(Version 1.1)

Users’ Manual for Handling Resampled Micro Data of
Sri Lanka Household Income and Expenditure Survey
HIES 2012/13

2018
The Institute of Statistical Mathematics (ISM)
andStatistical Information Institute for Consulting and Analysis (SINFONICA)

1

History of revision of the manualVersion 1.1 in March 2018
- Revised based on the discussion during the Tenth International Workshop on Analysis of
Micro Data of Official Statistics in November 2017.

First draft version 1.0 in November 2017
for the discussion at the Workshop.

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2

CONTENTS

HIES 2012/13
1. About this Manual

Page 4

2. Outline of the survey

5

3. Data and metadata provided

7

4. Data import11Strategy to import micro data into RImport CSV files to R

Structure of each data frame14

ID: Household identifier32



PID: Individual identifier

33

Append weight in R25 to all data files

35

5. Data check5.1 Summary of each variable37

5.2 Frequency table of categorical variables

58

5.3 Primary key and foreign key of each data set

72

5.4 Sample allocation

81

5.5 Population estimates85

6. Household income
6.1 Definition of household income

86

6.2 Process of estimating household income

89

6.3 Generating the variable of monthly monetary income

93

6.4 Creating individual-level income data file101

6.5 Income receivers’ income

106

6.6 Creating data set of household income at household level

111

6.7 Non-monetary income (income in-kind) at household level

113

6.8 The variable of HIncPM in R25

123

7. Household expenditure
7.1 Definition of household expenditure124

7.2 Process of estimating household expenditure

126

7.3 Converting to monthly expenditure129

7.4 Estimating household expenditure by 4-digit item

134

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7.5 Household expenditure by subgroup

144

8. Resampling method

157

Attachments:
1. HIES 2012/13 Questionnaire165

2. HIES 2012/13 Data dictionary

200

3. HIES 2012/13 Final Buletin

239

4. Excerpt of “Introduction” in HIES 2012/13 Final Report

247

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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 Sri
Lanka Household Income and Expenditure Survey 2012/13.
Survey year

Data files

Contents

HIES 2012/13

25 files

R01 to R24: HIES 2012/13 Questionnaire
R25: Summary file

CSV files"R01_80.csv""R02_80.csv""R03_80.csv""R04_80.csv""R05_80.csv""R06_80.csv""R07_80.csv""R08_80.csv""R09_80.csv""R10_80.csv""R11_80.csv""R12_80.csv""R13_80.csv""R14_80.csv""R15_80.csv""R16_80.csv""R17_80.csv""R18_80.csv""R19_80.csv""R20_80.csv""R21_80.csv""R22_80.csv""R23_80.csv""R24_80.csv""R25_80.csv"
R data frames"R01.80" "R02.80" "R03.80" "R04.80" "R05.80" "R06.80" "R07.80" "R08.80""R09.80" "R10.80" "R11.80" "R12.80" "R13.80" "R14.80" "R15.80" "R16.80""R17.80" "R18.80" "R19.80" "R20.80" "R21.80" "R22.80" "R23.80" "R24.80""R25.80"

2. The original micro data sets composed of all the samples were provided by DCS, Sri
Lanka based on the Charter for Experimental Laboratory for Research Purpose StatisticalUse of Micro Data, and resampled at the rate of 80% by Sinfonica.

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

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

Sri Lanka HIES 2012/13 Manual (Version 1.1)5

2. Outline of HIES 2012/13
Objective

To provide information on household income and expenditure in order tomeasure the levels and changes in the living condition of the people, andcompute various other indicators such as poverty etc.

Frequency

HIES had been conducted since 1990 as a separate survey once in every five
years until HIES 2006/07. Thereafter, as rapidly changing economic conditions
demanded for frequent monitoring of income and expenditure patterns in the
country, the DCS decided to conduct the HIES once in every three years.

Topics covered

In general, the survey gathers information on the next three main topics;


Demographic characteristics

Household expenditureHousehold income

After HIES 2006/07, the following six topics have been introduced; Education,
Health, Durable goods and debts, Access to facilities, Housing,
Agriculture holdings and livestock.
Data collection

Direct personal interview.
The data collection in the field is done in twelve monthly rounds from July 2012
to June 2013.

