64

Section 9B: Pre and post-natal care

S09B

s09B_

No

6

32

Section 9C: Family planning

S09C

s09C_

No

35

30

Section 9D: Household decisions (PART I)

S09D

s09D_

No

15

41

Section 10A: Time Use

S10A

s10A_

No

35

65

Section 10B: Jobs

S10B

s10B_

No

60

79

Section 11: Unemployment / Under-employment and past job

S11

s11__

No

35

34

Section 12: Wage Jobs

S12

s12__

No

15

145

Section 13A1: Landholding - land owned

S13A1

s13A1

No

25

110

Section 13A2: Land sharecropped/rented/mortgaged -in

S13A2

s13A2

No

20

76

Section 13B: Production and uses

S13B

s13B_

No

40

94

Section 13C1: Expenditures on seeds and young plants

S13C1

s13C1

No

16

46

Section 13C2: Expenditures on fertilizers and insecticides

S13C2

s13C2

No

8

55

Section 13C3: Hiring labour

S13C3

s13c3

No

12

90

Section 13D1: Agriculture Earnings

S13D1

s13D1

No

8

44

Section 13D2: Agriculture Expenditures

S13D2

s13D2

No

15

45

Section 13E1: Livestock

S13E1

s13E1

No

10

83

Section 13E2: Income from livestock

S13E2

s13E2

No

8

45

Section 13E3: Livestock's expenditures

S13E3

s13E3

No

5

45

Section 13F: Ownership of farming assets

S13F

s13F_

No

10

75

Section 14: Non-agriculture enterprises/activities

S14

s14__

No

10

222

Section 15A: Borrowing

S15A

s15A_

No

10

94

Section 15B: Lending

S15B

s15B_

No

10

89

Section 15C: Other assets

S15C

s15C_

No

1

113

Section 15D: Household decisions (PART II)

S15D

s15D_

No

15

41

Section 16: Absentees information

S16

s16__

No

20

90

Section 17A: Remittances sent

S17A

s17A_

No

8

69

Section 17B: Remittances received

S17B

s17B_

No

8

72

Section 18A: Transfers, social assistance

S18A

s18A_

No

7

73

Section 18B: Social assistance

S18B

s18B_

No

1

17

Section 18C: Other Income

S18C

s18C_

No

9

63

Section 19:

S19

s19__

No

1

42

Section 20: Anthropometrics

Adequacy of consumption

S20

s20__

No

35

59

Section 21: Panel Household tracking

S21

s21__

No

1

78

Section 21X: Panel Household members tracking

S21X

s21X_

No

35

55L

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

380Record: Cover, Survey Information & Single Record
Datafile : xH00_S00-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

2

N

I

1

0

No

Yes

District

V00_DIST

12

1

Taplejung2

Panchthar`

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

Ward

V00_WARD

14

2

N

I

1

0

No

Yes

Subward

V00_SUBW

16

2

N

I

1

0

No

Yes

V00_VDC

18

3

N

I

1

0

No

Yes

Zone (Urban/Rural)

V00_ZONE

21

5

AN

I

1

0

No

No

GPS/Latitude (degrees, no decimals)

V00_GPS_LAT_DEG

26

2

N

I

1

0

No

No

V00_GPS_LAT_MIN

28

6

N

I

1

3

Yes

Yes

V00_GPS_LON_DEG

36

2

N

I

1

0

No

No

V00_GPS_LON_MIN

38

6

N

I

1

3

Yes

Yes

A. Household no.

V00_HNO

46

2

N

I

1

0

No

No

A. Household was interviewed

V00_A

48

1

N

I

1

0

No

No

V00_B

49

1

N

I

1

0

No

No

VDC/municipality
1:103

26:30

`

GPS/Latitude (minutes, 3 decimals)
0.000:59.999
GPS/Longitude (degrees, no decimals)
80:88
GPS/Longitude (minutes, 3 decimals)
0.000:59.999

1

Yes

2

No

`

B. Reason household not interviewed
1

Dwelling not found

2

Household not foundNepal NLSS Ⅲ 2010/11 Manual Version 2.0

381
3

RefusalC. Replaced by household number
0

V00_C

50

2

N

I

1

0

No

Yes

V00_D

52

2

N

I

1

0

No

Yes

V00_E

54

2

N

I

1

0

No

Yes

V00_F

56

2

N

I

1

0

No

Yes

Not a replacement

1:21
D. This household replaces household number
0This household does not replace any other one

1:21

Replaced household number

E. Religion of hhld head
1

Hindu

2

Bouddha

3

Islam

4

Kirant5

Jain

6

Christian

7

Shikh

8

Bahai

9

Other religion

F. Language used in household
1

Nepali2

Maithili

3

Bhojpuri

4

Tharu (Dagaura/Rana)

5

Tamang

6

Newar

7

Magar

8

Awadhi

9

Bantawa

10

Gurung

11

Limbu

12

Bajjika

13

Urdu

14

Rajbansi

15

Sherpa

16

Hindi

17

Chamling

18

Santhali19

Chepang20

Danuwar21

Jhangar/Dhangar

22

Sunuwar23

Bangla24

Marwari (Rajsthani)

25

Manjhi

26

Thami

27

Kulung28

Dhimal29

Angika30

Yakkha31

Thulung32

Sangpang

33

Bhujel/Khawas

34

Darai

35

Khaling

36

Kumal

37

Thakali

38

Chhantyal/Chhantel

39

Nepali Sign Language

40

Tibbetan

41

Dumi

42

Jirel

43

Wambule/ Umbule

44

Puma

45

Yholmo46

Nachhiring

47

Dura

48

Meche

49

Pahari50

Lepcha/ Lapche

51

Bote

52

Bahing53

Koi/ Koyu

54

Raji

55

Hayu

56

Byangshi

57

Yamphu/ Yamphe

58

Ghale

59

Khariya60

Chhiling

61

Lohorung

62

Punjabi

63

Chinese64

English65

Mewahang

66

Sanskrit

67

Kaike

68

Raute

69

Kisan

70

Churauti

71

Baram/ Maramu

72

Tilung73

Jero/ Jerung

74

Dungmali

75

Oriya

76

Lingkhim

77

Kusunda78

Sindhi

79

Koche

80

Hariyanwi

81

Magahi

82

Sam

83

Kurmali

84

Kagate85

Dzonkha

86

Kuki

87

Chhintang

88

Mizo

89

Nagamese

90

Lhomi

91

Assamise92

Sadhani

93

Other Language

V00_G

58

1

N

I

1

0

No

No

V00_TEAM

59

2

N

I

1

0

No

Yes

G. Interpreter used
1

Yes

2

No

Team number

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

382
1:20
Interviewer

V00_INT

61

3

N

I

1

12

Laxman Ban

13

Narayan Karki

14

Indira Khapangi Magar

15

Anjana Oli

22

Ramesh Bahadur Shrestha

23

Ishwor Lal Mandal

24

Khadka Kumari Lamichhane (Sapkota)

25

Pratima Gyawali (Adhikari)

32

Suresh Kumar Shrestha

33

Yam Bahadur Gurung

34

Ganga Bhujel

35

Bebi Deji Gupta

42

Loknath Lamichhane

43

Narayan Bahadur Thapa

44

Ramita Poudel

45

Usha Kumari Choudhari

52

Santosh Chandra Thapa

53

Santosh Karki

54

Gyani Dhakal

55

Gita Rimal

62

Kashi Mandal

63

Bij Kumar Yadav

64

Shushila Kumari Shah65

Uma Kumari

72

Ramprit Yadav

73

Jaya Kishan Prasad Sonar

74

Madhu Mallik

75

Kalyani Sah82

Mahantha Prasad Yadav

83

Rajendra Prasad Choudhari

84

Nargis Khatoon

85

Pinki Kumari Ojha

92

Gajendra Lal Shrestha

93

Pushkar Prasad Guragai

94

Sunita Dhakal (Pokhrel)

95

Sanu Maiya Shrestha

102

Krishna Prasad Choulagai

103

Kalkika Shrestha

104

Indira Birbal

105

Rama Kumari Thapa

106

Prativa Soti

112

Kiran Bista

113

Dipak Bhattarai

114

Dambar Kumari Ghale (Pariyar)

115

Binu Shrestha

122

Renu Kumari Ghimire

123

Rajendra Parajuli

124

Renuka Lamichhane

125

Kaushila Adhikari (Bohara)

132

Rabi Lal Shrestha

133

Surendra Raj Poudel

134

Samjhana Basnet

135

Bindu Pande142

Tilak Prasad Acharya

143

Ghanashyam Sapkota

144

Anjana Kumari Shrestha

145

Sharada Pande

152

Yubraj Khadka

153

Ramchandra Gautam

154

Kalpana Bhandari (Thapa)

155

Sakuntala Dhakal

162

Surya Giri

163

Kamal Prasad Poudel

164

Shila G.C.

165

Shushila Kumal

172

Saroj Pokhrel

173

Prem Kumari Sharma

174

Shira Sharma

175

Shushma Adhikari

182

Megh Nath Regmi

183

Lanka Bahadur Bam

184

Shanta Kumari Pokhrel

185

Laxmi Gupta

192

Tej Narayan Adhikari

193

Hari Bahadur Gharti

194

Prativa Shoti

195

Pooja Thapa

196

Prathana Bhatta202

Ram Datta Bhatta

203

Shanbhu Narayan Panta

204

Pushpa Joshi

205

Sapana Bhatta

Supervisor

V00_SUP

64

3

N

11

Nava Raj Shrestha

21

Suman Kumar Shrestha

31

Kedar Prasad Neupane

41

Janak Babu Kafle

51

Balaram Dulal

61

Pawan Kumar Thakur

71

Raj Kishor Sah81

Sanjaya Kumar Yadav

91

Damodar Shrestha

101

Deepak Koirala

111

Urmila Shahi

121

Ram Prasad Wagle

131

Kanta Lal Choudhari

141

Bishnu Raj Regmi

151

Jeevnath Acharya

161

Damodar Neupane

171

Damodar Gautam

181

Ram Bahadur Thapa

191

Bishwo Darshan Dhungana

201

Mohan Dev Bhatta

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

I

1

0

No

Yes

0

No

Yes

383
V00_ENT

Entry operator

67

3

N

I

13

Narayan Karki

22

Ramesh Bahadur Shrestha

33

Yam Bahadur Gurung

43

Narayan Bahadur Thapa

52

Santosh Chandra Thapa

64

Shushila Kumari Shah74

Madhu Mallik

84

Nargis Khatoon

93

Pushkar Prasad Guragai

102

Krishna Prasad Choulagai

106

Prativa Soti

113

Dipak Bhattarai

122

Renu Kumari Ghimire

133

Surendra Raj Poudel

142

Tilak Prasad Acharya

152

Yubraj Khadka

162

Surya Giri172

Saroj Pokhrel

183

Lanka Bahadur Bam

193

Hari Bahadur Gharti

194

Prativa Shoti202

Ram Datta Bhatta

1st interview/Day
0

1

0

No

Yes

V00_INT1_D

70

2

N

I

1

0

No

Yes

V00_INT1_M

72

2

N

I

1

0

No

Yes

V00_INT1_Y

74

4

N

I

1

0

No

Yes

V00_INT2_D

78

2

N

I

1

0

No

Yes

V00_INT2_M

80

2

N

I

1

0

No

Yes

V00_INT2_Y

82

4

N

I

1

0

No

Yes

V00_INT3_D

86

2

N

I

1

0

No

Yes

V00_INT3_M

88

2

N

I

1

0

No

Yes

V00_INT3_Y

90

4

N

I

1

0

No

Yes

V00_SUP_D

94

2

N

I

1

0

No

Yes

V00_SUP_M

96

2

N

I

1

0

No

Yes

V00_SUP_Y

98

4

N

I

1

0

No

Yes

V00_HEADN

102

20

AN

I

1

0

No

No
No

date not given

1:32
1st interview/Month
1:12
1st interview/Year
2066:2067
2nd interview/Day
0!end-of-list!

1:32
2nd interview/Month
1:12
2nd interview/Year
2066:2067
3rd interview/Day
0!end-of-list!

1:32
3rd interview/Month
1:12
3rd interview/Year
2066:2067
Supervision/Day
0date not given

1:32
Supervision/Month
1:12
Supervision/Year
2066:2067
Name of household Head
Telephone

V00_TELEF

122

20

AN

I

1

0

No

Address

V00_ADDRESS

142

50

AN

I

1

0

No

No

Locality

V00_LOCALITY

192

25

AN

I

1

0

No

No

V13_02

12

1

N

I

1

0

No

No

{13.18} Household has land sharecropped/rented/V13_18

13

1

N

I

1

0

No

No

14

1

N

I

1

0

No

No

{13.02} Household has own agricultural land
1

Yes

2

No

1

Yes

2

No

{13.29} Household did sell, transfer land1

V13_29Yes

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

384
2

No

{13.30} 1st area sold, transfered/Unit (in RopaV13_30_1U
1

15

1

N

I

1

0

No

No

Ropani{13.30} 1st area sold, transfered/Ropani

V13_30_1R

16

3

N

I

1

0

No

No

{13.30} 1st area sold, transfered/Aana

V13_30_1A

19

2

N

I

1

0

No

No

V13_30_1P

21

2

N

I

1

0

No

No

{13.30} 2nd area sold, transfered/Unit (in BighV13_30_2U

23

1

N

I

1

0

No

No

0:15
{13.30} 1st area sold, transfered/Paisa
0:3

2

Bigha

{13.30} 2nd area sold, transfered/Bigha

V13_30_2B

24

3

N

I

1

0

No

No

{13.30} 2nd area sold, transfered/Kattha

V13_30_2K

27

2

N

I

1

0

No

No

V13_30_2D

29

2

N

I

1

0

No

No

{13.31} 1st area sold, transfered/Amount receivV13_31

31

10

N

I

1

0

No

No

{13.32} Household did acquired land

V13_32

41

1

N

I

1

0

No

No

V13_33_1U

42

1

N

I

1

0

No

No

{13.33} 1st area acquired/Ropani

V13_33_1R

43

3

N

I

1

0

No

No

{13.33} 1st area acquired/Aana

V13_33_1A

46

2

N

I

1

0

No

No

V13_33_1P

48

2

N

I

1

0

No

No

V13_33_2U

50

1

N

I

1

0

No

No

{13.33} 2nd area acquired/Bigha

V13_33_2B

51

3

N

I

1

0

No

No

{13.33} 2nd area acquired/Kattha

V13_33_2K

54

2

N

I

1

0

No

No

V13_33_2D

56

2

N

I

1

0

No

No

V13_34

58

10

N

I

1

0

No

No

{13.38} 98-Control total of the "amount of totaV13_38D_TOTAL

68

10

N

I

1

0

No

No

{13.39} Household purchased/received seeds, youV13_39

78

1

N

I

1

0

No

No

79

1

N

I

1

0

No

No

80

1

N

I

1

0

No

No

V13_48B

81

1

N

I

1

0

No

No

{13.49} Reason for unable to obtain all needed V13_49

82

1

N

I

1

0

No

No

83

1

N

I

1

0

No

No

0:19
{13.30} 2nd area sold, transfered/Dhur
0:19

1

Yes

2

No

{13.33} 1st area acquired/Unit (in Ropani)
1

Ropani0:15{13.33} 1st area acquired/Paisa
0:3{13.33} 2nd area acquired/Unit (in Bigha)
2

Bigha

0:19
{13.33} 2nd area acquired/Dhur
0:19{13.34} Area acquired/Amount paid (rs)

1

Yes

2

No

{13.43} Household purchased fertilizers, insectV13_43
1

Yes

2

No

{13.48A} Fertilizer/Place where it was obtainedV13_48A
1

Government

2

NGO

3

Private4

Cooperative, community{13.48B} Fertilizer/Could obtain as needed

13.50

1

Yes

2

No

1

Not available for purchase2

No money for purchase

3

Other

hire any casual farm workers

V13_50

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

385
1

Yes

2

No

3

Exchange labour only

{13.53) Exchange labor man-days, Male

V13_53_EL_MALE

178

4

N

I

1

0

No

{13_53} Exchange labor man-days, Female

V13_53_EL_FEMALE

182

4

N

I

1

0

No

No

{13.58} Total expenditure for hiring casual farV13_58

84

8

N

I

1

0

No

No

{13.59} Hired any permanent farm workers

V13_59

92

1

N

I

1

0

No

No

1

Yes

2

No

{13.60} Permanent workers/Number hiredNo

V13_60

93

4

N

I

1

0

No

No

{13.61} Permanent workers/Paid in cash and in-kV13_61

97

8

N

I

1

0

No

No

{13.62} Total expenditure on hired workers

V13_62

105

8

N

I

1

0

No

No

{13.65} Household owns livestock

V13_65

113

1

N

I

1

0

No

No

V13_73

114

1

N

I

1

0

V13_74

115

1

N

I

1

0

No

No

{13.82} Advice taken from Govt. Agriculture TecV13_82

116

1

N

I

1

0

No

No

117

1

N

I

1

0

No

No

118

1

N

I

1

0

No

No

119

1

N

I

1

0

No

No

V14_01

120

1

N

I

1

0

No

No

V15_01

121

1

N

I

1

0

No

No

V15_14

122

1

N

I

1

0

No

No

V16_00

123

1

N

I

1

0

No

No

V17_01

124

1

N

I

1

0

No

No

V17_11

125

1

N

I

1

0

No

No

1

Yes

2

No

{13.73} Hired labor for tending livestock
No

No
1

Yes

2

No

{13.74} Household owned any equipment
1

Yes

2

No

1

Yes

2

No

{13.83} Reason for not taking advice from Govt.V13_83
1Service far away

2

Service is not good3

Not necessary

4

Other

{13.84} Advice taken from Govt. Livestock ServiV13_84
1

Yes

2

No

{13.85} Reason for not taking advice from Govt.V13_85
1Service far away

2

Service is not good3

Not necessary

4

Other

{14.01} Any self-employment activities
1

Yes

2

No

{15.01} Any outstanding loans
1

Yes

2

No

{15.14} Any loans owed to the household
1

Yes

2

No

{16.00} Any absentees
1

Yes

2

No

{17.01} Any remittances sent out
1

Yes

2

No

{17.11} Any remittances received

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

386
1

Yes

2

No

{1.00} Respondent id-code0

V01_00

126

2

N

I

1

0

No

Yes

!information not gathered at the field!

1:35{2.A} Respondent id-code

V02_A

128

2

N

I

1

0

No

Yes

{3.00} Respondent id-code

V03_A

130

2

N

I

1

0

No

Yes

{5.00} Respondent id-code

V05_00

132

2

N

I

1

0

No

Yes

{6.00} Respondent id-code

V06_00

134

2

N

I

1

0

No

Yes

{9.36} Respondent id-code

V09_36

136

2

N

I

1

0

No

Yes

{13.01} Respondent id-code

V13_01

138

2

N

I

1

0

No

Yes

{13.17} Respondent id-code

V13_17

140

2

N

I

1

0

No

Yes

{15.38} Respondent id-code

V15_38

142

2

N

I

1

0

No

Yes

0

No competent respondent

1:99{19.01} Respondent id-code

V19_01

144

2

N

I

1

0

No

Yes

{n-01} Number of Lines in Section 1

N_S01

146

2

N

I

1

0

No

No

{n-09A} Number of lines in Section 9A

N_S9A

148

2

N

I

1

0

No

No

{n-09B} Number of lines in Section 9B

N_S9B

150

2

N

I

1

0

No

No

{n-10B} Number of lines in Section 10B

N_S10B

152

2

N

I

1

0

No

No

{n-12} Number of lines in Section 12

N_S12

154

2

N

I

1

0

No

No

{n-14} Number of lines in Section 14

N_S14

156

2

N

I

1

0

No

No

{n-15A} Number of lines in Section 15-A

N_S15A

158

2

N

I

1

0

No

No

{n-15B} Number of lines in Section 15-B

N_S15B

160

2

N

I

1

0

No

No

{n-16} Number of lines in Section 16

N_S16

162

2

N

I

1

0

No

No

{n-17A} Number of lines in Section 17A

N_S17A

164

2

N

I

1

0

No

No

{n-17B} Number of lines in Section 17B

N_S17B

166

2

N

I

1

0

No

No

{n-13A1} Number of plots 13-A1

N_S13A1

168

2

N

I

1

0

No

No

{n-13A2} Number of plots 13-A2

N_S13A2

170

2

N

I

1

0

No

No

{n-13B} Number of lines 13-B

N_S13B

172

2

N

I

1

0

No

No

{n-13C1} Number of plots 13-C1

N_S13C1

174

2

N

I

1

0

No

No

{n-13C3} Number of plots 13-C3

N_S13C3

176

2

N

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

387

Section 1: Household Roster
Datafile : XH01_S01-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

id codeV01_IDC

12

2

N

I

1

0

No

Yes

1.01 name

V01_01

14

25

AN

I

1

0

No

No

1.02 sex

V01_02

39

1

N

I

1

0

No

No

1.03 age

V01_03

40

2

N

I

1

0

No

No

{1.04} Relationship to head

V01_04

42

2

N

I

1

0

No

Yes

2

N

I

1

0

No

Yes

1

Male

2

Female1

Head2

Husband/wife

3

Son/daughter

4Grandchild5

Father/mother

6

Brother/sister

7

Nephew/niece

8

Son/daughter-in-law

9Brother/sister-in-law

10

Father/mother-in-law

11

Other family relative

12

Servant/servant's relatives

13

Tenant/tenant's relatives

14

Other non related{1.05} Birth place: district/foreign

V01_05A

44

1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

73Dadeldhura

388
74 Baitadi

75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

90

United arab emirates

91

United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country{1.05} Birth place: Urban/Rural
1

Urban

2

Rural

1.06 marital status
1

Never married

2Single married

3

Poly married

4

Re-married

5

Widow/widower

6

Divorced

7

Separated{1.07} Spouse id code
{1.08} Caste/ethnic

V01_05B

46

1

N

I

1

0

No

No

V01_06

47

1

N

I

1

0

No

No

V01_07

48

2

N

I

1

0

No

Yes

3

N

I

1

0

No

Yes

V01_0850

1Chhetri2

Brahman (hill)

3

Magar

4

Tharu

5

Tamang

6

Newar

7

Muslim

8

Kami

9

Yadav

10

Rai

11

Gurung

12

Damain/dholi

13

Limbu

14

Thakuri

15

Sarki

16

Teli

17

Chamar/harijan/ram

18

Koiri

19

Kurmi

20

Sanyasi

21

Dhanuk22

Musahar23

Dusadh/Paswan/Pasi

24

Sherpa

25

Sonar

26

Kewat

27

Brahman (terai)

28

Baniya29

Gharti/bhujel

30

Mallah31

Kalwar

32

Kumal

33

Hajam/thakur

34

Kanu

35

Rajbansi

36

Sunuwar

37

Sudhi

38

Lohar

39

Tatma

40

Khatwe

41

Dhobi

42

Majhi

43

Nuniya44

Kumhar45

Danuwar46

Chepang/praja

47

Haluwai48

Rajpur49

Kayastha

50

Badhae51

Marwadi52

Santhal/satar

53

Dhagar/jhagar

54

Bantar

55

Barae

56

Kahar

57

Gangai

58

Lodh

59

Rajbhar

60

Thami

61

Dhimal

62

Bhote

63

Bing/binda

64

Bhediyar/gaderi

65

Nurang

66

Yakkha

67

Darai

68

Tajpuriya

69

Thakali

70

Chidimar

71

Pahari

72

Mali

73

Bangali

74

Chhantal

75

Dom

76

Kamar

77

Bote

78

Brahmu/baramu

79

Gaine

80

Jirel

81

Adibasi/janajati

82

Dura

83

Churaute

84

Badi

85

Meche

86

Lepcha87

Halkhor88

Punjabi/sikh

89

Kisan

90

Raji

91

Byangsi

92

Hayu

93

Koche

94

Dhunia95

Walung

96

Jain

97

Munda

98

Raute

99

Yehlmo100

Patharkata/kuswadiya

101

Kusunda102

Other dalit

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

389
103

Other caste

1.09 months at home

V01_09

53

2

N

I

1

0

No

No

V01_10

55

1

N

I

1

0

No

No

V01_JDC

56

2

N

I

1

0

No

Yes

V01_11

58

1

N

I

1

0

No

No

{1.12} Father id code

V01_12

59

2

N

I

1

0

No

No

{1.13} Father: highest degree completed

V01_13

61

2

N

I

1

0

No

Yes

0:12
1.10 member or not?

i

1

Yes

2

No

d

c

o

d

e

1.11 father living in home?
1

Yes

2

No

3

Died

0

Pre-school/kindergarden

1

Class 1

2

Class 2

3

Class 3

4

Class 4

5Class 5

6

Class 6

7

Class 7

8Class 89Class 910Class 10

11

SLC

12

Intermediate/Class 12

13

Bachelor level

14

Master level or higher

15

Professional degree

16

Literate (levelless)17

Illiterate

98

DK

1.14 mother living in home?

