(Version 1.1)

Users’ Manual for Handling Resampled Micro Data of
Vietnamese Household Living Standard Survey(VHLSS)

VHLSS – Overall and Survey Process

2015
The Institute of Statistical Mathematics (ISM)
andStatistical Information Institute for Consulting and Analysis (SINFONICA)

1

History of revision of the manual Version 1.1 in February 2015
- Revised based on the discussion during the Sixth International Workshop on Analysis of Micro
Data of Official Statistics in December 2014
- Added chapter 6.8 Education

 First draft version 1.0 in September 2014

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CONTENTS

VHLSS – Overall and survey process
1. About this Manual

Page 3

2. Outline of VHLSS
2.1 Objective of the survey

4

2.2 Topics covered by the survey

4

2.3 Coverage of the survey

5

2.4 Sample design6

2.5 Data collection and editing10

2.6 Data processing

11

3. Data and metadata provided
3.1 VHLSS 2002/2004/2006

12

3.2 Other source of metadata33

4. Map of provinces and regions38

5. Panel data (Trial)40

6. Population and household
6.1 Ethnicity

66

6.2 Religion

68

6.3 Age heaping

69

6.4 Literacy

71

6.5 Polygamy

74

6.6 Relationship to the household head78

6.7 Family type
6.7.1 Family type based on the number of persons by generation

84

6.7.2 Family type based on the generation and number of married couples

94

6.8 Education

100

7. Agricultural land

103

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1. About this Manual
1. This manual was prepared for users to use the next 80% resampled micro data sets of VietnameseHousehold Living Standard Survey (VHLSS). It described mainly overall and survey process of
VHLSS 2002, 2004 and 2006.

Survey year

Data files

Contents

VHLSS 2002

31 filesHousehold questionnaire for Income and Expenditure Survey

VHLSS 2004

47 filesHousehold questionnaire for Income and Expenditure Survey

VHLSS 2006

49 filesHousehold questionnaire for Income and Expenditure Survey

2. The questionnaires, file layout and data dictionary of each VHLSS are described in the manual foreach year’s VHLSS, respectively.

3. The original micro data sets composed of all the samples were provided by NSO, Viet Nam basedon the Charter for Experimental Laboratory for Research Purpose Statistical Use of Micro Data, andresampled at the rate of 80% by Sinfonica.

4. This manual was first compiled in September 2014 by;
Hiroshige Furuta
Visiting Senior Research Fellow, Sinfonica

Acknowledgements
Special thanks to Ms. Van Nghiem, GSO, Vietnam, who assisted my work of compilingthe manuals by properly answering to my queries via email.

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2. Outline of VHLSSThis chapter mainly describes on VHLSS 2002, 2004 and 2006.

2.1 Objective of the survey
In order to evaluate the living standards for policy and socio-economic plan making, the General
Statistics Office (GSO) has conducted many household living standards surveys. From 2002 to 2010
particularly, the household living standards survey (VHLSS) is conducted every 2 years by the GSO
in the years end with even numbers in order to monitor systematically living standards of Vietnam
population’s groups; monitor and assess the implementation of the Comprehensive Poverty
Reduction and Growth Strategy; making contribution to evaluating results of realization of theMillennium Development Goals (MDGs) and Vietnam Development Goals (VDGs).
The GSO publishes the official results of the VHLSS. Data of other statistical areas compiled fromthe VHLSS are not for replacing data of these areas which were published before rather than forclarifying and further analyzing factors affecting to living standards.

VHLSS collects information to be used as basis for assessment of living standard, including poverty
and the gap between the rich and the poor serving for policy making, planning and national targeted
programs of the party and the State in order to continuously improve the living standard of
population across the country, in all regions and localities.
In addition to that, information is collected to serve for research, analysis of some topics on health,education, employment and to calculate weight to compile consumer price index and national
account.

2.2 Topics covered by the survey
VHLSS includes main content reflecting living standard of households in the entire country, andmain socio-economic conditions of communes in the rural areas which affect living standard of
population in their particular area.

VHLSS consists of two surveys; for household and for commune.
2.2.1 For households- Some demographic characteristics of household’s members including age, sex, ethnicity, marital
status.
- Household’s income includes: income level; income by different sources (salary, wage; selfagricultural, forestry, fishery production; self-household’s business production/service; others);

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income by economic sector and industry.
- Household’s expenditure: expenditure level, expenditure by purpose and item (expenditure on food,clothes, accommodation, travel, education, health, culture, etc... and others.
- Education level, professional level of each household’s member.
- Illness and use of type of health clinic.
- Employment status, working hours.
- Assets, housing and facilities, such as appliances, electricity, water, sanitation
condition.
- Participation in hunger elimination and poverty reduction, credit status.
If budget is available, extended content on education and health will be surveyed.

2.2.2 For communes which have surveyed households- Some information on demography, ethnicity.
- Basic socio-economic infrastructure including: situation of electricity, roads, schools, medical
stations, markets, post offices, water sources.
- Economic situation, including: Agriculture production (land, production increase/decrease
tendency of some main crops and causes, assistances for production development such as irrigation,
agricultural encouragement); non-agricultural job opportunities.
- Some main information on social order and safety.

Note: Micro data of the survey for commune was not provided.

2.3 Coverage of the survey

2.3.1 VHLSS covers the whole country. Scope of the survey includes all selected enumeration
areas and communes in 64 provinces and cities (in VHLSS 2004 and 2006) under central
management (hereafter called province/city).
2.3.2 Target population
The target population comprises the civilian, non-institutional population.
Only persons considered as permanent residents are eligible for inclusion in the survey.
The following persons are included;-persons on vacation-temporarily in a hospital-students living away from home during the school year
The following households are ineligible;

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-housing unit containing only students living away from home during the school year
-military compounds-hospitals-prisons

2.4 Sample design: three-stage stratified cluster design

Sample of the VHLSS is selected in the way to represent the entire country (in which:urban/rural areas), 8 regions (in which: urban/rural areas), and provinces/cities.
Master sample
Two-stage area sample from enumeration areas of the 1999 Population and Housing
Census.
Strata: province and urban/rural
PSU (primary sampling unit): communes
Out of 10,476 communes as of 1999 Population Census, 3,063 communes/words wereselected.
Communes contain on average about 1,600 households while EAs were set up during thecensuses to contain about 100 households.
SSU (Secondary sampling unit): EAs
Three EAs were selected per commune.
Only one of EA was used for each year of VHLSS survey.
This is technically a three-stage design counting the selection of households, but it isoperationally equivalent to a two-stage design.
Sample allocation
The sample was allocated over strata proportional to the square root of the number ofhouseholds.
Both communes and EAs were selected with probability proportionate to size (PPS), thesize being the number of households according to 1999 Population Census.
The master sample is designed specifically for VHLSSs in the period 2002-2010. It contains
3,063 communes/wards, and 3 EAs are selected in each commune/ward from EAs of the
Population and Housing Census 1999.

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Figure 1

Structure of VHLSS 2006

Type of survey

Target and size (2006)

Questionnaire/Questions

Commune survey

3,063 commune/wards

Basic socio-economic

which have surveyed

information

Objective

households
Income survey

36,756 households (Short) Questionnaire 1A;
Questions on income

To assess living
standards at
provincial/city level

Income and9,189 households (Long) Questionnaire 1B;

expenditure

Questions on income andstandards at national

surveyexpenditureand regional level

In total

To assess living

45,945 households

Sample size
Sample size of the VHLSS 2006 includes 45,945 households selected from 3,063 areas ofthe master sample frame, and is divided into 2 types: Sample for income survey includes
36,756 households to collect information as mentioned above, excluding household’s
expenditure, to assess living standard at national level, regional and provincial/citylevel; Sample for income-expenditure survey includes 9,189 to collect sufficient information
for further assessment and analysis of living standard at national and regional level.

Sample for income survey and sample for income-expenditure survey was divided for two
rounds are as follows:

Time for
Data collection
Total

Survey on income& expenditure

Survey on
income

9.189

Total

36.75645.945

Of which:(households)

May 2006

4.593

18.37222.965September 2006

4.596

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Sample selection for each year of VHLSS: 50% rotation of households

The Socio-Environmental Statistics Department coordinates with provinces/cities to selectsample as follows:
Step 1: Select EAs.
EAs of the VHLSS 2006 will be selected rotationally, specifically: re-select 50%
enumeration areas of the VHLSS 2004 (in which half of the areas were surveyed in the2002 and 2004 VHLSSs and another half of the areas were only surveyed in the 2004
VHLSS) and the other 50% areas will be newly selected from the master sample, whichwere not selected in the 2002 and 2004 VHLSSs.

Figure 2

Selection method of EAs

VHLSS 2002
VHLSS 2004
VHLSS 2006
New
New

Note:
in VHLSS 2006 were surveyd in 2002 and 2004.
in VHLSS 2006 were surveyd only in 2004.

Panel data
The sample design enables to make use of panel data. The questionnaire of VHLSS 2004
and 2006 included questions about the identification code of the previous VHLSS, whichwere recorded by the enumeration team leader.
Remarks:
Identifiers of VHLSS 2002 differ from the later VHLSS, as follows.
VHLSS 2004/2006

VHLSS 2002

tinh

3

tinh

huyen

2

xa

2

diaban

3

hoso

2

xa

hoso

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Updating of sample frame of households
The Socio- Environmental Statistics Department is responsible for selecting areas andsending the list of selected areas to Provincial Statistics Offices for reviewing and updating
attached with the map and list of areas of the 1999 Population and Housing Census of the
new areas.
Provincial Statistics Offices review and propose to change some areas for more suitability
with geographical, socio-economic characteristics of provinces with less than the change of
5% of total number of provinces’ areas with an agreement of the GSO (the SocioEnvironmental Statistics Department) during the year prior to implementing the survey.
Step 2: Select households.
Provincial Statistics Offices select households, specifically:- For areas which are re-selected from the 2004 VHLSS, select all 15 households in which
12 households were already surveyed with income (income households) in 2004 to be
surveyed with income for the 2006 VHLSS and 3 households were already surveyed withincome-expenditure in 2004 to be surveyed with income-expenditure for the 2006 VHLSS.
In case of households which were surveyed in 2002 or 2004 moved from the area, findalternate households to be assured of 12 households for income and 3 households forincome-expenditure in each enumeration area.
- For new areas, select 20 households from the updated household’s list. From these 20
households, select 15 households (12 official households, 3 spare households) for income
survey, the 5 remaining households ( 3 official and 2 spare households) for
income-expenditure survey.
Household selection follows the methods in the 2006 VHLSS manual.
Provincial Statistics Office will equally divide EAs by urban/rural areas and geographicalregion for the two rounds of the surveys conducted in May and September.
Communes which have selected EAs for the household interview will simultaneouslyinterview commune questionnaire.
The list of selected households will be kept in two places: PSOs and Socio-environmental
Statistics Department for the implementation, monitoring, and supervision.
Remarks:

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Sample design was changed based on the results of analysis of VHLSS 2002.
In VHLSS 2002, the cluster size (the number of sample households within EA ) was 25 households.
The cluster sizes for the expenditure module in VHLSS 2004 and 2006 were smaller, 3 households
per EA as compared to 5 or 20 households per EA in VHLSS 2002.

Definition of urban/rural
The definition of urban/rural is not clear.
There are three types of local administrative unit; ward, district town and commune. They areclassified into urban and rural.
Urban: ward and district town
Rural: commune

Note: The area of urban/rural might vary year by year.

2.5

Data collection method

Field work
The field work was done about one month for each round of the survey.
In VHLSS 2002, it was implemented in 4 quarters (starting at first month of each quarter). Each longquestionnaire was completed within one and a half day, and short questionnaire was completed
within one day.
In VHLSS 2004, the survey was implemented in May and September. The long questionnaire(including two special modules) was often completed within 2.5 days and the short is completed in
one and a half.

QuestionnaireThis survey uses two types of questionnaire: questionnaire for commune and questionnaire forhousehold, as in Figure 1.
The questionnaire for household includes: income-expenditure questionnaire (applied for the sampleof the income-expenditure survey) which contains all information of the survey; and income
questionnaire (applied for sample of the income survey) which contains all information of the survey
excluding information on household’s expenditure. Questionnaires are designed in details so that it
will facilitate the recording of interviewers, help avoid the missing of items and increase theconsistency among interviewers. Thus the survey data will be improved.

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Direct interview is applied in this survey. Interviewers visit households, meet with heads ofhouseholds and related households’ members to interview and fill information in questionnaires forhouseholds.

2.6 Data processing1) The data is first checked by team leader before sending to PSO.
2) The PSO checks the data again and send it to data entry staffs.
3) The data entry staffs uses CSPro for entering data twice and data cleaning. The program gives awarning for inconsistent or out of range for each suspicious numbers in each question.
4) After the data entry is completed at PSO, it is sent to Center of Information and Statistics as wellas to Social and Environment Statistics Department. The second data cleaning process is done at theSocial and Environment Statistics Department. Total time for data cleaning often takes from 8 to 12
months.

[VHLSS 2006]
After finishing data entering and cleaning up, Provincial Statistics Offices have to preliminarily
compile the survey data. Leaders from Provincial Statistics Offices examine and send thepreliminarily compiled data and data testing report to the Socio- Environmental Statistics
Department for assessment. The compilation and assessment of the preliminary results should befinished in 1 month, of them 20 days are for preliminary results and 10 days are for assessment of
the preliminary results.
After the preliminary results have been assessed, Provincial Statistics Offices proceed to officiallycompilation, as well as send the entered raw data to Hanoi Statistical Informatics Center.
The Statistical Computing Centre of Hanoi chairs the cooperation with Socio- Environmental
Statistics Department to compile the nationwide data.
The General Statistics Office publishes the preliminary results of the VHLSS 2006 in June 2007 andofficial results in December 2007.

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3 Data and metadata provided by NSO
3.1 Viet Nam Household Living Standard Survey 2002/2004/2006
The data and metadata were provided to Sinfonica in February 2012 by Ms. Ngheim Thi Van, GSO
official cum TIU student at that time, with the permission of NSO.

In the above data set, the weight data had not been included. The weight data files were submitted to
Sinfonica by Ms. Van, GSO official in July 2014 upon request.

Data and metadata provided

Organization of data files
Summary sheet.xlsx

Description of list of data files including the next items;
Year, STATA filenames, Section in the questionnaire, Main contents,
Number of variables
Sheet ‘2002’

Number of data files is 31.
Number of sections is 9.

Sheet ‘2004’

Number of data files is 47.
Number of sections is 9.

Sheet ‘2006’

Number of data files is 49.
Number of sections is 8.

Example of Sheet ‘2006’

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VHLSS 2002 - List of data files
Year

No.

