Users’ Manual for Handling Resampled Micro Data of
Bangladesh Household Income and Expenditure Survey

Bangladesh HIES 2010
(Version 1.0)

2017
The Institute of Statistical Mathematics (ISM)
andStatistical Information Institute for Consulting and Analysis (SINFONICA)

1

History of revision of the manual
.Version 1.0: Finalized in March 2017 based on the discussion during the Workshop in December
2016.
 Provisional version in November 2016 for the Workshop in December 2016.

Bangladesh HIES 2010 Manual (Version 1.0)

2

CONTENTS

HIES 2010
1. About this Manual

Page 4

2. Outline of the survey

5Objective of the survey

Topics covered by the surveyFrequency of the survey

Survey period

Coverage of the survey

Sample designData collection methodData entry and data checkPublication

Technical and financial assistance

3. Data and metadata provided
3.1 Data files

7

3.2 Division and district

10

3.3 Key findings

16

4. Data import
4.1 Import STATA data files into R

21

4.2 Generated list of variable names in each data frame

23

4.3 Identifier

36

4.4 Weight
4.5 Total income and expenditure per household

39
40

5. Data check5.1 Structure of each data file41

5.2 Summary of each variable55

5.3 Frequency tables of categorical variables

74

5.4 Consistency check of identifier

87

5.5 Sample design

92

Bangladesh HIES 2010 Manual (Version 1.0)

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6. Household income

95

7. Household consumption

119

8. Micro data to be provided

142

Attachments:

146

1. The questionnaire147

2. Data dictionary

190

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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
Bangladesh Household Income and Expenditure Survey (HIES) 2010.

CSV format[1] "rt001_80.csv" "rt002_80.csv" "rt003_80.csv" "rt004_80.csv"[5] "rt005_80.csv" "rt006_80.csv" "rt007_80.csv" "rt008_80.csv"[9] "rt009_80.csv" "rt010_80.csv" "rt011_80.csv" "rt012_80.csv"[13] "rt013_80.csv" "rt014_80.csv" "rt015_80.csv" "rt016_80.csv"[17] "rt017_80.csv" "rt018_80.csv" "rt019_80.csv" "rt020_80.csv"
R format
The elements of the list object of “outfiles.80”correspond to;[1] "rt001.80" "rt002.80" "rt003.80" "rt004.80" "rt005.80"[6] "rt006.80" "rt007.80" "rt008.80" "rt009.80" "rt010.80"[11] "rt011.80" "rt012.80" "rt013.80" "rt014.80" "rt015.80"[16] "rt016.80" "rt017.80" "rt018.80" "rt019.80" "rt020.80"

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

3. This manual was first compiled in March 2017 by;
Hiroshige Furuta
Visiting Senior Research Fellow, Sinfonica

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2. Outline of HIES 2010
The below table describes mainly on the income and expenditure survey.
Objectives of the

To provide with very important data like household expenditure, income,surveyconsumption, savings, housing conditions, education, employment, health,sanitation, water supply, electricity usage, etc.
The HIES data series generated by the BBS is considered to be the main data
source for estimation of poverty and its correlates in Bangladesh.

Topics covered by

The 2010 HIES consists of nine major modules, covering various aspects ofthe survey

household activities and characteristics. The 2010 HIES saw the introductionof four new sub-modules – to gather information on: (i) Microcredit; (ii)Disability; (iii) Migration and Remittance; (iv) and Crisis Management(Shocks and Coping). Besides, the Social Safety Net module, introduced in
2005, was redesigned and somewhat shortened. This reflects several rounds oftechnical discussions on questionnaire design and content.

Frequency of theEvery five years.

surveyThe first round of HIES was conducted in the Year 1973-74 in the independent
Bangladesh. Since then, including the latest survey in 2010, BBS has
successfully completed 15 rounds of HIES.

Survey periodThe survey was duly completed without interruption in one year
(February 1, 2010 to January 31, 2011).



Data was collected over a year to capture the seasonal variations inincome, expenditure, and consumption patterns.



The one-year period was divided into 18 terms. A total of 34 PSUs werecovered in each term, to collect data from 680 sample households.

Coverage of the

Geographical coverage: National

survey

The target population comprised the civilian, non-institutional population.

Sample designTwo-stage stratified design
Master: The framework of Integrated Multipurpose Sample (IMPS)
design developed on the basis of the sampling frame based on the
Population and Housing Census 2001. The IMPS design consisted of1000 Primary Sampling Units (PSUs) throughout the country.
Strata: 16 - of which 6 are urban, 6 are rural, and 4 are Statistical
Metropolitan Areas (SMAs).
PSU: 612 (392 rural and 220 urban) were selected systematically.
FSU: 20 households from each PSU

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Sample size
In total 12,240 households
7,840 households from rural area
4,400 households from urban area
Data collectionFace-to-facemethod

There were 36 (including two reserve) enumeration teams for the survey.
Each enumeration team was comprised of a supervisor, 2 enumerator cum
data entry operators and two female facilitators. This team of fivemembers was assigned to 1 PSU to collect data for a continuous period of
20 days.



During this period, for collecting information on food consumption, thehouseholds were divided into two groups each consisting of 10
households. Each enumerator collected information on food consumptionof the households for 14 days by paying 7 visits. Information on foodconsumption of previous two days were collected during each visit.

The questionnaire has been revised and updated for HIES 2010. In the
HIES-2010 the consumption module was included in the mainquestionnaire as it was done in HIES 2005 & HIES 2000. Therefore, only
one questionnaire was used for all the sections covered under the previewof the HIES 2010. The HIES questionnaire contains nine separate
sections. The following four new sub- modules were included in
HIES-2010: 1. Disability, 2. Internal and external migration,
3. Indebtedness, opening of bank accounts and savings,
4. Household crisis and crisis management

Data entry anddata check

Data collection and data entry were done in the field level and for thispurpose each survey team was provided with a laptop computer.

Data were sent to HQ office through internet. These new techniques
helped improving the data collection and data entry processes to a great
extent and also facilitated to compile the Final results quickly.

Publication“Report of the Household Income & Expenditure Survey 2010” in December
2011Technical andWorld BankfinancialassistanceBangladesh HIES 2010 Manual (Version 1.0)

7

3. Data and metadata provided
3.1 Data filesHousehold data files

File names in STATA format;[1] "rt001.dta" "rt002.dta" "rt003.dta" "rt004.dta" "rt005.dta" "rt006.dta"[7] "rt007.dta" "rt008.dta" "rt009.dta" "rt010.dta" "rt011.dta" "rt012.dta"[13] "rt013.dta" "rt014.dta" "rt015.dta" "rt016.dta" "rt017.dta" "rt018.dta"[19] "rt019.dta" "rt020.dta"
Questionnaire and table of contents

Household

Questionnaire

for

Income&

HIES English Questionnaire 2010.xls

Expenditure Survey in English
Table of contents
Page
No.

Order of Sections
1

2

3

4

HOUSEHOLD INFORMATION ROSTER
PART A:

HOUSEHOLD INFORMATION

1

rt002

PART B:EMPLOYMENT INFORMATION

2

rt002

PART C:SOCIAL SAFETY NETS PROGRAMME

3

rt002

PART A:

LITERACY AND EDUCATIONAL ATTAINMENT

4

rt002

PART B:

CURRENT ENROLLMENT

5-6

rt002

PART A:ILLNESSES AND INJURIES

7-9

rt002

PART B:

CHILD HEALTH AND IMMUNIZATION

10

rt002

PART C:

PRE-NATAL AND POST-NATAL CARE

11

rt002

PART D:DISABILITY

12

rt002

13
14

rt003
rt003

15-16

rt004

EDUCATIONHEALTHECONOMIC ACTIVITIES AND WAGE EMPLOYMENTPART A:PART B:

5

Data File

ACTIVITIESWAGE EMPLOYMENTNON-AGRICULTURAL ENTERPRISES

Bangladesh HIES 2010 Manual (Version 1.0)

rt001

8

6

7

HOUSING
HOUSING INFORMATION

17

PART B:SHOCKS AND COPING

18

rt005

19
20-21

rt006

AGRICULTURAL ENTERPRISESPART A:PART B:PART C:

89✅

PART A:LANDHOLDINGCROP PRODUCTIONNON-CROP ACTIVITIESLIVESTOCK AND POULTRYLIVESTOCK PRODUCTSFISH FARMING AND FISH CAPTURE
FARM FORESTRY

23

rt007
rt008
rt009
rt010

PART D:

EXPENSES ON AGRICULTURAL INPUTS

24

rt011

PART E:AGRICULTURAL ASSETS

25

rt012

22

OTHER INCOME AND ASSETSPART A:PART B:OTHER PROPERTY AND ASSETSOTHER INCOME

26
27

PART C:MIGRATION AND REMITTANCES

28

rt013

PART D:MICRO CREDIT

29

rt014

CONSUMPTIONPART A:

rt015

DAILY CONSUMPTION
DAY 1

30-31

DAY 2
DAY 3

32-33
34-35

DAY 4

36-37

DAY 5

38-39

DAY 6
DAY 7

40-41
42-43

PART B:WEEKLY CONSUMPTION (DAY 1-7)

PART A:

DAY 8

45-46

DAY 9

47-48

DAY 1049-50

DAY 11
DAY 12

51-52
53-54

DAY 1355-56

DAY 1457-58

PART B:WEEKLY CONSUMPTION (DAY 8-14)

PART C:PART D:PART E:MONTHLY NON-FOOD EXPENDITUREANNUAL NON-FOOD EXPENDITURE
INVENTORY OF DURABLE GOODS

44

rt016

59
60-61
62-65
66

rt017
rt018, rt019
rt020

Note: rt001 is the summary file and check marks indicate where items are included in rt001.

Bangladesh HIES 2010 Manual (Version 1.0)

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✅

✅

✅
✅
✅
✅

✅

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

Codebook was not provided by NSO.



Community survey

Questionnaire[1] "HIEScover.pdf" "HIESC1.pdf"[5] "HIESC4.pdf""HIESC5.pdf""HIESC2.pdf""HIESC3.pdf""HIESC6.pdf""HIESC7.pdf"[9] "HIESC8.pdf"
Data files[1] "t1_sec_1.dta""t2_sec_1a.dta" "t3_sec_2.dta""t4_sec_3.dta"[5] "t5_sec_4a.dta" "t6_sec_4b.dta" "t7_sec_4b1.dta" "t8_sec_4c.dta"[9] "t9_sec_5.dta"
Codebook"t10_sec_6.dta" "t11_sec_6b.dta"[1] "sec_1.xls""sec_1a.xls""sec_2.xls""sec_3.xls""sec_4a.xls"[6] "sec_4b.xls""sec_4b1.xls" "sec_4c.xls""sec_5.xls""sec_6.xls"[11] "sec_6b.xls"

Remarks: Data set of community survey is not included in the resampled micro data.


PSU List

PSU list file[1] "hies_sam.dta"

It includes area information for each PSU.
> library(foreign)> d<-read.dta("hies_sam.dta" )> dim(d)[1] 612 17> head(d)psu
spc stratum
str_name div div_name zila zl_name uz
1 001
Rural
01 Barisal Rural 10 BARISAL
04 BARGUNA 09
2 002
Rural
01 Barisal Rural 10 BARISAL
04 BARGUNA 09
3 003
Rural
01 Barisal Rural 10 BARISAL
04 BARGUNA 28
4 004
Rural
01 Barisal Rural 10 BARISAL
04 BARGUNA 47
5 005
Rural
01 Barisal Rural 10 BARISAL
04 BARGUNA 85
6 006 Municipality
02 Barisal Urban 10 BARISAL
04 BARGUNA 09
uz_name unun_name mz
mza_name vill
1
AMTALI 39
BARABAGI 073 BARA NISHANBARIA CH
1
2
AMTALI 71
HALDIA 847
PURBA CHILA
1
3 BARGUNA SADAR 28
BARGUNA 112
BANSHBANIA
44BETAGI 23
BIBICHINI 860
PHULTALA
1
5
PATHARGHATA 11 CHAR DUANTI 969
TAFALBARIA
1
6AMTALI 04 WARD NO-04 823
SADARPARA-vill_name rmo
1
KABIRAJ PARA
1
2
PURBA CHILA
13UTTAR HELIBUNIA
1
4
PHULTALA
1
5
TAFALBARIA
1
6------------------2

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3.2 Division and District code
Using PSU list, division and district codes are available.

There are 6 divisions in HIES 2010.
> division<-unique(paste(d$div,d$div_name,sep=":"))> division[1] "10:BARISAL""20:CHITTAGONG" "30:DHAKA"[5] "50:RAJSHAHI""60:SYLHET""40:KHULNA"

Remarks: In January 2010, Rangpur Division was split off from Rajshahi Division.
Therefore, there were 6 divisions at the time of sample design, but the country wasgrouped into 7 divisions in the micro data and in the report.

Bangladesh HIES 2010 Manual (Version 1.0)

11> unique(outfiles[[1]]$region) # rt001[1] 10 20 30 40 55 50 60

There are 64 districts in HIES 2010.
> district<-unique(paste(d$zila,d$zl_name,sep=":"))> length(district)[1] 65> district[43:44][1] "68:NARAYANGANJ" "68:NARSINGDI"> district[order(district)][-44][1] "01:BAGERHAT""03:BANDARBAN""04:BARGUNA"[5] "09:BHOLA""10:BOGRA""12:BRAHMANBARIA" "13:CHANDPUR"[9] "15:CHITTAGONG""18:CHUADANGA""19:COMILLA""22:COX'S BAZAR"[13] "26:DHAKA""27:DINAJPUR""29:FARIDPUR""30:FENI"[17] "32:GAIBANDHA""33:GAZIPUR""35:GOPALGANJ""36:HABIGANJ"[21] "38:JOYPURHAT""39:JAMALPUR""41:JESSORE""42:JHALOKATI"[25] "44:JHENAIDAH""46:KHAGRACHHARI" "47:KHULNA""48:KISHORGONJ"[29] "49:KURIGRAM""50:KUSHTIA""51:LAKSHMIPUR""52:LALMONIRHAT"[33] "54:MADARIPUR""55:MAGURA""56:MANIKGANJ""57:MEHERPUR""61:MYMENSINGH""64:NAOGAON"[37] "58:MAULVIBAZAR" "59:MUNSHIGANJ""06:BARISAL"[41] "65:NARAIL""67:NARAYANGANJ" "68:NARAYANGANJ" "69:NATORE"[45] "70:NAWABGANJ""72:NETRAKONA""73:NILPHAMARI""75:NOAKHALI"[49] "76:PABNA""77:PANCHAGARH""78:PATUAKHALI""79:PIROJPUR"[53] "81:RAJSHAHI""82:RAJBARI""84:RANGAMATI""85:RANGPUR"[57] "86:SHARIATPUR""87:SATKHIRA""88:SIRAJGANJ""89:SHERPUR"[61] "90:SUNAMGANJ""91:SYLHET""93:TANGAIL""94:THAKURGAON"

Note: Both "68:NARAYANGANJ" and "68:NARSINGDI" are used for the same district in PSU list.
So, the latter is dropped.



Relationship between 6 Divisions and Districts

The number of psu by division and district> d$division<-paste(d$div,d$div_name,sep=":")

Bangladesh HIES 2010 Manual (Version 1.0)

12

> d$district<-paste(d$zila,d$zl_name,sep=":")> table(d$district,d$division)

10:BARISAL 20:CHITTAGONG 30:DHAKA 40:KHULNA 50:RAJSHAHI 60:SYLHET
01:BAGERHAT

0

0

0

8

0

0

03:BANDARBAN

0

7

0

0

0

0

04:BARGUNA

8

0

0

0

0

0

06:BARISAL

10

0

0

0

0

0

09:BHOLA

8

0

0

0

0

0

10:BOGRA

0

0

0

0

12

0

12:BRAHMANBARIA

0

10

0

0

0

0

13:CHANDPUR

0

9

0

0

0

0

15:CHITTAGONG

0

24

0

0

0

0

18:CHUADANGA

0

0

0

8

0

0

19:COMILLA

0

15

0

0

0

0

22:COX'S BAZAR

0

6

0

0

0

0

26:DHAKA

0

0

20

0

0

0

27:DINAJPUR

0

0

0

0

10

0

29:FARIDPUR

0

0

8

0

0

0

30:FENI

0

8

0

0

0

0

32:GAIBANDHA

0

0

0

0

8

0

33:GAZIPUR

0

0

12

0

0

0

35:GOPALGANJ

0

0

8

0

0

0

36:HABIGANJ

0

0

0

0

0

9

38:JOYPURHAT

0

0

0

0

6

0

39:JAMALPUR

0

0

10

0

0

0

41:JESSORE

0

0

0

12

0

0

42:JHALOKATI

8

0

0

0

0

0

44:JHENAIDAH

0

0

0

9

0

0

46:KHAGRACHHARI

0

7

0

0

0

0

47:KHULNA

0

0

0

13

0

0

48:KISHORGONJ

0

0

10

0

0

0

49:KURIGRAM

0

0

0

0

9

0

50:KUSHTIA

0

0

0

9

0

0

51:LAKSHMIPUR

0

7

0

0

0

0

52:LALMONIRHAT

0

0

0

0

6

0

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54:MADARIPUR

0

0

7

0

0

0

55:MAGURA

0

0

0

8

0

0

56:MANIKGANJ

0

0

8

0

0

0

57:MEHERPUR

0

0

0

7

0

0

58:MAULVIBAZAR

0

0

0

0

0

11

59:MUNSHIGANJ

0

0

6

0

0

0

61:MYMENSINGH

0

0

22

0

0

0

64:NAOGAON

0

0

0

0

9

0

65:NARAIL

0

0

0

7

0

0

67:NARAYANGANJ

0

0

12

0

0

0

68:NARAYANGANJ

0

0

4

0

0

0

68:NARSINGDI

0

0

6

0

0

0

69:NATORE

0

0

0

0

8

0

70:NAWABGANJ

0

0

0

0

6

0

72:NETRAKONA

0

0

8

0

0

0

73:NILPHAMARI

0

0

0

0

7

0

75:NOAKHALI

0

10

0

0

0

0

76:PABNA

0

0

0

0

10

0

77:PANCHAGARH

0

0

0

0

7

0

78:PATUAKHALI

8

0

0

0

0

0

79:PIROJPUR

7

0

0

0

0

0

81:RAJSHAHI

0

0

0

0

18

0

82:RAJBARI

0

0

8

0

0

0

84:RANGAMATI

0

7

0

0

0

0

85:RANGPUR

0

0

0

0

11

0

86:SHARIATPUR

0

0

6

0

0

0

87:SATKHIRA

0

0

0

9

0

0

88:SIRAJGANJ

0

0

0

0

10

0

89:SHERPUR

0

0

8

0

0

0

90:SUNAMGANJ

0

0

0

0

0

11

91:SYLHET

0

0

0

0

0

12

93:TANGAIL

0

0

14

0

0

0

94:THAKURGAON

0

0

0

0

6

0

Bangladesh HIES 2010 Manual (Version 1.0)

14Remarks: Relationship between 7 divisions and districts
Divisions in the data set rt001> df<-outfiles[[1]]> dim(df)[1] 12240

151> table(df$region)
10

20

30

40

50

55

60

980 2200 3540 1800 1580 1280 860

Relationship between 7 divisions and 6 divisions> d<-read.dta("hies_sam.dta")> df<-merge(df,d,by="psu",all.x=T)> table(df$region,df$div)
10

20

30

40

50

60

10 980

0

0

0

0

0

20

0 2200

0

0

0

0

30

0

0 3540

0

0

0

40

0

0

0 1800

0

0

50

0

0

0

0 1580

0

55

0

0

0

0 1280

0

60

0

0

0

0



0 860

Relationship between the divided divisions and districts> df50<-subset(df,div==50)> table(df50$district,df50$region)
50 55
10 240

0

27

0 200

32

0 160

38 120

0

49

0 180

52

0 120

64 1800

69 1600

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70 12073

0 140

76 200
77



0

0 220

88 200
94

0

0 140

81 360
85

0

0

0 120

Lists of occupation code and industry code are not available.

Bangladesh HIES 2010 Manual (Version 1.0)

16



IHSN
http://catalog.ihsn.org/index.php/catalog/2257/(Accessed on 23 April 2016)

Survey report

Report on The Household Income and Expenditure Survey 2010

Questionnaires

Household Income and Expenditure Survey 2010 - Questionnaire

Micro data set

In addition to rt001 to rt020, the data file of “hhold_exp_hies2010” (12,240
records and 25 variables) is listed, which includes the variables of monthly
household income and monthly household consumption.

3.3 Key findings

The survey report includes the next table of key findings;

NOTE: Errors in key findings
The figures in 17. Expenditure should be read as the next, according to the Table 4.7 (page 38) of thesurvey report.
17. Expenditure (taka per month)
Food expenditure per h/h

HIES 2010
Total

Rural

Urban

errors

2,491

2,122

3,526

to be read as

6,031

5,543

7,362

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KEY FINDINGSKey Findings

HIES 2010
Rural Urban

Total
1

Total

HIES 2005
Rural Urban

2

3

4

5

6

7

1. Total sample household

12,240

7,840

4,400

10,080

6,400

3,680

2.Total household (Million)

33.03

24.17

8.86

28.64

21.38

7.26

Male headed
Female headed

28.44
4.59

20.57
3.60

7.87
0.99

25.70
2.93

19.09
2.28

6.61
0.65

3. Total population (million)
Male
FemaleSex ratio

148.49
73.57
74.91
98.21

109.46
54.05
55.41
97.54

39.03
19.52
19.50
100.12138.82
69.85
68.97
101.27104.5052.77
51.72
102.04

34.32
17.07
17.25
98.99

4. Average household size
4.50
5. Number of household by size (in million)
1 member
0.80
2-3 member
9.04
4-5 member
15.37
6+ member
7.82

4.53

4.41

4.85

4.89

4.72

0.66
6.45
11.13
5.93

0.14
2.59
4.24
1.89

0.60
6.51
12.70
8.83

0.50
4.74
9.26
6.88

0.10
1.77
3.44
1.95

6. Number of household by number of earner (in million)
0.67
2.36
0 earner
3.03
5.10
20.30
15.21
1 earner
2.23
4.77
7.01
2 earner
1.83
0.85
2.68
3+ earner

2.11
16.86
6.60
3.07

1.73
12.78
4.71
2.16

0.38
4.08
1.89
0.91

7. Housing structure (head of households) roof material (percent)
28.71
7.69
3.65
Brick/Cement
10.37
82.25
68.28
81.52
86.38
CIS/wood
10.06
8.11
9.97
3.01
Straw/hay/bamboo/others

2.07
85.84
12.09

24.24
71.68
4.08

8. Housing structure (head of households) Wall material (percent)
Brick/Cement
CIS/Brick/Wood
Mud/unburnt brickHay/bamboo/leaf/others

25.12
38.46
16.72
19.71

13.59
43.24
20.57
22.60

56.59
25.40
6.22
11.78

19.63
35.53
18.05
26.79

9.92
38.14
22.15
29.79

48.23
27.87
5.97
17.93

SupplyTube well
Others*

10.62
85.37
4.01

1.47
94.97
3.56

35.57
59.18
5.04

7.63
89.04
3.33

0.54
95.28
4.18

28.49
70.70
0.81

10. Electricity (percent)

55.26

42.49

90.10

44.23

31.19

82.61

9. Source of drinking water (percent)xix
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Key Findings

HIES 2010
Total
Rural Urban

1

2

11. Toilet facilities (percent)
Sanitary/Pucca
KatchaOpen space/others

HIES 2005
Total
Rural Urban

3

4

5

6

7

51.05
44.54
4.40

41.87
52.39
5.73

76.12
23.11
0.77

51.74
36.95
11.56

41.53
43.99
14.44

80.65
16.22
3.09

36.1
63.9

46.4
53.4

8.3
91.7

47.5
52.5

57.6
42.4

12.2
87.8

12. Main occupation (percent)Agriculture/Forestry/Fisheries
Non-agricultural

13. School enrollment CNB (Lower poverty line)
Aged 6-10 years
Poor
Noon-poor
Aged 11-15
Poor
Noon-poor

78.33
89.00

78.48
87.92

77.54
91.71

72.35
84.40

72.71
83.32

69.90
87.61

70.20
85.52

72.28
85.25

60.75
86.21

53.92
75.04

53.65
72.52

55.39
73.62

81.64
11.89
6.46

83.59
10.25
6.16

75.18
17.33
7.49

81.55
11.85
6.60

82.89
10.34
6.77

77.97
16.01
6.02

14. Type of school attended
Government
Govt. subsidised
Non-government & others

15. Income accruing to: (taka per household per month)(Household expenditure scall)Top 5% household
Bottom 5% household

35,695
5,149

27,8185,138

39,717
5,284

33471
1605

25044
1485

47447
2113

11,479
8,795
2,553

9,648
7,592
2,130

16,475
11,778
3,741

7203
5145
1485

9095
4449
1246

10463
6975
2217

11,2009,612 15,531
11,0039,436 15,276--------------------------------2,491
2,122
3,526

6134
5964
3209

5319
5165
3023

8533
8315
3756

53.85
54.81

52.31
53.81

56.83
58.54

44.02
45.17

16. Income ( taka per month)
Income per household
Income per earner
Income per capita
17. Expenditure (taka per month)
Total expenditure per h/h
Consum. expen. per h/hFood expen. per h/h
18. Share of food expenditure on
Total expenditure
Consum. expenditure

57.67
58.74

47.40
48.19

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Key Findings

HIES 2010
Total
Rural Urban

1

2

HIES 2005
Total
Rural Urban

3

4

5

6

7

485.6
13.2
170.0
45.8
14.7
5.8
31.8
42.6
65.24
2344.6

402.9
17.2
155.0
59.9
33.3
10.9
39.2
50.4
69.11
2244.5

469.2
14.2
157.0
42.1
20.8
5.2
32.4
32.5
62.52
2238.5

485.6
12.7
156.5
39.7
17.6
4.4
31.0
32.4
61.74
2253.2

419.3
18.6
158.7
49.6
30.7
7.4
36.6
32.9
64.88
2193.8

17.6
3.1
0.8

21.1
3.7
1.0

7.7
1.3
0.4

25.1
4.6
1.3

28.6
5.3
1.5

14.7
2.6
0.7

31.5
6.5
9.0

35.2
7.4
2.2

21.3
4.3
1.3

40.0
9.0
2.9

43.8
9.8
3.1

28.4
6.5
2.1

19. Food Intake (gram per capita per day)
Cereals
Pulse
Vegetable
Fish
Meat
Egg
Milk & Milk product
Fruit
ProteinCalorie (K.cal/capita/day)

463.9
14.3
166.1
49.5
19.0
7.2
33.7
44.7
66.26
2318.3

20. Incidence of poverty CBN method
Lower Poverty Line
Head count (%)
Poverty gap
Squared poverty gap
Upper Poverty Line
Head count (%)
Poverty gap
Squared poverty gap

21. Incidence of poverty and literacy of head of household CBN method
Lower Poverty Line
Literate
Illiterate
Upper Poverty Line
Literate
Illiterate

9.2
25.1

12.4
27.2

3.9
15.6

12.3
36.3

15.3
37.5

6.7
29.9

19.0
42.8

23.3
43.5

11.4
39.4

23.0
54.7

27.0
55.1

15.7
52.3

22. Poverty incidence and sex of head of household CBN method
Lower Poverty Line
Male
FemaleUpper Poverty Line
Male
Female

17.9
14.6

21.5
17.3

7.9
5.5

25.4
21.9

29.0
23.6

14.5
16.2

32.1
26.6

35.9
29.3

21.7
17.5

40.8
29.5

44.9
31.0

28.7
24.4

xxi
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Key Findings

HIES 2010
Total
Rural Urban

1

2

3

4

HIES 2005
Total
Rural Urban
5

23. Poverty incidence and age of head of household CBN method
Lower Poverty Line
<= 29
19.4
22.8
9.1
30.2
30-39
21.6
26.4
9.3
32.1
40-49
17.3
21.2
7.3
26.0
50-59
13.7
16.9
5.4
19.7
60+
15.6
17.6
8.0
18.3
Upper Poverty Line
<= 29
35.6
39.5
24.1
45.9
30-39
37.0
42.0
24.1
47.7
40-49
31.4
34.9
22.4
40.7
50-59
25.8
29.4
16.4
33.5
60+
28.1
30.5
19.2
31.1
24. Percent of household receiving
benefit from Social Safety Nets Programme
24.57
30.12
9.42
13.06

6

7

34.0
37.8
29.8
22.4
19.9

20.1
16.4
15.0
11.9
11.0

50.1
52.4
45.8
37.7
34.4

34.6
34.9
26.1
21.5
27.3

15.64

5.45

HIES-2010
25. Percent of household reporting migration
Total
Within country
Abroad
26. Percent of household reporting

Total
12.28
3.97
8.60

Rural
13.72
4.84
9.25

Urban
8.33
1.62
6.85

Opening new bank accountDeposited money in micro/Financial institution
Deposited money for saving in any Informal
Financial Institution
Received Loan from Financial Inst. friends, etc.

7.41
14.51
5.64

5.05
15.94
5.80

13.85
10.61
5.22

32.03

35.08

23.70

27. Average Amount of Loan Taken per household

28,06221,804

54,122

28. Percent of household who faced any kind of crisis

0.84

1.03

0.30

29. Percent of disabled person (Net) HIES-2010
30. Disability: Intensity of difficulty (Percent) 2010(a) Eye Sight(b) Hearing(c) Walking & Climbing(d) Remembering & Concentrating(e) Self care(f) Speaking & Communicating

9.07
Some
5.58
1.93
1.84
0.94
0.57
0.52

9.63
Severe
0.53
0.33
0.53
0.24
0.30
0.24

7.49
Fully Unable
0.08
0.06
0.07
0.08
0.08
0.09

xxii
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4. Data import
4.1 Import STATA data files into R> STATA.files<-list.files()[3:22]> STATA.files[1] "rt001.dta" "rt002.dta" "rt003.dta" "rt004.dta" "rt005.dta" "rt006.dta"[7] "rt007.dta" "rt008.dta" "rt009.dta" "rt010.dta" "rt011.dta" "rt012.dta"[13] "rt013.dta" "rt014.dta" "rt015.dta" "rt016.dta" "rt017.dta" "rt018.dta"[19] "rt019.dta" "rt020.dta"# Imported STATA files into R> library(foreign)> outfiles<-list()> for(j in 1:20){+ outfiles<-c(outfiles,list(read.dta(STATA.files[j],convert.factors=F)))+ }> length(outfiles)[1] 20# 20 R data frames were stored in the list “outfiles”.

# Made list of data file names, number of records and variables> for(j in 1:20){+ cat(STATA.files[j],": ",dim(outfiles[[j]]),"\n")+ }rt001.dta :12240 151

rt002.dta :55580 149

rt003.dta :18067 28

rt004.dta :3497 35

rt005.dta :220012 13

rt006.dta :186980 19

rt007.dta :80424 11

rt008.dta :72327 9rt009.dta :25241 9rt010.dta :45643 7

Bangladesh HIES 2010 Manual (Version 1.0)

22

rt011.dta :247753 7

rt012.dta :228631 11

rt013.dta :2100 21

rt014.dta :5137 16

rt015.dta :438559 62

rt016.dta :200008 13

rt017.dta :362670 7

rt018.dta :285162 6

rt019.dta :830454 5

rt020.dta :341813 8

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4.2 Generated list of variable names in each data frame

NAMING RULE OF VARIABLES
1. In principle, the variable name corresponds to the combination of the section number of thequestionnaire and the question number.

2. If the variable label differs from the variable name in the following list, the variable label
corresponds to the questionnaire.

For instance, in the next case of rt003, the variable of s04b_q_3 means the question of s04b_q05t in
Section 4 of the questionnaire.
###

rt003.dta ###variablequestion

24 s04b_q_3

s04b_q05t: What was the total price received per day? taka

So, the variable label is proper for data dictionary. Regardless of lack of codebook, the next list
will take a role of codebook.

> for(k in 1:20){+ df<-outfiles[[k]]+ cb<-vector(length=ncol(df),mode="list")+ # cb: data frame of codebook+ for(j in 1:length(cb)){+ cb[[j]]$varname<-names(df)[j]+ cb[[j]]$varlabel<-attributes(df)$var.labels[j]+ cb[[j]]$vallabel<-(attributes(df)$label.table[attributes(df)$val.labels])[[j]]+ }+ # print codebook+ cat("\n\n### ",STATA.files[k]," ###\n")+ for(j in 1:length(cb)){+ cat(format(j,width=2),format(cb[[j]]$varname,width=10),+
format(cb[[j]]$varlabel,width=30),"\n")+ if(length(cb[[j]])==3){ # including value labels+t<-cb[[j]]$vallabel+
for(k in 1:length(t)){+
cat(rep(" ",10),format(t[k],width=3),format(names(t)[k],width=30),"\n")+} # end of for+ } # end of if+ } # end of for j+ } # end of for k

Bangladesh HIES 2010 Manual (Version 1.0)

24### rt001.dta ###1 stratum
Stratum
2 psu
Primary Sampling Unit
3 hhold
Household Number
4 team5 term6 region
Division
7 district
District code
8 thanaUpazila9 unionUnion code
10 mouza
Mouza Code
11 spcRural/Urban(Municipality+SMA)
12 urbanrur
13 wgtWeight14 s06a_q01
id code of the respondent
15 s06a_q02
How many rooms occupy?
16 s06a_q03
Your dwelling possesa separate dining
17 s06a_q04
Your dwelling possesa separate kitchan
18 s06a_q05
Material of the walls
19 s06a_q06
Material of the roof
20 s06a_q07
Total usable space (Sq.feet)
21 s06a_q08
What type of latrine?
22 s06a_q09
Main sousce of drinking water
23 s06a_q10
Tubwell tested for arsenic
24 s06a_q11
Was Arsenic found?
25 s06a_q12
Alternative source of drinking water
26 s06a_q13
Source of water for other use
27 s06a_q14
Electricity connection
28 s06a_q15
How many hours you have electicity?
29 s06a_q16
Mobile phone
30 s06a_q17
Landline phone connection
31 s06a_q18
Coumputer
32 s06a_q19
Access to internet
33 s06a_q20
How you access the internet?
34 s06a_q21
Present occupancy ststus
35 s06a_q22
Amount you need to buy/build such house
36 s07a_q01
Total cultivable land owned. decimals
37 s07a_q02
total homestead land? decimals
38 s07a_q03
Total not uncultivated land? decimals
39 s07a_q04
Total agriculture land rented out? decimals
40 s07a_q05
Total cultivable land rented out? decimals
41 s07a_q06
Total operating land? decimals
42 s07b_q01
Did any member cultivated any land for crop...
43 s07c_q01
Household raise any livestock
44 s07c_q09
Household engage in any fishing
45 s07c_q13
Household engage in any farm forestry
46 s08a_q01
Own any unused land?
47 s08a_q_1
Amount of unused land? decimals
48 s08a_q02
present price of your own land?
49 s08a_q03
household purchased any land in last 12 months
50 s08a_q04
Cost of purchasing this land?
51 s08a_q05
Did your household purchase any house?
52 s08a_q06
Cost of purchasing this house?
53 s08a_q07
Household own other assets
54 s08a_q08
What is the total value of such assets?
55 s08a_q09
Household purchased any assets in last 12 months

Bangladesh HIES 2010 Manual (Version 1.0)

25

56 s08a_q10
57 s08a_q11
58 s08a_q12
59 s08b_q01
60 s08b_q02
61 s08b_q03
62 s08b_q_1
63 s08b_q_2
64 s08b_q04
65 s08b_q05
66 s08b_q06
67 s08b_q07
68 s08b_q08
69 s08b_q09
70 s08b_q10
71 s08b_q11
72 s08b_q12
73 s08b_q13
74 s08c_q01
75 s08c_q02
76 s08d_q01
77 s08d_q02
78 s08d_q03
79 s08d_q04
80 s09a1d01
81 s09a1d_1
82 s09a1d_2
83 s09a1d_3
84 s09a1d_4
85 s09a1d02
86 s09a1d_5
87 s09a1d_6
88 s09a1d_7
89 s09a1d_8
90 s09a1d03
91 s09a1d_9
92 s09a1_10
93 s09a1_11
94 s09a1_12
95 s09a1d04
96 s09a1_13
97 s09a1_14
98 s09a1_15
99 s09a1_16
100 s09a1d05
101 s09a1_17
102 s09a1_18
103 s09a1_19
104 s09a1_20
105 s09a1d06
106 s09a1_21
107 s09a1_22
108 s09a1_23
109 s09a1_24
110 s09a1d07
111 s09a1_25

What was the cost of such purchase?
Household sold any assets
How much household received from selling
Income from rent of land (share cropping)
Income from rent of other property
income from Life insuranceincome from Health insuranceincome from General insurance
Profit and dividend received
Lottery or similary income in cash or in-kind
Gift, Charity or other received? In cash
Gift, Charity or other received? In-kindRemittances received from within the country
Remittances received from outside the country
Where did you invast remittance money
Pension, Gratuity, other benefit received
Profit,Interest received
Other cash or in-kind received
Any member of household migrated?
Household received remittances from outside?
Any member opened bank account?
Any member deposited in any credit union?
Any member deposited in non-inst. group?
Any member borrowed money
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls

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26

112 s09a1_26
113 s09a1_27
114 s09a1_28
115 s09a1d08
116 s09a1_29
117 s09a1_30
118 s09a1_31
119 s09a1_32
120 s09a1d09
121 s09a1_33
122 s09a1_34
123 s09a1_35
124 s09a1_36
125 s09a1d10
126 s09a1_37
127 s09a1_38
128 s09a1_39
129 s09a1_40
130 s09a1d11
131 s09a1_41
132 s09a1_42
133 s09a1_43
134 s09a1_44
135 s09a1d12
136 s09a1_45
137 s09a1_46
138 s09a1_47
139 s09a1_48
140 s09a1d13
141 s09a1_49
142 s09a1_50
143 s09a1_51
144 s09a1_52
145 s09a1d14
146 s09a1_53
147 s09a1_54
148 s09a1_55
149 s09a1_56
150 s09b1w1_
151 s09b1w2_

Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Date
Date

### rt002.dta ###1 psu
Primary sampling unit
2 hhold
household3 idcode
personal identification code
4 resid1a
respondent identification code - section 1.a
5 s01a_q01
s01a_q01: name(to be deleted)
6 s01a_q02
s01a_q02: sex
7 s01a_q03
s01a_q03: relationship to head
8 s01a_q04
s01a_q04: age in years
9 s01a_q05
s01a_q05: religion
10 s01a_q06
s01a_q06: marital status
11 s01a_q07
s01a_q07: earner
12 s01a_q08
s01a_q07: spouse ID
13 s01a_q09
s01a_q09: father id

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27

14 s01a_q10
15 s01a_q11
16 s01a_q12
17 s01b_q01
18 s01b_q02
19 s01b_q03
20 s01b_q04
21 resid1c
22 s01c_q01
23 s01c_q02
24 s01c_q03
25 s01c_q_1
26 s01c_q04
27 s01c_q_2
28 s01c_q_3
29 s01c_q05
30 s01c_q_4
31 s01c_q_5
32 s01c_q06
33 s01c_q07
34 resid2a
35 s02a_q03
36 s02a_q04
37 s02a_q05
38 s02a_q06
39 s02a_q07
40 resid2b
41 s02b_q01
42 s02b_q02
43 s02b_q03
44 s02b_q04
45 s02b_q05
46 s02b_q06
47 s02b_q07
48 s02b_q08
49 s02b_q_1
50 s02b_q_2
51 s02b_q_3
52 s02b_q_4
53 s02b_q_5
54 s02b_q_6
55 s02b_q_7
56 s02b_q_8
57 s02b_q_9
58 s02b__10
59 s02b__11
60 s02b__12
61 s02b__13
62 s02b__14
63 s02b__15
64 s03a_q01
65 s03a_q02
66 s03a_q_1
67 s03a_q03
68 s03a_q_2
69 s03a_q04

s01a_q10: mother id
s01a_q11: Lived abroad more than 6 m?
s01a_q12: why did return?
s01b_q01: did work for livelihood during last 7 days?
s01b_q02: was avaible for work during last 7 days?
s01b_q03: did look for work during last 7 days
s01b_q04: Reason for not looking for work
respondent identification code - section 1.c
s01c_q01: has benefitted from social safety?
s01c_q02: In what program has been included?
s01c_q03m: when enlisted (month)s01c_q03y: when enlisted (year)
s01c_q04t: how much entitled cash (taka)
s01c_q04m: how much entitled in kind (code of item)
s01c_q04k: how much entitled in kind (kg)s01c_q05t: how much actually received in cash?
s01c_q05n: how much actually received in kind (code of item)
s01c_q05k: how much actually received in kind (kg)s01c_q06: amount spent for inclusions01c_q07: cause why not included
respondent identification code - section 2.as02a_q03: can read a letter?
s02a_q04: can write a letter?
s02a_q05: highest class passed
s02a_q06: where attended/attending?
s02a_q07: what type of school attended/attending?
respondent identification code - section 2.b
s02b_q01: are you attending education institution?
s02b_q02: what class attending?
s02b_q03: receiving stipend for primary?
s02b_q04: how much you received in 12 m?
s02b_q05: receiving secondary stipend?
s02b_q06: how much received in 12 m?
s02b_q07: received benefit in the tuition waiver?
s02b_q08a: Amount spent on admission
s02b_q08b: Amount spent on annual session fee
s02b_q08c: Amount spent on registration
s02b_q08d: Amount spent on examination fee
s02b_q08e: Amount spent on tuituion fee
s02b_q08f: Amount spent on text books
s02b_q08g: Amount spent on exercise books
s02b_q08h: Amount spent on uniform
s02b_q08i: Amount spent on private tution
s02b_q08j: Amount spent on hostel/messs02b_q08k: Amount spent on conveyances02b_q08l: Amount spent on tiffins02b_q08l: Amount spent on internet/e-mails02b_q08n: Amount spent on schooling donation
s02b_q08o: Amount spent on schooling others02b_q08p: Amount spent on schooling total
s03a_q01: have suffered chronic illness?
s03a_q02: what chronic illness? 1
s03a_q02: what chronic illness? 2
s03a_q03y: for how long had illness? (years)
s03a_q03m: for how long had illness? (months)
s03a_q04: have suffered illness in last 30 days?

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70 s03a_q05
71 s03a_q_3
72 s03a_q_4
73 s03a_q06
74 s03a_q07
75 s03a_q08
76 s03a_q_5
77 s03a_q09
78 s03a_q10
79 s03a_q11
80 s03a_q12
81 s03a_q13
82 s03a_q_6
83 s03a_q14
84 s03a_q_7
85 s03a_q15
86 s03a_q16
87 s03a_q17
88 s03a_q_8
89 s03a_q_9
90 s03a__10
91 s03a__11
92 s03a__12
93 s03a__13
94 s03a__14
95 s03a__15
96 s03a__16
97 s03a__17
98 s03a_q18
99 s03a__18
100 s03a__19
101 s03b_q01
102 s03b_q_1
103 s03b_q02
104 s03b_q03
105 s03b_q04
106 s03b_q05
107 s03b_q_2
108 s03b_q_3
109 s03b_q_4
110 s03b_q_5
111 s03b_q_6
112 s03b_q_7
113 s03b_q_8
114 s03b_q_9
115 s03b_q06
116 s03b_q07
117 s03b_q08
118 s03c_q01
119 s03c_q02
120 s03c_q03
121 s03c_q04
122 s03c_q05
123 s03c_q06
124 s03c_q07
125 s03c_q08

s03a_q05_1: what type of illness? 1
s03a_q05_2: what type of illness? 2
s03a_q05_3: what type of illness? 3
s03a_q06: have sought medical treatment?
s03a_q07: If not why not?
s03a_q08_1: If yes who was consulted (1st)s03a_q08_2: who was consulted (2nd)s03a_q09: After how many days began consultation?
s03a_q10: From where got medicines?
s03a_q11: did you pay for the medicines?
s03a_q12: how did travel to rervice provider?
s03a_q13h: how much time it took? (hour)s03a_q13m: how much time it took? (minutes)
s03a_q14h: how long you had to wait? (hour)s03a_q14m: how long you had to wait? (minutes)
s03a_q15: why you chose this provider?
s03a_q16: Did provider give enough time for you?
s03a_q17a: Amount paid for consultation/visit
s03a_q17b: Amount paid for hospital/clinic
s03a_q17c: Amount paid for medicines
s03a_q17d: Amount paid for cost of test/investigation
s03a_q17e: Amount paid for conveyances03a_q17f: Amount paid for tips on
s03a_q17g: Amount paid for other services
s03a_q17hc: total cost of treatment? (maternity clinic)
s03a_q17hm: total cost of treatment? (maternity midwife)
s03a_q17ho: total cost of treatment? (maternity others)
s03a_q17t: total cost of treatment? (total cost)
s03a_q18_1: how financed treatment? 1
s03a_q18_2: how financed treatment? 2
s03a_q18_3: how financed treatment? 3
s03b_q01m: when the child was born? (month)s03b_q01y: when the child was born? (year)
s03b_q02: present age of child? (in months)
s03b_q03: has he/she ever been immunized?
s03b_q04: Do you have immunization card?
s03b_q05bcg: bcg?
s03b_q05dpt1: dpt1?
s03b_q05dpt2: dpt2?
s03b_q05dpt3: dpt3?
s03b_q05p1: polio1?
s03b_q05p2: polio2?
s03b_q05p3: polio3?
s03b_q05m: measles?
s03b_q05h: hepatitis?
s03b_q06: where was most recent immunization?
s03b_q07: who influenced you to immunize?
s03b_q08: received vitamin-a capsules in last 12m?
s03c_q01: have you ever given birth?
s03c_q02: in what year you gave the last one?
s03c_q03: did you attend prenatal consultations?
s03c_q04: From where you received this care?
s03c_q05: In what month you went first time?
s03c_q06: did you receive tetanus vaccine?
s03c_q07: who assisted with this birth?
s03c_q08: where did you give birth?