V01_14

63

1

N

I

1

0

No

No

1.15 id code of mother

V01_15

64

2

N

I

1

0

No

Yes

{1.16} Mother: highest degree completed

V01_16

66

2

N

I

1

0

No

Yes

1

Yes

2

No

3

Died

0

Pre-school/kindergarden

1

Class 1

2

Class 2

3

Class 3

4

Class 4

5Class 5

6

Class 6

7

Class 7

8Class 89Class 910Class 10

11

SLC

12

Intermediate/Class 12

13

Bachelor level

14

Master level or higher

15

Professional degree

16

Literate (levelless)17

Illiterate

98

DK

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

390

Section 2: Housing
Datafile : xH02_S02-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

V02_01

12

1

N

I

1

0

No

No

V02_02A

13

2

N

I

1

0

No

No

V02_02B

15

2

N

I

1

0

No

No

V02_02C

17

2

N

I

1

0

No

No

V02_02D

19

2

N

I

1

0

No

No

V02_02E

21

2

N

I

1

0

No

No

V02_02F

23

2

N

I

1

0

No

No

V02_02G

25

2

N

I

1

0

No

No

V02_02H

27

2

N

I

1

0

No

No

V02_03

29

1

N

I

1

0

No

No

V02_04

30

1

N

I

1

0

No

No

V02_05

31

1

N

I

1

0

No

No

V02_06

32

1

N

I

1

0

No

No

V02_07

33

1

N

I

1

0

No

No

2.01 dwelling unit occupied by own hhld only

2.02

1

Yes

2

No

a) total room
1:50.

b) kitchen
0:50.

c) toilet/bathroom
0:50.

d) bedrooms
0:50.

e) living/dining rooms
0:50.

f) business rooms
0:50.

g) mixed used rooms
0:50.

h) other rooms
0:50

2.03 having kitchen garden
1

Yes

2

No

2.04 material of outside wall
1

Cement bonded bricks/stones

2

Mud bonded bricks/stones

3

Wood

4

Bamboo/leaves

5

Unbaked bricks

6

Other material

7

No outside walls

2.05 foundation of dwelling
1

Pillar bonded

2

Cement bonded

3

Mod bonded

4

Wooden pillar

5

Other

2.06 main roofing material
1

Straw/thatch

2

Earth/mud3

Wood/planks

4

Galvanized iron

5

Concrete/cement

6

Tiles/slate

7

Other

2.07 types of window
1

No windows/no covering window

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

391
2

Shutters

3

Screens/glass

4

Other

2.08 size of housing plot

- unitV02_08U

34

1

N

I

1

0

No

No

- ropani/bigha

V02_08RB

35

3

N

I

1

0

No

No

- aana/kattha

V02_08AK

38

2

N

I

1

0

No

No

- paisa/dhur

V02_08PD

40

2

N

I

1

0

No

No

2.09 area inside of the dwelling (sq.ft.)

V02_09

42

4

N

I

1

0

No

No

2.10 year built

V02_10

46

4

N

I

1

0

No

No

V02_11

50

1

N

I

1

0

No

No

1

Ropani

2

Bigha

.
0:999
.
0:19
.
0:19

1990:2067
2.11 dwelling owned by you?
1

Yes

2

No

2.12 sale value(rs.)

V02_12

51

10

N

I

1

0

No

No

2.13 rental value(rs.)

V02_13

61

8

N

I

1

0

No

No

2.14 rent part of dwelling?

V02_14

69

1

N

I

1

0

No

No

(rs.)

V02_15

70

8

N

I

1

0

No

No

2.16 present occupancy status

V02_16

78

1

N

I

1

0

No

No

1

Yes

2

No

2.15 rent received

1

Renter2

Providing free of cost

3

Squatting

4

Other

2.17 rental value(rs.)

V02_17

79

8

N

I

1

0

No

No

2.18 rent paid(rs.)

V02_18

87

8

N

I

1

0

No

No

2.19 source of drinking water

V02_19

95

1

N

I

1

0

No

No

V02_20

96

1

N

I

1

0

No

No

V02_21

97

3

N

I

1

0

No

No

V02_22

100

7

N

I

1

0

No

No

V02_23

107

1

N

I

1

0

No

No

V02_24

108

1

N

I

1

0

No

No

1

Piped water2

Covered well3

Hand pump/Tubewell

4Open well

5

Spring water

6

River

7

Other source

2.20 piped water connected to house
1

Yes

2

No

2.21 water comes hours/week
0Less than 1 hour a week

1:167
168

All the week without interruption

2.22 amount paid for water(rs.)

2.23 sanitation system for liquid waste
1

Underground drain

2Open drains

3

Soak pit

4No2.24 garbage disposal method
1

Collected by garbage truck

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

3922

Private collector

3

Dumped4

Burned/buried

5

Dumped and used for fertilizer

6

Other

2.25 amount paid for garbage(rs.)

2.26 type of toilet used
1

Flush - municipal sewer

2

Flush - septic tank3

Non-flush4

Communal latrine

5

No toilet

2.27 source of light1

Electricity

2

Solar

3

Biogas4

Kerosene5

Other

2.28 types of meter
1

Individual

2

Joint

3

No meter

2.29 amount paid for electricity(rs.)

2.30 how many hrs/week not have eletricity

V02_25

109

6

N

I

1

0

No

No

V02_26

115

1

N

I

1

0

No

No

V02_27

116

1

N

I

1

0

No

No

V02_28

117

1

N

I

1

0

No

No

V02_29

118

7

N

I

1

0

No

No

V02_30

125

3

N

I

1

0

No

No

V02_31A

128

1

N

I

1

0

No

No

V02_31B

129

1

N

I

1

0

No

No

V02_31C

130

1

N

I

1

0

No

No

V02_31D

131

1

N

I

1

0

No

No

V02_32

132

7

N

I

1

0

No

No

V02_33

139

1

N

I

1

0

No

No

V02_34

140

1

N

I

1

0

No

No

0:126
2.31 a) having telephone

.

.

.

1

Yes

2

No

b) having mobile phone
1

Yes

2

No

c) having cable TV
1

Yes

2

No

d) having email/internet
1

Yes

2

No

2.32 amount paid for facilities(rs)

2.33 main cooking fuel1

Firewood

2

Dung

3

Leaves/rubbish/straw/thatch

4

Cylinder gas

5

Kerosene

6

Bio-gas7

Other

2.34 stove for cooking
1Open fireplace2

Mud fireplace

3

Smokeless oven4

Kerosene stove5

Gas stove

6

Other

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

393
V02_35

141

1

N

I

1

0

No

No

V02_36

142

1

N

I

1

0

No

No

V02_37A

143

1

N

I

1

0

No

No

- nuV02_37B

144

4

N

I

1

0

No

No

V02_37C

148

7

N

I

1

2

Yes

No

V02_38A

155

2

N

I

1

0

No

No

- minuV02_38B

157

2

N

I

1

0

No

No

V02_39

159

1

N

I

1

0

No

No

2.35 use firewood during past 12 months
1

Yes

2

No

2.36 collect firewood during past 12 months?
1

Yes

2

No

2.37 avg. no. of firewood collection - unit
1

Bhari

2

Chart

3

Kg

.
.

1 Bhari/chartequal to kg

2.38 time to collect firewood - hour
0:10
.
0:59
2.39 place of collecting firewood
1

Own land2

Community managed forest

3

Govt. forest

4

Other

2.40 price per unit of firewood

V02_40

160

4

N

I

1

0

No

No

2.41 collect fodder

V02_41

164

1

N

I

1

0

No

No

V02_42

165

1

N

I

1

0

No

No

V02_43

166

1

N

I

1

0

No

No

V02_44

167

1

N

I

1

0

No

No

1

Yes

2

No

2.42 place of collecting fodder1

Own land2

Community managed forest

3

Govt. forest

4

Other

2.43 Type of salt used for cooking
1

Large crystal salt

2

Crushed salt

3

Packet salt (two child logo)

4

Other

2.44 Iodine level in salt used for cooking
1

0 PPM

2

Below 15 PPM

315 PPM or more

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

394

Section 3: Access to Facilities
Datafile : xH03_S03-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

V03_IDC

12

3

N

I

1

0

No

No

{3.00} facility description

V03_TXT

15

25

AN

I

1

0

No

No

{3.01} Way to travel to the closest facility

V03_01

40

1

N

I

1

0

No

No

V03_02D

41

2

N

I

1

0

No

No

V03_02H

43

2

N

I

1

0

No

No

V03_02M

45

2

N

I

1

0

No

No

{3.03} Distance to closest facility/Kms

V03_03A

47

4

N

I

1

0

No

No

{3.03} Distance to closest facility/Mts

V03_03B

51

3

N

I

1

0

No

No

{3.04} Was the facility used past 12 months

V03_04

54

1

N

I

1

0

No

No

V03_05

55

1

N

I

1

0

No

No

{3.00} facility code
101:124

1

Foot (without load)

2

Bicycle, rickshaw

3

Motorcycle, tamppo

4Car, bus

5

Mixed (foot and vehicle)

6

Facility is next to the household

7

NOT APPLICABLE (no facility around){3.02} Time to go to/Days
0:7{3.02} Time to go to/Hours
0:23{3.02} Time to go to/Minutes
0:59

1

Yes

2

No

{3.05} How often was used
1

Daily

2

Weekly3Monthly4

RarelyNepal NLSS Ⅲ 2010/11 Manual Version 2.0

395

Section 4: Migration
Datafile : xH04_S04-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

id codeV04_IDC

12

2

N

I

1

0

No

Yes

{4.01} Respondent id-code

V04_01

14

2

N

I

1

0

No

Yes

4.02

V04_02

16

1

N

I

1

0

No

No

V04_03A

17

2

N

I

1

0

No

No

4.03

ever migrated?
1

Yes

2

No

migrated from - district/country
1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

90

United arab emirates

91

United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country

4.03- urban/rural
1

Urban

2

Rural

V04_03B

19

1

N

I

1

0

No

No

4.04

when move to this place

V04_04

20

4

N

I

1

0

No

No

4.05

reason for migration

V04_05

24

2

N

I

1

0

No

No

1

Marriage

2

Follow the family

3

Other family reason

4

Education

5

Training

6

Looking for work

7

Start new job

8Start new business

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

4.06

9

Job transfer

396
10 Conflict

11

Natural disaster

12

13

Other

primary activity [NSCO]
011

ARMED FORCES

111

LEGISLATORS

Easier life style

V04_06

26

3

N

I

1

0

No

No

V04_07

29

1

N

I

1

0

No

No

V04_08

30

2

N

I

1

0

No

No

V04_09_Y

32

4

N

I

1

0

No

Yes

V04_09_M

36

2

N

I

1

0

No

Yes

V04_10_Y

38

4

N

I

1

0

No

Yes

V04_10_M

42

2

N

I

1

0

No

Yes

- distric/counV04_11A

44

2

N

I

1

0

No

No

. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .
999
4.07

4.08

NOT REPORTED

live outside more than 2 cont. month?
1

Yes

2

No

no.of times lives outside
1:30{4.09} Last time left to live outside, Year
2061:2067{4.09} Last time left to live outside, Month
1:12{4.10} Last time came back, Year
2061:2067
98

Still away

{4.10} Last time came back, Month
1:12
98
4.11

Still away

place of living last time
1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

90United arab emirates

91

397
United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country

1

Urban

2

Rural

.

- urban/rural V04_11B

46

1

N

I

1

0

No

No

V04_IDC2

47

2

N

I

1

0

No

Yes

49

2

N

I

1

0

No

No

id code4.12

4.13

reason for migration

V04_12

1

Marriage

2

Follow the family

3

Other family reason

4

Education

5

Training

6

Looking for work

7

Start new job

8Start new business

9

Job transfer

10

Conflict11

Natural disaster

12

Easier life style

13

Other

primary activity [NSCO] before living
11

ARMED FORCES

111

LEGISLATORS

V04_13

51

3

N

I

1

0

No

No

V04_14

54

1

N

I

1

0

No

No

V04_15

55

1

N

I

1

0

No

No

V04_16

56

1

N

I

1

0

No

No

V04_17

57

3

N

I

1

0

No

No

60

2

N

I

1

0

No

No

. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .
999
4.14

4.15

4.16

4.17

NOT REPORTED

have any job there?
1

Yes

2

No

who help to find job/start business?
1

Relatives

2

Friends,neighbours

3

Other

4

Nobody helped

5

Not applicabletypes of work done
1

Wage job

2

Self employment

primary activity [NSCO] there

Note: {TODO: Dol, is there any code 998-Not workinh here?
011

ARMED FORCES. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .
999
4.18

NOT REPORTED

main reason to come backV04_18

1

Family reasons

2Work finished3

Dissatisfied with work

4

Start new job

5Start new business6

Job transfer

7

Conflict8

Natural disaster

9

Easier life style

10

Other

99

Not Returned

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

398

Section 5: Food consumption
Datafile : xH05_S05-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

food code

V05_IDC

12

3

N

I

1

0

No

Yes

food item name

V05_ITM

15

25

AN

I

1

0

No

No

5.01

V05_01

40

1

N

I

1

0

No

No

V05_02

41

2

N

I

1

0

No

No

V05_03A

43

8

N

I

1

3

Yes

No

V05_03B

51

2

N

I

1

0

No

Yes

consumed (yes/no)
1

Yes

2

No

{05.02} Home-Prod-12-mo/Number of months
0:12{05.03} A: Home-Prod-12-mo/Quantity{05.03} B: Home-Prod-12-mo/Measurement unit
1

Kg

2

Gram

3

Maund

4

Liter

5

Muri

6

Pathi

7

Manna

8

Kuruwa

9

Piece

10

Dozen

11

Quintal{05.04} Home-Prod-12-mo/Value

V05_04

53

7

N

I

1

0

No

No

{05.05} Purchases-12-mo/Number of months

V05_05

60

2

N

I

1

0

No

No

{05.06} A: Purchases-12-mo/Quantity

V05_06A

62

8

N

I

1

3

Yes

No

{05.06} B: Purchases-12-mo/Measurement unit

V05_06B

70

2

N

I

1

0

No

Yes

0:12

1

Kg

2

3

Maund

4

Gram
Liter

5

Muri

6

Pathi

7

Manna

8

Kuruwa

9

Piece

10

Dozen

11

Quintal{05.07} Purchases-12-mo/Value

V05_07

72

7

N

I

1

0

No

No

{05.08} In-kind-12-mo/Value

V05_08

79

7

N

I

1

0

No

No

{05.09} Consumed-7-days/Number of days

V05_09

86

2

N

I

1

0

No

No

{05.10} A: Consumed-7-days/Quantity

V05_10A

88

8

N

I

1

3

Yes

No

{05.10} B: Consumed-7-days/Measurement unit

V05_10B

96

2

N

I

1

0

No

Yes

0:7

1

Kg

2

Gram

3

Maund

4

Liter

5

Muri

6

Pathi

7

Manna

8

Kuruwa

9

Piece

10

Dozen

11

Quintal{05.11} Consumed-7-days/Value

V05_11

98

7

N

I

1

0

No

No

{05.12} Consumed-7-days/Source

V05_12

105

1

N

I

1

0

No

No

1

Home production

2

Purchase

3

In-kind

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

399
Section 6A: Frequent non-food expenditures
Datafile :xH06_S06A-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes
No

frq. non-food code

V06A_IDC

12

3

N

I

1

0

No

frq. non-food item

V06A_ITM

15

25

AN

I

1

0

No

No

6.01

V06_01

40

1

N

I

1

0

No

No

consumed (yes/no)
1

Yes

2

No

6.02

amount of past 30 days (rs.)

V06_02A

41

7

N

I

1

0

No

No

6.02

amount of past 12 months (rs.)

V06_02B

48

7

N

I

1

0

No

No

Section 6B: Infrequent non-food expenditures
Datafile : xH07_S06B-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes
No

infrq. non-food code

V06B_IDC

12

3

N

I

1

0

No

infrq. non-food item

V06B_ITM

15

25

AN

I

1

0

No

No

6.03

V06_03

40

1

N

I

1

0

No

No

V06_04

41

7

N

I

1

0

No

No

6.04

purchased/received (yes/no)
1

Yes

2

No

amount of past 12 months (rs.)

Section 6C: Inventory of durable goods
Datafile : xH08_S06C-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes
No

code of durable goods

V06C_IDC

12

3

N

I

1

0

No

durable goods item

V06C_ITM

15

25

AN

I

1

0

No

No

6.05

V06_05

40

1

N

I

1

0

No

No

having durable goods (yes/no)
1

Yes

2

No

6.06

no. of durable goods having now

V06_06

41

3

N

I

1

0

No

No

6.07

years before bring

V06_07

44

2

N

I

1

0

No

No

6.08

how received?

V06_08

46

1

N

I

1

0

No

No

1

Purchased2

Gift/payment

3

Dowry/inheritance

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

6.09

price at that period

400
V06_09

47

7

N

I

1

0

No

No

6.10

current sale value

V06_10

54

7

N

I

1

0

No

No

Section 6D: Own account production of goods
Datafile : xH09_S06D-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

HHNO(id) HNUM

4

N

I

1

0

No

Yes

10

2

N

I

1

0

No

Yes

own account prod. goods code

V06D_IDC

12

3

N

I

1

0

No

No

own account prod. goods item

V06D_ITM

15

25

AN

I

1

0

No

No

6.11

V06_11

40

1

N

I

1

0

No

No

produced (yes/no)
1

Yes

2

No

6.12

amount of past 30 days (rs.)

V06_12A

41

7

N

I

1

0

No

No

6.12

amount of past 12 months (rs.)