Reference period

Twelve months to capture the seasonal variation of income and expenditurepatterns of households.
Reference period of income and expenditure depends on item; a week, a month,
6 months or 12 months.

Coverage

and

scope

HIES 2012/13 can be considered as very important milestone as it covered all 25
districts in the country since its inception.
Private households, excluding collective (institutional) households.
A household may be a one-person household or a multi person household. A
one-person household is a unit where a person lives by himself and makes
separate provision for his food, either cooking himself or purchasing. A multiperson household is a group of two or more persons who lives together and has acommon arrangement for cooking and partaking food. Boarders and servants
who share the meals and housing facilities with other members of the household
are also considered as members of the household.

Sample frame

List of census blocks prepared for Census of Population and Housing (CPH) 2011
List of housing units prepared for CPH 2011
HIES 2012/13 was the first HIES sampled from this sampling frame.

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Sample design

Two stage stratified random sample design
Strata: district (25) and sector (3)PSU: census blocks. 2,500 psu were selected.
List of housing units in each selected psu was updated about one month prior to
the scheduled interviewing.
SSU: housing units.
For each psu, 10 housing units were selected for the survey.
Note: Sample households of different year surveys are completely independent.

Sample size andTotal sample size is 25,000 housing units.

sampleNeymann (optimal) method was employed for allocation of psu for districts andallocation

sectors.
The district sample was equally distributed among the 12 monthly rounds.
In total, 20,540 households responded to the survey.

Data processing

According to the delegates from Sri Lanka for the International Workshop in
2014,“Data cleaning is done in districts and head office concurrently withthe field work using an online and interactive computer data editing andcleaning program, which reports errors in the data as identified
according to the conditions pre-prepared and the corrections are madereferring to the hard questionnaire or to the enumerator or the respondent
household.”
Preliminary

Based on only the first 3 monthly rounds of the survey to fulfil urgent data needs

reportof the country.

Final results

Based on the 100% data, published in August 2014.

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3. Data and metadata provided by NSO
The following micro data and metadata were downloaded with permission of DCS,
Sri Lanka.
Type

Filename

Description

Included inthismanualData
Sri Lanka HIES-2012-13-Data_CSV
Format

The next 25 data files in CSV format
including weight data[1] "HIES-2012-13-100%-Data-SEC_1_DEMOGRAPHIC.CSV"[2] "HIES-2012-13-100%-Data-SEC_2_SCHOOL_EDUCATION.CSV"[3] "HIES-2012-13-100%-Data-SEC_3_HEALTH.CSV"[4] "HIES-2012-13-100%-Data-SEC_4_1_FOOD_EXP.CSV"[5] "HIES-2012-13-100%-Data-SEC_4_2_NONFOOD.CSV"[6] "HIES-2012-13-100%-Data-SEC_4_3_BOARDERS.CSV"[7] "HIES-2012-13-100%-Data-SEC_4_3_IS_BOADERS.CSV"[8] "HIES-2012-13-100%-Data-SEC_5_1_EMP_INCOME.CSV"[9] "HIES-2012-13-100%-Data-SEC_5_1_IS_EMP_INCOME.CSV"[10] "HIES-2012-13-100%-Data-SEC_5_2_AGRI_INCOME.CSV"[11] "HIES-2012-13-100%-Data-SEC_5_2_IS_AGRI_INCOME.CSV"[12] "HIES-2012-13-100%-Data-SEC_5_3_IS_OTHER_AGRI_INCOME.CSV"[13] "HIES-2012-13-100%-Data-SEC_5_3_OTHER_AGRI_INCOME.CSV"[14] "HIES-2012-13-100%-Data-SEC_5_4_IS_NON_AGRI_INCOME.CSV"[15] "HIES-2012-13-100%-Data-SEC_5_4_NON_AGRI_INCOME.CSV"[16] "HIES-2012-13-100%-Data-SEC_5_5_1_IS_OTHER_INCOME.CSV"[17] "HIES-2012-13-100%-Data-SEC_5_5_1_OTHER_INCOME.CSV"[18] "HIES-2012-13-100%-Data-SEC_5_5_2_IS_WINDFALL_INCOME.CSV"[19] "HIES-2012-13-100%-Data-SEC_5_5_2_WINDFALL_INCOME.CSV"[20] "HIES-2012-13-100%-Data-SEC_6_B_DEBTNESS.CSV"[21] "HIES-2012-13-100%-Data-SEC_6A_DURABLE_GOODS.CSV"[22] "HIES-2012-13-100%-Data-SEC_7_BASIC_FACILITIES.CSV"[23] "HIES-2012-13-100%-Data-SEC_8_HOUSING.CSV"[24] "HIES-2012-13-100%-Data-SEC_9_LAND_ANIMAL.CSV"[25] "HIES-2012-13-100%-Data-WEIGHT_INCOME_EXPENDITURE.CSV"