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126 s03c_q09
127 s03c_q10
128 s03d_q02
129 s03d_q03
130 s03d_q04
131 s03d_q05
132 s03d_q06
133 s03d_q07
134 s03d_q08
135 s03d_q09
136 s03d_q10
137 s03d_q11
138 s03d_q12
139 s03d_q13
140 s03d_q14
141 s03d_q15
142 s03d_q16
143 s03d_q17
144 s03d_q18
145 s03d_q19
146 s03d_q20
147 s03d_q_1
148 s03d_q_2
149 s03d_q21

s03c_q09: did you visit post-natal checkup?
s03c_q10: where did you go for checkup?
s03d_q02: difficulty seeing?
s03d_q03: how old was when difficulty began?
s03d_q04: what was the cause?
s03d_q05: difficulty hearing?
s03d_q06: how old was when it began?
s03d_q07: what was the cause?
s03d_q08: difficulty walking?
s03d_q09: how old was when it began?
s03d_q10: what was the cause?
s03d_q11: difficulty remembering?
s03d_q12: how old was when it began?
s03d_q13: what was the cause?
s03d_q14: difficulty with self care?
s03d_q15: how old was when it began?
s03d_q16: what was the cause?
s03d_q17: difficulty in comunicating?
s03d_q18: how old was when it began?
s03d_q19: what was the cause?
s03d_q20h: difficulty reduced work at home?
s03d_q20s: difficulty reduced work at school?
s03d_q20w: difficulty reduced work at work?
s03d_q21: what measures taken to improve?

### rt003.dta ###
1 psu
PSU
2 hhold
household3 serial
Activity serial
4 idcode
Id code5 s04a_q01
s04a_q01d: what activities did you do? description
6 s04a_q_1
s04a_q01o: what activities did you do? occupation code (2-digit)
7 s04a_q_2
s04a_q01i: what activities did you do? industry code (2-digit)
8 s04a_q02
s04a_q02: how many month you do this activity?
9 s04a_q03
s04a_q03: how many days per month?
10 s04a_q04
s04a_q04: how many hours per day?
11 s04a_q05
s04a_q05ru: where did you do this activity? R/U code
12 s04a_q_3
s04a_q05d: where did you do this activity? district code
13 s04a_q06
s04a_q06: what kind of activities?
14 s04a_q07
s04a_q07: what was your work status? agriculture
15 s04a_q08
s04a_q08: whats was your work status? non agri
16 idcode2
Id Code in 4B
17 s04b_q01
s04b_q01: were you paid on daily basis?
18 s04b_q02
s04b_q02_1: highest daily wage in cash?
19 s04b_q_1
s04b_q02_2: lowest daily wage in cash?
20 s04b_q_2
s04b_q02_3: average daily wage in cash?
21 s04b_q03
s04b_q03: Did you receive payments in-kind?
22 s04b_q04
s04b_q04: what type of item received in payment?
23 s04b_q05
s04b_q05kg: how much you received per day? KG
24 s04b_q_3
s04b_q05t: What was the total price received per day? taka
25 s04b_q06
s04b_q06: What type of org. you work for?
26 s04b_q07
s04b_q07: Gross monthly cash remuneration
27 s04b_q08
s04b_q08: Net cash/remuneration take-home monthly?
28 s04b_q09
s04b_q09: Other benefits you received in last 12 months?

Bangladesh HIES 2010 Manual (Version 1.0)

30### rt004.dta ###
1 psu
psu
2 hhold
household3 enumberenterprise number
4 s05a_q01
s05a_q01d: what kind of entreprise? description
5 s05a_q_1
s05a_q01i: what kind of entreprise? industry
6 s05a_q02
s05a_q02_1: ID of member who work in the enterprise?
7 s05a_q_2
s05a_q02_2: ID of member who work in entreprise?
8 s05a_q_3
s05a_q02_3: ID of member who work in an entreprise?
9 s05a_q_4
s05a_q02_4: ID of member who work in an entreprise?
10 s05a_q_5
s05a_q02_5: ID of member who work in an entreprise?
11 s05a_q03
s05a_q03y: for how long has been operating? years
12 s05a_q_6
s05a_q03m: for how long has been operating? months
13 s05a_q04
s05a_q04: where do you operate the entreprise?
14 s05a_q05
s05a_q05: Months the enterprise was operational in the last 12 months?
15 s05a_q06
s05a_q06: what is houselhold's share in this entreprise?
16 s05a_q07
s05a_q07: what is the share of profit?
17 s05a_q08
s05a_q08_1: who are your costumers? 1st
18 s05a_q_7
s05a_q08_2: who are your costumers? 2nd
19 s05a_q09
s05a_q09: is the entreprise registred officially?
20 s05a_q10
s05a_q10_1: Source of finance when established? 1st
21 s05a_q_8
s05a_q10_2: Source of finance when established? 2nd
22 s05b_q11
s05b_q11: Number of salaried persons in the last 12 months
23 s05b_q12
s05b_q12_1: what problems faced in running your business? 1
24 s05b_q_1
s05b_q12_2: what problems faced in running your business? 2
25 s05b_q13
s05b_q13: gross revenues last 12m?
26 s05b_q14
s05b_q14: expenditures on salary/wages last 12m?
27 s05b_q15
s05b_q15: expenditure on rent last 12m?
28 s05b_q16
s05b_q16: expenditure on raw materials last 12m?
29 s05b_q17
s05b_q17: expenditure on fuel, kerosene, electricity.. last 12m?
30 s05b_q18
s05b_q18: value of finished good last 12m?
31 s05b_q19
s05b_q19: other operating expenses last 12m?
32 s05b_q20
s05b_q20: net revenues last 12m
33 s05b_q21
s05b_q21: expenditure on capital goods last 12m?
34 s05b_q22
s05b_q22: sale of assets last 12m?
35 s05b_q23
s05b_q23: What is the selling price of this entreprise?

### rt005.dta ###
1 psu
psu
2 hhold
household3 shock_coshock code
4 s06b_q02
s06b_q02: did you experience this shock?
5 s06b_q03
s06b_q03: when did the shock first occur?
6 s06b_q04
s06b_q04: how long did the shock last? (days)
7 s06b_q05
s06b_q05a: was a decline in income?
8 s06b_q_1
s06b_q05b: was a decline in assets?
9 s06b_q_2
s06b_q05c: was a decline in food production?
10 s06b_q_3
s06b_q05d: was a decline in food purchased?
11 s06b_q06
s06b_q06_1: how coped with this shock? 1st
12 s06b_q_4
s06b_q06_2: how coped with this shock? 2nd
13 s06b_q_5
s06b_q06_3: how coped with this shock? 3rd###

rt006.dta###

Bangladesh HIES 2010 Manual (Version 1.0)

31

1 psu
2 hhold
3 ln
4 crop_cod
5 s07b_q02
6 s07b_q03
7 s07b_q04
8 s07b_q_1
9 s07b_q05
10 s07b_q06
11 s07b_q07
12 s07b_q08
13 s07b_q_2
14 s07b_q_3
15 s07b_q_4
16 s07b_q_5
17 s07b_q_6
18 s07b_q_7
19 pricepsu
householdline number
crop code
s07b_q02: did you cultivate this crop?
s07b_q03: how much land you cultivated? decimals
s07b_q04k: how much crop produced? kg
s07b_q04t: price of each KG? takas07b_q05: how much your household consumed?
s07b_q06: how much your household sold?
s07b_q07: how much your household stored?
s07b_q08: how much given to landlord?
s07b_q08: how much paid for wages?
s07b_q08: how much used as seed?
s07b_q08: how much as feed for animals?
s07b_q08: how much waste?
s07b_q08: how much in other uses?
s07b_q08: how much in total?

### rt007.dta ###
1 psu
psu
2 hhold
household3 liv_code
livestock code
4 s07c_q02
s07c_q02n: how many of them you have? number
5 s07c_q_1
s07c_q02t: Total value of them? taka
6 s07c_q03
s07c_q03n: how many born/purchased? number
7 s07c_q_2
s07c_q03t: Total value of them? taka
8 s07c_q04
s07c_q04n: how many died/sell? number
9 s07c_q_3
s07c_q04t: Total value of them? taka
10 s07c_q05
s07c_q05n: how many household consume? number
11 s07c_q_4
Total value of them? taka### rt008.dta ###
1 psu
psu
2 hhold
household3 prod_cod
product code
4 s07c_q06
s07c_q06q: how much produced? quantity
5 s07c_q_1
s07c_q06t: value? taka
6 s07c_q07
s07c_q07q: how much did you sell? quantity
7 s07c_q_2
s07c_q07t: value? taka
8 s07c_q08
s07c_q08q: how much did you consume? quantity
9 s07c_q_3
s07c_q08t: value? taka### rt009.dta ###
1 psu
psu
2 hhold
household3 fish_act
code fishing
4 s07c_q10
s07c_q10kg: how much you produce? kg
5 s07c_q_1
value? taka
6 s07c_q11
s07c_q11kg: how much your household sold? kg
7 s07c_q_2
s07c_q11t: value? taka
8 s07c_q12
s07c_q12kg: how much your household consumed? kg

Bangladesh HIES 2010 Manual (Version 1.0)

32

9 s07c_q_3

value? kg### rt010.dta ###
1 psu
psu
2 hhold
household
3 forestry
code forestry
4 s07c_q14
s07c_q14n: how many trees? no.
5 s07c_q_1
s07c_q14t: value? taka
6 s07c_q15
s07c_q15: value of trees sold? taka
7 s07c_q16
s07c_q16: value of trees consumed? taka### rt011.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 exp_agri
agric code of the sector
5 s07d_q01
s07d_q01: did your household spend any money on...?
6 s07d_q02
s07d_q02kg: how much your household spend? kg
7 s07d_q_1
s07d_q02t: household spend? taka### rt012.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 agric_asagricultural asset code
5 s07e_q01
s07e_q01n: how many of them do you own? no.
6 s07e_q_1
s07e_q01t: value? taka
7 s07e_q02
s07e_q02n: how many your household bought? no.
8 s07e_q_2
s07e_q02t: value? taka
9 s07e_q03
s07e_q03n: how many your household sold? no.
10 s07e_q_3
s07e_q03t: value? taka
11 s07e_q04
s07e_q04: how much household earned from rental? taka### rt013.dta ###
1 psu
psu
2 hhold
household3 migrant_
migrant number
4 s08c_q03
s08c_q03: name
5 s08c_q04
s08c_q04: relationship to head
6 s08c_q05
s08c_q05m: how long ago did he/she migrate? months
7 s08c_q_1
s08c_q05y: how long ago did he/she migrate? years
8 s08c_q06
s08c_q06: where is working?
9 s08c_q07
s08c_q07: in-country zila code
10 s08c_q08
s08c_q08: if abroad, write the country code
11 s08c_q09
s08c_q09: age
12 s08c_q10
s08c_q10: sex
13 s08c_q11
s08c_q11: level of education
14 s08c_q12
s08c_q12: occupation
15 s0bc_q13
s0bc_q13: how many times he/she sent money?
16 s08c_q14
s08c_q14: total amount of money sent?
17 s08c_q15
s08c_q15: how he/she sent money?
18 s08c_q16
s08c_q16_1: goods that he/she sent 1 (code)

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33

19 s08c_q_2
20 s08c_q_3
21 s08c_q17

s08c_q16_2: goods that he/she sent 2 (code)
s08c_q16_3: goods that he/she sent 3 (code)
s08c_q17: total value of goods that he/she sent?

### rt014.dta ###
1 psu
psu
2 hhold
household3 loan_num
loan number
4 idcode
id code5 s08d_q05
s08d_q05: what was the source of this credit?
6 s08d_q06
s08d_q06: how much money was borrowed?
7 s08d_q07
s08d_q07: how long is the payment period?
8 s08d_q08
s08d_q08m: interest applied per month
9 s08d_q_1
s08d_q08y: interest applied per year
10 s08d_q09
s08d_q09f: how often you make payments? frequency
11 s08d_q_2
s08d_q09a: how often you make payments? amount
12 s08d_q10
s08d_q10: completed repayment?
13 s08d_q11
s08d_q11: amount of unpaid loan?
14 s08d_q12
s08d_q12: what was the purpose of loan?
15 s08d_q13
s08d_q13: would you like to borrow more money?
16 s08d_q14
s08d_q14: how much would you want to borrow?

### rt015.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemfood item code
5 s09a1d01
s09a1d01_qty: quantity
6 s09a1d_1
s09a1d01_u: unit
7 s09a1d_2
s09a1d01_v: value
8 s09a1d_3
s09a1d01_s: major source
9 s09a1d02
s09a1d02_qty: quantity
10 s09a1d_4
s09a1d02_u: unit
11 s09a1d_5
s09a1d02_v: value
12 s09a1d_6
s09a1d02_s: major source
13 s09a1d03
s09a1d03_qty: quantity
14 s09a1d_7
s09a1d03_u: unit
15 s09a1d_8
s09a1d03_v: value
16 s09a1d_9
s09a1d03_s: major source
17 s09a1d04
s09a1d04_qty: quantity
18 s09a1_10
s09a1d04_u: unit
19 s09a1_11
s09a1d04_v: value
20 s09a1_12
s09a1d04_s: major source
21 s09a1d05
s09a1d05_qty: quantity
22 s09a1_13
s09a1d05_u: unit
23 s09a1_14
s09a1d05_v: value
24 s09a1_15
s09a1d05_s: major source
25 s09a1d06
s09a1d06_qty: quantity
26 s09a1_16
s09a1d06_u: unit
27 s09a1_17
s09a1d06_v: value
28 s09a1_18
s09a1d06_s: major source
29 s09a1d07
s09a1d07_qty: quantity
30 s09a1_19
s09a1d07_u: unit
31 s09a1_20
s09a1d07_v: value

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32 s09a1_21
33 s09a1d08
34 s09a1_22
35 s09a1_23
36 s09a1_24
37 s09a1d09
38 s09a1_25
39 s09a1_26
40 s09a1_27
41 s09a1d10
42 s09a1_28
43 s09a1_29
44 s09a1_30
45 s09a1d11
46 s09a1_31
47 s09a1_32
48 s09a1_33
49 s09a1d12
50 s09a1_34
51 s09a1_35
52 s09a1_36
53 s09a1d13
54 s09a1_37
55 s09a1_38
56 s09a1_39
57 s09a1d14
58 s09a1_40
59 s09a1_41
60 s09a1_42
61 t
62 kcals09a1d07_s: major source
s09a1d08_qty: quantity
s09a1d08_u: unit
s09a1d08_v: value
s09a1d08_s: major source
s09a1d09_qty: quantity
s09a1d09_u: unit
s09a1d09_v: value
s09a1d09_s: major source
s09a1d10_qty: quantity
s09a1d10_u: unit
s09a1d10_v: value
s09a1d10_s: major source
s09a1d11_qty: quantity
s09a1d11_u: unit
s09a1d11_v: value
s09a1d11_s: major source
s09a1d12_qty: quantity
s09a1d12_u: unit
s09a1d12_v: value
s09a1d12_s: major source
s09a1d13_qty: quantity
s09a1d13_u: unit
s09a1d13_v: value
s09a1d13_s: major source
s09a1d14_qty: quantity
s09a1d14_u: unit
s09a1d14_v: value
s09a1d14_s: major source
total indicator (auxiliary variable)
kcal per 100 gm or ml### rt016.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemfood item code
5 s09b1w1_
s09b1w1_q: quantity
6 s09b1w_1
s09b1w1_gm: unit
7 s09b1w_2
s09b1w1_v: value
8 s09b1w_3
s09b1w1_m: major source
9 s09b1w2_
s09b1w2_q: quantity
10 s09b1w_4
s09b1w2_gm: unit
11 s09b1w_5
s09b1w2_v: value
12 s09b1w_6
s09b1w2_m: major source
13 kcalkcal per 100 gm or ml### rt017.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemnon-food item code
5 s09c1_q0
s09c1_q01: value of goods received wage/cash?
6 s09c1__1
s09c1_q02: value of goods produced/gifts?

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7 s09c1__2

s09c1_q03: sum of col.1 & 2### rt018.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemnon-food item code
5 s09d1_q0
s09d1_q01: how many did you buy/produce/receive?No.
6 s09d1__1
s09d1_q02: what is the value?

### rt019.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemnon-food item code
5 s09d2_q0
s09d2_q01: what is the value?

### rt020.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 dg_code
durable good code
5 s09e_q01
s09e_q01: owns item?
6 s09e_q02
s09e_q02: how many do you own?
7 s09e_q03
s09e_q03: Expected value if want to sell
8 s09e_q04
s09e_q04: how much did it cost (if bought past 12m)



rt002 and rt013 include person name, which should be dropped in the resampled micro data.

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4.3 IdentifierHousehold identifier is the combination of psu and hhold.

> d<-outfiles[[1]]> dim(d)[1] 12240 151> head(d[,1:13])
stratum psu hhold team term region district thana union mouzaspc
1
01 010
002
1
2
10
6
7
33
513 Rural
2
01 010
010
1
2
10
6
7
33
513 Rural
3
01 010
026
1
2
10
6
7
33
513 Rural
4
01 010
038
1
2
10
6
7
33
513 Rural
5
01 010
046
1
2
10
6
7
33
513 Rural
6
01 010
058
1
2
10
6
7
33
513 Rural
urbanrurwgt
1
1 2507.01
2
1 2507.01
3
1 2507.01
4
1 2507.01
5
1 2507.01
6
1 2507.01> hhid<-paste(d$psu,d$hhold,sep="")> length(unique(hhid))[1] 12240> sum(duplicated(hhid))
[1] 0



Generated hhid for each data frame> outfiles.save<-outfiles> for(j in 1:20){+ d<-outfiles[[j]]+ d$hhid<-paste(d$psu,d$hhold,sep="")+ outfiles[[j]]<-d+ cat(j,":",ncol(d),"\n")+ }# Number of variables for each data frame
1 : 152
2 : 150
3 : 294 : 36
5 : 14
6 : 20
7 : 12
8 : 10
9 : 10
10 : 8
11 : 8
12 : 12
13 : 22
14 : 17
15 : 63
16 : 14
17 : 8Bangladesh HIES 2010 Manual (Version 1.0)

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18 : 7
19 : 6
20 : 9# Example of rt019; outfiles[[19]]> head(outfiles[[19]])
psu hhold ln item s09d2_q0
hhid
1 010
002 1 360
2200 010002
2 010
002 2 361
800 010002
3 010
002 6 365
900 010002
4 010
002 12 372
500 010002
5 010
002 14 390
1000 010002
6 010
002 17 393
1000 010002

Person identifier is the combination of psu, hhold and idcode for rt002 and rt003.

# rt002> d<-outfiles[[2]]> d$pid<-paste(d$psu,d$hhold,d$idcode,sep="")> dim(d)[1] 55580 151> head(d[,c(1:5,150,151)])
psu hhold idcode resid1as01a_q01
hhid
pid
1 010
002
01
2 Mofajall gazi 010002 01000201
2 010
002
02
2
paria begum 010002 01000202
3 010
002
03
2
sadia 010002 01000203
4 010
002
04
2 Aminul islam 010002 01000204
5 010
002
05
2
Farjana 010002 01000205
6 010
002
06
2
suraia 010002 01000206> outfiles[[2]]<-d
The variable of pid is an unique identifier.
> length(unique(pid))[1] 55580> sum(duplicated(d$pid))[1] 0# rt003> d<-outfiles[[3]]> d$pid<-paste(d$psu,d$hhold,formatC(d$idcode,width=2,flag="0"),sep="")> dim(d)[1] 18067
30> head(d[,c(1:5,29,30)])
psu hhold serial idcode
s04a_q01
hhid
pid
1 010
002
01
1 Service holder 010002 01000201
2 010
002
02
1
Farmer 010002 01000201
3 013
168
01
1
Rickshaw 013168 01316801
4 010
010
01
1
Sales man 010010 01001001
5 010
026
01
3
Labour 010026 01002603
6 010
026
02
2
Labour 010026 01002602> outfiles[[3]]<-dBangladesh HIES 2010 Manual (Version 1.0)

38

Remarks: There are 1,821 duplicated pid in rt003. This means that some persons are conducting
multiple activities.
> sum(duplicated(d$pid))[1] 1821
Number of persons by the number of activities> addmargins(table(tapply(d$pid,d$pid,length)))
1
2
3
4
Sum
14498 1677
69
2 16246> 14498+1677*2+69*3+2*4[1] 18067

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4.4 WeightHousehold weight is included in the data frame “rt001”.



The estimated number of households is 33.03 million.

> d<-outfiles[[1]]> dim(d)[1] 12240 152> sum(d$wgt)[1] 33028014



The estimated number of household members is 148.49 million.

> df<-outfiles[[2]]> df<-merge(df,d[,c("hhid","wgt")],by="hhid",all.x=T)> dim(df)[1] 55580 152> sum(df$wgt)[1] 148488530

The estimated household size is 4.50 persons.

> nrow(df)/nrow(d)[1] 4.54085> sum(df$wgt)/sum(d$wgt)[1] 4.495836

Summary
Un-weighted

Weighted

Number of households

12,24033,028,014

Number of household members

55,580148,488,530

Household size

4.54

4.50

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40

4.5 Total income and expenditure per household

The variables of household total income and expenditure are not found in the provided data set.



Table 1 (at page 201) of the survey report indicates that the average monthly income perhousehold is 11479.47 taka, and the average monthly expenditure per household is 11199.59 taka.

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5. Data Check
5.1 Structure of each data file
# Displayed the names and types of variables> Rnames<-sub(".dta","",STATA.files)> for(j in 1:20){+ cat("##",j,"#### ",Rnames[j]," #########\n")+ print(str(outfiles[[j]]))+ cat("\n\n")+ }## 1 #### rt001 #########'data.frame':
12240 obs. of 151 variables:$ stratum : chr "01" "01" "01" "01" ...
$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "010" "026" "038" ...
$ team: int 1 1 1 1 1 1 1 1 1 1 ...
$ term: int 2 2 2 2 2 2 2 2 2 2 ...
$ region : int 10 10 10 10 10 10 10 10 10 10 ...
$ district: int 6 6 6 6 6 6 6 6 6 6 ...
$ thana : int 7 7 7 7 7 7 7 7 7 7 ...
$ union : int 33 33 33 33 33 33 33 33 33 33 ...
$ mouza : int 513 513 513 513 513 513 513 513 513 513 ...
$ spc: chr "Rural" "Rural" "Rural" "Rural" ...
$ urbanrur: int 1 1 1 1 1 1 1 1 1 1 ...
$ wgt: num 2507 2507 2507 2507 2507 ...
$ s06a_q01: int 2 2 1 2 1 1 2 2 2 1 ...
$ s06a_q02: int 4 2 3 3 2 3 3 3 6 4 ...
$ s06a_q03: int 2 1 2 2 1 1 1 1 1 1 ...
$ s06a_q04: int 1 1 1 2 1 1 1 1 1 1 ...
$ s06a_q05: int 2 2 2 2 2 2 1 2 2 2 ...
$ s06a_q06: int 2 2 2 2 2 2 2 2 2 2 ...
$ s06a_q07: int 720 180 700 208 1200 238 810 192 990 408 ...
$ s06a_q08: int 3 1 5 1 1 1 3 1 3 1 ...
$ s06a_q09: int 2 2 2 2 2 2 2 2 3 2 ...
$ s06a_q10: int 2 1 2 1 2 2 2 1 0 1 ...
$ s06a_q11: int 0 2 0 2 0 0 0 2 0 2 ...
$ s06a_q12: int 0 2 0 2 0 0 0 2 0 2 ...
$ s06a_q13: int 3 3 3 3 2 3 3 3 3 3 ...
$ s06a_q14: int 2 1 2 1 2 2 1 1 1 1 ...
$ s06a_q15: int 0 14 0 14 0 0 22 14 22 14 ...
$ s06a_q16: int 1 1 2 1 2 2 1 1 1 1 ...
$ s06a_q17: int 2 2 2 2 2 2 2 2 2 2 ...
$ s06a_q18: int 2 2 2 2 2 2 2 2 2 2 ...
$ s06a_q19: int 2 2 2 2 2 2 2 2 2 2 ...
$ s06a_q20: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06a_q21: int 2 1 1 1 2 1 1 1 1 1 ...
$ s06a_q22: int 100000 60000 60000 250000 500000 80000 400000 75000 300000 200000 ...
$ s07a_q01: int 100 0 20 30 0 70 0 0 400 172 ...
$ s07a_q02: int 10 15 30 6 0 20 10 3 20 40 ...
$ s07a_q03: int 0 0 0 0 0 2 0 0 0 0 ...
$ s07a_q04: int 0 0 0 0 0 0 0 0 160 0 ...
$ s07a_q05: int 0 0 0 30 0 70 0 0 350 0 ...
$ s07a_q06: int 110 15 50 6 0 22 10 3 230 212 ...

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$ s07b_q01: int$ s07c_q01: int$ s07c_q09: int$ s07c_q13: int$ s08a_q01: int$ s08a_q_1: int$ s08a_q02: int$ s08a_q03: int$ s08a_q04: int$ s08a_q05: int$ s08a_q06: int$ s08a_q07: int$ s08a_q08: int$ s08a_q09: int$ s08a_q10: int$ s08a_q11: int$ s08a_q12: int$ s08b_q01: int$ s08b_q02: int$ s08b_q03: int$ s08b_q_1: int$ s08b_q_2: int$ s08b_q04: int$ s08b_q05: int$ s08b_q06: int$ s08b_q07: int$ s08b_q08: int$ s08b_q09: int$ s08b_q10: int$ s08b_q11: int$ s08b_q12: int$ s08b_q13: int$ s08c_q01: int$ s08c_q02: int$ s08d_q01: int$ s08d_q02: int$ s08d_q03: int$ s08d_q04: int$ s09a1d01: int$ s09a1d_1: int$ s09a1d_2: int$ s09a1d_3: int$ s09a1d_4: chr$ s09a1d02: int$ s09a1d_5: int$ s09a1d_6: int$ s09a1d_7: int$ s09a1d_8: chr$ s09a1d03: int$ s09a1d_9: int$ s09a1_10: int$ s09a1_11: int$ s09a1_12: chr$ s09a1d04: int$ s09a1_13: int$ s09a1_14: int

1 2 2 1 2 2 2 2 1 1 ...
1 2 2 1 2 2 2 1 1 1 ...
2 2 2 2 2 2 1 2 2 2 ...
2 2 2 2 2 2 2 2 2 2 ...
2 2 2 2 2 1 2 2 2 2 ...
0 0 0 0 0 2 0 0 0 0 ...
0 0 0 0 0 8000 0 0 0 0 ...
2 2 2 2 2 2 2 2 2 2 ...
0 0 0 0 0 0 0 0 0 0 ...
2 2 2 2 2 2 2 2 2 2 ...
0 0 0 0 0 0 0 0 0 0 ...
2 1 2 1 2 2 2 2 2 2 ...
0 90000 0 500 0 0 0 0 0 0 ...
2 2 2 2 2 2 2 2 2 2 ...
0 0 0 0 0 0 0 0 0 0 ...
2 2 2 2 2 2 2 2 2 2 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 7000 0 0 1200 14000 ...
0 0 4000 3000 0 0 0 0 20000 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 600 0 0 0 0 0 0 0 ...
30000 0 0 0 0 0 0 0 0 0 ...
70000 0 0 0 0 0 0 40000 0 25000 ...
5 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
1 2 2 2 2 2 2 1 2 1 ...
1 2 2 2 2 2 2 1 2 1 ...
1 2 2 2 2 2 1 2 1 2 ...
1 2 2 2 2 2 2 2 2 2 ...
1 2 2 2 2 2 2 2 2 2 ...
1 2 2 1 2 2 2 2 2 2 ...
0 1 0 0 2 0 1 1 0 1 ...
1 0 0 1 1 0 1 0 0 1 ...
8 0 2 2 0 1 1 2 2 1 ...
3 1 1 3 1 0 1 1 2 2 ...
"21/02/2010" "21/02/2010" "21/02/2010" "21/02/2010" ...
0 1 0 2 2 0 2 1 0 0 ...
2 0 0 0 1 0 1 0 0 0 ...
3 0 2 1 0 1 2 3 2 1 ...
3 1 1 3 1 0 3 4 2 2 ...
"22/02/2010" "22/02/2010" "22/02/2010" "22/02/2010" ...
1 1 0 1 2 0 1 3 0 1 ...
1 0 0 0 1 0 1 3 0 2 ...
3 0 2 1 0 1 1 5 2 1 ...
2 1 1 2 1 0 2 5 2 2 ...
"23/02/2010" "23/02/2010" "23/02/2010" "23/02/2010" ...
1 1 0 1 2 0 1 0 0 0 ...
1 0 0 0 1 0 1 0 0 0 ...
2 1 2 1 0 2 1 3 2 1 ...

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$ s09a1_15: int 2 0 1 2 1 1 2 2 2 2 ...
$ s09a1_16: chr "24/02/2010" "24/02/2010" "24/02/2010" "24/02/2010" ...
[list output truncated]- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 11:57"- attr(*, "formats")= chr "%2s" "%3s" "%3s" "%8.0g" ...
- attr(*, "types")= int 2 3 3 251 251 251 251 251 251 252 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "Stratum" "Primary Sampling Unit" "Household Number" "" ...
- attr(*, "version")= int 12
NULL## 2 #### rt002 #########'data.frame':
55580 obs. of 149 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ idcode : chr "01" "02" "03" "04" ...
$ resid1a : int 2 2 2 2 2 2 2 2 2 2 ...
$ s01a_q01: chr "Mofajall gazi" "paria begum" "sadia" "Aminul islam" ...
$ s01a_q02: int 1 2 2 1 2 2 2 2 1 2 ...
$ s01a_q03: int 1 2 3 3 3 5 4 5 1 2 ...
$ s01a_q04: int 54 42 10 14 21 4 23 3 33 26 ...
$ s01a_q05: int 1 1 1 1 1 1 1 1 1 1 ...
$ s01a_q06: int 1 1 2 2 1 2 1 2 1 1 ...
$ s01a_q07: int 1 2 2 2 2 2 2 2 1 2 ...
$ s01a_q08: int 2 1 0 0 99 0 99 0 2 1 ...
$ s01a_q09: int 99 99 1 1 1 99 99 99 99 99 ...
$ s01a_q10: int 99 99 2 2 2 5 99 7 99 99 ...
$ s01a_q11: int 2 2 2 2 2 2 2 2 2 2 ...
$ s01a_q12: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01b_q01: int 1 2 2 2 2 0 2 0 1 2 ...
$ s01b_q02: int 0 2 2 2 2 0 2 0 0 2 ...
$ s01b_q03: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01b_q04: int 0 2 3 3 11 0 2 0 0 2 ...
$ resid1c : int 2 2 2 2 2 NA 2 NA 2 2 ...
$ s01c_q01: int 2 2 2 2 2 0 2 0 2 2 ...
$ s01c_q02: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q03: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q_1: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q04: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q_2: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q_3: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q05: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q_4: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q_5: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q06: int 0 0 0 0 0 0 0 0 0 0 ...
$ s01c_q07: int 2 2 2 2 2 0 2 0 2 2 ...
$ resid2a : int 2 2 2 2 2 NA 2 NA 2 2 ...
$ s02a_q03: int 1 1 1 1 1 0 1 0 1 1 ...
$ s02a_q04: int 1 1 1 1 1 0 1 0 1 1 ...
$ s02a_q05: int 5 5 4 7 6 0 10 0 10 9 ...
$ s02a_q06: int 1 1 1 1 1 0 4 0 1 1 ...
$ s02a_q07: int 1 1 1 2 2 0 2 0 2 2 ...
$ resid2b : int 2 2 2 2 2 NA 2 NA 2 2 ...
$ s02b_q01: int 2 2 1 1 2 0 2 0 2 2 ...

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$ s02b_q02: int$ s02b_q03: int$ s02b_q04: int$ s02b_q05: int$ s02b_q06: int$ s02b_q07: int$ s02b_q08: int$ s02b_q_1: int$ s02b_q_2: int$ s02b_q_3: int$ s02b_q_4: int$ s02b_q_5: int$ s02b_q_6: int$ s02b_q_7: int$ s02b_q_8: int$ s02b_q_9: int$ s02b__10: int$ s02b__11: int$ s02b__12: int$ s02b__13: int$ s02b__14: int$ s02b__15: int$ s03a_q01: int$ s03a_q02: int$ s03a_q_1: int$ s03a_q03: int$ s03a_q_2: int$ s03a_q04: int$ s03a_q05: int$ s03a_q_3: int$ s03a_q_4: int$ s03a_q06: int$ s03a_q07: int$ s03a_q08: int$ s03a_q_5: int$ s03a_q09: int$ s03a_q10: int$ s03a_q11: int$ s03a_q12: int$ s03a_q13: int$ s03a_q_6: int$ s03a_q14: int$ s03a_q_7: int$ s03a_q15: int$ s03a_q16: int$ s03a_q17: int$ s03a_q_8: int$ s03a_q_9: int$ s03a__10: int$ s03a__11: int$ s03a__12: int$ s03a__13: int$ s03a__14: int$ s03a__15: int$ s03a__16: int$ s03a__17: int

0 0 5 8 0 0 0 0 0 0 ...
0 0 2 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 2 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 150 150 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 400 400 0 0 0 0 0 0 ...
0 0 500 500 0 0 0 0 0 0 ...
0 0 700 1000 0 0 0 0 0 0 ...
0 0 2400 2400 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 1000 1200 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 5150 5650 0 0 0 0 0 0 ...
2 2 2 2 2 2 2 2 2 2 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
2 2 2 2 2 2 2 2 2 1 ...
0 0 0 0 0 0 0 0 0 9 ...
0 0 0 0 0 0 0 0 0 2 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 1 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 1 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 5 ...
0 0 0 0 0 0 0 0 0 1 ...
0 0 0 0 0 0 0 0 0 11 ...
0 0 0 0 0 0 0 0 0 1 ...
0 0 0 0 0 0 0 0 0 30 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 30 ...
0 0 0 0 0 0 0 0 0 6 ...
0 0 0 0 0 0 0 0 0 1 ...
0 0 0 0 0 0 0 0 0 50 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 550 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 200 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 800 ...

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$ s03a_q18: int 0 0 0 0 0 0 0 0 0 1 ...
$ s03a__18: int 0 0 0 0 0 0 0 0 0 0 ...
[list output truncated]- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:29"- attr(*, "formats")= chr "%3s" "%3s" "%2s" "%8.0g" ...
- attr(*, "types")= int 3 3 2 251 30 251 251 252 251 251 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "Primary sampling unit" "household" "personal identificationcode" "respondent identification code - section 1.a" ...
- attr(*, "version")= int 12
NULL## 3 #### rt003 #########'data.frame':
18067 obs. of 28 variables:$ psu: chr "010" "010" "013" "010" ...
$ hhold : chr "002" "002" "168" "010" ...
$ serial : chr "01" "02" "01" "01" ...
$ idcode : int 1 1 1 1 3 2 1 1 1 1 ...
$ s04a_q01: chr "Service holder" "Farmer" "Rickshaw" "Sales man" ...
$ s04a_q_1: chr "53" "61" "59" "42" ...
$ s04a_q_2: chr "15" "01" "60" "67" ...
$ s04a_q02: int 2 10 12 12 10 2 8 12 12 3 ...
$ s04a_q03: int 26 30 28 26 20 25 26 26 30 10 ...
$ s04a_q04: int 8 8 10 10 10 5 10 8 11 4 ...
$ s04a_q05: int 2 1 2 2 1 1 2 2 1 1 ...
$ s04a_q_3: chr "26" "06" "06" "26" ...
$ s04a_q06: int 2 1 2 2 2 2 2 2 2 2 ...
$ s04a_q07: int 0 2 NA 0 0 0 0 0 0 0 ...
$ s04a_q08: int 4 0 1 4 1 1 1 4 2 2 ...
$ idcode2 : int 1 NA 1 1 3 2 1 1 NA NA ...
$ s04b_q01: int 2 0 1 2 1 1 1 2 0 0 ...
$ s04b_q02: int 0 0 180 0 200 80 500 0 0 0 ...
$ s04b_q_1: int 0 0 100 0 100 80 300 0 0 0 ...
$ s04b_q_2: int 0 0 130 0 125 80 400 0 0 0 ...
$ s04b_q03: int 0 0 2 0 2 1 2 0 0 0 ...
$ s04b_q04: int 0 0 NA 0 0 2 0 0 0 0 ...
$ s04b_q05: int 0 0 NA 0 0 2 0 0 0 0 ...
$ s04b_q_3: int 0 0 NA 0 0 40 0 0 0 0 ...
$ s04b_q06: int 3 0 NA 3 0 0 0 3 0 0 ...
$ s04b_q07: int 5000 0 NA 6000 0 0 0 5000 0 0 ...
$ s04b_q08: int 5000 0 NA 6000 0 0 0 5000 0 0 ...
$ s04b_q09: int 10000 0 NA 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:00"- attr(*, "formats")= chr "%3s" "%3s" "%2s" "%8.0g" ...
- attr(*, "types")= int 3 3 2 251 48 2 2 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "PSU" "household" "Activity serial" "Id code" ...
- attr(*, "version")= int 12
NULL## 4 #### rt004 #########'data.frame':
3497 obs. of

35 variables:

Bangladesh HIES 2010 Manual (Version 1.0)

46

$ psu
: chr "010" "010" "010" "010" ...
$ hhold : chr "026" "026" "063" "083" ...
$ enumber : int 1 2 1 1 1 1 1 2 1 1 ...
$ s05a_q01: chr "" "" "Grocer" "Busness" ...
$ s05a_q_1: chr "61" "81" "74" "74" ...
$ s05a_q02: int 0 0 1 1 0 6 1 1 1 1 ...
$ s05a_q_2: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05a_q_3: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05a_q_4: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05a_q_5: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05a_q03: int 1 3 13 10 2 3 14 1 4 1 ...
$ s05a_q_6: int 2 4 6 6 1 0 0 0 3 6 ...
$ s05a_q04: int 4 4 4 1 4 4 5 1 1 1 ...
$ s05a_q05: int 12 12 12 8 12 12 12 12 9 12 ...
$ s05a_q06: int 100 100 100 100 100 100 100 100 100 100 ...
$ s05a_q07: int 100 100 100 100 100 100 100 100 100 100 ...
$ s05a_q08: int 0 0 1 1 0 1 3 1 1 1 ...
$ s05a_q_7: int 0 0 0 0 0 0 1 0 0 0 ...
$ s05a_q09: int 0 0 3 2 0 3 2 2 2 2 ...
$ s05a_q10: int 0 0 2 2 0 1 1 1 1 2 ...
$ s05a_q_8: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05b_q11: int 0 0 0 1 0 0 2 0 0 0 ...
$ s05b_q12: int 0 0 4 1 0 1 2 1 1 1 ...
$ s05b_q_1: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05b_q13: int 37500 2000 400000 100000 60480 182500 192000 16800 5400 48000 ...
$ s05b_q14: int 0 0 0 12000 0 0 72000 0 0 0 ...
$ s05b_q15: int 0 0 2400 0 0 4800 0 0 0 0 ...
$ s05b_q16: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05b_q17: int 0 0 2000 0 0 600 0 0 0 0 ...
$ s05b_q18: int 0 0 280000 60000 0 125000 0 0 0 0 ...
$ s05b_q19: int 0 0 1000 1000 0 2000 0 0 0 0 ...
$ s05b_q20: int 37500 2000 114600 27000 60480 50100 120000 16800 5400 48000 ...
$ s05b_q21: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05b_q22: int 0 0 0 0 0 0 0 0 0 0 ...
$ s05b_q23: int 0 0 400000 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:02"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%45s" ...
- attr(*, "types")= int 3 3 251 45 2 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "enterprise number" "s05a_q01d: what kind ofentreprise? description" ...
- attr(*, "version")= int 12
NULL## 5 #### rt005 #########'data.frame':
220012 obs. of 13 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ shock_co: int 101 102 103 104 105 106 107 108 109 110 ...
$ s06b_q02: int 2 2 2 2 2 2 2 2 2 2 ...
$ s06b_q03: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06b_q04: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06b_q05: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06b_q_1: int 0 0 0 0 0 0 0 0 0 0 ...

Bangladesh HIES 2010 Manual (Version 1.0)

47

$ s06b_q_2: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06b_q_3: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06b_q06: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06b_q_4: int 0 0 0 0 0 0 0 0 0 0 ...
$ s06b_q_5: int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:04"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 252 251 251 252 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "shock code" "s06b_q02: did you experience thisshock?" ...
- attr(*, "version")= int 12
NULL## 6 #### rt006 #########'data.frame':
186980 obs. of 19 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 2 7 10 23 2 26 1 2 8 21 ...
$ crop_cod: int 2 7 10 24 2 27 1 2 8 22 ...
$ s07b_q02: int 1 1 1 1 1 1 1 1 1 1 ...
$ s07b_q03: int 100 20 0 1 20 1 50 210 70 5 ...
$ s07b_q04: int 1200 96 2000 20 180 5 400 3600 240 50 ...
$ s07b_q_1: int 15 5 2 10 15 40 12 13 50 5 ...
$ s07b_q05: int 800 23 1000 10 150 5 150 500 37 50 ...
$ s07b_q06: int 0 50 0 10 0 0 200 500 150 0 ...
$ s07b_q07: int 0 10 400 0 25 0 30 2450 45 0 ...
$ s07b_q08: int 200 0 0 0 0 0 0 0 0 0 ...
$ s07b_q_2: int 150 0 600 0 0 0 0 0 0 0 ...
$ s07b_q_3: int 0 0 0 0 5 0 20 100 8 0 ...
$ s07b_q_4: int 50 13 0 0 0 0 0 50 0 0 ...
$ s07b_q_5: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07b_q_6: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07b_q_7: int 400 13 600 0 5 0 20 150 8 0 ...
$ price : int 18 2 2 10 18 10 20 18 40 5 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:05"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 251 251 252 253 252 253 253 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "crop code" ...
- attr(*, "version")= int 12
NULL## 7 #### rt007 #########'data.frame':
80424 obs. of 11 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ liv_code: int 201 202 203 204 205 206 207 208 209 210 ...
$ s07c_q02: int 2 0 0 0 12 4 4 0 0 22 ...
$ s07c_q_1: int 2500 0 0 0 800 600 500 0 0 4400 ...
$ s07c_q03: int 1 0 0 0 7 0 0 0 0 0 ...
$ s07c_q_2: int 3000 0 0 0 250 0 0 0 0 3250 ...

Bangladesh HIES 2010 Manual (Version 1.0)

48

$ s07c_q04: int 0 0 0 0 0 0 4 0 0 0 ...
$ s07c_q_3: int 0 0 0 0 0 0 500 0 0 500 ...
$ s07c_q05: int 0 0 0 0 2 2 0 0 0 0 ...
$ s07c_q_4: int 0 0 0 0 300 300 0 0 0 600 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:06"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 252 252 253 252 253 252 253 252 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "livestock code" "s07c_q02n: how many of them
you have? number" ...
- attr(*, "version")= int 12
NULL## 8 #### rt008 #########'data.frame':
72327 obs. of 9 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ prod_cod: int 211 212 213 214 215 216 217 218 220 211 ...
$ s07c_q06: int 0 0 0 120 100 0 500 0 0 0 ...
$ s07c_q_1: int 0 0 0 3600 600 0 500 500 5200 0 ...
$ s07c_q07: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q_2: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q08: int 0 0 0 120 100 0 500 0 0 0 ...
$ s07c_q_3: int 0 0 0 3600 600 0 500 500 5200 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:08"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%12.0g" ...
- attr(*, "types")= int 3 3 252 253 253 253 253 252 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "product code" "s07c_q06q: how much produced?
quantity" ...
- attr(*, "version")= int 12
NULL## 9 #### rt009 #########'data.frame':
25241 obs. of 9 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "046" "046" "046" "046" ...
$ fish_act: int 221 222 223 224 225 226 227 229 230 221 ...
$ s07c_q10: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q_1: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q11: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q_2: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q12: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q_3: int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:10"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 252 252 253 252 253 252 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "code fishing" "s07c_q10kg: how much you
produce? kg" ...
- attr(*, "version")= int 12

Bangladesh HIES 2010 Manual (Version 1.0)

49

NULL

## 10 #### rt010 #########'data.frame':
45643 obs. of 7 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "046" "046" "046" "046" ...
$ forestry: int 231 232 233 234 235 236 237 238 239 240 ...
$ s07c_q14: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q_1: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q15: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07c_q16: int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:11"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%12.0g" ...
- attr(*, "types")= int 3 3 252 253 253 253 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "code forestry" "s07c_q14n: how many trees?
no." ...
- attr(*, "version")= int 12
NULL## 11 #### rt011 #########'data.frame':
247753 obs. of 7 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 1 2 3 4 5 6 7 8 9 10 ...
$ exp_agri: int 301 302 303 304 305 306 307 308 309 311 ...
$ s07d_q01: int 1 2 1 2 2 1 2 1 2 2 ...
$ s07d_q02: int 25 0 40 0 0 0 0 0 0 0 ...
$ s07d_q_1: int 500 0 480 0 0 1250 0 80 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:12"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 252 251 252 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" " agric code of the sector" ...
- attr(*, "version")= int 12
NULL## 12 #### rt012 #########'data.frame':
228631 obs. of 11 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 1 2 3 4 5 6 7 8 9 10 ...
$ agric_as: int 401 402 403 404 405 406 407 408 409 411 ...
$ s07e_q01: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07e_q_1: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07e_q02: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07e_q_2: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07e_q03: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07e_q_3: int 0 0 0 0 0 0 0 0 0 0 ...
$ s07e_q04: int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""Bangladesh HIES 2010 Manual (Version 1.0)

50

- attr(*, "time.stamp")= chr "13 Jul 2011 12:13"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 252 252 253 252 253 252 253 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "agricultural asset code" ...
- attr(*, "version")= int 12
NULL## 13 #### rt013 #########'data.frame':
2100 obs. of 21 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "080" "086" ...
$ migrant_: int 91 92 91 91 92 91 91 91 92 91 ...
$ s08c_q03: chr "Liton" "Milton" "" "Hamayet" ... (to be deleted)$ s08c_q04: int 3 3 3 3 3 4 2 2 3 3 ...
$ s08c_q05: int 9 0 6 0 6 0 0 0 0 0 ...
$ s08c_q_1: int 0 1 2 1 1 2 8 20 5 1 ...
$ s08c_q06: int 2 1 2 2 2 2 2 2 2 1 ...
$ s08c_q07: chr "" "33" "" "" ...
$ s08c_q08: int 5 0 6 3 1 2 1 3 3 0 ...
$ s08c_q09: int 30 26 22 34 22 37 42 51 23 14 ...
$ s08c_q10: int 1 1 1 1 1 1 1 1 1 1 ...
$ s08c_q11: int 10 10 10 12 11 10 8 10 10 5 ...
$ s08c_q12: chr "83" "79" "01" "07" ...
$ s0bc_q13: int 3 5 4 2 2 12 12 4 4 3 ...
$ s08c_q14: int 70000 30000 40000 15000 10000 1000 180000 120000 120000 12000 ...
$ s08c_q15: int 4 8 4 4 4 4 4 4 4 8 ...
$ s08c_q16: int 0 0 0 0 0 0 0 0 0 0 ...
$ s08c_q_2: int 0 0 0 0 0 0 0 0 0 0 ...
$ s08c_q_3: int 0 0 0 0 0 0 0 0 0 0 ...
$ s08c_q17: int 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:15"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%25s" ...
- attr(*, "types")= int 3 3 251 25 251 251 251 251 2 251 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "migrant number" "s08c_q03: name" ...
- attr(*, "version")= int 12
NULL## 14 #### rt014 #########'data.frame':
5137 obs. of 16 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "110" "117" "122" ...
$ loan_num: int 20 20 20 20 21 20 20 21 20 21 ...
$ idcode : int 1 1 2 1 1 1 1 1 1 1 ...
$ s08d_q05: int 8 9 8 8 8 12 1 2 12 8 ...
$ s08d_q06: int 32000 13000 25000 80000 20000 10000 60000 48000 12000 15000 ...
$ s08d_q07: int 24 12 24 24 12 24 24 36 24 24 ...
$ s08d_q08: num 0 0 0 0 0 0 0 0 0 0 ...
$ s08d_q_1: num 10 10 20 10 10 10 10 10 10 10 ...
$ s08d_q09: int 7 3 3 3 3 3 5 5 3 3 ...
$ s08d_q_2: int 22500 9450 700 61000 10000 4000 3000 47600 5000 2800 ...
$ s08d_q10: int 2 2 2 2 2 2 2 2 2 2 ...