V06_12B

48

7

N

I

1

0

No

No

Section 7: Education
Datafile : xH10_S07-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU
HHNO
i

d

c

o

d

e

(id) HPSU

6

4

N

I

1

0

No

Yes

(id) HNUM

10

2

N

I

1

0

No

Yes

12

2

N

I

1

0

No

Yes

V07_IDC{7.01} Respondent id-code

V07_01

14

2

N

I

1

0

No

Yes

7.02

V07_02

16

1

N

I

1

0

No

No

V07_03

17

1

N

I

1

0

No

No

V07_04

18

1

N

I

1

0

No

No

V07_05

19

1

N

I

1

0

No

No

V07_06

20

1

N

I

1

0

No

No

V07_07

21

1

N

I

1

0

No

No

7.03

7.04

7.05

7.06

7.07

can read a letter?
1

Yes

2

No

can write a letter?
1

Yes

2

No

where did person learn?
1Formal schooling2

Taught at home

3

Govt. literacy course

4

NGO literacy course

5

Other

ever been refused admission
1

Yes

2

No

reason to refuse admission
1

Poor academic

2

Couldn't pay

3

Caste/ethnicity

4

Religion

5

Otherstypes of school/college refused

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

401

7.08

1

Community/government2

Institutional/private

3

Gurukul/madarsha/gumba

4

Other
V07_08

22

1

N

I

1

0

No

No

V07_09

23

2

N

I

1

0

No

No

V07_IDC2

25

2

N

I

1

0

No

Yes

V07_10

27

1

N

I

1

0

No

No

V07_11

28

2

N

I

1

0

No

No

V07_12

30

2

N

I

1

0

No

No

V07_13A

32

2

N

I

1

0

No

No

{7.13} B: Times the SLC exam was given

V07_13B

34

2

N

I

1

0

No

No

7.14

V07_14

36

2

N

I

1

0

No

No

7.09

i

educational background1

Never attended school

2

Attended school/college in past3

Currently attending school/college

reason for never attending school1

Too young2

Too expensive

3

Too far away

4

Had to help at home5

Education not useful

6

Parents did not want

7

Not willing to attend

8

Disable9

Not allowed admission

10

Other reasons

c

o

d

7.10

7.11

7.12

d

e

type of school/college
1

Community/government2

Institutional/private

3

Technical/vocational

4

Gurukul/madarsha/gumba

5Community (public) campus

6

Private campus

7Constituent campus

8

Other

highest level completed
0

Nursery/kg/pre-school

1

Class 01

2

Class 02

3

Class 03

4

Class 04

5

Class 05

6

Class 06

7

Class 07

8

Class 08

9

Class 09

10

Class 10

11

SLC

12

Intermediate

13

Bachelor

14

Master15

Profesional degree

16

Levellessyears to complete primary level (1-5)
0:10
98Complete without attending school{7.13} A: Years takes to complete class 10
0:20

age when left school

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

7.15

last attending before leaving school0

Nursery/kg/pre-school

1

Class 01

2

Class 02

3

Class 03

4

Class 04

5

Class 05

6

Class 06

7

Class 07

8

Class 08

9

Class 09

10

Class 10

11

SLC

12

Intermediate

13

Bachelor

14

Master15

Profesional degree

16

Levelless{7.16} reason for leaving schooli1Further schooling not available

2

Too expensive

3

Too far away

4

Had to help at home5

Parents did not want

6

Completed desired schooling

7

Marriage

8

Moved away

9

Poor academic progress

10

Started working/got a job

11

Lack of toilet facility

12

Language problem

13

Not allowed admission

14

Mistreatment at school

15

Other reasons

c

o

d

7.17

7.18

d

e

type of school/college
1

Community/government2

Institutional/private

3

Technical/vocational

4

Gurukul/madarsha/gumba

5Community (public) campus

6

Private campus

7Constituent campus

8

Other

class currently attending
0

Nursery/kg/pre-school

1

Class 01

2

Class 02

3

Class 03

4

Class 04

5

Class 05

6

Class 06

7

Class 07

8

Class 08402
V07_15

38

2

N

I

1

0

No

No

V07_16

40

2

N

I

1

0

No

No

V07_IDC3

42

2

N

I

1

0

No

Yes

V07_17

44

1

N

I

1

0

No

No

V07_18

45

2

N

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0403
9Class 09

10

Class 10

11

SLC

12

Intermediate

13

Bachelor

14

Master15

Profesional degree

7.19

no. days attending school

V07_19

47

1

N

I

1

0

No

No

7.20

no. days supposed to go school

V07_20

48

1

N

I

1

0

No

No

V07_21

49

1

N

I

1

0

No

No

V07_22

50

1

N

I

1

0

No

No

: hrs

V07_23A

51

2

N

I

1

0

No

No

: minute

V07_23B

53

2

N

I

1

0

No

No

take any private tuition

V07_24

55

1

N

I

1

0

No

No

V07_25

56

1

N

I

1

0

No

No

e

V07_IDC4

57

2

N

I

1

0

No

Yes

expenditure on

0:7

0:7
{7.21} reason for absenting from school

7.22

7.23

1

Illness/injury

2

Had to help with hhld chores

3

Had to work

4

Family members sick or died5

Family function

6

Quit school

7

Othersmode of transport
1

Walk

2

Bus/Motor

3

Bicycle/rickshaw

4

Mixed (walk+vehicle)

5

Other

time taken
0:6

.
0:59
7.24

7.25

1

Yes

2

No

tuition teacher from own school?
1

Yes

2
i

d

7.26

No
c

o

d

: monthly tuition fee (rsV07_26A

59

7

N

I

1

0

No

No

.

: on other fee (rs)V07_26B

66

7

N

I

1

0

No

No

.

: on uniform (rs)V07_26C

73

7

N

I

1

0

No

No

.

: on textbook/supplies (rV07_26D

80

7

N

I

1

0

No

No

.

: on transportation (rs) V07_26E

87

7

N

I

1

0

No

No

.

: on private tuition (rs)V07_26F

94

7

N

I

1

0

No

No

.

: on others (rs)V07_26G

101

7

N

I

1

0

No

No

V07_27

108

1

N

I

1

0

No

No

V07_28

109

7

N

I

1

0

No

No

V07_29

116

1

N

I

1

0

No

No

V07_30

117

1

N

I

1

0

No

No

7.27

receive scholarship?
1

Yes

2

No

7.28

value of scholarship7.29

form of scholarship

7.30

1

Tuition fees

2

Uniforms/books

3

Hostel4

Othertypes of scholarship1(rs)

Poor and talented

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

404

7.31

7.32

2

Girls

3

Dalits4

Conflict affected

5

Disabled

6

HIV/AIDS

7

Karnali zone

8

Other

class attending in last academic year
0

Nursery/kg/pre-school

1

Class 01

2

Class 02

3

Class 03

4

Class 04

5

Class 05

6

Class 06

7

Class 07

8

Class 08

9

Class 09

10

Class 10

11

SLC

12

Intermediate

13

Bachelor

14

Master15

Profesional degree98

Did not attend last year

years to complete pri. level excl. ECD

V07_31

118

2

N

I

1

0

No

No

V07_32

120

2

N

I

1

0

No

No

V07_33A

122

2

N

I

1

0

No

No

V07_33B

124

2

N

I

1

0

No

No

0:10
98

Complete primary level without attending school{7.33} A: Years took completing class 10
0:20{7.33} B: Times the SLC exam was given
0:10

Section 8: Health
Datafile : xH11_S08-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

12

2

N

I

1

0

No

Yes

id codeV08A_IDC{8.01} Respondent id-code

V08_01

14

2

N

I

1

0

No

Yes

{8.02} Type of disability

V08_02

16

1

N

I

1

0

No

No

1

Physical2

Visual3

Hearing

4

Visual and hearing

5

Speech6

Mental7

Multiple

8

None

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

8.03

how long suffered from disability?

405
V08_03

17

2

N

I

1

0

No

No

V08_04

19

1

N

I

1

0

No

No

V08_05

20

2

N

I

1

0

No

No

V08_06

22

2

N

I

1

0

No

No

V08_07A

24

8

N

I

1

0

No

No

V08_07B

32

8

N

I

1

0

No

No

V08_08

40

1

N

I

1

0

No

No

0:75
98
8.04

8.05

By birth

suffer from chronic illness?
1

Yes

2

No

primary chronic illness
1

Heart related

2

Respiratory

3

Asthma4

Epilepsy5

Cancer6

Diabetes

7

Kidney/liver deseases

8

Rheumatism related

9

Gynecological problems

10

Occupational illnesses

11

High/low blood pressure

12

Gastrointestinal deseases

13

Other

8.06

years of illness start

8.07

amount spent : medicine

.

(rs)

: other expenses (rs)8.08

received in-kind medication
1

Yes

2

No

8.09

days of work missed

V08_09

41

4

N

I

1

0

No

No

8.10

present health status

V08_10

45

1

N

I

1

0

No

No

V08B_IDC

46

2

N

I

1

0

No

Yes

V08_11

48

1

N

I

1

0

No

No

V08_12

49

2

N

I

1

0

No

Yes

V08_13

51

1

N

I

1

0

No

No

1

Excellent

2

Good

3

Fair

4
i

d

8.11

8.12

8.13

Poor
c

o

d

e

any health problem & injured
1

Yes

2

No

types of illness or injury
1

Diarrhoea

2

Dysentry

3

Respiratory problems

4

Malaria5Cold/fever/flu

6

Other fever

7

Skin disease

8

TB

9

Measles10

Jaundice

11

Parasites

12

Injury13

Dental problems

14

Other

consult with anyone?
1

Yes

2

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

8.14

main reason for not consulting
1

Illness/injury not serious

2

Health facilty too far

3

No transport facility

4

Health care too expensive

5

Transport too expensive

6

Health7

Health workers not present

8Health care not good quality

9

Other

1

Govt/SHP

2

Govt/HP

3

Govt/PHC

4

Govt/Hospital

5

Govt/Mobile clinic

6

Govt/Ayurved centre

7

Priv/Pharmacy

8

Priv/Clinic

9

Priv/Hospital

10

Priv/Health worker's home

11

Other

{8.16} Consulted with
1

Govt. Doctor

2

Govt. Paramedic

3

Govt. Kaviraj/vaidya

4

Pvt. Doctor

5Pvt. Pharmacist

6

Pvt. Paramedic

7

Pvt. Kaviraj/vaidya

8

Traditional

9
d

52

1

N

I

1

0

No

No

V08_15

53

2

N

I

1

0

No

No

V08_16

55

1

N

I

1

0

No

No

workers unfriendly{8.15} Place of last consultationi406
V08_14

healerOther
V08B_IDC2

56

2

N

I

1

0

No

No

(rs)

V08_17A

58

8

N

I

1

0

No

No

.

:medicine (rs)V08_17B

66

8

N

I

1

0

No

No

.

:travelV08_17C

74

8

N

I

1

0

No

No

8.17

c

o

d

e

cost of consultation

:fees

(rs)

8.18

total cost spent (rs)V08_18

82

8

N

I

1

0

No

No

8.19

received in-kind medication

V08_19

90

1

N

I

1

0

No

No

V08_20

91

1

N

I

1

0

No

No

V08_21

92

4

N

I

1

0

No

No

V08_22

96

1

N

I

1

0

No

No

V08_23

97

1

N

I

1

0

No

No

V08_24

98

1

N

I

1

0

No

No

8.20

8.21

1

Yes

2

No

stopped work?
1

Yes

2

No

days of work missed

8.22 anything to treat the diarrhoea?

8.23

8.24

1

Yes

2

No

treatment given
1

ORS

2

Allopathic medicine

3

Traditional medicine

4

Other

obtain the ors from1

SHP

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

407

i

2

HP

3

PHC

4

Hospital

5

Mobile clinic

6

Made at home

7

Non-govt. inst.

8

Medicine shop

9

Other

d

8.25

8.26

8.27

c

o

d

e

heard about HIV/AIDS
1

Yes

2

No

3

Person not contacted

first media to know about hiv/aids
1

Radio

2

Television3

Newspapers/pamphlets/posters

4

Friends/relatives

5

Health workers

6

Textbook7

Othermethods of prevention - first

V08C_IDC

99

2

N

I

1

0

No

Yes

V08_25

101

1

N

I

1

0

No

No

V08_26

102

1

N

I

1

0

No

No

No

V08_27A

103

2

N

I

1

0

No

.

- second

V08_27B

105

2

N

I

1

0

No

No

.

- thirdV08_27C

107

2

N

I

1

0

No

No

- fourth

V08_27D

109

2

N

I

1

0

No

No

V08D_IDC

111

2

N

I

1

0

No

Yes

V08_28

113

1

N

I

1

0

No

No

V08_29

114

1

N

I

1

0

No

No

: bcg

V08_30A

115

1

N

I

1

0

No

No

: dpt1

V08_30B

116

1

N

I

1

0

No

No

.

i

0

!end-of-list!

1

Abstain from sex

2

Use condoms

3

Limit sex to one partner

4

Limit number of sexual partners

5

Avoid sex with prostitutes

6

Avoid sex with persons who have many partners

7

Avoid sex with homosexuals

8

Avoid sex with persons who inject drugs intravenously

9

Avoid blood transfusions

10

Avoid injections

11

Avoid sharing razors/blades

12

Avoid kissing

13

Avoid mosquito bites

14

Seek protection from traditional practitioner

15

Other

c

o

d

d

e

8.28 ever been immunized?
1

Yes

2

No

3

Don't know

8.29 have immunization card?
1

Yes, seen

2

Yes, not available

3No

8.30 received vaccine

.

1

Yes

2

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

408
1

Yes

2

No

.
1

Yes

2

No

.
1

Yes

2

No

1

Yes

2

No

1

Yes

2

No

1

Yes

2

No

1

Yes

2

No

.

.

.

.

.
1

Yes

2

No

.
1

Yes

2

No

1

Yes

2

No

.

8.31

8.32

8.33

8.34

8.35

: dpt2

V08_30C

117

1

N

I

1

0

No

No

: dpt3

V08_30D

118

1

N

I

1

0

No

No

: polio 1

V08_30E

119

1

N

I

1

0

No

No

: polio 2

V08_30F

120

1

N

I

1

0

No

No

: polio 3

V08_30G

121

1

N

I

1

0

No

No

: measles

V08_30H

122

1

N

I

1

0

No

No

: HP-B 1

V08_30I

123

1

N

I

1

0

No

No

: HP-B 2

V08_30J

124

1

N

I

1

0

No

No

: HP-B 3

V08_30K

125

1

N

I

1

0

No

No

V08_31

126

1

N

I

1

0

No

No

V08_32

127

1

N

I

1

0

No

No

V08_33

128

1

N

I

1

0

No

No

V08_34

129

1

N

I

1

0

No

No

V08_35

130

1

N

I

1

0

No

No

received BCG vaccine
1

Yes

2

No

3

Don't know

received Polio vaccine
1

Yes

2

No

3

Don't know

when first polio vaccine received
1

Just after birth

2

Later

3

Don't know

No. of time polio vaccine received
1

Once

2

Twice

3

Thrice4More than 3 times

5

Don't know

received DPT vaccine
1

Yes

2

No

3

Don't know

8.36

No. of time DPT vaccine received

V08_36

131

2

N

I

1

0

No

No

8.37

received measles vaccine

V08_37

133

1

N

I

1

0

No

No

1

Yes

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

409

8.38

8.39

2

No

3

Don't know

received Hp-B vaccine
1

Yes

2

No

3

Don't know

place of immunization
1

SHP

2

HP

3

PHC

4

Hospital5

Outreach clinic

6

Other health inst.

V08_38

134

1

N

I

1

0

No

No

V08_39

135

1

N

I

1

0

No

No

Section 9A: Maternity history
Datafile : xH12_S09A-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

ID_S9A

12

2

N

I

1

0

No

Yes

unique ids e r i a l

n o.

o f

w o m e n

V09A_SN

14

1

N

I

1

0

No

No

d

o f

w o m e n

V09A_IDC

15

2

N

I

1

0

No

Yes

V09A_RSP

17

2

N

I

1

0

No

Yes

V09A_BO

19

2

N

I

1

0

No

No

{9.03} Child name

V09_03

21

25

AN

I

1

0

No

No

{9.04} Date of birth, Month

V09_04A

46

2

N

I

1

0

No

No

V09_04B

48

4

N

I

1

0

No

No

V09_05

52

1

N

I

1

0

No

No

V09_06

53

1

N

I

1

0

No

No

V09_07

54

1

N

I

1

0

No

No

i

d

i d

c

o

c o d eb i r t heo fr e s p o n d e n t

o r d e r

1:12
{9.04} Date of birth, Year
2026:2067
9.05

9.06

9.07

sex of child
1

Male

2

Femalestill alive
1

Yes

2

No

currently live with you?
1

Yes

2

No

9.08

id code of child

9.09

lived forV09_08

55

2

N

I

1

0

No

Yes

: yearV09_09A

57

2

N

I

1

0

No

No

:

month

V09_09B

59

2

N

I

1

0

No

No

:

day

V09_09C

61

2

N

I

1

0

No

No

highest level completed

V09_10

63

2

N

I

1

0

No

No

0:32
.
0:11
.
0:31
9.10

0

Nursery/kg/pre-school

1

Class 01

2

Class 02

3

Class 03

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

4104Class 04

5

Class 05

6

Class 06

7

Class 07

8

Class 08

9

Class 09

10

Class 10

11

SLC

12

Intermediate

13

Bachelor

14

Master15

Profesional degree98

Never attending school

Section 9B: Pre and post-natal care
Datafile : xH13_S09B-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i n eYesn u m b e r

V09B_LN1

12

2

N

I

1

0

No

Yes

9.11

id code of women

V09B_IDC

14

2

N

I

1

0

No

Yes

9.12

went for pre-natal consultation

V09_12

16

1

N

I

1

0

No

No

V09_13

17

2

N

I

1

0

No

Yes

V09_14

19

1

N

I

1

0

No

No

V09_15

20

1

N

I

1

0

No

No

V09_16

21

1

N

I

1

0

No

No

V09_17

22

1

N

I

1

0

No

No

9.13

9.14

9.15

1

Yes

2

No

place where first care received
1

SHP

2

HP

3

PHC

4

Hospital

5

Mobile clinic

6

Ayurved centre

7

Pharmacy

8

Clinic9

Pvt. hospital

10

Health worker's home

11

Other

care provider
1

Doctor2

Nurse/anm ha/sahw /ahw/mchw/ vhw

3

TBA

4

SBA

5

Other

at what month
1:9

9.16

9.17

given tetanus injection after birth
1

Yes

2

No

no. of times
1

Once

2

Twice

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

411
3
9.18

reason for not pre-natal consultation
1

9.20

9.21

Health facilty too far

3

No transport

4

Health care too expensive

5

Transport too expensive

6

Health

23

1

N

I

1

0

No

No

7

Health workers not present

8Health care not good quality

9

Other

workers unfriendly

n u m b e r

V09B_LN2

24

2

N

I

1

0

No

Yes

where given birth

V09_19

26

1

N

I

1

0

No

No

V09_20

27

1

N

I

1

0

No

No

V09_21

28

1

N

I

1

0

No

No

V09_22

29

2

N

I

1

0

No

Yes

V09_23

31

1

N

I

1

0

No

No

V09_24

32

1

N

I

1

0

No

No

1

Home

2

SHP

3

HP

4

PHC

5

Hospital

6

Pvt. hospital

7

Other

who assisted
1

Family member or relatives

2

Neighbours

3

TBA

4

SBA

5

HA/SAHW/AHW/MCHW/CHW/VHW

6

ANM/nurse/doctor

7

Other

8

No one9

Don't know

post-natal checkup
1

Yes

2

No

9.22 place of post-natal check-up
1

SHP

2

HP

3

PHC

4

Hospital

5

Mobile clinic

6

Ayurved centre

7

Pharmacy

8

Clinic9

Pvt. hospital

10

Health worker's home

11

Other

9.23 care provider

9.24

V09_18

Don't think is necessary2

l i n e9.19

More than twice

1

Doctor2

Nurse/AMN/HA/SAHW/AHW/MCHW/VHW

3

TBA

4

SBA

5

Other

reason for not post-natal consultation
1

Don't think necessary

2

Health facilty too far

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

4123

No transport

4

Health care too expensive

5

Transport too expensive

6

Health7

Health workers not present

8Health care not good quality

9

Other

workers unfriendly

Section 9C: Family planning
Datafile : xH14_S09C-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

HHNOi(id) HNUM

d

9.25

c

o

d

e

is this 15-49 married women?
1

Yes

2

No

6

4

N

I

1

0

No

Yes

10

2

N

I

1

0

No

Yes

V09C_IDC

12

2

N

I

1

0

No

Yes

V09_25

14

1

N

I

1

0

No

No

9.26

age at first marriage

V09_26

15

2

N

I

1

0

No

No

9.27

know any method to prevent pregnancy

V09_27

17

1

N

I

1

0

No

No

V09_28

18

1

N

I

1

0

No

No

V09_29

19

1

N

I

1

0

No

No

V09_30

20

1

N

I

1

0

No

No

V09_31

21

1

N

I

1

0

No

No

V09_32

22

1

N

I

1

0

No

No

9.28

9.29

9.30

9.31

9.32

1

Yes

2

No

medium to learn
1

Radio

2

Television3

Newspapers/ pamphlets/ posters

4

Friends/ relatives

5

Health worker

6

Husband7

Textbook8

Otherever used
1

Yes

2

No

currently using
1

Yes

2

No

which method currently used
1

Pill

2

IUD

3

Injectables

4

Implants

5

Condom6

Trad. method

7

Female sterilization

8

Male sterilization

9

Other

where obtained
1

Public health2

Private health inst.

inst.

3

Pharmacy

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

413

9.33

9.34

9.35

4

VSC

5

Other

why not?
1

Not available

2

Too expensive

3

Husband away

4

Want more children

5

Religious reasons

6

Scared of side-effects

7

Husband does not want

8

Other

health worker visit?
1

Yes

2

No

child like to have

V09_33

23

1

N

I

1

0

No

No

V09_34

24

1

N

I

1

0

No

No

: totalV09_35A

25

2

N

I

1

0

No

No

.

: boys

V09_35B

27

2

N

I

1

0

No

No

.

: girlV09_35C

29

2

N

I

1

0

No

No

Section 9D: Household decisions (PART I)
Datafile : xH15_S09D-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes
Yes

decision number

V09D_IDC

12

2

N

I

1

0

No

Yes

decision description

V09D_TXT

14

25

AN

I

1

0

No

No

9.37

V09_37

39

1

N

I

1

0

No

No

V09_38

40

1

N

I

1

0

No

No

V09_39

41

1

N

I

1

0

No

No

9.38

9.39

have to make decision ?
1

Yes

2

No

involvement in decision making
1

A lot

2

A little3

Not involved

who made final decision
1

Me

2

My spouse

3

Both

4

Other

Section 10A: Time Use
Datafile : xH16_S10A-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

ID CODE
10.01

hour spent on

fetching water

10

2

N

I

1

0

No

Yes

V10_IDC

12

2

N

I

1

0

No

Yes

V10_01A

14

3

N

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

414
0:56
.

firewood collecting

V10_01B

17

3

N

I

1

0

No

No

fodder collecting

V10_01C

20

3

N

I

1

0

No

No

care animal

V10_01D

23

3

N

I

1

0

No

No

knitting/weaving/tailorinV10_01E

26

3

N

I

1

0

No

No

processing preserved foodV10_01F

29

3

N

I

1

0

No

No

total [A-F]

V10_01G

32

3

N

I

1

0

No

No

hhld repairing

V10_01H

35

3

N

I

1

0

No

No

cooking/serving foodV10_01I

38

3

N

I

1

0

No

No

clearing house

V10_01J

41

3

N

I

1

0

No

No

shopping

V10_01K

44

3

N

I

1

0

No

No

caring for elderly/sick/dV10_01L

47

3

N

I

1

0

No

No

baby sitting/child caringV10_01M

50

3

N

I

1

0

No

No

other volunteer/comm. serV10_01N

53

3

N

I

1

0

No

No

56

10

AN

I

1

0

No

No

0:56
.
0:56
.
0:56
.
0:56
.
0:56
.
0:336
.
0:56
.
0:56
.
0:56
.
0:56
.
0:126
.
0:126
.
0:56
{10Name} Person, short name

V10_NAME

Section 10B: Jobs
Datafile : xH17_S10B-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

Person-job seqno

V10B_LN

12

2

N

I

1

0

No

Yes

{10.02} Person id-code

V10_02

14

2

N

I

1

0

No

Yes

{10.02} Job-id

V10_02_JOB

16

2

N

I

1

0

No

No

1

1st job

2

2nd job

3

3rd job

4

4th job

5

5th job99

Total number of hours worked during the past 7 days

{10.03} Occupation description

V10_03_TXT

18

25

AN

I

1

0

No

No

{10.03} Occupation code (NSCO)

V10_03

43

3

N

I

1

0

No

Yes

011

ARMED FORCES

111

LEGISLATORS. . . . . . . . . . . . . . .
. . . . . . . . . . . . . . .