STATA

SectionMain contents

Number offilenames

variables

2002

1

muc1

Section 1: List of household members

List of household members

13

2002

2

muc2

Section 2: Education

Education level and expense of household members

25

2002

3

muc2hoSection 2: Education

Total expense for education of household

9

2002

4

muc3

Section 3: Employment

Employment

27

2002

5

muc4

Section 4: Health

Health situation and expense of household members

11

2002

6

muc4hoSection 4: Health

Total expense for health of household

14

2002

7

muc5a

Section 5: Income and other inflows of money

income from salary, wage of household members

18

2002

8

muc5ahoSection 5: Income and other inflows of money

Total income from salary, wage of household

10

2002

9

muc5b1

Section 5: Income and other inflows of money

Agricultural, forestry and fishery activities

68

2002

10

muc5b2ho

Section 5: Income and other inflows of money

Agriculture production, Income from Annual and perennial industrial

26

crops,

Income from Fruit crops , Income from Crop by-production,

Agricultural production expenditure
2002

11

muc5b4

Section 5: Income and other inflows of money

Farm services,

Income from Farm services, Expenses for Farm

88

services of household
2002

12

muc5b5

Section 5: Income and other inflows of money

Sylviculture,

Income from sylvicuture,

2002

13

muc5b21_23

Section 5: Income and other inflows of money

Income from rice and other food crops,
perennial industrial crops,

2002

14

muc5b25

Section 5: Income and other inflows of money

Sylvicultural expenditure
Income from Annual and62
13

Income from Fruit crops of household

Agricultural production expenditure

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14

2002

15

muc5b31

Section 5: Income and other inflows of money

Income from livestock

108

2002

16

muc5b32

Section 5: Income and other inflows of money

Expenditure on livestock

72

2002

17

muc5b61

Section 5: Income and other inflows of money

income from aquaculture

73

2002

18

muc5b62

Section 5: Income and other inflows of money

Expenditure on planting and growing aquacultural products

49

2002

19

muc5c1

Section 5: Income and other inflows of money

Income from non-farm, non-forestry and non-aquaculture businesses

43

2002

20

muc5c2

Section 5: Income and other inflows of money

EXPENDITURE FOR non-farm, non-forestry and non-aquaculture55

businesses
2002

21

muc5d

Section 5: Income and other inflows of money

Other sources of income

27

2002

22

muc6a1

Secton 6: Expenditure

expenditure on food and drinks during holidays

10

2002

23

muc6a2

Secton 6: Expenditure

Daily Expenditure on food and drink

15

2002

24

muc6b1

Secton 6: Expenditure

Expenditure on daily consumption

10

2002

25

muc6b2

Secton 6: Expenditure

annual expenditure

104

2002

26

muc6b34

Secton 6: Expenditure

other expenses included and not included in expenditure

30

2002

27

muc7

Section 7: Fixed assets and durable things

fixed assets and durable things

13

2002

28

muc7hoSection 7: Fixed assets and durable things

fixed assets and durable things

9

2002

29

muc8

Section 8: Housing

housing

120

2002

30

muc9

Section 9: Participation in poverty alleviation

Participation in poverty alleviation programs

49

summary

58

programs
2002

31

tongcong

Summary

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VHLSS 2004 - List of data files
Year

2004

No

STATA

.

filenames1

M1_2_3A

SectionMain contents

Number ofvariables

Section 1,2,3: List of household members,

list of household members, Education, training and vocational

Education and Healthtraining, health and health care

49

2004

2

M1B

Section 1: List of household members

list of household members

14

2004

3

M3B

Section 3: Health and health insurance

Health and health care and health insurance

17

2004

4

M4A

Section 4: Income

Employment

45

2004

5

M4B11

Section 4: Income

Rice

20

2004

6

M4B12_14

Section 4: Income

other starchy, vegetable, annual and perennial industrial crops, Fruit

14

Crops
2004

7

M4B15

Section 4: Income

income from crop by-products

11

2004

8

M4B16

Section 4: Income

Crop Planting Expenditure

12

2004

9

M4B21

Section 4: Income

Income from Livestock breeding

15

2004

10

M4B22

Section 4: Income

Livestock breeding expenditure

19

2004

11

M4B31

Section 4: Income

Income from Agricultural Services

10

2004

12

M4B32

Section 4: Income

Expenses on Agricultural Services

18

2004

13

M4B41

Section 4: Income

Income from forestry and hunting, trapping and domesticating forest

9

animals and birds
2004

14

M4B42

Section 4: Income

expenditure for forestry and hunting, trapping and domesticating

21

forest animals and birds
2004

15

M4B51

Section 4: Income

income from aquaculture

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2004

16

M4B52

Section 4: Income

expenditure for aquaculture

20

2004

17

M4C1

Section 4: Income

Non-agriculture activity

25

2004

18

M4C2

Section 4: Income

expenditure for from non-agriculture, non-forestry, non-aquaculture

19business and production trades; theprocess of agricultural,

forestry, and aquacultural products
2004

19

M4D

Section 4: Income

Other income

20

2004

20

M5A1

Section 5: Expenditure

Expenditure on foods and drinks during holidays

11

2004

21

M5A2

Section 5: Expenditure

Daily expenditure on foods and drinks

16

2004

22

M5B1

Section 5: Expenditure

Expenditure on daily consumption

11

2004

23

M5B2

Section 5: Expenditure

Annual consumption expenditure

9

2004

24

M5B3_4

Section 5: Expenditure

Other spending that is considered and is not considered as

22household expenditure,
2004

25

M6

Secton 6: Fixed assets and consumer durables

Fixed assets and durable appliances

68

2004

26

M6A

Secton 6: Fixed assets and consumer durables

Fixed assets and durable appliances

13

2004

27

M6B

Secton 6: Fixed assets and consumer durables

Fixed assets and durable appliances

12

2004

28

M7

Section 7: Housing, water and sanitation

Accommodation

52

2004

29

M8

SectionParticipation in the poverty alleviation and hunger eradication

42

8:Participation

in

the

poverty

alleviation and hunger eradication programme

programand credit
2004

30

M8_XDGN

Section8:

Participation

in

the

poverty

alleviation and hunger eradication programmeParticipation in the poverty alleviation and hunger eradication
programand credit

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2004

31

M10A_E3

Section 10: Business other than agriculture,

Information on time, location, and labor, Business history,

forestry and aquaculture (expanded)

involvement in business associations and clubs, Contact with
relevant

agencies

at different

levelsand89

industries, othercharacteristics
2004

2004

32

33

M10F

M91

Section 10: Business other than agriculture,

Occupations, industries which existed during the past 10 years but

forestry and aquaculture (expanded)

ceded operations over the12 months

Section 9: Agriculture, forestry and aquaculture

FARMING, FORESTRY LAND AND WATER SURFACE FOR(expanded)

AQUACULTURE, residential LAND AND GARDEN, POND NEXT

15

23

TO HOUSING LAND
2004

34

M92

Section 9: Agriculture, forestry and aquaculture

land that is rented/borrowed/temporarily exchanged

14

Land that is temporaily rented out/ lent out

14

rice

11

Section 9: Agriculture, forestry and aquaculture

staple food, food crops and other annual crops, annual and

10(expanded)

perennial industrial crops, fruit crops

Section 9: Agriculture, forestry and aquaculture

access to extension services

11

Section 9: Agriculture, forestry and aquaculture

Bought, bid, inherited land or use right-transferred land over the last19

(expanded)

10 years

Section 9: Agriculture, forestry and aquaculture

Sold, tender-expired, acquired land or inheritance right-transfered(expanded)

land in the last 10 years(expanded)
2004

35

M93

Section 9: Agriculture, forestry and aquaculture(expanded)

2004

36

M941

Section 9: Agriculture, forestry and aquaculture(expanded)

2004

2004

37

38

M942_44

M946(expanded)
2004

2004

39

40

M948

M949

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2004

41

M9451

Section 9: Agriculture, forestry and aquaculture

Cropping structure

10

Cropping structure

15

Cropping structure

13(expanded)
2004

42

M9452

Section 9: Agriculture, forestry and aquaculture(expanded)

2004

43

M9453

Section 9: Agriculture, forestry and aquaculture(expanded)

2004

2004

44

45

M9471

M9472

Section 9: Agriculture, forestry and aquaculture

conversions in agricultural, forestry and aquacultural land and(expanded)

the past 10 years

Section 9: Agriculture, forestry and aquaculture

conversions in agricultural, forestry and aquacultural land and(expanded)

the past 10 years

over

17

over

15

2004

46

ho1

Summary

Summary

128

2004

47

ho2

Summary

Summary39

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VHLSS 2006 - List of data files
Year

No.

STATA

SectionMain contents

filenames

Number ofvariables

2006

1

muc1a.dta

Section 1: List of household members

List of household members

17

2006

2

muc1b.dta

Section 1: List of household members

List of household members

16

2006

3

muc2a.dta

Section 2: Education, training and vocational training

General information

34

2006

4

muc2b.dta

Section 2: Education, training and vocational training

Detail information on general and tertiary education

21

2006

5

muc2c.dta

Section 2: Education, training and vocational training

Extra classes

31

2006

6

muc2d.dta

Section 2: Education, training and vocational training

Repeated the school year

19

2006

7

muc2e.dta

Section 2: Education, training and vocational training

Vocational training

16

2006

8

muc3a1.dta

Section 3: Health and health insurance

Health and health care

12

2006

9

muc3a2.dta

Section 3: Health and health insurance

Health and health care

18

2006

10

muc3b.dta

Section 3: Health and health insurance

Disability

93

2006

11

muc3c.dta

Section 3: Health and health insurance

Health

17

2006

12

muc3d.dta

Section 3: Health and health insurance

Fertility

15

2006

13

muc3e.dta

Section 3: Health and health insurance

Behavoriours that have impacts on health

11

2006

14

muc3f.dta

Section 3: Health and health insurance

Health insurance

28

2006

15

muc3g.dta

Section 3: Health and health insurance

Out-patient diagnosis treatment

28

2006

16

muc3h.dta

Section 3: Health and health insurance

In-patient diagnosis treatment

31

2006

17

muc3i.dta

Section 3: Health and health insurance

Self-treatment

16

2006

18

muc4a.dta

Section 4: Income

Employment

52

2006

19

muc4b0.dta

Section 4: Income

Land for agriculture, forestry and water surface for

16

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20

aquaculture
2006

20

muc4b11.dta

Section 4: Income

Rice

19

2006

21

muc4b12.dta

Section 4: Income

Other starchy, vegetable

12

2006

22

muc4b13.dta

Section 4: Income

Annual and perennial industrial crops

13

2006

23

muc4b14.dta

Section 4: Income

Fruit crops

13

2006

24

muc4b15.dta

Section 4: Income

Income from crops by products

10

2006

25

muc4b16.dta

Section 4: Income

Crop planning expenditure

11

2006

26

muc4b161.dta

Section 4: Income

Table of quantity of chemical fertilizers used for types of

11

trees
2006

27

muc4b21.dta

Section 4: Income

Income from livestock breeding

14

2006

28

muc4b22.dta

Section 4: Income

Livestock breeding expenditure

18

2006

29

muc4b31.dta

Section 4: Income

Income from agricultural services

9

2006

30

muc4b32.dta

Section 4: Income

Expenses on agricultural services

17

2006

31

muc4b41.dta

Section 4: Income

Incomefromforestryandhunting,trapping

and13

Expenditure from forestry and hunting, trapping and20

domesticating forest animals and birds
2006

32

muc4b42.dta

Section 4: Income

domesticating forest animals and birds
2006

33

muc4b51.dta

Section 4: Income

Income from aquaculture

14

2006

34

muc4b52.dta

Section 4: Income

Expenditure from aquaculture

19

2006

35

muc4c.dta

Section 4: Income

Non-agriculture, non-forestry, non-aquaculture business33

and production trades
2006

36

muc4c2.dta

Section 4: Income

Expenditureon-nNon-agriculture,

non-forestry,

non-aquaculture business and production trades

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21

2006

37

muc4d.dta

Section 4: Income

Other income

22

2006

38

muc5a1.dta

Section 5: Expenditure

Expenditure on foods and drinks during holidays

10

2006

39

muc5a2.dta

Section 5: Expenditure

Daily expenditure on food and drinks

15

2006

40

muc5b1.dta

Section 5: Expenditure

Daily non-foods expenditure and other expenditures

10

2006

41

muc5b2.dta

Section 5: Expenditure

Annual consumption expenditure

8

2006

42

muc5b3_4.dta

Section 5: Expenditure

Other spending that is considered and not considered as

23

household expenditure
2006

43

muc6.dta

Section 6: Fixed assets and durable appliances

Fixed assets and durable appliances

67

2006

44

muc6a.dta

Section 6: Fixed assets and durable appliances

Fixed assets and durable appliances

12

2006

45

muc6b.dta

Section 6: Fixed assets and durable appliances

Fixed assets and durable appliances

11

2006

46

muc7.dta

Section 7: Accommodation

Accommodation

51

2006

47

muc8.dta

Section 8: Participation in the poverty alleviation andParticipation in the poverty alleviation and hunger

37

hunger eradication programme

eradication programme

Section 8: Participation in the poverty alleviation andParticipation in the poverty alleviation and hunger

hunger eradication programme

eradication programme

Summary

Summary

2006

2006

48

49

muc8_vayvon.dta

ttchung.dta

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[VHLSS 2002]
Outline of the survey;
General Instruction of

Description of survey objectives, survey contents, survey methodology,

VHLSS2002.doc

training and field survey, main contents of VHLSS2002

Data in STATA and SAS format
File names in STATA format;[1] "muc1.dta""muc2.dta""muc2ho.dta""muc3.dta"[5] "muc4.dta""muc4ho.dta""muc5a.dta""muc5aho.dta"[9] "muc5b1.dta""muc5b21_23.dta" "muc5b25.dta""muc5b2ho.dta"[13] "muc5b31.dta""muc5b32.dta""muc5b4.dta""muc5b5.dta"[17] "muc5b61.dta""muc5b62.dta""muc5c1.dta""muc5c2.dta"[21] "muc5d.dta""muc6a1.dta""muc6a2.dta""muc6b1.dta"[25] "muc6b2.dta""muc6b34.dta""muc7.dta""muc7ho.dta"[29] "muc8.dta""muc9.dta""tongcong.dta"

Number of records and variables in each data file;muc1.dta : 132384 13
muc2.dta : 132374 25
muc2ho.dta : 29532 9
muc3.dta : 109715 27
muc4.dta : 29522 11
muc4ho.dta : 29532 14
muc5a.dta : 27220 18
muc5aho.dta : 29532 10
muc5b1.dta : 29530 68
muc5b21_23.dta : 109865 13
muc5b25.dta : 153565 11
muc5b2ho.dta : 29532 26
muc5b31.dta : 29530 108
muc5b32.dta : 29530 72
muc5b4.dta : 29530 88
muc5b5.dta : 29530 62
muc5b61.dta : 29530 73
muc5b62.dta : 29530 49
muc5c1.dta : 29530 43
muc5c2.dta : 29530 55

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muc5d.dta : 29530 27
muc6a1.dta : 436795 10
muc6a2.dta : 904003 15
muc6b1.dta : 350823 10
muc6b2.dta : 29530 104
muc6b34.dta : 29530 30
muc7.dta : 250928 13
muc7ho.dta : 29532 9
muc8.dta : 29532 120
muc9.dta : 29532 49
tongcong.dta : 29532 51

Questionnaire
Household Questionnaire for Income & Expenditure Survey in English includes 11 Excel files.
File nameCover.xls
Sec01xls to Sec09.xls
Balance.xls
Note: Vietnamese version was also provided.

Data dictionary
HouseholdDic.doc

Describes variable name, type, format and label for Section 1 to 9

ExampleCodebook
Codebook_2002.xls

Describes variable name, topics, scope, length, code and description in each
sheet of dataset

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ExampleProvince code
Code of provinces and regions 2002.xls

Includes the next two list;
List of provinces in VHLSS 2002 (61 provinces)
List of regions in VHLSS 2002 (8 regions)
Note: Definition of regions was not included here.

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[VHLSS 2004]
Outline of the survey;
Description

Survey plan.pdf

ofobjectives,contents,

methodology,

direction

implementation of VHLSS2004
Guidance to using

1. Contents of the VHLSS2004

database of

2. Survey sample of income and expenditure and representative level

VHLSS2004.doc

3. Content and structure of VHLSS2004 database
4. Some items to pay attention to when using the VHLSS2004 database

Data in STATA and SAS format
File names in STATA format;[1] "ho1.dta""ho2.dta""m1_2_3a.dta" "m10a_e3.dta"[5] "m10f.dta""m1b.dta"[9] "m4b11.dta""m4b12_14.dta" "m4b15.dta""m4b16.dta"[13] "m4b21.dta""m4b22.dta""m4b31.dta""m4b32.dta"[17] "m4b41.dta""m4b42.dta""m4b51.dta""m4b52.dta"[21] "m4c1.dta""m4c2.dta""m4d.dta""m5a1.dta"[25] "m5a2.dta""m5b1.dta""m5b2.dta""m5b3_4.dta"[29] "m6.dta""m6a.dta""m6b.dta""m7.dta"[33] "m8.dta""m8_xdgn.dta" "m91.dta""m92.dta"[37] "m93.dta""m941.dta""m942_44.dta" "m9451.dta"[41] "m9452.dta""m9453.dta""m946.dta"[45] "m9472.dta""m948.dta""m949.dta""m3b.dta""m4a.dta""m9471.dta"

Number of records and variables in each data file;
ho1.dta : 9188 128
ho2.dta : 9188 39
m1_2_3a.dta : 40438 49
m10a_e3.dta : 4376 89
m10f.dta : 416 15
m1b.dta : 20209 14
m3b.dta : 16777 17
m4a.dta : 37368 45
m4b11.dta : 11102 20
m4b12_14.dta : 31444 14
m4b15.dta : 8493 11
m4b16.dta : 54161 12

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26

m4b21.dta : 18225 15
m4b22.dta : 11311 19
m4b31.dta : 301 10
m4b32.dta : 299 18
m4b41.dta : 4593 9
m4b42.dta : 2533 21
m4b51.dta : 4243 15
m4b52.dta : 3046 20
m4c1.dta : 4544 25
m4c2.dta : 29152 19
m4d.dta : 9188 20
m5a1.dta : 129993 11
m5a2.dta : 290143 16
m5b1.dta : 108735 11
m5b2.dta : 116575 9
m5b3_4.dta : 9188 22
m6.dta : 9188 68
m6a.dta : 16512 13
m6b.dta : 79235 12m7.dta : 9188 52
m8.dta : 9188 42
m8_xdgn.dta : 5233 19
m91.dta : 35317 23
m92.dta : 1219 14
m93.dta : 805 14
m941.dta : 11095 11
m942_44.dta : 31444 10m9451.dta : 25119 10m9452.dta : 16003 15
m9453.dta : 3347 13m946.dta : 12229 11m9471.dta : 737 17m9472.dta : 262 15
m948.dta : 6506 19
m949.dta : 1061 20
muc6b34.dta : 29530 30

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muc7.dta : 250928 13
muc7ho.dta : 29532 9
muc8.dta : 29532 120
muc9.dta : 29532 49
tongcong.dta : 29532 51

Questionnaire
Household Questionnaire for Income & Expenditure Survey in English includes 12 Excel files.
File nameCover.xls
Section01xls to Section10.xls
Indicators.xls
Note: Vietnamese version was also provided.