Bangladesh HIES 2010 Manual (Version 1.0)

51

$ s08d_q11: int 9500 3550 12000 19000 10000 6000 57000 400 7000 12200 ...
$ s08d_q12: int 3 3 2 5 5 5 8 5 8 8 ...
$ s08d_q13: int 2 1 1 2 2 2 2 2 2 1 ...
$ s08d_q14: int 0 50000 25000 0 0 0 0 0 0 50000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:34"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 251 251 253 252 254 254 251 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "loan number" "id code" ...
- attr(*, "version")= int 12
NULL## 15 #### rt015 #########'data.frame':
438559 obs. of 62 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 1 2 4 7 8 12 15 16 18 22 ...
$ item: int 10 11 13 16 17 22 30 31 33 40 ...
$ s09a1d01: int 4100 0 4050 0 0 50 250 0 250 1500 ...
$ s09a1d_1: chr "" "gm" "gm" "gm" ...
$ s09a1d_2: int 11700 0 11200 0 0 500 1000 0 1000 25000 ...
$ s09a1d_3: int 0 0 3 0 0 4 0 0 1 0 ...
$ s09a1d02: int 2500 0 2500 0 0 0 0 0 0 0 ...
$ s09a1d_4: chr "" "gm" "gm" "gm" ...
$ s09a1d_5: int 7500 0 7500 0 0 0 0 0 0 0 ...
$ s09a1d_6: int 0 0 3 0 0 0 0 0 0 0 ...
$ s09a1d03: int 3550 1000 1500 0 1000 50 0 0 0 0 ...
$ s09a1d_7: chr "" "gm" "gm" "gm" ...
$ s09a1d_8: int 14200 7000 4500 0 2200 500 0 0 0 0 ...
$ s09a1d_9: int 0 1 3 0 1 4 0 0 0 0 ...
$ s09a1d04: int 2550 0 2500 0 0 50 200 200 0 700 ...
$ s09a1_10: chr "" "gm" "gm" "gm" ...
$ s09a1_11: int 8000 0 7500 0 0 500 2200 2200 0 9000 ...
$ s09a1_12: int 0 0 3 0 0 4 0 1 0 0 ...
$ s09a1d05: int 2550 0 2500 0 0 50 200 200 0 200 ...
$ s09a1_13: chr "" "gm" "gm" "gm" ...
$ s09a1_14: int 8000 0 7500 0 0 500 2200 2200 0 2000 ...
$ s09a1_15: int 0 0 3 0 0 4 0 1 0 0 ...
$ s09a1d06: int 2500 0 2500 0 0 0 100 100 0 500 ...
$ s09a1_16: chr "" "gm" "gm" "gm" ...
$ s09a1_17: int 7500 0 7500 0 0 0 1200 1200 0 5000 ...
$ s09a1_18: int 0 0 3 0 0 0 0 1 0 0 ...
$ s09a1d07: int 2650 0 2500 100 0 50 0 0 0 500 ...
$ s09a1_19: chr "" "gm" "gm" "gm" ...
$ s09a1_20: int 8600 0 7500 600 0 500 0 0 0 5000 ...
$ s09a1_21: int 0 0 3 3 0 4 0 0 0 0 ...
$ s09a1d08: int 2750 0 2500 200 0 50 0 0 0 200 ...
$ s09a1_22: chr "" "gm" "gm" "gm" ...
$ s09a1_23: int 9200 0 7500 1200 0 500 0 0 0 2000 ...
$ s09a1_24: int 0 0 3 3 0 4 0 0 0 0 ...
$ s09a1d09: int 2750 0 2500 200 0 50 0 0 0 200 ...
$ s09a1_25: chr "" "gm" "gm" "gm" ...
$ s09a1_26: int 9200 0 7500 1200 0 500 0 0 0 2000 ...
$ s09a1_27: int 0 0 3 3 0 4 0 0 0 0 ...

Bangladesh HIES 2010 Manual (Version 1.0)

52

$ s09a1d10: int 2800 0 2500 200 0 100 0 0 0 200 ...
$ s09a1_28: chr "" "gm" "gm" "gm" ...
$ s09a1_29: int 9700 0 7500 1200 0 1000 0 0 0 2000 ...
$ s09a1_30: int 0 0 3 3 0 4 0 0 0 0 ...
$ s09a1d11: int 2750 0 2500 200 0 50 0 0 0 200 ...
$ s09a1_31: chr "" "gm" "gm" "gm" ...
$ s09a1_32: int 9200 0 7500 1200 0 500 0 0 0 2000 ...
$ s09a1_33: int 0 0 3 3 0 4 0 0 0 0 ...
$ s09a1d12: int 1800 0 1500 200 0 100 0 0 0 0 ...
$ s09a1_34: chr "" "gm" "gm" "gm" ...
$ s09a1_35: int 6700 0 4500 1200 0 1000 0 0 0 0 ...
$ s09a1_36: int 0 0 3 3 0 4 0 0 0 0 ...
$ s09a1d13: int 1750 0 1500 250 0 0 100 100 0 200 ...
$ s09a1_37: chr "" "gm" "gm" "gm" ...
$ s09a1_38: int 6000 0 4500 1500 0 0 1200 1200 0 2000 ...
$ s09a1_39: int 0 0 3 3 0 0 0 1 0 0 ...
$ s09a1d14: int 1600 0 1500 100 0 0 0 0 0 200 ...
$ s09a1_40: chr "" "gm" "gm" "gm" ...
$ s09a1_41: int 5100 0 4500 600 0 0 0 0 0 2000 ...
$ s09a1_42: int 0 0 1 1 0 0 0 0 0 0 ...
$ t: int 1 0 0 0 0 0 1 0 0 1 ...
$ kcal: int 0 346 346 354 341 534 0 343 372 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:38"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 252 252 253 3 253 251 253 3 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "food item code" ...
- attr(*, "version")= int 12
NULL## 16 #### rt016 #########'data.frame':
200008 obs. of 13 variables:$ psu: chr "007" "007" "007" "007" ...
$ hhold : chr "001" "001" "001" "001" ...
$ ln: int 1 2 3 4 5 6 7 8 9 11 ...
$ item: int 210 211 212 213 214 215 216 217 218 221 ...
$ s09b1w1_: int 1440 150 250 500 100 100 250 50 20 20 ...
$ s09b1w_1: chr "" "gm" "gm" "gm" ...
$ s09b1w_2: int 16000 3500 1000 2000 2000 3500 400 600 1000 2000 ...
$ s09b1w_3: int 0 1 1 1 1 1 1 1 1 1 ...
$ s09b1w2_: int 1690 150 250 750 100 100 250 50 20 20 ...
$ s09b1w_4: chr "" "gm" "gm" "gm" ...
$ s09b1w_5: int 17600 3500 1000 3600 2000 3500 400 600 1000 2000 ...
$ s09b1w_6: int 0 1 1 1 1 1 1 1 1 1 ...
$ kcal: int 0 245 103 50 145 349 67 67 356 229 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:42"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 252 252 2 253 251 252 2 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "food item code" ...
- attr(*, "version")= int 12
NULL

Bangladesh HIES 2010 Manual (Version 1.0)

53## 17 #### rt017 #########'data.frame':
362670 obs. of 7 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 1 3 5 10 11 12 14 18 19 20 ...
$ item: int 240 242 244 249 250 251 253 257 260 261 ...
$ s09c1_q0: int 108 0 100 8 120 40 60 20 130 50 ...
$ s09c1__1: int 200 200 0 0 0 0 0 0 0 0 ...
$ s09c1__2: int 308 200 100 8 120 40 60 20 130 50 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:43"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 252 253 252 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "non-food item code" ...
- attr(*, "version")= int 12
NULL## 18 #### rt018 #########'data.frame':
285162 obs. of 6 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 1 2 3 4 5 8 16 22 23 26 ...
$ item: int 300 301 302 303 304 307 316 323 324 330 ...
$ s09d1_q0: int 37 4 4 2 10 4 4 6 3 0 ...
$ s09d1__1: int 7870 800 1200 300 2500 800 120 2000 150 2500 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:44"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 252 252 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "non-food item code" ...
- attr(*, "version")= int 12
NULL## 19 #### rt019 #########'data.frame':
830454 obs. of 5 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 1 2 6 12 14 17 36 39 49 51 ...
$ item: int 360 361 365 372 390 393 410 413 430 432 ...
$ s09d2_q0: int 2200 800 900 500 1000 1000 1000 1000 5650 150 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:47"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 252 252 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "non-food item code" ...
- attr(*, "version")= int 12
NULL## 20 ####

rt020#########

Bangladesh HIES 2010 Manual (Version 1.0)

54

'data.frame':
341813 obs. of 8 variables:$ psu: chr "010" "010" "010" "010" ...
$ hhold : chr "002" "002" "002" "002" ...
$ ln: int 1 2 3 4 5 6 7 8 9 10 ...
$ dg_code : int 561 562 563 564 565 566 567 568 569 571 ...
$ s09e_q01: int 1 2 2 2 2 2 2 2 2 2 ...
$ s09e_q02: int 1 0 0 0 0 0 0 0 0 0 ...
$ s09e_q03: int 200 0 0 0 0 0 0 0 0 0 ...
$ s09e_q04: int 350 0 0 0 0 0 0 0 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr "13 Jul 2011 12:48"- attr(*, "formats")= chr "%3s" "%3s" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 3 3 251 252 251 252 253 253- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "psu" "household" "line number" "durable good code" ...
- attr(*, "version")= int 12
NULL

Bangladesh HIES 2010 Manual (Version 1.0)

55

5.2 Summary of each variable> for(j in 1:20){+ cat("##",j,"#### ",Rnames[j]," #########\n")+ print(summary(outfiles[[j]]))+ cat("\n\n")+ }## 1 #### rt001 #########
stratumpsu
hhold
Length:12240
Length:12240
Length:12240
Class :character
Class :character
Class :character
Mode :character
Mode :character
Mode :character

team
term
Min.
: 1.0
Min.
: 1.0
1st Qu.: 9.0
1st Qu.: 5.0
Median :17.5
Median : 9.5
Mean
:17.5
Mean
: 9.5
3rd Qu.:26.0
3rd Qu.:14.0
Max.
:34.0
Max.
:18.0
regiondistrict
thana
union
mouza
Min.
:10.00Min.
: 1.00Min.
: 2.00Min.
: 1.00Min.
: 3.0
1st Qu.:20.00
1st Qu.:27.00
1st Qu.:28.00
1st Qu.: 9.00
1st Qu.:252.2
Median :30.00
Median :51.00
Median :51.00
Median :31.00
Median :519.0
Mean:35.38Mean:50.78Mean:49.96Mean:36.18Mean:509.5
3rd Qu.:50.00
3rd Qu.:75.00
3rd Qu.:72.00
3rd Qu.:63.00
3rd Qu.:781.0
Max.
:60.00Max.
:94.00Max.
:95.00Max.
:95.00Max.
:995.0spc
urbanrurwgt
s06a_q01
s06a_q02
Length:12240
Min.
:1.000Min.
: 897.1
Min.
: 0.000Min.
: 1.000
Class :character
1st Qu.:1.000
1st Qu.:1308.2
1st Qu.: 1.000
1st Qu.: 1.000
Mode :character
Median :1.000
Median :2946.4
Median : 2.000
Median : 2.000
Mean:1.641
Mean:2698.4
Mean: 1.712Mean: 2.304
3rd Qu.:2.000
3rd Qu.:3115.6
3rd Qu.: 2.000
3rd Qu.: 3.000
Max.
:4.000Max.
:6882.2
Max.
:22.000Max.
:16.000
s06a_q03
s06a_q04
s06a_q05
s06a_q06
s06a_q07
Min.
:0.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:
0.0
1st Qu.:2.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.: 200.0
Median :2.000
Median :1.000
Median :2.000
Median :2.000
Median : 306.0
Mean:1.869Mean:1.246Mean:2.289Mean:2.037
Mean: 402.1
3rd Qu.:2.000
3rd Qu.:1.000
3rd Qu.:3.000
3rd Qu.:2.000
3rd Qu.: 480.0
Max.
:2.000Max.
:7.000Max.
:5.000Max.
:5.000Max.
:20000.0s06a_q08
Min.
:1.000
1st Qu.:2.000
Median :3.000
Mean:3.177
3rd Qu.:4.000
Max.
:6.000s06a_q09
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.0143rd Qu.:2.000
Max.
:6.000s06a_q10
Min.
:0.000
1st Qu.:1.000
Median :1.000
Mean:1.258
3rd Qu.:2.000
Max.
:2.000s06a_q14s06a_q15
Min.
:0.000Min.
: 0.000
1st Qu.:1.000
1st Qu.: 0.000
Median :1.000
Median : 7.000
Mean:1.423Mean: 8.071
3rd Qu.:2.000
3rd Qu.:16.000
Max.
:2.000Max.
:30.000
s06a_q20
s06a_q21
Min.
:0.00000
Min.
:1.000
1st Qu.:0.00000
1st Qu.:1.000
Median :0.00000
Median :1.000
Mean:0.02369
Mean:1.342
3rd Qu.:0.00000
3rd Qu.:1.000

s06a_q11
Min.
:0.000
1st Qu.:0.000
Median :1.000
Mean:1.001
3rd Qu.:2.000
Max.
:2.000s06a_q12Min.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean:0.9641
3rd Qu.:2.0000
Max.
:6.0000

s06a_q13
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.2443rd Qu.:3.000
Max.
:6.000s06a_q16
s06a_q17s06a_q18
s06a_q19
Min.
:0.00
Min.
:0.000Min.
:0.000Min.
:0.00
1st Qu.:1.00
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:2.00
Median :1.00
Median :2.000
Median :2.000
Median :2.00
Mean
:1.35
Mean
:1.978Mean:1.973Mean:1.98
3rd Qu.:2.00
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.00
Max.
:2.00
Max.
:2.000Max.
:2.000Max.
:2.00
s06a_q22
s07a_q01
s07a_q02
Min.
:
0
Min.
:
0.00
Min.
: 0.000
1st Qu.:78000
1st Qu.:0.00
1st Qu.: 2.000
Median : 150000
Median :0.00
Median : 5.000
Mean: 516188
Mean: 55.83
Mean: 8.283
3rd Qu.: 400000
3rd Qu.: 50.00
3rd Qu.: 10.000

Bangladesh HIES 2010 Manual (Version 1.0)

56

Max.
:5.00000
Max.
:6.000Max.
:80000000
Max.
:3960.00
Max.
:500.000s07a_q03
s07a_q04
s07a_q05
s07a_q06
s07b_q01
Min.
: 0.000
Min.
:
0.00
Min.
:
0.00
Min.
:
0.00
Min.
:0.000
1st Qu.: 0.000
1st Qu.:0.00
1st Qu.:0.00
1st Qu.:3.00
1st Qu.:1.000
Median : 0.000
Median :0.00
Median :0.00
Median : 10.00
Median :2.000
Mean: 5.696
Mean: 18.88
Mean: 25.81
Mean: 62.88
Mean:1.575
3rd Qu.: 0.000
3rd Qu.:0.00
3rd Qu.:0.00
3rd Qu.: 70.00
3rd Qu.:2.000
Max.
:1000.000
Max.
:1825.00
Max.
:3960.00
Max.
:3210.00
Max.
:2.000s07c_q01
s07c_q09
s07c_q13s08a_q01
s08a_q_1
Min.
:0.000Min.
:0.000Min.
:0.000Min.
:0.000Min.
:
0.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:0.000
Median :1.000
Median :2.000
Median :2.000
Median :2.000
Median :0.000
Mean:1.387Mean:1.868Mean:1.683Mean:1.961Mean:
3.818
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:0.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000Max.
:17520.000
s08a_q02
s08a_q03
s08a_q04
s08a_q05
s08a_q06
Min.
:
0
Min.
:0.000
Min.
:
0
Min.
:0.000Min.
:
0
1st Qu.:0
1st Qu.:2.000
1st Qu.:0
1st Qu.:2.000
1st Qu.:0
Median :0Median :2.000
Median :0Median :2.000
Median :0
Mean: 16623
Mean:1.969
Mean
:
5444
Mean
:1.996Mean:
978
3rd Qu.:0
3rd Qu.:2.000
3rd Qu.:0
3rd Qu.:2.000
3rd Qu.:0Max.
:9000000
Max.
:2.000Max.
:3000000
Max.
:2.000Max.
:6500000s08a_q07
s08a_q08
s08a_q09
s08a_q10s08a_q11
Min.
:0.000
Min.
:
0
Min.
:0.000
Min.
:
0
Min.
:0.000
1st Qu.:1.000
1st Qu.:0
1st Qu.:2.000
1st Qu.:0
1st Qu.:2.000
Median :2.000
Median :0Median :2.000
Median :0Median :2.000
Mean:1.541
Mean: 19737
Mean:1.979
Mean
:
2151
Mean
:1.967
3rd Qu.:2.000
3rd Qu.: 15000
3rd Qu.:2.000
3rd Qu.:0
3rd Qu.:2.000
Max.
:2.000Max.
:4000000
Max.
:2.000Max.
:3600000
Max.
:2.000s08a_q12s08b_q01
s08b_q02
s08b_q03
s08b_q_1
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:
0.00
Min.
:0.0000000
1st Qu.:0
1st Qu.:0
1st Qu.:0
1st Qu.:0.00
1st Qu.:0.0000000
Median :0Median :0Median :0Median :0.00
Median :0.0000000
Mean: 3744Mean: 3073
Mean
:
3148
Mean
:
65.51
Mean
:0.0003268
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0.00
3rd Qu.:0.0000000
Max.
:3500000
Max.
:540000
Max.
:1032000
Max.
:93000.00
Max.
:2.0000000
s08b_q_2
s08b_q04
s08b_q05
s08b_q06
s08b_q07
Min.
:
0.0
Min.
:
0.0
Min.
:
0.00
Min.
:
0.0
Min.
:
0.0
1st Qu.:0.0
1st Qu.:0.0
1st Qu.:0.00
1st Qu.:0.0
1st Qu.:0.0
Median :0.0Median :0.0Median :0.00
Median :0.0Median :0.0
Mean: 11.5
Mean
:
231.7
Mean
:
19.36
Mean
:
434.2
Mean
:
472.5
3rd Qu.:0.0
3rd Qu.:0.0
3rd Qu.:0.00
3rd Qu.:0.0
3rd Qu.:0.0
Max.
:80000.0Max.
:500000.0
Max.
:50000.00
Max.
:140000.0
Max.
:160000.0
s08b_q08
s08b_q09
s08b_q10s08b_q11
s08b_q12
Min.
:
0
Min.
:
0
Min.
:0.00
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
1st Qu.:0.00
1st Qu.:0
1st Qu.:0
Median :0Median :0Median :0.00
Median :0Median :0
Mean: 3146
Mean: 14300
Mean
:0.71
Mean
:
1636
Mean
:
419
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0.00
3rd Qu.:0
3rd Qu.:0Max.
:530000
Max.
:5000000
Max.
:7.00
Max.
:6152392
Max.
:500000
s08b_q13s08c_q01
s08c_q02
s08d_q01
s08d_q02
Min.
:
0
Min.
:0.000Min.
:0.000Min.
:0.000Min.
:0.000
1st Qu.:0
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:2.000
Median :0Median :2.000
Median :2.000
Median :2.000
Median :2.000
Mean: 2999Mean:1.873Mean:1.872Mean:1.932Mean:1.846
3rd Qu.:0
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000

Bangladesh HIES 2010 Manual (Version 1.0)

57

Max.
:2000000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000s08d_q03
s08d_q04
s09a1d01
s09a1d_1
s09a1d_2
Min.
:0.000Min.
:0.000Min.
:0.0000Min.
: 0.0000Min.
: 0.00
1st Qu.:2.000
1st Qu.:1.000
1st Qu.:0.0000
1st Qu.: 0.0000
1st Qu.: 1.00
Median :2.000
Median :2.000
Median :0.0000
Median : 0.0000
Median : 1.00
Mean
:1.939Mean:1.667Mean:0.5306
Mean: 0.5155
Mean: 1.733rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:1.0000
3rd Qu.: 1.0000
3rd Qu.: 2.00
Max.
:2.000Max.
:2.000Max.
:9.0000Max.
:14.0000Max.
:16.00s09a1d_3
s09a1d_4
s09a1d02
s09a1d_5
s09a1d_6
Min.
: 0.000
Length:12240
Min.
:0.0000
Min.
:0.0000Min.
: 0.000
1st Qu.: 1.000
Class :character
1st Qu.:0.0000
1st Qu.:0.0000
1st Qu.: 1.000
Median : 2.000
Mode :character
Median :0.0000
Median :0.0000
Median : 1.000
Mean: 1.838
Mean:0.5267
Mean:0.5142Mean: 1.736
3rd Qu.: 2.000
3rd Qu.:1.0000
3rd Qu.:1.0000
3rd Qu.: 2.000
Max.
:16.000
Max.
:9.0000
Max.
:9.0000Max.
:65.000
s09a1d_7
s09a1d_8
s09a1d03
s09a1d_9
s09a1_10
Min.
: 0.00Length:12240
Min.
:0.0000
Min.
:0.0000Min.
: 0.000
1st Qu.: 1.00
Class :character
1st Qu.:0.0000
1st Qu.:0.0000
1st Qu.: 1.000
Median : 2.00
Mode :character
Median :0.0000
Median :0.0000
Median : 1.000
Mean: 1.84Mean:0.5284
Mean:0.5141
Mean: 1.725
3rd Qu.: 2.00
3rd Qu.:1.0000
3rd Qu.:1.0000
3rd Qu.: 2.000
Max.
:32.00Max.
:9.0000
Max.
:9.0000Max.
:15.000
s09a1_11
s09a1_12
s09a1d04
s09a1_13
s09a1_14
Min.
: 0.000
Length:12240
Min.
:0.0000Min.
: 0.000Min.
: 0.000
1st Qu.: 1.000
Class :character
1st Qu.:0.0000
1st Qu.: 0.000
1st Qu.: 1.000
Median : 2.000
Mode :character
Median :0.0000
Median : 0.000
Median : 1.000
Mean: 1.834
Mean:0.5282
Mean: 0.515
Mean: 1.725
3rd Qu.: 2.000
3rd Qu.:1.0000
3rd Qu.: 1.000
3rd Qu.: 2.000
Max.
:12.000
Max.
:9.0000Max.
:10.000Max.
:25.000
s09a1_15
s09a1_16
s09a1d05
s09a1_17
s09a1_18
Min.
: 0.000
Length:12240
Min.
: 0.0000
Min.
:0.0000Min.
: 0.000
1st Qu.: 1.000
Class :character
1st Qu.: 0.0000
1st Qu.:0.0000
1st Qu.: 1.000
Median : 2.000
Mode :character
Median : 0.0000
Median :0.0000
Median : 1.000
Mean: 1.838
Mean: 0.5249
Mean:0.5105
Mean: 1.714
3rd Qu.: 2.000
3rd Qu.: 1.0000
3rd Qu.:1.0000
3rd Qu.: 2.000
Max.
:20.000
Max.
:11.0000
Max.
:6.0000
Max.
:34.000
s09a1_19
s09a1_20
s09a1d06
s09a1_21
s09a1_22
Min.
: 0.000
Length:12240
Min.
:0.0000
Min.
:0.0000Min.
: 0.000
1st Qu.: 1.000
Class :character
1st Qu.:0.0000
1st Qu.:0.0000
1st Qu.: 1.000
Median : 2.000
Mode :character
Median :0.0000
Median :0.0000
Median : 1.000
Mean: 1.828
Mean:0.5222Mean:0.5077
Mean: 1.708
3rd Qu.: 2.000
3rd Qu.:1.0000
3rd Qu.:1.0000
3rd Qu.: 2.000
Max.
:15.000
Max.
:9.0000
Max.
:6.0000Max.
:20.000
s09a1_23
s09a1_24
s09a1d07
s09a1_25
s09a1_26
Min.
: 0.000
Length:12240
Min.
: 0.0000
Min.
:0.0000Min.
: 0.000
1st Qu.: 1.000
Class :character
1st Qu.: 0.0000
1st Qu.:0.0000
1st Qu.: 1.000
Median : 2.000
Mode :character
Median : 0.0000
Median :0.0000
Median : 1.000
Mean: 1.822Mean: 0.5199
Mean:0.5087
Mean: 1.696
3rd Qu.: 2.000
3rd Qu.: 1.0000
3rd Qu.:1.0000
3rd Qu.: 2.000
Max.
:20.000Max.
:15.0000
Max.
:9.0000Max.
:22.000
s09a1_27
s09a1_28
s09a1d08
s09a1_29
s09a1_30
Min.
: 0.000
Length:12240
Min.
:0.0000Min.
: 0.0000Min.
: 0.000
1st Qu.: 1.000
Class :character
1st Qu.:0.0000
1st Qu.: 0.0000
1st Qu.: 1.000
Median : 2.000
Mode :character
Median :0.0000
Median : 0.0000
Median : 1.000
Mean: 1.819Mean:0.5191
Mean: 0.5086
Mean: 1.696
3rd Qu.: 2.000
3rd Qu.:1.0000
3rd Qu.: 1.0000
3rd Qu.: 2.000

Bangladesh HIES 2010 Manual (Version 1.0)

58

Max.
:27.000
s09a1_31
Min.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 1.821
3rd Qu.: 2.000
Max.
:11.000
s09a1_35
Min.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 1.814
3rd Qu.: 2.000
Max.
:30.000
s09a1_39
Min.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 1.817
3rd Qu.: 2.000
Max.
:10.000
s09a1_43
Min.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 1.812
3rd Qu.: 2.000
Max.
:20.000
s09a1_47
Min.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 1.805
3rd Qu.: 2.000
Max.
:20.000
s09a1_51
Min.
: 0.000
1st Qu.: 1.000
Median : 2.000
Mean: 1.799
3rd Qu.: 2.000
Max.
:11.000
s09a1_55
Min.
: 0.00
1st Qu.: 1.00
Median : 2.00
Mean
: 1.81
3rd Qu.: 2.00
Max.
:20.00Max.
:5.0000
Max.
:11.0000Max.
:23.000
s09a1d09
s09a1_33
s09a1_34
Min.
:0.0000Min.
: 0.0000Min.
: 0.000
1st Qu.:0.0000
1st Qu.: 0.0000
1st Qu.: 1.000
Median :0.0000
Median : 0.0000
Median : 1.000
Mean:0.5144Mean: 0.5078
Mean: 1.685
3rd Qu.:1.0000
3rd Qu.: 1.0000
3rd Qu.: 2.000
Max.
:6.0000Max.
:10.0000Max.
:13.000
s09a1d10s09a1_37
s09a1_38
Min.
: 0.0000
Min.
:0.0000Min.
: 0.000
1st Qu.: 0.0000
1st Qu.:0.0000
1st Qu.: 1.000
Median : 0.0000
Median :0.0000
Median : 1.000
Mean: 0.5208
Mean:0.5102Mean: 1.691
3rd Qu.: 1.0000
3rd Qu.:1.0000
3rd Qu.: 2.000
Max.
:11.0000
Max.
:6.0000Max.
:13.000
s09a1d11
s09a1_41
s09a1_42
Min.
:0.000Min.
: 0.0000Min.
: 0.000
1st Qu.:0.000
1st Qu.: 0.0000
1st Qu.: 1.000
Median :0.000
Median : 0.0000
Median : 1.000
Mean:0.519Mean: 0.5093
Mean: 1.698
3rd Qu.:1.000
3rd Qu.: 1.0000
3rd Qu.: 2.000
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Bangladesh HIES 2010 Manual (Version 1.0)

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Bangladesh HIES 2010 Manual (Version 1.0)

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Bangladesh HIES 2010 Manual (Version 1.0)

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Bangladesh HIES 2010 Manual (Version 1.0)

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Bangladesh HIES 2010 Manual (Version 1.0)

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Bangladesh HIES 2010 Manual (Version 1.0)

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Bangladesh HIES 2010 Manual (Version 1.0)

65

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Bangladesh HIES 2010 Manual (Version 1.0)

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0.0
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:
361.8

Bangladesh HIES 2010 Manual (Version 1.0)

67

3rd Qu.:0.00
Max.
:81000.00

3rd Qu.:0Max.
:3950100## 9 #### rt009 #########
psu
hhold
Length:25241
Length:25241
Class :character
Class :character
Mode :character
Mode :character

s07c_q11
Min.
:
0.00
1st Qu.:0.00
Median :0.00
Mean: 24.05
3rd Qu.:0.00
Max.
:26500.00

s07c_q_2
Min.
:0
1st Qu.:0
Median :0
Mean: 2319
3rd Qu.:0Max.
:992000## 10 #### rt010
psu
Length:45643
Class :character
Mode :character#########
hhold
Length:45643
Class :character
Mode :character

s07c_q15
Min.
:
0.0
1st Qu.:0.0
Median :0.0
Mean: 253.2
3rd Qu.:0.0
Max.
:200000.0

s07c_q16
Min.
:0
1st Qu.:0
Median :
0
Mean
: 212
3rd Qu.:0Max.
:88000## 11 #### rt011
psu
Length:247753
Class :character
Mode :character#########
hhold
Length:247753
Class :character
Mode :character

s07d_q02
Min.
: 0.00
1st Qu.: 0.00
Median : 0.00
Mean
: 22.67
3rd Qu.: 0.00
Max.
:9908.00

s07d_q_1
Min.
:0
1st Qu.:0
Median :
0
Mean
:
827
3rd Qu.:0Max.
:750000## 12 #############

rt012

3rd Qu.:0.0
Max.
:22650.0

3rd Qu.:0.0
Max.
:327520.0

fish_act
Min.
:221.0
1st Qu.:223.0
Median :225.0
Mean:225.2
3rd Qu.:227.0
Max.
:230.0s07c_q12
Min.
:
0.00
1st Qu.:0.00
Median :
0.00
Mean
:
10.02
3rd Qu.:0.00
Max.
:12000.00

s07c_q10
Min.
:
0.00
1st Qu.:0.00
Median :
0.00
Mean
:
33.91
3rd Qu.:0.00
Max.
:26550.00
s07c_q_3
Min.
:
0.0
1st Qu.:0.0
Median :
0.0
Mean
:
921.9
3rd Qu.:0.0
Max.
:904500.0

s07c_q_1
Min.
:0
1st Qu.:0
Median :0
Mean: 31943rd Qu.:0Max.
:991250

forestryMin.
:231.0
1st Qu.:233.0
Median :235.0
Mean:235.5
3rd Qu.:238.0
Max.
:240.0s07c_q14
Min.
:
0.00
1st Qu.:0.00
Median :
0.00
Mean
:
24.04
3rd Qu.:2.00
Max.
:40000.00

s07c_q_1
Min.
:0
1st Qu.:0
Median :0
Mean: 5130
3rd Qu.:600
Max.
:922000ln
Min.
: 1.00
1st Qu.: 6.00
Median :11.00
Mean:10.81
3rd Qu.:16.00
Max.
:21.00

exp_agriMin.
:301.0
1st Qu.:306.0
Median :312.0
Mean:311.7
3rd Qu.:317.0
Max.
:330.0

Bangladesh HIES 2010 Manual (Version 1.0)

s07d_q01
Min.
:0.000
1st Qu.:2.000
Median :2.000
Mean:1.807
3rd Qu.:2.000
Max.
:2.00068

psu
Length:228631
Class :character
Mode :character

hhold
Length:228631
Class :character
Mode :characters07e_q_1
Min.
:
0.0
1st Qu.:0.0
Median :0.0
Mean: 246.3
3rd Qu.:0.0Max.
:900000.0
s07e_q04
Min.
:
0.00
1st Qu.:0.00
Median :
0.00
Mean
:
44.99
3rd Qu.:0.00
Max.
:175000.00

s07e_q02
Min.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 0.024
3rd Qu.: 0.000
Max.
:800.000## 13 #### rt013
psu
Length:2100
Class :character
Mode :character#########
hhold
Length:2100
Class :character
Mode :character

s08c_q05
Min.
: 0.00
1st Qu.: 0.00
Median : 2.00
Mean
: 3.143rd Qu.: 6.00
Max.
:14.00NA's:1s08c_q09Min.
: 0.00
1st Qu.:25.00
Median :30.00
Mean:31.72
3rd Qu.:38.00
Max.
:83.00s08c_q14
Min.
:0
1st Qu.: 20000
Median : 60000
Mean: 92891
3rd Qu.: 120000
Max.
:5000000ln
agric_ass07e_q01
Min.
: 1
Min.
:401.0Min.
: 0.0000
1st Qu.: 5
1st Qu.:405.0
1st Qu.: 0.0000
Median :10
Median :411.0
Median : 0.0000
Mean
:10
Mean
:410.5Mean: 0.0424
3rd Qu.:15
3rd Qu.:416.0
3rd Qu.: 0.0000
Max.
:19
Max.
:420.0Max.
:400.0000
s07e_q_2
s07e_q03
s07e_q_3
Min.
:
0.0
Min.
:0.0e+00
Min.
:0.00e+00
1st Qu.:0.0
1st Qu.:0.0e+00
1st Qu.:0.00e+00
Median :0.0Median :0.0e+00
Median :0.00e+00
Mean
:
44.5
Mean
:9.4e-03
Mean:5.59e+00
3rd Qu.:0.0
3rd Qu.:0.0e+00
3rd Qu.:0.00e+00
Max.
:820000.0
Max.
:7.0e+02
Max.
:2.50e+05

migrant_Min.
:91.00
1st Qu.:91.00
Median :91.00
Mean:91.243rd Qu.:91.00
Max.
:95.00s08c_q03
Length:2100
Class :character
Mode :character

s08c_q_1
Min.
: 0.000
1st Qu.: 2.000
Median : 3.000
Mean: 5.152
3rd Qu.: 7.000
Max.
:98.000

s08c_q06
Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.6533rd Qu.:2.000
Max.
:2.000s08c_q07
Length:2100
Class :character
Mode :character

s08c_q10
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.032
3rd Qu.:1.000
Max.
:2.000s08c_q11
Min.
: 0.000
1st Qu.: 5.000
Median : 8.000
Mean: 7.268
3rd Qu.:10.000
Max.
:19.000

s08c_q12
Length:2100
Class :character
Mode :character

s08c_q15
Min.
:0.000
1st Qu.:4.000
Median :4.000
Mean:4.571
3rd Qu.:5.000
Max.
:8.000s08c_q16
Min.
:0.000
1st Qu.:0.000
Median :2.000
Mean:3.286
3rd Qu.:6.000
Max.
:8.000NA's:3s08c_q_2
Min.
:0.000
1st Qu.:0.000
Median :0.000
Mean:1.368
3rd Qu.:1.000
Max.
:8.000NA's:59

Bangladesh HIES 2010 Manual (Version 1.0)s08c_q04
Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.083
3rd Qu.: 3.000
Max.
:14.000

s08c_q08Min.
: 0.000
1st Qu.: 0.000
Median : 1.000
Mean: 4.853
3rd Qu.: 9.000
Max.
:26.000NA's:65s0bc_q13
Min.
: 0.00
1st Qu.: 3.00
Median : 4.00
Mean
: 4.15
3rd Qu.: 5.00
Max.
:17.00s08c_q_3
Min.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean:0.8716
3rd Qu.:0.0000
Max.
:8.0000
NA's
:83

69

s08c_q17
Min.
:0
1st Qu.:0
Median : 1000
Mean
: 6517
3rd Qu.: 6000
Max.
:300000## 14 #### rt014
psu
Length:5137
Class :character
Mode :character#########
hhold
Length:5137
Class :character
Mode :character

s08d_q06
Min.
:400
1st Qu.: 7000
Median : 10000
Mean: 30211
3rd Qu.: 20000
Max.
:3000000s08d_q_2
Min.
:0
1st Qu.: 205
Median : 330
Mean: 5182
3rd Qu.: 1200
Max.
:700000
s08d_q14
Min.
:0
1st Qu.:0
Median :0
Mean: 13253
3rd Qu.: 8000
Max.
:5000000s08d_q07
Min.
: 0.0
1st Qu.: 11.0
Median : 12.0
Mean
: 14.43rd Qu.: 12.0Max.
:240.0s08d_q10
Min.
:0.000
1st Qu.:2.000
Median :2.000
Mean:1.982
3rd Qu.:2.000
Max.
:7.000## 15 #### rt015
psu
Length:438559
Class :character
Mode :character#########
hhold
Length:438559
Class :character
Mode :characters09a1d_1
Length:438559
Class :character
Mode :characters09a1d_2
Min.
:0
1st Qu.:0
Median :0
Mean: 10333rd Qu.: 1000

loan_num
idcodes08d_q05
Min.
:20.00Min.
: 1.000Min.
: 1.00
1st Qu.:20.00
1st Qu.: 1.000
1st Qu.: 8.00
Median :20.00
Median : 2.000
Median :12.00
Mean:20.25Mean: 1.765
Mean:11.26
3rd Qu.:20.00
3rd Qu.: 2.000
3rd Qu.:14.00
Max.
:25.00Max.
:11.000Max.
:23.00s08d_q08
s08d_q_1
s08d_q09Min.
: 0.000Min.
: 0.00Min.
:0.000
1st Qu.: 0.000
1st Qu.:10.00
1st Qu.:3.000
Median : 0.000
Median :13.00
Median :3.000
Mean: 1.324Mean:14.25Mean:3.288
3rd Qu.: 0.000
3rd Qu.:20.00
3rd Qu.:3.000
Max.
:80.000Max.
:75.00Max.
:9.000s08d_q11s08d_q12s08d_q13
Min.
:
0
Min.
:1.000Min.
:0.000
1st Qu.:3450
1st Qu.:4.000
1st Qu.:1.000
Median :6800
Median :4.000
Median :2.000
Mean: 20993
Mean:4.969Mean:1.712
3rd Qu.: 13800
3rd Qu.:6.000
3rd Qu.:2.000
Max.
:3000000
Max.
:8.000Max.
:8.000lnMin.
: 1
1st Qu.: 24Median : 52
Mean
: 57
3rd Qu.: 81
Max.
:142
NA's
:3
s09a1d_3
Min.
:0.000
1st Qu.:0.000
Median :0.000
Mean:0.307
3rd Qu.:1.000

item
Min.
: 10.00
1st Qu.: 42.00
Median : 82.00
Mean: 87.16
3rd Qu.:122.00
Max.
:204.00
NA's:3
s09a1d02
Min.
:
0.0
1st Qu.:0.0
Median :0.0
Mean: 231.9
3rd Qu.: 125.0

Bangladesh HIES 2010 Manual (Version 1.0)

s09a1d01
Min.
:
0.0
1st Qu.:0.0
Median :0.0
Mean: 236.2
3rd Qu.: 150.0
Max.
:33500.0
NA's:3s09a1d_4
Length:438559
Class :character
Mode :character

70

Max.
:416000
Max.
:4.000Max.
:33500.0
NA's
:3
NA's
:32
NA's
:3
s09a1d_5
s09a1d_6
s09a1d03
s09a1d_7
s09a1d_8
Min.
:
0
Min.
:0.0000
Min.
:
0.0
Length:438559
Min.
:0
1st Qu.:0
1st Qu.:0.0000
1st Qu.:0.0
Class :character
1st Qu.:0
Median :0Median :0.0000
Median :0.0Mode :character
Median :0
Mean: 1004Mean:0.3018
Mean: 231.7
Mean: 1018
3rd Qu.: 900
3rd Qu.:0.0000
3rd Qu.: 125.0
3rd Qu.:900
Max.
:506900
Max.
:4.0000
Max.
:16000.0Max.
:266000
NA's
:3
NA's
:32
NA's
:22
NA's
:22
s09a1d_9
s09a1d04
s09a1_10
s09a1_11
s09a1_12
Min.
:0.0000
Min.
:
0.0
Length:438559
Min.
:
0
Min.
:0.0000
1st Qu.:0.0000
1st Qu.:0.0
Class :character
1st Qu.:0
1st Qu.:0.0000
Median :0.0000
Median :0.0Mode :character
Median :0Median :0.0000
Mean:0.2966
Mean: 231.4
Mean
:
1032
Mean
:0.2957
3rd Qu.:0.0000
3rd Qu.: 125.0
3rd Qu.:900
3rd Qu.:0.0000
Max.
:4.0000
Max.
:70000.0Max.
:1375000
Max.
:4.0000
NA's
:29
NA's
:22
NA's
:21
NA's
:27
s09a1d05
s09a1_13
s09a1_14
s09a1_15
s09a1d06
Min.
:
0.0
Length:438559
Min.
:
0.0
Min.
:0.0000
Min.
:
0.0
1st Qu.:0.0
Class :character
1st Qu.:0.0
1st Qu.:0.0000
1st Qu.:0.0
Median :0.0Mode :character
Median :0.0Median :0.0000
Median :0.0
Mean: 229.2
Mean
:
977.4
Mean
:0.2931
Mean: 226.9
3rd Qu.: 120.0
3rd Qu.:900.0
3rd Qu.:0.0000
3rd Qu.: 105.0
Max.
:24500.0Max.
:320000.0
Max.
:4.0000
Max.
:23000.0
NA's
:23
NA's
:24
NA's
:29
NA's
:25
s09a1_16
s09a1_17
s09a1_18
s09a1d07
s09a1_19
Length:438559
Min.
:
0
Min.
:0.0000
Min.
:
0.0
Length:438559
Class :character
1st Qu.:0
1st Qu.:0.0000
1st Qu.:0.0
Class :character
Mode :character
Median :0Median :0.0000
Median :0.0Mode :character
Mean
:
965
Mean
:0.2927
Mean: 225.8
3rd Qu.:900
3rd Qu.:0.0000
3rd Qu.: 103.0
Max.
:206000
Max.
:4.0000Max.
:55000.0
NA's
:25
NA's
:31
NA's
:25
s09a1_20
s09a1_21
s09a1d08
s09a1_22
s09a1_23
Min.
:
0.0
Min.
:0.000Min.
:
0.0
Length:438559
Min.
:
0.0
1st Qu.:0.0
1st Qu.:0.000
1st Qu.:0.0
Class :character
1st Qu.:0.0
Median :0.0Median :0.000
Median :0.0Mode :character
Median :
0.0
Mean
:
957.4
Mean
:0.291
Mean: 226.7
Mean
:
950.3
3rd Qu.:900.0
3rd Qu.:0.000
3rd Qu.: 120.0
3rd Qu.:900.0
Max.
:1050000.0
Max.
:4.000Max.
:13000.0Max.
:170000.0
NA's
:25
NA's
:31
NA's
:23
NA's
:23
s09a1_24
s09a1d09
s09a1_25
s09a1_26
s09a1_27
Min.
:0.0000
Min.
:
0.0
Length:438559
Min.
:
0.0
Min.
:0.0000
1st Qu.:0.0000
1st Qu.:0.0
Class :character
1st Qu.:0.0
1st Qu.:0.0000
Median :0.0000
Median :0.0Mode :character
Median :0.0Median :0.0000
Mean:0.2947
Mean: 223.7
Mean
:
935.7
Mean
:0.2888
3rd Qu.:0.0000
3rd Qu.: 102.0
3rd Qu.:840.0
3rd Qu.:0.0000
Max.
:4.0000
Max.
:30000.0Max.
:132000.0
Max.
:4.0000
NA's
:29
NA's
:26
NA's
:26
NA's
:32
s09a1d10s09a1_28
s09a1_29
s09a1_30
s09a1d11Min.
:
0.0
Length:438559
Min.
:
0.0
Min.
:0.00
Min.
:
0.0
1st Qu.:0.0
Class :character
1st Qu.:0.0
1st Qu.:0.00
1st Qu.:0.0
Median :0.0Mode :character
Median :0.0Median :0.00
Median :0.0
Mean: 226.4
Mean
:
990.7
Mean
:0.29
Mean
: 227.3
3rd Qu.: 117.0
3rd Qu.:900.0
3rd Qu.:0.00
3rd Qu.: 110.0

Bangladesh HIES 2010 Manual (Version 1.0)