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

415
999
10.04

NOT REPORTED

work done in
1

Yes

2

No

1

Yes

2

No

.

.
1

Yes

2

No

.
1

Yes

2

No

.
1

Yes

2

No

1

Yes

2

No

1

Yes

2

No

1

Yes

2

No

.

.

.

.
1

Yes

2

No

.
1

Yes

2

No

1

Yes

2

No

1

Yes

2

No

.

.

10.05

baishakh

V10_04A

46

1

N

I

1

0

No

No

jesthaV10_04B

47

1

N

I

1

0

No

No

aashadhV10_04C

48

1

N

I

1

0

No

No

shravan

V10_04D

49

1

N

I

1

0

No

No

bhadraV10_04E

50

1

N

I

1

0

No

No

ashwinV10_04F

51

1

N

I

1

0

No

No

kartik

V10_04G

52

1

N

I

1

0

No

No

mangsir

V10_04H

53

1

N

I

1

0

No

No

poush

V10_04I

54

1

N

I

1

0

No

No

magh

V10_04J

55

1

N

I

1

0

No

No

falgunV10_04K

56

1

N

I

1

0

No

No

chaitraV10_04L

57

1

N

I

1

0

No

No

V10_05A

58

2

N

I

1

0

No

No

V10_05B

60

2

N

I

1

0

No

No

V10_06A

62

2

N

I

1

0

No

No

V10_06B

64

2

N

I

1

0

No

No

tuesday

V10_06C

66

2

N

I

1

0

No

No

wednesday

V10_06D

68

2

N

I

1

0

No

No

thursday

V10_06E

70

2

N

I

1

0

No

No

fridayV10_06F

72

2

N

I

1

0

No

No

saturday

V10_06G

74

2

N

I

1

0

No

No

on average no.of days/month work
1:30

10.05

on average no.of hours/day work
1:18

(10.06a) Hours worked past 7 days/Sunday
0:20(10.06b) Hours worked past 7 days/Monday
0:20
.
0:20
.
0:20
.
0:20
.
0:20
.

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

416
0:20
.

total

V10_06H

76

3

N

I

1

0

No

No

V10_07

79

1

N

I

1

0

No

No

0:140
{10.07} Sector of employment
1

wage employment in agri.

2

wage employment in non-agri.

3

self-employment in agri.

4

self-employment in non-agri.

Section 11: Unemployment / Under-employment and past job
Datafile :xH18_S11-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

i d

c o d eV11_IDC

12

2

N

I

1

0

No

Yes

11.01

Total hours work [10.01(G)+10.06(H)]V11_01

14

3

N

I

1

0

No

No

11.02

available for work?

V11_02

17

1

N

I

1

0

No

No

V11_03

18

1

N

I

1

0

No

No

reason for not available/not look for woV11_04

19

2

N

I

1

0

No

Yes

V11_05

21

1

N

I

1

0

No

No

V11_06

22

1

N

I

1

0

No

No

reason for not available/not look for adV11_07

23

2

N

I

1

0

No

Yes

11.03

11.04

11.05

11.06

11.07

1

Yes

2

No

look for work
1

Yes

2

No

1

Attending school

2

Household duties

3

Old/sick4

Disabled5

Thought no work available

6

Awaiting reply to earlier enquiries

7

Waiting to start arranged job/business8

Off season

9

Not available

10

Othersavailable for additional work?
1

Yes

2

No

look for additional work
1

Yes

2

No

1

Could not find more work/lack of business2

Lack of finance, raw materials

3

Machinery, electrical, other breakdow

4

Off season inactivity

5

Industrial dispute

6

Other involuntary

7

Have sufficient work

8

Household duties

9

Student, unpaid training

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

417
10

Illness, disability

11

Vacation, family reason

12

Pregnant/delivery

13

Other voluntary{11Name} Person short name

V11_NAME

25

10

AN

I

1

0

No

No

Section 12: Wage Jobs
Datafile : xH19_S12-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU
HHNO
l i n en u m b e r(id) HPSU

6(id) HNUM

10

2

N

I

1

0

No

Yes

12

2

N

I

1

0

No

Yes
Yes

V12_LN1

4

N

I

1

0

No

Yes

{12.01} Person id-code

V12_01

14

2

N

I

1

0

No

{12.01} Job-id

V12_01_JOB

16

2

N

I

1

0

No

No

{12.02} Work activity description

V12_02_TXT

18

25

AN

I

1

0

No

No

{12.02}

V12_02

43

3

N

I

1

0

No

Yes

V12_03

46

1

N

I

1

0

No

No

Work activity code [NSIC]
1

AGRICULTURE AND RELATED SERVICE ACTIVITIES2FORESTRY, LOGGING AND RELATED SERVICE ACTIVITIES. . . . . . . . . . . . . . .
. . . . . . . . . . . . . . .
99
12.03

EXTRA-TERRITORIAL ORGANIZATION AND BODIES

basis of payment receive
1

Daily basis

2

Long term basis

3

Contract/ piece-rate

12.04

cash per day (rs.)

12.05

in-kind payment

.

12.06

0

!end-of-list!

1

Paddy/rice

2

Wheat

3

Maize

4

Meals

5

Clothing

6

Othervalue of in kind

12.07

47

6

N

I

1

0

No

No

V12_05A

53

1

N

I

1

0

No

No

- second

V12_05B

54

1

N

I

1

0

No

No

- rs./day.
l i n eV12_04- firstV12_06A

55

7

N

I

1

0

No

No

- other (rs/whole peV12_06B

62

7

N

I

1

0

No

No

V12_LN2

69

2

N

I

1

0

No

No

V12_07

71

1

N

I

1

0

No

No

V12_08

72

7

N

I

1

0

No

No

n u m b e r
sector of long term worker
1

Wage job in agriculture

2

Wage job in non-agri.

12.08

cash received

12.09

in-kind received : first

V12_09A

79

1

N

I

1

0

No

No

: second

V12_09B

80

1

N

I

1

0

No

No

.

(rs.)

0

!end-of-list!

1

Paddy/rice

2

Wheat

3

Maize

4

Meals

5

Clothing

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

418
6
12.10

Other

value of in-kind

.
12.11

- rs/day

V12_10A

81

6

N

I

1

0

No

No

- rs/whole period

V12_10B

87

6

N

I

1

0

No

No

V12_11

93

1

N

I

1

0

No

No

V12_12

94

1

N

I

1

0

No

No

V12_13

95

1

N

I

1

0

No

No

V12_14

96

1

N

I

1

0

No

No

V12_LN3

97

2

N

I

1

0

No

No
No

takin loan from employer

12.12

1

Yes

2

No

other member also work same employer

12.13

1

Yes

2

No

share crop with employer

12.14

1

Yes

2

No

tent livestock for emploer
1

Yes

2
l i n e12.15

Non u m b e r

V12_15A

99

7

N

I

1

0

No

.

received as salary/monthtranspor/monthV12_15B

106

7

N

I

1

0

No

No

.

bonus/year

V12_15C

113

7

N

I

1

0

No

No

.

uniform/year

V12_15D

120

7

N

I

1

0

No

No

.

other/year

V12_15E

127

7

N

I

1

0

No

No

V12_16

134

1

N

I

1

0

No

No

V12_17

135

1

N

I

1

0

No

No

V12_18

136

1

N

I

1

0

No

No

V12_19

137

1

N

I

1

0

No

No

V12_20

138

1

N

I

1

0

No

No

V12_21

139

7

N

I

1

0

No

No

12.16

tax already deducted

12.17

1

Yes

2

No

provision of provident fund

12.18

1

Yes

2

No

provision of pension

12.19

1

Yes

2

No

provision of subsidized medical care

12.20

1

Yes

2

No

no. of employee

12.21

1

One

2

Two to nine3

Ten or more

how much receive in-kind and cash

Section 13A1: Landholding - land owned
Datafile : xH20_S13A1-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

V13_PLT1

12

2

N

I

1

0

No

Yes

p l o tn u m b e r

13.03

name of plot

13.04

area

.

1

Ropani2

Bigha

V13_03

14

25

AN

I

1

0

No

No

- unit

V13_04U

39

1

N

I

1

0

No

No

- ropani/bigha

V13_04RB

40

3

N

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

419
0:999
.

- aana/kattha

V13_04AK

43

2

N

I

1

0

No

No

- paisa/dhur

V13_04PD

45

2

N

I

1

0

No

No

V13_05

47

2

N

I

1

0

No

No

0:19
.
0:19
13.05

district where plot located

13.06

13.07

13.08

13.09

13.10

1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchulatypes of land
1

Up Land2

Low Landplot irrigated?
1

Yes

2

No

seasonal or year round irrigation1

Seasonal

2

Year roundmode of irrigation
1

Tubewell/Boring

2

Canal

3

Pond/tank

4

Other natural sources

5

Mixed

value of this plot (rs.)

V13_06

49

1

N

I

1

0

No

No

V13_07

50

1

N

I

1

0

No

No

V13_08

51

1

N

I

1

0

No

No

V13_09

52

1

N

I

1

0

No

No

V13_10

53

10

N

I

1

0

No

No

n u m b e r

V13_PLT2

63

2

N

I

1

0

No

Yes

{13.11} Dry season: Use of plot

V13_11

65

1

N

I

1

0

No

No

p l o t1

Croped yourself

2

Sharedcroped out

3

Fixed rent out

4

Mortgaged out

5Left fallow

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

420
6

Other

{13.12} Dry season: Net rent received/Cash (rs)V13_12C

66

7

N

I

1

0

No

No

{13.12} Dry season: Net rent received/In-kind (V13_12K

73

7

N

I

1

0

No

No

{13.13} Dry season: 1st crop

V13_13A

80

2

N

I

1

0

No

Yes

{13.13} Dry season: 2nd crop

V13_13B

82

2

N

I

1

0

No

Yes

{13.13} Dry season: 3rd crop

V13_13C

84

2

N

I

1

0

No

Yes

86

2

N

I

1

0

No

Yes

{13.13} Dry season: 4th crop
0

NO CULTIVATE IN DRY SEASON

V13_13D
1

EARLY PADDY

2MAIN PADDY

3

UPLAND PADDY

4

WHEAT

5

SPRING/WINTER MAIZE

6SUMMER MAIZE

7MILLET

8

BARLEY9BUCKWHEAT10OTHER CEREALS

11

SOYBEANS12BLACK GRAM

13

RED GRAM

14

GRASS PEA15LENTIL

16

GRAM

17

PEA

18

GREEN GRAM

19

COARSE GRAM

20

COW PEA

21

OTHER LEGUMES

22

WINTER POTATO

23

SUMMER POTATO24

SWEET POTATO

25

COLOCASIA

26

OTHER TUBERS

27MUSTARD

28

GROUND NUT

29

LINSEED

30

SESAME

31

OTHER OILSEED32SUGARCANE

33

JUTE

34

TOBACCO35

OTHER CASH CROPS

36

CHILIES37

ONIONS38GARLIC

39

GINGER

40

TURMERIC

41

CARDAMOM

42

CORIANDER SEED43

OTHER SPICES

44

WINTER VEGETABLES

45

SUMMER VEGETABLES

46

ORANGE

47

LEMON

48

LIME

49

SWEET LIME50

OTHER CITRUS

51

MANGO

52

BANANA

53

GUAVA

54

JACK FRUIT55

PINEAPPLE56

LICHEE

57

PEAR

58

APPLE

59

PLUM

60

PAPAYA

61

POMEGRANATE62

OTHER FRUIT

63

TEA

64

THATCH

65

FODDER TREES

66

BAMBOO67

OTHER TREES{13.14} Wet season: Use of plot

V13_14

88

1

N

I

1

0

No

No

{13.15} Wet season: Net rent received/Cash (rs)V13_15C

89

7

N

I

1

0

No

No

{13.15} Wet season: Net rent received/In-kind (V13_15K

96

7

N

I

1

0

No

No

{13.16} Wet season: 1st crop

V13_16A

103

2

N

I

1

0

No

Yes

{13.16} Wet season: 2nd crop

V13_16B

105

2

N

I

1

0

No

Yes

{13.16} Wet season: 3rd crop

V13_16C

107

2

N

I

1

0

No

Yes

{13.16} Wet season: 4th crop

V13_16D

109

2

N

I

1

0

No

Yes

1

Croped yourself

2

Sharedcroped out

3

Fixed rent out

4

Mortgaged out

5Left fallow

6

Other

0

NO CULTIVATE IN DRY SEASON1

EARLY PADDY

2MAIN PADDY

3

UPLAND PADDY

4

WHEAT

5

SPRING/WINTER MAIZE

6SUMMER MAIZE

7MILLET

8

BARLEY9BUCKWHEAT10OTHER CEREALS

11

SOYBEANS12BLACK GRAM

13

RED GRAM

14

GRASS PEA15LENTIL

16

GRAM

17

PEA

18

GREEN GRAM

19

COARSE GRAM

20

COW PEA

21

OTHER LEGUMES

22

WINTER POTATO

23

SUMMER POTATO24

SWEET POTATO

25

COLOCASIA

26

OTHER TUBERS

27MUSTARD

28

GROUND NUT

29

LINSEED

30

SESAME

31

OTHER OILSEED32SUGARCANE

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

421
33

JUTE

34

TOBACCO35

OTHER CASH CROPS

36

CHILIES37

ONIONS38GARLIC

39

GINGER

40

TURMERIC

41

CARDAMOM

42

CORIANDER SEED43

OTHER SPICES

44

WINTER VEGETABLES

45

SUMMER VEGETABLES

46

ORANGE

47

LEMON

48

LIME

49

SWEET LIME50

OTHER CITRUS

51

MANGO

52

BANANA

53

GUAVA

54

JACK FRUIT55

PINEAPPLE56

LICHEE
PLUM

57

PEAR

58

APPLE

59

60

PAPAYA

61

POMEGRANATE62

OTHER FRUIT

63

TEA

64

THATCH

65

FODDER TREES

66

BAMBOO67

OTHER TREES

Section 13A2: Land sharecropped/rented/mortgaged -in
Datafile : xH21_S13A2-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU
HHNO(id) HPSU

6

4

N

I

1

0

No

Yes

(id) HNUM

10

2

N

I

1

0

No

Yes

p l o tn u m b e r

V13_PLT3

12

2

N

I

1

0

No

Yes

13.19

name of the plot

V13_19

14

25

AN

I

1

0

No

No

13.20

contractual arrangement

V13_20

39

1

N

I

1

0

No

No

1

Sharedcroped out

2

Rent-in3

Mortgaged-in

4

Other

13.21

rent paid (cash only)

13.22

Area of other's land
1

Ropani

2

Bigha

.
.

V13_21

40

7

N

I

1

0

No

No

- unitV13_22U

47

1

N

I

1

0

No

No

- ropani/bigha

V13_22RB

48

3

N

I

1

0

No

No

- aana/kattha

V13_22AK

51

2

N

I

1

0

No

No

- paisa/dhur

V13_22PD

53

2

N

I

1

0

No

No

V13_23

55

1

N

I

1

0

No

No

V13_24

56

1

N

I

1

0

No

No

V13_25

57

1

N

I

1

0

No

No

V13_26

58

1

N

I

1

0

No

No

V13_PLT4

59

2

N

I

1

0

No

Yes

0:19
.
0:19
13.23

13.24

13.25

13.26

types of land
1

Up Land2

Low Landplot irrigated?
1

Yes

2

No

seasonal or year round irrigation1

Seasonal

2

Year roundmode of irrigation
1

Tubewell

2

Canal

3

Pond/tank

4

Other natural sources5
p l o tMixedn u m b e r

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

13.27422
V13_27A

dry season : crop 1

61

2

N

I

1

0

No

No

.

: crop 2

V13_27B

63

2

N

I

1

0

No

No

.

: crop 3

V13_27C

65

2

N

I

1

0

No

No

67

2

N

I

1

0

No

No

.

13.28

: crop 4

V13_27D

0

NO CULTIVATE IN DRY SEASON1

EARLY PADDY

2MAIN PADDY

3

UPLAND PADDY

4

WHEAT

5

SPRING/WINTER MAIZE

6SUMMER MAIZE

7MILLET

8

BARLEY9BUCKWHEAT10OTHER CEREALS

11

SOYBEANS12BLACK GRAM

13

RED GRAM

14

GRASS PEA15LENTIL

16

GRAM

17

PEA

18

GREEN GRAM

19

COARSE GRAM

20

COW PEA

21

OTHER LEGUMES

22

WINTER POTATO

23

SUMMER POTATO24

SWEET POTATO

25

COLOCASIA

26

OTHER TUBERS

27MUSTARD

28

GROUND NUT

29

LINSEED

30

SESAME

31

OTHER OILSEED32SUGARCANE

33

JUTE

34

TOBACCO35

OTHER CASH CROPS

36

CHILIES37

ONIONS38GARLIC

39

GINGER

40

TURMERIC

41

CARDAMOM

42

CORIANDER SEED43

OTHER SPICES

44

WINTER VEGETABLES

45

SUMMER VEGETABLES

46

ORANGE

47

LEMON

48

LIME

49

SWEET LIME50

OTHER CITRUS

51

MANGO

52

BANANA

53

GUAVA

54

JACK FRUIT55

PINEAPPLE56

LICHEE

57

PEAR

58

APPLE

59

PLUM

60

PAPAYA

61

POMEGRANATE62

OTHER FRUIT

63

TEA

64

THATCH

65

FODDER TREES

66

BAMBOO67

OTHER TREES

wet season : crop 1

V13_28A

69

2

N

I

1

0

No

No

.

: crop 2

V13_28B

71

2

N

I

1

0

No

No

.

: crop 3

V13_28C

73

2

N

I

1

0

No

No

.

: crop 4

V13_28D

75

2

N

I

1

0

No

No

0

NO CULTIVATE IN DRY SEASON1

EARLY PADDY

2MAIN PADDY

3

UPLAND PADDY

4

WHEAT

5

SPRING/WINTER MAIZE

6SUMMER MAIZE

7MILLET

8

BARLEY9BUCKWHEAT10OTHER CEREALS

11

SOYBEANS12BLACK GRAM

13

RED GRAM

14

GRASS PEAPEA

15LENTIL

16

GRAM

17

18

GREEN GRAM

19

COARSE GRAM

20

COW PEA

21

OTHER LEGUMES

22

WINTER POTATO

23

SUMMER POTATO24

SWEET POTATO

25

COLOCASIA

26

OTHER TUBERS

27MUSTARD

28

GROUND NUT

29

LINSEED
SUGARCANE

30

SESAME

31

OTHER OILSEED

32

33

JUTE

34

TOBACCO35

OTHER CASH CROPS

36

CHILIES37

ONIONS38GARLIC

39

GINGER

40

TURMERIC

41

CARDAMOM

42

CORIANDER SEED43

OTHER SPICES

44

WINTER VEGETABLES

45

SUMMER VEGETABLES

46

ORANGE

47

LEMON

48

LIME

49

SWEET LIME50

OTHER CITRUS

51

MANGO

52

BANANA

53

GUAVA

54

JACK FRUIT55

PINEAPPLE56

LICHEE

57

PEAR

58

APPLE

59

PLUM

60

PAPAYA

61

POMEGRANATE62

OTHER FRUIT

63

TEA

64

THATCH

65

FODDER TREES

66

BAMBOO67

OTHER TREES

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

423

Section 13B: Production and uses
Datafile :xH22_S13B-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

V13B_LN

12

2

N

I

1

0

No

Yes

V13_35NM

14

23

AN

I

1

0

No

No

V13_35CC

37

2

N

I

1

0

No

Yes

l i n e13.35

n u m b e r
n a m eo f

c r o p{13.35} Crop code
1

13.36

EARLY PADxDY

2

MAIN PADDY

3

UPLAND PADDY

4

WHEAT

5

SPRING/WINTER MAIZE

6SUMMER MAIZE

7MILLET

8

BARLEY9BUCKWHEAT10OTHER CEREALS

11

SOYBEANS12BLACK GRAM

13

RED GRAM

14

GRASS PEA15LENTIL

16

GRAM

17

PEA

18

GREEN GRAM

19

COARSE GRAM

20

COW PEA

21

OTHER LEGUMES

22

WINTER POTATO

23

SUMMER POTATO24

SWEET POTATO

25

COLOCASIA

26

OTHER TUBERS

27MUSTARD

28

GROUND NUT

29

LINSEED

30

SESAME

31

OTHER OILSEED32SUGARCANE

33

JUTE

34

TOBACCO35

OTHER CASH CROPS

36

CHILIES37

ONIONS38GARLIC

39

GINGER

40

TURMERIC

41

CARDAMOM

42

CORIANDER SEED43

OTHER SPICES

44

WINTER VEGETABLES

45

SUMMER VEGETABLES

46

ORANGE

47

LEMON

48

LIME

49

SWEET LIME50

OTHER CITRUS

51

MANGO

52

BANANA

53

GUAVA

54

JACK FRUIT55

PINEAPPLE56

LICHEE

57

PEAR

58

APPLE

59

PLUM

60

PAPAYA

61

POMEGRANATE62

OTHER FRUIT

65

FODDER TREES

63

TEA

64

THATCH

66

BAMBOO67

OTHER TREES

used improved seed?
1

Yes

2

No

13.37

a.

production unit

1

KG

2

Gram

3

Mund

4

Liter

5

Muri

6

Pathi

7

Mana

8

Kuruwa

9

Piece

10

Dozen

11

QuintalV13_36

39

1

N

I

1

0

No

No

V13_37A

40

2

N

I

1

0

No

No

.

b.

total qty. harvested

V13_37B

42

7

N

I

1

0

No

No

.

c.

given to landlord

V13_37C

49

7

N

I

1

0

No

No

.

d.

amount sold/to be soldV13_37D

56

10

N

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

13.38a.

sold unit

1

KG

2

Gram

3

Mund

4

Liter

5

Muri

6

Pathi

7

Mana

8

Kuruwa

9

Piece

10

Dozen

11

424
V13_38A

66

2

N

I

1

0

No

No

Quintal.

b.

total qty. soldV13_38B

68

7

N

I

1

0

No

No

.

c.

price per unit (rs.)