Codebook
Codebook_2004.xls

Describes variable name, topics, scope, length, code and description in each
sheet of dataset

ExampleProvince code
Province code.xls

Includes the next list;
List of provinces in VHLSS 2004 (64 provinces)

Remarks: Three provinces increased.

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[VHLSS 2006]
Outline of the survey;
Description

Survey plan.pdf

ofobjectives,contents,

methodology,

direction

implementation of VHLSS2006

Data in STATA and SAS format
File names in STATA format;[1] "muc1a.dta""muc1b.dta""muc2a.dta""muc2b.dta"[5] "muc2c.dta""muc2d.dta""muc2e.dta""muc3a1.dta"[9] "muc3a2.dta""muc3b.dta""muc3c.dta""muc3d.dta"[13] "muc3e.dta""muc3f.dta""muc3g.dta""muc3h.dta"[17] "muc3i.dta""muc4a.dta""muc4b0.dta""muc4b11.dta"[21] "muc4b12.dta""muc4b13.dta""muc4b14.dta""muc4b15.dta"[25] "muc4b16.dta""muc4b161.dta""muc4b21.dta""muc4b22.dta"[29] "muc4b31.dta""muc4b32.dta""muc4b41.dta""muc4b42.dta"[33] "muc4b51.dta""muc4b52.dta""muc4c.dta""muc4c2.dta"[37] "muc4d.dta""muc5a1.dta""muc5a2.dta""muc5b1.dta"[41] "muc5b2.dta""muc5b3_4.dta""muc6.dta""muc6a.dta"[45] "muc6b.dta""muc7.dta""muc8.dta""muc8_vayvon.dta"[49] "ttchung.dta"
Number of records and variables in each data file;
muc1a.dta : 39071 17
muc1b.dta : 18792 16
muc2a.dta : 39071 34
muc2b.dta : 39071 21
muc2c.dta : 39071 31
muc2d.dta : 39071 19
muc2e.dta : 39071 16
muc3a1.dta : 39071 12
muc3a2.dta : 18524 18
muc3b.dta : 39071 93
muc3c.dta : 39071 17
muc3d.dta : 6647 15
muc3e.dta : 33826 11
muc3f.dta : 39071 28
muc3g.dta : 11058 28

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29

muc3h.dta : 9214 31
muc3i.dta : 21622 16
muc4a.dta : 39071 52
muc4b0.dta : 24080 16
muc4b11.dta : 14961 19
muc4b12.dta : 15499 12
muc4b13.dta : 3932 13
muc4b14.dta : 9791 13
muc4b15.dta : 8379 10
muc4b16.dta : 52513 11
muc4b161.dta : 16592 11
muc4b21.dta : 16577 14
muc4b22.dta : 10222 18
muc4b31.dta : 279 9
muc4b32.dta : 277 17
muc4b41.dta : 3932 13
muc4b42.dta : 2283 20
muc4b51.dta : 3653 14
muc4b52.dta : 2706 19
muc4c.dta : 4379 33
muc4c2.dta : 29333 9
muc4d.dta : 9189 22
muc5a1.dta : 131726 10
muc5a2.dta : 288564 15
muc5b1.dta : 114114 10
muc5b2.dta : 114952 8
muc5b3_4.dta : 9189 23
muc6.dta : 9189 67
muc6a.dta : 15897 12
muc6b.dta : 85890 11
muc7.dta : 9189 51
muc8.dta : 9189 37
muc8_vayvon.dta : 4987 20
ttchung.dta : 9189 141

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Questionnaire
Household Questionnaire for Income & Expenditure Survey in English
File name

VHLSS06_questionnaire.pdf (85 pages)

Codebook
Codebook_2006.xls

Describes variable name, topics, scope, length, code and description in each
sheet of dataset

ExampleProvince code
Province code.xls

Includes the next list;
List of provinces in VHLSS 2006 (64 provinces)

Note: Not confirmed whether this is the same as VHLSS 2004.

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Survey results
Survey report of

Includes the results of VHLSS 2002, 2004 and 2006 as time series

VHLSS 2006
File name

Introduction.pdf and Part 01.pdf to Part 11.pdf

Content of the report

Note

The survey report is also available at NSO’s website.
http://www.gso.gov.vn/default_en.aspx?tabid=515&ItemID=8183(Accessed on 11 July 2014)

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[Weight data]
Weight is unique within xa (commune), which is psu. The following weight data by xa wasprovided;
File

Description

No of records

weight_by_xa02.dta

Weight for VHLSS 2002 by psu

2,901

weight_by_xa04.dta

Weight for VHLSS 2004 by psu

3,061

weight_by_xa06.dta

Weight for VHLSS 2006 by psu

3,063

Example of VHLSS 2006:

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33

3.2 Other sources of metadata

IHSN (International Household Survey Network)

VHLSS 2002 (http://catalog.ihsn.org/index.php/catalog/3284/related_materials)(Accessed on 11 July 2014)

The following documents are available;

Document

Description

VHLSS 2002 and 2004

This document was prepared by the officials from Social & Environment

Basic Information

Statistics Department, Vietnam General Statistics Office. It describes on(47 pages)

sample design of VHLSS 2002 and 2004 in detail.

Main difference

VHLSS 2002 is a development of VLSS conducted during 1990’s.

between VLSSIt describes on the main difference between VLSS and VHLSS.

1992/1993, VLSS
1997/1998 and VHLSS
2001/2002(5 pages)

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VHLSS 2004 (http://catalog.ihsn.org/index.php/catalog/3285/related_materials)
The following documents are available;

Document

Description

VHLSS 2004

It is the operational handbook in the field. It describes on the tasks andOperational Handbook

responsibilities of team leader, enumerator and supervisor, as well as how(124 pages)

to fill in questionnaire in detail.

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World bank: Living Standards Measurement Study(http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTLSMS/0,,c
ontentMDK:21709814~pagePK:64168445~piPK:64168309~theSitePK:3358997,00.html
Accessed on 07 August 2014)

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“Survey design and sample design in household budget surveys” by Hans Pettersson,
Statistics Sweden (14 pages)(http://unstats.un.org/unsd/hhsurveys/pdf/Chapter_24.pdf) (Accessed on 11 July 2014)

This paper addresses some issues on survey design and sample design for household budget surveys.
It explained the square root allocation method as one compromise among conflicting demands from
National Accounts, and government planners and policy analysts. Square root allocation has beenused for VHLSS. He had designed the sampling for VHLSS.

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

Enumerators’ manual of VHLSS 2004

“Operational Handbook: VHLSS 2004” by GSO, April 2004, 124 pages(Available at IHSN website. Contents copying and changing documents are restricted.)

Below is the contents of the manual. Especially, “Section 1. Part A. List of household members” isvery important.

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4. Map of Provinces and Regions (as of 2006)
Map of Provinces
The below map of Vietnam exhibiting its 59 provinces and 5 centrally controlled municipalities at
the time of VHLSS 2004 and 2006. Ha Tay province, located in the south west of “2 Ha Noi” wasmerged with Ha Noi municipality in 2008. The number of provinces is 63 in 2014.

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Map of regions
Provinces are grouped into eight regions.
Note: From VHLSS 2010, Vietnam is divided into 6 regions in GSO’s publication.
Code

RegionProvince codes

Note: Regions from 2001

1

Red River Delta

101-117

Red River Delta

2

North East

201-225

Northern

3

North West301-305

Mountains

4

North Central

401-411

North Central and Central

5South Central Coast

501-511

Coast

6

Central Highlands

601-607

Central Highlands

7South East

701-717

South East

8

Mekong River Delta

801-823

Mekong River Delta

MidlandVietnam VHLSS Manual - Overall and survey process (Version 1.1)

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40

5. Panel Data (Trial)
It is an advantage of survey design of VHLSS to make use of panel data.

What is panel data?
The term panel data refers to observations obtained over multiple time periods for the same
households or individuals.
{Xijt} i: household or individual,

j: variable, t: year

In statistical surveys, panel data is essentially a set of pairs of household identifiers (ID06, ID04) andpairs of individual identifiers (PID06, PID04). Each identifier is uniquely linked with household orindividual data in VHLSS 2006 and 2004.
pair of (ID06, ID04)

ID06 Household data in 2006

ID04 Household data in 2004

pair of (PID06, PID04)

ID06 PID06 Individual data in 2006

ID04 PID04 Individual data in 2004

Where,
ID: Household identifier
PID: Individual identifier
06: VHLSS 2006
04: VHLSS 2004

How can the design of VHLSS make panel data?
VHLSS 2006 has the question on whether the household was surveyed in VHLSS 2004 in Section
1b. And if yes, household identification of VHHLSS 2004 was recorded by the survey team leader
based on the sample list of VHLSS 2004. Finally the data set ttchung has the variable of “ghepho”
on whether matched with VHLSS 2004.

As for matching household members, the data set MUC1B, list of household members at the time ofVietnam VHLSS Manual - Overall and survey process (Version 1.1)

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VHLSS 2004 was prepared. Basic information was recorded by the survey team leader based on thelist of household members of VHLSS 2004. It has the variables of person number in 2004, sex in2004 and age in 2004 in addition to name in 2004, as well as person number in VHLSS 2006 if aperson is the household member at the time of VHLSS 2006. And if a person is no longer living in
the household at the time of VHLSS 2006, the reason was asked. Finally, the data set has the variableof ghep on whether matched with VHLSS 2004.

VHLSS 2006

VHLSS 2004

◆list of sample householda half surveyed in 2004
pairs (ID06, ID04)◆list of sample households→questions on household◆list of household members(PID06)◆list of household members→questions on household members◆list of household members
surveyed in 2004
If a person is a member in 2006,
YES: pairs (PID06, PID04)
No: Move-out/died info.

Micro data sets in 2006

Micro data sets in 2004

What is available from panel data?
Household panel data show the transition of household-level variables over time. It also
provides information on the household which was selected for VHLSS 2006 but had removed
to other EA.

Transition of households
VHLSS 2004

VHLSS 2006

A half of sample
households in 2004

Re-selected
in 2006

Another half ofsample households
in 2004

Not selected

Surveyed in 2006
Removed out of EA

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For instance, as described in the survey report, GSO makes use of panel data to compare
directly household income per capita at household level.

Individual panel data provide information on changes of individual-level variables of persons
who were surveyed both in 2004 and 2006, that is, remained in the same households during
2004 and 2006.
In addition, it reveals persons who were surveyed in 2004 but have moved out of the household
or died, as well as persons who moved in the household or were born during 2004 to 2006.

Transition of household members
VHLSS 2004

During 2004-06

A h ou se h old

Household members
surveyed in 2004

Remained in
the household
Moved out of
the household/died

VHLSS 2006
Th e same h ou se h old
Householdmembers
Household members
in 2004
surveyed in 2006
Moved in/born

Possible errors during conducting surveys
If the pairs of (ID06, ID04) and (PID06, PID04) are perfect, making panel data is a task of merging
data sets between 2006 and 2004. However, it is natural to consider errors in the pairs of (ID06,
ID04) and (PID06, PID04) during the survey process.
Possible errors are as the next;Recording errors when a survey team leader filled in questionnaire from the lists of VHLSS
2004.



Data entry errors by an operator.

Response errors by a sample household

Therefore, we will treat pairs of (ID06, ID04) and (PID06, PID04) available in data sets ascandidates of panel data.

Vietnam VHLSS Manual - Overall and survey process (Version 1.1)

43Strategy for creating panel data
1) To prepare data sets and variables for matching2) To verify the information for matching3) To drop the unqualified records from candidates of panel4) To create panel at household level and individual level

Preparation for making panel data
Goal: The below data sets with household and individual identifiers will be prepared.

Data set

HLD06

Original

TTCHUNG

Description

All households surveyed in

HouseholdIndividual

identifier

identifier

ID06

ID04

2004 and link data
HLD04

HO1

All households

ID04

LIN06

MUC1B

Individual-level link data

ID06

MEM06

MUC1A

All household members

ID06

ID04

PID06

PID04

PID06

belonging to households
surveyed in 2004
MEM04

M1_2_3A

All household members

ID04

PID04

VHLSS 2006
List of data sets and variables related to panel (VHLSS 2006)
Data set

Variable

TTCHUNG

Description
Summary file for all households (9,189)

m1c1

Surveyed in 2004?

(Yes: 4,298 No: 4,891)

ghephoMatched with VHLSS 2004?
(0: 4,891 Not surveyed in 2004 1: 4,267 Data in 2004 9: 31)

tinh04huyen04
xa04
diaban04

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ttnt04hoso04

MUC1A

All household members (39,071)

MUC1B

Household members surveyed in VHLSS 2004 (18,792)
m1bc3

Person number in 2004

m1bc4

Sex in 2004

m1bc5

Age in 2004

ghep

Matched with VHLSS 2004?

(0: 671 Not surveyed in 2004 1:

17,039 Data in 2004 9: 37 NA: 1,045)
m1bc6

Household member in 2006? (1: 17,076 Yes 2: 1,716 No)

m1bc7

Person number in 2006

m1bc8

Reason for no longer living in the household

m1bc9

Reason for moving out of the household

m1bc10

Province code work in

Generating household-level data frame HLD06 from TTCHUNG> d<-subset(ttchung,m1c1==1)> dim(d)[1] 4298 144# Renamed the household identifier ID as ID06
> d["ID06"]<-d$ID
Generated the household identifier ID04.

> d["ID04"]<-as.character(d$hoso04+(10^2)*d$diaban04+(10^5)*d$xa04++ (10^7)*d$huyen04+(10^9)*d$tinh04)> HLD06<-d# Remarks: Out of 4,298 households, 3,667 households have the same ID both in 2004 and 2006.

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> table(HLD06$ID==HLD06$ID04,useNA="ifany")
FALSE

TRUE <NA>

631 3667

4891

Generating individual-level data frame MEM06 from MUC1A> d<-muc1a> str(d[1:6])'data.frame':$ tinh39071 obs. of 6 variables:: chr "101" "101" "101" "101" ...

$ huyen : chr "01" "01" "01" "01" ...
$ xa: chr "03" "03" "03" "03" ...

$ diaban: chr "014" "014" "014" "014" ...
$ hoso : int 15 15 15 15 19 19 19 19 24 24 ...
$ matv : int 1 2 3 4 1 2 3 4 1 2 ...
Generated household identifier ID06 in MUC1A.

> d["ID06"]<-as.character(d$hoso+(10^2)*as.integer(d$diaban)+(10^5)*as.integer(d$xa)++ (10^7)*as.integer(d$huyen)+(10^9)*as.integer(d$tinh))Generated individual identifier PID06 as a combination of ID06 and person number (matv).

> d["PID06"]<-paste(d$ID06,formatC(d$matv,width=2,flag="0"),sep="")> MEM06<-subset(d,is.element(d$ID06,HLD06$ID06))> dim(MEM06)[1] 18419

23

Generating individual-level data frame LIN06 from MUC1B> d<-muc1b# Renamed the household identifier ID as ID06
> d["ID06"]<-d$ID
Generated individual identifier PID06 as a combination of ID06 and person number in 2006
(m1bc7) if the person was a household member in 2006.