71

Max.
:16666.0
NA's:18s09a1_31
Length:438559
Class :character
Mode :characterMax.
:200000.0
Max.
:4.00
Max.
:16666.0
NA's
:18
NA's
:26
NA's
:26
s09a1_33
s09a1d12
s09a1_34
Min.
:0.0000
Min.
:
0.0
Length:438559
1st Qu.:0.0000
1st Qu.:0.0
Class :character
Median :0.0000
Median :0.0Mode :character
Mean:0.2882
Mean: 225.3
3rd Qu.:0.0000
3rd Qu.: 101.0
Max.
:4.0000
Max.
:18000.0
NA's
:31
NA's
:25
s09a1d13
s09a1_37
s09a1_38
Min.
:
0.0
Length:438559
Min.
:
0.0
1st Qu.:0.0
Class :character
1st Qu.:0.0
Median :0.0Mode :character
Median :0.0
Mean: 223.7
Mean
:
960.1
3rd Qu.: 115.0
3rd Qu.:900.0
Max.
:13000.0Max.
:350000.0
NA's
:23
NA's
:23
s09a1_40
s09a1_41
s09a1_42
Length:438559
Min.
:
0.0
Min.
:0.0000
Class :character
1st Qu.:0.0
1st Qu.:0.0000
Mode :character
Median :0.0Median :0.0000
Mean
:
967.7
Mean
:0.2914
3rd Qu.:900.0
3rd Qu.:0.0000
Max.
:174500.0
Max.
:4.0000
NA's
:25
NA's
:31

s09a1_32
Min.
:0
1st Qu.:0
Median :0
Mean: 1016
3rd Qu.:900
Max.
:390000
NA's:26
s09a1_35
s09a1_36
Min.
:
0.0
Min.
:0.0000
1st Qu.:0.0
1st Qu.:0.0000
Median :0.0Median :0.0000
Mean: 984.1
Mean:0.2886
3rd Qu.: 800.0
3rd Qu.:0.0000
Max.
:550000.0
Max.
:4.0000
NA's
:25
NA's
:31
s09a1_39s09a1d14Min.
:0.0000
Min.
:
0.0
1st Qu.:0.0000
1st Qu.:0.0
Median :0.0000
Median :0.0
Mean:0.2875
Mean: 226.6
3rd Qu.:0.0000
3rd Qu.: 125.0
Max.
:7.0000
Max.
:14500.0
NA's
:30
NA's
:25
t
kcal
Min.
:0.0000
Min.
: 0.0
1st Qu.:0.0000
1st Qu.: 0.0
Median :0.0000
Median : 76.0
Mean
:0.2808
Mean:155.8
3rd Qu.:1.0000
3rd Qu.:273.0
Max.
:1.0000Max.
:900.0NA's:39## 16 #### rt016
psu
Length:200008
Class :character
Mode :character#########
hhold
Length:200008
Class :character
Mode :characters09b1w_1
Length:200008
Class :character
Mode :characters09b1w_2
Min.
:0
1st Qu.:0
Median :700
Mean: 1695
3rd Qu.: 1600
Max.
:214200

s09b1w_5
Min.
:
1st Qu.:00

s09b1w_6
Min.
:0.0000
1st Qu.:0.0000

lnMin.
: 1.000
1st Qu.: 5.000
Median : 9.000
Mean: 9.806
3rd Qu.:15.000
Max.
:21.000

s09b1w_3
Min.
:0.0000
1st Qu.:0.0000
Median :1.0000
Mean:0.6382
3rd Qu.:1.0000
Max.
:4.0000
NA's
:1
kcal
Min.
: 0.0
1st Qu.: 50.0

item
Min.
:210.0
1st Qu.:214.0
Median :218.0
Mean:221.1
3rd Qu.:231.0
Max.
:237.0s09b1w1_Min.
:
0.0
1st Qu.:0.0
Median :50.0
Mean: 252.8
3rd Qu.: 250.0Max.
:30400.0

s09b1w2_Min.
:
0.0
1st Qu.:0.0
Median :50.0
Mean: 252.4
3rd Qu.: 250.0
Max.
:11700.0NA's:20s09b1w_4
Length:200008
Class :character
Mode :character

Bangladesh HIES 2010 Manual (Version 1.0)

72Median : 700
Mean: 1681
3rd Qu.: 1600
Max.
:102000
NA's:20

Median :1.0000
Mean:0.6331
3rd Qu.:1.0000
Max.
:4.0000
NA's:20

Median :208.0
Mean:180.2
3rd Qu.:304.0
Max.
:356.0## 17 #### rt017
psu
Length:362670
Class :character
Mode :character#########
hhold
Length:362670
Class :character
Mode :characters09c1__1
Min.
: 0.00
1st Qu.: 0.00
Median : 0.00
Mean
: 14.78
3rd Qu.: 0.00
Max.
:5000.00

s09c1__2
Min.
:0
1st Qu.:0
Median :
20
Mean
: 110
3rd Qu.: 100
Max.
:96140## 18 #### rt018
psu
Length:285162
Class :character
Mode :character#########
hhold
Length:285162
Class :character
Mode :characterlnMin.
: 1.00
1st Qu.:11.00
Median :20.00
Mean:22.36
3rd Qu.:32.00
Max.
:49.00item
Min.
:240.0
1st Qu.:250.0
Median :261.0
Mean:262.7
3rd Qu.:273.0
Max.
:292.0s09c1_q0
Min.
:
0.00
1st Qu.:0.00
Median :
15.00
Mean
:
95.27
3rd Qu.:75.00
Max.
:96140.00

lnMin.
: 1.00
1st Qu.: 7.00
Median :20.00
Mean:21.28
3rd Qu.:35.00
Max.
:47.00item
Min.
:300.0
1st Qu.:306.0
Median :321.0
Mean:322.7
3rd Qu.:339.0
Max.
:352.0s09d1_q0
Min.
:
0.000
1st Qu.:0.000
Median :
1.000
Mean
:
3.228
3rd Qu.:4.000
Max.
:6400.000

ln
Min.
: 1.0
1st Qu.: 35.0
Median : 67.0
Mean
: 68.9
3rd Qu.:103.0
Max.
:142.0item
Min.
: 27.0
1st Qu.:401.0
Median :450.0
Mean:451.9
3rd Qu.:504.0
Max.
:718.0s09d2_q0
Min.
:0
1st Qu.:0
Median :
0
Mean
:
1060
3rd Qu.:170
Max.
:1618000lnMin.
: 1.00
1st Qu.: 7.00
Median :14.00

dg_codeMin.
:561.0
1st Qu.:567.0
Median :575.0

s09e_q01
Min.
:0.000
1st Qu.:2.000
Median :2.000

s09d1__1
Min.
:0
1st Qu.:0
Median : 150
Mean
: 576
3rd Qu.: 550
Max.
:69800## 19 #### rt019
psu
Length:830454
Class :character
Mode :character#########
hhold
Length:830454
Class :character
Mode :character## 20 #### rt020
psu
Length:341813
Class :character
Mode :character#########
hhold
Length:341813
Class :character
Mode :character

Bangladesh HIES 2010 Manual (Version 1.0)

73s09e_q02
Min.
: 0.000
1st Qu.: 0.000
Median : 0.000
Mean: 1.113
3rd Qu.: 0.000
Max.
:6000.000

s09e_q03
Min.
:0
1st Qu.:0
Median :
0
Mean
:
2174
3rd Qu.:0Max.
:4090500

Mean:14.49Mean:575.9
3rd Qu.:21.00
3rd Qu.:583.0
Max.
:28.00Max.
:600.0s09e_q04
Min.
:
0.0
1st Qu.:0.0
Median :
0.0
Mean
:
351.7
3rd Qu.:0.0
Max.
:2963800.0

Bangladesh HIES 2010 Manual (Version 1.0)

Mean
:1.717
3rd Qu.:2.000
Max.
:5.00074

5.3 Frequency tables of categorical variables
DATA CHECK OF CATEGORICAL VARIBLES> # file.names: Rnames[j]> # file.list: outfiles[[j]]> # list of column numbers of categorical variables> ck<-list()> ck[[1]]<-c(16:19,21:27,29:34,42:46,49,51,53,55,57,74:79)> ck[[2]]<-c(6,7,9:11,15:20,23,33,35:39,41:43,45,47,64:66,69:76,78:80,85:86,98:100,+
104:118,120:121,123:128,130:131,133:134,136:137,139:140,142:143,145:149)> ck[[3]]<-c(11,13:15,17,21:22,25)> ck[[4]]<-c(13,17:21,23:24)> ck[[5]]<-c(4,7:13)> ck[[6]]<-c(5)> ck[[7]]<-c()> ck[[8]]<-c()> ck[[9]]<-c()> ck[[10]]<-c()> ck[[11]]<-c(5)> ck[[12]]<-c()> ck[[13]]<-c(5,8,10,12,13,15,17)> ck[[14]]<-c(5,10,12,14:15)> ck[[15]]<-seq(6,60,by=2)> ck[[16]]<-c(6,8,10,12)> ck[[17]]<-c()> ck[[18]]<-c()> ck[[19]]<-c()> ck[[20]]<-c(5)> for(j in 1:20){+ if(length(ck[[j]])==0) {next}+ cat("\n\n#### FREQUENCY OF VARIABLES IN", Rnames[j],+ "#########################################\n\n")+ varlabels<-attributes(outfiles[[j]])$var.labels+ for(k in ck[[j]]){+ if(length(ck[[j]])>0){+ variable.name<-colnames(outfiles[[j]])[k]+ cat(k," -----",variable.name," :",varlabels[k],"------------------------")+ print(table(outfiles[[j]][k],useNA="ifany"))+ }}}#### FREQUENCY OF VARIABLES IN rt001 #########################################
16----- s06a_q03 : Your dwelling possesa separate dining -----------------------0
1
2
1 1601 10638
17 ----- s06a_q04 : Your dwelling possesa separate kitchan -----------------------12
3
79238 3000
1
1
18 ----- s06a_q05 : Material of the walls -----------------------123
453148 4850 1854 2330
58
19 ----- s06a_q06 : Material of the roof -----------------------123
45Bangladesh HIES 2010 Manual (Version 1.0)

751275 9992 288 614
71
21 ----- s06a_q08 : What type of latrine? -----------------------1
2
3
4
5
6
2419 2110 1926 2935 2376 474
22 ----- s06a_q09 : Main sousce of drinking water -----------------------1
2
3
4
5
6
980 10825
112
119
10
194
23 ----- s06a_q10 : Tubwell tested for arsenic -----------------------0
1
21367 6350 4523
24 ----- s06a_q11 : Was Arsenic found? -----------------------0
1
25889 455 5896
25 ----- s06a_q12 : Alternative source of drinking water -----------------------0
1
2
3
4
5
6
6420
53 5666
62
1
3
35
26 ----- s06a_q13 : Source of water for other use -----------------------1
2
3
4
5
6
1238 7125 3662 140
1560
27 ----- s06a_q14 : Electricity connection -----------------------0
1
2
3 7056 5181
29 ----- s06a_q16 : Mobile phone -----------------------0
1
2
1 7960 4279
30 ----- s06a_q17 : Landline phone connection -----------------------01
2
5
262 11973
31 ----- s06a_q18 : Coumputer -----------------------0
1
2
2
328 11910
32 ----- s06a_q19 : Access to internet -----------------------0
1
2
52
142 12046
33 ----- s06a_q20 : How you access the internet? -----------------------0
1
2
3
5
12083
38
113
2
4
34 ----- s06a_q21 : Present occupancy ststus -----------------------1
2
3
4
5
6
9920 1394 279 444 110
93
42 ----- s07b_q01 : Did any member cultivated any land for crop... -----------------------0
1
2
6 5195 7039
43 ----- s07c_q01 : Household raise any livestock -----------------------0
1
2
5 7488 4747
44 ----- s07c_q09 : Household engage in any fishing -----------------------0
1
2
6 1607 10627
45 ----- s07c_q13 : Household engage in any farm forestry -----------------------0
1
2
9 3856 8375
46 ----- s08a_q01 : Own any unused land? -----------------------01
2
15
448 11777
49 ----- s08a_q03 : household purchased any land in last 12 months ------------------------

Bangladesh HIES 2010 Manual (Version 1.0)

76

0
1
2
6
368 11866
51 ----- s08a_q05
0
1
2
3
39 12198
53 ----- s08a_q07
01
2
2 5610 6628
55 ----- s08a_q09
01
2
3
251 11986
57 ----- s08a_q11
01
2
8383 11849
74 ----- s08c_q01
0
1
2
5 1540 10695
75 ----- s08c_q02
01
2
5 1554 10681
76 ----- s08d_q01
01
2
5822 11413
77 ----- s08d_q02
01
2
5 1880 10355
78 ----- s08d_q03
01
2
5739 11496
79 ----- s08d_q04
01
2
5 4071 8164

: Did your household purchase any house? ------------------------: Household own other assets ------------------------: Household purchased any assets in last 12 months -----------------------: Household sold any assets ------------------------: Any memberof household migrated? ------------------------: Household received remittances from outside? ------------------------: Any member opened bank account? ------------------------: Any member deposited in any credit union? ------------------------: Any member deposited in non-inst. group? ------------------------: Any member borrowed money ------------------------#### FREQUENCY OF VARIABLES IN rt002 #########################################
6----- s01a_q02 : s01a_q02: sex -----------------------1
227533 28047
7 ----- s01a_q03 : s01a_q03: relationship to head -----------------------1
2
3
4
5
6
7
8
9
10
11
12
13
12240 10314 24612 2013 2699 1572
751
463
234
214
152
166
52
14
98
9 ----- s01a_q05 : s01a_q05: religion -----------------------123
4548735 6245
385
196
19
10 ----- s01a_q06 : s01a_q06: marital status -----------------------123
4525972 26658 2582
167
201
11 ----- s01a_q07 : s01a_q07: earner -----------------------1
215968 39612
15 ----- s01a_q11 : s01a_q11: Lived abroad more than 6 m? -----------------------1
2

Bangladesh HIES 2010 Manual (Version 1.0)

77

210 55370----- s01a_q12 : s01a_q12: why did return? -----------------------0
1
2
3
4
5
6
7
55370
32
29
59
37
31
6
16
17 ----- s01b_q01 : s01b_q01: did work for livelihood during last 7 days? ----------------------0
1
25691 16152 33737
18 ----- s01b_q02 : s01b_q02: was avaible for work during last 7 days? ----------------------0
1
2
21852
236 33492
19 ----- s01b_q03 : s01b_q03: did look for work during last 7 days -----------------------0
1
2
55328
171
81
20 ----- s01b_q04 : s01b_q04: Reason for not looking for work -----------------------0
1
2
3
4
5
6
7
8
9
10
11
25
22015
948 12964 13507 2195 1985
262
400
448
135
124
596
1
23 ----- s01c_q02 : s01c_q02: In what program has been included? -----------------------0
1
2
3
4
5
6
7
8
10
11
13
14
52072
568
238
32
4
16
14
55
278
42
9
17
5
15
16
17
18
19
20
21
22
23
24
25
26
27
560
8
10
123
32
503
4
20
630
6
35
278
5
28
29
30
2
9
5
33 ----- s01c_q07 : s01c_q07: cause why not included -----------------------0
1
2
3
4
5
6
9138 2045 29939 1853 1769 9975
861
35 ----- s02a_q03 : s02a_q03: can read a letter? -----------------------0
1
25690 28950 20940
36 ----- s02a_q04 : s02a_q04: can write a letter? -----------------------0
1
2
426627 28132
820
1
37 ----- s02a_q05 : s02a_q05: highest class passed -----------------------0
1
2
3
4
5
6
7
8
9
10
11
12
27788
817 1187 1591 2297 4996 1852 2167 2804 3438 3139 1833
938
13
14
15
16
17
18
19
50
503
21
35
1
40
5
127
1
38 ----- s02a_q06 : s02a_q06: where attended/attending? -----------------------0
1
2
3
4
5
6
7
8
27436 23108 2755
432 1403
13
29
309
95
39 ----- s02a_q07 : s02a_q07: what type of school attended/attending? ----------------------0
1
2
3
4
5
6
27882 11627 14152
688
157
830
244
41 ----- s02b_q01 : s02b_q01: are you attending education institution? ----------------------01
25677 14088 35815
42 ----- s02b_q02 : s02b_q02: what class attending? -----------------------0
1
2
3
4
5
6
7
8
9
10
11
12
42070 1996 1563 1538 1287 1112
887
821
761
669
909
904
702
13
14
15
16
17
18
19
113
6
15
2
26
1
198
43 ----- s02b_q03 : s02b_q03: receiving stipend for primary? -----------------------16

Bangladesh HIES 2010 Manual (Version 1.0)

78

0
1
2
48076 1326 6178
45 ----- s02b_q05
0
1
2
50595
735 4250
47 ----- s02b_q07
-0
1
2
54824
388
368
64 ----- s03a_q01
01
2
15 8157 47408
65 ----- s03a_q02
0
1
2
47423
224
317
13
14
15
171
35 1331
66 ----- s03a_q_1
0
1
2
53895
38
54
13
14
15
11
3
212
69 ----- s03a_q04
01
2
39 10931 44609
70 ----- s03a_q05
0
1
2
44649
543 6318
13
14
15
23
17
114
71 ----- s03a_q_3
0
1
2
53830
78
210
13
14
15
1
6
18
72 ----- s03a_q_4
0
1
2
55125
100
10
13
14
15
1
1
5
73 ----- s03a_q06
01
244655 10086
838
74 ----- s03a_q07
0
1
2
54742
7
484
75 ----- s03a_q08
0
1
2
45402
253
33
13
237
76 ----- s03a_q_5
0
1
2
55251
9
5
13

: s02b_q05: receiving secondary stipend? ------------------------: s02b_q07: received benefit in the tuition waiver? ----------------------: s03a_q01: have suffered chronic illness? ------------------------: s03a_q02: what chronic illness? 1 -----------------------3
4
5
6
7
8
9
10
11
12
602
737
112 1976
905 1091
123
472
30
31

: s03a_q02: what chronic illness? 2 -----------------------3
4
5
6
7
8
9
10
11
12
77
82
25
385
297
353
31
111
3
1
16
25
1
1
: s03a_q04: have suffered illness in last 30 days? ----------------------41: s03a_q05_1: what type of illness? 1 -----------------------3
4
5
6
7
8
9
10
11
12
232 1135
181
250
87
250
274
159
105
112
16
17
18
19
20
21
22
23
24
25
90
30
8
17
28
12
195
32
9
710
: s03a_q05_2: what type of illness? 2 -----------------------3
4
5
6
7
8
9
10
11
12
66
554
23
59
20
150
149
102
38
24
16
17
19
20
22
23
24
25
18
3
18
3
45
6
4
155
: s03a_q05_3: what type of illness? 3 -----------------------3
4
5
6
7
8
9
10
11
12
5
25
3
9
4
16
125
94
7
3
16
19
21
22
23
24
25
4
8
1
6
1
2
25
: s03a_q06: have sought medical treatment? -----------------------81: s03a_q07: If not why not? -----------------------3
4
5
6
7
9
10
11
12
121
10
2
1
28
15
5
3
162
: s03a_q08_1: If yes who was consulted (1st) -----------------------3
4
5
6
7
8
9
10
11
12
368
97
36 1035 1572
20 2373 3989
101
64

: s03a_q08_2: who was consulted (2nd) -----------------------3
4
5
6
7
8
9
10
11
12
16
17
5
25
44
1
54
122
11
10

Bangladesh HIES 2010 Manual (Version 1.0)

7910----- s03a_q10 : s03a_q10: From where got medicines? -----------------------0
1
2
3
4
5
6
7
8
9
45400
290
31
50
343 923489
42137
79 ----- s03a_q11 : s03a_q11: did you pay for the medicines? -----------------------0
1
2
3
7
8
45408 9725
254
191
1
1
80 ----- s03a_q12 : s03a_q12: how did travel to rervice provider? -----------------------0
1
2
3
4
5
6
8
9
10
11
12
13
45403
81
195
812
376 1699 131971
46
20 5071191
296
85 ----- s03a_q15 : s03a_q15: why you chose this provider? -----------------------0
1
2
3
4
5
6
7
8
9
10
20
45409 3273 2065
666
34
59 3271
48
95
504
155
1
86 ----- s03a_q16 : s03a_q16: Did provider give enough time for you? ----------------------0
12
3645411 9519
191
458
1
98 ----- s03a_q18 : s03a_q18_1: how financed treatment? 1 -----------------------0
1
2
3
4
5
6
7
8
9
10
11
44726 8715 1419
24
30
30
9
9
202
57
159
200
99 ----- s03a__18 : s03a_q18_2: how financed treatment? 2 -----------------------0
1
2
3
4
5
6
7
8
9
10
11
54605
75
725
6
3
16
2
3
69
7
52
17
100 ----- s03a__19 : s03a_q18_3: how financed treatment? 3 -----------------------0
1
2
3
4
5
8
9
10
11
50
95
55540
5
1
5
1
1
5
2
6
12
1
1
104 ----- s03b_q03 : s03b_q03: has he/she ever been immunized? -----------------------0
1
2
348964 6340
199
77
105 ----- s03b_q04 : s03b_q04: Do you have immunization card? -----------------------0
1
249239 5107 1234
106 ----- s03b_q05 : s03b_q05bcg: bcg? -----------------------0
1
249238 6320
22
107 ----- s03b_q_2 : s03b_q05dpt1: dpt1? -----------------------0
1
249240 6255
85
108 ----- s03b_q_3 : s03b_q05dpt2: dpt2? -----------------------0
1
249243 6149
188
109 ----- s03b_q_4 : s03b_q05dpt3: dpt3? -----------------------0
1
249243 6057
280
110 ----- s03b_q_5 : s03b_q05p1: polio1? -----------------------0
1
249249 6144
187
111 ----- s03b_q_6 : s03b_q05p2: polio2? -----------------------0
1
249252 6060
268
112 ----- s03b_q_7 : s03b_q05p3: polio3? -----------------------0
1
249253 5984
343
113 ----- s03b_q_8 : s03b_q05m: measles? -----------------------0
1
249259 5765
556
78

Bangladesh HIES 2010 Manual (Version 1.0)

80

114

----- s03b_q_9 : s03b_q05h: hepatitis? -----------------------0
1
2
449258 5371
950
1
115 ----- s03b_q06 : s03b_q06: where was most recent immunization? -----------------------0
1
2
3
4
5
6
7
8
9
49238 1848 1934
556
211
112
61
140 143842
116 ----- s03b_q07 : s03b_q07: who influenced you to immunize? -----------------------0
1
2
3
4
5
6
7
8
9
10
11
30
49239 2966
352
987 1689
132
2
5
60
115
27
5
1
117 ----- s03b_q08 : s03b_q08: received vitamin-a capsules in last 12m? ----------------------0
1
249245 5806
529
118 ----- s03c_q01 : s03c_q01: have you ever given birth? -----------------------0
1
2
539339 14789 1451
1
120 ----- s03c_q03 : s03c_q03: did you attend prenatal consultations? ----------------------0
1
2
3
6
7
40787 6666 8122
2
2
1
121 ----- s03c_q04 : s03c_q04: From where you received this care? -----------------------0
1
2
3
4
5
6
7
8
48910 1080 2135 1202
571
188
143 1055
296
123 ----- s03c_q06 : s03c_q06: did you receive tetanus vaccine? -----------------------0
1
2
4
5
6
7
8
48908 6440
226
1
2
1
1
1
124 ----- s03c_q07 : s03c_q07: who assisted with this birth? -----------------------0
1
2
3
4
5
6
7
40787 6195 2320 1935 2500
493 1333
17
125 ----- s03c_q08 : s03c_q08: where did you give birth? -----------------------0
1
2
3
4
5
6
7
8
9
40786 12767
156
105
300
363
49
183
757
114
126 ----- s03c_q09 : s03c_q09: did you visit post-natal checkup? -----------------------0
1
240787 1981 12812
127 ----- s03c_q10 : s03c_q10: where did you go for checkup? -----------------------0
1
2
3
4
5
6
7
8
9
53599
165
115
320
360
210
45
102
562
102
128 ----- s03d_q02 : s03d_q02: difficulty seeing? -----------------------0
12
34
1 52185 3049
307
38
130 ----- s03d_q04 : s03d_q04: what was the cause? -----------------------0
1
2
3
4
5
6
52184
78
263 1098 1638
39280
131 ----- s03d_q05 : s03d_q05: difficulty hearing? -----------------------0
12
3
4
1 54287 1065191
36
133 ----- s03d_q07 : s03d_q07: what was the cause? -----------------------0
1
2
3
4
5
6
54287
118
180
437
433
23
102
134 ----- s03d_q08 : s03d_q08: difficulty walking? -----------------------0
12
3
42 54275981
280
42
136 ----- s03d_q10 : s03d_q10: what was the cause? -----------------------0
1
2
3
4
5
6
54276
89
170
439
487
21
98

Bangladesh HIES 2010 Manual (Version 1.0)

81

137

----- s03d_q11 : s03d_q11: difficulty remembering? -----------------------0
12
3
42 54878513
138
49
139 ----- s03d_q13 : s03d_q13: what was the cause? -----------------------0
1
2
3
4
5
6
54878
111
37
211
292
15
36
140 ----- s03d_q14 : s03d_q14: difficulty with self care? -----------------------0
12
342 55069296
167
46
142 ----- s03d_q16 : s03d_q16: what was the cause? -----------------------0
1
2
3
4
5
6
55071
89
47
153
182
14
24
143 ----- s03d_q17 : s03d_q17: difficulty in comunicating? -----------------------012
3
42 55109281
134
54
145 ----- s03d_q19 : s03d_q19: what was the cause? -----------------------0
1
2
3
4
5
6
55109
149
21
131
133
12
25
146 ----- s03d_q20 : s03d_q20h: difficulty reduced work at home? -----------------------01
250631 4156
793
147 ----- s03d_q_1 : s03d_q20s: difficulty reduced work at school? -----------------------0
1
251155 1045 3380
148 ----- s03d_q_2 : s03d_q20w: difficulty reduced work at work? -----------------------0
1
250847 3276 1457
149 ----- s03d_q21 : s03d_q21: what measures taken to improve? -----------------------0
1
2
3
4
5
6
7
8
9
10
50618 2153
207 2150
168
121
1
3
102
38
19

#### FREQUENCY OF VARIABLES IN rt003 #########################################
11----- s04a_q05 : s04a_q05ru: where did you do this activity? R/U code ------------------1
211319 6748
13 ----- s04a_q06 : s04a_q06: what kind of activities? -----------------------1
26796 11271
14 ----- s04a_q07 : s04a_q07: what was your work status? agriculture ----------------------0
12
34 <NA>
10725 2571 4048
63
121
539
15 ----- s04a_q08 : s04a_q08: whats was your work status? non agri -----------------------0
12
34 <NA>
6301 3464 3752
53 4013 484
17 ----- s04b_q01 : s04b_q01: were you paid on daily basis? -----------------------0
1
2 <NA>
7364 6056 4152 495
21 ----- s04b_q03 : s04b_q03: Did you receive payments in-kind? -----------------------0
1
2 <NA>
11305
464 5585
713
22 ----- s04b_q04 : s04b_q04: what type of item received in payment? ----------------------0
12345 <NA>
16598
16
132
80
216
20 1005
25 ----- s04b_q06 : s04b_q06: What type of org. you work for? ------------------------

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0
13097

1
642

2
566

3
2161

4
13

5
306

6
34

7
174

8
161

9
107

<NA>
806

#### FREQUENCY OF VARIABLES IN rt004 #########################################
13----- s05a_q04 : s05a_q04: where do you operate the entreprise? -----------------------12
3
4
5
534 593 75 1386 909
17 ----- s05a_q08 : s05a_q08_1: who are your costumers? 1st -----------------------0
12345 <NA>
14 3140 48 248
6383
18 ----- s05a_q_7 : s05a_q08_2: who are your costumers? 2nd -----------------------0
12345 <NA>
2607
66 142 360
14 170 138
19 ----- s05a_q09 : s05a_q09: is the entreprise registred officially? ---------------------0
1
2
3 <NA>
13 878 1619 984
3
20 ----- s05a_q10 : s05a_q10_1: Source of finance when established? 1st -------------------0
1
2
3
4
5
6
7
8
9
10
11 <NA>
13 620 2192 186
1815
74
57 15627
34 102
3
21 ----- s05a_q_8 : s05a_q10_2: Source of finance when established? 2nd -------------------0
1
2
3
4
5
6
7
8
9
10
11
71 <NA>
2377
74 267 315
21
58
36
53
81
31
11
48
1 124
23 ----- s05b_q12 : s05b_q12_1: what problems faced in running your business? 1 -----------0
1
2
3
4
5
6
7
8
9
10
11
12
13 <NA>
13 1831 1224
36
25
63
14
51
10
20
43 10044
20
324 ----- s05b_q_1 : s05b_q12_2: what problems faced in running your business? 2 -----------0
1
2
3
4
5
6
7
8
9
10
11
12
13
16
2442
27 14233
38 12413
82
17
36
63 211
89
49
1
<NA>
130

#### FREQUENCY OF VARIABLES IN rt005 #########################################
4----- s06b_q02 : s06b_q02: did you experience this shock? -----------------------1
2
1760 218252
7 ----- s06b_q05 : s06b_q05a: was a decline in income? -----------------------0
1
2
52182611366
384
18 ----- s06b_q_1 : s06b_q05b: was a decline in assets? -----------------------0
1
2218258899
855
9 ----- s06b_q_2 : s06b_q05c: was a decline in food production? -----------------------0
1
22182641117
631
10 ----- s06b_q_3 : s06b_q05d: was a decline in food purchased? -----------------------0
1
2218266876
870
11 ----- s06b_q06 : s06b_q06_1: how coped with this shock? 1st -----------------------0
1
2
3
4
5
6
7
8
9
10
218252
315
15
102
108
30
74
34
618
257
25
11
12
13
14
16
19
33
41
2
87

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12

----- s06b_q_4 : s06b_q06_2: how coped with this shock? 2nd -----------------------0
1
2
3
4
5
6
7
8
9
10
219164
65
11
119
69
48
68
20
198
143
10
11
12
13
14
15
16
14
5
43
1
5
29
13 ----- s06b_q_5 : s06b_q06_3: how coped with this shock? 3rd -----------------------0
1
2
3
4
5
6
7
8
9
10
219595
27
5
66
16
19
54
6
55
41
19
11
12
13
14
15
16
2
12
12
1
7
75

#### FREQUENCY OF VARIABLES IN rt006 #########################################
5----- s07b_q02
1224780 162200: s07b_q02: did you cultivate this crop? ------------------------#### FREQUENCY OF VARIABLES IN rt011 #########################################
5----- s07d_q01 : s07d_q01: did your household spend any money on...? --------------------0
1
27419 33047 207287#### FREQUENCY OF VARIABLES IN rt013 #########################################
5----- s08c_q04 : s08c_q04: relationship to head -----------------------1
2
3
4
5
6
7
8
9
10
11
14
51 539 1299258
42 1042
1
242
3
8 ----- s08c_q06 : s08c_q06: where is working? -----------------------1
2
728 1372
10 ----- s08c_q08 : s08c_q08: if abroad, write the country code -----------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
657 485 52
94
57 13238
5
2
14 271
4
3
32
12
15
16 20
21
22
23
25
26 <NA>
5
1
2
28
6
3
15 11765
12 ----- s08c_q10 : s08c_q10: sex -----------------------1
2
2032
68
13 ----- s08c_q11 : s08c_q11: level of education -----------------------0
1 2
3
4
5
6
7
8
9 10 11 12 13 14 15 17 19
201 15 31 53 70 375 82 103 220 247 371 167 83 60
592
615 ----- s0bc_q13 : s0bc_q13: how many times he/she sent money? -----------------------0
1 2
3
4
5
6
7
8
9 10 11 12 15 17
9 208 255 317 410 635 102 45 22
4 15
1 75
1
1
17 ----- s08c_q15 : s08c_q15: how he/she sent money? -----------------------0
1
2
3
4
5
6
7
8
68
84 23
41 1167 294
44
88 291#### FREQUENCY OF VARIABLES IN rt014 #########################################
5----- s08d_q05: s08d_q05: what was the source of this credit? ------------------------

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1
88
16
4

2
3
4
5
6
7
8
9
10
11
12
13
14
15
84 3381281
4
12 1055 539
89
37 99425 774 357
17 18
19
20
21
22
23
205 10856 200
5 160
10 ----- s08d_q09 : s08d_q09f: how often you make payments? frequency ---------------------0
1
2
3
4
5
6
7
8
9
99 327 76 3614
51 569 300
89
111
12 ----- s08d_q10 : s08d_q10: completed repayment? -----------------------0
1
2
5
7
6
87 50421
1
14 ----- s08d_q12 : s08d_q12: what was the purpose of loan? -----------------------1
2
3
4
5
6
7
8
102 219 963 1345 690 539 197 1082
15 ----- s08d_q13 : s08d_q13: would you like to borrow more money? -----------------------0
1
2
83 1488 3644
2#### FREQUENCY OF VARIABLES IN rt015 #########################################
6----- s09a1d_1 : s09a1d01_u: unit -----------------------gm
cup
gm
ml
no
No
127461
16654 276081
7003 11036 10323
8 ----- s09a1d_3 : s09a1d01_s: major source -----------------------0
1
2
3
4
<NA>
328527 98717
303
8627
2353
32
10 ----- s09a1d_4 : s09a1d02_u: unit -----------------------gm
cup
gm
ml
no
No
12746016654 276082
7003 11036 10323
12 ----- s09a1d_6 : s09a1d02_s: major source -----------------------0
1
2
3
4
<NA>
330564 96864
302
8328
2469
32
14 ----- s09a1d_7 : s09a1d03_u: unit -----------------------cup
gm
ml
no
No
1274546653 276084
7003 11042 10323
16 ----- s09a1d_9 : s09a1d03_s: major source -----------------------0
1
2
3
4
<NA>
331169 96935
288
7976
2162
29
18 ----- s09a1_10 : s09a1d04_u: unit -----------------------cup
gm
ml
no
No
1274546655 276088
7003 11036 10323
20 ----- s09a1_12 : s09a1d04_s: major source -----------------------0
1
2
3
4
<NA>
331847 96185
285
7935
2280
27
22 ----- s09a1_13 : s09a1d05_u: unit -----------------------cup
gm
ml
no
No
1274566655 276088
7003 11034 10323
24 ----- s09a1_15 : s09a1d05_s: major source -----------------------0
1
2
3
4
<NA>
332806 95301
292
7862
2269
29
26 ----- s09a1_16 : s09a1d06_u: unit -----------------------cup
gm
mil
ml
no
No
1274576654 276084
27003 11036 10323
28 ----- s09a1_18 : s09a1d06_s: major source -----------------------0
1
2
3
4
<NA>
332974 95150
280
7856
2268
31

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30

----- s09a1_19 : s09a1d07_u: unit -----------------------cup
gm
ml
no
No
1274576655 276085
7003 11036 10323
32 ----- s09a1_21 : s09a1d07_s: major source -----------------------0
1
2
3
4
<NA>
333218 95115
272
7745
2178
31
34 ----- s09a1_22 : s09a1d08_u: unit -----------------------500
cup
gm
ml
no
No
12745716654 276083
7003 11038 10323
36 ----- s09a1_24 : s09a1d08_s: major source -----------------------0
1
2
3
4
<NA>
332244 95852
257
7850
2327
29
38 ----- s09a1_25 : s09a1d09_u: unit -----------------------cup
gm
gn
ml
no
No
1274586655 276083
17003 11036 10323
40 ----- s09a1_27 : s09a1d09_s: major source -----------------------0
1
2
3
4
<NA>
333629 94954
241
7578
2125
32
42 ----- s09a1_28 : s09a1d10_u: unit -----------------------cup
gm
ml
no
No
1274506655 276088
7005 11038 10323
44 ----- s09a1_30 : s09a1d10_s: major source -----------------------0
1
2
3
4
<NA>
333517 94926
256
7583
2251
26
46 ----- s09a1_31 : s09a1d11_u: unit -----------------------cup
gm
ml
no
No
1274576655 276085
7003 11036 10323
48 ----- s09a1_33 : s09a1d11_s: major source -----------------------0
1
2
3
4
<NA>
334180 94345
234
7485
2284
31
50 ----- s09a1_34 : s09a1d12_u: unit -----------------------cup
gm
ml
no
No
1274576655 276085
7003 11036 10323
52 ----- s09a1_36 : s09a1d12_s: major source -----------------------0
1
2
3
4
<NA>
334443 93921
258
7497
2409
31
54 ----- s09a1_37 : s09a1d13_u: unit -----------------------cuo
cup
gm
ml
no
No
12745516654 276085
7003 11038 10323
56 ----- s09a1_39 : s09a1d13_s: major source -----------------------0
1
2
3
4
7
<NA>
333896 94872
264
7309
2187
1
30
58 ----- s09a1_40 : s09a1d14_u: unit -----------------------cup
gm
ml
no
No
1274576655 276085
7003 11036 10323
60 ----- s09a1_42 : s09a1d14_s: major source -----------------------0
1
2
3
4
<NA>
331916 96947
244
7323
2098
31

#### FREQUENCY OF VARIABLES IN rt016 #########################################
6----- s09b1w_1
gm
27420 172588
8 ----- s09b1w_3: s09b1w1_gm: unit ------------------------: s09b1w1_m: major source ------------------------

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0
1
2
3
4
<NA>
76488 121662
77
1290
490
1
10 ----- s09b1w_4 : s09b1w2_gm: unit -----------------------gm
27440 172568
12 ----- s09b1w_6 : s09b1w2_m: major source -----------------------0
1
2
3
4
<NA>
76864 121537
72
1122
393
20

#### FREQUENCY OF VARIABLES IN rt020 #########################################
5----- s09e_q01 : s09e_q01: owns item? -----------------------0
12
512061 72603 257147
2

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87

5.4 Consistency check of identifierFound one unmatched household identifier hhid in rt007 and three in rt015.

> HHID<-outfiles[[1]]$hhid> for(j in 1:20){+ cat("\n",j,":",sum(!is.element(outfiles[[j]]$hhid,HHID)))
+ }
1 : 0
2 : 0
3 : 0
4 : 0
5 : 0
6 : 0
7 : 1
8 : 0
9 : 0
10 : 011 : 012 : 013 : 014 : 0
15 : 3
16 : 017 : 018 : 019 : 020 : 0# rt007> d<-outfiles[[7]]> d[!is.element(d$hhid,HHID),]psu hhold liv_code s07c_q02 s07c_q_1 s07c_q03 s07c_q_2 s07c_q04 s07c_q_3
56287
0
0
0
0
0
0
0
s07c_q05 s07c_q_4 hhid
56287
0
NA


This record should be dropped because it is empty.

> d<-d[-56287,]> dim(d)[1] 80423
12> sum(!is.element(d$hhid,HHID))[1] 0> outfiles[[7]]<-dBangladesh HIES 2010 Manual (Version 1.0)

88

# rt015> d<-outfiles[[15]]> d[!is.element(d$hhid,HHID),]psu hhold ln item s09a1d01 s09a1d_1 s09a1d_2 s09a1d_3 s09a1d02
30444 238
NA NA
NA
NA
NA
NA
30445 238
NA NA
NA
NA
NA
NA
30446 238
NA NA
NA
NA
NA
NA
s09a1d_4 s09a1d_5 s09a1d_6 s09a1d03 s09a1d_7 s09a1d_8 s09a1d_9
30444
NA
NA
NA
NA
NA
30445
NA
NA
NA
NA
NA
30446
NA
NA
NA
NA
NA
s09a1d04 s09a1_10 s09a1_11 s09a1_12 s09a1d05 s09a1_13 s09a1_14
30444
NA
NA
NA
NA
NA
30445
NA
NA
NA
NA
NA
30446
NA
NA
NA
NA
NA
s09a1_15 s09a1d06 s09a1_16 s09a1_17 s09a1_18 s09a1d07 s09a1_19
30444
NA
NA
NA
NA
NA
30445
NA
NA
NA
NA
NA
30446
NA
NA
NA
NA
NA
s09a1_20 s09a1_21 s09a1d08 s09a1_22 s09a1_23 s09a1_24 s09a1d09
30444
NA
NA
NA
NA
NA
NA
30445
NA
NA
NA
NA
NA
NA
30446
NA
NA
NA
NA
NA
NA
s09a1_25 s09a1_26 s09a1_27 s09a1d10 s09a1_28 s09a1_29 s09a1_30
30444
NA
NA
NA
NA
NA
30445
NA
NA
NA
NA
NA
30446
NA
NA
NA
NA
NA
s09a1d11 s09a1_31 s09a1_32 s09a1_33 s09a1d12 s09a1_34 s09a1_35
30444
NA
NA
NA
NA
NA
30445
NA
NA
NA
NA
NA
30446
NA
NA
NA
NA
NA
s09a1_36 s09a1d13 s09a1_37 s09a1_38 s09a1_39 s09a1d14 s09a1_40
30444
NA
NA
NA
NA
NA
30445
NA
NA
NA
NA
NA
30446
NA
NA
NA
NA
NA
s09a1_41 s09a1_42 t kcal hhid
30444
NA
NA NA
0 238
30445
NA
NA NA
0 238
30446
NA
NA NA
0 238


The above three records should be dropped, because they do not have household
number (hhold).

> d<-d[is.element(d$hhid,HHID),]> dim(d)[1] 438556
63> sum(!is.element(d$hhid,HHID))[1] 0> outfiles[[15]]<-dBangladesh HIES 2010 Manual (Version 1.0)

89

Found 29 unmatched person identifier pid in rt003. Their person ID code “idcode” aremissing, that is, NA or 0, but information about economic activities are filled in.

> PID<-outfiles[[2]]$pid> length(PID)[1] 55580> sum(!is.element(outfiles[[3]]$pid,PID))[1] 29> d[!is.element(d$pid,PID),c(1:10,29,30)]
psu hhold serial idcode
s04a_q01 s04a_q_1 s04a_q_2
81030 07303NA
6101
228 066 054
05
NA
81
36
283 079 132
02
NA
HANDI CRAFTS
6302284 079 132
03
NA(BAMBOO/CAIN)
63
02
296 087 001
02
NALabour61
01
309 087 178
01
NA
BEGGER99
99
327 102 181
01
NA
13
80
328 102 181
02
NA
85
37
376 122 122
08
NA
1
60
01
441 154 061
11
NA
61
01
516 189 183
01
NA Agriculture work
6101
632 209 192
04
NA
46
60
1358 440 032
02NAAgriculture
61011830 558 082
04
NA
0
75
17
1840 558 147
02NA
Weaver75
172223 101261
02
0
AGRICULTURE
61012403 208 074
01
0
61
01
2747 439 031
010
Mudir Shope
42
522748 439 031
02
0 Puffed Rice Shope
42
522760 439 150
02
0 Puffed Rice Shope
42
522775 439 283
02
0 Puffed Rice Shope42522808 488 003
030Agriculture
61012809 488 003
040
agriculture
61012884 518 175
020
Chakury
39
752933 543 097
02
0
Business
42
522935 543 099
02
0
Day Labour
61
012942 543 166
020Agri Labour
610112179 009 024
020
Contakter874512197 009 087
020Carpenter
81
36

81
228
283
284
296

s04a_q02 s04a_q03 s04a_q04 hhid
pid
12
20
2 030073 030073NA
1225
10 066054 066054NA
1230
12 079132 079132NA
1230
12 079132 079132NA
1220
4 087001 087001NA

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309
327
328
376
441
516
632
1358
1830
1840
2223
2403
2747
2748
2760
2775
2808
2809
2884
2933
2935
2942
12179
12197



12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
6
10

39
20
30
30
12
20
26
10
10
25
30
26
30
30
30
30
15
15
20
30
20
20
24
28

10 087178 087178NA
10 102181 102181NA
5 102181 102181NA
8 122122 122122NA
5 154061 154061NA
5 189183 189183NA
10 209192 209192NA
4 440032 440032NA
6 558082 558082NA
8 558147 558147NA
2 101261 10126100
2 208074 20807400
10 439031 43903100
10 439031 43903100
10 439150 43915000
10 439283 43928300
3 488003 48800300
2 488003 48800300
10 518175 51817500
10 543097 54309700
3 543099 54309900
5 543166 54316600
8 009024 00902400
8 009087 00908700

In case of hhid=101261, there are 4 household members in rt002. There are 2 household members
worked for livelihood in rt003. One is household head but the other is unable to identify.

> d<-outfiles[[2]]> d[d$hhid=="101261",c(1:4,6:8,17)]
psu hhold idcode resid1a s01a_q02 s01a_q03 s01a_q04 s01b_q01
6827 101 261
01
2
1
1
54
1
6828 101 261
02
2
2
2
50
2
6829 101 261
03
2
1
3
15
2
6830 101 261
04
2
2
7
44
2
> df<-outfiles[[3]]> df[df$hhid=="101261",1:10]
psu hhold serial idcode
s04a_q01 s04a_q_1 s04a_q_2 s04a_q02
2222 101 261
01
1
BUSINESS46
67
52223 101 261
02
0 AGRICULTURE
610112s04a_q03 s04a_q04
2222
25
8
2223
30
2

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91

Summary:
For 29 unmatched records in rt003, if the counterpart in rt002 is not identified, it is impossible tolink working information and personal information.

This problem should be inquired for BBS.

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92

5.5 Sample design# data frame rt001# stratum# region: division# thana: upazila# spc: rural/urban(Municipality+SMA)# urbanrur: 1-4> d<-outfiles[[1]]> table(d$stratum)
01 02 03 04
05 06 07
08 09 10 11 12
680 300 1420 540 240 2100 900 540 1100 540 160 1880
13 14 15 16
800 180 660 200> table(d$region)
10 20 30 40
50 55 60
980 2200 3540 1800 1580 1280 860> table(d$thana)
2 3
4 5 6
7 8 9 10 11 12 13 14 15 16
160 60 180 20 100 220 80 120 180 120 60 100 180 100 60
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
60 220 100 120 40 120 160 100 160 40 80 160 180 240 100
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
120 80 120 20 200 40 120 140 200 60 80 160 120 80 80
47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
360 20 220 100 200 160 100 100 100 180 180 380 20 160 120
62 63 64 65 66 67 68 69 70 71 72 73 74 75 76
160 80 180 180 200 180 40 120 120 120 240 260 140 40 240
78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
120 160 120 120 140 60 60 220 120 280 140 60 140 80 40
94 95
300 140> table(d$spc)
Municipality
3280

Rural
7840

SMA
1120

> table(d$urbanrur)
12
347240 3280 600 1120



There are 16 strata, of which 6 are urban (Municipality), 6 are rural, and 4 are Statistical
Metropolitan Areas (SMAs).