V13_38C

75

10

N

I

1

2

Yes

No

.

d.

total sales amount (rs)V13_38D

85

10

N

I

1

0

No

No

Section 13C1: Expenditures on seeds and young plants
Datafile :xH23_S13C1-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i n eV13C1_LN

12

2

N

I

1

0

No

Yes

{13.40} Crop description

V13_40_TXT

14

23

AN

I

1

0

No

No

{13.40} Crop code

V13_40

37

2

N

I

1

0

No

No

1

0

No

No

13.41

n u m b e r

1

EARLY PADDY

2MAIN PADDY

3

UPLAND PADDY

4

WHEAT

5

SPRING/WINTER MAIZE

6SUMMER MAIZE

7MILLET

8

BARLEY9BUCKWHEAT10OTHER CEREALS

11

SOYBEANS12BLACK GRAM

13

RED GRAM

14

GRASS PEA15LENTIL

16

GRAM

17

PEA

18

GREEN GRAM

19

COARSE GRAM

20

COW PEA

21

OTHER LEGUMES

22

WINTER POTATO

23

SUMMER POTATO24

SWEET POTATO

25

COLOCASIA

26

OTHER TUBERS

27MUSTARD

28

GROUND NUT

29

LINSEED

30

SESAME

31

OTHER OILSEED32SUGARCANE

33

JUTE

34

TOBACCO35

OTHER CASH CROPS

36

CHILIES37

ONIONS38GARLIC

39

GINGER

40

TURMERIC

41

CARDAMOM

42

CORIANDER SEED43

OTHER SPICES

44

WINTER VEGETABLES

45

SUMMER VEGETABLES

46

ORANGE

47

LEMON

48

LIME

49

SWEET LIME50

OTHER CITRUS

51

MANGO

52

BANANA

53

GUAVA

54

JACK FRUIT55

PINEAPPLE56

LICHEE

57

PEAR

58

APPLE

59

PLUM

60

PAPAYA

61

POMEGRANATE62

OTHER FRUIT

63

TEA

64

THATCH

65

FODDER TREES

66

BAMBOO67

OTHER TREES

97

TRANSPORTATION COST98

EXPENDITURE ON SEEDS/PLANT

where did you obtain: firstV13_41A

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

39

1

N

I

.

: second0!end-of-list!

1Agri. dev. office2

Seeds company/serv.centre

3

Other farmer

4

Private dealer

5

From india

6

Landlord

7

Other

13.42 amount spent on seed/young plant425
V13_41B

40

1

N

I

1

0

No

No

V13_42

41

6

N

I

1

0

No

No

Section 13C2: Expenditures on fertilizers and insecticides
Datafile :xH24_S13C2-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i n en u m b e r

V13C2_LN

12

2

N

I

1

0

No

Yes

{13.44}

fertilizer/insecticide description

V13_44_TXT

14

25

AN

I

1

0

No

No

V13_45YN

39

1

N

I

1

0

No

No

13.45

13.46

fertilizer used
1

Yes

2

No
: crop 1

V13_46A

40

2

N

I

1

0

No

Yes

.

fertilizer used

: crop 2

V13_46B

42

2

N

I

1

0

No

Yes

.

: crop 3

V13_46C

44

2

N

I

1

0

No

No

1

EARLY PADDY

2MAIN PADDY

3

UPLAND PADDY

4

WHEAT

5

SPRING/WINTER MAIZE

6SUMMER MAIZE

7MILLET

8

BARLEY9BUCKWHEAT10OTHER CEREALS

11

SOYBEANS12BLACK GRAM

13

RED GRAM

14

GRASS PEA15LENTIL

16

GRAM

17

PEA

18

GREEN GRAM

19

COARSE GRAM

20

COW PEA

21

OTHER LEGUMES

22

WINTER POTATO

23

SUMMER POTATO24

SWEET POTATO

25

COLOCASIA

26

OTHER TUBERS

27MUSTARD

28

GROUND NUT

29

LINSEED

30

SESAME

31

OTHER OILSEED32SUGARCANE

33

JUTE

34

TOBACCO35

OTHER CASH CROPS

36

CHILIES37

ONIONS38GARLIC

39

GINGER

40

TURMERIC

41

CARDAMOM

42

CORIANDER SEED43

OTHER SPICES

44

WINTER VEGETABLES

45

SUMMER VEGETABLES

46

ORANGE

47

LEMON

48

LIME

49

SWEET LIME50

OTHER CITRUS

51

MANGO

52

BANANA

53

GUAVA

54

JACK FRUIT55

PINEAPPLE56

LICHEE

57

PEAR

58

APPLE

59

PLUM

60

PAPAYA

61

POMEGRANATE62

OTHER FRUIT

63

TEA

64

THATCH

65

FODDER TREES

66

BAMBOO67

OTHER TREES

13.47

qty. purchased

V13_47A

46

4

N

I

1

0

No

No

.

amount paid (rs.)

V13_47B

50

6

N

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

426

Section 13C3: Hiring labour
Datafile :xH25_S13C3-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i n eV13C3_LN

12

2

N

I

1

0

No

Yes

13.51

detail of work

V13_51

14

20

AN

I

1

0

No

No

13.52

hire worker on piece-rate basis

V13_52

34

1

N

I

1

0

No

No

13.53

n u m b e r

1

Yes

2

No

working days : male (rs/day/worker)

13.53
13.54

cash rate

13.54
13.55

total rate

13.56
13.57

35

4

N

I

1

0

No

No

V13_53F

39

4

N

I

1

0

No

No

: male(rs/day/worker)

V13_54M

43

4

N

I

1

0

No

No

: female (rs/day/worker)

V13_54F

47

4

N

I

1

0

No

No

(rs/day/worker)

V13_55M

51

4

N

I

1

0

No

No

: female (rs/day/worker)

in-kind rate : male

13.55
13.56

V13_53M: female (rs/day/worker)

total exp.

V13_55F

55

4

N

I

1

0

No

No

: male [B+C] (rs/day/workerV13_56M

59

4

N

I

1

0

No

No

: female [B+C] (rs/day/workV13_56F

63

4

N

I

1

0

No

No

: male [A*D] (rs.)

V13_57M

67

8

N

I

1

0

No

No

13.57

: female [A*D] (rs.)

V13_57F

75

8

N

I

1

0

No

No

13.57

: total (rs.)

V13_57T

83

8

N

I

1

0

No

No

Section 13D1: Agriculture Earnings
Datafile :xH26_S13D1-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

s o u r c e

n u m b e r

V13D1_SN

12

1

N

I

1

0

No

No

s o u r c e

n a m eV13D1_NM

13

25

AN

I

1

0

No

No

V13_63

38

7

N

I

1

0

No

No

13.63

yearly income

Section 13D2: Agriculture Expenditures
Datafile :xH27_S13D2-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU
HHNO(id) HPSU

6

4

N

I

1

0

No

Yes

(id) HNUM

10

2

N

I

1

0

No

Yes

e x p e n d i t u r en u m b e r

V13D2_EN

12

2

N

I

1

0

No

Yes

e x p e n d i t u r e

n a m eV13D2_NM

14

25

AN

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

13.64

yearly expenditure427
V13_64

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

39

7

N

I

1

0

No

No

428
Section 13E1: Livestock
Datafile :xH28_S13E1-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i v e s t o c k

c o d e13.66

l i v e s t o c k

13.66

yes/no
1

Yes

2

No

n a m eV13E1_LC

12

2

N

I

1

0

No

Yes

V13_66NM

14

25

AN

I

1

0

No

No

V13_66YN

39

1

N

I

1

0

No

No

V13_67A

40

4

N

I

1

0

No

No

13.67

owned now

(no.)

13.67

selling value(rs.)

V13_67B

44

7

N

I

1

0

No

No

13.68

owned 12 months ago(no.)

V13_68A

51

4

N

I

1

0

No

No

13.68

selling value(rs.)

V13_68B

55

7

N

I

1

0

No

No

13.69

sold past 12 months(no.)

V13_69A

62

4

N

I

1

0

No

No

13.69

amount receive(rs.)

V13_69B

66

7

N

I

1

0

No

No

13.70

bought past 12 month (no.)

V13_70A

73

4

N

I

1

0

No

No

13.70

amount paid

V13_70B

77

7

N

I

1

0

No

No

(rs.)

Section 13E2: Income from livestock
Datafile :xH29_S13E2-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

12

1

N

I

1

0

No

Yes

V13E2_NM

13

25

AN

I

1

0

No

No

V13_71

38

8

N

I

1

0

No

No

i n c o m e

c o d ei n c o m e

i t e m13.71

V13E2_ID

Total income

Section 13E3: Livestock's expenditures
Datafile :xH30_S13E3-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU
HHNO(id) HPSU

6

4

N

I

1

0

No

Yes

(id) HNUM

10

2

N

I

1

0

No

Yes

e x p e n d i t u r e

c o d eV13E3_ID

12

1

N

I

1

0

No

Yes

e x p e n d i t u r e

i t e mV13E3_NM

13

25

AN

I

1

0

No

No

V13_72

38

8

N

I

1

0

No

No

13.72

total expenditure

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

429
Section 13F: Ownership of farming assets
Datafile :xH31_S13F-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

e q u i p m e n t

c o d e13.75

e q u i p m e n t

13.75

yes/no
1

Yes

2

No

n a m eV13F_EC

12

2

N

I

1

0

No

Yes

V13_75NM

14

25

AN

I

1

0

No

No

V13_75YN

39

1

N

I

1

0

No

No

13.76

owned now(no.)

V13_76

40

4

N

I

1

0

No

No

13.77

selling value(rs.)

V13_77

44

8

N

I

1

0

No

No

13.78

sold

(no.)

V13_78

52

4

N

I

1

0

No

No

13.79

amount received(rs.)

V13_79

56

8

N

I

1

0

No

No

13.80

bought(no.)

V13_80

64

4

N

I

1

0

No

No

13.81

amount paid(rs.)

V13_81

68

8

N

I

1

0

No

No

Section 14: Non-agriculture enterprises/activities
Datafile :xH32_S14-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

e n t e r p r i s e

V14_EC1

12

2

N

I

1

0

No

Yes

14.02 detail of activities

c o d eV14_02A

14

25

AN

I

1

0

No

No

14.02 produced goods and services

V14_02B

39

25

AN

I

1

0

No

No

14.02

V14_02C

64

2

N

I

1

0

No

Yes

66

2

N

I

1

0

No

Yes

68

2

N

I

1

0

No

Yes

70

2

N

I

1

0

No

Yes

72

2

N

I

1

0

No

Yes

74

2

N

I

1

0

No

Yes

produced goods and services(NSIC)
1

AGRICULTURE AND RELATED SERVICE ACTIVITIES2FORESTRY, LOGGING AND RELATED SERVICE ACTIVITIES. . . . . . . . . . . . . .
. . . . . . . . . . . . . .
. . . . . . . . . . . . . .
99{14.03}EXTRA-TERRITORIAL ORGANIZATION AND BODIES

A-PID of 1st person working in this acV14_03A
1:99{14.03}

B-PID of 2nd person working in this acV14_03B
0!end-of-list!

1:99{14.03}

C-PID of 3rd person working in this acV14_03C0!end-of-list!

1:99{14.03}

D-PID of 4th person working in this acV14_03D0!end-of-list!

1:99{14.03}

E-PID of 5th person working in this acV14_03E
0!end-of-list!

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

430
1:99
{14.04} Respondent id-code

V14_04

76

2

N

I

1

0

No

Yes

14.05

operating since - years

V14_05A

78

4

N

I

1

0

No

No

- months

V14_05B

82

2

N

I

1

0

No

No

V14_06

84

1

N

I

1

0

No

No

V14_07

85

2

N

I

1

0

No

No

V14_08

87

1

N

I

1

0

No

No

V14_09

88

5

N

I

1

1

No

No

Yes

.
0:12
14.06

14.07

place operated
1

Home

2

Other fixed location

3

Other changing location

months operating
1:12

14.08

14.09

owned by1

Owned by household only2

partnership/shared with otherprofit (in %)5.0:100.0

e n t e r p r i s e

V14_EC2

93

2

N

I

1

0

No

{14.10} 1st customer

V14_10A

95

2

N

I

1

0

No

Yes

{14.10} 2nd customer

V14_10B

97

2

N

I

1

0

No

Yes

V14_11

99

1

N

I

1

0

No

No

V14_12

100

2

N

I

1

0

No

Yes

V14_13

102

1

N

I

1

0

No

No

V14_14

103

1

N

I

1

0

No

No

14.11

14.12

14.13

14.14

c o d e0!end-of-list!

1

Other households or individuals

2

Small enterprises

3

Large private enterprises

4

Govt. or other public firm

5

Local retail traders

6

Exporters or foreign purchasers

7

Contractor

8

Tourists9

NGO/internat. organization

10

Other

registered with govt. agency
1

Yes

2

No

main source of money
1

Didn't need any money2

Own savings

3

Relatives/friends

4Agri. dev. bank5

Commercial bank

6

Rural dev. bank

7

Other financial

8

Local group (dhukuti)

9

NGO or relief agency

10

Sale of assets

11

Other

institution

tried to borrow
1

Yes, successfully

2

Yes, but unsuccessfully

3

Noborrowed from1

Relatives/friends

2Agri. dev. bank3

Commercial bank

4

Rural dev. bank

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

431

14.15

14.16

5

Other financial

institution

6

Local group (dhukuti)

7

NGO or relief agency

8

Other

hired anyone
1

Yes

2

No

no. of workers

V14_15

104

1

N

I

1

0

No

No

V14_16

105

4

N

I

1

0

No

No

No

1

No major problem

2

Capital or credit problems

3

Lack of technical know-how

4

Supply of power5

Problems with equipment or spare parts

6

Lack of adequate labor

7

Government regulations

8

Lack of raw materials

9

Lack of customers

10

Transport problems

11

Intimidation and labor unrest

12

Supply of water

13

Other

e n t e r p r i s e

V14_EC3

111

2

N

I

1

0

No

14.18

gross revenues

c o d e(rs.)

V14_18

113

10

N

I

1

0

No

No

14.19

exp. on wages(rs.)

V14_19

123

10

N

I

1

0

No

No

14.20

exp. in fuel, elect, etc.(rs.)

V14_20

133

10

N

I

1

0

No

No

14.21

exp. on raw materials

V14_21A

143

10

N

I

1

0

No

No

.

: cash(rs.)

V14_21B

153

10

N

I

1

0

No

No

14.22

other operating exp.

: in-kind (rs.)(rs.)

V14_22

163

10

N

I

1

0

No

No

14.23

net revenues(rs.)

V14_23

173

10

N

I

1

0

No

No

14.24

exp. on capital goods(rs.)

V14_24

183

10

N

I

1

0

No

No

14.25

sale of assets(rs.)

V14_25

193

10

N

I

1

0

No

No

14.26

value of enterprise(rs.)

V14_26

203

10

N

I

1

0

No

No

14.27

value one year ago(rs.)

V14_27

213

10

N

I

1

0

No

No

Section 15A: Borrowing
Datafile :xH33_S15A-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

{15.02} Loan number

V15A_LN1

12

2

N

I

1

0

No

Yes

{15.02} Loan description

V15_02

14

23

AN

I

1

0

No

No

15.03

id code of borrower

V15_03

37

2

N

I

1

0

No

Yes

15.04

loan obtained: month

V15_04A

39

2

N

I

1

0

No

Yes

: yearV15_04B

41

4

N

I

1

0

No

Yes

V15_05

45

2

N

I

1

0

No

Yes

1:12
.
15.05

obtained from1

Relatives/friends

2Agri. dev. bank3

Commercial bank

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

4324

Rural dev. bank

5

Other financial institution

6

NGO or relief agency

7

Landlord/employer.

8

Shopkeeper

9

Money lender

10

Co-operative

11

Other

{15.06} Purpose of borrowing
1

Purchase of inputs

2

Purchase of equipment

3

Purchase of land

4

Purchase of livestock

5

Building improvements for business6

Other business or farm use

7

Household consumption needs

8

Purchase/improvement of dwelling

9

Marriage/family events

10

Consumer durables

11

To travel abroad

12

Other personal use

15.07

amount borrowed

l o a nn u m b e r

15.08

amount of interest (rs.)

.
15.09

(rs.)

interest rate (%)

V15_06

47

2

N

I

1

0

No

Yes

V15_07

49

9

N

I

1

0

No

No

V15A_LN2

58

2

N

I

1

0

No

No

V15_08A

60

9

N

I

1

0

No

No

V15_08B

69

5

N

I

1

1

Yes

No

: month

V15_09A

74

2

N

I

1

0

No

Yes

: yearV15_09B

76

4

N

I

1

0

No

Yes

finished repaying loan

V15_10

80

1

N

I

1

0

No

No

V15_11

81

9

N

I

1

0

No

No

V15_12

90

1

N

I

1

0

No

No

V15_13

91

4

N

I

1

0

No

No

repayment time
1:12

.
15.10

1

Fully paid

2Partly paid3

Not paid at all

15.11

amount repaid

15.12

collateral

15.13

(rs.)

1

Agricultural land2

Buildings or other property

3Gold/silver

4

Property documents

5

Personal guarantee

6

Past borrowing record

7

Other

8

No collateraldays taken

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

433
Section 15B: Lending
Datafile :xH34_S15B-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes
Yes

{15.15} Loan number

V15B_LN1

12

2

N

I

1

0

No

{15.15} Loan description

V15_15

14

23

AN

I

1

0

No

No

15.16

id code of lender

V15_16

37

2

N

I

1

0

No

Yes

15.17

loan given: month

V15_17A

39

2

N

I

1

0

No

Yes

: year

V15_17B

41

4

N

I

1

0

No

Yes

V15_18

45

1

N

I

1

0

No

No

46

2

N

I

1

0

No

Yes

1:12
.
15.18

15.19

15.20

relationship to lender
1

Employee or tenant farmer

2

Business customer

3

Other business associate

4

Friend/neighbour

5

Relative6

Other

purpose of lending

V15_191

Purchase of agri.inputs

2

Purchase of equipment

3

Purchase of land

4

Purchase of livestock

5

Building improvements for business6

Other business or farm use

7

Household consumption needs

8

Purchase/improvement of dwelling

9

Marriage/family events

10

Consumer durables

11

To travel abroad

12

Other personal use

V15_20

48

9

N

I

1

0

No

No

l o a namount lentn u m b e r

V15B_LN2

57

2

N

I

1

0

No

Yes

15.21

amount of interest

V15_21A

59

9

N

I

1

0

No

No

V15_21B

68

5

N

I

1

1

Yes

No

: month

V15_22A

73

2

N

I

1

0

No

No

: yearV15_22B

75

4

N

I

1

0

No

No

finished repaying loan

V15_23

79

1

N

I

1

0

No

No

V15_24

80

9

N

I

1

0

No

No

V15_25

89

1

N

I

1

0

No

No

.
15.22

(rs.)

interest rate (%)
repayment time
1:12

.
15.23

1

Fully paid

2Partly paid3

Not paid at all

15.24

amount repaid

15.25

collateral

(rs.)

1

Agricultural land2

Buildings or other property

3Gold/silver

4

Property documents

5

Personal guarantee

6

Past borrowing record

7

Other

8

No collateral

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

434
Section 15C: Other assets
Datafile :xH35_S15C-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

12

1

N

I

1

0

No

No

15.26 own other land or property?
1

Yes

2

No

V15_2615.27 value of other property(rs.)

V15_27

13

10

N

I

1

0

No

No

15.28 value a year ago(rs.)

V15_28

23

10

N

I

1

0

No

No

15.29 value of assets purchased (rs.)

V15_29

33

10

N

I

1

0

No

No

15.30 value of sold assets(rs.)

V15_30

43

10

N

I

1

0

No

No

15.31 rent received 12 months(rs.)

V15_31

53

10

N

I

1

0

No

No

V15_32

63

1

N

I

1

0

No

No

15.32 own other assets?
1

Yes

2

No

15.33 value of other assets(rs.)

V15_33

64

10

N

I

1

0

No

No

15.34 value a year ago(rs.)

V15_34

74

10

N

I

1

0

No

No

15.35 value of assets purchased (rs.)

V15_35

84

10

N

I

1

0

No

No

15.36 value of sold assets

V15_36

94

10

N

I

1

0

No

No

V15_37

104

10

N

I

1

0

No

No

(rs)

15.37 rent received in 12 months(rs.)

Section 15D: Household decisions (PART II)
Datafile: xH36_S15D-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

{15.39} Decision number

V15D_IDC

12

2

N

I

1

0

No

Yes

{15.39} Decision description

V15D_TXT

14

25

AN

I

1

0

No

No

{15.39} Have to make decision

V15_39

39

1

N

I

1

0

No

No

V15_40

40

1

N

I

1

0

No

No

V15_41

41

1

N

I

1

0

No

No

1

Yes

2

No

{15.40} Involvement in decision making
1

A lot

2

A little3

Not involved{15.41} Who made final decision
1

Me

2

My spouse/female head

3

Both

4

Other

Section 16: Absentees information
Datafile : xH37_S16

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

435-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i n en u m b e r

V16A_LN1

12

2

N

I

1

0

No

Yes

n a m eo f

V16_NME

14

20

AN

I

1

0

No

No

V16_02

34

1

N

I

1

0

No

No

V16_03

35

2

N

I

1

0

No

Yes

V16_04

37

2

N

I

1

0

No

No

V16_05

39

2

N

I

1

0

No

No

V16_06

41

1

N

I

1

0

No

No

16.02

16.03

a b s e n t e e s

sex of absentees
1

Male

2

Femalerelations to hhld head2

Husband/wife

3

Son/daughter

4Grandchild5

Father/mother

6

Brother/sister

7

Nephew/niece

8

Son/daughter-in-law

9Brother/sister-in-law

10

Father/mother-in-law

11

Other family relative

12

Servant, servant's relatives

13

Tenat, tenant's relatives

14

Other person not related

16.04

age

16.05

highest grate completed ?

16.06

0

Nursery/kg/pre-school

1

Class 01

2

Class 02

3

Class 03

4

Class 04

5

Class 05

6

Class 06

7

Class 07

8

Class 08

9

Class 09

10

Class 10

11

SLC

12

Intermediate

13

Bachelor

14

Master15

Profesional degree

16

Literate (levelless)17

Illiterate

97

Don't know

marital status
1

Never married

2Single married

3

Poly married

4

Re-married

5

Widow, widower

6

Divorced

7

Separated

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

436
8
16.07

Don't know

when leave this hhld ?