> t<-table(d$m1bc6)> names(t)<-c("Member in 2006","No")Vietnam VHLSS Manual - Overall and survey process (Version 1.1)

46>tMember in 2006

No

17076

1716

> d["PID06"]<-ifelse(d$m1bc6==1,paste(d$ID06,formatC(d$m1bc7,width=2,flag="0"),sep=""),NA)

Appended ID04 by merging with HLD06, and generated PID04 as a combination of ID04 andperson number in 2004 (m1bc3).

> d<-merge(d,HLD06[c("ID06","ID04")],key="ID06",all.x=T)> d["PID04"]<-paste(d$ID04,formatC(d$m1bc3,width=2,flag="0"),sep="")> LIN06<-d

LIN06 consists of 18,876 household members at the time of VHLSS 2004 in sample
households surveyed in VHLSS 2004, of which 17,076 persons are household members in
VHLSS 2006, that is, they have PID06.

> table(is.na(LIN06$PID04),useNA="ifany")
FALSE
18792
> table(is.na(LIN06$PID06),useNA="ifany")
FALSE

TRUE

17076 1716

VHLSS 2004
List of data sets and variables related to panel (VHLSS 2004)
Data set

Variable

HO1

Description
Summary file for all households (9,188)m1c1

Surveyed in 2002?

(1 Yes: 4,476 2 No: 4,712)

tinh02xa02
ttnt02hoso02
quy02

Quarter code in 2002

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M1_2_3A

All household members (40,438)

M1B

Household members surveyed in VHLSS 2002 (20,209)
m1bc3

Person number in 2002

m1bc4

Sex in 2002

m1bc5

Age in 2002

m1bc6

Household member in 2004? (1: 18,353 Yes 2: 1,856 No)

m1bc7

Person number in 2004

m1bc8

Reason for no longer living in the household

m1bc9

Reason for moving out of the household

Generating household-level data frame HLD04 from HO1> d<-ho1# Generated household identifier ID04.
> d["ID04"]<-as.character(d$hoso+(10^2)*d$diaban+(10^5)*d$xa+(10^7)*d$huyen+(10^9)*d$tinh)

Generating individual-level data frame MEM04 from M1_2_3A> d<-m1_2_3a

Generated household identifier ID04 in M1_2_3A.

> d["ID04"]<-as.character(d$hoso+(10^2)*d$diaban+(10^5)*d$xa+(10^7)*d$huyen+(10^9)*d$tinh)Generated individual identifier PID04 as a combination of ID04 and person number (matv).

> d["PID04"]<-paste(d$ID04,formatC(d$matv,width=2,flag="0"),sep="")> MEM04<-dVietnam VHLSS Manual - Overall and survey process (Version 1.1)

48

Verifying data sets and variables for matchingTo generate data set hp1 as pairs (ID06, ID04) from HLD06, which is a candidate forhousehold-level panel.

> hp1<- HLD06[c("ID06","ID04")]> dim(hp1)[1] 42982> head(hp1)
ID06

ID04

1

101010301415 101010301415

2101010301419 101010301419

16 101012301813 101012301813
31 101051100213 101051100213
32 101051100215 101051100215
33 101051100219 101051100219

 To state two conditions for hp1 and seven conditions for LIN06 essential for panel, andprepare flags on whether conditions are satisfied or not for each record in the datasets.

Tow conditions for hp1
1) ID04 in hp1 should be in HLD04.
2) This linkage should be one to one relationship. There should be no duplicated
ID04 in hp1.
Condition 1: ID04 in hp1 should be in HLD04.



Out of 4,298 households surveyed in VHLSS 2004, the number of households linked with
HLD04 is 4,219.

# Appended flag1 for unmatched ID04 in hp1> hp1["flag1"]<-ifelse(is.element(hp1$ID04,HLD04$ID04),1,0)> table(hp1$flag1)0

1

79 4219

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# Example of errors> head(subset(hp1,flag1==0))
ID06

ID04 flag1

105 101082500115 101082500115

0

727 105213700813 105213700813

0742 105231900313 105231900313

0743 105231900314 105231900314

0744 105231900315 105231800318

01165 109051100413 109051100413

0Condition 2: This linkage should be one to one relationship. There should be no duplicated ID04
in hp1.



There are 13 duplicated ID04 in hp1 as follows.

> sum(duplicated(subset(hp1,flag1==1)$ID04))[1] 13# List of duplicated ID04
> dup.ID04<-hp1[duplicated(hp1$ID04),"ID04"]> dup.ID04[1] "101093301019" "209214100214" "211033700214" "211073300814"[5] "211095700513" "211114700114" "217012100813" "221072300615"[9] "305101500215" "715130300514" "813070501613" "813191301219"[13] "819070700115"# Appended flag2 for duplicated records in hp1.
> hp1["flag2"]<-ifelse(!is.element(hp1$ID04,dup.ID04),1,0)> t<-addmargins(table(hp1$flag1,hp1$flag2))[c(3,2,1),c(3,2,1)]> rownames(t)<-c("Surveyed in 2004","Linked with HLD04","No")> colnames(t)<-c("Total","No duplication","Duplicated")> t
Total No duplication DuplicatedSurveyed in 2004

4298

4272

26

Linked with HLD04 4219

4193

26

79

0

No

79

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# List of 26 duplicated records> hp1[hp1$flag2==0,c("ID06","ID04")]
ID06

ID04

130 101093301015 101093301019
131 101093301019 101093301019
2417 209214100214 209214100214
2418 209214100215 209214100214
2438 211033700214 211033700214
2439 211033700215 211033700214
2471 211073300814 211073300814
2472 211073300815 211073300814
2491 211095700513 211095700513
2492 211095700514 211095700513
2521 211114700114 211114700114
2522 211114700115 211114700114
2800 217012100813 217012100813
2801 217012100815 217012100813
2992 221072300613 221072300615
2994 221072300615 221072300615
3626 305101500214 305101500215
3627 305101500215 305101500215
7178 715130300514 715130300514
7179 715130300515 715130300514
8362 813070501613 813070501613
8363 813070501614 813070501613
8456 813191301214 813191301219
8457 813191301215 813191301219
8866 819070700113 819070700115
8868 819070700115 819070700115



There are two different ID06 for each duplicated ID04. While we assume that one of the twosame ID04 might be right and the other might be a result of data entry miss, removed the above
26 records tentatively because we cannot discuss on which is the right linkage at this moment.

Created household-level panel hp2 consisted of 4,193 pairs of ID06 and ID04.

> hp2<-subset(hp1,flag1==1&flag2==1)[c("ID06","ID04")]

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> dim(hp2)[1] 4193

2> head(hp2)
ID06

ID04

1

101010301415 101010301415

2101010301419 101010301419

16 101012301813 101012301813
31 101051100213 101051100213
32 101051100215 101051100215
33 101051100219 101051100219

Seven conditions for LIN06 in order to link the identical households3) A person with PID04 should be in MEM04.
4) There should be no duplicated PID04 in LIN06.
5) The person’s sex and age in LIN06 and MEM04 should be identical.
6) ID06 in LIN06 should be the same as ID06 in hp.
7) If a person in LIN06 was a household member in VHLSS 2006, that is, PID06 was definedin LIN06, the person with the PID06 should be in MEM06.
8) There should be no duplicated PID06 in LIN06.
9) The person’s sex and age in LIN06 and MEM06 should be identical.

Condition 3: A person with PID04 in LIN06 should be in MEM04.



Out of 18,792 persons in LIN06, the number of those linked with records in MEM04 is 18,348.

# Appended flag3 for unmatched PID04 in LIN06> LIN06["flag3"]<-ifelse(is.element(LIN06$PID04,MEM04$PID04),1,0)> t<-addmargins(table(LIN06$m1bc6,LIN06$flag3))[c(3,1,2),c(3,2,1)]> rownames(t)<-c("Person in LIN04","Member in 2006","No (move-out/died)")> colnames(t)<-c("Total","Linked with MEM04","No")> tVietnam VHLSS Manual - Overall and survey process (Version 1.1)

52

Total Linked with MEM04 No
Person in LIN04

18792

18348 444

Member in 2006

17076

16685 391

No (move-out/died)

1716

1663 53

Condition 4: There should be no duplicated PID04 in LIN06.



Among 18,348 PID04 in LIN06 which linked with MEM04, there are 42 duplicated PID04 as
follows. Which record is right one will be discussed in the next condition 5.

> sum(duplicated(subset(LIN06,flag3==1)$PID04)==T)[1] 42# List of duplicated ID04
> dup.PID04<- subset(LIN06,flag3==1) [duplicated(subset(LIN06,flag3==1)$PID04)==T,"PID04"]> length(dup.PID04)[1] 42# Appended flag4 for duplicated records in LIN06> LIN06["flag4"]<-ifelse(!is.element(LIN06$PID04,dup.PID04),1,0)> t<-addmargins(table(LIN06$flag3,LIN06$flag4))[c(3,2,1),c(3,2,1)]> rownames(t)<- c("Person in LIN06","Linked with MEM04","No")> colnames(t)<-c("Total","No duplication","Duplicated")> t
Total No duplication Duplicated
Person in LIN06

18792

18708

84

Linked with MEM04 18348

18264

84

444

0

No

444

# Examples of duplicated records> for(j in dup.PID04) print(LIN06[LIN06$PID04==j,c("PID06","PID04")])
PID06

PID04

178 10109330101502 10109330101902
182 10109330101902 10109330101902
PID06

PID04

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179 10109330101501 10109330101901
183 10109330101901 10109330101901
:
PID06

PID04

18011 81907070011303 81907070011503
18027 81907070011503 81907070011503

Condition 5: The person’s sex and age in LIN06 and MEM04 should be identical.

> d<-subset(LIN06,flag3==1)> df1<-d[c("PID06","ID06","PID04","ID04","m1bc4","m1bc5","flag3","flag4")]> dim(df1)[1] 18348

8> colnames(df1)[5:6]<-c("lsex","lage")> head(df1)
PID06

ID06

PID04

ID04 lsex lage flag3 flag4

1 10101030141501 101010301415 10101030141502 101010301415

2

46

1

1

2 10101030141503 101010301415 10101030141504 101010301415

1

24

1

1

3 10101030141502 101010301415 10101030141503 101010301415

1

26

1

1

4 10101030141504 101010301415 10101030141505 101010301415

1

18

1

1

5

<NA> 101010301415 10101030141501 101010301415

1

57

1

1

6 10101030141902 101010301419 10101030141902 101010301419

1

24

1

1

> df2<-MEM04[c("PID04","ID04","m1ac2","m1ac5")]> dim(df2)[1] 40438

4> colnames(df2)<-c("PID04","ID04","sex04","age04")> head(df2)
PID04

ID04 sex04 age04

1 10101030141301 101010301413

243

2 10101030141302 101010301413

150

3 10101030141303 101010301413

1

20

4 10101030141304 101010301413

1

17

5 10101030141501 101010301415

1

50"

6 10101030141502 101010301415

2

46

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> cond5<-merge(df1[c("PID06","PID04","lsex","lage","flag3","flag4")],+ df2[c("PID04","sex04","age04")],by="PID04",all.x=T)> dim(cond5)[1] 18348

8> head(cond5)
PID04
1 10101030141501

PID06 lsex lage flag3 flag4 sex04 age04
<NA>

1

57

1

1

1

50

2 10101030141502 10101030141501

2

46

1

1

2

46

3 10101030141503 10101030141502

1

26

1

1

1

26

4 10101030141504 10101030141503

1

24

1

1

1

24

5 10101030141505 10101030141504

1

18

1

1

1

18

6 10101030141901 10101030141901

2

52

1

1

2

52



Out of 18,348, the number of records satisfying the condition 5 is 17,723.

# Appended flag5 for inconsistent sex and age> cond5["flag5"]<-ifelse(cond5$lsex==cond5$sex04 &+ abs(cond5$lage-cond5$age04)<=1,1,0)> table(cond5$flag5)01

625 17723# Renamed cond5 as ip1 and generated ID06 and ID04 from PID06 and PID04> ip1<-cond5> ip1["ID06"]<-substr(ip1$PID06,1,12)> ip1["ID04"]<-substr(ip1$PID04,1,12)# Example of records with inconsistent sex and age between LIN06 and MEM04> head(ip1[ip1$flag5==0,c(2,1,3,4,7,8,5,6,9)])
PID06

PID04 lsex lage sex04 age04 flag3 flag4 flag5

1<NA> 10101030141501

1

57

1

50

1

1

0

59

<NA> 10106070171305

2

21

2

23

1

1

0

125 10108030011401 10108030011401

2

44

2

42

1

1

0

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129 10108030011501 10108030011501

1

46

1

44

1

1

0

174 10109330101501 10109330101901

1

42

2

49

1

0

0

176 10109330101502 10109330101902

2

41

2

18

1

0

0

Interpretation of errors:The pair of PID06=10109330101502 and PID04=10109330101902 is not proper.



For PID04=10109330101902, there are two candidates, PID06=10109330101502 and PID06=
10109330101902 (condition 4).

The pair of PID04=10109330101902 and PID06=10109330101902 is proper.

Out of 84 records regarded as duplicated under the condition 4, that is, given flag4=0, a half of
the 84 records are identified with the records with the same PID04 and the other half are not
identified.
Therefore, data set ip2, a subset of records with flag5=1 in ip1 is candidates for panel at the
moment.

> t<-addmargins(table(ip1$flag4,ip1$flag5))[c(3,1,2),c(3,2,1)]> rownames(t)<-c("Linked with MEM04","Duplicated","Not duplicated")> colnames(t)<-c("Total","Identified","Not identified")> t
Total Identified Not identified
Linked with MEM04 18348
DuplicatedNot duplicated

17723

625

84

42

42

18264

17681

583

> ip2<-subset(ip1,flag5==1)> dim(ip2)[1] 17723



11

Again, applied the condition 4 for ip2 and found one duplication. We cannot distinguish fromdata of sex and age because they both satisfy the condition 5. We have to check householdcomposition.

> sum(duplicated(ip2$PID04))[1] 1> ip2[ip2$PID04==ip2[duplicated(ip2$PID04),"PID04"],c("PID06","ID06","PID04","ID04")]

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PID06

ID06

PID04

ID04

17598 81907070011302 819070700113 81907070011502 819070700115
17599 81907070011502 819070700115 81907070011502 819070700115# Household composition of ID04=="819070700115"> MEM04[MEM04$ID04=="819070700115",c("PID04","ID04","m1ac2","m1ac5")]
PID04

ID04 m1ac2 m1ac5

38860 81907070011504 819070700115

2

938861 81907070011503 819070700115

1

1138862 81907070011502 819070700115

23038863 81907070011501 819070700115

1

30# Household composition of ID06=="819070700113"> MEM06[MEM06$ID06=="819070700113",c("PID06","ID06","m1ac2","m1ac5")]
PID06

ID06 m1ac2 m1ac537561 81907070011301 819070700113

1

3437562 81907070011302 819070700113

2

3237563 81907070011303 819070700113

2

2# Household composition of ID06=="819070700115"> MEM06[MEM06$ID06=="819070700115",c("PID06","ID06","m1ac2","m1ac5")]
PID06

ID06 m1ac2 m1ac5

37571 81907070011501 819070700115

1

3237572 81907070011502 819070700115

2

3237573 81907070011503 819070700115

1

1337574 81907070011504 819070700115

2

11



As a result of comparing household composition, the pair of PID06=”81907070011502” andPID04=”81907070011502”

isright.

The

pair

of

PID06=”81907070011302”

PID04=”81907070011502” should be dropped from ip2.
> ip2[ip2$PID04==ip2[duplicated(ip2$PID04),"PID04"],c("PID06","ID06","PID04","ID04")]
PID06

ID06

PID04

ID04

17598 81907070011302 819070700113 81907070011502 819070700115←Should be dropped!

17599 81907070011502 819070700115 81907070011502 819070700115←Right!

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> ip2[!is.na(ip2$PID06)&ip2$PID06=="81907070011302",c("PID06","ID06","PID04","ID04")]
PID06

ID06

PID04

ID04

17598 81907070011302 819070700113 81907070011502 819070700115> ip3<-ip2[is.na(ip2$PID06)|ip2$PID06!="81907070011302",]> dim(ip3)[1] 17722

11> sum(duplicated(ip3$PID04))[1] 0Condition 6: ID06 in LIN06 should be the same as ID06 in hp.



As for LIN06, a set of ID06 is the same as a set of ID06 in HLIN.

> table(unique(LIN06$ID06)==HLIN$ID06)
TRUE
4298



As for ip3, the number of unique ID06 is 4,167.

Appended flag6 to hp2.