> addmargins(table(d$stratum,d$spc))

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# Number of sample households by stratum and spc
Municipality Rural SMA Sum
01
0 680
0 680
02
300
0
0 300
03
0 14200 1420
04
540
0
0 540
05
0
0 240 24006
0 21000 2100
07
900
0
0 900
08
0
0 540 54009
0 11000 1100
10
540
0
0 540
11
0
0 160 160
120 18800 1880
13
800
0
0 800
14
0
0 180 180
15
0 660
0 660
16
200
0
0 200
Sum
3280 7840 1120 12240



Relationship between division and spc> table(d$region,d$spc)

10
20
30
40
50
55
60



Municipality Rural
300 680
540 1420
900 2100
540 1100420 980
380 900
200 660

SMA
0
240
540
160
180
0
0

Relationship between urban/rural and spc> table(d$urbanrur,d$spc)
Municipality Rural SMA
1
0 7240
0
2
3280
0
0
3
0
600
0
4
0
0 1120What’s the difference between 1 and 3 in Rural?

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> table(d$stratum,d$urbanrur)
1
01 66002
003 126004
005
006 1900
07
008
009 1080100
110
12 1760
130
14
0
15 580
16
0

2
3
4
0 20
0
300
0
0
0 160
0
540
0
0
0
0 240
0 200
0
900
0
0
0
0 540
0 20
0
540
0
0
0
0 160
0 120
0
800
0
0
0
0 180
0 80
0
200
0
0

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6. Household IncomeThe questions related to household income are asked in the section 4 to the section 8 of thequestionnaire, and compiled in data frame rt001 and rt003 to rt012, as in the next table.

Questionnaire/Data

Revenue/Cost

Variables

Remarks

file

Generated
income variable

4B Wage employment

Daily wage

(rt003)

Salaried wage

s04b_q_2

daily wage in cash (1Day)

s04b_q_3

in-kind per day (1Day)

s04a_q02

months per year

s04a_q03

days per months04b_q07

gross monthly cash (1M)s04a_q02

months per year

s04a_q09

in-kind in past 12M

5 Non-agricultural

Profit of shares05a_q07

share of profit

enterprise

Net revenues

s05b_q20

net revenue in past 12M

6 Housing

Imputed rent is

s06a_q21

housing occupancy status(rt001)

unavailable

s06a_q22

amount to buy/build such

inc4b

inc5

(rt004)house
9D Annual non-food

Imputed rent

expenditure

item=382

imputed rent

s09d2_q0

annual value

inc9d(rt019)7B Crop production

Production ofs07b_q04

quantity produced 12M (kg)inc7b(rt006)

crop/by product/s07b_q_1

unit price per kg

s07c_q_1

value produced 12M

inc7c1s07c_q_1

value produced 12M

inc7c2inc7c3vegetable/fruits
7C Livestock products

Livestock(rt008)production

7C Fish farming and

Fishery

fish capture

production(rt009)7C Farm forestry

Forestrys07c_q15

value sold 12M(rt010)production (sells07c_q16

value consumed 12M+ consume)

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7D Expenses on(minus)agricultural inputs

Expenditures07d_q_1

value spend 12M

inc7ds07e_q04

value earned from rental 12M

inc7e(rt011)
7E Agricultural assets

Earn from rental(rt012)none

8A Other property andassets(rt001)8B Other income

1 to 13

V59 to V73

inc8e

8C Migration andMoney/in kinds08c_q14

amount of money sent 12M

remittance

These ares08c_q17

value of in-kind sent 12M(rt013)included in 8B.

(rt001)

8D Micro credit

inc8c

none

(rt014)

Strategy to generate household-level variable of household incomeMonthly income per household.



Total household income and disaggregation as follows;

Wage income

Business income

Imputed rent

Agricultural income

Other income

Remittance

The household-level summary income data will be provided in the resampled micro data set.

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Other income: rt001 (Section 8B)


The data frame rt001 includes all households, therefore it is used as the basis for household-level
data frame of household income.

> d<-outfiles[[1]]> dim(d)[1] 12240



152

Computed the monthly mean value of the variable V59 to V73.

> t<-apply(d[,59:73],2,function(x) round(weighted.mean(x,d$wgt)/12))> lbl<-c("Income from rent of land - share cropping",+ "Income from rent of other property",+ "income from Life insurance",+ "income from Health insurance",+ "income from General insurance",+ "Profit and dividend received",+ "Lottery or similary income in cash or in-kind",+ "Gift, Charity or other received? In cash",+ "Gift, Charity or other received? In-kind",+ "Remittances received from within the country",+ "Remittances received from outside the country",+ "Where did you invast remittance money",+ "Pension, Gratuity, other benefit received",+ "Profit,Interest received",+ "Other cash or in-kind received")> m<-data.frame(variable=names(t),mean=t,label=lbl,row.names=NULL)> mvariable meanlabel

1 s08b_q01 233

Income from rent of land - share cropping

2 s08b_q02 297

Income from rent of other property

3 s08b_q03

5income from Life insurance

4 s08b_q_1

0income from Health insurance

5 s08b_q_2

1

income from General insurance

6 s08b_q04

24

Profit and dividend received

7 s08b_q05

1 Lottery or similary income in cash or in-kind

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98

8 s08b_q06

36

Gift, Charity or other received? In cash

9 s08b_q07

36

Gift, Charity or other received? In-kind

10 s08b_q08 237 Remittances received from within the country
11 s08b_q09 1140 Remittances received from outside the country
12 s08b_q10

0

Where did you invast remittance money

13 s08b_q11 135

Pension, Gratuity, other benefit received

14 s08b_q12

33

Profit,Interest received

15 s08b_q13 237

Other cash or in-kind received> sum(m$mean)[1] 2415Generated the variable inc8e of monthly other income.

> d<-d[order(d$hhid),]> d$inc8e<-round(rowSums(d[,59:73])/12,2)> inc<-d[,c("hhid","region","urbanrur","wgt","inc8e")]> head(inc)hhid region urbanrur

wgt inc8e

6121 001006

10

1 2507.01 416.67

6122 001036

10

1 2507.01

0.00

6123 001054

10

1 2507.01

0.00

6124 001061

10

1 2507.01

0.00

6125 001062

10

1 2507.01

0.00

6126 001065

10

1 2507.01 83.33> weighted.mean(inc$inc8e,inc$wgt)[1] 2413.934

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Wage income: rt003 (Section 4B)

4B Wage employment

Daily wage

(rt003)

Salaried wage

s04b_q_2

daily wage in cash

s04b_q_3

in-kind per day

s04a_q02

months per year

s04a_q03

days per months04b_q07

gross monthly cash

s04a_q02

months per year

s04a_q09

in-kind in past 12M# Individual-level data frame> d<-outfiles[[3]]> dim(d)[1] 18067

30> d[is.na(d)]<-0# Yearly income> d$dailywage<-(d$s04b_q_2+d$s04b_q_3)*d$s04a_q03*d$s04a_q02> d$salary<-d$s04b_q07*d$s04a_q02+d$s04b_q09> head(d[,c(30:32,8,9,20,24,26,28)])
pid dailywage salary s04a_q02 s04a_q03 s04b_q_2 s04b_q_3 s04b_q07 s04b_q09
1 01000201

0 20000

2

26

0

0

5000

10000

2 01000201

0

0

10

30

0

0

0

0

3 01316801

43680

0

12

28

130

0

0

0

0 72000

12

26

0

0

6000

0

4 01001001
5 01002603

25000

0

10

20

125

0

0

0

6 01002602

6000

0

2

25

80

40

0

0

# Monthly wage income inc4b> d$inc4b<-round((d$dailywage+d$salary)/12,2)# Aggregated at household-level> t<-aggregate(d$inc4b,list(d$hhid),sum)> colnames(t)<-c("hhid","inc4b")> head(t)hhid inc4b
1 001006

0

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100

2 001036 5625
3 001054 6000
4 001061 8960
5 001062 3250
6 001065 8400
> dim(t)[1] 11089

2# Appended to data frame inc.
> inc<-merge(inc,t,by="hhid",all.x=T)> head(inc)hhid region urbanrur

wgt inc8e inc4b

1 001006

10

1 2507.01 416.67

02 001036

10

1 2507.01

0.00 5625

3 001054

10

1 2507.01

0.00 6000

4 001061

10

1 2507.01

0.00 8960

5 001062

10

1 2507.01

0.00 3250

6 001065

10

1 2507.01 83.33 8400

> inc[is.na(inc)]<-0> weighted.mean(inc$inc4b,inc$wgt)[1] 4292.464

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Business income: rt004 (Section 5)

5 Non-agricultural enterprise

Profit of shares05a_q07

share of profit(rt004)

Net revenues

s05b_q20

net revenue in past 12M> d<-outfiles[[4]]> dim(d)[1] 3497

36# Share of profit in percentage.
> table(d$s05a_q07)
1

10

12

13

16

17

20

25

30

33

50

60

70

75

80

99 100

1

8

3

1

1

1

4

22

3

29 15412

4

7

1 3255# Monthly business income> d$business<-round(d$s05b_q20*d$s05a_q07/100/12,2)# Aggregated at household level> t<-aggregate(d$business,list(d$hhid),sum,row.names=NULL)> colnames(t)<-c("hhid","inc5")> dim(t)[1] 3127

2# Appended to inc> inc.save<-inc> inc<-merge(inc,t,by="hhid",all.x=T)> dim(inc)[1] 12240

7> inc[is.na(inc)]<-0> head(inc[inc$inc5>0,])hhid region urbanrur

wgt

inc8e inc4b

inc5

21 002001

10

1 2507.01 25416.67

0 20141.67

22 00201510

1 2507.01

458.330 5891.67

24 002028

10

1 2507.01

0.00

0 9850.00

27 002099

10

1 2507.01 6000.00

0 1208.33

28 002101

10

1 2507.01

0.00

0 7941.67

30 002123

10

1 2507.01

0.00

0 10833.33

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102

> weighted.mean(inc$inc5,inc$wgt)[1] 2027.652

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103

Imputed rent: rt019 (Section 9D)

6 Housing

Imputed rent

(rt001)s06a_q21

housing occupancy status

s06a_q22

amount to buy/build suchhouse

> d<-outfiles[[1]]> dim(d)[1] 12240

152# Housing occupancy status> t<-table(d$s06a_q21)> names(t)<-c("1 Owner","2 Renter","3 Squatter","4 Provided free by relatives/employer",+ "5 Government residence","6 Other, specify")> t
1 Owner

2 Renter

9920

1394

3 Squatter 4 Provided free by relatives/employer



279

444

5 Government residence

6 Other, specify

110

93

Imputed rent is unable to be calculated from rt001. Instead, expenditure data in the section
9 should be used.

9D Annual non-food expenditure

Imputed rent

(rt019)

item=382

imputed rent

s09d2_q0

annual value> d<-outfiles[[19]]> dim(d)[1] 830454

6> d1<-subset(d,item==382)> dim(d1)[1] 10363

6> head(d1)psu hhold ln item s09d2_q0

hhid

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489 021

007 28 382

8400 021007

530 021019 28 382

7200 021019

569 021049 28 382

4800 021049

588 021056 28 382

9600 021056

613 021059 28 382

6000 021059

653 021064 28 382

4800 021064> sum(duplicated(d1$hhid))[1] 0# Household-level monthly imputed rent
> t<-d1[,c("hhid","s09d2_q0")]> colnames(t)<-c("hhid","inc9d")> t$inc9d<-round(t$inc9d/12,2)> head(t)hhid inc9d
489 021007

700

530 021019

600

569 021049

400

588 021056

800

613 021059

500

653 021064

400# Appended to inc> inc.save<-inc> inc<-merge(inc,t,by="hhid",all.x=T)> inc[is.na(inc)]<-0> head(inc)hhid region urbanrur

wgt inc8e

1 001006

10

1 2507.01 416.67

2 001036

10

3 001054

inc4b inc5 inc9d
0.00

0

500

1 2507.01

0.00 11718.75

0

300

10

1 2507.01

0.00 6000.00

0400

4 001061

10

1 2507.01

0.00 20906.67

0200

5 001062

10

1 2507.01

0.00 3250.00

0300

6 001065

10

1 2507.01 83.33 21000.00

0300

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105Agricultural income: rt006 to rt012 (Section 7B, 7C, 7D and 7E)

7B Crop production

Production of crop/

s07b_q04

quantity produced 12M (kg)(rt006)by product/vegetable/fruits

s07b_q_1

unit price per kg

7C1 Livestock products

Livestock production

s07c_q_1

value produced 12M

Fishery production

s07c_q_1

value produced 12M

7C3 Farm forestry

Forestry production (sell +s07c_q15

value sold 12M(rt010)consume)s07c_q16

value consumed 12M

7D Expenses on agricultural inputs(minus) Expenditures07d_q_1

value spend 12M

Earn from rental

s07e_q04

value earned from rental(rt008)
7C2 Fish farming and fish capture(rt009)(rt011)
7E Agricultural assets(rt012)12M

7B Crop production> d<-outfiles[[6]]> dim(d)[1] 186980

20> d$crop<-d$s07b_q04*d$s07b_q_1# Household-level monthly crop production> t<-aggregate(d$crop,list(d$hhid),sum,row.names=NULL)> colnames(t)<-c("hhid","inc7b")> t$inc7b<-round(t$inc7b/12,2)> head(t)
hhid

inc7b

1 001006 11003.33
2 001066 1491.67
3 001113

670.004 001116 1384.58
5 001144 4731.67
6 001145 4823.33# Appended to inc> inc.save<-inc> inc<-merge(inc,t,by="hhid",all.x=T)

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> inc[is.na(inc)]<-0> head(inc[inc$inc7b>0,])hhid region urbanrur

wgt

inc8e

inc4b inc5 inc9d

inc7b

1 001006

10

1 2507.01 416.67

0.00

0

500 11003.33

7 001066

10

1 2507.01

0.00

0.00

0

300 1491.67

11 001113

10

1 2507.01 6916.67

0.00

0 2500

12 001116

10

1 2507.01 2500.00 10333.33

0 3000 1384.58

16 001144

10

1 2507.01

0.00

0.00

0

500 4731.67

17 001145

10

1 2507.01 1792.08

0.00

0

600 4823.33

670.00

7C1 Livestock production: rt008> d<-outfiles[[8]]> dim(d)[1] 72327

10> d1<-subset(d,prod_cod==220) # Total value> dim(d1)[1] 8043

10# Household-level monthly livestock production> d1$inc7c1<-round(d1$s07c_q_1/12,2)> inc.save<-inc> inc<-merge(inc,d1[,c("hhid","inc7c1")],by="hhid",all.x=T)> inc[is.na(inc)]<-0> head(inc)hhid region urbanrur

wgt inc8e

1 001006

10

1 2507.01 416.67

2 001036

10

3 001054

inc4b inc5 inc9d

inc7b inc7c1

0.00

0

500 11003.33 726.67

1 2507.01

0.00 11718.75

0300

0.00 35.00

10

1 2507.01

0.00 6000.00

0400

0.00 46.67

4 001061

10

1 2507.01

0.00 20906.67

0200

0.00 87.50

5 001062

10

1 2507.01

0.00 3250.00

0300

0.00 50.00

6 001065

10

1 2507.01 83.33 21000.00

0300

0.00 110.00

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7C2 Fishery: rt009> d<-outfiles[[9]]> dim(d)[1] 25241

10> d1<-subset(d,fish_act==230) # Total value> dim(d1)[1] 2806

10# Household-level monthly fishery product> d1$inc7c2<-round(d1$s07c_q_1/12,2)> inc.save<-inc> inc<-merge(inc,d1[,c("hhid","inc7c2")],by="hhid",all.x=T)> inc[is.na(inc)]<-0> head(inc)hhid region urbanrur

wgt inc8e

1 001006

10

1 2507.01 416.67

2 001036

10

3 001054

inc4b inc5 inc9d

inc7b inc7c1 inc7c2

0.00

0

500 11003.33 726.67

1 2507.01

0.00 11718.75

0300

0.00 35.00

0

10

1 2507.01

0.00 6000.00

0400

0.00 46.67

04 001061

10

1 2507.01

0.00 20906.67

0200

0.00 87.50

05 001062

10

1 2507.01

0.00 3250.00

0300

0.00 50.00

06 001065

10

1 2507.01 83.33 21000.00

0300

0.00 110.00

0

7C3 Forestry: rt010> d<-outfiles[[10]]> dim(d)[1] 25241

10> d1<-subset(d,forestry==240) # Total value> dim(d1)[1] 4583

8# Household-level monthly fishery product> d1$inc7c3<-round((d1$s07c_q15+d1$s07c_q16)/12,2)

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108

> head(d1[d1$inc7c3>0,])psu hhold forestry s07c_q14 s07c_q_1 s07c_q15 s07c_q16

hhid inc7c3

70 013

255

240

22

15000

3000

4000 013255 583.33

90 021

049

240

99

60200

0

100 021

056

240

104

1144002000

500 021056 208.33

140 021

126

240

0

10200

0

4500 021126 375.00

150 021

158

240

145

85000

5000

7000 021158 1000.00

160 021

172

240

58

33000

0

1500 021172 125.00

300 021049

25.00> inc.save<-inc> inc<-merge(inc,d1[,c("hhid","inc7c3")],by="hhid",all.x=T)> inc[is.na(inc)]<-0> head(inc[inc$inc7c3>0,])hhid region urbanrur

wgt

inc8e inc4b

inc5

inc9d

inc7b inc7c1

1 001006

10

1 2507.01

416.6700.00 500.00 11003.33 726.67

9 001100

10

1 2507.01 3333.33

0

0.00

10 001110

10

1 2507.01 1500.42 25500

0.00 1000.00

0.00 50.00

20 001208

10

1 2507.01 7083.33

00.00 333.33

957.50 846.67

21 002001

10

1 2507.01 25416.67

0 20141.67 1500.00

250.83 220.00

22 00201510

1 2507.01

0 5891.67 500.00

176.67 40.00

458.33inc7c2 inc7c3
1

800 166.67

90 41.6710

900 83.33

20675 25.00

21

900 541.67

22

0 50.00

7d Expenses on agricultural inputs: rt011> d<-outfiles[[11]]> dim(d)[1] 247753

8> d1<-subset(d,exp_agri==330) # Total value> dim(d1)Bangladesh HIES 2010 Manual (Version 1.0)

0.00

0.00

0.00

109

[1] 7418

8# Household-level monthly expenses on agricultural inputs> d1$inc7d<--round(d1$s07d_q_1/12,2)> head(d1)psu hhold ln exp_agri s07d_q01 s07d_q02 s07d_q_1

hhid

inc7d

21 010002 21330

0654350 010002 -362.50

62 010026 213300

8725 010026

162 010083 21

330

0

120

8630 010083 -719.17

183 010086 21330

0407220 010086 -601.67

244 010110 21

330

0

1350

13450 010110 -1120.83

265 010117 21330

0

0

200 010117-60.42-16.67> inc.save<-inc> inc<-merge(inc,d1[,c("hhid","inc7d")],by="hhid",all.x=T)> inc[is.na(inc)]<-0> head(inc[inc$inc7d<0,])hhid region urbanrur

wgt

inc8e

1 001006

10

1 2507.01 416.67

2 001036

10

4 001061

inc4b inc5 inc9d

inc7b inc7c1 inc7c2

0.00

0

500 11003.33 726.67

1 2507.01

0.00 11718.75

0300

0.00 35.00

0

10

1 2507.01

0.00 20906.67

0200

0.00 87.50

05 001062

10

1 2507.01

0.00 3250.00

0300

0.00 50.00

07 001066

10

1 2507.01

0.00

0.00

0

300 1491.67 388.33

350

9 001100

10

1 2507.01 3333.33

0.00

0

inc7c3inc7d

1 166.67 -1254.17
2

0.00

-2.50

4

0.00

-1.67

5

0.00

-2.50

7

0.00

-32.92

9 41.67-1.67

Bangladesh HIES 2010 Manual (Version 1.0)

0

0.00

0.00

800

0

110

7E Agricultural assets: rt012 (Section 7E)> d<-outfiles[[12]]> dim(d)[1] 228631

12# Household-level monthly earn from rental> d1$inc7e<-round(d1$s07e_q04/12,2)> head(d1[d1$s07e_q04>0,])psu hhold ln agric_as s07e_q01 s07e_q_1 s07e_q02 s07e_q_2 s07e_q03 s07e_q_3
2143 035

131 19

420

0

1000

0

1000

0

0

2941 050

117 19

420

0

70400

0

20400

0

0

4043 061

152 19420

010400000005012 078

049 19

420

0

300

0

900

0

0

6361 101

261 19

420

0

40600

0

0

0

0

8185 136

006 19

420

0

1500

0

0

0

0

s07e_q04

hhid

inc7e

2143

1000 035131

83.33

2941

30000 050117 2500.00

4043

73000 061152 6083.33

5012

1000 078049

6361

20000 101261 1666.67

8185

1500 136006 125.00

83.33> inc.save<-inc> inc<-merge(inc,d1[,c("hhid","inc7e")],by="hhid",all.x=T)> inc[is.na(inc)]<-0> head(inc[inc$inc7e>0,])hhid region urbanrur

wgt

inc8e

inc4b

inc5 inc9d

171 009084

10

1 2507.01 3000.00

235 012160

10

1 2507.01

0.00 5000.00 1750.00

0 975.00 33.33

287 015052

10

1 2507.01

0.00

0 4617.50

0.00

293 015113

10

1 2507.01 583.33 7666.67

0.00

0

0.00

0.00

692 035131

10

1 2507.01 4266.67

0.00

0.00

0

0.00

0.00

731 037174

10

1 2507.01

0.00

0.00

inc7c2 inc7c3

inc7d

0.00

0.00 29333.33

0.00 8833.33

inc7e

171

0

0.00

0.00 833.33

235

00.00 -142.50 1750.00

Bangladesh HIES 2010 Manual (Version 1.0)0

inc7b inc7c1
0.00

0.00

200 5498.67 23.33

111

287

0

0.00 -199.17 2083.33

293

0

0.00

-91.6741.67

3000 833.33

0.00

83.33

692
731

0

0.00 -1803.33 1037.50

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8C Remittance: rt013 (Section 8C)

8C Migration and remittance

Money/in kind

(rt013)s08c_q14

amount of money sent 12M

s08c_q17

value of in-kind sent 12M> d<-outfiles[[13]]> dim(d)[1] 2100

22# Generated the variable of monthly remittances> d$remit<-round((d$s08c_q14+d$s08c_q17)/12,2)> head(d[d$s08c_q14>0&d$s08c_q17>0,-4])
psu hhold migrant_ s08c_q04 s08c_q05 s08c_q_1 s08c_q06 s08c_q07 s08c_q08 s08c_q09
18 030

003

91

3

6

30

1

15

0

46

20 030

010

91

2

7

4

1

26

0

49

21 030

032

91

2

4

8

1

26

0

30

22 030

034

91

3

4

4

1

26

0

22

24 030

064

91

2

8

3

2

3

43

25 030

073

91

3

2

3

1

0

38

26

s08c_q10 s08c_q11 s08c_q12 s0bc_q13 s08c_q14 s08c_q15 s08c_q16 s08c_q_2 s08c_q_3
18

1

8

59

3

30000

5

2

3

6

20

1

8

36

4

15000

5

7

6

0

21

1

9

59

4

10000

3

6

0

0

22

1

8

59

7

300000

6

6

0

0

24

1

3

59

4

60000

4

8

0

0

25

1

12

44

4

20000

8

6

8

0

s08c_q17

hhid

remit

18

20000 030003 4166.67

202400 030010 1450.00

211000 030032

222500 030034 25208.33

24

20000 030064 6666.67

25

4000 030073 2000.00

916.67# Aggregated at household-level> t<-aggregate(d$remit,list(d$hhid),sum,row.names=NULL)

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> dim(t)[1] 16882> colnames(t)<-c("hhid","inc8c")> head(t)
hhid

inc8c

1 001110 4833.34
2 001113 833.33
3 001170 2416.67
4 001208 5333.34
5 002080 1100.00
6 002099 6416.67> inc.save<-inc> inc<-merge(inc,t,by="hhid",all.x=T)> inc[is.na(inc)]<-0> head(inc[inc$inc8c>0,])hhid region urbanrur

wgt

inc8e inc4b

inc5

inc9d

inc7b inc7c1

10 001110

10

1 2507.01 1500.42 25500

0.00 1000.00

0.00

50.00

11 001113

10

1 2507.01 6916.67

00.00 2500.00 670.00

70.00

18 001170

10

1 2507.01 2000.00

0

0.00 416.67 221.67 125.00

20 001208

10

1 2507.01 7083.33

00.00 333.33 957.50 846.67

26 002080

10

1 2507.01 1000.42

00.00 300.00 321.67 202.50

27 002099

10

1 2507.01 6000.00

0 1208.33 400.00 6231.67 2420.00

inc7c2 inc7c3

inc7d inc7e

inc8c

10 900.00 83.33-4.17

0 4833.34

11

0.00

0.00

-4.17

0 833.33

18 333.33

0.00-20.170 2416.67

20 675.00 25.00 -101.17

0 5333.34

26

0.00 83.33-14.580 1100.00

27

0.00

0.00 -3232.50

0 6416.67

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Remarks: Remittances – relationship between Part B, Section 8 and Part C, Section 8> d<-outfiles[[1]]> dim(d)[1] 12240

152> rem<-d[,c("hhid","s08b_q08","s08b_q09")+ ]> dim(rem)[1] 12240

3> head(rem)hhid s08b_q08 s08b_q09
1 010002

30000

70000

2 010010

00

3 010026

00

4 010038

00

5 010046

00

6 01005800> df<-outfiles[[13]]> df$remit<-df$s08c_q14+df$s08c_q17> t<-aggregate(df$remit,list(df$hhid),sum,row.names=NULL)> colnames(t)<-c("hhid","remit")> head(t)hhid remit
1 001110 58000
2 001113 10000
3 001170 29000
4 001208 64000
5 002080 13200
6 002099 77000> rem<-merge(rem,t,by="hhid",all.x=T,row.names=NULL)> rem[is.na(rem)]<-0> head(rem[rem$remit>0,])hhid s08b_q08 s08b_q09 remit
10 001110

18000

0 58000

11 001113

10000

0 10000

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18 001170

24000

0 29000

20 001208

55000

0 64000

26 002080

12000

0 13200

27 002099

72000

0 77000> head(rem[rem$remit>0&rem$s08b_q09>0,])hhid s08b_q08 s08b_q09 remit
49 003108

0

100000 140000

68 004063

0150000 175000

99 005212

0

6000

100 005215

040000 50000

108 006106

0180000 180000

142 008024

50000

100000 170000

6000> table((rem$s08b_q08+rem$s08b_q09)==0&rem$remit>0)

FALSE TRUE
12237

3

> table((rem$s08b_q08+rem$s08b_q09)>0&rem$remit==0)

FALSE TRUE
11166 1074
> head(rem[(rem$s08b_q08+rem$s08b_q09)>0&rem$remit==0,])hhid s08b_q08 s08b_q09 remit
17 001145

1500

20000

0

74 004128

16000

0

0

161 009008

0

40000

0

162 009024

0

48000

0

171 009084

0

26000

0

223 012019

12000

0

0

Remarks:
The coverage of remittances received from relatives in Part B seems larger than PartC, which is those from members of the household migrated. That is, because Part C is
included in Part B, Part C should not be summed up in total household income.

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

Generated the variable of monthly total household income.

> inc$ttinc<-rowSums(inc[,5:15])> head(inc)hhid region urbanrur

wgt inc8e inc4b inc5 inc9d

1 001006

10

1 2507.01 416.67

2 001036

10

3 001054

inc7b inc7c1 inc7c2

00

500 11003.33 726.67

1 2507.01

0.00 5625

0300

0.00 35.00

0

10

1 2507.01

0.00 6000

0400

0.00 46.67

04 001061

10

1 2507.01

0.00 8960

0200

0.00 87.50

05 001062

10

1 2507.01

0.00 3250

0300

0.00 50.00

06 001065

10

1 2507.01 83.33 8400

0300

0.00 110.00

0inc7c3inc7d inc7e inc8c

800

ttinc

1 166.67 -1254.17

00 12359.17

2

0.00

-2.50

0

0 5957.50

3

0.00

0.00

0

0 6446.67

4

0.00

-1.67

0

0 9245.83

5

0.00

-2.50

0

0 3597.50

6

0.00

0.00

0

0 8893.33> weighted.mean(inc$ttinc,inc$wgt)[1] 12394.19



Mean value of each sub-group of household income (monthly, per household)> apply(inc[5:16],2,function(x) weighted.mean(x,inc$wgt))
inc8e

inc4b

inc5

2413.93431 4292.46394 2027.65159
inc7c2
256.05141

inc7c3inc7d

76.26206 -734.70098

inc9d

inc7b

inc7c1

534.37409 1925.13140

203.25625

inc7e

inc8c

ttinc

37.39272 1362.37535 12394.19215> t<-apply(inc[5:16],2,function(x) weighted.mean(x,inc$wgt))> lbl<-c("Other income","Wage income","Business income","Imputed rent",+ "Crop product","Livestock product","Fishery product","Forestry product",+ "Agri cost","Earn from rent","Renittance","Total income")> m<-data.frame(item=names(t),value=round(t,1),label=lbl,row.names=NULL)
> m
item
1

value

label

inc8e 2413.9

Other income

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117

2

inc4b 4292.5

Wage income

3

inc5 2027.7

Business income

4

inc9d

534.4

Imputed rent

5

inc7b 1925.1

Crop product6 inc7c1

203.3 Livestock product7 inc7c2

256.1

8 inc7c3

76.3 Forestry product9

Fishery productinc7d -734.7

10 inc7eAgri cost

37.4

Earn from rent

11 inc8c 1362.4

Renittance

12 ttinc 12394.2

Total income

Remarks# Mean agricultural income> sum(t[5:10])[1] 1763.393# Household-level agricultural income> inc$agri<-rowSums(inc[,9:14])> weighted.mean(inc$agri,inc$wgt)[1] 1763.393> table(inc$agri==0)
FALSE TRUE
8009 4231> table(inc$agri<0)
FALSE TRUE
11404

836

> table(inc$agri>0)
FALSE TRUE
5067 7173



Out of 12,240 households, 4,231 households have no agricultural income, and 836
households have negative income.
How to treat negative income in the aggregation?

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118



Monthly income per household
Monthly income

Trial calculation

12,394

Figures in the Report

11,479

Gap

+915

+8.0%

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7. Household Consumption

The questions of household consumption are asked in the section 9 of the questionnaire, andcompiled in data frame rt015 to rt020, as the next.

File

rt015

rt016

rt017

rt018

rt019

Part of Section 9:

Item/

Reference

Consumption

item groupperiodPart A Daily consumption:

010-204

14 daysDay1 to Day14

g1-g15

B Weekly consumption:

210-237

Day1-7, Day8-14

g16-g17

C Monthly non-food

240-292

expenditure

g18-g21

D Annual non-food

300-352

expenditureg22-g24

D Annual non-food

360-553

expenditureg25-g38

Remarks

Summary
file

Value in taka and paisa,

exp015in-kind, own production
14 days

Value in taka and paisa,

exp016

in-kind, own production
1 monthfuel, cosmetics, washing, transport

exp017

12 months

garment, cloth, footwear

exp01812 months

textile, medical, housing,

exp019education, remittances, recreation,
tax, cooking equipment, furniture,
personal articles, household
durables, insurance

rt020



E Inventory of durable

561-600goods

g39 (gd)

12 months

cost if procured within last 12M

exp020Strategy to generate household-level variable of household consumption.
Monthly consumption per householdTotal household consumption and disaggregation by subgroups having item codes ending
with 0.

For each data frame “rt0xx”, to generate the household-level summary file “exp0xx” including
variables of subgroups.



To merge exp015 to exp020, and convert to monthly expenditure.

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120

Remarks:Multiplier to convert 14 days (2 weeks) to monthly.
Multiplier = 2 for g1 to g17

Multiplier to convert yearly to monthly.
Multiplier = 1/12 for g22 to g38 and dg



The values of food; part A and part B are written in Taka and Paisa.
Others are in Taka.

The currency of Bangladesh is Taka and Paisa.
100 paisa = 1 taka
1 US$ = about 72 taka

Bangladesh HIES 2010 Manual (Version 1.0)

121

rt015: Daily food (Day 1 to Day 14)> d<-outfiles[[15]]> dim(d)[1] 438556
63> d<-d[order(d$hhid,d$item),]> head(d)psu hhold ln item s09a1d01 s09a1d_1 s09a1d_2 s09a1d_3 s09a1d02
213714 001
006 1
10
7200
26400
0
7200
213715 001
006 3
12
6000
gm
22800
3
6000
213716 001
006 5
14
0
gm
0
0
0
213717 001
006 6
15
0
gm
0
0
0
213718 001
006 7
16
200
gm
1200
3
200
213719 001
006 8
17
960
gm
2400
1
1000
s09a1d_4 s09a1d_5 s09a1d_6 s09a1d03 s09a1d_7 s09a1d_8 s09a1d_9
213714
26400
0
6300
24600
0
213715
gm
22800
3
6000
gm
22800
3
213716
gm
0
0
0
gm
0
0
213717
gm
0
0
0
gm
0
0
213718
gm
1200
1
300
gm
1800
1
213719
gm
2400
1
0
gm
0
0
s09a1d04 s09a1_10 s09a1_11 s09a1_12 s09a1d05 s09a1_13 s09a1_14
213714
5200
19500
0
7500
27600
213715
5000
gm
17500
3
6000
gm
24000
213716
0
gm
0
0
0
gm
0
213717
0
gm
0
0
0
gm
0
213718
0
gm
0
0
0
gm
0
213719
0
gm
0
0
1500
gm
3600
s09a1_15 s09a1d06 s09a1_16 s09a1_17 s09a1_18 s09a1d07 s09a1_19
213714
0
7000
26400
0
7500
213715
3
6000
gm
24000
3
6000
gm
213716
0
0
gm
0
0
0
gm
213717
0
0
gm
0
0
0
gm
213718
0
0
gm
0
0
0
gm
213719
1
1000
gm
2400
1
1500
gm
s09a1_20 s09a1_21 s09a1d08 s09a1_22 s09a1_23 s09a1_24 s09a1d09
213714
27600
0
7500
27600
0
6300
213715
24000
1
6000
gm
24000
3
6000
213716
0
0
0
gm
0
0
0
213717
0
0
0
gm
0
0
0
213718
0
0
0
gm
0
0
300
213719
3600
1
1500
gm
3600
1
0
s09a1_25 s09a1_26 s09a1_27 s09a1d10 s09a1_28 s09a1_29 s09a1_30
213714
29400
0
6300
29100
0
213715
gm
27600
1
6000
gm
27600
1
213716
gm
0
0
0
gm
0
0
213717
gm
0
0
300
gm
1500
1
213718
gm
1800
1
0
gm
0
0
213719
gm
0
0
0
gm
0
0
s09a1d11 s09a1_31 s09a1_32 s09a1_33 s09a1d12 s09a1_34 s09a1_35
213714
6400
30800
0
6500
31600
213715
6000
gm
27600
1
6000
gm
27600
213716
0
gm
0
0
0
gm
0
213717
0
gm
0
0
0
gm
0
213718
200
gm
1200
1
0
gm
0
213719
0
gm
0
0
0
gm
0

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s09a1_36 s09a1d13 s09a1_37 s09a1_38 s09a1_39 s09a1d14 s09a1_40
0
6400
26000
0
6400
3
6000
gm
24000
3
6000
gm
0
200
gm
800
1
200
gm
0
0
gm
0
0
0
gm
0
200
gm
1200
1
200
gm
0
0
gm
0
0
0
gm
s09a1_41 s09a1_42 t kcal
hhid
21371426000
0 1
0 001006
21371524000
3 0 346 001006
213716800
1 0 346 001006
21371700 0 325 001006
2137181200
1 0 354 001006
21371900 0 341 001006
213714213715213716213717213718213719# Data frame consisted of sub-total records> d2<-subset(d,is.element(d$item,c(10,30,40,60,70,80,100,110,120,130,150,160,170,180,200)))> dim(d2)[1] 123134
63# Sum of values from Day 1 to Day 14> d2$ttl<-rowSums(d2[,seq(7,59,by=4)])> head(d2[,c(1:4,seq(7,59,by=4),63:64)])
psu hhold ln item s09a1d_2 s09a1d_5 s09a1d_8 s09a1_11 s09a1_14 s09a1_17
213714 001
006 1
10
26400
26400
24600
19500
27600
26400
213721 001
006 15
30
2500
2500
2500
2000
0
0
213724 001
006 22
40
2500
3000
0
0
0
0
213726 001
006 50
80
3300
5100
4100
1900
2000
2400
213737 001
006 67 100
1000
1000
1000
0
1000
1000
213739 001
006 79 120
2100
2100
1200
1200
2100
1200
s09a1_20 s09a1_23 s09a1_26 s09a1_29 s09a1_32 s09a1_35 s09a1_38 s09a1_41
213714
27600
27600
29400
29100
30800
31600
26000
26000
213721
0
0
0
0
1000
0
1200
1200
213724
2000
2000
1000
2000
1000
1000
2000
2000
213726
2900
3000
4200
3200
1700
2500
4600
3900
213737
1000
1000
2000
1000
1000
1000
1000
1000
213739
1500
1500
2100
2100
1200
1200
1200
1700
hhid
ttl
213714 001006 379000
213721 001006 12900
213724 001006 18500
213726 001006 44800
213737 001006 14000
213739 001006 22400# Household-level data frame of the amount of daily consumption within 14 days by sub-groups> t<-tapply(d2$ttl,list(d2$hhid,d2$item),sum,na.rm=T)> dim(t)[1] 12239
15> df<-data.frame(hhid=rownames(t),t,row.names=NULL)> colnames(df)<-c("hhid",paste("g",1:15,sep=""))> df[is.na(df)]<-0

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> head(df)
hhid
g1
g2
g3 g4
g5
g6
g7 g8
g9 g10 g11
g12
1 001006 379000 12900 18500 00 44800 14000 0 22400
0 4700 14000
2 001036 86600
0 10600 0 15000 18600
0 0 14800
0003 001054 135200 8450 64500 0 24000 22800
0 0 17900 2600 600 3000
4 001061 70350
0 31000 0
0 21900
0 0 15900 600 1000 1500
5 001062 41000 2000 25000 0 8000 9200
0 0 13800
00 25006 001065 67800
0 19500 00 16900
0 0 16150
000g13 g14 g15
1
0
0
0
2
0 1500 8400
30 1600040 600 3600
500 24006
0 3800 8700
> exp015<-df

Remarks: The number of records of exp015 is 12,239 and one household’ record might be missing.
> setdiff(HHID,exp015$hhid)[1] "545041"> d3<-subset(d,hhid== "545041")> dim(d3)[1] 13 63> d3[,c(1:4,seq(7,59,by=4),63)]
psu hhold ln item s09a1d_2 s09a1d_5 s09a1d_8 s09a1_11 s09a1_14
130027 545
041
2
11
1500
1500
1500
1500
1500
130028 545
041 16
31
500
500
500
500
500
130029 545
041 23
41
0
0
4000
0
0
130030 545
041 27
45
0
0
0
0
3000
130031 545
041 30
48
0
500
0
0
0
130032 545
041 31
49
1000
0
0
0
0
130033 545
041 39
61
0
0
600
0
0
130034 545
041 51
81
300
300
300
300
300
130035 545
041 59
89
400
0
0
0
0
130036 545
041 64
95
200
200
0
0
400
130037 545
041 65
96
0
0
200
200
0
130038 545
041 81 122
500
500
500
500
500
130039 545
041 133 191
600
600
600
600
600
s09a1_17 s09a1_20 s09a1_23 s09a1_26 s09a1_29 s09a1_32 s09a1_35
130027
1500
1500
0
0
0
0
0
130028
500
500
0
0
0
0
0
130029
4000
0
0
0
0
0
0
130030
0
0
0
0
0
0
0
130031
0
1000
0
0
0
0
0
130032
0
0
0
0
0
0
0
130033
0
0
0
0
0
0
0
130034
300
300
0
0
0
0
0
130035
0
0
0
0
0
0
0
130036
0
0
0
0
0
0
0
130037
200
200
0
0
0
0
0
130038
500
500
0
0
0
0
0
130039
600
600
0
0
0
0
0

Bangladesh HIES 2010 Manual (Version 1.0)

124130027130028130029130030130031130032130033130034130035130036130037130038130039s09a1_38 s09a1_41
hhid
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041
00 545041

Summary:
For hhid=545041, there are values between Day1 and Day7, but no values between Day8 and Day14.
Furthermore, there are no items of subtotal.

Bangladesh HIES 2010 Manual (Version 1.0)

125

rt016: Weekly consumption (Day 1-7 and Day 8-14)> d<-outfiles[[16]]> dim(d)[1] 200008
14> d<-d[order(d$hhid),]> head(d)psu hhold ln item s09b1w1_ s09b1w_1 s09b1w_2 s09b1w_3 s09b1w2_
125702 001
006 1 210
3050
26100
0
2800
125703 001
006 2 211
400
gm
6000
1
400
125704 001
006 4 213
1000
gm
2400
1
1000
125705 001
006 5 214
200
gm
2400
1
200
125706 001
006 6 215
400
gm
12000
1
400
125707 001
006 7 216
1000
gm
1300
1
750
s09b1w_4 s09b1w_5 s09b1w_6 kcal
hhid
12570225700
00 001006
125703gm
6000
1 245 001006
125704gm
2400
1
50 001006
125705gm
2400
1 145 001006
125706gm
12000
1 349 001006
125707gm
900
1
67 001006> table(d$item)
210
211
212
213
214
215
216
217
218
219
221
222
12229 11521 11511 12212 11787 12225 12221 9903 10817 8511 9159 8817
223
230
231
232
233
234
235
236
237
7143 9306 8666 8672 7928 8629 6262 6604 5885

# Data frame consisted of sub-total records> d2<-subset(d,item==210|item==230)> dim(d2)[1] 21535
14> head(d2)psu hhold ln item s09b1w1_ s09b1w_1 s09b1w_2 s09b1w_3 s09b1w2_
125702 001
006 1 210
3050
26100
0
2800
125709 001
006 14 230
435
6100
0
435
125714 001
036 1 210
1550
14100
0
1550
125721 001
054 1 210
1450
10500
0
1700
125727 001
054 14 230
600
6200
0
600
125732 001
061 1 210
1500
11900
0
1750
s09b1w_4 s09b1w_5 s09b1w_6 kcal
hhid
12570225700
00 001006
1257095600
00 001006
12571414100
00 001036
12572111100
00 001054
1257276200
00 001054
12573212500
00 001061# Household-level data frame of the amount of weekly consumption within 14 days by sub-groups> t<-tapply(d2$s09b1w_2+d2$s09b1w_5,list(d2$hhid,d2$item),sum,na.rm=T)> dim(t)[1] 12230
2> df<-data.frame(hhid=rownames(t),t,row.names=NULL)

Bangladesh HIES 2010 Manual (Version 1.0)

126

> colnames(df)<-c("hhid",paste("g",16:17,sep=""))> df[is.na(df)]<-0> head(df)hhid g16
g17
1 001006 51800 11700
2 001036 28200
03 001054 21600 12400
4 001061 24400 14800
5 001062 14800
06 001065 18400 6000
> exp016<-df

Bangladesh HIES 2010 Manual (Version 1.0)

127rt017: Monthly non-food consumption (item 240-292)> d<-outfiles[[17]]> dim(d)[1] 362670
8> d<-d[order(d$hhid),]> head(d)psu hhold ln item s09c1_q0 s09c1__1 s09c1__2
hhid
200916 001
006 1 240123
300
423 001006
200917 001
006 2 2410
150
150 001006
200918 001
006 3 2420
150
150 001006
200919 001
006 4 243000 001006
200920 001
006 5 244
1150
115 001006
200921 001
006 6 245000 001006> table(d$item)
240
241
242
243
244
245
246
247
248
249
250
251
12221 8867 7264 5858 10147 7613 6091 9207 4896 11542 12168 10301
252
253
254
255
256
257
260
261
262
263
264
265
5772 11131 11633 7738 6651 8269 12192 12070 12120 6097 5526 5806
266
267
268
269
270
271
272
273
274
275
276
277
5073 5677 7216 4997 11852 9067 9584 7492 5314 4977 5123 5659
278
279
281
282
283
284
285
286
287
288
289
291
4996 4862 4880 4947 4858 4896 5002 9746 5010 4968 4864 4900
292
5530

# Data frame consisted of sub-total records> d2<-subset(d,item==240|item==250|item==260|item==270)> dim(d2)[1] 48433
8> head(d2)psu hhold ln item s09c1_q0 s09c1__1 s09c1__2
hhid
200916 001
006 1 240123
300
423 001006
200926 001
006 11 250
2200
220 001006
200934 001
006 19 26075
075 001006
200944 001
006 29 2703500
350 001006
200965 001
036 1 24080
275
355 001036
200975 001
036 11 250
2300
230 001036# Household-level data frame of the amount of monthly non-food consumption sub-groups> t<-tapply(d2$s09c1__2,list(d2$hhid,d2$item),sum,na.rm=T)> dim(t)[1] 12231
4> df<-data.frame(hhid=rownames(t),t,row.names=NULL)> colnames(df)<-c("hhid",paste("g",18:21,sep=""))> df[is.na(df)]<-0> head(df)hhid g18 g19 g20 g21
1 001006 423 220 75 350
2 001036 355 230 44 240
3 001054 388 130 65 360
4 001061 329 155 53 60

Bangladesh HIES 2010 Manual (Version 1.0)

128

5 001062 306 130
6 001065 556 180

70 30030 250> exp017<-df

Bangladesh HIES 2010 Manual (Version 1.0)

129

rt018: Annual non-food consumption (item 300-352)> d<-outfiles[[18]]> dim(d)[1] 285162
7> d<-d[order(d$hhid),]> head(d)psu hhold ln item s09d1_q0 s09d1__1
hhid
155544 001
006 1 300
61
7660 001006
155545 001
006 2 301
4
900 001006
155546 001
006 3 3022500 001006
155547 001
006 5 304
41300 001006
155548 001
006 6 3058700 001006
155549 001
006 7 306
12
1400 001006> table(d$item)
300
301
302
12225 11485 10502
313
314
315
4872 3722 5489
326
330
331
3739 8459 4895
341
342
343
5408 4645 7960

303
304
305
8227 11642 10555
316
317
318
7435 5940 4316
332
333
334
3226 3112 3120
344
345
346
5974 5145 10309

306
7833
319
6320
335
3118
347
3634

307
6170
321
3867
336
3110
348
3420

308
3614
322
4419
337
3326
349
3184

309
5652
323
5758
338
8006
351
5268

311
312
8845 4979
324
325
9178 3941
339
340
3912 12081
352
3125

# Data frame consisted of sub-total records> d2<-subset(d,item==300|item==330|item==340)> dim(d2)[1] 32765
7> head(d2)psu hhold ln item s09d1_q0 s09d1__1
hhid
155544 001
006 1 300
61
7660 001006
155554 001
006 26 3300
600 001006
155556 001
006 36 340
131500 001006
155560 001
036 1 300
152350 001036
155565 001
036 36 3403
350 001036
155567 001
054 1 300
38
5260 001054# Household-level data frame of the amount of monthly non-food consumption sub-groups> t<-tapply(d2$s09d1__1,list(d2$hhid,d2$item),sum,na.rm=T)> dim(t)[1] 12226
3> head(t)300 330 340
001006 7660 600 1500
001036 2350 NA 350
001054 5260 300 1250
001061 4220 550 775
001062 2200 550 250
001065 1950 150 650> df<-data.frame(hhid=rownames(t),t,row.names=NULL)> head(df)Bangladesh HIES 2010 Manual (Version 1.0)

130hhid X300 X330 X340
1 001006 7660 600 1500
2 001036 2350
NA 3503 001054 5260 300 1250
4 001061 4220 550 775
5 001062 2200 550 250
6 001065 1950 150 650> str(df)'data.frame':
12226 obs. of 4 variables:$ hhid: Factor w/ 12226 levels "001006","001036",..: 1 2 3 4 5 6 7 8 9 10 ...
$ X300: num 7660 2350 5260 4220 2200 1950 4320 4020 1100 6740 ...
$ X330: num 600 NA 300 550 550 150 300 300 NA 600 ...
$ X340: num 1500 350 1250 775 250 650 740 670 200 2060 ...
> colnames(df)<-c("hhid","g22","g23","g24")> df[is.na(df)]<-0> head(df)hhid g22 g23 g24
1 001006 7660 600 1500
2 001036 2350
0 350
3 001054 5260 300 1250
4 001061 4220 550 775
5 001062 2200 550 250
6 001065 1950 150 650> exp018<-df

Bangladesh HIES 2010 Manual (Version 1.0)

131rt019: Annual non-food expenditure (item 360-553)> d<-outfiles[[19]]> dim(d)[1] 830454
6> d<-d[order(d$hhid,d$item),]> head(d)psu hhold ln item s09d2_q0
hhid
505484 001
006 26 380
6000 001006
505485 001
006 28 382
6000 001006
505479 001
006 14 390
1555 001006
505480 001
006 17 393
700 001006
505481 001
006 19 395
450 001006
505482 001
006 20 396
5 001006> table(d$item)
27
4813
372
6584
390
11050
410
11382
430
8657
443
5359
456
7132
475
4760
496
4729
511
5255
525
5238
538
4687

360 361362
363
9459 5930 5267 4744
373
380
381
382
4933 11662 5817 10363
391
392
393
394
7632 4929 10812 4812
411
412
413
414
7969 5052 11143 4853
431
432
433
434
5253 7549 5475 6082
444
445
446
447
5646 6509 7732 4788
457
458
459
461
4728 4858 5275 4888
476
477
478
479
4823 4755 6600 5009
497
500
501
502
4747 9576 6835 4757
512
513
514
515
5022 4740 4861 4777
526
528
529
530
6134 5002 5385 8151
539
541
542
543
4710 7221 5000 4690

364
3654724 7289
383
384
4842 5206
395
396
5575 4841
415
416
5798 4903
435
436
6645 7864
448
450
7360 10207
462
463
4901 5191
481
490
6791 6144
503
504
4792 7795
516
517
4782 4954
531
532
4715 4692
550
551
6122 4902

366
4949
385
4777
397
4784
417
4796
437
4867
451
5072
470
8718
491
4769
505
5772
520
8155
533
4713
552
4681

367
5110
386
4787
398
4777
419
4725
438
7461
452
4980
471
4957
492
4692
506
5171
521
5030
534
4764
553
4827

368
5267
387
5422
399
4723
421
4729
440
8547
453
8062
472
4820
493
4699
507
4792
522
4790
535
4691
718
4774

369
4804
388
5335
401
4732
422
4906
441
5188
454
6011
473
4694
494
4833
508
5436
523
5992
536
4748

371
4849
389
4889
402
6625
423
6857
442
7462
455
6906
474
4716
495
4844
510
6847
524
4803
537
4691

Remarks:
Item 718 (spectacles) is located between item 417 and 419.
Item 27 (Tie-pin, cigarette, lighter etc.) is located between item 526 and 528.
They seem to be typos!