V16_07

42

4

N

I

1

0

No

No

2

N

I

1

0

No

No

0:98
99
16.08

Birth at there

current place of living- district/foreV16_08A

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

90

United arab emirates

91

United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country

1

Urban

2

Rural

.

urban/rural

16.09

reason for leaving
1

Together family/relatives

2

Other family reasons

3

Education

4

Training

5

Looking for work

6

Start new job

7Start new business

8

Job transfer

9

Conflict10

Natural disaster

11

Easier life style12
l i n e
16.10

46

1

V16_08B

48

1

N

I

1

0

No

No

V16_09

49

2

N

I

1

0

No

Yes

Other
n u m b e r

primary activity [NSCO]

V16A_LN2

51

2

N

I

1

0

No

Yes

V16_10

53

3

N

I

1

0

No

Yes

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

437
11

ARMED FORCES

111

LEGISLATORS. . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . .
999
16.11

16.12

16.13

NOT REPORTED

types of job
1

Wage job

2

Self-employed

3Ext. economic work

4

Don't know

how find current job?
1

Relatives

2

Friends/ neighbors

3

Employment agency

4

Other

5

Don't know

received money or goods
1

Yes

2

No

V16_11

56

1

N

I

1

0

No

No

V16_12

57

1

N

I

1

0

No

No

V16_13

58

1

N

I

1

0

No

No

16.14

no. of times received

V16_14

59

2

N

I

1

0

No

No

16.15

methods of sending

V16_15

61

1

N

I

1

0

No

No

1

Formal financial institutions

2

Hundi

3

Self

4

Through friends

5

Through other family members/relatives

6

Through acquaintances

7

Others16.16

cash receive(rs.)

V16_16

62

9

N

I

1

0

No

No

16.17

in-kind receive(rs.)

V16_17

71

9

N

I

1

0

No

No

16.18

major use of remittance: firstV16_18A

80

1

N

I

1

0

No

No

: second

V16_18B

81

1

N

I

1

0

No

No

V16_19

82

9

N

I

1

0

No

No

.

16.19

0

!end-of-list!

1

daily consumption

2

education

3

capital formation

4

business or investment

5household assets

6

savings

7

repay loan

8otherstotal amount sent

(rs.)

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

438
Section 17A: Remittances sent
Datafile : xH38_S17A-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i n eV17A_LN

12

2

N

I

1

0

No

Yes

{17.02} Respondent id-code

V17_02

14

2

N

I

1

0

No

Yes

17.03

name of recipient

V17_03

16

25

AN

I

1

0

No

No

17.04

id code of remit. sender

V17_04

41

2

N

I

1

0

No

Yes

17.05

relationship to the recipient

V17_05

43

2

N

I

1

0

No

Yes

V17_06

45

2

N

I

1

0

No

No

V17_07A

47

2

N

I

1

0

No

Yes

17.06

17.07

n u m b e r2

Husband/wife

3

Son/daughter

4Grandchild5

Father/mother

6

Brother/sister

7

Nephew/niece

8

Son/daughter-in-law

9Brother/sister-in-law

10

Father/mother-in-law

11

Other family relative

12

Servant, servant's relative

13

Tenant, tenant's relatives

14

Other person not related

sex of receipant
1

Male

2

Femaleplace of living- district/country

1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

73Dadeldhura

74

439
Baitadi75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

90

United arab emirates

91

United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country

.

17.08

17.09

- urban/rural
1

Urban

2

Rural

primary work activity of recipient
1

Wage employee in agri.

2

Wage employee in non-agri.

3

Self employee in agri.

4

Self employee in non-agri.

5

Study

6

Other

7

Don't know

cash sent

(rs.)

.

in-kind sent

(rs.)

17.10

how usually send the amount?

V17_07B

49

1

N

I

1

0

No

No

V17_08

50

1

N

I

1

0

No

No

V17_09A

51

9

N

I

1

0

No

No

V17_09B

60

9

N

I

1

0

No

No

V17_10

69

1

N

I

1

0

No

No

1

Formal financial institutions

2

Hundi

3

Self

4

Through friends

5

Through other family members/relatives

6

Through acquaintances

7

OthersSection 17B: Remittances received
Datafile :xH39_S17B-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

l i n eV17B_LN

12

2

N

I

1

0

No

Yes

{17.12} Respondent id-code

n u m b e r

V17_12

14

2

N

I

1

0

No

Yes

17.13

name of donor

V17_13

16

25

AN

I

1

0

No

No

17.14

id code of remit. receiver

V17_14

41

2

N

I

1

0

No

Yes

17.15

relationship to the recipient?

V17_15

43

2

N

I

1

0

No

Yes

2

Husband/wife

3

Son/daughter

4Grandchild5

Father/mother

6

Brother/sister

7

Nephew/niece

8

Son/daughter-in-law

9Brother/sister-in-law

10

Father/mother-in-law

11

Other family relative

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

440

17.16

17.17

12

Servant, servant's relative

13

Tenant, tenant's relatives

14

Other person not related

sex of donor
1

Male

2

Femaleage of donor{17.18} Place of residence - district/country

V17_16

45

1

N

I

1

0

No

V17_17

46

2

N

I

1

0

No

No

V17_18A

48

2

N

I

1

0

No

Yes

1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

90

United arab emirates

91

United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country{17.18} Place of residence- urban/rural
1

Urban

2

Rural

{17.19} Primary work activity
1

Wage employee in agri.

2

Wage employee in non-agri.

3

Self employee in agri.

4

Self employee in non-agri.

5

Study

6

Other

7

Don't knowNoV17_18B

50

1

N

I

1

0

No

No

V17_19

51

1

N

I

1

0

No

No

17.20

cash received(rs.)

V17_20A

52

9

N

I

1

0

No

No

.

in-kind received (rs.)

V17_20B

61

9

N

I

1

0

No

No

17.21

how usually send the amount?

V17_21

70

1

N

I

1

0

No

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

441

17.22

1

Formal financial institutions

2

Hundi

3

Self

4

Through friends

5

Through other family members/relatives

6

Through acquaintances

7

Othersmajor use of remittance : first

V17_22A

71

1

N

I

1

0

No

No

: second

V17_22B

72

1

N

I

1

0

No

No

.
0

!end-of-list!

1

Daily consumption

2

Education

3

Capital formation

4

Business or investment

5

Household assets

6

Savings

7

Repay loan

8

OthersSection 18A: Transfers, social assistance
Datafile :xH40_S18A-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

{18.00} Source serial number

V18A_SN

12

2

N

I

1

0

No

Yes

{18.00} Source description

V18A_TXT

14

25

AN

I

1

0

No

No

18.01

V18_01

39

1

N

I

1

0

No

No

hhld member receive payment
1

Yes

2

No

3

Not Applicable

18.02

no. of person receiving

V18_02

40

2

N

I

1

0

No

No

18.03

how frequently

V18_03

42

1

N

I

1

0

No

No

V18_04

43

8

N

I

1

0

No

No

1

Monthly2

Quarterly

3

Half yearly4Yearly5

Once only{18.04} Amount is supposed to receive (rs)
100:5000
9999

Don't know

18.05

month ago last payment received

V18_05

51

3

N

I

1

0

No

No

18.06

received amountV18_06

54

8

N

I

1

0

No

No

18.07

no.of month due

V18_07

62

3

N

I

1

0

No

No

18.08

anyone currently payment

V18_08

65

1

N

I

1

0

No

No

V18_09

66

8

N

I

1

0

No

No

18.09

1

Yes

2

No

(rs.)

total amount owed?

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

442
Section 18B: Social assistance
Datafile :xH41_S18B-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU
HHNO
18.10

18.10

18.10

18.10

18.10

18.10

(id) HPSU

6

4

N

I

1

0

No

Yes

(id) HNUM

10

2

N

I

1

0

No

Yes

V18_101

12

1

N

I

1

0

No

No

2) receive nutritional program for childV18_102

13

1

N

I

1

0

No

No

14

1

N

I

1

0

No

No

V18_104

15

1

N

I

1

0

No

No

V18_105

16

1

N

I

1

0

No

No

V18_106

17

1

N

I

1

0

No

No

1) receive public food distrb. system
1

Yes

2

No

1

Yes

2

No

3) receive nutritional program for motheV18_103
1

Yes

2

No

4) receive food for work
1

Yes

2

No

5) receive cash for work
1

Yes

2

No

6) receive RCIWP programme
1

Yes

2

No

Section 18C: Other Income
Datafile :xH42_S18C-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

{18.11} Other income code

V18C_IC

12

2

N

I

1

0

No

Yes

{18.11} Other income description

V18C_TXT

14

25

AN

I

1

0

No

No

18.11

V18_11

39

1

N

I

1

0

No

No

having income
1

Yes

2

No

18.12

current amount/value

V18_12

40

8

N

I

1

0

No

No

18.13

Amount/value a year ago

V18_13

48

8

N

I

1

0

No

No

18.14

amount received

V18_14

56

8

N

I

1

0

No

No

(rs.)

(rs.)

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

443
Section 19:
Datafile :Adequacy of consumption

xH43_S19-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

V19_02

12

1

N

I

1

0

No

No

V19_03

13

1

N

I

1

0

No

No

V19_04

14

1

N

I

1

0

No

No

V19_05

15

1

N

I

1

0

No

No

V19_06

16

1

N

I

1

0

No

No

V19_07

17

1

N

I

1

0

No

No

V19_08

18

1

N

I

1

0

No

No

V19_09

19

1

N

I

1

0

No

No

V19_10

20

1

N

I

1

0

No

No

V19_11

21

1

N

I

1

0

No

No

19.02

19.03

19.04

19.05

19.06

19.07

19.08

19.09

19.10

19.11

opinion on food consumption
1

Less than adequate

2

Just adequate

3

More than adequate

4

Not applicable

opinion on housing
1Less than adequate

2

Just adequate

3

More than adequate

4

Not applicable

opinion on clothing
1Less than adequate

2

Just adequate

3

More than adequate

4

Not applicable

opinion on health care getting
1

Less than adequate

2

Just adequate

3

More than adequate

4

Not applicable

opinion on children's schooling1Less than adequate

2

Just adequate

3

More than adequate

4

Not applicable

opinion on family's total income
1

Less than adequate

2

Just adequate

3

More than adequate

4

Not applicableeats too little food to live
1

Yes

2

No

status of health facility
1

Good

2

Fair

3

Bad

4

Not applicablestatus of education facility
1

Good

2

Fair

3

Bad

4

Not applicablestatus of drinking water facility
1

Good

2

Fair

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

444

19.12

19.13

19.14

19.15

19.16

3

Bad

4

Not applicablestatus of electricity facility
1

Good

2

Fair

3

Bad

4

Not applicablestatus of road facility
1

Good

2

Fair

3

Bad

4

Not applicablestatus of postal facility
1

Good

2

Fair

3

Bad

4

Not applicablestatus of telephone facility
1

Good

2

Fair

3

Bad

4

Not applicableenough food or money to buy food
1

Yes

2

No

V19_12

22

1

N

I

1

0

No

No

V19_13

23

1

N

I

1

0

No

No

V19_14

24

1

N

I

1

0

No

No

V19_15

25

1

N

I

1

0

No

No

V19_16

26

1

N

I

1

0

No

No

V19_17

27

2

N

I

1

0

No

No

29

1

N

I

1

0

No

No

V19_1802

30

1

N

I

1

0

No

No

V19_1803

31

1

N

I

1

0

No

No

V19_1804

32

1

N

I

1

0

No

No

V19_1805

33

1

N

I

1

0

No

No

V19_1806

34

1

N

I

1

0

No

No

V19_1807

35

1

N

I

1

0

No

No

V19_1808

36

1

N

I

1

0

No

No

V19_1809

37

1

N

I

1

0

No

No

19.17

no.of days facing scarcity

19.18

1) rely on less preferred/expensive foodV19_1801

1:30

19.18

19.18

19.18

19.18

19.18

19.18

19.18

19.18

1

Yes

2

No

2) borrow food or money
1

Yes

2

No

3) purchase food on credit
1

Yes

2

No

4) gather wild food/eat unmature crops
1

Yes

2

No

5) consume seed stock
1

Yes

2

No

6) send hhld member to eat elsewhere
1

Yes

2

No

7) send hhld member to beg
1

Yes

2

No

8) limit portion sizes at mealtime
1

Yes

2

No

9) restrict consumption of adults
1

Yes

2

No

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

19.18

19.18

19.18

19.18

19.18

10) feed working members of hhld
1

Yes

2

No

11) ration the money
1

Yes

2

No

12) reduced the meals eaten in a day
1

Yes

2

No

13) skip entire days without eating
1

Yes

2

No

14) sell assets, jewelry
1

Yes

2

No

445
V19_1810

38

1

N

I

1

0

No

No

V19_1811

39

1

N

I

1

0

No

No

V19_1812

40

1

N

I

1

0

No

No

V19_1813

41

1

N

I

1

0

No

No

V19_1814

42

1

N

I

1

0

No

No

Section 20: Anthropometrics
Datafile :xH44_S20-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

{20-id} Id-code

V20_IDC

12

2

N

I

1

0

No

Yes

{20.01} Has birth certificate

V20_01

14

1

N

I

1

0

No

No

V20_02D

15

2

N

I

1

0

No

Yes

V20_02M

17

2

N

I

1

0

No

Yes

V20_02Y

19

4

N

I

1

0

No

Yes

V20_03

23

2

N

I

1

0

No

No

V20_04

25

1

N

I

1

0

No

No

1

Yes

2

No

{20.02} Date of birth/Day
1:32{20.02} Date of birth/Month
1:12{20.02} Date of birth/Year
2061:2067{20.03} Age in months
0:59{20.04} measurement finished
1

Yes

2

Could not meet the child after multiple visits3

Too ill or disabled

4

Refused5

Other{20.05} Height (cms, 1 decimal)

V20_05

26

5

N

I

1

1

Yes

No

{20.06} Way was measured

V20_06

31

1

N

I

1

0

No

No

{20.07} Weight (kgs, 1 decimal)

V20_07

32

5

N

I

1

1

Yes

No

{20.08} Date of measurement/Day

V20_08D

37

2

N

I

1

0

No

Yes

V20_08M

39

2

N

I

1

0

No

Yes

V20_08Y

41

4

N

I

1

0

No

Yes

1

Standing

2

Lying

1:32
{20.08} Date of measurement/Month
1:12{20.08} Date of measurement/Year
2066:2067

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

446
V20_09{20.09} Did breastfeed{20.09} Didbreastfeed
1

Yes

2

No

1

N

I

1

0

No

No

46

2

N

I

1

0

No

No

48

2

N

I

1

0

No

No

50

10

AN

I

1

0

No

No

(V20_09_VS1){20.10} Months breastfeeding
045

V20_10Less than one month (or age less than 1 month)

1:59
90

Did not breastfeed{20.11} Months after birth was given complimentV20_11
0Less than one month

1:59
90

Not yet{20.name}

V20_NAME

Section 21: Panel Household tracking
Datafile :xH45_S21-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

12

2

N

I

1

0

No

Yes

PSU geo-codes: District

V21_DIST

1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

District16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

name

V21_DIST_TXT

14

21

AN

I

1

0

No

No

Ward

V21_WARD

35

2

N

I

1

0

No

Yes

Subward

V21_SUBW

37

2

N

I

1

0

No

Yes

VDC/municipality

V21_VDC

39

3

N

I

1

0

No

Yes

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

VDC/municipality

447
V21_VDC_TXT

name

Serial number

V21_SNUM

42

21

AN

I

1

0

No

No

63

3

N

I

1

0

No

No

66

1

N

I

1

0

No

No

67

1

N

I

1

0

No

No

V21_03

68

1

N

I

1

0

No

No

{21.04} A: Where-when did the Household move, DV21_04A

69

2

N

I

1

0

No

Yes

{21.01} The Household is in the NLSS1/NLSS2 forV21_01
1

Yes

2

No

{21.02} The Household was found in the NLSS3 fiV21_02
1

Yes

2

No

{21.03} Reason not found
1

Moved

2

Other

3

Don't know

1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

19

Sarlahi20

Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

90

United arab emirates

91

United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country{21.04} B: Where-when did the Household move, ZV21_04B

71

1

N

I

1

0

No

No

{21.04} C: Where-when did the Household move, YV21_04C

72

4

N

I

1

0

No

Yes

{21.05} Respondent for the reason not found

76

1

N

I

1

0

No

No

1

Urban

2

Rural

1

Relative

2

Neighbor

3

Knowledgeable person

4

Other

V21_05

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

448{21-NUM} Number of Household members (in 2002) V21_NUM

77

2

N

I

1

0

No

No

Section 21X: Panel Household members tracking
Datafile :xH46_S21X-------------------------------------------------------------------------------------------------------------Data Item
Item Label

Item Name

Start

Len Type Type

Dec Zero
Occ

Dec Char Fill-------------------------------------------------------------------------------------------------------------PSU(id) HPSU

6

4

N

I

1

0

No

Yes

HHNO

(id) HNUM

10

2

N

I

1

0

No

Yes

{V21_PID} Id code (in 2002)

V21_IDC

12

2

N

I

1

0

No

Yes

{21_06} Name

V21_06

14

25

AN

I

1

0

No

No

{21_07} Sex

V21_07

39

1

N

I

1

0

No

No

V21_08

40

2

N

I

1

0

No

Yes

V21_09

42

2

N

I

1

0

No

No

{21_10} In 2010: New person in the Household roV21_10

44

1

N

I

1

0

No

No

1

Male

2

Female{21.08} Relationship to head
1

Head

2

Husband/wife

3

Son/daughter

4Grandchild5

Father/mother

6

Brother/sister

7

Nephew/niece

8

Son/daughter-in-law

9Brother/sister-in-law

10

Father/mother-in-law

11

Other family relative

12

Servant/servant's relatives

13

Tenant/tenant's relatives

14

Other non related{21.09} Age (in 1996/2002)

1

Yes

2

No

{21_11} In 2010: ID-code the new Household rostV21_11

45

2

N

I

1

0

No

Yes

{21_12} In 2010: Reason this person is not in tV21_12

47

1

N

I

1

0

No

No

V21_13

48

1

N

I

1

0

No

No

{21_13} In 2010: A: Current residence, DistrictV21_13A

49

2

N

I

1

0

No

Yes

1

Died

2

Household split

3

Moved for work

4

Moved due to marriage

5

Moved for studies

6

Other

{21_13} In 2010: Current residence, Same PSU
1

Yes

2

No

1

Taplejung2

Panchthar

3

Ilam

4

Jhapa

5

Morang6Sunsari

7

Dhankuta8

Terhathum

9

Sankhuwasabha

10

Bhojpur

11

Solukhumbu

12

Okhaldhunga

13

Khotang

14

Udayapur

15

Saptari

16

Siraha17

Dhanusa18

Mahottari

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

19Sarlahi

20449
Sindhuli

21

Ramechhap

22

Dolakha23

Sindhupalchok

24

Kavre

25

Lalitpur

26

Bhaktapur

27

Kathmandu

28

Nuwakot

29

Rasuwa30

Dhading

31

Makwanpur

32

Rautahat

33

Bara

34

Parsa

35

Chitawan

36

Gorkha37

Lamjung

38

Tanahu39

Syangja

40

Kaski

41

Manang42

Mustang43

Myagdi44

Parbat45

Baglung

46

Gulmi

47

Palpa

48

Nawalparasi

49

Rupandehi

50

Kapilbastu

51

Arghakhanchi

52

Pyuthan

53

Rolpa

54

Rukum

55

Salyan

56

Dang

57

Banke

58

Bardiya59

Surkhet60

Dailekh61

Jajarkot

62

Dolpa

63

Jumla

64

Kalikot

65

Mugu

66

Humla

67

Bajura68

Bajhang69

Achham

70

Doti

71

Kailali

72

Kanchanpur

73

Dadeldhura

74

Baitadi75

Darchula

81

India

82

Bhutan

83

China

84

Bangladesh

85

Hongkong

86

Malayasia

87

Japan

88

Saudi Arabia

89

Qatar

90

United arab emirates

91

United kingdom

92

United states of america

93South korea

94

Australia

95

Israel96

Other country{21_13} In 2010: B: Current residence, Zone U/RV21_13B
1

Urban

2

Rural

{V21_14} Year this person did die or move

V21_14

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0

51

1

N

I

1

0

No

No

52

4

N

I

1

0

No

Yes

450

CHAPTER X: CONSUMPTION
10.1 Introduction
Consumption and income are widely used monetary indicators of well-being. Consumption may be abetter proxy than income for three reasons: actual consumption measures a person’s well-being in termsof meeting current basic needs while income is just an element that allows such consumption;
consumption is usually measured with more reliability than income; and consumption better reflects afamily’s long-term welfare as it captures that family’s ability to smooth out income fluctuations. But
income also has its own advantages if one wants to analyze welfare in terms of monetary sources, and tolook at welfare opportunity rather than achievement. This chapter looks at consumption while the next
chapter explores income from NLSS 2010/11.
There have been changes in the consumption level of the people in Nepal over the years. At the national
level, nominal per capita consumption increased more than five times in the past 15 years (between
1995/96 and 2010/11). During this period, the gap in consumption shares between the bottom twenty
percent and the top twenty percent of population has become even worse. A summary of consumption
indicators is shown in Box 10.1.

10.2 Computation of Consumption Aggregates
The method of computing consumption aggregates outlined below is based on the method described in(Datt, Jolliffe, Redaelli & Shrestha) 1 which according to them is guided by earlier poverty assessments 2
done in NLSS-I and NLSS-II, and “construction of consumption aggregates” described in the“Statistical Report” of NLSS-II 3.
The consumption aggregates for NLSS-III are constructed by adding together the various goods andservices consumed by each household during a period of 12 months. Various components ofconsumption are grouped together into three main categories: consumption of food items, consumptionof housing and consumption of other non-food items. A detail breakdown of the consumption aggregate
is presented below, along with the appropriate section of the NLSS-III questionnaire, from which these
expenses are computed (Figure 10.1).
In what follows, the methodology used to ascribe a rupee value to consumption of the specific items
included in each component is outlined.

1

Datt, Jolliffe, Redaelli and Shrestha (2011), The World Bank Group: “Poverty in Nepal 2010: Estimates Basedon NLSS-III”.

2

Lanjouw,Prennushi and Zaidi (1998), “Building Blocks for a Consumption Based Analysis of Poverty in Nepal”
and Deaton and Zaidi (2002): World Bank Living Standards Survey Working Paper # 135, “Guidelines forConstructing Consumption Aggregates for Welfare Analysis” .

3

Central Bureau of Statistics, Nepal (2004), “Nepal Living Standards Survey 2003/04: Statistical Report Volume
Two”.