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Yes

8

Metadata

HIES-2012_13-Schedule English new.pdf 35 pages

Yes

39 pages

Yes

HIES-2012-13-Data
Layout_Complete.pdf

HIES2012_13_Study document.pdf
HIES2012PrelimineryReport.pdf

Results from the first three months survey

HIES200213FinalBuletin4.pdf

Final results, August 2014, 8 pages

HIES2012_13FinalReport.pdf

Final report, , 173 pages

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Yes

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

Changes from the previous HIES 2009/10

1. The questionnaire of HIES 2012/13 was almost the same as HIES 2009/10.
2. The frequency of the HIES survey has changed from once in five years to once in three years.
3. Geographically, all districts were covered after 26 years.
4. Sample design was based on the census blocks prepared for CPH2011.
5. The household-level variables of total income and total expenditure except disaggregation were
provided in the micro dataset.

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Other sources

Homepage of DCS
http://www.statistics.gov.lk/page.asp?page=Income%20and%20Expenditure(Accessed on 29 May 2017)

It provides the preliminary report and the final report of HIES 2012/13.


IHSN Survey Catalog:

Sri Lanka - Household Income and Expenditure Survey 2012-2013

http://catalog.ihsn.org/index.php/catalog/6086(Accessed on 29 May 2017)

The study description and data description are available on the web.
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4. Data Import
Strategy to import micro data into R
The provided data set consisted of 25 CSV data files with header labels.
Weight data was included in one of the above files.
1) To import CSV data files into R, making use of the variable names prepared by NSO.
2) To combine three variables; A0, SNUMBER and HHNO of each data file and generate thevariable ID, household identifier.
3) To generate the variable of PID (person id) as a combination of ID and person number for data
files at individual level.
4) To append weight data to all data files.
*******************************************************************************

Import CSV files to R
# CSV files to be imported> csv.names[1] "HIES-2012-13-100%-Data-SEC_1_DEMOGRAPHIC.CSV"[2] "HIES-2012-13-100%-Data-SEC_2_SCHOOL_EDUCATION.CSV"[3] "HIES-2012-13-100%-Data-SEC_3_HEALTH.CSV"[4] "HIES-2012-13-100%-Data-SEC_4_1_FOOD_EXP.CSV"[5] "HIES-2012-13-100%-Data-SEC_4_2_NONFOOD.CSV"[6] "HIES-2012-13-100%-Data-SEC_4_3_BOARDERS.CSV"[7] "HIES-2012-13-100%-Data-SEC_4_3_IS_BOADERS.CSV"[8] "HIES-2012-13-100%-Data-SEC_5_1_EMP_INCOME.CSV"[9] "HIES-2012-13-100%-Data-SEC_5_1_IS_EMP_INCOME.CSV"[10] "HIES-2012-13-100%-Data-SEC_5_2_AGRI_INCOME.CSV"[11] "HIES-2012-13-100%-Data-SEC_5_2_IS_AGRI_INCOME.CSV"[12] "HIES-2012-13-100%-Data-SEC_5_3_IS_OTHER_AGRI_INCOME.CSV"[13] "HIES-2012-13-100%-Data-SEC_5_3_OTHER_AGRI_INCOME.CSV"[14] "HIES-2012-13-100%-Data-SEC_5_4_IS_NON_AGRI_INCOME.CSV"[15] "HIES-2012-13-100%-Data-SEC_5_4_NON_AGRI_INCOME.CSV"[16] "HIES-2012-13-100%-Data-SEC_5_5_1_IS_OTHER_INCOME.CSV"[17] "HIES-2012-13-100%-Data-SEC_5_5_1_OTHER_INCOME.CSV"[18] "HIES-2012-13-100%-Data-SEC_5_5_2_IS_WINDFALL_INCOME.CSV"[19] "HIES-2012-13-100%-Data-SEC_5_5_2_WINDFALL_INCOME.CSV"[20] "HIES-2012-13-100%-Data-SEC_6_B_DEBTNESS.CSV"[21] "HIES-2012-13-100%-Data-SEC_6A_DURABLE_GOODS.CSV"