# Data frame consisted of sub-total records> d2<-subset(d,is.element(d$item,c(360,380,390,410,430,440,450,470,490,500,510,520,530,550)))> dim(d2)[1] 124677
6> head(d2)psu hhold ln item s09d2_q0
hhid

Bangladesh HIES 2010 Manual (Version 1.0)

132505484 001
505479 001
505486 001
505491 001
505488 001
505496 001

006 26006 14006 36006 71006 58006 99

380
390
410
440
450
500

6000 001006
1555 001006
400 001006
1530 001006
700 001006
450 001006# Household-level data frame of the amount of monthly non-food consumption sub-groups> t<-tapply(d2$s09d2_q0,list(d2$hhid,d2$item),sum,na.rm=T)> dim(t)[1] 12217
14> df<-data.frame(hhid=rownames(t),t,row.names=NULL)> colnames(df)<-c("hhid",paste("g",25:38,sep=""))> df[is.na(df)]<-0> head(df)hhid g25 g26 g27
g28 g29 g30 g31 g32 g33 g34 g35 g36 g37 g38
1 001006 0 6000 1555
400
0 1530 700
0
0 450
0
0
0
0
2 001036 0 3600 900 1105
0
0
0 55
0
0
0
0
0
0
3 001054 0 6300 400
700 8750
0 200 60
0 250
0
0
0
0
4 001061 0 2900 300 11800
0 505 150
0
0
0
0
0
0
0
5 001062 0 4600 200
250
0
0 50
0
0
0
0
0
0
0
6 001065 0 3600 3515
300
0
0
0
0
0
0
0
0
0
0
> exp019<-df

Bangladesh HIES 2010 Manual (Version 1.0)

133

rt020: Inventory of consumer durable goods> d<-outfiles[[20]]> dim(d)[1] 341813
9> d<-d[order(d$hhid,d$dg_code),]> head(d)psu hhold ln dg_code s09e_q01 s09e_q02 s09e_q03 s09e_q04
hhid170606 001
006 1
561
2
0
0
0 001006
170607 001
006 2
562
2
0
0
0 001006
170608 001
006 3
563
2
0
0
0 001006
170609 001
006 4
564
2
0
0
0 001006
170610 001
006 5
565
2
0
0
0 001006
170611 001
006 6
566
2
0
0
0 001006> table(d$dg_code,useNA="ifany")
561
562
563
564
565
566
567
568
569
571
572
573
12215 12218 12215 12215 12215 12215 12215 12215 12216 12215 12215 12215
574
575
576
577
578
579
581
582
583
584
585
586
12215 12215 12215 12216 12216 12215 12216 12217 12216 12216 12216 12217
587
588
589
600
12217 12215 12147 12060# Data frame consisted of sub-total records> d2<-subset(d,dg_code==600)> dim(d2)[1] 12060
9> head(d2)psu hhold ln dg_code s09e_q01 s09e_q02 s09e_q03 s09e_q04
hhid170633 001
006 28600
003900
1350 001006
170661 001
036 28600
00700
0 001036
170689 001
054 28600
003300
250 001054
170717 001
061 28600
00
0
150 001061
170745 001
062 28600
0030003250 001062
170773 001
065 28600
00400
0 001065> df<-data.frame(hhid=d2$hhid,dg=d2$s09e_q04)> head(df)hhid dg
1 001006 1350
2 001036
0
3 001054 250
4 001061 150
5 001062 3250
6 001065
0> exp020<-df

Bangladesh HIES 2010 Manual (Version 1.0)

134

Aggregation of expenditureMerged exp015 to exp020 as exp> exp<-outfiles[[1]][,c("hhid","wgt")]> dim(exp)[1] 12240
2> exp<-exp[order(exp$hhid),]> head(exp)hhidwgt
6121 001006 2507.01
6122 001036 2507.01
6123 001054 2507.01
6124 001061 2507.01
6125 001062 2507.01
6126 001065 2507.01> exp.s1<-exp> exp<-merge(exp,exp015,by="hhid",all.x=T)> dim(exp)[1] 12240
17> head(exp)
hhid
wgt
g1
g2
g3 g4
g5
g6
g7 g8
g9 g10 g11
1 001006 2507.01 379000 12900 18500 00 44800 14000 0 22400
0 47002 001036 2507.01 86600
0 10600 0 15000 18600
0 0 14800
003 001054 2507.01 135200 8450 64500 0 24000 22800
0 0 17900 2600 600
4 001061 2507.01 70350
0 31000 0
0 21900
0 0 15900 600 1000
5 001062 2507.01 41000 2000 25000 0 8000 9200
0 0 13800
006 001065 2507.01 67800
0 19500 00 16900
0 0 16150
00g12 g13 g14 g15
1 14000 0002
0 0 1500 8400
3 3000 0 1600
04 1500 0 600 3600
5 2500 0
0 24006
0 0 3800 8700
> exp.s2<-exp> exp<-merge(exp,exp016,by="hhid",all.x=T)> dim(exp)[1] 12240
19> head(exp)
hhid
wgt
g1
g2
g3 g4
g5
g6
g7 g8
g9 g10 g11
1 001006 2507.01 379000 12900 18500 00 44800 14000 0 22400
0 47002 001036 2507.01 86600
0 10600 0 15000 18600
0 0 14800
003 001054 2507.01 135200 8450 64500 0 24000 22800
0 0 17900 2600 600
4 001061 2507.01 70350
0 31000 0
0 21900
0 0 15900 600 1000
5 001062 2507.01 41000 2000 25000 0 8000 9200
0 0 13800
006 001065 2507.01 67800
0 19500 00 16900
0 0 16150
00g12 g13 g14 g15
g16
g17
1 14000 000 51800 11700
2
0 0 1500 8400 28200
03 3000 0 1600
0 21600 12400
4 1500 0 600 3600 24400 14800
5 2500 0
0 2400 14800
06
0 0 3800 8700 18400 6000

Bangladesh HIES 2010 Manual (Version 1.0)

135

> exp.s3<-exp> exp<-merge(exp,exp017,by="hhid",all.x=T)> dim(exp)[1] 12240
23> head(exp)
hhid
wgt
g1
g2
g3 g4
g5
g6
g7 g8
g9 g10 g11
1 001006 2507.01 379000 12900 18500 00 44800 14000 0 22400
0 47002 001036 2507.01 86600
0 10600 0 15000 18600
0 0 14800
003 001054 2507.01 135200 8450 64500 0 24000 22800
0 0 17900 2600 600
4 001061 2507.01 70350
0 31000 0
0 21900
0 0 15900 600 1000
5 001062 2507.01 41000 2000 25000 0 8000 9200
0 0 13800
006 001065 2507.01 67800
0 19500 00 16900
0 0 16150
00g12 g13 g14 g15
g16
g17 g18 g19 g20 g21
1 14000 000 51800 11700 423 220 75 3502
0 0 1500 8400 28200
0 355 230 44 240
3 3000 0 1600
0 21600 12400 388 130 65 360
4 1500 0 600 3600 24400 14800 329 155 53 60
5 2500 0
0 2400 14800
0 306 130 70 300
6
0 0 3800 8700 18400 6000 556 180 30 250> exp.s4<-exp> exp<-merge(exp,exp018,by="hhid",all.x=T)> dim(exp)[1] 12240
26> head(exp)
hhid
wgt
g1
g2
g3 g4
g5
g6
g7 g8
g9 g10 g11
1 001006 2507.01 379000 12900 18500 00 44800 14000 0 22400
0 47002 001036 2507.01 86600
0 10600 0 15000 18600
0 0 14800
003 001054 2507.01 135200 8450 64500 0 24000 22800
0 0 17900 2600 600
4 001061 2507.01 70350
0 31000 0
0 21900
0 0 15900 600 1000
5 001062 2507.01 41000 2000 25000 0 8000 9200
0 0 13800
006 001065 2507.01 67800
0 19500 00 16900
0 0 16150
00g12 g13 g14 g15
g16
g17 g18 g19 g20 g21 g22 g23 g24
1 14000 000 51800 11700 423 220 75 350 7660 600 1500
2
0 0 1500 8400 28200
0 355 230 44 240 2350
0 350
3 3000 0 1600
0 21600 12400 388 130 65 360 5260 300 1250
4 1500 0 600 3600 24400 14800 329 155 53 60 4220 550 775
5 2500 0
0 2400 14800
0 306 130 70 300 2200 550 250
6
0 0 3800 8700 18400 6000 556 180 30 250 1950 150 650> exp.s5<-exp> exp<-merge(exp,exp019,by="hhid",all.x=T)> dim(exp)[1] 12240
40> head(exp)
hhid
wgt
g1
g2
g3 g4
g5
g6
g7 g8
g9 g10 g11
1 001006 2507.01 379000 12900 18500 00 44800 14000 0 22400
0 47002 001036 2507.01 86600
0 10600 0 15000 18600
0 0 14800
003 001054 2507.01 135200 8450 64500 0 24000 22800
0 0 17900 2600 600
4 001061 2507.01 70350
0 31000 0
0 21900
0 0 15900 600 1000
5 001062 2507.01 41000 2000 25000 0 8000 9200
0 0 13800
006 001065 2507.01 67800
0 19500 00 16900
0 0 16150
00g12 g13 g14 g15
g16
g17 g18 g19 g20 g21 g22 g23 g24 g25 g26 g27
1 14000 000 51800 11700 423 220 75 350 7660 600 1500
0 6000 1555
2
0 0 1500 8400 28200
0 355 230 44 240 2350
0 350
0 3600 900

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3
4
5
6

3000 0 1600
0 21600 12400 388 130 65 360 5260 300 1250
1500 0 600 3600 24400 14800 329 155 53 60 4220 550 775
2500 00 2400 14800
0 306 130 70 300 2200 550 250
0 0 3800 8700 18400 6000 556 180 30 250 1950 150 650
g28 g29 g30 g31 g32 g33 g34 g35 g36 g37 g38
1400
0 1530 700
0
0 450
0
0
0
0
2 1105
0
0
0 55
0
0
0
0
0
0
3
700 8750
0 200 60
0 250
0
0
0
0
4 11800
0 505 150
0
0
0
0
0
0
0
5
250
0
0 50
0
0
0
0
0
0
0
6
300
0
0
0
0
0
0
0
0
0
0

0 6300 400
0 2900 300
0 4600 200
0 3600 3515

> exp.s6<-exp> exp<-merge(exp,exp020,by="hhid",all.x=T)> dim(exp)[1] 12240
41> head(exp)
hhid
wgt
g1
g2
g3 g4
g5
g6
g7 g8
g9 g10 g11
1 001006 2507.01 379000 12900 18500 00 44800 14000 0 22400
0 47002 001036 2507.01 86600
0 10600 0 15000 18600
0 0 14800
003 001054 2507.01 135200 8450 64500 0 24000 22800
0 0 17900 2600 600
4 001061 2507.01 70350
0 31000 0
0 21900
0 0 15900 600 1000
5 001062 2507.01 41000 2000 25000 0 8000 9200
0 0 13800
006 001065 2507.01 67800
0 19500 00 16900
0 0 16150
00g12 g13 g14 g15
g16
g17 g18 g19 g20 g21 g22 g23 g24 g25 g26 g27
1 14000 000 51800 11700 423 220 75 350 7660 600 1500
0 6000 1555
2
0 0 1500 8400 28200
0 355 230 44 240 2350
0 350
0 3600 900
3 3000 0 1600
0 21600 12400 388 130 65 360 5260 300 1250
0 6300 400
4 1500 0 600 3600 24400 14800 329 155 53 60 4220 550 775
0 2900 300
5 2500 0
0 2400 14800
0 306 130 70 300 2200 550 250
0 4600 200
6
0 0 3800 8700 18400 6000 556 180 30 250 1950 150 650
0 3600 3515
g28 g29 g30 g31 g32 g33 g34 g35 g36 g37 g38
dg
1400
0 1530 700
0
0 450
0
0
0
0 13502 1105
0
0
0 55
0
0
0
0
0
0
0
3
700 8750
0 200 60
0 250
0
0
0
0 250
4 11800
0 505 150
0
0
0
0
0
0
0 150
5
250
0
0 50
0
0
0
0
0
0
0 3250
6
300
0
0
0
0
0
0
0
0
0
0
0
> exp.s7<-exp> exp[is.na(exp)]<-0> exp.old<-expConverted to monthly expenditure in takaMultiplier = 1 month/2 weeks/ 100 = 0.02 for g1 to g17
Multiplier = 1/12 for g22 to g38 and dg
Multiplier = 1 for g18 to g21> for(j in 1:17) exp[,j+2]<-round(exp[,j+2]*0.02)> for(j in 22:39) exp[,j+2]<-round(exp[,j+2]/12)> head(exp)
hhid
wgt
g1 g2
g3 g4 g5 g6 g7 g8 g9 g10 g11 g12 g13 g14 g15
1 001006 2507.01 7580 258 370 00 896 280 0 448
0 94 2800002 001036 2507.01 1732
0 212 0 300 372
0 0 2960000 30 1683 001054 2507.01 2704 169 1290 0 480 456
0 0 358 52 12 60
0 32
04 001061 2507.01 1407
0 620 00 438
0 0 318 12 20 30
0 12 72

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5 001062 2507.01 820 40 500 0 160 184
0 0 276
0
0 50
0
0 48
6 001065 2507.01 1356
0 390 00 338
0 0 32300
00 76 174
g16 g17 g18 g19 g20 g21 g22 g23 g24 g25 g26 g27 g28 g29 g30 g31 g32 g33
1 1036 234 423 220 75 350 638 50 125
0 500 130 33
0 128 58002 564
0 355 230 44 240 196
0 29
0 300 75 92
0
0
0
5
0
3 432 248 388 130 65 360 438 25 104
0 525 33 58 729
0 17
50
4 488 296 329 155 53 60 352 46 65
0 242 25 983
0 42 1200
5 296
0 306 130 70 300 183 46 21
0 383 17 21
0
0
4
0
0
6 368 120 556 180 30 250 162 12 54
0 300 293 25
0
0
0
0
0
g34 g35 g36 g37 g38 dg
1 38
0 0
0
0 112
2
0
0 0
0
0
0
3 21
0 0
0
0 21
4
0
0 0
0
0 12
5
0
0 0
0
0 271
6
0
0 0
0
0
0



Monthly household expenditure by expenditure group in taka

> exp.item<-c("1 Food grains","2 Pulses","3 Fish","4 Eggs","5 Meat","6 Vegetables",+ "7 Milk & Daily","8 Sweetmeat","9 Oil & Fats","10 Fruits","11 Drinks",+ "12 Sugar & molasses","13 Miscellaneous Food","14 Dining out",+ "15 Tobacco & tobacco produts", "16 Spices","17 Betel leaf & Chewgoods",+ "18 Fuel & lighting","19 Cosmetics & other expenses","20 Washing & cleaning expenses",+ "21 Transport/travel & other misc. charges","22 Ready-made garments",+ "23 Clothing material & tailoring","24 Footwear","25 Household-use textiles, etc.",+ "26 Housing related expenses","27 Medical treatment expenses (male)",+ "28 Medical treatment expenses (female)","29 Educatonal expenses (male)",+ "30 Educational expenses (female)","31 Remittances, ceremonies, gift, etc.",+ "32 Recreation & leisure, etc.","33 Taxes, interest, fines, etc.",+ "34 Cooking equipent","35 Furniture & related paraphernalia","36 Personal articles",+ "37 Misc. household durable","38 Insurance expenditure","dg Durable goods")> for(j in 1:39){+
cat("\n",formatC(round(weighted.mean(exp[,j+2],exp$wgt)),width=6,format="d"),":",exp.item[j])+ }1974 : 1 Food grains
141 : 2 Pulses
751 : 3 Fish
90 : 4 Eggs
471 : 5 Meat
428 : 6 Vegetables
165 : 7 Milk & Daily
41 : 8 Sweetmeat
237 : 9 Oil & Fats
223 : 10 Fruits
40 : 11 Drinks
73 : 12 Sugar & molasses
8 : 13 Miscellaneous Food
162 : 14 Dining out
125 : 15 Tobacco & tobacco produts
415 : 16 Spices
131 : 17 Betel leaf & Chewgoods

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620 : 18 Fuel & lighting
173 : 19 Cosmetics & other expenses
132 : 20 Washing & cleaning expenses
693 : 21 Transport/travel & other misc. charges
428 : 22 Ready-made garments
34 : 23 Clothing material & tailoring
83 : 24 Footwear
43 : 25 Household-use textiles, etc.
1088 : 26 Housing related expenses
186 : 27 Medical treatment expenses (male)
230 : 28 Medical treatment expenses (female)
334 : 29 Educatonal expenses (male)291 : 30 Educational expenses (female)419 : 31 Remittances, ceremonies, gift, etc.
43 : 32 Recreation & leisure, etc.
70 : 33 Taxes, interest, fines, etc.
38 : 34 Cooking equipent
74 : 35 Furniture & related paraphernalia
99 : 36 Personal articles
29 : 37 Misc. household durable
26 : 38 Insurance expenditure
378 : dg Durable goods# Monthly total expenditure and food expenditure in taka
> exp$ttexp<-rowSums(exp[,3:41])> exp$food<-rowSums(exp[,3:19])> head(exp)
hhid
wgt
g1 g2
g3 g4 g5 g6 g7 g8 g9 g10 g11 g12 g13 g14 g15
1 001006 2507.01 7580 258 370 00 896 280 0 448
0 94 2800002 001036 2507.01 1732
0 212 0 300 372
0 0 2960000 30 1683 001054 2507.01 2704 169 1290 0 480 456
0 0 358 52 12 60
0 32
04 001061 2507.01 1407
0 620 00 438
0 0 318 12 20 30
0 12 72
5 001062 2507.01 820 40 500 0 160 184
0 0 276
0
0 50
0
0 48
6 001065 2507.01 1356
0 390 00 338
0 0 32300
00 76 174
g16 g17 g18 g19 g20 g21 g22 g23 g24 g25 g26 g27 g28 g29 g30 g31 g32 g33
1 1036 234 423 220 75 350 638 50 125
0 500 130 33
0 128 58002 564
0 355 230 44 240 196
0 29
0 300 75 92
0
0
0
5
0
3 432 248 388 130 65 360 438 25 104
0 525 33 58 729
0 17
50
4 488 296 329 155 53 60 352 46 65
0 242 25 983
0 42 1200
5 296
0 306 130 70 300 183 46 21
0 383 17 21
0
0
4
0
0
6 368 120 556 180 30 250 162 12 54
0 300 293 25
0
0
0
0
0
g34 g35 g36 g37 g38 dg ttexp food
1 38
0 0
0
0 112 14356 11476
2
0
0 0
0
0
0 5240 3674
3 21
0 0
0
0 21 9212 6293
4
0
0 0
0
0 12 6089 3713
5
0
0 0
0
0 271 4126 2374
6
0
0 0
0
0
0 5007 3145

Monthly expenditure per household in taka
> round(weighted.mean(exp$ttexp,exp$wgt))[1] 10986> round(weighted.mean(exp$food,exp$wgt))[1] 5476

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

Monthly expenditure per household
Total expenditure

Food

Trial

10.9685,476

Report11,200

6.031

Gap

-232

-555



Generated data frame of trial estimation of income and expenditure> table(inc$hhid==exp$hhid)
TRUE
12240
> incexp<-cbind(inc,exp[,c(-1,-2)])> dim(incexp)[1] 12240
57> head(incexp)
hhid region urbanrur
wgt inc8e
inc4b inc5 inc9d
inc7b inc7c1
1 001006
10
1 2507.01 416.67
0.00
0 500 11003.33 726.67
2 001036
10
1 2507.01 0.00 11718.75
0 300
0.00 35.00
3 001054
10
1 2507.01 0.00 6000.00
0 400
0.00 46.67
4 001061
10
1 2507.01 0.00 20906.67
0 200
0.00 87.50
5 001062
10
1 2507.01 0.00 3250.00
0 300
0.00 50.00
6 001065
10
1 2507.01 83.33 21000.00
0 300
0.00 110.00
inc7c2 inc7c3
inc7d inc7e inc8c
ttinc g1 g2 g3 g4 g5 g6 g7 g81
800 166.67 -1254.17
00 12359.17 7580 258 370 0 0 896 280 02
0 0.00-2.50
00 12051.25 1732 0 212 0 300 372 0 0
30 0.000.00
00 6446.67 2704 169 1290 0 480 456
0 0
4
0 0.00-1.67
00 21192.50 1407 0 620 0 0 438 0 0
50 0.00-2.50
00 3597.50 820 40 500 0 160 184 0 0
60 0.000.00
00 21493.33 1356 0 390 0 0 338
0 0
g9 g10 g11 g12 g13 g14 g15 g16 g17 g18 g19 g20 g21 g22 g23 g24 g25 g26
1 448 0 94 280 0 0
0 1036 234 423 220 75 350 638 50 125 0 500
2 296 0 0
0 0 30 168 564
0 355 230 44 240 196 0 29 0 300
3 358 52 12 60 0 32
0 432 248 388 130 65 360 438 25 104 0 525
4 318 12 20 30 0 12 72 488 296 329 155 53 60 352 46 65 0 242
5 276 0 0 50 0 0 48 296
0 306 130 70 300 183 46 21 0 383
6 323 0 0
0 0 76 174 368 120 556 180 30 250 162 12 54 0 300
g27 g28 g29 g30 g31 g32 g33 g34 g35 g36 g37 g38 dg ttexp food
1 130 33 0 128 58 0
0 38 0 0 0 0 112 14356 11476
2 75 92 00 0 5
0 0 0 0 0 0 0 5240 3674
3 33 58 729
0 17 5
0 21 0 0 0 0 21 9212 6293
4 25 983 0 42 12 0
0 0 0 0 0 0 12 6089 3713
5 17 21 00 4 0
0 0 0 0 0 0 271 4126 2374
6 293 25 00 0 0
0 0 0 0 0 0 0 5007 3145

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> var<-colnames(incexp)> var[1] "hhid""region"[7] "inc5""inc9d"[13] "inc7d""inc7e"[19] "g3""g4"[25] "g9""g10"[31] "g15""g16"[37] "g21""g22"[43] "g27""g28"[49] "g33""g34"[55] "dg""ttexp"> lbl[1] "Other income"[4] "Imputed rent"[7] "Fishery product"[10] "Earn from rent""urbanrur" "wgt""inc7b""inc7c1""inc8c""ttinc"
"g5"
"g6"
"g11"
"g12"
"g17"
"g18"
"g23"
"g24"
"g29"
"g30"
"g35"
"g36"
"food""Wage income""Crop product""Forestry product""Renittance""inc8e""inc7c2"
"g1"
"g7"
"g13"
"g19"
"g25"
"g31"
"g37"

"inc4b""inc7c3"
"g2"
"g8"
"g14"
"g20"
"g26"
"g32"
"g38"

"Business income""Livestock product""Agri cost""Total income"> label<-c(rep("",4),lbl,exp.item,"Total expenditure","Total food")> data.frame(variable=var,label=label)variable
label
1
hhid
2
region3 urbanrur
4
wgt
5
inc8e
Other income
6
inc4b
Wage income
7
inc5
Business income
8
inc9d
Imputed rent9
inc7b
Crop product10 inc7c1
Livestock product11 inc7c2
Fishery product12 inc7c3
Forestry product13
inc7d
Agri cost14
inc7eEarn from rent
15
inc8c
Renittance16
ttinc
Total income
17
g1
1 Food grains
18
g2
2 Pulses
19
g3
3 Fish20
g4
4 Eggs21
g5
5 Meat22
g6
6 Vegetables
23
g7
7 Milk & Daily
24
g8
8 Sweetmeat25
g9
9 Oil & Fats
26
g10
10 Fruits27
g11
11 Drinks
28
g12
12 Sugar & molasses

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29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57

g13
13 Miscellaneous Food
g14
14 Dining out
g15
15 Tobacco & tobacco produts
g16
16 Spices
g17
17 Betel leaf & Chewgoods
g18
18 Fuel & lighting
g19
19 Cosmetics & other expenses
g20
20 Washing & cleaning expenses
g21 21 Transport/travel & other misc. charges
g22
22 Ready-made garments
g23
23 Clothing material & tailoring
g24
24 Footwear
g25
25 Household-use textiles, etc.
g26
26 Housing related expenses
g27
27 Medical treatment expenses (male)g28
28 Medical treatment expenses (female)g29
29 Educatonal expenses (male)g30
30 Educational expenses (female)g31
31 Remittances, ceremonies, gift, etc.
g32
32 Recreation & leisure, etc.
g33
33 Taxes, interest, fines, etc.
g34
34 Cooking equipent
g35
35 Furniture & related paraphernalia
g36
36 Personal articles
g37
37 Misc. household durable
g38
38 Insurance expendituredg
dg Durable goods
ttexp
Total expenditurefood
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8. Micro data to be provided
Strategy

1. Resampling
The data files; rt001 to rt020 which were provided by NSO, and trial estimation of incexp will beresampled as follows;
1.1

To append household identifier hhid to all data files.

1.2

To append personal identifier pid to rt002 and rt003.

1.3

To delete person name variables; s01a_q01 in rt002 and s08c_q03 in rt013.

1.4

To select 80% of hhid by systematic sampling method.

1.5

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

2. The weight wgt in rt001 and incexp will be adjusted by dividing by 0.8.

3.

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

Set incexp as outfiles[[21]]> outfiles[[21]]<-incexp

Original data files excluded personal names> d<-outfiles[[2]]> d<-d[-5]> outfiles[[2]]<-d> dim(outfiles[[2]])[1] 55580

150> d<-outfiles[[13]]> d<-d[-4]> outfiles[[13]]<-d> dim(outfiles[[13]])[1] 2100

21

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Resampling# Selected 80% of hhid
> hhid<-outfiles[[1]]$hhid> length(hhid)[1] 12240> Int<-5> (St<-sample(1:5,1))[1] 2> hhid.selected<-hhid[(1:length(hhid))%%Int!=(St-1)]> length(hhid.selected)/length(hhid)[1] 0.8> hhid.selected<-hhid.selected[order(hhid.selected)]> head(hhid.selected)[1] "001036" "001054" "001061" "001062" "001066" "001070"# Resampled at the rate of 80%# outfiles[[1]] to outfiles[[21]]> Rnames<-c(Rnames,"incexp")> Rnames.80<-paste(Rnames,".80",sep="")> Rnames.80[1] "rt001.80" "rt002.80" "rt003.80" "rt004.80" "rt005.80" "rt006.80"[7] "rt007.80" "rt008.80" "rt009.80" "rt010.80" "rt011.80" "rt012.80"[13] "rt013.80" "rt014.80" "rt015.80" "rt016.80" "rt017.80" "rt018.80"[19] "rt019.80" "rt020.80" "incexp.80"> outfiles.80<-list()> for(j in 1:21){+ d<-outfiles[[j]]+ outfiles.80[[j]]<-subset(d,is.element(d$hhid,hhid.selected))+ }> length(outfiles.80)[1] 21> for(j in 1:21){+ cat(format(Rnames.80[j],width=10),": ",Bangladesh HIES 2010 Manual (Version 1.0)

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+ format(nrow(outfiles.80[[j]]),width=6),",",+ format(ncol(outfiles.80[[j]]),width=3),"\n")+ }rt001.80:

9792 , 152

rt002.80:

44306 , 150

rt003.80:14400 , 30

rt004.80:2758 , 36

rt005.80: 176002 , 14

rt006.80: 149553 , 20

rt007.80:64236 , 12

rt008.80:

57774 , 10

rt009.80:20217 , 10

rt010.80:

36357 ,

8

rt011.80: 198280 ,8

rt012.80: 182824 , 12

rt013.80:

1689 , 21

rt014.80:

4112 , 17

rt015.80: 350172 , 63

rt016.80: 159864 , 14

rt017.80: 290025 ,8

rt018.80: 228131 ,7

rt019.80: 663824 ,6

rt020.80: 273404 ,9incexp.80 :9792 , 57# Adjusted weight of rt001 and incexp, and generated WT variable> d<-outfiles.80[[1]]> d$WT<-d$wgt/0.8> outfiles.80[[1]]<-d> sum(d$WT)[1] 33028014> sum(outfiles[[1]]$wgt)[1] 33028014

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> d<-outfiles.80[[21]]> d$WT<-d$wgt/0.8> outfiles.80[[21]]<-d> sum(outfiles[[1]]$wgt)[1] 33028014> save(outfiles.80,file="Resampled_80%.RData")# Converted to CSV> CSVnames<-gsub("\\.","_",Rnames.80)> CSVnames<-paste(CSVnames,".csv",sep="")> CSVnames[1] "rt001_80.csv" "rt002_80.csv" "rt003_80.csv" "rt004_80.csv"[5] "rt005_80.csv" "rt006_80.csv" "rt007_80.csv" "rt008_80.csv"[9] "rt009_80.csv" "rt010_80.csv" "rt011_80.csv" "rt012_80.csv"[13] "rt013_80.csv" "rt014_80.csv" "rt015_80.csv" "rt016_80.csv"[17] "rt017_80.csv" "rt018_80.csv" "rt019_80.csv" "rt020_80.csv"[21] "incexp_80.csv"> for(j in 1:21){+ cmd<-paste("write.csv(outfiles.80[[",j,"]],'",CSVnames[j],"',row.names=F)",sep="")+ eval(parse(text=cmd))+ }# Resampled data set in csv format> list.files()[1] "rt001_80.csv" "rt002_80.csv" "rt003_80.csv" "rt004_80.csv"[5] "rt005_80.csv" "rt006_80.csv" "rt007_80.csv" "rt008_80.csv"[9] "rt009_80.csv" "rt010_80.csv" "rt011_80.csv" "rt012_80.csv"[13] "rt013_80.csv" "rt014_80.csv" "rt015_80.csv" "rt016_80.csv"[17] "rt017_80.csv" "rt018_80.csv" "rt019_80.csv" "rt020_80.csv"[21] "incexp_80.csv"

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Attachments
1. The questionnaire

2. Data dictionary(1) rt001 to rt020: Refer to “4.2 Generated list of variable names in each data frame”.

(2) incexp: trial estimation of household-level income and expenditure by the author.

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HIES Q-1

147

GOVERNMENT OF THE PEOPLE'S REPUBLIC OF BANGLADESH

Bangladesh Bureau of Statistics
Household Income-Expenditure Survey, 2009-12 Project
Parishankhan Bhaban, E-27/A, Agargaon, Sher-E-Bangla Nagar

HOUSEHOLD INCOME AND EXPENDITURE SURVEY-2010(Write all number in English)

PSU serial number

Team number

TermHousehold number

Geographic Codes
AREA

CODE

NAME

RegionDistrict
Thana/ Upazilla
Union/ Ward
Mouza/ Mohalla
Rural/ PSA/ SMA

Female facilitator
Interviewer
Supervisor

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T A B L E
Order of Sections
1

HOUSEHOLD INFORMATIONEMPLOYMENT INFORMATIONSOCIAL SAFETY NETS PROGRAMMEILLNESSES AND INJURIESCHILD HEALTH AND IMMUNIZATION
PRE-NATAL AND POST-NATAL CARE
DISABILITY

OTHER PROPERTY AND ASSETSOTHER INCOMEMIGRATION AND REMITTANCESMICRO CREDIT

26
27
28
29

4
5-6

9

7-9
10
11
12

CONSUMPTIONPART A: DAILY CONSUMPTION
DAY 1
DAY 2
DAY 3
DAY 4
DAY 5
DAY 6
DAY 7

30-31
32-33
34-35
36-37
38-39
40-41
42-43

PART B: WEEKLY CONSUMPTION (DAY 1-7)
PART A: DAY 8
DAY 9
DAY 10
DAY 11
DAY 12
DAY 13
DAY 14PART B: WEEKLY CONSUMPTION (DAY 8-14)
PART C: MONTHLY NON-FOOD EXPENDITURE
PART D: ANNUAL NON-FOOD EXPENDITURE
PART E: INVENTORY OF DURABLE GOODS

44
45-46
47-48
49-50
51-52
53-54
55-56
57-58
59
60-61
62-65
66

ECONOMIC ACTIVITIES AND WAGE EMPLOYMENTPART A: ACTIVITIESPART B: WAGE EMPLOYMENT

5

NON-AGRICULTURAL ENTERPRISES

6

HOUSINGPART A: HOUSING INFORMATIONPART B: SHOCKS AND COPING

7

OTHER INCOME AND ASSETSPART A:PART B:PART C:PART D:HEALTHPART A:PART B:PART C:PART D:

4

8
1
2
3

Page No.

EDUCATION
PART A: LITERACY AND EDUCATIONAL ATTAINMENTPART B: CURRENT ENROLLMENT

3

C ON T E N T S
Order of Sections

HOUSEHOLD INFORMATION ROSTER
PART A:PART B:PART C:

2

Page No.

O F

13
14
15-16

17
18

AGRICULTURAL ENTERPRISESPART A: LANDHOLDINGPART B: CROP PRODUCTION
PART C: NON-CROP ACTIVITIESLIVESTOCK AND POULTRYLIVESTOCK PRODUCTSFISH FARMING AND FISH CAPTURE
FARM FORESTRYPART D: EXPENSES ON AGRICULTURAL INPUTSPART E: AGRICULTURAL ASSETS

19
20-21
22

23
24
25

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SECTION 1: HOUSEHOLD INFORMATION ROSTER`
ID CODE as in Roster

Respondent ID Code

2
Sex

3
Relationship of members with the headof the household

PART A: HOUSEHOLD INFORMATION (all members listed in the roster)

4
Age

5
Religion

6
Marital status

7
Earner
1 Yes
2 No

01 Head02 Husband/ wife
03 Son/Daughter
1 Male04 Spouse of
2 Female
Son/Daughter
05 Grandchild
06 Father/Mother07 Brother/Sister
08 Niece/Nephew

09 Father/Motherin- law
10 Brother/Sisterin-law
11 Other relative,
specify
12 Servant
13 Employee
14 Other, specify

WRITE AGE
IN FULLYEARSWRITE "00"
FOR LESS
THAN
1 (one) YEAR

1
2
3
4
5

Islam
1 Currently Married
Hinduism 2 Never Married
Buddhism 3 Widowed
Christianity 4 Divorced
Other
5 Separated(specify)

8
9
10
11
SPOUSE FATHER MOTHER Whether(name) was
abroad morethan 6monthsduring last 5
PLEASE WRITE THE ID
years
CODE.
IF NOT EXIST INROSTER, WRITE '99'.

1
2

Yes
No >> Next
person12
Why did (name) return tothe household fromabroad?

1 Lose job
2 Due to illness
3 End of employment
contract
4 Disagreement withauthorities
5 Homesick
6 Due to Economic Recession

7 Other (specify)
ID CodeID CodeID Code

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

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HIES 2010
SECTION 1: HOUSEHOLD INFORMATION ROSTER
ID CODE as in Roster1
Did you work for livelihood
during the past 7 days?

1
2

Yes
No

>>Next person

2
Were you available for work
during the past 7 days?

1
2

Yes
No >>Q4

PART B: EMPLOYMENT INFORMATION (ALL PERSONS 5 YEARS AND OLDER)

3
Did you looking for work
during the past 7 days?

1
2

Yes
No

4
Why were you not available/
did you not looking for work?

>>Next person

01
02
03
04
05
06
07
08
09
10
11

Engaged in domestic work
Housewife
Student
Too old/ retired
Too youngTemporarily sick
Disabled
Waiting to start new job
No work available
On leave/looking for job/business
Other (Specify)

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

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SECTION 1: HOUSEHOLD INFORMATION ROSTER
ID CODE as in Roster

Respondent ID

1
Has (name)included of anysocial safety nets
programme in thelast 12 months?

2 If Yes,
What was the main program in which(name) has Included in the last 12(Programme Code)

HIES 2010

PART C: SOCIAL SAFETY NETS PROGRAMME (ALL PERSONS 5 YEARS & OLDER)
151

3
When did (name)enlist in thisprogramme?

4
How much was (name) entitled toreceive in cash/in kinds?

5
How much was (name) actually
receive in cash/in kinds?

1 Rice2 Maize
3 Wheat
4 Powder Milk
5 Clothing
6 Other (specify)

1 Yes
2 No >>Question 7

1 Rice2 Maize
3 Wheat
4 Powder Milk
5 Clothing
6 Other, specify

NAME OFGOODS(CODE)
MONTH

YEAR

TAKA

KG

TAKA

NAME OFGOODS(CODE)KG

6

7

How much
What was thedid [name]cause for not
spend to be included (Code)included theprogramme?

Taka

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
Code for Name of Program

Code for Question-7

1-Old age Allowance (MOSW)

19-Test Relief (TR) Food (MOFDM)

1. Didn't Know about the programme

2-Allowance for the Widowed, Deserted and Destitute

20-Gratuitious Relief

2. Not fit for that programme

3-Allowance for the Financiallly Insolvent Disabled

21-Food for work (FFW)

3. Fit for the programme but not apply

4-Maternity allowance Program for the Poor Lactating

22-Employment Generation for Hard-core Poor or 100 days

4. Due to shortness of budget

5-Honorarium for Insolvent Freedon Fighters (MOFWA)

23-Stipened for Primary Students (MOPMED)

5. Selection was not proper

6-Honorarium for Injured Freedom Fighters

24-School Feeding Programme (MOPMED)

6. Not, any programme is this area

7-Gratuitous Relief (Cash)

25-Stipend for drop out students

8-General Relief Activities

26-Stipened for Secondary and Higher Secondary/Female Student

9-Allowances for distressed cultural personalities/Activitists

27-Maternal Health Voucher Allowance

10-Allowance for beneficiaries in ctg. Hill tract area

28-Rural Employment opportunities for protection of public

11-Stipend for Disabled Students (MOSW)

29-Char livelihood
30-Rural Employment, Social forestration and Rural Maintenance
Program (LGD)

12-Grants for the schools for the Disabled (MOSW)
13-Cash for Work (MOFDM)
14-Housing Support
15-Agriculture Rehabilitation (MOA)
16-Subsidy for open market sales (OMS)
17-Vulnerable group development (VGD) (MOWCA)
18-Vulnerable group feeding (VGF) (MOFDM)

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SECTION 2: EDUCATION
ID CODE as in Roster

Respondent ID code3
Can you reada letter?

1
2

Yes
No

>>Next Person

PART A: LITERACY AND EDUCATION ATTAINMENT (ALL PERSONS 5 YEARS AND OLDER)
4
Can you write
a letter?

1
2

Yes
No

>>Next Person

5What was the highest class that youcompleted?

6
Where did you do last study / are you currently
studying?

00 No class passed
01 Class 1
02 Class 2
03 Class 3
04 Class 405 Class 5
06 Class 6
07 Class 7
08 Class 8
09 Class 9
10 SSC/equivalent
11 HSC/equivalent

1 Formal School
2 Formal College
3 Formal University
4 Madrasha
5 Taught by family
6 Govt. informal literacy programme
7 NGO literacy course
8 Other (specify)

12 Graduate/equivalent
13 Post graduate/
equivalent
14 Medical
15 Engineering
16 Vocational
17 Technical Education
18 Nursing
19 Other (Specify)>>Next Person>>Next Person>>Next Person>>Next Person

7
What type of school/
institution did you last attended/
are you currently attending?
1
2
3
4
5
6

Government
Private (Govt. grants)
Private (Not govt. grants)
NGO run institution
Madrasa (Govt. affiliated)
Madrasa (Kowmi)

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

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HIES-2010
SECTION 2: EDUCATIONPART B: CURRENT ENROLLMENT (ALL PERSONS 5 YEARS AND OLDER)ASK ONLY PRIMARY STUDENTS

ID CODE as in Roster

Respondent ID CODE1
Are you currently
attending school/
educational
institution?