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10.2.1 Food Consumption
Section 5 of the NLSS-III questionnaire collects information on consumption of 72 food items in 13
categories. Like the previous surveys, the current survey asks questions on consumption of food items in
the “typical” month in which a particular food item is consumed. This practice of collecting
consumption data is slightly unusual 4 compared to the standard practice elsewhere. The current survey
tries to improve on this limitation by adding in questions that ask about consumption of the same
categories of food items in the past seven days 5.
Figure 10.1: Composition of total consumption

Food

Total
Consumption

Nonfood

Consumption of
housing

Food items
excluding tobacco

Section 5

Tobacco andtobacco products

Section 5

Selected nonfood

Section 6a, 6b

Expenditure in
durable goods

Section 6c,
estimation

Expenditure inutilities

Section 2

Expenditure ineducation

Section 7, 6

Rental value of
housing

Section 2,
imputation4

One possible issue with this format would be the inherent vagueness in the definition of a ‘typical’ month.
Another issue is recall bias. People tend to forget or mis-value items consumed over a month, more so when the
typical month could actually be several months prior to the interview time.

5

The main advantage of this format is that it leads to less recall bias and the time horizon is very specific. One
downside of this approach is that it fails to capture items that are consumed seasonally. However, sincefieldwork in each of the stratum is randomly divided all across the seasons and interviews take place year-round,the seasonality pattern is expected to be reflected in the aggregates for each of the strata.

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The current survey keeps the old “typical month” format of the questionnaire for comparability of
estimates with previous surveys, but at the same time administers a more standard way of collecting
food consumption data 6 . To prevent respondents’ response to the “typical month” format affect theconsumption numbers in the past 7 days, enumerators are asked specifically to administer the newformat, one day after administering the old-format.

10.2.2 The "Typical Month" Criterion
For all the food items that were consumed in the past 12 months, the old format (typical month format)of the food consumption module separately collects information on food items that were purchased,home produced or received in-kind. For food items that were purchased or home produced, it asks thenumber of months in which the food item was consumed and the amount of money spent (or the value of,for home produced) in a ‘typical month’ in which the food item was consumed. For food items received
in-kind, it asks the total value of such food items consumed in past 12 months. For each of the purchased
or home produced food items, consumption value in the typical month is multiplied by the number ofmonths consumed to get annual consumption of food items that were purchased or home produced. Thisannual value is added to the in-kind value to get the annual consumption of that particular food item.
Then, this value for all the food items (except tobacco and tobacco products) consumed by thehousehold in the past 12 months is aggregated to get annual food consumption.

10.2.3 The “Past 7 Days” Criterion
For each of the food items consumed in the past 7 days, the survey asks the total amount of money spent(or equivalent value if the food item was home produced or received as gift or payment) in the fooditems that was consumed during the past 7 days. It is important to note that the question specifically asks
for the value of food items consumed in the past 7 days and not the value of items purchased or
produced. This information for all the food items (except tobacco and tobacco products) consumed by
the household is aggregated to get weekly household consumption of food items. Which is thenconverted to annual food consumption.

10.2.4 Items Excluded From Food Consumption Aggregates
As with the previous assessments, consumption of tobacco is excluded from the estimates of foodconsumption and instead put this as a separate category of non-food consumption. The method of
calculation is, however, exactly as described above.

6

These 7 day estimates are also used in the poverty assessment.

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10.2.5 Selected Non-food Consumption
Section 6 of the questionnaire collects information on household expenditure on several frequent andnon-frequent items. The items are categorized into: fuels, apparel and personal care, other frequent
expenses, infrequent expenses, miscellaneous expenses and durable goods. The following presents thedetails of the methods and norms used to compute non-food expenditures from the questionnaires.

10.2.6 The Excluded Non-food Expenditures
Certain items were excluded from the consumption aggregates. The exclusion list is exact to the ones
used in previous rounds of the survey. A brief summary of what is excluded and the reasons is provided
below:•

Expenditure on firewood is excluded because of the difficulty in imputing prices of firewood due towidespread use of non-metric and non-uniform units.

•

Expenditure on education is calculated separately (as described below) by combining informationfrom non-food expenditure section of the questionnaire with the education module.

•

Expenditure on health is excluded. Expenditure on health, though very important in its own right,
is a bit different from other expenditures. Unlike other expenditures, most health expenses occur
only in response to health shocks. Given that there is a health shock, health expenditure is generally
welfare enhancing. But the incidence of health shocks itself is a sign of reduction in welfare.
Because of this oddity, expenditure on health is excluded as in the previous round.

•

Expenditures on repair and maintenance and home construction and improvements are excluded
as these items are actually investment and pose a risk of double counting.

•Expenditures on taxes and fines are excluded.

•

Expenditures on marriages, dowries, funerals, charity and other social and religious functions
are excluded because of the lumpiness of these expenditures on a relatively short time horizon forthese kinds of expenditures to accurately reflect household welfare.

•

Expenditures on durable goods (items 412, 414-432) are excluded because of the lumpy nature ofthese investments. In lieu of this, the flow of services from durable goods under ‘expenditures on
durable goods’ section is estimated.

10.2.7 Expenditure on Frequent Non-food Items
For the ‘frequent’ non-food expenditures (Section 6A), the survey collects information on the expenses
incurred in the past 30 days, and, separately, in the past 12 months. It is not always clear as to which
reported figure gives the best estimate of the expenses. The 30 day estimates have lower recall bias but
would miss items that are not consumed every month (for example, LPG gas and clothing), the 12 monthestimates includes expenses on non-regular items but would have greater recall bias. To get around this

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problem, these items are divided into categories that are regular, and those aren’t that regular. For the
regular items, the reported expenses in the past 30 days are multiplied by 12 to get annual expenses. For
non-regular items, the reported expenses in the past 12 months are taken as they are.
Table A: Classification of frequent non-food expenditure items into regular and non-regular
Item

Group

Code

Mean:
12m

Mean:
30d

Median:12m

Median:30d

Kerosene oil

212

989

1,081

700

780

Coal, charcoal

213

2,014

1,951

500

0

Cylinder gas (LPG)

214

7,975

7,708

7,200

2,880

Matches, candles, etc

215

356

408

180

180

Ready-made clothing

221

4,684

6,761

3,000

0

Cloth, wool, yarn, threads

222

3,060

3,700

2,000

0

Tailoring expenses

223

992

1,160

700

0

Footwear

224

1,828

2,710

1,200

720

Toilet soap

225

619

630

480

480

Regular

Toothpaste, toothbrushes etc

226

451

434

300

300

Regular

Other personal care items

227

936

1,095

500

300

RegularDry cleaning, washing expenses

228

2,772

2,998

700

600

Regular

Personal services

229

531

493

300

240

RegularPublic transportation

231

2,969

4,194

1,500

1,200

RegularPetrol, diesel, motor oil

232

14,681

16,263

8,400

9,600

RegularEntertainment

233

809

1,013

400

240

Newspapers, books, supplies

234

1,264

1,259

600

480

Regular

Pocket money to children

235

3,218

3,503

1,500

1,680

RegularWages paid to watchmen, etc

239

16,50917,442950

600Light bulbs, shades, batteries, etc

241

522

754

280

0

Household cleaning article

242

1,147

1,204

800

840

Regular

Regular

Regular

Note: Unweighted means and medians, including panel data

Table A is used to guide the grouping of these items into regular or non-regular. This approach isdifferent from the one used in earlier assessments. The NLSS-II, for instance, uses monthly expenditures
where available and replaces this by annual expenditures when items weren’t consumed in the pastmonth whereas NLSS-I uses annual expenditures. The table presents the means and medians using thetwo sources of estimates. Items are classified as regular when the median expenditure using past monthis within 20 percent of the median expenditure using past 12 months. For these items, the past month’s

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expenditure is used to avoid recall bias. In cases where these items were not consumed in the past month,then the expenditures on past 12 months are taken whenever available. For other items, the past 12
month’s expenditures are used so that the expenditures on non-regular items are not missed out.

10.2.8 Expenditure on Selected Infrequent Non-food Items
Calculating the expenditure on selected infrequent non-food items is fairly straightforward as the survey
asks the expenditure made in the past 12 months.
For all the selected frequent and infrequent non-food items, total annual expenditure on each of the item
is computed and is aggregated across all the items to get expenditures on selected non-food items.

10.2.9 Expenditure on Durable Goods
Section 6C of the NLSS-III questionnaire asks, for a list of durable items, the number of such items
owned, the year of purchase, the price of purchase and their estimate of the current value of the item.
From this information, the flow of services accrued to the household is estimated in the following way:
1. For each of the items owned by the household, their purchase price in current prices are expressedusing historic rates of inflation over the past 37 years from the Central Bank of Nepal (Nepal Rastra
Bank, 2010).
2. A rate of depreciation 𝛿𝛿𝑖𝑖 for each of the items is obtained using the purchase value per-item(expressed at current prices, from 1.) 𝑉𝑉𝑖𝑖0 , the age of the item 𝐴𝐴𝑖𝑖 , and the current value per-item 𝑉𝑉𝑖𝑖𝑖𝑖
1𝑉𝑉𝐴𝐴
using the formula: 𝛿𝛿𝑖𝑖 = �1 − � 𝑖𝑖𝑖𝑖 � �
𝑉𝑉𝑖𝑖0

3. For each of the items, the item-specific median depreciation rates are obtained from the sample. The
depreciation rates thus obtained are listed in
4. This rate of depreciation is applied to the current value (scaled back to last year) of the item on all
the items to impute the annual flow of services from all items owned by the household.
5. The flow of services across all durable goods owned by each household is aggregated to get theconsumption of durable goods.

10.2.10 Expenditure on Utilities
Annual expenditures by household on electricity, garbage collection and telephone charges (reported in
Section 2 of the questionnaire) is included in expenditure on utilities. As with the previous surveys,
expenditure on water is excluded from consumption aggregates.

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Table B: Median depreciation rates by durable goods
Item

Code

Median annual
depreciation rate

Radio

501

0.278

Camera502

0.227

Bi-cycle

503

0.265

Motorcycle/Scooter

504

0.202

Cars, etc

505

0.146

Fridge506

0.187

Washing machine

507

0.197

Fans

508

0.253

Heater509

0.311

TV/VCR/DVD player

510

0.208

Petromax

511

0.228

Telephone/mobile

512

0.351

Sewing machine

513

0.124

Computer/Printer

517

0.242

Unweighted estimates including panel data

10.2.11 Expenditure on Education
Section 7 collects information on educational expenditure in the past 12 months for each of the membersof the household that are currently going to school. The expenses per person in monthly fees, admission
and other fees, uniform, textbooks and supplies, transportation, private tuition, and other expenses
related to education are added together. The value of scholarship received is added to this amount. Thisis aggregated across each of the household members to get the total expenditure on education.
This method has one huge caveat 7 . To overcome this limitation, the estimates from Section 7, arecompared with the household estimate provided in Section 6 (item 236), and take the maximum of these
two estimates as the estimate of expenditure on education. The NLSS-II consumption aggregates seems
to have only included expenditures reported in the education module, but the NLSS-I seems to havefollowed the same approach as done here.

10.2.12 Consumption of Housing
Consumption of housing is an important indicator of welfare. Section 2 collects information that allows
one to calculate (or estimate) the consumption of housing. The actual monthly rent paid by households if

7

Though it accurately determines the expenditure on education for members that are currently going to school, it
completely misses the educational expenditures for members that are not currently going to school. This would
be a concern if members have dropped out of school in the past 12 months and therefore have incurrededucational expenses. It also misses expenditure on education of members (maybe former, or absent) that are
not in the current household roster.

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they rent-in their housing unit is available. For households that owned their dwelling unit, the survey
asks the expected rent if someone were to rent the dwelling that they are using. For households that live
in housing provided for free, the survey asks them to estimate the monthly rent for the part of thedwelling that they are using. Even with this, a small fraction (269 out of 5988 households) reports
unlikely or zero rental values. Reported rents less than NRs 100 per month and greater than NRs 30,000
per month are treated as unlikely and values are imputed in such cases. 8
For such 269 households, the rental value of housing are imputed by estimating a hedonic housing
regression model, very similar to the one used in previous poverty assessments, on the sample ofhouseholds reporting non-zero rent. Specifically, the following relation is used for the purpose:ln(𝑅𝑅𝑖𝑖 ) = 𝛽𝛽𝑋𝑋𝑖𝑖 + 𝜖𝜖𝑖𝑖
where 𝑅𝑅𝑖𝑖 is the reported rent (or rental estimates) for household 𝑖𝑖, and 𝑋𝑋𝑖𝑖 includes the set of explanatory
variables as detailed in and a dummy for each of the survey strata.
Table C: Estimation of consumption of housing
Coefficient

Std. Err.

Log(total area of dwelling)

0.179***

0.018

Number of rooms

0.103***

0.006

Dwelling has a kitchen

0.268***

0.023

Dwelling has cemented wall

0.269***

0.037

Dwelling has cemented foundation

0.241***

0.036

Cemented or Tin roof

0.214***

0.025

Dwelling has a window

0.210***

0.025

Piped water supply-0.089***0.029

Piped water inside dwelling

0.201***

0.031

Communal garbage collection

0.207***

0.038

Has municipal sewage

0.177***

0.047

Electricity for lighting

0.183***

0.026

Dwelling has telephone facility

0.359***

0.030

Paved road next to dwelling

0.432***

0.031

log(Value of durable goods)

0.060***

0.008

Constant

4.198***

0.125

Number of observations

6,861

R2

0.668

Adjusted R2
0.666
Note: *** p<0.01, ** p<0.05, * p<0.1. Regression also includes stratum dummies. Dependent
variable is log(reported rent). Regression is unweighted and includes panel households

8

NLSS-II poverty assessment uses NRs 25 and NRs 25,000 as the cut-off

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The predicted value of the rent, ln�(𝑅𝑅𝚤𝚤 ), from the above regression is then used to construct predicted
9rents, 𝑅𝑅�𝚤𝚤 . Predicted rent is used in cases where households do not report an estimated rent. The value of
housing thus obtained is multiplied by 12 to get annual consumption of housing.

10.3 Distribution of Nominal per Capita Consumption
Table 10.1 and 10.2 present nominal per capita consumption and cumulative shares for deciles andquintiles respectively. The richest ten percent of the population consumes about 10-fold (30 percent) ofwhat the poorest ten percent of the population consumes (3 percent). Bottom quintile accounts for 8
percent of consumption while the top quintile for 47 percent.
Table 10.3 shows population distribution by quintile and analytical domain. This table reports that morethan 80 percent of the Kathmandu urban population and about one half of population in other urban
areas are in the top quintile. Among analytic domains, the rural-mid & far western hills, the mountains,
the rural eastern hills, and the rural eastern Tarai have higher share of their population in lower quintiles.

10.4 Distribution of Household Consumption by Expenditure Category
Table 10.4 presents nominal household consumption and its distribution by expenditure category. The
nominal household consumption for Nepal is estimated at NRs. 170,735. The richest twenty percent of
the population consumes about 4-fold (NRs. 304,616 per household) of what the poorest twenty percentof the population consumes (NRs. 81,714 per household). Regarding the composition, on average, 62
percent of household consumption is spent on food expenditures in the country. Share of housing
consumption is 11 percent, that of education is 5 percent and the rest 22 percent is accounted for byother non-food items. Urban households have a lower share in the food consumption expenses than thatof rural households. The urban households spend 46 percent on food, and especially in the Kathmandu
valley urban areas such share is the lowest (36 percent). Not surprisingly, urban households tend to
spend more on house rental and education relative to their rural counterparts.

9�
Because our regression variable is logarithmic, we get: 𝑅𝑅�𝚤𝚤 = 𝛼𝛼 exp(ln(𝑅𝑅𝚤𝚤 )), where 𝛼𝛼 is not necessarily 1. There1
are several ways to estimate 𝛼𝛼. Most estimation, including NLSS-II, use the smearing correction 𝛼𝛼 = ×n∑ni=1 exp(ϵ�)ı , which leads to 𝛼𝛼 > 1 in all cases. Instead, we use the data driven method and run a regression of�𝚤𝚤 )) without any intercept to estimate 𝛼𝛼 and use this to get our imputed rent
reported rent 𝑅𝑅𝑖𝑖 on exp(ln(𝑅𝑅(Wooldridge, 2009).

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Box 10.1: Nominal per capita consumption, 1995/96 – 2010/11
Nepal Living Standards Survey
Description
1995/96

2003/04

2010/11*

All Nepal

6,802

15,84834,829Poorest 20% of population

2,571

4,913

13,168Richest 20% of population

15,24342,23678,504

Poorest 20% of population

7.6

6.2

7.6

Richest 20% of population

44.9

53.3

45.1

Nominal per capita consumption (in NRs.)

Share of nominal per capita consumption*Unlike in previous two surveys, the consumption in 2010/11 has been calculated on the basis of the last
seven days consumption of the household.

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Table 10.1: Nominal per capita consumption by decile
DecileMean
consumption(NRs.)

Share of
Cumulative shareconsumption(%)(%)

Poorest (First)

11093

3.2

3.2

Second

15241

4.4

7.6

Third

17893

5.1

12.7

Fourth

20744

6.0

18.7

Fifth

24238

7.0

25.6

sixth

28270

8.1

33.7

Seventh

33196

9.5

43.3

Eighth

40730

11.7

55.0

Ninth

54203

15.6

70.5

Richest (Tenth)

102772

29.5

100.0

Nepal

34829

100.0

100.0

Table 10.2: Nominal per capita consumption by quintile
Quintile

Mean
consumption(NRs.)

Share of
Cumulative shareconsumption(%)(%)

Poorest (First)

13168

7.6

7.6

Second

19317

11.1

18.7

Third

26253

15.1

33.7

Fourth

36962

21.2

55.0

Richest (Fifth)

78504

45.1

100.0

Nepal

34829

100.0

100.0

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Table 10.3: Distribution of population by nominal per capita consumption quintile and geographic group
Poorest(First)SecondThird

FourthRichest(Fifth)Total

Eastern

16.7

22.2

23.4

23.2

14.5

100.0

Central

15.5

14.8

17.9

21.6

30.2

100.0

Western

17.9

21.2

19.9

20.0

20.9

100.0

Mid West

29.3

25.1

22.1

15.7

7.8

100.0

Far West

37.7

25.2

16.9

11.2

9.0

100.0

Mountains

24.3

26.2

20.1

18.9

10.5

100.0

Hills

19.5

17.3

18.0

20.0

25.3

100.0

Tarai

19.8

21.6

21.9

20.1

16.6

100.0

Urban

4.8

10.4

10.8

22.5

51.5

100.0

Rural

23.6

22.3

22.2

19.4

12.6

100.0

Mountains

24.3

26.2

20.1

18.9

10.5

100.0

Urban-Kathmandu Valley

0.5

0.8

3.7

13.2

81.8

100.0

Urban-Other Hills

4.4

7.5

11.7

26.1

50.3

100.0

Urban- Tarai

8.0

18.5

14.8

26.7

32.0

100.0

Rural Hills- Eastern

15.9

21.1

28.2

24.2

10.7

100.0

Rural Hills- Central

22.3

15.7

15.6

24.3

22.1

100.0

Rural Hills- Western

18.9

21.6

23.5

22.6

13.3

100.0

Rural Hills- Mid & Far Western

39.1

26.9

19.5

11.3

3.2

100.0

Rural Tarai- Eastern

20.8

22.0

24.0

22.2

11.0

100.0

Rural Tarai- Central

19.8

19.9

25.7

19.8

14.8

100.0

Rural Tarai- Western

24.2

27.4

20.1

14.6

13.7

100.0

Rural Tarai - Mid & Far Western

28.7

22.7

20.7

14.3

13.5

100.0

Nepal

20.0

20.0

20.0

20.0

20.0

100.0

Development Region

Ecological Zone

Urban/Rural

Analytical Domain

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Table 10.4: Nominal household consumption and its distribution by expenditure category
HouseholdShare ofConsumption
food
(NRs)

Share ofhousingrentshare of
Share ofother noneducation
food

Total

Development Region
Eastern

150,759

65.3

8.8

4.7

21.2

100.0

Central212,252

56.3

13.5

5.7

24.5

100.0

Western

164,897

61.7

9.6

5.4

23.3

100.0

Mid West

133,125

66.7

8.5

4.7

20.1

100.0

Far West

119,597

64.9

13.1

5.4

16.6

100.0

Mountains

134,683

66.8

11.2

4.2

17.8

100.0

Hills

180,666

60.5

11.5

5.7

22.3

100.0

Tarai

165,913

61.7

10.4

5.0

22.9

100.0

Urban

277,134

46.0

18.6

8.5

26.9

100.0

Rural

142,641

65.6

9.0

4.4

21.0

100.0

Mountains

134,683

66.8

11.2

4.2

17.8

100.0

Urban-Kathmandu Valley

374,148

36.0

23.7

10.3

30.0

100.0

Urban-Other Hills

247,890

49.2

16.4

8.5

25.9

100.0

Urban- Tarai

221,357

51.5

16.0

7.2

25.4

100.0

Rural Hills- Eastern

135,130

71.3

6.6

3.9

18.2

100.0

Rural Hills- Central

167,737

60.1

12.2

5.3

22.4

100.0

Rural Hills- Western

125,413

66.8

6.2

4.5

22.5

100.0

Rural Hills- Mid & Far Western

107,762

70.7

8.4

4.0

17.0

100.0

Rural Tarai- Eastern

140,154

64.6

8.3

4.9

22.2

100.0

Rural Tarai- Central

166,875

64.8

8.5

3.3

23.4

100.0

Rural Tarai- Western

162,932

62.9

10.4

4.9

21.7

100.0

Rural Tarai - Mid & Far Western

138,545

63.0

10.7

5.6

20.8

100.0

Poorest81,714

71.6

7.2

3.4

17.8

100.0

Second108,152

70.2

7.4

3.9

18.6

100.0

Third

131,337

66.6

8.2

4.6

20.6

100.0

Fourth161,503

61.3

10.6

5.7

22.4

100.0

Richest304,616

45.8

18.1

7.6

28.5

100.0

Nepal

170,735

61.5

11.0

5.3

22.2

100.0

Ecological Zone

Urban/Rural

Analytical Domain

Consumption Quintile

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CHAPTER XI: INCOME

11.1 Introduction
Income, as defined in this survey, measures the flow of resources in a household in the past 12 months.
It intends to capture the flow of resources which enable the household to achieve its living standard.
This chapter describes the methodology followed in constructing household income aggregates, andincome levels, sources and distribution.
In the last 15 years, there have been notable changes in household income structure. In nominal terms,
average household income grew by more than 360 percent (between 1995/96 and 2010/11). Mean percapita income increased by almost 490 percent during the same period. The gap in per capita income
between the poorest 20 percent of population and the richest 20 percent has narrowed down. Box 11.2
presents a summary of income indicators.