Sri Lanka HIES 2012/13 Manual (Version 1.1)

12[22] "HIES-2012-13-100%-Data-SEC_7_BASIC_FACILITIES.CSV"[23] "HIES-2012-13-100%-Data-SEC_8_HOUSING.CSV"[24] "HIES-2012-13-100%-Data-SEC_9_LAND_ANIMAL.CSV"[25] "HIES-2012-13-100%-Data-WEIGHT_INCOME_EXPENDITURE.CSV"# Names of data frames in R
# using the sequential number in alphabetical order of CSV file names> (Rnames<-paste("R",formatC(1:25,width=2,flag="0"),sep=""))[1] "R01" "R02" "R03" "R04" "R05" "R06" "R07" "R08" "R09" "R10" "R11" "R12" "R13"[14] "R14" "R15" "R16" "R17" "R18" "R19" "R20" "R21" "R22" "R23" "R24" "R25"# Imported 25 CSV files to R and stored R data frames in the list outfiles.
Note: In read.csv command, the parameter of stringsAsFactor=F should be set.
> outfiles<-list()+ for(j in 1:25){+ outfiles[[j]]<-read.csv(csv.names[j], header=T, stringsAsFactor=F )+ }> length(outfiles)[1] 25# List of the number of rows and columns of the created data frames> filenames<-sub("HIES-2012-13-100%-Data-","",csv.names)> filenames<-sub(".CSV","",filenames)> filenames[1] "SEC_1_DEMOGRAPHIC""SEC_2_SCHOOL_EDUCATION"[3] "SEC_3_HEALTH""SEC_4_1_FOOD_EXP"[5] "SEC_4_2_NONFOOD""SEC_4_3_BOARDERS"[7] "SEC_4_3_IS_BOADERS""SEC_5_1_EMP_INCOME"[9] "SEC_5_1_IS_EMP_INCOME""SEC_5_2_AGRI_INCOME"[11] "SEC_5_2_IS_AGRI_INCOME""SEC_5_3_IS_OTHER_AGRI_INCOME"[13] "SEC_5_3_OTHER_AGRI_INCOME""SEC_5_4_IS_NON_AGRI_INCOME"[15] "SEC_5_4_NON_AGRI_INCOME""SEC_5_5_1_IS_OTHER_INCOME"[17] "SEC_5_5_1_OTHER_INCOME""SEC_5_5_2_IS_WINDFALL_INCOME"[19] "SEC_5_5_2_WINDFALL_INCOME""SEC_6_B_DEBTNESS"[21] "SEC_6A_DURABLE_GOODS""SEC_7_BASIC_FACILITIES"[23] "SEC_8_HOUSING""SEC_9_LAND_ANIMAL"

Sri Lanka HIES 2012/13 Manual (Version 1.1)

13[25] "WEIGHT_INCOME_EXPENDITURE"> for(j in 1:25){+ cat(Rnames[j],": ",format(filenames[j],width=30),": ",+ format(nrow(outfiles[[j]]),width=6),",",+ format(ncol(outfiles[[j]]),width=4),"\n")
+ }
R01 :