1
2

2
What class are you currently attending?

00 Nursery/Kindergarten
01 Class 1
02 Class 2
03 Class 3
Yes
04 Class 4
No
05 Class 5>> Next person 06 Class 6
07 Class 7
08 Class 8
09 Class 9
10 Class 10/
equivalent

11 HSC/equivalent
12 Graduate/equivalent
13 Post graduate/
equivalent
14 Medical
15 Engineering
16 Vocational
17 Technical Education
18 Nursing
19 Other, specify

3
Are you receiving astipend for primaryeducation?
1
2

4 If Yes,
How much did you
receive in total in the past
12 months?

ASK ONLY SECONDARY/HIGHER SECONDARY STUDENTS

5
Are you receiving
stipend insecondary/Higher
secondary level?

Yes
No >>Q8

1
2

TAKA

6
How much did
you receive in
total from thisstipend in thepast 12months?

Yes
No >>Q8

7
Do you benefit
from thetuition waiver?

1
2

Yes
No

TAKA

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

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SECTION 2: EDUCATIONPART B: CURRENT ENROLLMENT (ALL PERSONS 5 YEARS AND OLDER)

ID CODE as in Roster

EXPENDITURE DURING THE PAST 12 MONTHS FOR EDUCATION

8
How much did your household spend during the past 12 months on your schooling?
WRITE THE EXPENSES IN TAKAIF NOTHING WAS SPENT, WRITE "0" (ZERO)IF UNKNOWN, LEAVE BLANK
A

B

Admission

Annual/fees

session fees

C

D

Registration Examination
fees

fees

E

F

G

H

I

J

K

L

TuitionText books,

Exercisebooks,

Uniform
dress,

PrivateHostelExpenses

Transport

Tiffinfees

note bookstationary

footweartutoring(incl food)

cost

cost

M
Cost ofInternet ande-mail(edu. related)

N

O

P

Donation

Other,
specify

Total(edu. related)

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

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SECTION 3: HEALTH

HIES 2010

PART A: ILLNESSES AND INJURIES (ALL HOUSEHOLD MEMBERS)

ID code as in Roster

1
2
3
Have you suffered What chronic illness/ disability For how longfrom any chronic are you suffering from?
have you had
illness/ disability 01 Chronic Feverthis illness?
in the last 12
02 Injuries / Disability
months?
03 Chronic Heart Disease
04 Asthma/Breathing trouble
05 Chronic Dysentery
1 Yes
06 Gastric/ ulcer
2 No >>Q4
07 Blood pressure
08 Arthritis/ Rheumatism
09 Eczema
10 Diabetes
11 Cancer
12 Leprosy
13 Paralysis
14 Epilepsy
YEAR MONTHS
15 Other (specify)
1
2

4
Have you sufferedfrom any symptoms
of illness/ injury in
the last 30 days?

1
2

Yes
No >>Next person

5What Symptoms/Diseases did you
suffer from13 Tuberculosis
01 Diarrhoea
14 Malaria
02 Fever15 Jaundice
03 Dysentery
16 Female diseases
04 Pain17 Pregnancy related
05 Injury
18 Cancer
06 Blood pressure
19 Leprosy
07 Heart disease
20 Paralysis
08 Breathingtrouble21 Epilepsy
22 Scabies
09 Weakness
23 Kidney Diseases
10 Dizziness
24 Gall stone Diseases
11 Pneumonia
25 Other, specify
12 Typhoid6
Have you sought
any type of
medical treatment
related to yourhealth problems
mentioned inquestion 5 col. 1

1
2

Yes >>Q8
No

7
Why not receive any treatment?
01 Quality of healthcare
08 There was none
is not goodto accompany
02 Problem was not
09 It is a hassle
serious
to go outside
03 Treatment cost is
10 Not able to maketoo muchown decision about
04 Distance is too long
healthcare
05 Afraid of discovering
11 Didn't know whereserious illness
to go
06 Afraid to take action
12 Other, specify
07 Nobody at home
to pay any attention/ take care of me>>Q 17

IN ORDER OF IMPORTANCE
1

2

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HIES 2010
SECTION 3: HEALTHPART A: ILLNESSES AND INJURIES (ALL HOUSEHOLD MEMBERS)

8Which of the following were consulted for this illness/injury (in the order in which they were consulted)?
ID CODE as in Roster

01 Govt. Health Worker
02 NGO Health Worker
03 Homeopath
04 Ayurved/Kabiraji/Hekim
05 Other Traditional/
Spiritual/Faith Healer
06 Govt. Doctor(Govt. Facility)
07 Govt. Doctor(Private Facility)
1st

08 Doctor from NG0
Facility
09 Doctor from Private
Facility
10 Salesman of a
Pharmacy/Dispensary
11 Family treatment
12 Self treatment
13 Other, specify

2nd

9
How many days
after symptoms
began did you first
consult this healthprovider?

10
Where did you get the
medicines from?

01
02
IF IMMEDIATELY,
03
WRITE "00"
04
05
06
07
08
09
No. of days

11
Did you pay forthe medicines?

Govt. health centre
NGO health facility
1
Private health facility
2
Other facility, specify
3
Pharmacy/dispensary
Other shop
Not available >>Q12
Could not afford >>Q12
Other, specify

12
How did you travelto the provider?
01 Private car
02 Taxi
03 BusYes, totally
04 Auto rickshaw
Yes, partially 05 Rickshaw
No
06 Rickshaw van
07 Bullock cart
08 Country boat
09 Engine boat
10 Ambulance
11 Walking
12 Calling doctor at home
13 Other, specify

13
How much time
did it take toreach the service
provider?

HOUR

MINUTES14
How long did you
have to wait atprovider to be
examined?

HOUR

MINUTES

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

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8 (Version 1.0)
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HIES 2010

ID CODE as in Roster

SECTION 3: HEALTHPART A: ILLNESSES AND INJURIES (ALL HOUSEHOLD MEMBERS)

15
Why did you choose thisprovider?

16
In your opinion,
did the provider
spend enoughtime with you?

01 Nearby
02 Acceptable cost
03 Availability of doctor
04 Availability of female doctor
05 Availability of equipment
1
06 Quality of treatment
2
07 Referred by other provider
3
08 Referred by relatives/friends
09 Reputation
10 Other, specify

17What was the total cost of treatment during the past 30 days?

18
How did you finance treatment for all
illness in the last 30 days?
01 Regular income
02 Household saving
03 Sold personal belonging
04 Sold Livestock
05 Sold Agricultural product/Tree
06 Sold permanent assets
07 Mortgage of Assets/Land
08 Borrowed from Friends/

WRITE THE VALUE IN TAKAIF NOTHING WAS SPENT, WRITE "0" (ZERO)IF UNKNOWN, LEAVE BLANK

Yes
No
Don't know

Consultation

Hospital/

fees (visit)

Clinic charges

Cost of
Medicines

Cost of Test/
Investigation

Transport
cost

Tips

Other
chargesMaternity cost
ClinicMidwife

Others

Total
Cost

09
10
11

Relatives/Office
Borrowed from Money Lender
Assistance from friends & relatives
Other Specify(Mention three answers inorder of rank)
1
2
3

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

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HIES 2010
SECTION 3: HEALTHPART B: CHILD HEALTH AND IMMUNIZATION (ALL CHILDREN 5 YEARS AND UNDER)

ID CODE as in Roster

1
2
3
In which month Present Has ..[name]..
and year was
Age of ever been..[NAME OF
immunized?
the
CHILD]..
Children
born?

1 Yes
2 No
>>PartNext
D Child
3 Don't know>>PartNext
D Child

MONTH

YEAR

MONTH

4
Do you have an
immunization cardfor ..(name)?

567
Who influenced you to
If immunization card is available, collect information from Where was the mostrecent immunization given? immunize your children?
the card.

Yes-1

01 Satellite Clinic
02 Union health &Family welfare center
03 Thana health complex
IF NO CARD, ASK RESPONDENT ABOUT
04 District hospital
IMMUNIZATIONS.
05 NGO health center
06 Private clinic/Hospital/Doctor
07 Other, specify
At home, by:
DPT1 DPT2 DPT3 Polio1 Polio2 Polio3 Measles Hepatitis 08 Govt. health worker
Yes-1 Yes-1 Yes-1 Yes-1 Yes-1 Yes-1 Yes-1
Yes-1
09 NGO health worker

No-2

No-2

CHECK FROM CARD WHETHERIMMUNIZATION HAS TAKEN PLACEASK TO SEE CARDSFOR ALL CHILDRENFOR WHOM CARDSARE AVAILABLE
1 Yes,
card available
2 No, or card notavailable

BCG

No-2

No-2

No-2

No-2

No-2

No-2

8
Did
(name)
receive a
Vitamin-A
Capsulesin last 12
months?

01 Self
02 Friends/Relatives
03 EPI program staff
04 Govt. health worker
05 NGO health worker
06 Private practitioner
07 RadioYes-1
08 Television
No-2
09 Union sub-center
10 Thana health complex
11 Hospital

No-2

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

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HIES 2010
SECTION 3: HEALTH
1Have you evergiven birth?

ID CODE as in Roster

2
In which year
did you givebirth to yourlast child?

1 Yes
2 No>> Next
married womenPART C: PRE-AND POST-NATAL CARE (ALL EVER MARRIED WOMEN)
3
When pregnant withyour last child,
did you
attendprenatal
consultations
at a healthcare Centre/Doctor?
1
2

YEAR

Yes
No

>>Q7

4
Where did you first receive
this care?
01 Satellite Clinic
02 Union health &Family welfare center
03 Thana health complex
04 District hospital
05 NGO health center
06 Med. College hospital07 Private clinic/Hospital/Doctor
08 Other, specify

5
At which monthof pregnancy
did you go foryour first visit?

6
Did youreceive atetanusvaccine at
this time?

1
2

Yes
No

7
Who assisted you
with this birth?

8
Where did you
give birth?

01 At homemember/relative
02 Neighbor
03 Midwife(trained)
04 Midwife(untrained)
05 Nurse
06 Doctor
07 Other, specify

01 At Home
02 Satellite Clinic
03 Union health &Family welfare center
04 Thana health complex
05 District hospital
06 NGO health center
07 Med. College hospital
08 Private clinic/Hospital/Doctor
09 Other, specify

9After the birth,
did you visit ahealth care facility
within 6 weeks ofdelivery for post
-natal checkup?
1 Yes
2 No>> Next
Married Women

10
Where did you go
for this checkup?
01 At Home
02 Satellite Clinic
03 Union health &family welfare center
04 Thana health complex
05 District hospital
06 NGO health center
07 Med. College hospital
08 Private clinic/Hospital/Doctor
09 Other, specify

MONTH

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

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HIES 2010
SECTION 3: HEALTHPART D: DISABILITY (ALL HOUSEHOLD MEMBERS)

ID CODE as in Roster

20
21
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Does
Does
FOR CODES 2-4 IN COLUMN 2, 5, 8,
Does
How old What Does
How old What
Does How old What
Does How old What
How old What
How old What(name) have was(name) was
was the (name) was
11, 14, 17:(name)
waswas(name)
waswas(name) was
was
was
was
difficulty (name)have
REHABILITATION
Does this difficulty
have(name) thehave(name) cause?
have (name) the
have (name) thethe(name) the(with self
difficulty(Code)
reducetheamountofdifficulty whencause? difficulty when
difficulty whencause? difficulty whencause?
when the cause?
when
cause?
care such
communiforthe(Code) hearing, theforthe(Code) rememb the(Code) as) washing difficulty (Code) cating; for the(Code) work [NAME] can do During the pastat home, at work or 12 months,
seeing, difficultyeven ifdifficulty
walking difficulty
ering or difficulty
all over or began?
example difficultyat school?
Yes-1, what measureseven ifbegan?
he/she is began?
orbegan?
concent- began?
dressing, (By bornunderstan began?
are taken toNo-2
feeding, write "00")ding or (By born
he/she is (By born
wearing a (Byclimbing (By born
rating? (By born
improve
toileting
etc?
being
wearing write
hearing bornsteps? If write
If
write(Age inwrite(name)’s
If Answer years)
under- "00")
performance ofglasses? "00")aid? If
write
Answer "00")
Answer "00")code isstood? if
activities?
If Answer (Age in
Answer "00")code is (Age incode is (Age in(Age in
1>>Q17
Answer(Code)
code isyears)
code is(Age in
1>>Q11 years)
1>>Q14 years)
years)
code is1>>Q5
1>>Q8
years)
1>>Q20At
AtHome School

At
Work

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
Code of Question Answer: 2,5,8,11,14 and 17
1-No Difficulty
2-Yes, Some Difficulty
3-Yes, Severe Difficulty
4-Yes, Can't see/hear/

walk/remember/selfcare/communicate at all

CODES FOR 4, 7, 10, 13,16, 19
1 From birth
2 Accident
3 Illness
4 Old Age
5 Malpractice
6 Other, specify

CODES FOR 21
01
09
None
Spiritual/traditional healer
02
Surgical operation 10
Other, specify
03
Medication
Assistive devices (glasses, wheelchair, braces, hearing aid, artificial limbs, stick, etc)
04
05
Special education
06
Skills training (vocational)
Activity of Daily Living (ADL) training
07
Counseling
08

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HIES 2010
SECTION 4: ECONOMIC ACTIVITIES SELF AND WAGE EMPLOYMENT

S
E
R
I
A
L

ID code as in Roster

A
C
T
I
V
I
T
Y

1
What economic activities did (name) do in thepast 12 months? Beside this,What other activities did you do?
Activities: Service/Wage employment/All
activities(agric/non-agric) conducted under
self or joint ownerships.

DESCRIPTION OF ACTIVITY

2
How
many
monthsdid youdo thisactivity inthe last
12months?

OCCUPATION INDUSTRY
CODE

CODE

MONTH

PART A: ACTIVITIES (ALL PERSONS 5 YEARS AND OLDER)

3
4
5
6
7
8
What was major field If you were engaged in agricultural sector, what If you were engaged in non agriculture sector,
On
On
Where did you do thisof economic activities was your employment status?
what was your work status?
average, average
activity?
you engaged in?
how many how manydays per hours per Was it rural or urban area?
month?
day?
>>Part-B>>Part-B
1 Rural1 Agriculture
1 Day labourer
1 Day labourer
2 Urban
2 Non Agriculture
2 Self employed >> Section-7
2 Self employed >>Section-5
WRITE DISTRICT CODE>>Q8
3 Employer>>Section-7
3 Employer>>Section-5>>Part-B>> Part-B
4 Employee
4 Employee
FROM CODE LIST
DAYS/
MONTH

HOURS/DAY

R/U

DISTRICT CODE

A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S

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HIES 2010
SECTION 4: ECONOMIC ACTIVITIES AND WAGE EMPLOYMENTPART B: WAGE EMPLOYMENTDAILY WAGE

S
E
R
I
A
L

ID CODE as in Roster

A
C
T
I
V
I
T
Y

1
Were youpaid on adaily basis?

1
2

SALARIED WAGE

2
3
What was the daily wage in Did you receive
cash in the past 12 months? payments in-kind?

1
2

Yes
No >>Q6

Highest Lowest
1
2

Average3

4
5What type of in- How much did you
kind payment receive per day?
did youreceive?
WRITE THE QUANTITY
Yes
1 Paddy
IN KG AND THE TOTAL
No >>Next Activity/ 2 RiceVALUE IN TAKA.
Next person
3 Wheat
4 Meal5 Other
QUANTITY

VALUE

KG

TAKA

6
What type of org. do/
did you work for?
1 Govt. organization
2 Autonomous body
3 Private office
4 Public mill/ factory
5 Private mill/ factory
6 Local govt
7 NGO
8 Household
9 Other, specify

7What is yourgross
remuneration
per month?

8
What is yourtotal net takehome monthly
remuneration
after alldeduction atsource?

9What is the total value of
in-kind or other benefits
you received over the
past 12 months (tips,
bonuses or transport) for
this job?
>> Next Activity/
Next Person

TAKA

TAKA

TAKA

A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S

Bangladesh HIES 2010 Manual (Version 1.0)

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163

HIES 2010
SECTION 5: NON-AGRICULTURAL ENTERPRISES
E
N
T
E
R
P
R
I
S
E

1
What kind of enterprise did/ do your household
solely or jointly own and operate in the past 12
months?

CROSS CHECK ACTIVITIESREPORTED IN SECTION 4 PART A

2
3
For how long
Which people in
the household work has thisenterprise/in this enterprise/
activity been
activity?
operating?

1
2
3
4

WRITE ID CODE
FROM ROSTER

5

WRITE DESCRIPTION IN FULL
N
U
M
B
E
R

DESCRIPTION OF ACTIVTITY

4
Where do you
operate theenterprise/ activity?

56
7
8
How many What is the What share Who are yourmonths did household' of profit is customers?
thes share of owned byenterprise / thishousehold? 1 Households/
Individuals
activity
enterprise/
Own house
2 Govt. or otheroperate in activity?
Rented house
public firm
the past 12
Govt land/house months?
3 Private
Fixed locationenterprises
outside house
4 Foreign individual
Variable/organization
location
5 Others, specify
WRITE IN
ACCORDING TO
IMPORTANCE

INDUSTRYCODE

ID CODE

YEAR

MONTH

MONTHSPERCENTAGE PERCENTAGE

1st

2nd

9
Is theenterprise/
activity
registered by
the govt. or
local govt.?
1
2
3

10
What was your main
source of finance forsetting up the business?

01 Inherited/ through gift
02 Own savings
03 Borrowing fromYes
relatives/ friends
No
04 Agricultural dev. bankNot
05 Commercial bankapplicable 06 Grameen bank
07 Other financial Inst.
08 NGO/ Relief agency
09 Sale of assets
10 Supplyer's credit
11 Other, specify
1st

2nd

1
2
3
4
5

Bangladesh HIES 2010 Manual (Version 1.0)

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164

HIES 2010
SECTION 5: NON-AGRICULTURAL ENTERPRISESINCOME FROM THE ENTERPRISE [WRITE THE VALUE IN TAKA]

E
N
T
E
R
P
R
I
S
E
N
U
M
B
E
R

11
How many
employees
wereengaged in
the past 12
months?

NUMBER

12
What problems, if any, have you
had in running your business in
the last 12 months?
01 No problem
02 Inadequate capital or credit
03 Inadequate tech. knowledge
04 Lack of required no. of expertise
05 High-cost of running ent.
06 Water supply problem
07 Power supply problem
08 Problems with equipment/
spare parts
09 Government regulations
10 Lack of raw materials
11 Lack of customers
12 Transport problems
13 Other, specify
1

2

13
Gross
revenues overthe past 12
months14
15
16
Expenditures Expenditure Expenditure
on raw
on wages in on rent in
the past 12 materials in
the last 12
monthsthe past 12
monthsmonths17
18
19
Expenditure Expenditure Other
on Fuel,on finished operating
Kerosene, goods
expenses
Electricity, purchased in the pastetc. in thefor reselling 12 months
past 12in the pastmonths
12 months

20
Net
revenues

21
Expenditureon capital
goods inpast 12months22
Sales ofassets inpast 12months23
If someone
wanted to buy
this enterprise
today, howmuch would hehave to pay?

TAKA

TAKA

TAKA

[13-(14+15+16
17+18+19)]

BOTH CASH
AND IN KIND

TAKA

TAKA

TAKA

TAKA

TAKA

TAKA

TAKA

TAKA

1
2
3
4
5

Bangladesh HIES 2010 Manual (Version 1.0)

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5B

HIES 2010

165

SECTION 6: HOUSINGRESPONDENT SHOULD BE HOUSEHOLD HEAD OR OTHER RESPONSIBLE HOUSEHOLD MEMBER

PART-A: HOUSING INFORMATION

1 ID code of the respondent

12 What is the alternative source of drinking water?
1 Supply water 4 Well
2 Tubewell
5 Waterfall/string
3 Pond/river
6 Other, specify

2 How many rooms does your household occupy?
(Excluding rooms for business) Number of rooms:

13 What is the main source of water for other use?
1 Supply water 4 Well
2 Tubewell
5 Waterfall/string
3 Pond/river
6 Other, specify

3 Does your dwelling posses a separate dining room?
1 Yes
2 No
4 Does your dwelling posses a separate kitchen?
1 Yes
2 No

14 Does the household have an electricity connection?
1 Yes
2 No >>Q16

5 What is the construction material of the walls of the main room?
1 Brick/cement
4 Hemp/hay/bamboo
2 C.I. Sheet/wood
5 Other, specify
3 Mud brick

15 How many hours in a day normally have electricity in your house?
hrs.

6 What is the construction material of the roof of the main room?
1 Brick/cement
4 Hemp/hay/bamboo
2 C.I. Sheet/wood
5 Other, specify
3 Tile/wood
7 What is the total usable space/area of covered rooms
SQ FT
8 What type of latrine does the household use?
1 Sanitary
4 Kacha latrine (perm)
2 Pacca latrine (water seal) 5 Kacha latrine (temp)
3 Pacca latrine (pit)
6 Other, specify
9 What is the main source of drinking water?
1 Supply water
4 Well2 Tubewell
5 Waterfall/string
3 Pond/river
6 Other, specify

16 Does this household own a mobile phone?
1 Yes
2 No
Does the household have a landline telephone connection?
17 1 Yes
2 No
18 Does your household own a computer?
1 Yes
2 No
19 Does this household have access to internet/e-mail facilities?
1 Yes
2 No >>Q21
20 How does your household most often access the internet?
1 Mobile phone 4 Community information Center
2 Home computer 5 Other, specify
3 Cybercafe

10 Has your Tubewell been tested for arsenic?
1 Yes
2 No >>Q13

21 What is your present occupancy status?
1 Owner
4 Provided free by relatives/employer
2 Renter
5 Government residence
3 Squatter
6 Other, specify

11 Was arsenic found?
1 Yes

22 If you want to buy or construct a dwelling just like thistoday, how much money would you have to pay?

2 No

Bangladesh HIESPage
2010 Manual
17 (Version 1.0)

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6A

HIES-2010

166

SECTION 6: HOUSING
CodePART-B: SHOCKS AND COPING

1
Description of distress events

101

Drought / Irregular rains

102
103

FloodsLandslides/ Erosion

104
105
106
107
108

110
111
112
113
114
115
116
117

Unusually High Level of Crop Pests and Disease
Unusually High Level of Livestock Disease
Unusually High Prices of Agricultural Inputs
Unusually Low Prices of Agricultural Output
Reduction in the Earnings of currently (Off-Farm)
Employed Household Member(s)Loss of Employment of Previously Employed
Household member(s) (not due to illness/accident)
Serious Illness or Accident of income Earner(s)Serious Illness or Accident of other HH Member(s)Death of Income Earner(s)Death of other HH Member(s)Theft of Money/Valuables/Non Agricultural Assets
Theft of Agricultural Assets/Output(Crop or Livestock)
Conflict/Violence
Fire/Tornedo/Earthquake, etc

118

Other (Specify)

109

2

3

4

Did you
When did theexperienceshock first occur?
[shock] during
the past 12
months?
1-Jan
7-Jul
2-Feb

8-Aug

3-Mar

9-Sep

1-Yes>>Q3

4-Apr

10-Oct2-No >>
Next shock

5-May

11-Nov

6-Jun

12-Dec56How long
As a result of the [SHOCK], was there a decline
did thein your households:
shock last?
(If Shocks
continued till
now write"99")

NUMBEROF DAYS

How did yourhousehold cope withthis [shock]? Up tothree answers withrank for each shock
experienced, use
code below:

IncomeAssetsFood
Production

Foodpurchased

1 Yes

1 Yes

1 Yes

1 Yes

1st

2 No

2 No

2 No

2 No

CODE CODE CODE

2nd

3rd

CODES FOR Q6 - COPING STRATEGIES
01 Unconditional help provided by relatives/friends

09 Obtained credit

02 Unconditional help provided by local government

10 Sold durable household assets

03 Change dietary patterns involuntarily

11 Sold land or building

04 Change cropping practices (crop choices or technology)
05 Household member(s) took on more non-farm (wage or self employment)

12 Rented out land/building
13 Distress sales of animal stock

06 Household members took on more farm ways employment

14 Send children to live elsewhere

07 Household members migrated

15 Reduce expenditure on health and education

08 Relied on savings

16 Other, specify

Bangladesh HIES 2010 Manual (Version 1.0)

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167

HIES 2010
SECTION 7: AGRICULTURE

PART A: LANDHOLDINGWRITE '0' IF NONE.

1

Total cultivable agricultural land owned:

2

Total dwelling-house/Homested land owned:

3

Total Non-cultivated Land

4

Total cultivable agricultural land rented/ share-cropped/ mortgaged in:5

Total cultivable agricultural land rented/ share-cropped/ mortgaged out:

6

Total operating land (1+2+3+4-5)

Acres

DecimalAcres

DecimalAcres

DecimalAcres

DecimalAcres

DecimalAcres

DecimalBangladesh HIES 2010 Manual (Version 1.0)

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7A

168

HIES 2010
SECTION 7: AGRICULTURE
1
Did you or anyone in yourhousehold cultivate any following
crops in the last 12 months?
1 Yes
2 No >> Other Crop part B

Crop/by product

Code

Aus

01

Aman

02

Boro

03

Wheat

04

Maize

05

Jute

06

Sugarcane07

Pulses08

Oil Seed

09

PART B: CROP PRODUCTION

2
3
4
5
6
7
8
Did you
How much
How much in total of How much did How much did How much did How much of it was used for the following purposes in the lastcultivate (crop) land did you crop did you produce your household your household your household 12 months?
in the last 12 cultivatein the last 12
consumed insell in the last stock in the last WRITE TOTAL QUANTITY USED (IN KG)
months?
under thismonths?
the last 12
12 months?
12 months?
crop in themonths?
1 Yeslast 12Given for Used as
Feed forPRODUCED (IN KG) AND
Given to2 No
months?
Waste
Other
Total
landlord
THE UNIT PRICE (IN TK)
Wages
Seed
animals>>Nextkg
kg
kg
Acres Decimalkg
Taka/kg
kg
kg
kg
kg
kg
kg
kg
Crop

By ProductBy product of paddy

10

By product of wheat11

By product of jute12

By product of sugarcane

13

Bangladesh HIES 2010 Manual (Version 1.0)

Page 20

7B1

SECTION 7: AGRICULTURE

PART B: CROP PRODUCTION

169

Other Crops: Write the name of the other crops Cultivated by the household
2 Did you
Did you or any one in your
3 How much 4 How much 5 How much
cultivatehousehold member cultivate
land did you
in total of
did yourany crop in the last 12 months? (crop) in the cultivate under crop did you household
last 12
this crop in
produce in
consume inmonths?
the last 12
1=Yes,
the last 12
the last 12
months?
2=No>> Next
months?
months?

6. How much
did yourhousehold sellin the last 12
months?

7 How much did 8 How much of it was used for the following
your household purposes in the last 12 months?
stock in the last
12 months?

crop

Vegetable
Potato

Code
15

Onion

16

Garlic17

Ginger18

Turmeric

19

Tomato20

Brinjal21

Green Banana/Green Papaya

22

Cauliflower/Cabbage

23

Pumpkin24

Radish25

Green Chili

26

Bean

27

Patal

28

Lady's Finger
Puisak29

Others (Code)

Acres

DecimalKg

Taka/kg

Kg

Kg

Kg

given inlandlord

given forwages

Used asseed

Feed foranimals

Waste

Other

30
31

FruitsMango

40

Jackfruit

41

Ripe Banana

42

Ripe Papaya

43

Pineapple

44

Leechee45

Melon/Bangi

46

Guava

47

Others (Code)48

Bangladesh HIES 2010 Manual (Version 1.0)

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7B2

Total

170

HIES-2010
SECTION 7: AGRICULTURE

PART C: NON-CROP ACTIVITIES (Livestock and poultry)

1. Did you or anyone in your household raise any livestock or poultry birds in the last 12 months?
1 Yes
2 No >> Q9
LIVESTOCK AND POULTRY (Do you or any member of your household)2
How many..[NAME OF
THE ANIMAL]..
own?
What is their
total value?
IF NONE,
WRITE '0'.

Name of domestic
animals

AnimalCode

NumberTaka

3
4
How many were born/ did
How many died/ didyour household
your household sellpurchase in the lastin the last 12 months?
12 months?
WRITE TOTAL NO.
WRITE TOTAL NO.
AND THE TOTALAND THE TOTALVALUE IN TAKAVALUE IN TAKA

NumberTaka

NumberTaka

LIVESTOCK PRODUCTS

5
How many did
your household
consume in the
12 months?
WRITE TOTAL NO.
AND THE TOTALVALUE IN TAKA

NumberTaka

6
7
8
How much did How much did
How much did
you produce
you sell
you consume inin the lastin the last 12
the last 12
12 months?
months?
months?
WRITE TOTALWRITE TOTALQUANTITY AND QUANTITY AND
VALUE IN TAKA VALUE IN TAKA

Animal products and byproductAnimal product

Code

Unit

Cattle

201

Beef

211

Kg

Goat

202

Chicken

212

Kg

Sheep

203

Mutton213

Kg

Buffalo

204

Milk

214

Lt

Chicken

205

Eggs

215

No

Duck

206

Animal skins

216

No

Pigeon207

Cow dung

217

Kg

Other domestic Birds

208

Other

218

Other (specify)

209

Total value

220

Total value

210

Bangladesh HIES 2010 Manual (Version 1.0)

Page 22

Quantity

Taka

Quantity

Taka

Quantity

Taka

7C1

171

HIES-2010
SECTION 7: AGRICULTURE

PART C: NON-CROP ACTIVITIES

9. Did you or anyone in your household engage in any fishing or fish farming in the last 12 months?
1
Yes
2
No >> Q13

13. Did you or anyone in your household engage in any farm forestry in the last 12 months?
1
Yes
2
No >> Part D
FARM FORESTRYFISH FARMING AND FISH CAPTURE

10
How much did you
produce (catch) in
the past 12 months?
WRITE TOTALQUANTITY AND
VALUE IN TAKA

Source

Code

Kg

Taka

11
How much did
your household
sell in the past
12 months?
WRITE TOTALQUANTITY AND
VALUE IN TAKA

kg

Taka

12
How much did
your household
consume in the
12 months?
WRITE TOTALQUANTITY AND
VALUE IN TAKA

kg

14
How many trees doyou presently own?
What is their total
value?

Forest Product

Taka

Tree

Code

Fish farm

221

Bamboo231

Fish Hatchery

222

Timber trees

232

Marine fishing

223

Fruit trees

233

Canal/river fishing224

Firewood trees

234

Swampland/marsh/fen fishing

225

Honey

235

Pond/sink fishing226

Sapling/seedling sale

236

227

Flower sale237

Dry fish229

Flower /fruit seed sale

238

Total value

230

Other, specify

239

Total value

240

No of trees

Taka

15
16
How much did How much did
your household your household
sell in the lastconsume in the
12 months?
12 months?
WRITE THE
WRITE THETOTAL VALUETOTAL VALUE

Taka

Taka

Fish fry, crab frog and Other, specify

Page
23 (Version 1.0)
Bangladesh HIES2010 Manual

7C2

172

HIES 2010
SECTION 7: AGRICULTURE

PART D: EXPENSES ON AGRICULTURAL INPUTS
1
Did your householdspend any money onthe (item) in last 12
months?

Expenditure item and code2
How much did your householdspend on the (item) in the last 12
months?

1 Yes
2 No >> NEXT ITEM
Items of expenditure

Code

Seed seedling (crop seedling)

301

Seed (forest seedling)302

Fertilizer (chemical)

303

Fertilizer (compose/mix)

304

Food of livestock/ draft animal

305

Tractor/ tiller/ power tiller (rental)

306

Irrigation expenses

307

Insecticides

308Land revenue (agricultural land)

309

Rent (agricultural land)

311

Carrying cost of goods and transportation expenses (agricultural goods)

312

Salary/wages of laborer employed in agriculture

313

Insurance expenses (agriculture related)

314

Interest of the Agriculture loan

315

Electricity and fuel cost

316

Bees culture expenses

317

Fish production expenses

318

Livestock rearing expenses (Treatment cost etc.)

319

Poultry rearing expenses (Treatment cost etc.)

321

Other

322

Total value

330

Kg

Bangladesh HIES 2010 Manual (Version 1.0)

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Taka

7D

173

HIES 2010
SECTION 7: AGRICULTURAL ENTERPRISES

Equipment & assets used inagriculture

Farming asset

Code

Tractor

401

Thresher

402

Power tiller

403

Power pump

404

Hand pump

405

Plough and yoke

406

Deep tube-well407

Shallow tube-well

408

Sprayer409

Husking machine

411

Ginning machine

412

Country boat

413

Engine boat

414

Fishing net

415

Cage incubator

416

Brooder

417

Bees-box

418

Other (specify)

419

Total value

420

PART E: AGRICULTURAL ASSETS

1
How many ..[NAME OF ASSET]..
do you presently own?
What is their total value?

NumberTaka

2
How many did your household
buy in the last 12 months?

3
How many did your household sellin the last 12 months?

TOTAL NUMBER AND

TOTAL NUMBER AND

THE TOTAL VALUE IN TAKATHE TOTAL VALUE IN TAKA

NumberTaka

NumberBangladesh HIES 2010 Manual (Version 1.0)

Page 25

Taka

4
How much did your household
earn from rental of this itemin the last 12 months?
WRITE TOTAL VALUE IN TAKA
Taka

7E

174

HIES 2010
SECTION 8: OTHER ASSETS AND INCOME

PART A: OTHER PROPERTY AND ASSETS
Acres

Decimals

1 Is there any land or property which your household owns
but doesn't operate?
(don't include property already reported in sections 5, 6 & 7)
1 Yes
2 No
>> Q3
2 How much would it cost to buy land or property owned
by your household?

TAKA

3 Did your household purchase any land or property
during the last 12 months?
1 Yes
2 No
>> Q5
4 How much did your household spend on purchasing thisland or property?

TAKA

5 Did your household purchase any house or flat over the
past 12 months?
1 Yes
2 No

>> Q7

6 How much did your household spend on purchasingthat house or flat?

TAKA

7 Does your household own any other assets (e.g. stocks,
bonds, other financial assets, jewelry, etc. not reported
earlier)
1 Yes
2 No
>> Q9
8 How much in total are these worth?

TAKA

9 Did your household purchase any assets other than land over the past 12 months?
1 Yes
2 No
>> Q11
10 How much did your household spend on purchasing these assets?

TAKA

11 Did your household sell any assets over the past 12 months?
1 Yes
2 No
>> Part B
12 How much did your household get from selling these assets?

TAKA

Bangladesh HIES 2010 Manual (Version 1.0)

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175

SECTION 8: OTHER ASSETS AND INCOME
1 Income from rent of land received during the past TAKA
12 months:

2 Income from rent of other property received
during the past 12 months:

TAKA

3 Social and insurrance (life and non-life) income
received during the past 12 months:
Life Insurance TAKA

HIES 2010

PART B: OTHER INCOME
6 Charity, gift, royalty, help, zakat, fitra or othersuch assistance, etc. received during the past
12 months: IN CASH

TAKA

7 Charity, gift, royalty, help, zakat, fitra or othersuch assistance, etc. received during the past
12 months: IN-KIND

TAKA

TAKA
8 Remittances received from relatives during thepast 12 months:(CASH AND IN-KIND FROM WITHIN THE COUNTRY)

Health Insurance TAKA

General Insurance TAKA

4 Profit and dividend received as partner/ shareholder during the past 12 months:

TAKA(DO NOT INCLUDE INCOME ALREADY REPORTED IN SECTION 5)

5 Lottery/ prize bond/ other similar income received TAKAin cash or in-kind during the past 12 months:

TAKA
9 Remittances received from relatives during thepast 12 months:(CASH AND IN-KIND FROM OUTSIDE THE COUNTRY)
10 where did you invest/spend the received money?
1. Construction 2. Business 3. Ecucation 4. Marriage
5. Consumption 6. Treatment 7. Others (Specify)
11 Gratuity,separation payment, retirement benefit
received during the past 12 months:
12 Interest received during the past 12 months:(FROM BANKS AND OTHER SOURCES)

TAKA

TAKA

13 Other cash or in-kind receipts during the past 12
TAKA(DO NOT INCLUDE RECEIPTS REPORTED ELSEWHERE)

Bangladesh HIES 2010 Manual (Version 1.0)

Page 278B

SECTION 8: OTHER ASSETS AND INCOME

HIES-2010

PART C: MIGRATION
AND REMITTANCE
176

1 Has any member of your household migrated, either within the country or abroad, during the last 5 years?
1- Yes >>Q3, 2-No
2 Has this household received any remittances from anyone living outside the household, either within Bangladesh or abroad, in the last 12 months?
1-Yes
2- No >> Part D
3
Name
P
E
R
S
O
N

4
Relationship tohead ofthe H.H.

5
When did(name)
migrate?

6
Where is(name)
working?

78
9
10
If inIf
Age Sexcountry, abroad,write zila write
code.
country
code.

11
12
Level of Occupation
education
Literacy
SEE
SECTION 4
FOR CODE

I
D

CODE
91

1 In country
2 Abroad>>Q8
MONT YEAR
H

1
2

>>Q9

Male
Female

CODE

CODE

13
14
15
16
17
How
What is How did Write the code of What ismany
the total (name) any goods/
the total
times
amount send
things thatvalue of(name)
of money money (name) has sent all the
send
that
to theto the household goods/
money(name)
house- in the last 12
thingsduring the has sent hold?
months.
thatlast 12over the(name)
months? last 12
sent inmonths?
the last
LIST UP TO 3 IN
12
ORDER OFmonths?
IMPORTANCE
CODE

TAKA

CODE

1

CODE
2
3

TAKA

92
93
94
95
96
97
98
99
CODES FOR Q4 - RELATIONSHIP TO HOUSEHOLD HEAD

CODES FOR Q16 - ITEMCODES FOR Q8 - COUNTRY CODE

1 Food

01

Head

08

Niece/Nephew

01 Saudi Arabia

10 United Arab Emirates

02

Husband/ wife

09

Father/Mother-in-law

02 Qatar

11 Canada

19 Sweden

2 Electronic Goods (TV, VCD, etc.)

03

Son/Daughter

04
05
06
07

Spouse of Son/Daughter
Grandchild
Father/Mother
Brother/Sister

10
11
12
13
14

Brother/Sister-in-law
Other relative, specify
Servent
Employee
Other, specify

03 Kuwait
04 Oman
05 Malaysia
06 Singapore
07 Iraq
08 Iran
09 Libya

12 Australia
13 U.K.
14 USA
15 Korea (South)
16 Japan
17 Turkey
18 Germany

20 Federation of Russia
21 Italy
22 Other European Country
23 Brunai
24 Mauritius
25 South Africa
26 Other, specify

3 Mobile Phone
4 Computer
5 Vehicle
6 Clothing
7 Household Appliance
8 Other, specify

CODES FOR Q11 - EDUCATION CODES
00 No class passed
06 Class 6

10 SSC/equivalent

15 Engineering

02 Class 2

07 Class 7

11 HSC/equivalent

16 Vocational

1 Once4 Four times

Western Union

03 Class 3

08 Class 8

12 Graduate/equivalent

17 Technical Education

2 Twice

5 Once a month

Money Gram

6 Through Travel Agency

04 Class 4

09 Class 9

13 Post Graduate/equivalent

18 Nursing

3 Three times 6 Never send

Postal Money Order

7 Agent/Broker

14 Medical

19 Other,specify HIES 2010 Manual (Version 1.0)7 Other (Specify)
Bangladesh

Through Bank

8 Other, specify

05 Class 5

CODES FOR Q13 - FREQUENCYCODES FOR Q15 - METHOD

01 Class 1

Page-28

5 Through Friend/Relatives

8C

SECTION 8: OTHER ASSETS AND INCOME

HIES-2010

PART D: MICRO CREDIT
177

Respondent: Head of household
12
3
Have you or any member of your Have you or any member of your household deposit
Have you or any member of yourhousehold open a bank accountmoney in the credit or microfinance institution (BRAC, household deposit money in any
during the last 12 months?
Grammeen Bank, etc) in the last 12 months?
informal depositor organisation in
the last 12 months?
1 Yes
2 No

1 Yes
2 No

4
Have you or anyone in your householdborrowed money from a family member,friend, micro finance institution, bank orother source in the last 12 months?

1 Yes
2 No

1 Yes
2 No >>Section 9

INTERVIEWER: Use a separate line for every line, put in the ID code of the person who borrowed the money.

ID CODE in Roster

L
O
A
N

5
What wasthe sourceof this loanor credit?

6
7
8
How much How long is Interest rate applied on loan
money was the(%)
borrowed repayment(in taka)? period (inmonths)?

FILL IN ONLY MONTH ORONLY YEARUSE
CODES.

CODE

9
How often do you makepayments and what is theamount of each payment?

10
Have youcompletedrepaymentof your
loan?

11
12
Amount of What was
unpaid loan the purpose(in taka)? of receiving
loan?

1 Yes

USE
CODES.

2 No>>Q12

TAKA

NUMBEROF
MONTHSMONTHLY
RATE (%)

YEARLYRATE (%)

FREQUENCY AMOUNT
CODE
TAKA

TAKA

13
14
Yes-1
Would you like to Are you
have borrowed
willing tomore money at the take moresame rate ofloan at theinterest?
same rate ofinterest andhow much?
1 Yes
2 No >> Next
person

CODE

TAKA

20
21
22
23
24
25
26
27
28
CODES FOR Q9 - FREQUENCY OF PAYMENTCODES FOR Q5 - SOURCE OF LOAN OR CREDIT

CODES FOR Q12 - PURPOSE OF LOAN

01 Private Commercial Bank

09 BRAC

17 Money Lender1 Once1 Education

02 Public Commercial Bank

10 BRDB

18 Land Lord

2 Daily

2 Health

03 Krishi Bank/Rajshahi Krishi Bank

11 Other Govt. Department

19 Employer

3 Weekly

3 Agriculture

04 Co-operative Bank

12 ASA

20 Friends

4 Fortnightly

4 Business

05 Co-operative association

13 Proshika

21 Relatives

5 Monthly

5 Housing06 BSIC
07 Youth Development

14 Other NGO
15 Other Micro Finance Establishment

22 Grocery Store
23 Other, specify

6 Yearly
7 Other, specify

6 Food Purchase
7 Marriage

08 Grameen Bank

16 Input supplier

Bangladesh HIES 2010 Manual (Version 1.0)

Page-29

8 Other, specify

8D

178

HIES 2010
SECTION 9: CONSUMPTION
Less than 10 years old

No. of boys
No. girls

PART A: DAILY CONSUMPTION

10 years and older

No. of men
No. of women

Date (Date/ Month/ Year)
Quantity

DAY 1

2

3

Food grains

010

Rice - Fine

011

Rice - Medium

012

Rice - CoarsePs

1. Purchase
2. Wage in-kind
3. Self-prod

Value
Quantity

DAY 1

4. GiftTaka

1

1. Purchase
2. Wage in-kind
3. Self Prod.

Value

4. Gift

Major Source

Taka

Dried fish

052

gm

gm

Tangra/ Eelfish

053

gm

gm

sea fish

054

gm

013

gm

Baila/ Tapashi

055

gm

Beaten rice

014

gm

Other (specify)

056

gm

Pop rice

015

gm

Eggs

060

Puffed rice

016

gm

Hen egg

061

No

Wheat (Atta)

017

gm

Duck egg

062

No

Flour

018

gm

Other (specify)

063

No

Vermicelli/ Suji

019

gm

Meat

070

Bread/ Bonroti

021

gm

Beef

071

gm

Biscuits

022

gm

Buffalo

072

gm

Cake

023

gm

Mutton073

gm

Other (specify)

025

gm

Sheep

074

gm

Pulses

030

Hen

075

gm

Lentil (musur)

031

gm

Duck

076

gm

Chickling-Vetch (mug)

032

gm

gm

Green gram (boot)

033

gm

Pea gram (kheshari)034

Mashkalai

035

Other (specify)

036

Fish

040

Hilsa

041

Rhui/ Katla/ Mrigel/ Kal baush

042

Pangash/ Boal/ Air

4

5

Other (specify)

077

Vegetables

080

gm

Potato

081

gm

gm

Brinjal

082

gm

gm

White gourd/ Pumpkin

083

gm

Water gourd

084

gm

gm

Balsam apple

085

gm

gm

Perbol (Patal)

086

gm

043

gm

Snake gourd/ Ribbed gourd

087

gm

Kai/ Magur/ Shinghi/ Khalisha

044

gm

Green banana/ Green papaya

088

gm

koi

045

gm

Arum/ Ol-kachu/ Kachur-mukhi

089

gm

Silver carp/ Grass carp/ Miror carp

046

gm

Cauliflower/ Cabbage

091

gm

Shoal/ Gajar/ Taki

047

gm

Bean/ Lobey

092

gm

Puti/ Big Puti/ Telapia/ Nilotica

048

gm

Tomato093

gm

Mala-kachi/ Chala-chapila

049

gm

Radish094

gm

Shrimp051

gm

Ladies' finger

095

gm

(All types of leafy veg.(Spinach/ Amaranta/ Basil)

096
097

gm
gm

6

Other (specify)
Bangladesh HIES 2010 Manual (Version 1.0)

Page 30

Ps

Major Source

9A

SECTION 9: CONSUMPTIONPART A: DAILY CONSUMPTION
Quantity

Value

DAY 1
Taka

7

8

9

1. Purchase
2. Wage in-kind
3. Self-prod
4. Gift
Ps

179

Value

1. Purchase
2. Wage in-kind
3. Self-prod
4. Gift

Quantity

DAY 1

Major

Taka

Milk & Dairy

100

Liquid milk

101

ml

Soft drinks(peepsi/RC/Mojo/Coke, Sherbat, etc.