> ip3.ID06<-unique(ip3$ID06)> length(ip3.ID06)[1] 4167> hp2["flag6"]<-ifelse(is.element(hp2$ID06,ip3.ID06),1,0)> head(hp2)
ID06

ID04 flag6

1101010301415 101010301415

1

2101010301419 101010301419

1

16 101012301813 101012301813

1

31 101051100213 101051100213

1

32 101051100215 101051100215

1

33 101051100219 101051100219

1> addmargins(table(hp2$flag6))
0

1 Sum

40 4153 4193

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Defined hp3 as a subset of hp2 satisfying the condition 6.

> hp3<-subset(hp2,flag6==1)> dim(hp3)[1] 4153

3

Condition 7: If a person in LIN06 was a household member in VHLSS 2006, that is, PID06 was definedin LIN06, the person with the PID06 should be in MEM06.

(Satisfied!)If a person in LIN06 is a household member of VHLSS 2006, the person belongs to MEM06.

> t<-table(is.na(LIN06$PID06),!is.element(LIN06$PID06,MEM06$PID06))> rownames(t)<-c("Member in LIN06","No")> colnames(t)<-c("Belong to MEM06","No")> tMember in LIN06

Belong to MEM06

No

17076

0

0

1716

No

Condition 8: There should be no duplicated PID06 in LIN06. (Satisfied!)> sum(duplicated(subset(LIN06,m1bc6==1)$PID06))[1] 0Condition 9: The person’s sex and age in LIN06 and MEM06 should be identical.

> df1<-ip3[c("PID04","PID06","lsex","lage")]> dim(df1)[1] 17722

4> head(df1)
PID04

PID06 lsex lage

2 10101030141502 10101030141501

2

46

3 10101030141503 10101030141502

1

26

4 10101030141504 10101030141503

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5 10101030141505 10101030141504

1

18

6 10101030141901 10101030141901

252

7 10101030141902 10101030141902

1

24> df2<-MEM06[c("PID06","m1ac2","m1ac5")]> colnames(df2)<-c("PID06","sex06","age06")> dim(df2)[1] 18419

3> head(df2)
PID06 sex06 age06
1 10101030141501

248

2 10101030141502

1

28

3 10101030141503

1

26

4 10101030141504

1

20

5 10101030141901

2

54

6 10101030141902

1

26> cond9<-merge(df1,df2,by="PID06",all.x=T)> dim(cond9)[1] 17722

6> head(cond9)
PID06

PID04 lsex lage sex06 age06

1 10101030141501 10101030141502

2

46

2

48

2 10101030141502 10101030141503

1

26

1

28

3 10101030141503 10101030141504

1

24

1

26

4 10101030141504 10101030141505

1

18

1

20

5 10101030141901 10101030141901

2

52

2

54

6 10101030141902 10101030141902

1

24

1

26



Out of 17,722, the number of records satisfying the condition 9 is 16,103.

# Appended flag9 for inconsistent sex and age> cond9["flag9"]<-ifelse(cond9$lsex==cond9$sex06 &+ abs(cond9$lage+2-cond9$age06)<=2,1,0)> table(cond9$flag9)

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0

1

18 16102# List of not identified records in ip3> subset(cond9,flag9==0)
PID06

PID04 lsex lage sex06 age06 flag93644

20705090041302 20705090041302

2

38

2

43

0

3708

20709010061403 20709010061404

1

16

1

21

0

7990

41101490141403 41101490141405

2

36

1

38

0

8064

41107310111305 41107310111305

2

5

2

10

0

10125 60321020051303 60321020051303

2

11

1

13

0

10126 60321020051304 60321020051304

2

5

1

7

0

10972 70109030301402 70109030301402

1

42

1

47

0

14571 81303050081404 81303050081406

2

8

1

16

0

14687 81311010131504 81311010131504

1

21

2

22

0

14726 81313050161303 81313050161905

2

21

1

23

0

14929 81525030151901 81525030151901

1

47

1

59

0

14931 81525030151903 81525030151903

1

28

1

36

0

15029 81601090041501 81601090041502

2

46

2

52

0

15030 81601090041502 81601090041503

2

43

2

50

0

15091 81607130081301 81607130081301

1

36

1

43

0

15190 81611070141504 81611070141504

2

8

1

10

0

16064 82309150131501 82309150131501

1

41

1

46

0

16077 82311070171503 82311070171506

2

15

2

27

0



Generated data frame ip4 consisted of pairs of PID06 and PID04 as a subset of cond9 with
flag9=1. Each pair of PID06 and PID04 identifies uniquely records in MEM06 and MEM04.

> ip4<-subset(cond9,flag9==1)[c("PID06","PID04")]> ip4["ID06"]<-substr(ip4$PID06,1,12)> ip4["ID04"]<-substr(ip4$PID04,1,12)> dim(ip4)[1] 16102

4> head(ip4)
PID06

PID04

ID06

ID04

1 10101030141501 10101030141502 101010301415 101010301415

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2 10101030141502 10101030141503 101010301415 101010301415
3 10101030141503 10101030141504 101010301415 101010301415
4 10101030141504 10101030141505 101010301415 101010301415
5 10101030141901 10101030141901 101010301419 101010301419
6 10101030141902 10101030141902 101010301419 101010301419

Comparing household size of individual panel data in 2004 with HLD04, 2,821 households areinconsistent but in 1,344 households the number of records of panel data within the household
is smaller than the household size of the same household surveyed in 2004.

> t<-tapply(ip4$PID04,ip4$ID04,length)> ip4.hhsz<-data.frame(ID04=names(t),ip.hhsz=as.vector(t),row.names=NULL)> ip4.hhsz2<-merge(ip4.hhsz,HLD04[c("ID04","tsnguoi")],by="ID04",all.x=T)> head(ip4.hhsz2)
ID04 ip.hhsz tsnguoi
1 101010301415

4

5

2 101010301419

3

3

3 101012301813

44

4 101051100213

2

6

5 101051100215

6

6

6 101051100219

4

4> table(ip4.hhsz2$ip.hhsz==ip4.hhsz2$tsnguoi,useNA="ifany")
FALSE TRUE
1344 2821

Finally, the individual-level panel data consisted of 11,411 pairs of (PID06, PID04) weregenerated, which is consistent with the household size in 2004.

> ip4.hhsz["flag"]<-ifelse(ip4.hhsz2$ip.hhsz==ip4.hhsz2$tsnguoi,1,0)> table(ip4.hhsz$flag)
011344 2821> ip5.ID04<-subset(ip4.hhsz,flag==1)$ID04> length(ip5.ID04)[1] 2821

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> ip5<-subset(ip4,is.element(ip4$ID04,ip5.ID04))> dim(ip5)[1] 11411

4> head(ip5)
PID06

PID04

ID06

ID04

5

10101030141901 10101030141901 101010301419 101010301419

6

10101030141902 10101030141902 101010301419 101010301419

710101030141904 10101030141903 101010301419 101010301419

810101230181301 10101230181301 101012301813 101012301813

910101230181302 10101230181302 101012301813 101012301813

10 10101230181303 10101230181303 101012301813 101012301813

Example:Panel data ID06=”101010301419” and ID04=”101010301419”
Household members of ID04=="101010301419" in 2004 and 2006
MEM04:> d<-MEM04[MEM04$ID04=="101010301419",c(51,10:15)]> colnames(d)<-c("PID04","sex","relation","month","year","age","marital")> d
PID04 sex relation month year age marital
10 10101030141901

2

1

1 195252

3

11 10101030141902

1

3

1 198024

1

12 10101030141903

1

3

2 198717

1

MEM06:> d1<-MEM06[MEM06$ID06=="101010301419",c(23,7:12)]> colnames(d1)<-c("PID06","sex","relation","month","year","age","marital")> d1
PID06 sex relation month year age marital
5 10101030141901

2

1

1 1952 54

3

6 10101030141902

1

3

1 1980 26

2

7 10101030141903

2

3

11 1980 25

2

8 10101030141904

1

3

2 1987 19

1

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In the above case, the household consisted of a widowed mother and two sons of age 24 and 17
in 2004. The elder son got married during 2004 and 2006, and his wife was surveyed in 2006.



Topic: Household size in 2004

Comparing LIN06 with HLD04, the household sizes in 2004 of LIN06 are consistent in 4,066 households.
> t<-tapply(LIN06$PID04,LIN06$ID04,length)> ip.hhsz<-data.frame(ID04=names(t),ip.hhsz=as.vector(t),row.names=NULL)> ip.hhsz2<-merge(ip.hhsz,HLD04[c("ID04","tsnguoi")],by="ID04",all.x=T)> head(ip.hhsz2)
ID04 ip.hhsz tsnguoi
1 101010301415

5

52 101010301419

3

3

3 101012301813

44

4 101051100213

6

6

5 101051100215

6

6

6 101051100219

4

4> table(ip.hhsz2$ip.hhsz==ip.hhsz2$tsnguoi,useNA="ifany")
FALSE TRUE <NA>
140 066



79

Topic 2: Difficulties of matching individual data

Case of ID04=="101010301415"
MEM04:> d<-MEM04[MEM04$ID04=="101010301415",c(51,10:15)]> colnames(d)<-c("PID04","sex","relation","month","year","age","marital")> d
PID04 sex relation month year age marital
5 10101030141501

1

1

5 195450

2

6 10101030141502

22

7 1957 46

2

7 10101030141503

1

3

11 1977 26

1

8 10101030141504

1

3

3 1980 24

1

9 10101030141505

1

3

3 1986 18

1

LIN06:

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> d1<-LIN06[LIN06$ID04=="101010301415",c("PID04","m1bc4","m1bc5","m1bc6")]> colnames(d1)<-c("PID04","sex","age","member in 2006")> d1<-d1[order(d1$PID04),]> d1
PID04 sex age member in 2006
5 10101030141501

1

57

2

1 10101030141502

2

46

1

3 10101030141503

1

26

1

2 10101030141504

1

24

1

4 10101030141505

1

18

1



In the above case, the age 57 of PID04=”10101030141501”in LIN06 should be read as 50.
It may be possible to revise errors manually in such way. However, how to make it programmable?

Example of usage at household level:
Changes of consumption expenditure per capita between 2004 and 2006
# Household-level panel> hp5<-ip5[!duplicated(ip5$ID06),c("ID06","ID04")]> HLD06["pcexp06"]<-HLD06$chidsbq> HLD04["pcexp04"]<-HLD04$chidsbq# Linked the pcexp data of HLD06 and HLD04> pcexp.panel<-merge(hp5,HLD06[c("ID06","pcexp06")])> pcexp.panel<-merge(pcexp.panel,HLD04[c("ID04","pcexp04")])> dim(pcexp.panel)[1] 2821

4# Generated the variable of increasing rate of pcexp between 2004 and 2006.
> pcexp.panel["rate"]<-(pcexp.panel$pcexp06/pcexp.panel$pcexp04-1)*100> head(pcexp.panel)
ID04

ID06 pcexp06 pcexp04

rate

1 101010301419 101010301419

1226

753 62.81541

2 101012301813 101012301813

1111

814 36.48649

3 101051100215 101051100215

430

373 15.28150

4 101051100219 101051100219

942

494 90.68826

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5 101053301214 101053301214

778

554 40.43321

6 101053301215 101053301215

968

700 38.28571

Summary:Regarding VHLSS 2004 and 2006, household-level panel consisted of 4,193 household
identifiers was generated.



Also, individual-level panel consisted of 11,411 person identifier was generate.

To make panel more complete, data entry and data check of identification items should be
carried out more carefully.

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6. Population and Household

6.1 Ethnicity
Kinh people are the majority ethnic group of Vietnam.
Regarding household head’s ethnicity, 88% of households are Kinh. The share of Kinh
households is high especially in Red River Delta (99%), North Central (91%), South Central
Coast (95%), South East (92%) and Mekong River Delta (93%).
On the other, the share of Kinh is only 11% in rural of North West.

> d<-ttchung[c("ID","wt","tinh","ttnt","tsnguoi","dantoc","phdich")]# Generated the variable kinh: 1=Kinh, 2=Non-kinh> d["kinh"]<-ifelse(d$dantoc==1,1,2)> table(d$kinh)
127745 1444# Weighted number of households by region and urban/rural> denominator<-addmargins(tapply(d$wt,list(substr(d$tinh,1,1),d$ttnt),sum,na.rm=T))# Weighted number of Kinh households by region and urban/rural> d1<-subset(d,kinh==1)> numerator<-addmargins(tapply(d1$wt,list(substr(d1$tinh,1,1),d1$ttnt),sum,na.rm=T))# Share of Kinh households by region and urban/rural> share<-round(numerator/denominator*100,1)[c(9,1:8),c(3,1,2)]> colnames(share)<-c("Total","Urban","Rural")> rownames(share)<-c("Vietnam",region.name)> shareTotal Urban Rural
Vietnam

87.8 94.3

85.3

Red River Delta

99.4 100.0

99.2

North East

62.4 85.7

55.8

North West24.2 84.6

11.1

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North Central

91.2 98.2

90.0South Central Coast 94.8 98.7

93.1

Central Highlands

71.1 86.2

64.3

South East

92.1 92.4

91.6a

Mekong River Delta

93.2 95.0

92.7

Out of 1,444 non-kinh sample households, 381 households needed the interpretation service
when conducting the survey.
It suggests that about one fourth non-Kinh households have communication problem with Kinh
people.

> m<-tapply(d$wt,list(d$kinh,d$phdich),length)> m[is.na(m)]<-0> m<- addmargins(m)[c(3,1,2),c(3,1,2)]> rownames(m)<-c("Total","Kinh","Non-kinh")> colnames(m)<-c("Total","Interpretation required","No")> m
Total Interpretation required

No

Total

9189

381 8808

Kinh

7745

0 7745

Non-kinh

1444

381 1063

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6.2 Religion
Ethnicity and religion are the very important factors when analyzing social and cultural structure of
the country.

In VHLSS, question on religion is not found in the questionnaire.

According to 2009 Population Census, only a small fraction of the Vietnamese adheres toinstitutional religions;
Non religious,

80.8%

Buddhism,

9.3%

Christianity,

7.2%

Roman Catholic,6.7%

Protestant,

0.5%

Hòa Hảo,1.5%

Cao Đài,1.1%“Although according to a 1999 census most Vietnamese list themselves as having no religious
affiliation, religion, as defined by shared beliefs and practices, remains an integral part ofVietnamese life, dictating the social behaviours and spiritual practices of Vietnamese individuals in
Vietnam and abroad. The triple religion (Vietnamese: tam giáo), referring to the syncretic
combination of Mahayana Buddhism, Confucianism, and Taoism remains a strong influence on the
beliefs and practices of the Vietnamese, even if the levels of formal membership in these religious
communities may not reflect that influence. One of the most notable and universal spiritual practices
common to Vietnamese is ancestor veneration, a practice shared with Chinese and most other Asian
cultures. Practically all Vietnamese, regardless of formal religious affiliation, have an altar in theirhome or business where prayers are offered to their ancestors. These offerings and practices aredone frequently during important traditional or religious celebrations (e.g., death anniversaries), thestarting of a new business, or even when a family member needs guidance or counsel. Belief inghosts and spirits is common; it is commonly believed that failing to perform the proper rituals for
one's ancestors will cause them to become hungry ghosts (Vietnamese: ma đói).” (Wikipedia)

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6.3 Age heaping
Age heaping is popular in developing countries’ censuses and surveys.
The below chart shows the number of sample household members of VHLSS 2006 by sex and single
year of age.
It is understood that age heaping is not significant in Vietnam. The reason might be that they use
twelve signs of Chinese Zodiac in daily life, which is listed in the questionnaire.

Chart Number of sample household members by sex and single year of age (2006)

100
96
92
88
84
80
76
72
68
64
60
56
52
48
44
40
36
32
28
24
20
16
12
8
4
0

MALE

600

500

400

300

200

100
Male

FEMALE

0

100

200

300

FemaleVietnam VHLSS Manual - Overall and survey process (Version 1.1)

400

500

600

70

> t<-tapply(muc1a$PID,list(muc1a$m1ac5,muc1a$m1ac2),length)> dim(t)[1] 1042> colnames(t)<-c("Male","Female")> m<-data.frame(age=rownames(t),t)> head(m)age Male Female
0

0 224

229

1

1 265

224

2

2 254

220

3

3 239

267

4

4 238

210

5

5 273

253

> write.csv(m,"pop_by_age_sex.csv",row.names=F)

Note: Buffalo is the second symbol, taking place of Ox in the Chinese zodiac.
Also the fourth symbol of Rabbit is replaced with Cat.