SEC_1_DEMOGRAPHIC:

89742 ,

27

R02 :SEC_2_SCHOOL_EDUCATION:

22029 ,

22

R03 :SEC_3_HEALTH:

80534 ,

27

R04 :SEC_4_1_FOOD_EXP:

815186 ,

15

R05 :

SEC_4_2_NONFOOD:

612085 ,

15

R06 :

SEC_4_3_BOARDERS

:

260 ,

25

R07 :

SEC_4_3_IS_BOADERS:

20540 ,

12

R08 :SEC_5_1_EMP_INCOME:

18594 ,

16

R09 :SEC_5_1_IS_EMP_INCOME:

20540 ,

12

R10 :SEC_5_2_AGRI_INCOME:

4196 ,

24

R11 :SEC_5_2_IS_AGRI_INCOME:

20540 ,

12

R12 :SEC_5_3_IS_OTHER_AGRI_INCOME:

20540 ,

12

R13 :SEC_5_3_OTHER_AGRI_INCOME:

3968 ,

19

R14 :SEC_5_4_IS_NON_AGRI_INCOME:

20540 ,

12

R15 :SEC_5_4_NON_AGRI_INCOME:

5700 ,

16

R16 :SEC_5_5_1_IS_OTHER_INCOME:

20540 ,

12

R17 :SEC_5_5_1_OTHER_INCOME:

12602 ,

24

R18 :SEC_5_5_2_IS_WINDFALL_INCOME:

20540 ,

12

R19 :SEC_5_5_2_WINDFALL_INCOME:

8575 ,

22

R20 :SEC_6_B_DEBTNESS:

20540 ,

29

R21 :SEC_6A_DURABLE_GOODS:

20540 ,

36

R22 :SEC_7_BASIC_FACILITIES:

20540 ,

48

R23 :SEC_8_HOUSING:

20540 ,

38

R24 :SEC_9_LAND_ANIMAL:

20540 ,40

R25 :WEIGHT_INCOME_EXPENDITURE:

20540 ,

14

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# Structure of each data frame> for(j in 1:25){+ cat(Rnames[j],": ",format(filenames[j],width=30),":\n")+ cat(str(outfiles[[j]]), "\n\n")
+ }

R01 :

SEC_1_DEMOGRAPHIC:'data.frame':

89742 obs. of$ REC_TYPE: int

1 1 1 1 1 1 1 1 1 1 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 39 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96375 ...

$ SNUMBER: int

1 1 2 2 2 2 3 3 3 3 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ PERSON_SERIAL_NO : int

1 2 1 2 3 41 1 2 3 4 ...

$ RELATIONSHIP: int

1 4 1 2 3 3 1 3 3 5 ...

$ SEX

: int

2 2 1 2 1 2 2 2 2 1 ...

$ BIRTH_YEAR: int

71 42 46 50 77 NA 61 92 89 83 ...

$ BIRTH_MONTH: int

2 7 11 7 3 NA 7 11 4 7 ...

$ AGE

: int

41 70 66 62 35 NA 51 20 23 29 ...

$ ETHNICITY: int

4 4 4 4 4 NA 4 4 4 4 ...

$ RELIGION: int

3 3 3 3 3 NA 3 3 3 3 ...

$ MARITAL_STATUS: int

3 5 2 2 1 NA 2 1 2 2 ...

$ CURR_EDUC: int

9 9 9 9 9 NA 9 4 9 9 ...

$ EDUCATION: int

5 4 6 4 12 NA 6 10 10 10 ...

$ IS_ACTIVE: int

2 2 2 2 1 NA 1 2 2 1 ...

$ MAIN_ACTIVITY: int

3 3 4 4 NA NA NA 3 3 NA ...

$ MAIN_OCCUPATION: int

NA NA NA NA 7422 NA 7512 NA NA 7512 ...

$ INDUSTRY: int

NA NA NA NA 33140 NA 10712 NA NA 10629 ...

$ EMPLOYMENT_STATUS: int

27 variables:

NA NA NA NA 3 NA 5 NA NA 5 ...