151

ml

Powder milk
Curd

102
103

gm

Ovaltine/ Horlicks
Tea/ Coffee leaf

152
153

gm
gm

Casein (ponir)/ Butter

104

gm

Liquid (Ros) of Sugarcane/ Date/Palm

154

ml

Milk drinks
Other (specify)

105
106

ml

Green coconut water
Other (specify)

155
156

ml

11 Drinks

12 Sugar & molasses

160

Sweetmeat

110
111

gm

Sugar/ Misri

161

gm

Jilapi/ Bundia/ Amriti

112

gm

Molasses (Sugarcane/ Date/ Palm)

162

gm

Halua/ Batasha/ Kadma
Other (specify)

113
114

gm
gm

Khaja/ Logenze/ Toffee
Chocolate

163
164

no
no

Oil & Fats
Mustard oil

120
121

Ice-cream
Other (specify)

165
166

no

gm

Soybean oil

122

gm

13 Miscellaneous Food

170

Dalda/ Vanashpati

123

gm

Pickles

171

gm

124
125

gm
gm

Jelly/ Jam
Amshatta

172
173

gm
gm

130
131

gm

Sauce/Sirka
Other (specify)

174
175

gm

Ripe banana
Mango

132

gm

Melon/ Bangi

133

gm

Meals(Rice/Biriani)

181

Jack fruit

134

gm

Fish

182

gm

Leeches

135

gm

Meat

183

gm

14 Dining out (Food outside)gm

Ripe papaya

136

gm

Patties/Cake

184

gm

137

gm

Sandwich

185

gm

Pineapple

138

gm

Burger186

gm

Safeda139

gm

Hotdog

187

gm

Palm

141

gm

Pizza

188

gm

Bedana142

gm

Samucha/Singara/Puri/Cake

189

gm

Apple

143

gm

Tea

191

cup

Orange144gm

Coffee

192

cup

Grape
Black berry

145
146

gm
gm

Soft drinks/bottle water
Other

193
194

ml

Amra/Kamranga
Others

147
148

gm
gm

200
201

no

Tobacco leaf

202

gm

BidiesGul and Other (specify)

203
204

no

Bangladesh HIES 2010 Manual (Version 1.0)

Page 31

Major

180

Guava

15 Tobacco & tobacco products
Cigarette

Ps

150

Rasogolla/ Chamcham/ Shandash

Ghee
Other (specify)
10 Fruits

HIES 2010

9A2

180

HIES 2010
SECTION 9: CONSUMPTIONPART B: WEEKLY CONSUMPTION
Date (Date/ Month/ Year)
Quantity

DAY 1-7

Value
TAKA

16 Spices

210

Dried chili

211

gm

Green chili

212

gm

Onion

213

gm

Garlic214

gm

Turmeric

215

gm

Salt

216

gm

Ginger217

gm

Cummins

218

gm

Coriander-seed

219

gm

Aromatic-seed

221

gm

Clove/ Black pepper/ Cassia-leaf

222

gm

Other (specify)

223

gm

17 Betel leaf & Chewgoods

PAISA

1. Purchase
2. Wage in-kind
3. Self-prod
4. Gift
Major

230

Betel leaf

231

gm

Betel nut

232

gm

Zorda/ tobacco leaf

233

gm

Lime

234

gm

Khoer

235

gm

Rolled betel leaf
Other (specify)

236
237

gm
gm

Bangladesh HIES 2010 Manual (Version 1.0)

Page 44

9B1

181

HIES 2010
SECTION 9: CONSUMPTIONPART B: WEEKLY CONSUMPTION
Date (Day/ Month/ Year)
Quantity

DAY 8-14

Value
TAKA

16 Spices

210

Dried chili

211

gm

Green chili

212

gm

Onion

213

gm

Garlic214

gm

Turmeric

215

gm

Salt

216

gm

Ginger217

gm

Cummins

218

gm

Coriander-seed

219

gm

Aromatic-seed

221

gm

Clove/ Black pepper/ Cassia-leaf

222

gm

Other (specify)

223

gm

17 Betel leaf & Chewgoods

PAISA

1. Purchase
2. Wage in-kind
3. Self-prod
4. Gift
Major

230

Betel leaf

231

gm

Betel nut

232

gm

Zorda/ tobacco leaf

233

gm

Lime

234

gm

Khoer

235

gm

Rolled betel leaf

236

gm

Other (specify)

237

gm

Bangladesh HIES 2010 Manual (Version 1.0)

Page 59

9B2

182

HIES 2010
SECTION 9: CONSUMPTIONPART C: MONTHLY NON-FOOD EXPENDITURE
1What was the value of goods
consumed that were bought incash/ credit or wages in-kind?

2
What was the value of goods
consumed that were producedby the household or received

Taka
18 FUEL AND LIGHTINGSUM OF COLUMN 1 AND 2

Taka

240

Firewood241

Cow dung/cakes/bhushi/wood-powder

242

Jute stick243

Kerosene

244

Agri by products fuel: paddy, hag, pressed sugarcane anddried com plants, etc.

245

Gas (natural, bio-gas)/LPG

246

Electricity

247

Pit coal, char coal, wood coal

248

Other fuels and light (e.g. matches and candles etc.)249

19 COSMETICS AND OTHER EXPENSES

Taka

3

250

Snow, cream, powder

251

Perfume etc.

252

Hair cutting, styling, shaving, etc.

253

Hair oil, hair cream, combs, clips, etc.

254

Razor, razor blades, shaving cream and lotions, etc.

255

Lipstick, nailpolish, etc.

256

Beautifying items (hair ribbon, churi, kajal, etc.)

257

20 WASHING AND CLEANING EXPENSES

260

Bath soap, shampoo, toothpaste, etc.

261

Washing soap, powder for cloths

262

Washing/ laundry expenses

263

Bleaching powder, soda etc.

264

Vim/ dish cleaning supplies

265

Bangladesh HIES 2010 Manual (Version 1.0)

Page 60

9C1

183

HIES 2010
SECTION 9: CONSUMPTIONPART C: MONTHLY NON-FOOD EXPENDITURE
1What was the value of goods
consumed that were bought incash/ credit or wages in-kind?

2
What was the value of goods
consumed that were producedby the household or received

Taka
Finis/ phenyl/ other household cleaning supplies

266

Toilet papers

267

Mosquito coil

268

Mosquito spray269

21 TRANSPORT/ TRAVEL AND OTHER MISC. CHARGES

Taka

3

SUM OF COLUMN 1 AND 2

Taka

270

Bus fare271

Rickshaw/ van fare272

Taxi/ tempoo/ mishuk fare273

Boat/ launch fare274

Train fare275

Other transport fare (specify)

276

Bicycle maintenance, tyres, tubes repairs etc.

277

Motor-cycle maintenance, repairs, etc.

278

Car maintenance, repairs, etc.

279

Boat maintenance, repairs, etc.

281

Petrol282

Diesel283

Motor oil/CNG. etc

284

Other transport, repair and maintenance.

285

Telephone bill/ charges/mobile286

Telegram, postal and courier service expenses, etc.

287

Salaries and wages of servants

288

Salaries and wages of drivers

289

Salaries and wages of others including guards, gardeners,
housekeepers etc.

291

Other contegencies expenses (specify)

292

Bangladesh HIES 2010 Manual (Version 1.0)

Page 61

9C2

HIES 2010

184

SECTION 9: CONSUMPTIONPART D: ANNUAL NON-FOOD EXPENDITURE
1
2
How many did you
What was the value
buy/produce atof these goods?
home/receive as gift?

Expenditure/ consumption during the past
12 monthNo

22 READY-MADE GARMENTS

300

1
2
How many did you
What was the value
buy/produce atof these goods?
home/receive as gift?

Expenditure/ consumption during the past
12 month

Taka

No

23

CLOTHING MATERIAL AND TAILORING330

For adult:
Lungi/dhuti
Shirts301

Mill-made cloth/ fabric

331

302

Hand loom cloth/ fabric

332

Pant

303

Drill and other cloth for trousers

333

Saree

304

Woolen cloth

334

Blouse/ peticoat

305

Silk

335

Salwar kameez/ Orna

306

Artificial silk etc.

336

Punjabi/ Pajamas

307

Other artificial-yarn made cloths

337

Suit, overcoat, ashkan, etc.
Sweaters, Jacket, pullovers,
mufflers, etc.
Underwear etc.
Socks, handkerchiefs, scarves,
caps, neckties etc.
Other (specify)

308

Tailoring expenses

338

309

Other clothing related expenses

339

Taka

311
312

24 FOOTWEAR

340

313

Leather shoes

341

For children:

314

Leather sandal-shoes

342

Full pant315

Leather sandal

343

Half pant

316

Plastic shoes

344

ShirtsT-shirt
Frocks, dresses, babysuit etc.
Socks, handkerchiefs, scarves,
caps, neckties etc.
Sweaters, Jacket, pullovers,
mufflers, etc.

317
318
319

Plastic sandal-shoes
Other sandal
Canvas shoes, sports shoes, etc.

345
346
347

321

Wooden sandals

348

322

Shoe brush, polish, cleaning supplies, etc.

349

Other for children (specify)

323

Maintenance and repair expenses of foot wear

351

Both
Towel, Gamcha
Chador, shawl, etc.

Other expenses regarding footwear

352

324
325

Other (specify)

326

Bangladesh HIES 2010 Manual (Version 1.0)

Page 62

9D1

185

HIES 2010
SECTION 9: CONSUMPTIONPART D: ANNUAL NON-FOOD EXPENDITURE
1What was the value of goodsbought/ produced at home/received as gift?

Expenditure/ consumption during the past 12 months

1
What was the value of goodsbought/ produced at home/received as gift?

Expenditure/ consumption during the past
12 monthTaka

25 HOUSEHOLD-USE TEXTILES, ETC.

360

Taka

26 HOUSING RELATED EXPENSES

361

House rent (rented house)

381

Toshok362

Imputed rent (owner-occupied or other)

382

Jajim

363

Water/ sewerage charges

383

Foam sheet

364

Home additions/ improvements

384

Bedsheets

365

Painting

385

Bed cover

366

Disaster-related maintenance/ repair

386

Pillows, cushions

367

Other routine maintenance/ repair

387

Pillow cover, cushion cover

368

Municipal tax

388

Table cover

369

Other related services/ expenses

389

Curtain

371

Mosquito netting

372

Other (specify)

373
28 MEDICAL TREATMENT EXPENSES (FEMALE)

27

380

Quilt/blanket/Katha

410

MEDICAL TREATMENT EXPENSES (MALE)

390

Doctor's fees

411

Doctor's fees

391

Other practitioner's fees (homeopath etc.)

412

Other practitioner's fees (homeopath etc.)

392

Medicines

413

Medicines

393

Ayurvedic/ Kbiraji

414

Ayurvedic/ Kbiraji

394

Medical Tests (X-ray, blood, urine etc.)

415

Medical Tests (X-ray, blood, urine etc.)

395

Hospitalization, clinic charges, etc.

416

Hospitalization, clinic charges, etc.

396

Dental related expenses

417

Dental related expenses

397

Spectacles

718

Spectacles

398

Hearing aids

419

Hearing aids

399

Crutches421

Crutches

401

Maternity expenses

422

Health-related travel/ incidental expenses

402

Health-related travel/ incidental expenses

423

Bangladesh HIES 2010 Manual (Version 1.0)

Page 63

9D2

186

HIES 2010
SECTION 9: CONSUMPTIONPART D: ANNUAL NON-FOOD EXPENDITURE

Expenditure/ consumption during the past 12 months

1
What was the value of goodsbought/ produced at home/received as gift?

1
What was the value of goodsbought/ produced at home/received as gift?

Expenditure/ consumption during the past
12 monthTaka

29 EDUCATIONAL EXPENSES (MALE)

31

430

Taka

30 EDUCATIONAL EXPENSES (FEMALE)

440

Registration fees

431

Registration fees

441

Examination fees

432

Examination fees

442

Annual fees

433

Annual fees

443

School fees

434

School fees

444

Personal Teaching expenses

435

Personal Teaching expenses

445

Text book/ note books/ stationary

436

Text book/ note books/ stationary

446

Hostel Expenses

437

Hostel Expenses

447

Other

438

Other

448

REMITTANCES, CEREMONIES,GIFTS, ETC.

450

Remittances to others living separately

451

Books, newspaper, magazines, story books

471

Zakat

452

Cinema

472

Fitra

453

Theater

473

Donation/ Sadqa

454

Variety shows, concerts, etc.

474

Qurbani455

Sporting expenses, club membership fees, etc.

475

Religious functions (milad etc.)

456

Video cassette purchases and rental etc.

476

Expenditure on Hajj

457

Audio cassette purchases etc.

477

Expenditure on Pilgrimage

458

Photography

478

Expenditure on marriage

459

TV/ video/ satelite license fees, etc.

479

Expenditure on births

461

Other recreation, marriage day/birthday, tourisum & leisure
related expenses

481

Expenditure on deaths

462

Other (specific)

463

32 RECREATION & LEISURE, ETC.

Bangladesh HIES 2010 Manual (Version 1.0)

Page 64

470

9D3

187

HIES 2010
SECTION 9: CONSUMPTIONPART D: ANNUAL NON-FOOD EXPENDITURE
1What was the value of goodsbought/ produced at home/received as gift?

Expenditure/ consumption during the past 12 months

33 TAXES, INTEREST, FINES, ETC.

490

1What was the value of goodsbought/ produced at home/received as gift?

Expenditure/ consumption during the past 12 Month

36 PERSONAL ARTICLES520

Income tax

491

Gold Jwelry

521

Bank interest charge, Payment of banking charge

492

Silver Jwelry

522

Fines

493

Imitation Jwelry

523

Legal practitioner fees

494

Purses/ money bags

524

Other legal expenses

495

Vanity bags

525

Property registration fees

496

Umbrella, walking stick

526

Other (specify)

497

Tie-pin, cigarette cases, lighter etc.

27

Wrist watch/ clock etc.

528

Other personal use items (belts, etc>)529

34 COOKING EQUIPMENT

500

Glass/china/clay plates and dishes etc.

501

Refrigerators

502

Stove (electric/ gas/ kerosene)

503

Radio

531

Pots/ pans504

Two-in-one

532

Other kitchen ware and utensils

505Black & White Television

533

spoons/ forks/ knives etc.

506

Colored Television

534

Micro oven/Pressure cooker

507

VDO game set

535

Others508

VCD/ VCR/dish antenna/cable membership fees

536

Washing machine, iron, etc.

537

510

Guitar/ orchestra/ harmonium

538

Khat/ chokey

511

Typewriter, personal computer etc.

539

Chai/ table/ dresing table etc.

512

Lenten/ chimney lamp etc.

541

Sofa

513

Electric fans, air-conditioners, coolers, etc.

542

Wooden/ steel almira/ wardrobe/ bookself

514

Cameras/ camcorders/vedio camera

543

Trunks, suitcases, etc.

515

Furniture repair/ polish etc.

516

Other furniture

517

35 FURNITURE & RELATED PARAPHERNALIA

37 MISC. HOUSEHOLD DURABLE

38 INSURANCE EXPENDITURE

530

550

Life insurance

551

Health insurance

552

General insurance

553

Bangladesh HIES 2010 Manual (Version 1.0)

Page 65

9D4

HIES 2010

188

SECTION 9: CONSUMPTIONPART E: INVENTORY OF CONSUMER DURABLE GOODS

1
Does you household own any of the following items?

2
How many ..[ITEM].. Does
your household own?

PUT A CROSS MARK (X) IN THE APPROPRIATE BOX
FOR ALL ITEMS IF THE ANSWER IS YES
Item

No

Yes

4
If it is procured within the last 12 months,
How much it costs?

OF THE ITEM OWNED
Code

Radio
Two-in-one, Cassette player
Camera/ camcorder

561
562
563

Bicycle564

Motorcycle/ scooter

565

Motor car etc.

566

Refrigerator or freezer

567

Washing machine

568

Fans
Heaters569
571

Television
VCR/ VCP/DVD

572
573

Dish antena/ decoder

574

Pressure lamps/ petromax

575

Sewing machine

576

Bedroom Furniture

577

Drawing room Furniture
Dining room Furniture
CarpetKitchen Items - Cutlery
Kitchen Items - Crockery
Mocrooven/Kitchen Items - Cooking
Tubewell (for drinking water only)

578
579
581
582
583
584
585

Wrist watch/Wall clock
MobileComputer/TV Card

586
587
588
589
600

Boat/Others
Total

3
If you wanted to sell this/these..[ITEM].. Today, how muchmoney would you receive

No

Taka

Bangladesh HIES 2010 Manual (Version 1.0)
Page 66

Taka

9E

189

SECTION 1:

HOUSEHOLD INFORMATION ROSTER
1

LIST THE NAME OF
EACH AND EVERY HOUSEHOLDMEMBERSTHEN GO TO SECTION 1 PART A
AND ASK THE QUESTIONS

SEX

AGE

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

Bangladesh HIES 2010 Manual (Version 1.0)

190

Attachment 2

Data dictionary### rt001.dta ###1 stratum
Stratum
2 psu
Primary Sampling Unit
3 hhold
Household Number
4 team5 term6 region
Division
7 district
District code
8 thanaUpazila9 unionUnion code
10 mouza
Mouza Code
11 spcRural/Urban(Municipality+SMA)
12 urbanrur
13 wgtWeight14 s06a_q01
id code of the respondent
15 s06a_q02
How many rooms occupy?
16 s06a_q03
Your dwelling possesa separate dining
17 s06a_q04
Your dwelling possesa separate kitchan
18 s06a_q05
Material of the walls
19 s06a_q06
Material of the roof
20 s06a_q07
Total usable space (Sq.feet)
21 s06a_q08
What type of latrine?
22 s06a_q09
Main sousce of drinking water
23 s06a_q10
Tubwell tested for arsenic
24 s06a_q11
Was Arsenic found?
25 s06a_q12
Alternative source of drinking water
26 s06a_q13
Source of water for other use
27 s06a_q14
Electricity connection
28 s06a_q15
How many hours you have electicity?
29 s06a_q16
Mobile phone
30 s06a_q17
Landline phone connection
31 s06a_q18
Coumputer
32 s06a_q19
Access to internet
33 s06a_q20
How you access the internet?
34 s06a_q21
Present occupancy ststus
35 s06a_q22
Amount you need to buy/build such house
36 s07a_q01
Total cultivable land owned. decimals
37 s07a_q02
total homestead land? decimals
38 s07a_q03
Total not uncultivated land? decimals
39 s07a_q04
Total agriculture land rented out? decimals
40 s07a_q05
Total cultivable land rented out? decimals
41 s07a_q06
Total operating land? decimals
42 s07b_q01
Did any member cultivated any land for crop...
43 s07c_q01
Household raise any livestock
44 s07c_q09
Household engage in any fishing
45 s07c_q13
Household engage in any farm forestry
46 s08a_q01
Own any unused land?
47 s08a_q_1
Amount of unused land? decimals
48 s08a_q02
present price of your own land?
49 s08a_q03
household purchased any land in last 12 months

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50 s08a_q04
51 s08a_q05
52 s08a_q06
53 s08a_q07
54 s08a_q08
55 s08a_q09
56 s08a_q10
57 s08a_q11
58 s08a_q12
59 s08b_q01
60 s08b_q02
61 s08b_q03
62 s08b_q_1
63 s08b_q_2
64 s08b_q04
65 s08b_q05
66 s08b_q06
67 s08b_q07
68 s08b_q08
69 s08b_q09
70 s08b_q10
71 s08b_q11
72 s08b_q12
73 s08b_q13
74 s08c_q01
75 s08c_q02
76 s08d_q01
77 s08d_q02
78 s08d_q03
79 s08d_q04
80 s09a1d01
81 s09a1d_1
82 s09a1d_2
83 s09a1d_3
84 s09a1d_4
85 s09a1d02
86 s09a1d_5
87 s09a1d_6
88 s09a1d_7
89 s09a1d_8
90 s09a1d03
91 s09a1d_9
92 s09a1_10
93 s09a1_11
94 s09a1_12
95 s09a1d04
96 s09a1_13
97 s09a1_14
98 s09a1_15
99 s09a1_16
100 s09a1d05
101 s09a1_17
102 s09a1_18
103 s09a1_19
104 s09a1_20
105 s09a1d06

Cost of purchasing this land?
Did your household purchase any house?
Cost of purchasing this house?
Household own other assets
What is the total value of such assets?
Household purchased any assets in last 12 months
What was the cost of such purchase?
Household sold any assets
How much household received from selling
Income from rent of land (share cropping)
Income from rent of other property
income from Life insuranceincome from Health insuranceincome from General insurance
Profit and dividend received
Lottery or similary income in cash or in-kind
Gift, Charity or other received? In cash
Gift, Charity or other received? In-kindRemittances received from within the country
Remittances received from outside the country
Where did you invast remittance money
Pension, Gratuity, other benefit received
Profit,Interest received
Other cash or in-kind received
Any member of household migrated?
Household received remittances from outside?
Any member opened bank account?
Any member deposited in any credit union?
Any member deposited in non-inst. group?
Any member borrowed money
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys

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106 s09a1_21
107 s09a1_22
108 s09a1_23
109 s09a1_24
110 s09a1d07
111 s09a1_25
112 s09a1_26
113 s09a1_27
114 s09a1_28
115 s09a1d08
116 s09a1_29
117 s09a1_30
118 s09a1_31
119 s09a1_32
120 s09a1d09
121 s09a1_33
122 s09a1_34
123 s09a1_35
124 s09a1_36
125 s09a1d10
126 s09a1_37
127 s09a1_38
128 s09a1_39
129 s09a1_40
130 s09a1d11
131 s09a1_41
132 s09a1_42
133 s09a1_43
134 s09a1_44
135 s09a1d12
136 s09a1_45
137 s09a1_46
138 s09a1_47
139 s09a1_48
140 s09a1d13
141 s09a1_49
142 s09a1_50
143 s09a1_51
144 s09a1_52
145 s09a1d14
146 s09a1_53
147 s09a1_54
148 s09a1_55
149 s09a1_56
150 s09b1w1_
151 s09b1w2_

Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Number of boys
Number of girls
Number of men
Number of women
Date
Date
Date

### rt002.dta ###1 psu
Primary sampling unit
2 hhold
household3 idcode
personal identification code
4 resid1a
respondent identification code - section 1.a
5 s01a_q01
s01a_q01: name(deleted)
6 s01a_q02
s01a_q02: sex
7 s01a_q03
s01a_q03: relationship to head

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8 s01a_q04
9 s01a_q05
10 s01a_q06
11 s01a_q07
12 s01a_q08
13 s01a_q09
14 s01a_q10
15 s01a_q11
16 s01a_q12
17 s01b_q01
18 s01b_q02
19 s01b_q03
20 s01b_q04
21 resid1c
22 s01c_q01
23 s01c_q02
24 s01c_q03
25 s01c_q_1
26 s01c_q04
27 s01c_q_2
28 s01c_q_3
29 s01c_q05
30 s01c_q_4
31 s01c_q_5
32 s01c_q06
33 s01c_q07
34 resid2a
35 s02a_q03
36 s02a_q04
37 s02a_q05
38 s02a_q06
39 s02a_q07
40 resid2b
41 s02b_q01
42 s02b_q02
43 s02b_q03
44 s02b_q04
45 s02b_q05
46 s02b_q06
47 s02b_q07
48 s02b_q08
49 s02b_q_1
50 s02b_q_2
51 s02b_q_3
52 s02b_q_4
53 s02b_q_5
54 s02b_q_6
55 s02b_q_7
56 s02b_q_8
57 s02b_q_9
58 s02b__10
59 s02b__11
60 s02b__12
61 s02b__13
62 s02b__14
63 s02b__15

s01a_q04: age in years
s01a_q05: religion
s01a_q06: marital status
s01a_q07: earner
s01a_q07: spouse ID
s01a_q09: father id
s01a_q10: mother id
s01a_q11: Lived abroad more than 6 m?
s01a_q12: why did return?
s01b_q01: did work for livelihood during last 7 days?
s01b_q02: was avaible for work during last 7 days?
s01b_q03: did look for work during last 7 days
s01b_q04: Reason for not looking for work
respondent identification code - section 1.c
s01c_q01: has benefitted from social safety?
s01c_q02: In what program has been included?
s01c_q03m: when enlisted (month)s01c_q03y: when enlisted (year)
s01c_q04t: how much entitled cash (taka)
s01c_q04m: how much entitled in kind (code of item)
s01c_q04k: how much entitled in kind (kg)s01c_q05t: how much actually received in cash?
s01c_q05n: how much actually received in kind (code of item)
s01c_q05k: how much actually received in kind (kg)s01c_q06: amount spent for inclusions01c_q07: cause why not included
respondent identification code - section 2.as02a_q03: can read a letter?
s02a_q04: can write a letter?
s02a_q05: highest class passed
s02a_q06: where attended/attending?
s02a_q07: what type of school attended/attending?
respondent identification code - section 2.b
s02b_q01: are you attending education institution?
s02b_q02: what class attending?
s02b_q03: receiving stipend for primary?
s02b_q04: how much you received in 12 m?
s02b_q05: receiving secondary stipend?
s02b_q06: how much received in 12 m?
s02b_q07: received benefit in the tuition waiver?
s02b_q08a: Amount spent on admission
s02b_q08b: Amount spent on annual session fee
s02b_q08c: Amount spent on registration
s02b_q08d: Amount spent on examination fee
s02b_q08e: Amount spent on tuituion fee
s02b_q08f: Amount spent on text books
s02b_q08g: Amount spent on exercise books
s02b_q08h: Amount spent on uniform
s02b_q08i: Amount spent on private tution
s02b_q08j: Amount spent on hostel/messs02b_q08k: Amount spent on conveyances02b_q08l: Amount spent on tiffins02b_q08l: Amount spent on internet/e-mails02b_q08n: Amount spent on schooling donation
s02b_q08o: Amount spent on schooling others02b_q08p: Amount spent on schooling total

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64 s03a_q01
65 s03a_q02
66 s03a_q_1
67 s03a_q03
68 s03a_q_2
69 s03a_q04
70 s03a_q05
71 s03a_q_3
72 s03a_q_4
73 s03a_q06
74 s03a_q07
75 s03a_q08
76 s03a_q_5
77 s03a_q09
78 s03a_q10
79 s03a_q11
80 s03a_q12
81 s03a_q13
82 s03a_q_6
83 s03a_q14
84 s03a_q_7
85 s03a_q15
86 s03a_q16
87 s03a_q17
88 s03a_q_8
89 s03a_q_9
90 s03a__10
91 s03a__11
92 s03a__12
93 s03a__13
94 s03a__14
95 s03a__15
96 s03a__16
97 s03a__17
98 s03a_q18
99 s03a__18
100 s03a__19
101 s03b_q01
102 s03b_q_1
103 s03b_q02
104 s03b_q03
105 s03b_q04
106 s03b_q05
107 s03b_q_2
108 s03b_q_3
109 s03b_q_4
110 s03b_q_5
111 s03b_q_6
112 s03b_q_7
113 s03b_q_8
114 s03b_q_9
115 s03b_q06
116 s03b_q07
117 s03b_q08
118 s03c_q01
119 s03c_q02

s03a_q01: have suffered chronic illness?
s03a_q02: what chronic illness? 1
s03a_q02: what chronic illness? 2
s03a_q03y: for how long had illness? (years)
s03a_q03m: for how long had illness? (months)
s03a_q04: have suffered illness in last 30 days?
s03a_q05_1: what type of illness? 1
s03a_q05_2: what type of illness? 2
s03a_q05_3: what type of illness? 3
s03a_q06: have sought medical treatment?
s03a_q07: If not why not?
s03a_q08_1: If yes who was consulted (1st)s03a_q08_2: who was consulted (2nd)s03a_q09: After how many days began consultation?
s03a_q10: From where got medicines?
s03a_q11: did you pay for the medicines?
s03a_q12: how did travel to rervice provider?
s03a_q13h: how much time it took? (hour)s03a_q13m: how much time it took? (minutes)
s03a_q14h: how long you had to wait? (hour)s03a_q14m: how long you had to wait? (minutes)
s03a_q15: why you chose this provider?
s03a_q16: Did provider give enough time for you?
s03a_q17a: Amount paid for consultation/visit
s03a_q17b: Amount paid for hospital/clinic
s03a_q17c: Amount paid for medicines
s03a_q17d: Amount paid for cost of test/investigation
s03a_q17e: Amount paid for conveyances03a_q17f: Amount paid for tips on
s03a_q17g: Amount paid for other services
s03a_q17hc: total cost of treatment? (maternity clinic)
s03a_q17hm: total cost of treatment? (maternity midwife)
s03a_q17ho: total cost of treatment? (maternity others)
s03a_q17t: total cost of treatment? (total cost)
s03a_q18_1: how financed treatment? 1
s03a_q18_2: how financed treatment? 2
s03a_q18_3: how financed treatment? 3
s03b_q01m: when the child was born? (month)s03b_q01y: when the child was born? (year)
s03b_q02: present age of child? (in months)
s03b_q03: has he/she ever been immunized?
s03b_q04: Do you have immunization card?
s03b_q05bcg: bcg?
s03b_q05dpt1: dpt1?
s03b_q05dpt2: dpt2?
s03b_q05dpt3: dpt3?
s03b_q05p1: polio1?
s03b_q05p2: polio2?
s03b_q05p3: polio3?
s03b_q05m: measles?
s03b_q05h: hepatitis?
s03b_q06: where was most recent immunization?
s03b_q07: who influenced you to immunize?
s03b_q08: received vitamin-a capsules in last 12m?
s03c_q01: have you ever given birth?
s03c_q02: in what year you gave the last one?

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120 s03c_q03
121 s03c_q04
122 s03c_q05
123 s03c_q06
124 s03c_q07
125 s03c_q08
126 s03c_q09
127 s03c_q10
128 s03d_q02
129 s03d_q03
130 s03d_q04
131 s03d_q05
132 s03d_q06
133 s03d_q07
134 s03d_q08
135 s03d_q09
136 s03d_q10
137 s03d_q11
138 s03d_q12
139 s03d_q13
140 s03d_q14
141 s03d_q15
142 s03d_q16
143 s03d_q17
144 s03d_q18
145 s03d_q19
146 s03d_q20
147 s03d_q_1
148 s03d_q_2
149 s03d_q21

s03c_q03: did you attend prenatal consultations?
s03c_q04: From where you received this care?
s03c_q05: In what month you went first time?
s03c_q06: did you receive tetanus vaccine?
s03c_q07: who assisted with this birth?
s03c_q08: where did you give birth?
s03c_q09: did you visit post-natal checkup?
s03c_q10: where did you go for checkup?
s03d_q02: difficulty seeing?
s03d_q03: how old was when difficulty began?
s03d_q04: what was the cause?
s03d_q05: difficulty hearing?
s03d_q06: how old was when it began?
s03d_q07: what was the cause?
s03d_q08: difficulty walking?
s03d_q09: how old was when it began?
s03d_q10: what was the cause?
s03d_q11: difficulty remembering?
s03d_q12: how old was when it began?
s03d_q13: what was the cause?
s03d_q14: difficulty with self care?
s03d_q15: how old was when it began?
s03d_q16: what was the cause?
s03d_q17: difficulty in comunicating?
s03d_q18: how old was when it began?
s03d_q19: what was the cause?
s03d_q20h: difficulty reduced work at home?
s03d_q20s: difficulty reduced work at school?
s03d_q20w: difficulty reduced work at work?
s03d_q21: what measures taken to improve?

### rt003.dta ###
1 psu
PSU
2 hhold
household3 serial
Activity serial
4 idcode
Id code5 s04a_q01
s04a_q01d: what activities did you do? description
6 s04a_q_1
s04a_q01o: what activities did you do? occupation code (2-digit)
7 s04a_q_2
s04a_q01i: what activities did you do? industry code (2-digit)
8 s04a_q02
s04a_q02: how many month you do this activity?
9 s04a_q03
s04a_q03: how many days per month?
10 s04a_q04
s04a_q04: how many hours per day?
11 s04a_q05
s04a_q05ru: where did you do this activity? R/U code
12 s04a_q_3
s04a_q05d: where did you do this activity? district code
13 s04a_q06
s04a_q06: what kind of activities?
14 s04a_q07
s04a_q07: what was your work status? agriculture
15 s04a_q08
s04a_q08: whats was your work status? non agri
16 idcode2
Id Code in 4B
17 s04b_q01
s04b_q01: were you paid on daily basis?
18 s04b_q02
s04b_q02_1: highest daily wage in cash?
19 s04b_q_1
s04b_q02_2: lowest daily wage in cash?
20 s04b_q_2
s04b_q02_3: average daily wage in cash?
21 s04b_q03
s04b_q03: Did you receive payments in-kind?
22 s04b_q04
s04b_q04: what type of item received in payment?
23 s04b_q05
s04b_q05kg: how much you received per day? KG

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24 s04b_q_3
25 s04b_q06
26 s04b_q07
27 s04b_q08
28 s04b_q09

s04b_q05t: What was the total price received per day? takas04b_q06: What type of org. you work for?
s04b_q07: Gross monthly cash remuneration
s04b_q08: Net cash/remuneration take-home monthly?
s04b_q09: Other benefits you received in last 12 months?

### rt004.dta ###
1 psu
psu
2 hhold
household3 enumberenterprise number
4 s05a_q01
s05a_q01d: what kind of entreprise? description
5 s05a_q_1
s05a_q01i: what kind of entreprise? industry
6 s05a_q02
s05a_q02_1: ID of member who work in the enterprise?
7 s05a_q_2
s05a_q02_2: ID of member who work in entreprise?
8 s05a_q_3
s05a_q02_3: ID of member who work in an entreprise?
9 s05a_q_4
s05a_q02_4: ID of member who work in an entreprise?
10 s05a_q_5
s05a_q02_5: ID of member who work in an entreprise?
11 s05a_q03
s05a_q03y: for how long has been operating? years
12 s05a_q_6
s05a_q03m: for how long has been operating? months
13 s05a_q04
s05a_q04: where do you operate the entreprise?
14 s05a_q05
s05a_q05: Months the enterprise was operational in the last 12 months?
15 s05a_q06
s05a_q06: what is houselhold's share in this entreprise?
16 s05a_q07
s05a_q07: what is the share of profit?
17 s05a_q08
s05a_q08_1: who are your costumers? 1st
18 s05a_q_7
s05a_q08_2: who are your costumers? 2nd
19 s05a_q09
s05a_q09: is the entreprise registred officially?
20 s05a_q10
s05a_q10_1: Source of finance when established? 1st
21 s05a_q_8
s05a_q10_2: Source of finance when established? 2nd
22 s05b_q11
s05b_q11: Number of salaried persons in the last 12 months
23 s05b_q12
s05b_q12_1: what problems faced in running your business? 1
24 s05b_q_1
s05b_q12_2: what problems faced in running your business? 2
25 s05b_q13
s05b_q13: gross revenues last 12m?
26 s05b_q14
s05b_q14: expenditures on salary/wages last 12m?
27 s05b_q15
s05b_q15: expenditure on rent last 12m?
28 s05b_q16
s05b_q16: expenditure on raw materials last 12m?
29 s05b_q17
s05b_q17: expenditure on fuel, kerosene, electricity.. last 12m?
30 s05b_q18
s05b_q18: value of finished good last 12m?
31 s05b_q19
s05b_q19: other operating expenses last 12m?
32 s05b_q20
s05b_q20: net revenues last 12m
33 s05b_q21
s05b_q21: expenditure on capital goods last 12m?
34 s05b_q22
s05b_q22: sale of assets last 12m?
35 s05b_q23
s05b_q23: What is the selling price of this entreprise?

### rt005.dta ###
1 psu
psu
2 hhold
household3 shock_coshock code
4 s06b_q02
s06b_q02: did you experience this shock?
5 s06b_q03
s06b_q03: when did the shock first occur?
6 s06b_q04
s06b_q04: how long did the shock last? (days)
7 s06b_q05
s06b_q05a: was a decline in income?
8 s06b_q_1
s06b_q05b: was a decline in assets?
9 s06b_q_2
s06b_q05c: was a decline in food production?
10 s06b_q_3
s06b_q05d: was a decline in food purchased?

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11 s06b_q06
12 s06b_q_4
13 s06b_q_5

s06b_q06_1: how coped with this shock? 1st
s06b_q06_2: how coped with this shock? 2nd
s06b_q06_3: how coped with this shock? 3rd### rt006.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 crop_cod
crop code
5 s07b_q02
s07b_q02: did you cultivate this crop?
6 s07b_q03
s07b_q03: how much land you cultivated? decimals
7 s07b_q04
s07b_q04k: how much crop produced? kg
8 s07b_q_1
s07b_q04t: price of each KG? taka
9 s07b_q05
s07b_q05: how much your household consumed?
10 s07b_q06
s07b_q06: how much your household sold?
11 s07b_q07
s07b_q07: how much your household stored?
12 s07b_q08
s07b_q08: how much given to landlord?
13 s07b_q_2
s07b_q08: how much paid for wages?
14 s07b_q_3
s07b_q08: how much used as seed?
15 s07b_q_4
s07b_q08: how much as feed for animals?
16 s07b_q_5
s07b_q08: how much waste?
17 s07b_q_6
s07b_q08: how much in other uses?
18 s07b_q_7
s07b_q08: how much in total?
19 price### rt007.dta ###
1 psu
psu
2 hhold
household3 liv_code
livestock code
4 s07c_q02
s07c_q02n: how many of them you have? number
5 s07c_q_1
s07c_q02t: Total value of them? taka
6 s07c_q03
s07c_q03n: how many born/purchased? number
7 s07c_q_2
s07c_q03t: Total value of them? taka
8 s07c_q04
s07c_q04n: how many died/sell? number
9 s07c_q_3
s07c_q04t: Total value of them? taka
10 s07c_q05
s07c_q05n: how many household consume? number
11 s07c_q_4
Total value of them? taka### rt008.dta ###
1 psu
psu
2 hhold
household3 prod_cod
product code
4 s07c_q06
s07c_q06q: how much produced? quantity
5 s07c_q_1
s07c_q06t: value? taka
6 s07c_q07
s07c_q07q: how much did you sell? quantity
7 s07c_q_2
s07c_q07t: value? taka
8 s07c_q08
s07c_q08q: how much did you consume? quantity
9 s07c_q_3
s07c_q08t: value? taka### rt009.dta ###
1 psu
psu
2 hhold
household

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3 fish_act
4 s07c_q10
5 s07c_q_1
6 s07c_q11
7 s07c_q_2
8 s07c_q12
9 s07c_q_3

code fishings07c_q10kg: how much you produce? kg
value? taka
s07c_q11kg: how much your household sold? kg
s07c_q11t: value? taka
s07c_q12kg: how much your household consumed? kg
value? kg### rt010.dta ###
1 psu
psu
2 hhold
household
3 forestry
code forestry
4 s07c_q14
s07c_q14n: how many trees? no.
5 s07c_q_1
s07c_q14t: value? taka
6 s07c_q15
s07c_q15: value of trees sold? taka
7 s07c_q16
s07c_q16: value of trees consumed? taka### rt011.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 exp_agri
agric code of the sector
5 s07d_q01
s07d_q01: did your household spend any money on...?
6 s07d_q02
s07d_q02kg: how much your household spend? kg
7 s07d_q_1
s07d_q02t: household spend? taka### rt012.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 agric_asagricultural asset code
5 s07e_q01
s07e_q01n: how many of them do you own? no.
6 s07e_q_1
s07e_q01t: value? taka
7 s07e_q02
s07e_q02n: how many your household bought? no.
8 s07e_q_2
s07e_q02t: value? taka
9 s07e_q03
s07e_q03n: how many your household sold? no.
10 s07e_q_3
s07e_q03t: value? taka
11 s07e_q04
s07e_q04: how much household earned from rental? taka### rt013.dta ###
1 psu
psu
2 hhold
household3 migrant_
migrant number
4 s08c_q03
s08c_q03: name(deleted)
5 s08c_q04
s08c_q04: relationship to head
6 s08c_q05
s08c_q05m: how long ago did he/she migrate? months
7 s08c_q_1
s08c_q05y: how long ago did he/she migrate? years
8 s08c_q06
s08c_q06: where is working?
9 s08c_q07
s08c_q07: in-country zila code
10 s08c_q08
s08c_q08: if abroad, write the country code
11 s08c_q09
s08c_q09: age
12 s08c_q10
s08c_q10: sex

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13 s08c_q11
14 s08c_q12
15 s0bc_q13
16 s08c_q14
17 s08c_q15
18 s08c_q16
19 s08c_q_2
20 s08c_q_3
21 s08c_q17

s08c_q11: level of education
s08c_q12: occupations0bc_q13: how many times he/she sent money?
s08c_q14: total amount of money sent?
s08c_q15: how he/she sent money?
s08c_q16_1: goods that he/she sent 1 (code)
s08c_q16_2: goods that he/she sent 2 (code)
s08c_q16_3: goods that he/she sent 3 (code)
s08c_q17: total value of goods that he/she sent?

### rt014.dta ###
1 psu
psu
2 hhold
household3 loan_num
loan number
4 idcode
id code5 s08d_q05
s08d_q05: what was the source of this credit?
6 s08d_q06
s08d_q06: how much money was borrowed?
7 s08d_q07
s08d_q07: how long is the payment period?
8 s08d_q08
s08d_q08m: interest applied per month
9 s08d_q_1
s08d_q08y: interest applied per year
10 s08d_q09
s08d_q09f: how often you make payments? frequency
11 s08d_q_2
s08d_q09a: how often you make payments? amount
12 s08d_q10
s08d_q10: completed repayment?
13 s08d_q11
s08d_q11: amount of unpaid loan?
14 s08d_q12
s08d_q12: what was the purpose of loan?
15 s08d_q13
s08d_q13: would you like to borrow more money?
16 s08d_q14
s08d_q14: how much would you want to borrow?

### rt015.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemfood item code
5 s09a1d01
s09a1d01_qty: quantity
6 s09a1d_1
s09a1d01_u: unit
7 s09a1d_2
s09a1d01_v: value
8 s09a1d_3
s09a1d01_s: major source
9 s09a1d02
s09a1d02_qty: quantity
10 s09a1d_4
s09a1d02_u: unit
11 s09a1d_5
s09a1d02_v: value
12 s09a1d_6
s09a1d02_s: major source
13 s09a1d03
s09a1d03_qty: quantity
14 s09a1d_7
s09a1d03_u: unit
15 s09a1d_8
s09a1d03_v: value
16 s09a1d_9
s09a1d03_s: major source
17 s09a1d04
s09a1d04_qty: quantity
18 s09a1_10
s09a1d04_u: unit
19 s09a1_11
s09a1d04_v: value
20 s09a1_12
s09a1d04_s: major source
21 s09a1d05
s09a1d05_qty: quantity
22 s09a1_13
s09a1d05_u: unit
23 s09a1_14
s09a1d05_v: value
24 s09a1_15
s09a1d05_s: major source
25 s09a1d06
s09a1d06_qty: quantity

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26 s09a1_16
27 s09a1_17
28 s09a1_18
29 s09a1d07
30 s09a1_19
31 s09a1_20
32 s09a1_21
33 s09a1d08
34 s09a1_22
35 s09a1_23
36 s09a1_24
37 s09a1d09
38 s09a1_25
39 s09a1_26
40 s09a1_27
41 s09a1d10
42 s09a1_28
43 s09a1_29
44 s09a1_30
45 s09a1d11
46 s09a1_31
47 s09a1_32
48 s09a1_33
49 s09a1d12
50 s09a1_34
51 s09a1_35
52 s09a1_36
53 s09a1d13
54 s09a1_37
55 s09a1_38
56 s09a1_39
57 s09a1d14
58 s09a1_40
59 s09a1_41
60 s09a1_42
61 t
62 kcals09a1d06_u: unit
s09a1d06_v: value
s09a1d06_s: major source
s09a1d07_qty: quantity
s09a1d07_u: unit
s09a1d07_v: value
s09a1d07_s: major source
s09a1d08_qty: quantity
s09a1d08_u: unit
s09a1d08_v: value
s09a1d08_s: major source
s09a1d09_qty: quantity
s09a1d09_u: unit
s09a1d09_v: value
s09a1d09_s: major source
s09a1d10_qty: quantity
s09a1d10_u: unit
s09a1d10_v: value
s09a1d10_s: major source
s09a1d11_qty: quantity
s09a1d11_u: unit
s09a1d11_v: value
s09a1d11_s: major source
s09a1d12_qty: quantity
s09a1d12_u: unit
s09a1d12_v: value
s09a1d12_s: major source
s09a1d13_qty: quantity
s09a1d13_u: unit
s09a1d13_v: value
s09a1d13_s: major source
s09a1d14_qty: quantity
s09a1d14_u: unit
s09a1d14_v: value
s09a1d14_s: major source
total indicator (auxiliary variable)
kcal per 100 gm or ml### rt016.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemfood item code
5 s09b1w1_
s09b1w1_q: quantity
6 s09b1w_1
s09b1w1_gm: unit
7 s09b1w_2
s09b1w1_v: value
8 s09b1w_3
s09b1w1_m: major source
9 s09b1w2_
s09b1w2_q: quantity
10 s09b1w_4
s09b1w2_gm: unit
11 s09b1w_5
s09b1w2_v: value
12 s09b1w_6
s09b1w2_m: major source
13 kcalkcal per 100 gm or ml###

rt017.dta###

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1 psu
2 hhold
3 ln
4 item
5 s09c1_q0
6 s09c1__1
7 s09c1__2

psuhouseholdline number
non-food item code
s09c1_q01: value of goods received wage/cash?
s09c1_q02: value of goods produced/gifts?
s09c1_q03: sum of col.1 & 2### rt018.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemnon-food item code
5 s09d1_q0
s09d1_q01: how many did you buy/produce/receive?No.
6 s09d1__1
s09d1_q02: what is the value?

### rt019.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 itemnon-food item code
5 s09d2_q0
s09d2_q01: what is the value?

### rt020.dta ###
1 psu
psu
2 hhold
household3 lnline number
4 dg_code
durable good code
5 s09e_q01
s09e_q01: owns item?
6 s09e_q02
s09e_q02: how many do you own?
7 s09e_q03
s09e_q03: Expected value if want to sell
8 s09e_q04
s09e_q04: how much did it cost (if bought past 12m)### incexp ###1
hhid2
region3 urbanrur
4
wgt
5
inc8e
6
inc4b
7
inc5
8
inc9d
9
inc7b
10
inc7c111
inc7c212
inc7c3
13
inc7d
14
inc7e
15
inc8c
16
ttinc
17
g1

Other income
Wage income
Business income
Imputed rent
Crop productLivestock productFishery product
Forestry productAgri cost
Earn from rent
Renittance
Total income
1 Food grains

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18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57

g2
2 Pulsesg3
3 Fishg4
4 Eggsg5
5 Meatg6
6 Vegetables
g7
7 Milk & Daily
g8
8 Sweetmeat
g9
9 Oil & Fats
g10
10 Fruits
g11
11 Drinks
g12
12 Sugar & molasses
g13
13 Miscellaneous Food
g14
14 Dining out
g15
15 Tobacco & tobacco produts
g16
16 Spices
g17
17 Betel leaf & Chewgoods
g18
18 Fuel & lighting
g19
19 Cosmetics & other expenses
g20
20 Washing & cleaning expenses
g21 21 Transport/travel & other misc. charges
g22
22 Ready-made garments
g23
23 Clothing material & tailoring
g24
24 Footwear
g25
25 Household-use textiles, etc.
g26
26 Housing related expenses
g27
27 Medical treatment expenses (male)g28
28 Medical treatment expenses (female)g29
29 Educatonal expenses (male)g30
30 Educational expenses (female)g31
31 Remittances, ceremonies, gift, etc.
g32
32 Recreation & leisure, etc.
g33
33 Taxes, interest, fines, etc.
g34
34 Cooking equipent
g35
35 Furniture & related paraphernalia
g36
36 Personal articles
g37
37 Misc. household durable
g38
38 Insurance expendituredg
dg Durable goods
ttexp
Total expenditurefood
Total foodBangladesh HIES 2010 Manual (Version 1.0)