11.2 Construction of Income Aggregate
The method of constructing income aggregates for the NLSS-III is similar to the method described in thestatistical report 10 of the Nepal Living Standards Survey 2003/04 (NLSS-II).
Like in NLSS II, the main components of household income are: crop income, non-crop farm income,valuation of housing consumption of own dwelling, income from wage employment, income from nonfarm enterprises, income from remittances, rental income and income from other sources. Eachcomponent is itself an aggregation over a number of possible revenues and costs. The components of
total household income are listed in Box 11.1.

11.2.1 Farm Income
Crop income: The gross values of crops harvested are calculated from section 13.B of the questionnaire,
which provides information on quantity harvested, quantity sold and unit selling price for each crop.
This calculation involves the following steps:•

10Firstly, the unit selling price reported by the household is used to evaluate the total production.
However, all of harvested quantity is sold in only a few cases. In most records, either a small share

Central Bureau of Statistics (2004), “Nepal Living Standards Survey, 2003/04: Statistical Report Volume Two”,
pp. 30-36.

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•

or none of the harvested amount is sold in the market. This necessitates price imputations for mostharvested crop.

•

To deal with cases where unit selling prices are not available, price is imputed by taking the average
price for each crop at progressively higher levels of aggregation. There are four such levels: ward,
district, region and country. Missing prices are first replaced with ward means (of those reported
prices at household level).

•

Imputation of missing prices for fodder tree, bamboo and other trees turned out to be problematic.
With no plausible options available, such cases were dropped.
Box 11.1: Components of household income

Main Component

Items to Add

Items to Deduct

Farm Income+ Value of total crop production (net of share paid - Cultivation costs (seeds, fertilizers, hired
to landlord)labour, irrigation etc.)+ Value of by-product production+ Net income from renting farm assets (draft
animal, tractor, thresher etc.)- maintenance expenditures on farm machineryand buildings+ Value of sales from non-crop farm production(milk, ghee, eggs etc.)- Fodder and other livestock expenditure(veterinary services)+ Earning from the sale of livestock- Expenditure for the purchase of livestock+ Value of home -produced non-crop consumption+ Total cash and in-kind received from tenants onland leased-out
Wage Income- Cash rent paid to landlord on land leased-in+ Value of cash and in-kind earning per year in
agriculture (includes daily, piece-rate andpermanent labour)+ Value of cash and in-kind earning per year
outside agriculture (includes daily, piece-rate andpermanent labour)

Non-farm Enterprises + Gross income from non-agriculture
enterprises/activities during past 12 months
Income- Wage paid both cash and in-kind- Energy expenditure- Expenditure on raw material- Other operating expenditure- Share of net revenues paid to partners (or keptby partners)

Non-agriculture
Rental-income+ Income from renting out non agriculture property+ Income from renting out non agriculture assets

Transfers+ Remittances (cash and in-kind)

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Owner-occupied
housing+ Imputed rental value of housing which would had
to be paid to purchase housing services

Other Income+ Interest, dividends, profit earning from shares andsavings/deposit accounts+ Social security payment+ Pension income (domestic and foreign)+ Commission fees and royalties, other incomes

For those households who are renting-in land, the in-kind payment to their land-lord is deducted fromthe total harvested quantity. The harvested values (net of in-kind rent payment to landlord) areaggregated across all crops for each household to obtain a household-level estimate from grossagricultural output per farming household.
The gross agricultural output value is combined with other agriculture earnings and expenditures data.
Income from selling of agriculture by-product and renting out of draft animal, tractor, thresher, othermachinery are added to gross agricultural output while expenditures on seeds, fertilizers, hired labor,
irrigation, and renting in of above items(from sections 13.C1, C2, C3 & D) are deducted from the grossagricultural output. Total crop income thus is gross agricultural output plus net income from this.
Income from livestock: Section 13.E gives information on livestock. Purchase and sale of cows,
buffaloes, goats, yaks, and poultry is combined with earnings from selling of milk, ghee, eggs, curd,meat and expenditures on animal feed, transportation of feed, veterinary services. Income from livestockis then calculated as total value of sold livestock minus total value of purchased livestock plus net
income from livestock byproduct.
Consumption of home produced non-crop goods: Section 5 has information on food expenses andhome products. Several home produced non-crop goods - eggs, milk, ghee, fish, mutton, buffalo meat,chicken and other meat - are aggregated to obtain a value of home produced non-crop consumption.
Land rent income: Section 13.A1 provides information on cash and in-kind received from renting-out
cultivation land to a tenant, while section 13.A2 gives information on rent paid in cash (in-kind payment
has already been deducted from gross agricultural output) to a landlord for renting-in land for crop
cultivation. Net income from these two sections is then added to household’s aggregate farm income.

11.2.2 Wage Income
The details of each wage employment activity are asked in section 12, separately for wage employment"in agriculture" and "outside agriculture": code 1 and 2 in section 10B. Each part collects wages
received by an employee in each activity either on daily, long-term or contract/piece-rate basis. i) Daily
wage income: Daily wage income is calculated as cash received per day plus value of any in-kindreceived per day multiplied by number of days worked in that particular activity, plus value of in-kindpayments for the whole period.

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worked plus in-kind payment received for the whole period. Wage outside of agriculture is
calculated as monthly payment and monthly transportation allowances times number of months
worked, plus bonuses, tips, allowances, clothing and any other payments received yearly from eachwork activity.
iii) Piece-rate/contract income: Piece rate or contract basis wage income is the total of cash and in-kindreceived by individual per each work.
For each individual wage earner in the household, all these components are annualized and then summed
up to obtain annual wage income. Wage income figures for every earner are then aggregated at thehousehold level to obtain total wage income to each household.

11.2.3 Non-farm Enterprises Income
Information on household enterprises either solely owned or in partnership, comes from section 14. Net
revenues from each household enterprise (or the share owned by the household) are aggregated to getannual non-farm enterprises income for each household.

11.2.4 Non-agricultural Rental Income
Rental income received by household on renting-out residential property, land property and other real
assets comes from section 15.C. This constitutes non-agricultural rental income.

11.2.5 Transfer Income
Remittances received by household member (as cash or in-kind) are reported in section 16 and section
17.B respectively. Total remittance amounts received by household during the past 12 months areaggregated for each recipient household.

11.2.6 Value of Owner-occupied Housing
If a household lives in a housing unit it owns or for free, this consumption needs to be included inincome as home-produced consumption. The value of such consumption is the reported rental value thatwould have to be paid if someone wanted to rent it. When such reported values are missing or
implausible, they are replaced with regression-based imputed rental values (the method is described inconsumption chapter 10).

11.2.7 Other Income
The remaining sources of income are compiled in section 18. These include earnings from social
assistance, savings or fixed account deposits, shares/stocks, treasury bills, employee provident

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fund/citizen investment fund, internal/external pension, commission fee, royalties etc. All these income
amounts are aggregated at the household level.

11.2.8 Total Income
The aggregate household income is finally obtained by summing income from sources described above:farm income, wage income, non-farm enterprises, non-agricultural rental income, remittance received,
owner occupied housing consumption and other income components. Per capita income is obtained by
dividing household income by household size.

11.2.9 Items Omitted From Income Aggregate
Net interest income: Flow of resources from interest earnings from money lent to others is difficult to
calculate for three reasons: NLSS does not have separate questions to distinguish between interest
payments and principal repayments; between outstanding principal amounts and interest amounts; rolled
over or extended loans may not involve actual transfer of payments even when a household reports a
positive interest rate on the amount. Because of these difficulties net interest income is excluded fromthe computation of total household income.
Farm machinery and housing property: Income from sales of farm machinery is not included in total
income because they represent investment or disinvestment of assets rather than current income(household whose income source is trading of agriculture machinery would be included in enterprise
section). Net proceeds from sale and purchase of housing or land-property are considered as changes in
assets and not included in total income. Similarly, expenditure on improvement of land or building andrepair and maintenance of agricultural equipment are also excluded because the reported values tend todenote investment flow and not income flow.

11.3 Levels, Sources and Distribution of Income
11.3.1 Income Levels
Table 11.1 reports the nominal mean and median household and per capita income 11. Nominal mean andmedian annual household incomes across the country in 2010/11 are Rs. 202,374 and Rs. 127,281
respectively. Nominal mean and median per capita incomes are Rs. 41,659 and Rs. 25,093 respectively.
Average household size 12 is 4.9.

11

One percent each of both ends of the per capita income distribution -- 120 households in total, 2 percent of allhouseholds -- are defined as outliers and excluded from the analysis. All subsequent tables are based on data
without the outliers.

12This is average household size of 5868 households after excluding outliers.

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There are wide variations in average per capita incomes among geographical areas. Both the mean andmedian per capita incomes in urban areas are greater than those in rural areas. Among urban areas, theKathmandu valley has the highest per capita income. Among rural areas, the rural eastern Tarai have thelargest mean per capita income while the rural-mid & far western hills have the lowest.
Among development regions, the far-western region is the poorest in terms of nominal average (mean ormedian) per capita income. Per capita mean and median income in the richest quintile are more than fivetimes the per capita income in the poorest quintile.

11.3.2 Income Sources
Table 11.2 describes the sectoral shares of household income 13 such as percentage share in total
household income from farm income, non-farm income, remittances, consumption of own-dwelling (orrent free dwelling) and other income. Other income sources include income from renting out nonagricultural property like building or assets and earnings from savings/deposit accounts, shares, pensions,etc.
At the national level, 28 percent of all household income comes from agriculture, 37 percent from nonfarm enterprises, 17 percent from remittances, and 16 percent from own housing consumption. The
difference is most marked between urban and rural areas regarding the share of farm income. Amongdevelopment regions, the central region is the least dependent on agriculture while the far-west is themost dependent on this sector. About one half of household income (47 percent) in urban areas comesfrom non-farm enterprises. Interestingly, the distribution of income by source does not change much
across the bottom three consumption quintiles (Table 11.2).
Table 11.3 reports the distribution of income by employment sources: wage employment, self
employment and other sources. Other sources include renting-out of non-agricultural property,
remittance, consumption of owner-occupied dwelling, renting out agricultural land and other income
sources (interest, dividends, profit, payment etc. from account, share, pension, commission fee, royaltiesetc.). Share of self employment in total household income is 47 percent, share of wage employment is 24
percent and the remaining 26 percent share is that of other sources. Share of self employment is higher
in rural areas than in urban areas. Shares of wage employment and that of other sources, on the other13

To preserve comparability with figures from NLSS 1995/96 and NLSS 2003/04 reports, average share of each
income source to total income is calculated as the mean of the household shares. That is, each share is
calculated at the household level first and then averaged across households in a particular region/group (Table
11.2). An alternative measure of income share in a given group is the ratio of sum of all household incomesfrom a certain source to sum of all incomes for all sources. Latter approach gives a lower share from farmincome in particular.

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hand, are higher in urban as compared to rural areas. Among consumption quintiles, share wage
employment decreases from lower to higher quintiles.

11.3.3 Distribution of Income
Table 11.4 and 11.5 present nominal per capita income and cumulative shares by deciles and quintiles
respectively. In nominal terms, bottom 80 percent of the population earn about 44 percent of total
income while the top 20 percent earn the other 56 percent of total income. The poorest 10 percent of thetotal population accounts for less than 2 percent of total income whereas the richest 10 percent of the
population accounts for 40 percent of the total income.
Table 11.6 reports the distribution of population by geographical area and nominal per capita income
quintile. About 43 percent of the urban population is in the richest quintile while the corresponding
proportion in rural areas is only 15 percent.
There are stark differences among analytic domains regarding the proportion of population in the richest
quintile. The percentage of the population in the richest quintile varies between 8 percent (in the rural
mid and far west hills) and 67 percent (in the Kathmandu valley urban areas). The mountains region andthe rural mid and far western hills are the other two notable domains where the proportion of populationin the richest quintile is rather low.

Box 11.2: Nominal income, 1995/96 – 2010/11
Description

Nepal Living Standards Survey
1995/96

2003/04

2010/11

43,73280,111

202,374

All Nepal

7,690

15,162

41,659Poorest 20% of population

2,020

4,003

15,888Richest 20% of population

19,32540,486

94,149

61.0

47.8

27.7

Non-farm income

22.0

27.6

37.2

Other income

16.0

24.5

35.1

Nominal average household income (NRs.)
Nominal average per capita income (NRs.)

Share of farm income in household income (inpercent)

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Table 11.1: Nominal household income and per capita income
Household income (NRs.)

Per Capita income (NRs.)

MeanMedianMeanMedianAverage
HH size

Eastern

182,326

122,164

37,81825,516

4.8

Central238,107

152,100

49,12829,2244.9

Western

212,694

132,298

45,651

26,970

4.7

Mid West

159,868

104,544

30,941

19,374

5.2

Far West

144,030

87,52428,584

17,721

5.1

Mountains

169,990

98,48034,633

20,7435.0

Hills

209,301

128,904

46,22426,923

4.6

Tarai

200,092

130,296

38,54924,716

5.2

Urban

318,167

210,800

71,72046,961

4.5

Rural

171,950

113,857

34,60722,406

5.0

Mountains

169,990

98,48034,633

20,743

5.0

Urban-Kathmandu Valley

404,511

282,400

98,08474,704

4.2

Urban-Other Hills

300,599

196,526

70,91547,311

4.3

Urban- Tarai

266,159

169,188

55,36232,393

4.8

Rural Hills- Eastern

165,150

111,167

34,94323,220

4.8

Rural Hills- Central

189,754

124,429

41,25925,127

4.7

Rural Hills- Western

157,991

106,349

37,10923,641

4.3

Rural Hills- Mid & Far Western

122,544

86,01023,89316,094

5.1

Rural Tarai- Eastern

173,637

118,230

35,451

25,827

4.9

Rural Tarai- Central

190,129

127,866

34,11022,654

5.6

Rural Tarai- Western

223,960

148,241

39,23923,663

5.7

Rural Tarai - Mid & Far Western

159,253

104,987

31,916

20,263

5.0

Poorest99,11576,872

15,88813,012

6.3

Second131,652

94,29823,341

17,475

5.7

Third165,010

114,965

32,791

24,500

5.1

Fourth186,337

141,025

42,79733,583

4.4

Richest356,555

258,577

94,14970,447

3.8

Nepal

202,374

127,281

41,65925,093

4.9

Development Region

Ecological Zone

Urban/Rural

Analytical Domain

Consumption Quintile

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Table 11.1A: Per capita income with own account production income included(Nomial NRs.)
MeanMedianDevelopment Region
Eastern

39,09526592

Central50,12330121

Western

46,89928021

Mid West

32,33221128

Far West

29,956

18728

Mountains

36,816

23260

Hills

47,536

28472

Tarai

39,496

25584

Urban

72,492

47698

Rural

35,923

23719

Mountains

36,81623260

Urban-Kathmandu Valley

98,336

74956

Urban-Other Hills

72,25949177

Urban- Tarai

56,10033201

Rural Hills- Eastern

36,75024670

Rural Hills- Central

42,76826923

Rural Hills- Western

38,51025415

Rural Hills- Mid & Far Western

25,334

17918

Rural Tarai- Eastern

36,491

26699

Rural Tarai- Central

34,90623130

Rural Tarai- Western

40,491

24932

Rural Tarai - Mid & Far Western

33,063

21017

Poorest16,85013938

Second24,582

18650

Third

34,15425798

Fourth44,18434969

Richest95,17271700

Ecological Zone

Urban/Rural

Analytical Domain

Consumption Quintile

Nepal

42,857

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Table 11.2: Shares of household income by sectoral source(Percent)
Farm
IncomeNon-Farm
IncomeRemittance

Own housing
consumption

Others

Total

Eastern

34.7

37.2

16.6

10.3

1.3

100.0

Central

16.3

41.5

16.0

23.9

2.4

100.0

Western

29.6

33.9

21.6

9.7

5.2

100.0

Mid West

36.9

35.6

15.7

10.9

0.9

100.0

Far West

38.6

28.7

16.2

14.7

1.8

100.0

Mountains

36.7

32.1

15.5

13.9

1.8

100.0

Hills

28.3

32.7

16.1

19.4

3.5

100.0

Tarai

25.7

42.5

18.7

11.6

1.5

100.0

Urban

9.8

47.1

15.4

23.0

4.6

100.0

Rural

32.4

34.5

17.7

13.5

1.9

100.0

Mountains

36.7

32.1

15.5

13.9

1.8

100.0

Urban-Kathmandu Valley

1.4

41.4

12.8

39.6

4.8

100.0

Urban-Other Hills

15.8

41.0

19.4

16.7

7.0

100.0

Urban- Tarai

12.5

55.0

15.0

14.3

3.1

100.0

Rural Hills- Eastern

55.1

22.3

12.4

8.7

1.6

100.0

Rural Hills- Central

10.5

36.7

14.9

35.8

2.2

100.0

Rural Hills- Western

37.6

28.9

21.4

6.5

5.6

100.0

Rural Hills- Mid & Far Western

45.1

29.1

14.1

11.0

0.7

100.0

Rural Tarai- Eastern

29.0

40.4

19.4

10.4

0.8

100.0

Rural Tarai- Central

27.8

42.6

18.3

10.6

0.7

100.0

Rural Tarai- Western

26.3

38.3

21.6

11.1

2.7

100.0

Rural Tarai - Mid & Far Western

32.4

33.4

20.6

12.7

0.9

100.0

Poorest

36.1

40.8

13.7

8.9

0.4

100.0

Second

37.9

40.5

12.1

8.7

0.8

100.0

Third

34.0

36.2

18.2

10.1

1.5

100.0

Fourth

21.3

28.3

19.9

28.1

2.6

100.0

Richest

16.5

41.1

19.8

17.3

5.4

100.0

Nepal

27.7

37.2

17.2

15.5

2.4

100.0

Development Region

Ecological Zone

Urban/Rural

Analytical Domain

Consumption Quintile

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Table 11.3: Shares of household income by employment source(Percent)
Wage
Development Region

l

Self
t

OthersEastern

24.3

47.3

28.4

Central

31.5

25.8

42.7

Western

23.1

40.2

36.8

Mid West

24.7

47.6

27.7

Far West

19.0

47.9

33.0

Mountains

21.9

46.4

31.7

Hills

25.4

35.3

39.3

Tarai

27.7

40.1

32.1

Urban

32.5

23.7

43.8

Rural

24.6

42.1

33.3

Mountains

21.9

46.4

31.7

Urban-Kathmandu Valley

40.2

1.5

58.3

Urban-Other Hills

25.9

30.4

43.6

Urban- Tarai

30.6

36.3

33.1

Rural Hills- Eastern

17.1

60.0

23.0

Rural Hills- Central

27.1

19.8

53.0

Rural Hills- Western

21.6

44.8

33.6

Rural Hills- Mid & Far Western

22.6

51.5

25.9

Rural Tarai- Eastern

26.4

42.8

30.8

Rural Tarai- Central

31.7

38.4

29.9

Rural Tarai- Western

26.6

37.7

35.6

Rural Tarai - Mid & Far Western

19.5

46.0

34.5

Poorest

34.5

42.5

23.0

Second

31.4

46.9

21.7

Third

25.1

44.9

29.9

Fourth

22.7

26.5

50.8

Richest

21.6

35.1

43.4

Nepal

26.2

38.3

35.5

Ecological Zone

Urban/Rural

Analytical Domain

Consumption Quintile

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Table 11.4: Nominal per capita income by decile
Mean per Capita
Income (NRs.)

Share ofincome (%)

Cumulative
share (%)

Poorest (First)

6222

1.5

1.5

Second

10779

2.6

4.1

Third

14350

3.4

7.5

Fourth

18238

4.4

11.9

Fifth

22655

5.4

17.3

sixth

28007

6.7

24.1

Seventh

35449

8.5

32.6

Eighth

46830

11.2

43.8

Ninth

69769

16.7

60.6

Richest (Tenth)164401

39.5

100.0

Nepal

41659

100.0

100.0

DecileTable 11.5: Nominal per capita income by quintile
Quintile

Mean per capita
income (NRs.)

Share of Cumulative shareincome(%)(%)

Poorest (First)

8498

4.1

4.1

Second

16294

7.8

11.9

Third

25329

12.2

24.1

Fourth

41138

19.7

43.8

Richest (Fifth)

117063

56.2

100.0

Nepal

41659

100.0

100.0

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Table 11.6: Distribution of population by nominal per capita income quintile and geographical group(Percent)
Poorest(First)SecondThird

fourthRichest(Fifth)Total

Eastern

16.4

21.8

23.5

22.1

16.3

100.0

Central

17.0

16.6

19.5

20.8

26.1

100.0

Western

18.7

19.6

18.6

20.0

23.1

100.0

Mid West

28.9

23.8

17.7

17.9

11.7

100.0

Far West

31.9

24.3

18.9

14.1

10.8

100.0

Mountains

27.4

21.0

19.1

20.1

12.5

100.0

Hills

20.8

17.8

17.2

19.3

25.0

100.0

Tarai

18.3

21.8

22.6

20.6

16.6

100.0

Urban

6.7

11.0

15.4

23.9

43.0

100.0

Rural

23.1

22.1

21.1

19.1

14.6

100.0

Mountains

27.4

21.0

19.1

20.1

12.5

100.0

Urban-Kathmandu Valley

1.6

2.5

6.3

22.8

66.8

100.0

Urban-Other Hills

8.9

7.8

15.0

25.1

43.2

100.0

Urban- Tarai

9.1

17.9

21.7

24.1

27.3

100.0

Rural Hills- Eastern

21.5

23.0

21.8

19.3

14.5

100.0

Rural Hills- Central

21.0

17.5

19.8

20.7

20.9

100.0

Rural Hills- Western

21.9

22.3

19.3

17.9

18.6

100.0

Rural Hills- Mid & Far Western

36.7

24.3

17.1

14.2

7.7

100.0

Rural Tarai- Eastern

14.4

21.9

25.5

22.8

15.4

100.0

Rural Tarai- Central

22.5

22.5

23.4

18.4

13.2

100.0

Rural Tarai- Western

21.5

21.2

20.0

21.7

15.5

100.0

Rural Tarai - Mid & Far Western

24.3

25.9

19.7

16.3

13.8

100.0

Nepal

20.0

20.0

20.0

20.0

20.0

100.0

Development Region

Ecological Zone

Urban/Rural

Analytical Domain

Nepal NLSS Ⅲ 2010/11 Manual Version 2.0