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6.4 LiteracyIn section 2A (Education) of the questionnaire, literacy was asked for those who finished 4-th
grade or below, and never go to school. Those who finished 5-th grade (primary school) or
upward are regarded as literacy.

Generated the variable literate for all household members
1: literate
2: Illiterate> d<-muc2a> literate<-ifelse(d$m2ac1>=5|d$m2ac2==1,1,2)# Number of records by literacy> table(literate)
literate1

233211 5860

Literacy rate of Vietnam is 86%.

> round(prop.table(tapply(d$wt,literate,sum,na.rm=T))*100,1)
1

2

86.1 13.9

Merged literate with MUC1A, and appended data of urban/rural and ethnicity in TTCHUNG.

> df<-merge(cbind(muc1a,literate),ttchung[,c("ID","ttnt","dantoc")],key="ID",all.x=T)> df["kinh"]<-ifelse(df$dantoc==1,1,2)Vietnam VHLSS Manual - Overall and survey process (Version 1.1)

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

Literacy rate by region and urban/rural
Literacy rate is low in rural of North West (67%) and rural of Central Highlands (77%).

> denominator<-addmargins(tapply(df$wt,list(substr(df$tinh,1,1),df$ttnt),sum,na.rm=T))> d2<-subset(df,literate==1)> numerator<-addmargins(tapply(d2$wt,list(substr(d2$tinh,1,1),d2$ttnt),sum,na.rm=T))> rate<-round(numerator/denominator*100,1)[c(9,1:8),c(3,1,2)]> colnames(rate)<-c("Total","Urban","Rural")> rownames(rate)<-c("Vietnam",region.name)> rateTotal Urban Rural
Vietnam

86.1 90.0

84.7

Red River Delta

90.0 91.9

89.4

North East

85.8 92.3

84.2

North West70.5 90.7 67.2

North Central

87.2 92.6

86.3

South Central Coast 87.4 89.5

86.5

Central Highlands

79.7 87.3 76.5

South East

88.2 90.6

85.7

Mekong River Delta

83.6 85.8

83.1

Literacy rate by ethnicity
Literacy rate of non-Kinh people is 15% points lower than Kinh people.
Range of Kinh people’s literacy rates by region is narrower compared to non-Kinh people.

> denominator<-addmargins(tapply(df$wt,list(substr(df$tinh,1,1),df$kinh),sum,na.rm=T))> d2<-subset(df,literate==1)> numerator<-addmargins(tapply(d2$wt,list(substr(d2$tinh,1,1),d2$kinh),sum,na.rm=T))> rate<-round(numerator/denominator*100,1)[c(9,1:8),c(3,1,2)]> colnames(rate)<-c("Total","Kinh","Non-kinh")> rownames(rate)<-c("Vietnam",region.name)> rateTotal Kinh Non-kinhVietnam

86.1 88.5

72.3

Red River Delta

90.0 90.0

88.9

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North East

85.8 90.3

79.8

North West70.5 93.3

65.3

North Central

87.2 88.4

76.7

South Central Coast 87.4 88.9

64.5

Central Highlands

79.7 90.4

59.2

South East

88.2 89.2

77.9

Mekong River Delta

83.6 84.8

69.2

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6.5 Polygamy
Summary of Marriage System

VHLSS allows the cases of multiple spouses. Examples of micro data of VHLSS 2006 areshown later.

The enumerator’s manual “Operational Handbook of VHLSS 2004” describes household
members as follows;“If the household owner has many wives, then enumerators write the names of the first wife andher children, then the second wife and her children and go on in this way.” (at page 21)In Vietnam, men belonging to the old generation (born around 1930s or earlier) usually had
more than one wife. That was the customs of those days.



According to the Law of Marriage and Family, which has begun since 1959 in North Vietnam at
the time and was applied for South Vietnam after the end of War in 1975, one husband has one
wife only. Until now, this provision is still in effect. Therefore, people got married before theimplementation of the Law could have more spouses if their marriage was carried out before
that time.



Vietnam has the Law of Family Register System. Accordingly, the second spouse cannot beregistered as an official spouse at the Local Authority. However, VHLSS is flexible and still
accepts the second spouse if the family answer that they are spouses.

VHLSS 2006 – Example of polygamyData file MUC1A: List of household members> head(muc1a)
tinh huyen xa diaban hoso matv m1ac2 m1ac3 m1ac4a m1ac4b m1ac5 m1ac6 m1ac7
1

101

01 03

014

15

1

2

1

7

1957

48

3

12

2

101

01 03

014

15

2

1

3

11

1977

28

1

12

3

101

01 03

014

15

3

1

3

3

1980

26

1

12

4

101

01 03

014

15

4

1

3

3

1986

20

1

12

5

101

01 03

014

19

1

2

1

1

1952

54

3

12

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6

101

01 03

014

19

2

m1ac8 m1ac9 m1ac10a m1ac10b

1

3
ID

1

1980

wt

xaid

26

2

12
PID

1

1

NA

NA

NA 101010301415 3107.318 1010103 10101030141501

2

1

NA

NA

NA 101010301415 3107.318 1010103 10101030141502

3

1

NA

NA

NA 101010301415 3107.318 1010103 10101030141503

4

1

NA

NA

NA 101010301415 3107.318 1010103 10101030141504

5

1

NA

NA

NA 101010301419 3107.318 1010103 10101030141901

6

1

NA

NA

NA 101010301419 3107.318 1010103 10101030141902



Generated subset of spouse.

> df<-muc1a[,c(18,6:8,11,12)]> colnames(df)[3:6]<-c("sex","relation","age","marital")> head(df)ID matv sex relation age marital
1 101010301415

1

2

1 48

3

2 101010301415

2

1

3 28

1

3 101010301415

3

1

3 26

1

4 101010301415

4

1

3 20

1

5 101010301419

1

2

1 54

3

6 101010301419

2

1

3 26

2

> spouse<-subset(df,relation==2)Made frequency table of the number of spouse within the household.
The results show that there are 4 sample households with two spouses.

> ns<-(tapply(spouse$ID,spouse$ID,length))> table(ns)
ns
1

2

7340

4



Example of household with multiple spouses within the household# IDs of such households> names(ns)[ns==2][1] "103117100913" "105253900215" "109060100814" "201150700215"> df[df$ID=="103117100913",]

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ID matv sex relation age marital
1333 103117100913

1

1

1

80

2

1334 103117100913

2

2

2

78

2

1335 103117100913

3

2

2

63

2

1336 103117100913

4

1

3

27

2

1337 103117100913

5

2

3

25

2

1338 103117100913

6

1

6

6

NA

1339 103117100913

7

1

6

3

NA

> df[df$ID=="105253900215",]
ID matv sex relation age marital
3137 105253900215

1

1

1

42

2

3138 105253900215

2

2

2

34

2

3139 105253900215

3

2

3

11

NA

3140 105253900215

4

2

2

30

2

3141 105253900215

5

1

3

5

NA

3142 105253900215

6

2

3

2

NA

> df[df$ID=="109060100814",]
ID matv sex relation age marital
4692 109060100814

1

1

1

47

2

4693 109060100814

2

2

2

49

2

4694 109060100814

3

2

2

46

2

4695 109060100814

4

1

3

14

1

4696 109060100814

5

2

3

12

NA

> df[df$ID=="201150700215",]
ID matv sex relation age marital
7705 201150700215

1

1

1

33

2

7706 201150700215

2

2

2

42

2

7707 201150700215

3

2

2

43

2

7708 201150700215

4

1

3

18

2

7709 201150700215

5

2

3

11

NA

7710 201150700215

6

2

3

7

NA

7711 201150700215

7

2

3

15

2

7712 201150700215

8

1

7

13

1

7713 201150700215

9

2

7

8

NA

7714 201150700215

10

1

7

9

NA

7715 201150700215

11

2

3

4

NA

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777716 201150700215

12

1

3

2

NA

Among 4 cases, the first case with spouses' ages 63 years old and 78 years. Remaining 3 cases,
location of which are Red River Delta and North East regions, are spouses with ages around 40-50
years old.

According to Ms. Van,
“Therefore, the first case may belong to the custom of old generation. The other 3 cases may be they
violate the Law, but with some special reasons (for example, to have children if the first wife could
not produce kids, ...). Because of the preference of son, if the wife has the daughters only, the man
could consider to have the second wife, though it is not permitted by the Law.”

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6.6 Relationship to the household head
Summary

According to “Operational Handbook of VHLSS 2004”, the variable of person number MATV
follows the next rule;

The code of relationship differs between VHLSS 2002 and 2004/2006.

For VHLSS 2002
Code

Relationship to head

1

Head

2

Wife/husband

3

Child

4

Child in law

5

Parents6

Sister/brother

7Grandfather/grandmother

8Grandchild

9

Other relationship

For VHLSS 2004 and 2006
Code

Relationship to head

1

Head

2

Spouse3Children

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4

Parents

5

Grandfather/Grandmother6Grandchild

7

Other relation

9

Missing

The code for “Child in law”, that is, son’s wife or daughter’s husband, seems not to be clearly
described in the manual of VHLSS 2004.

For this, Ms. Van describes, “Explaining for enumerator is that code 3 is the children of thehead. In other words, code 3 is for the one who is blood/adopted children of the head. Code 7 isfor other relationship.”

However, micro data of VHLSS 2006 suggests that there are two cases of code=3 (child) and
code=7 (other relation). Next are examples.
> df[df$ID=="715032100614",]
ID matv sex relation age marital
30336 715032100614
1
1
1 50
2
30337 715032100614
2
2
2 53
2
30338 715032100614
3
1
3 18
1
30339 715032100614
4
1
3 15
1
30340 715032100614
5
2
3 14
1
30341 715032100614
6
1
3 29
2
30342 715032100614
7
2
3 26
2
30343 715032100614
8
1
6
7
NA
30344 715032100614
9
1
6
5
NA
30345 715032100614
10
1
6
2
NA
30346 715032100614
11
1
6
1
NA
30347 715032100614
12
2
3 26
2
30348 715032100614 131
3 28
230349 715032100614
14
1
6
3
NA
30350 715032100614
15
1
3 21
2
30351 715032100614 162
3 21230352 715032100614
17
2
4 76
3

In the above case of relationship code=3, MATV=7 and 16 are regarded as sons’ wives, and
MATV=13 is a daughter’s husband.

> df[df$ID=="303090700214",]
ID matv sex relation age marital
14437 303090700214
1
1
1 56
2

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14439 303090700214
14440 303090700214
14441 303090700214
14442 303090700214
14443 303090700214
14444 303090700214
14445 303090700214
14446 303090700214
14447 303090700214
14448 303090700214
14449 303090700214
14450 303090700214
14451 303090700214
14452 303090700214
14453 303090700214

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

2
1
2
1
2
2
2
2
1
2
1
1
1
2
1
2

2
3
3
3
7
6
6
6
3
7
6
6
6
7
3
7

53
16
14
22
23
6
4
1
20
22
4
2
0
22
18
20

2
1
1
2
2
NA
NA
NA
2
2
NA
NA
NA
2
2
2

In the above case of relationship code=7, MATV=6, 11 and 17 are regarded as sons’ wives.
MATV=15 is supposed to be a son’s wife while the son (her husband) is away from home.

In summary,Types of code for spouse of son/daughter

Number of sample households

Case 1: code 3 was applied for daughter’s husband.

115

Case 1: code 3 was applied for son’s wife.

668

Case 2: code 7 was applied for daughter’s husband.

36

Case 2: code 7 was applied for son’s wife.

289# Made use of data frame ft, which was created in 6.7.1.
> child.couple<-function(df,k,j){# return 2 if the code of son/daughter’s spouse is 3.
# return 4 if the code of son/daughter’s spouse is 4.
+ flag<-0+ if(j>=2&df[k,j+27]==2&df[k,j+28]==2&df[k,j+44]*df[k,j+45]==2&+ (df[k,j+10]==3&df[k,j+11]==3)){+ flag<-2+ }else if(j>=2&df[k,j+27]==2&df[k,j+28]==2&df[k,j+44]*df[k,j+45]==2&+ (df[k,j+10]==3&df[k,j+11]==7)){+ flag<-4+ }+ return(flag)

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+ }

> childinlaw<-data.frame(ID=ft$ID,matrix(0,nrow=nrow(ft),ncol=5))> colnames(childinlaw)<-c("ID","m3","f3","m7","f7","other")> for(k in 1:nrow(ft)){+ for(j in 1:ft[k,2]){# Add 1 to childinlaw() if the spouse if female+ x<-child.couple(ft,k,j)+(ft[k,j+45]==2)+ if(x>0) childinlaw[k,x]<-childinlaw[k,x]+1+ }+ }> sum(childinlaw$m3)[1] 115> sum(childinlaw$f3)[1] 668> sum(childinlaw$m7)[1] 36> sum(childinlaw$f7)[1] 289> sum(childinlaw$other)
[1] 0



When discussing the family type, the sequence of person number MATV is indispensable fordetermining a couple.



The two household members within the household who satisfy the next conditions arepresumed as a couple in VHLSS 2006.
1) Their person numbers (MATV) are consecutive.
2) Their marital statuses are married.
3) Their sexes are opposite.
4) Their relationship codes are the same, orthe first person’s code is 3 (Child) and the second person’s code is 7 (Other relation).

“Brother/sister”, “other relatives” and “non-relatives” are not distinguished clearly in thequestionnaire.

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Background of the relationship codes
It is said that the traditional Vietnamese family is patriarchal, especially among Kinh people.
Traditional values of Vietnamese lifestyle were deeply affected by Confucian ethics. During
thousand years the Chinese invaded and maintained control Vietnam, Vietnamese culture waspermeated by their Confucian philosophical beliefs.

Regarding the reason why the response categories of relationship was simplified in VHLSS
2004/2006, Ms. Van replied, “We distinguish the direct relationship within family. The direct
relationship with the head is important to be distinguished clearly.”

For reference:
Relationship to the household head in Population Census
In Vietnam 1999 Population Census, the response categories of the question on the relationshipto the household head are as follows. Code 3 “Son/daughter” is limited only for biologicalchild.
1 Household head
2 Spouse
3 Son/Daughter
4 Parents
5 Other

The definition of each response category is described in the Enumerators’ manual;

Question 2: What is the relationship of (name) to the household head?
If respondent is the head of household, interviewers fill in code "1". For the rest of household members,interviewers fill in corresponding codes 2 to 5 depending on their relationship with the head of household.
Definition of each relationship to head of household is, as follow:
Head of household: is a household representative who is recognized by all members of household.
In households where there are only children because their father and mother work in ministry of defense
and/or ministry of public security (enumerating in special plan), head of household is the oldest child.

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For students of professional schools or college, students of stay-in general schools, interviewers registerthem by their room/apartment as one household. Head of household is a person who is recognized by all
household's members. Other household's members would have "other" relationship to the head.
Spouse: if a person has two spouses or more (wives or husbands) and lives together with them in one
household, these persons are all considered as spouses (wives or husbands) of the head of household (seequestion 15). Interviewers select code "2" if a person is a spouse of the head of household.
Biological child: is child by birth of the head of household. Interviewers select code "3" if a person is definedas biological child.
Parent: Father (mother) of the head of household includes biological father (mother), adopted father(mother), father-in-law and mother-in-law of the head of household.
Other relationship: Interviewers should report specific relationship with the head of household, such as
stepchild of the head of household, daughter-in-law/son-in-law, adopted child, other brother, older sister,
younger brother/sister, aunt, uncle, grandparent, grandchild, domestic employees, maid, friend, etc. A
person is defined as "other relationship" with the head of household has code "5".

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6.7 Family type
It is said that the Vietnamese household traditionally followed the extended multi-generational
pattern. The parents, their sons and their wives, their children, and unmarried siblings usuallyconstituted a Vietnamese household.

Objectives:
1) To determine the family type of each household.
2) To analyze living standards by family type.

6.7.1 Family type based on the number of persons by generation

Data file for determining the family type (1)Designed household-level data file consist of number of persons by relationship as well as dataof relationship, marital status, sex and age of each person.