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R02 :

SEC_2_SCHOOL_EDUCATION'data.frame':

22029 obs. of:
22 variables:$ REC_TYPE: int

2 2 2 2 2 2 2 2 2 2 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 39 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96375 ...

$ SNUMBER: int

3 4 4 6 7 7 7 7 9 9 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ R2_PERSON_SERIAL: int

2 4 5 3 3 4 5 6 3 4 ...

$ R2_SCHOOL_EDUCATION: int

3 3 1 1 1 1 1 1 3 1 ...

$ TYPE_OF_SCHOOL: int

NA NA 1 1 1 1 1 1 NA 1 ...

$ GRADE_THIS_YEAR: int

NA NA 8 12 10 9 5 1 NA 11 ...

$ GRADE_LAST_YEAR: int

NA NA 7 11 9 8 4 19 NA 10 ...

$ DISTANCE: int

NA NA 10 0 4 4 4 4 NA 0 ...

$ TRANSPORT_MEDIUM: int

NA NA 1 1 5 5 5 5 NA 1 ...

$ TIME_TO_SCHOOL: int

NA NA 5 5 15 15 15 15 NA 5 ...

$ NOSCHOOLING_REASON : int

NA NA NA NA NA NA NA NA NA NA ...

$ REASON_NOT_GOING

3 6 NA NA NA NA NA NA 9 NA ...

: int$ WHEN_STOP_SCHOOLING: int

R03 :2009 2011 NA NA NA NA NA NA 2010 NA ...

SEC_3_HEALTH'data.frame'::

80534 obs. of

27 variables:$ REC_TYPE: int

3 3 3 3 3 3 3 3 3 3 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 39 ...

$ A096375 ...

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375$ SNUMBER: int

1 1 2 2 2 3 3 3 3 3 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

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$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ R3_PERSON_SERIAL2: int

1 2 1 2 3 1 2 3 4 5 ...

$ ATTEND_GOVT_HOSPITAL3: int

2 2 2 2 2 1 2 2 2 2 ...

$ REASON_HOSPITAL4: int

NA NA NA NA NA 1 NA NA NA NA ...

$ ATTEND_PRIVATE_HOSPITAL5: int

2 2 2 1 2 2 2 2 2 2 ...

$ REASON_FOR_WHAT6: int

NA NA NA 1 NA NA NA NA NA NA ...

$ IS_STAY_GOVHOSPITAL7: int

2 2 2 2 2 2 2 2 2 2 ...

$ REASON_STAY8: int

NA NA NA NA NA NA NA NA NA NA ...

$ STAY_IN_PRIVTE_HOSPITAL9: int

2 2 2 2 2 2 2 2 2 2 ...

$ REASON_FOR_STAY10: int

NA NA NA NA NA NA NA NA NA NA ...

$ IS_ILL_DISABLE11: int

2 2 1 1 2 1 2 2 2 2 ...

$ WHAT_ILL_DISABLE12: int

NA NA 1 4 NA 10 NA NA NA NA ...

$ IS_EMPL_REASON13: int

NA NA 2 2 NA 2 NA NA NA NA ...

$ DURATION_YEARS14: int

NA NA 8 3 NA 2 NA NA NA NA ...

$ DURATION_MONTHS14: int

NA NA 4 6 NA 3 NA NA NA NA ...

$ IS_ABSENT_ACT15: int

NA NA 2 2 NA 2 NA NA NA NA ...

$ DAYS_ABSENT16: int

NA NA NA NA NA NA NA NA NA NA ...

R04 :SEC_4_1_FOOD_EXP:'data.frame':

815186 obs. of

15 variables:$ REC_TYPE: int

4 4 4 4 4 4 4 4 4 4 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 39 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96375 ...

$ SNUMBER: int

1 1 1 1 1 1 1 1 1 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ CODE: int

101 105 111 201 206 302 309 401 402 410 ...

$ QUANTITY: chr"000250" "003500" "000500" "000900" ...

$ VALUE: chr"0000016" "0000259" "0000050" "0000110" ...