Layout of record
ID

Number of persons by relationship

T

1



2

3

4

5

6

7

x

9

Relationship of each

Maritalpersoneach person

1

2

3

…

17

1

2

status3…

ofSex of each person

Age of each person

17

1

1

Verified MUC1A> df<-muc1a[,c(18,6:8,11,12)]> colnames(df)[3:6]<-c("sex","relation","age","marital")> table(df$sex,useNA="ifany")1219157 19914> table(df$relation,useNA="ifany")
1

2

3

4

5

6

7

9

9189 7348 18275

952

45

2203

1057

2

> table(subset(df,age>=13)$marital,useNA="ifany")

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2

3

…

17

2

3

…

17

85

1

2

3

4

5

10880 18029 2028

265

135

> table(is.na(df$age))
FALSE
39071



Created data frame ft: family type

# household size
> hhsize<-tapply(df$ID,df$ID,length)> ft<-data.frame(ID=names(hhsize),hhsz=hhsize,row.names=NULL)# Number of persons by relationship> for(j in c(1:7,9)){+ d<-subset(df,relation==j)+ nj<-tapply(d$ID,d$ID,length)+ dj<-data.frame(ID=names(nj),nj)+ ft<-merge(ft,dj,by.x="ID",by.y="ID",all.x=T)+ colnames(ft)[ncol(ft)]<-paste("n",j,sep="")+ }> dim(ft)[1] 918910> head(ft)ID hhsz n1 n2 n3 n4 n5 n6 n7 n9
1 101010301415

4 1 NA 3 NA NA NA NA NA

2 101010301419

4 1 NA 3 NA NA NA NA NA

3 101010301424

4 1 NA NA NA NA NA

4 101010901913

2 1

1 NA NA NA NA NA NA

5 101010901915

3 1

1 1 NA NA NA NA NA

6 101010901919

3 1

1 1 NA NA NA NA NA

3 NA> ft.old<-ft# Relationship, marital status, sex and age by person number> for(j in 1:17){+ d<-subset(df,matv==j)[,c(1,4,6,3,5)]+ colnames(d)<-c("ID",paste(c("r","m","s","a"),j,sep=""))

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+ ft<-merge(ft,d,by.x="ID",by.y="ID",all.x=T)+ }> dim(ft)[1] 9189

78> ft.old2<-ft> colnames(ft)[1] "ID""hhsz" "n1"

"n2"

"n3"

"n4"

"n5"

"n6"

"n7"

"n9"

[11] "r1"

"m1"

"s1"

"a1"

"r2"

"m2"

"s2"

"a2"

"r3"

"m3"

[21] "s3"

"a3"

"r4"

"m4"

"s4"

"a4"

"r5"

"m5"

"s5"

"a5"

[31] "r6"

"m6"

"s6"

"a6"

"r7"

"m7"

"s7"

"a7"

"r8"

"m8"

[41] "s8"

"a8"

"r9"

"m9"

"s9"

"a9"

"r10" "m10" "s10""a10"[51] "r11""m11" "s11""a11""r12" "m12""s12" "a12" "r13""m13"[61] "s13""a13" "r14""m14""s14" "a14""r15" "m15" "s15""a15"[71] "r16""m16" "s16""a16""r17" "m17""s17" "a17"# Ordered the variables as designed> ft<-ft[,c(1:10,seq(11,75,by=4),seq(12,76,by=4),seq(13,77,by=4),seq(14,78,by=4))]> colnames(ft)[1] "ID""hhsz" "n1"

"n2"

"n3"

"n4"

"n5"

"n6"

"n7"

"n9"

[11] "r1"

"r2"

"r3"

"r4"

"r5"

"r6"

"r7"

"r8"

"r9"

"r10"

[21] "r11""r12" "r13""r14""r15" "r16""r17" "m1"

"m2"

"m3"

[31] "m4"

"m5"

"m6"

"m7"

"m8"

"m9"

"m10" "m11" "m12""m13"[41] "m14""m15" "m16"

"m17"

"s1"

"s2"

"s3"

"s5"

"s6"

[51] "s7"

"s8"

"s9"

"s10"

"s11" "s12""s13" "s14" "s15""s16"[61] "s17"

"a1"

"a2"

"a3"

"a4"

"a6"

"a9"

[71] "a10""a11" "a12""a13""a14" "a15"

"a5"

"s4"

"a7"

"a8"

"a16" "a17"> head(ft[,c(1:16,28:33,45:50,62:67)])
ID hhsz n1 n2 n3 n4 n5 n6 n7 n9 r1 r2 r3 r4 r5 r6 m1 m2 m3 m4 m5 m6 s1 s2 s3
1 101010301415

4 1 NA 3 NA NA NA NA NA

1 3

3 3 NA NA 3

1 1

1 NA NA 21

1

2 101010301419

4 1 NA 3 NA NA NA NA NA

1 3

3 3 NA NA 3

2 2

1 NA NA 212

3 101010301424

4 1 NA NA NA NA NA

1 7

7 7 NA NA 3

2 2 NA NA NA 212

4 101010901913

2 1

1 NA NA NA NA NA NA

1 2 NA NA NA NA 2

2 NA NA NA NA 1

2 NA

5 101010901915

3 1

1 1 NA NA NA NA NA

1 2

3 NA NA NA 2

2 1 NA NA NA 122

6 101010901919

3 1

1 1 NA NA NA NA NA

1 2

3 NA NA NA 2

2 1 NA NA NA 2

1

2

3 NA

s4 s5 s6 a1 a2 a3 a4 a5 a6
1

1 NA NA 48 28 26 20 NA NA

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2

1 NA NA 54 26 25 19 NA NA

3

1 NA NA 74 28 24 1 NA NA

4 NA NA NA 77 74 NA NA NA NA
5 NA NA NA 48 39 20 NA NA NA
6 NA NA NA 62 59 21 NA NA NA> ft.old3<-ft> ft[is.na(ft)]<-0> head(ft[,c(1:16,28:33,45:50,62:67)])
ID hhsz n1 n2 n3 n4 n5 n6 n7 n9 r1 r2 r3 r4 r5 r6 m1 m2 m3 m4 m5 m6 s1 s2 s3
1 101010301415

4 1

0 3

0

0 0

0 0

1 3

3 3

0

0 3

1 1

1 0

0 2

1

1

2 101010301419

4 1

0 3

0

0 0

0 0

1 3

3 3

0

0 3

2 2

1 0

0 2

1

2

3 101010301424

4 1

0 0

0

0 0

3 0

1 7

7 7

0

0 3

2 2

0 0

0 2

1

2

4 101010901913

2 1

1 0

0

0 0

0 0

1 2

0 0

0

0 2

2 0

0 0

0 1

2

0

5 101010901915

3 1

1 1

0

0 0

0 0

1 2

3 0

0

0 2

2 1

0 0

0 1

2

2

6 101010901919

3 1

1 1

0

0 0

0 0

1 2

3 0

0

0 2

2 1

0 0

0 2

1

2

s4 s5 s6 a1 a2 a3 a4 a5 a6
1

1

0 0 48 28 26 20

0 0

2

1

0 0 54 26 25 19

0 0

3

1

0 0 74 28 24 1

0 0

4

0

0 0 77 74

0 0

0 0

5

0

0 0 48 39 20 0

0 0

6

0

0 0 62 59 21 0

0 0

Data file for determining the family type (2)Designed household-level data file consist of number of persons by generation as well as
number of couples by generation.

For each household;Generation based on the household head

Number of persons

Number of couples

1

Grandparents

p1

c1

2

Parentsp2

c2

3

Head/spousep3c3

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4Children

p4

c4

5

Grandchildren

p5

c5

6

Brother/sister

p6

c6

7

Other

p7

c7



Defined function couple(df, k, j)
df: data frame of family type, defined in the above
k: record number of df
j: person number of df

couple=1

if j-th and (j+1)-th persons are a couple

couple=0

else

The two household members within the household who satisfy the next conditions arepresumed as a couple in VHLSS 2006.
1) Their person numbers (MATV) are consecutive.
2) Their marital statuses are married.
3) Their sexes are opposite.
4) Their relationship codes are the same, orthe first person’s code is 3 (Child) and the second person’s code is 7 (Other relation).

> couple<-function(df,k,j){+ flag<-0+ if(j==1&df[k,j+11]==2){+ flag<-1+ return(flag)+ } else if(j>=2&df[k,j+27]==2&df[k,j+28]==2&df[k,j+44]*df[k,j+45]==2&+ (df[k,j+10]==df[k,j+11]|(df[k,j+10]==3&df[k,j+11]==7))){+ flag<-1+ }+ return(flag)+ }Examples;> couple(ft,2,2)[1] 1

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> couple(ft,5,1)[1] 1> couple(ft,1,1)[1] 0> couple(ft,3,1)[1] 0> couple(ft,3,2)
[1] 1



Defined function generation(df, k, j)
df: data frame of family type, defined in the above
k: record number of df
j: person number of df
generation: j-th person’s generation code defined in the above table> generation<-function(df,k,j){+ g<-7+ if(df[k,j+10]==1|df[k,j+10]==2) g<-3
+ else if(df[k,j+10]==3) g<-4
+ else if(df[k,j+10]==4) g<-2
+ else if(df[k,j+10]==5) g<-1
+ else if(df[k,j+10]==6) g<-5
+ # Child in law+ else if(df[k,j+10]==7&df[k,j+9]==3&couple(df,k,j-1)==1) g<-4
+ # Brother/sister+ flag<-0+ for(n in (j+1):df[k,2]){+ if(df[k,n+10]==5) flag<-flag+1+ } # end of for+ if(g==7&flag>0) g<-6
+ return(g)+ } # end of function

Remarks:
Code=6 (Brother/sister) is identified only if Grandparents exist in the household.

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A part of brothers/sisters might be grouped as code=7.

Created household-level data file ft2 consist of number of persons by generation as well as
number of couples by generation.

> ft2<-data.frame(ID=ft$ID,matrix(0,nrow=nrow(ft),ncol=14))> colnames(ft2)<-c("ID",paste("p",1:7,sep=""),paste("c",1:7,sep=""))> for(k in 1:nrow(ft)){+ for(j in 1:ft[k,2]){+ g<-generation(ft,k,j)+ ft2[k,g+1]<-ft2[k,g+1]+1+ ft2[k,g+8]<-ft2[k,g+8]+couple(ft,k,j)+ }+ }> head(ft2)
ID p1 p2 p3 p4 p5 p6 p7 c1 c2 c3 c4 c5 c6 c7
1 101010301415

0 0

1 3

0

0 0

0 0

0 0

0 0

0

2 101010301419

0 0

1 3

0

0 0

0 0

0 1

0 0

0

3 101010301424

0 0

1 0

0

0 3

0 0

0 0

0 0

1

4 101010901913

0 0

2 0

0

0 0

0 0

1 0

0 0

0

5 101010901915

0 0

2 1

0

0 0

0 0

1 0

0 0

0

6 101010901919

0 0

2 1

0

0 0

0 0

1 0

0 0

0

> ft2.old<-ft2# Generated the variable of hhsz: household size
> ft2["hhsz"]<-rowSums(ft2[,2:8])# Number of persons by generation> sapply(ft2[,2:8],sum)
p1

p2

p3

p4

p5

p6

p7

45

952 16537 18600 2203

4730

# Generated the variable of ct: total number of married couples with household> ft2["ct"]<-rowSums(ft2[,9:15])# Number of married couples by generation

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> sapply(ft2[,9:15],sum)
c1

c2

c3

c4

c5

c6

c7

2

115 7331 1109

3

0

40

# Frequency of sample households by total number of married couples within household> addmargins(table(ft2$ct))
0

1

2

3

4

5

Sum

1384 7068 687

43

6

1 9189Grouping sample household based on the number of persons by generation


Excluded 482 households with p7>0, and grouped 8,707 rest of households based on thenumber of household members by generation.

Two-generation household is the majority, which occupies 65%, followed by three-generation
household (19%) and one-generation household (10%).

Table Type of households by number of household members’ generations
Number ofHousehold consist of the below generations

Codegeneration
All households
1

2

3

4

Number ofPercentage

households

(%)

9,189

100.0

Total

h10

914

9.9

Single

h11

344

3.7

Plural members (head, spouse only)

h12

570

6.2

Plural members including siblings

h13

0

-

Total

h20

5,927

64.5

Head - Children

h21

5,855

63.7

Parents - Head

h22

72

0.8

Total

h30

1,779

19.4

Head – Children – Grandchildren

h31

1,074

11.7

Head - Grandchildren

h32

96

1.0

Parents – Head – Children

h33

606

6.6

Grandparents – Parents - Head

h34

0

-

Grandparents - Head

h35

3

0.0

Total

h40

84

0.9

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5

Parents – Head – Children - Grandchildren

h41

46

0.5

Parents – Head –Grandchildren

h42

4

0.0

Grandparents – Parents – Head - Children

h43

3

0.0

Grandparents –Head - Children

h44

31

0.3

h50

3

0.0

h60

482

5.2

Grandparents–(Parents)

–

Head

–

(Children)–

GrandchildrenxHouseholds with other relatives and/or non-relatives# Generated the variable code of household type in data frame ft2.
> d<-subset(ft2,p7==0)> d["code"]<-0> d["code"]<-ifelse(d$p3==1& d$hhsz==1,11,d$code)> d["code"]<-ifelse(d$p1==0& d$p2==0& d$p3>=2& d$p4==0& d$p5==0& d$p6==0,12,d$code)> d["code"]<-ifelse(d$p1==0& d$p2==0& d$p3>0& d$p4==0& d$p5==0&d$p6>0,13,d$code)> d["code"]<-ifelse(d$p1==0& d$p2==0& d$p4>0& d$p5==0,21,d$code)> d["code"]<-ifelse(d$p1==0& d$p2>0& d$p4==0& d$p5==0,22,d$code)> d["code"]<-ifelse(d$p1==0& d$p2==0& d$p4>0& d$p5>0,31,d$code)> d["code"]<-ifelse(d$p1==0& d$p2==0& d$p4==0& d$p5>0,32,d$code)> d["code"]<-ifelse(d$p1==0& d$p2>0& d$p4>0& d$p5==0,33,d$code)> d["code"]<-ifelse(d$p1>0& d$p2>0& d$p4==0& d$p5==0,34,d$code)> d["code"]<-ifelse(d$p1>0& d$p2==0& d$p4==0& d$p5==0,35,d$code)> d["code"]<-ifelse(d$p1==0& d$p2>0& d$p4>0& d$p5>0,41,d$code)> d["code"]<-ifelse(d$p1==0& d$p2>0& d$p4==0& d$p5>0,42,d$code)> d["code"]<-ifelse(d$p1>0& d$p2>0& d$p4>0& d$p5==0,43,d$code)> d["code"]<-ifelse(d$p1>0& d$p2==0& d$p4>0& d$p5==0,44,d$code)> d["code"]<-ifelse(d$p1>0& d$p5>0,50,d$code)> (t<-table(d$code))
11

12

21

344

570 5855

22

31

32

33

35

41

42

43

44

50

72 1074

96

606

3

46

4

3

31

3

31

32

33

35

41

42

43

44

50

0.8 11.7

1.0

6.6 0.0

0.5 0.0

0.0

0.3 0.0> sum(t)[1] 8707> round(t/nrow(ft2)*100,1)
11

12

21

3.7

6.2 63.7

22

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# Added code to ft2> ft2<-merge(ft2,d[c("ID","code")],by="ID",all=T)> ft2[is.na(ft2$code),"code"]<-60> table(ft2$code,useNA="ifany")
11

12

21

344

570 5855

22

31

32

33

35

41

42

43

44

50

60

72 1074

96

606

3

46

4

3

31

3

482

> ft2.old2<-ft2

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6.7.2 Family type based on the generation and number of married couples

Grouping sample household based on the generation and number of married couples
Excluded 482 sample households with other relation and 1,262 households without married
couple, then grouped 7,445 rest of households by the number of married couples’ generationand number of couples.

Nuclear family household is the major type, which occupies 61%, followed by the below types;
Household consist of head’s couple and children’s couple (6.5%)

Household consist of head’s couple, their parent(s) and their child(ren) (5.3%)

Household without head’s couple but with children’s couple(s) (4.4%)

Household consist of head’s couple, their child(ren) and their grandchild(ren) (1.9%)

As for number of married couples’ generation, households including one-generation couple(s) is
74%, and household including two-generation couples is 7.2%. On the other, household without
married couple is 14%.

Table Type of household by number of married couples’ generation and number of married couples
Number of

Numbercouples’ofgeneration

couplesHousehold consist of the below couples

Code2

Number ofPercentage

households(%)

All households

9,189

100.0

All households with marriage couples and without other

7,445

81.0

Total

6,774

73.7

Total

6,769

73.7

6,350

69.1

5,555

60.5

relation
1 generation1



Subtotal of a head’s couple



Nuclear family (Sum of 11 and 12)

10

Head’s couple only

11

570

6.2

Head’s couple and their children

12

4,985

54.2

Head’s couple, their children and their grandchildren

13

177

1.9

Head’s couple and their parents

14

26

0.3

Head’s couple, their parents and their children

15

488

5.3

Head’s couple, their parents, their children and their

16

15

0.2

grandchildren

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head’s couple, their grandparents and their parents

17

0

-

head’s couple, their grandparents, their parents and

18

1

0.0

Head’s couple and their grandchildren

19

57

0.6

Head’s couple, their grandparents and their children

20

24

0.3

Head’s couple, their parents and their grandchildren

21

3

0.0

Other

29

4

0.0

Subtotal of no head’s couple

30

419

4.6

No head’s couple but children’s couple

31

400

4.4

No head’s couple but parents’ couple

32

19

0.2

No head’s couple but siblings’ couple

33

0

-

No head’s couple but grandchildren’s gouple

34

0

-

Total

40

5

0.1

No head’s couple but children’s couples

41

5

0.1

Head’s couple and siblings’ couples

42

0

-

No head’s couple but siblings’ couple

43

0

-

Total

50

664

7.2

Head’s couple and children’s couple

51

598

6.5

Head’s couple and parents’ couple

52

64

0.7

Head’s couple and grandchildren’s couple

53

0

-

‘

Head’s couple and grandparents’ couple

54

2

0.0

3 generation

Head’s couple, parents’ couple and children’s couple

60

7

0.1

0 generation

No married couple without other relation

70

1,262

13.7

x

Households with other relations

80

482

5.2

their children



2

and

more

2 generation> table(ft2$p7>0)
FALSE TRUE
8707

482

> table(ft2$p7==0&ft2$ct==0)
FALSE TRUE
7927 1262> d<-subset(ft2,p7==0&ct>0)> dim(d)[1] 7445

18

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> d["code2"]<-0> d["code2"]<-ifelse(d$c1==0&d$c2>0&d$c4>0&d$c5==0,60,d$code2)> d["code2"]<-ifelse(d$c1==0&d$c2==0&d$c3>0&d$c4>0&d$c5==0,51,d$code2)> d["code2"]<-ifelse(d$c1==0&d$c2>0&d$c3>0&d$c4==0&d$c5==0,52,d$code2)> d["code2"]<-ifelse(d$c1==0&d$c2==0&d$c3>0&d$c4==0&d$c5>0,53,d$code2)> d["code2"]<-ifelse(d$c1>0&d$c2==0&d$c3>0&d$c4==0&d$c5==0,54,d$code2)> d["code2"]<-ifelse(d$ct==1,1,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1,29,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==0,30,d$code2)> d["code2"]<-ifelse(d$c1==0&d$c2==0&d$c3==0&d$c4==1&d$c5==0&d$c6==0,31,d$code2)> d["code2"]<-ifelse(d$c1==0&d$c2==1&d$c3==0&d$c4==0&d$c5==0&d$c6==0,32,d$code2)> d["code2"]<-ifelse(d$c1==0&d$c2==0&d$c3==0&d$c4==0&d$c5==0&d$c6==1,33,d$code2)> d["code2"]<-ifelse(d$c1==0&d$c2==0&d$c3==0&d$c4==0&d$c5==1&d$c6==0,34,d$code2)> d["code2"]<-ifelse(d$ct>=2&d$c1==0&d$c2==0&d$c3==0&d$c4>=2&d$c5==0&d$c6==0,41,d$code2)> d["code2"]<-ifelse(d$ct>=2&d$c1==0&d$c2==0&d$c3==1&d$c4>=2&d$c5==0&d$c6>=1,42,d$code2)> d["code2"]<-ifelse(d$ct>=2&d$c1==0&d$c2==0&d$c3==0&d$c4>=2&d$c5==0&d$c6>=2,43,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$hhsz==2,11,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1==0&d$p2==0&d$p4>0&d$p5==0&d$p6==0,12,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1==0&d$p2==0&d$p4>0&d$p5>0&d$p6==0,13,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1==0&d$p2>0&d$p4==0&d$p5==0&d$p6==0,14,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1==0&d$p2>0&d$p4>0&d$p5==0&d$p6==0,15,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1==0&d$p2>0&d$p4>0&d$p5>0&d$p6==0,16,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1>0&d$p2>0&d$p4==0&d$p5==0&d$p6==0,17,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1>0&d$p2>0&d$p4>0&d$p5==0&d$p6==0,18,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1==0&d$p2==0&d$p4==0&d$p5>0&d$p6==0,19,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1>0&d$p2==0&d$p4>0&d$p5==0&d$p6==0,20,d$code2)> d["code2"]<-ifelse(d$ct==1&d$c3==1&d$p1==0&d$p2>0&d$p4==0&d$p5>0&d$p6==0,21,d$code2)> table(d$code2)
11

12

13

570 4985 177
52

54

60

64

2

7

14

15

16

18

19

20

21

29

31

32

41

51

26 488

15

1

57

24

3

4

400

19

5 598

20

21

29

31

32

41

0.3 0.0

0.0

4.4 0.2> round(table(d$code2)/nrow(ft2)*100,1)
11

12

13

6.2 54.2 1.9
52

54

14

15

16

18

19

0.3 5.3

0.2

0.0 0.6

51

0.1 6.5

60

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0.7

0.0 0.1# Added code2 to ft2> ft2<-merge(ft2,d[c("ID","code2")],by="ID",all=T)> ft2["code2"]<-ifelse(ft2$p7>0,80,ft2$code2)> ft2["code2"]<-ifelse(ft2$p7==0&ft2$ct==0,70,ft2$code2)> table(ft2$code2,useNA="ifany")
11

12

13

14

15

16

18

19

20

21

29

31

32

41

51

570 4985 177

26 488

15

1

57

24

3

4

400

19

5 598

52

54

60

70

64

2

7 1262 482

80> ft2.old3<-ft2

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Family type by region
Comparing family type between Red River Delta and Mekong River Delta, the share of nuclear
family is higher in Red River Delta. Consequently, the average household size in Red River Delta issmaller than Mekong River Delta.
Composition of type of households by number of household members’ generation and by region> family<-cbind(ft2,ttchung[,c("tinh","ttnt","dantoc","tsnguoi","thunhap","chids","wt")])> t<-tapply(family$wt,list(substr(family$tinh,1,1),family$code),sum)> t[is.na(t)]<-0> region.name<-c("Red River Delta","North East","North West","North Central",+ "South Central Coast","Central Highlands","South East","Mekong River Delta")> rownames(t)<-region.name> round(prop.table(t,1)*100,1)
11

50

60

Red River Delta

4.6 10.3 62.0 0.7 10.6 1.3 6.9 0.0 0.5 0.1 0.0 0.1 0.0

2.9

North East

1.9 5.1 64.8 0.9 10.0 0.6 9.3 0.0 0.5 0.0 0.1 0.6 0.1

6.1

North West

1.6 3.6 66.6 0.6 10.8 0.4 5.5 0.0 0.5 0.0 0.0 0.0 0.0 10.4

North Central

4.1 7.3 65.9 0.5 10.4 0.4 7.6 0.1 0.7 0.0 0.0 0.4 0.0

2.6

South Central Coast 3.5 6.4 65.2 0.6 10.5 0.7 8.4 0.1 0.4 0.0 0.0 0.3 0.0

3.9

Central Highlands

1.9 4.1 72.0 0.2

7.8 0.7 5.9 0.0 0.4 0.0 0.0 1.0 0.0

5.8

South East

5.5 3.5 61.8 0.7 13.6 1.0 5.0 0.0 0.6 0.0 0.0 0.3 0.0

8.0

Mekong River Delta

4.0 5.2 59.7 1.3 16.5 1.8 4.4 0.0 0.3 0.1 0.0 0.1 0.0

6.4



12

21

22

31 32 33 35

41

42

43

44

Composition of type of households by number of married couples’ generation and number ofmarried couples, and by region> t<-tapply(family$wt,list(substr(family$tinh,1,1),family$code2),sum)> t[is.na(t)]<-0> round(prop.table(t,1)*100,1)
11

12 13 14 15 16 18 19 20 21 29 31 32

41

51 52 54 60

70

80

1 10.3 52.9 1.8 0.4 5.6 0.2

0 0.9 0.1 0.1 0.0 4.1 0.0 0.0

5.4 0.7 0.0 0.1 14.6

2.9

2

5.1 55.5 1.5 0.7 7.5 0.1

0 0.5 0.4 0.0 0.1 3.3 0.1 0.0

7.7 1.1 0.0 0.1 10.2

6.1

3

3.6 57.7 1.8 0.6 3.9 0.0

0 0.4 0.0 0.0 0.0 1.8 0.0 0.0 10.4 1.0 0.0 0.2 8.2 10.44

7.3 56.7 2.2 0.3 6.7 0.3

0 0.2 0.4 0.0 0.1 3.7 0.2 0.1

5.1 0.4 0.0 0.0 13.9

2.65

6.4 56.4 1.6 0.0 6.9 0.4

0 0.2 0.2 0.0 0.1 4.1 0.5 0.1

5.4 0.6 0.0 0.0 13.3

3.9

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6

4.1 62.8 1.9 0.2 5.1 0.2

0 0.5 0.7 0.0 0.1 2.1 0.0 0.0

4.2 0.6 0.2 0.3 11.2

5.8

7

3.5 50.2 2.3 0.1 3.7 0.0

0 0.4 0.3 0.0 0.0 6.6 0.2 0.1

5.6 0.4 0.0 0.0 18.5

8.0

8

5.2 50.6 2.3 0.2 3.5 0.0

0 0.9 0.0 0.1 0.0 6.3 0.4 0.1

8.7 0.7 0.1 0.1 14.5

6.4



Average household size by region> hhsz<-tapply(family$tsnguoi*family$wt,substr(family$tinh,1,1),sum)/+ tapply(family$wt,substr(family$tinh,1,1),sum)> names(hhsz)<-region.name> round(hhsz,2)
Red River Delta

North East

North West

North Central

3.83

4.27

4.81

4.27

South Central Coast

Central Highlands

4.27

4.88

South East Mekong River Delta
4.31

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100

6.8 EducationCompared school attendance rate (SRA) between male and female.



Data frames and variables used;

muc1a 39,071 records


MIAC2



M1AC5 age



wt



PID

sex

muc2a 39,071 records


M2AC5 Currently attending school?
1 Yes
2 Vacation
3 No



wt



PID

> d<-merge(muc1a[,c("PID","m1ac2","m1ac5")],muc2a[,c("PID","m2ac5","wt")])> d<-subset(d,m1ac5>=5&m1ac5<=24)> dim(d)[1] 15835
5> m<-matrix(as.vector(by(d$wt,list(d$m1ac5,d$m2ac5,d$m1ac2),sum)),nrow=20,ncol=6)> rownames(m)<-5:24> colnames(m)<-outer(c("Yes","Vacation","No"),c("M","F"),paste,sep="-")> round(m)Yes-M Vacation-M
No-M Yes-F Vacation-F No-F
5 256176
126693 196230 227789
98104 183635
6 280540
206955 102211 298711
172225 60424
7 306948
279764 17811 307781
180568 8203
8 300101
283586 14080 306068
262342 21313
9 357408
332475 18410 358804
304737 17060
10 452439
394041 20013 400681
365906 9140
11 382831
354612 37193 475993
386252 28489
12 420381
367452 40795 483817
406042 52599
13 446986
460903 94640 498525
462536 86768
14 457009
387374 159974 438114
418141 141029
15 455399
413641 258209 365051
384540 202645
16 445887
335297 376000 412619
345752 329362
17 335312
269601 483310 348163
272138 360918
18 271186
155011 582531 207864
139435 492442
19 270177
46581 645679 277946
40373 504163
20 226579
39338 576048 215340
33407 535029

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21 185678
22 154610
23 142607
24 55446

22555 641390 189922
11020 638537 132590
17782 617486 55919
1604 647971 48171

22000 575593
9215 572484
4953 679878
10649 603295# School attendance rate by sex and single year age> df<-as.data.frame(m)> df["male.SAR"]<-(df[,1]+df[,2])/rowSums(df[,1:3])*100> df["female.SAR"]<-(df[,4]+df[,5])/rowSums(df[,4:6])*100> (sar<-round(df[,7:8],1))
male.SAR female.SAR
5
66.1
64.0
6
82.7
88.6
7
97.1
98.3
8
97.6
96.4
9
97.4
97.5
10
97.7
98.8
11
95.2
96.8
12
95.1
94.4
13
90.6
91.7
14
84.1
85.9
15
77.1
78.7
16
67.5
69.7
17
55.6
63.2
18
42.3
41.4
19
32.9
38.7
20
31.6
31.7
21
24.5
26.9
22
20.6
19.9
23
20.6
8.2
24
8.1
8.9

> write.table(sar,file="clipboard",sep="¥t",row.names=F)

Summary:
Female’s school attendance rate (SAR) is slightly higher than male’s SAR.

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School attendance rate by sex and age
100
90
80
70
60
50
40
30
20
10
0

5

6
Age

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
male.SAR

female.SAR

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7. Agricultural land
7.1 HistoryIn Vietnam, land belongs to the entire people, which was approved by the National Assembly in
1980. The State has the powers and responsibilities for uniform administration of land as
representative.



In 1986, Doi Moi (Renovation) policy was launched.



1993 Land Law allowed ownership of a right to use land. This right is called the Land Use
Right (LUR).



In 1993, agricultural land was allocated free of charge for farmers for 20 years.



2003 Amended Land Law introduced market mechanism for LUR. Market price was applied forthe price of LUR.

7.2 Rice cultivated area by region
Comparison of the rice cultivated area by region shows that mean rice cultivated area per sample
household conditional on non-zero rice production is smaller in Red River Delta as compared to
Mekong River Delta. The coefficient of variation is also smaller in Red River Delta.

It is said that in North Vietnam agricultural land was equally distributed among farmers after theWorld War II, which resulted in the small mean area and the coefficient of variation. On the other, inSouth Vietnam landlord system had continued until the unity in 1976.

Table Share of sample households cultivated rice, mean and coefficient of variation of ricecultivated area per sample household (VHLSS 2006)
RegionShare of households

Mean rice cultivated area

cultivated rice (%)2)

for the past 12 months (mCoefficient ofvariation

Red River Delta

67.6

4,019

0.63

Mekong River Delta

37.2

23,141

1.12

Note: For example, in a period of one year, on a land lot of 360 m2 people grow two rice crops, totalrice cultivated area is twice the arable area, that is 720 m2.

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# Used data frame MUC4B11> d<-subset(muc4b11,m4b11ma>=5)> dim(d)[1] 6477

22# Aggregated the rice cultivated area for the past 12 months by household> t<-tapply(d$m4b11c3,d$ID,sum)# Created data frame land consist of variables of ID, area, wt for all households.
> land<-data.frame(ID=names(t),area=t,row.names=NULL)> land<-merge(land,ttchung[c("ID","wt")],all=T)> land[is.na(land)]<-0# Mean rice cultivated area by region> tapply(land$area,substr(land$ID,1,1),function(x) mean(x))
1

2

3

4

5

6

7

8

2718.764 2871.756 5305.886 3175.130 2344.995 2342.837 1607.811 8620.547# Coefficient of variation> tapply(land$area,substr(land$ID,1,1),function(x) sqrt(var(x))/mean(x))
1

2

3

4

5

6

7

8

1.029775 1.047827 1.154190 1.316736 1.316790 2.056574 3.938203 2.248628# Generated data frame land2, a subset of land, where household cultivated rice.
> land2<-subset(land,area>0)> dim(land2)[1] 4824

3# Share of sample households cultivated rice
> tapply(land2$wt,substr(land2$ID,1,1),length)/tapply(land$wt,substr(land$ID,1,1),length)
1

2

3

4

5

6

7

8

0.6764403 0.7031131 0.7715618 0.6607495 0.5516432 0.4106529 0.1506734 0.3725174# Mean rice cultivated area per sample household> tapply(land2$area,substr(land2$ID,1,1),function(x) mean(x))
1

2

3

4

5

4019.223 4084.343 6876.813

4805.346

4250.9286785705.151 10670.838 23141.324# Coefficient of variation> tapply(land2$area,substr(land2$ID,1,1),function(x) sqrt(var(x))/mean(x))
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8

0.6274495 0.6892151 0.8941031 0.8980131 0.7128223 1.0720696 1.2222574 1.1211101

Vietnam VHLSS Manual - Overall and survey process (Version 1.1)