$ INKIND_VALUE: chr"0000016" "0000259" "0000050" "0000110" ...

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R05 :

SEC_4_2_NONFOOD'data.frame'::

612085 obs. of

15 variables:$ REC_TYPE: int

5 5 5 5 5 5 5 5 5 5 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 39 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96375 ...

$ SNUMBER: int

1 1 1 1 1 1 1 1 1 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ NF_CODE: int

2001 2002 2003 2101 2104 2107 2109 2201 2202 2306 ...

$ NF_QUANTITY

: chr

"

$ NF_VALUE: chr"0000400" "0000048" "0000087" "0000350" ...

$ NF_INKIND_VALUE: int

R06 :

" "

" "

" ...

400 NA 87 350 NA 75 18 120 70 250 ...

SEC_4_3_BOARDERS'data.frame':" "260 obs. of:
25 variables:$ REC_TYPE: int

7 7 7 7 7 7 7 7 7 7 ...

$ DISTRICT: int

92 92 92 92 92 92 91 91 91 82 ...

$ SECTOR: int

2 2 3 2 1 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

7 7 7 3 2 2 10 7 1 2 ...

$ PSU

: int

5 5 8 64 56 60 27 2 56 50 ...

$ A0

: int

97449 97449 98645 97416 96890 98409 91403 91706 92146 89731 ...

$ SNUMBER : int

4 4 8 7 6 7 5 2 3 4 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ COL_2: int

2 3 1 8 6 8 5 2 6 4 ...

$ COL_3: int

1500 200 NA NA NA NA NA NA NA NA ...

$ COL_4: int

2300 NA NA NA NA NA NA NA NA NA ...

$ COL_5: int

NA NA NA NA NA NA NA NA NA NA ...

$ COL_6: int

NA NA NA NA NA NA NA NA NA NA ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

18

$ COL_7: int

NA NA 500 NA NA NA 2500 NA NA NA ...

$ COL_8: int1000 1200 NA NA NA NA 2000 60 300 NA ...

$ COL_9: int

NA NA NA NA NA NA NA NA NA NA ...

$ COL_10: int

3000 3000 NA NA NA NA 5500 NA NA NA ...

$ COL_11: int

5000 NA 14000 NA 6000 NA 5000 NA 1500 3000 ...

$ COL_12: int

12200 NA 140 NA NA NA 3750 NA NA NA ...

$ COL_13: int

1000 NA NA NA NA NA 3000 NA 150 NA ...

$ COL_14: int

4000 1500 NA NA NA NA 5000 NA NA 1000 ...

$ COL_15: int

NA NA 750 NA NA NA NA NA NA NA ...

R07 :SEC_4_3_IS_BOADERS'data.frame'::

20540 obs. of

12 variables:$ REC_TYPE: int

6 6 6 6 6 6 6 6 6 6 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_BOARDERS_SERVENTS: int

2 2 2 2 2 2 2 2 2 2 ...

R08 :SEC_5_1_EMP_INCOME'data.frame'::

18594 obs. of

16 variables:$ REC_TYPE: int

9 9 9 9 9 9 9 9 9 9 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 40 40 40 40 41 41 41 41 ...

$ A0

: int

96375 96375 96732 96732 96732 96732 96872 96872 96872 96872 ...

$ SNUMBER: int

2 10 1 3 5 5 7 7 8 10 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

19

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ SERIAL_NO_SEC_1: int

3 2 3 1 1 7 1 2 1 1 ...

$ PRI_SEC

1 1 1 1 1 1 1 1 1 1 ...

: int$ WAGES_SALARIES : int

2500 35000 12500 120000 12500 15000 18000 19520 22600 14000 ...

$ ALLOWENCES: int

500 NA NA NA NA NA NA NA NA NA ...

$ BONUS: int

NA NA NA NA NA NA NA NA NA NA ...

R09 :SEC_5_1_IS_EMP_INCOME'data.frame':

20540 obs. of:
12 variables:$ REC_TYPE: int

8 8 8 8 8 8 8 8 8 8 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_EMPLOYMENT_INCOME: int

2 1 2 2 2 2 2 2 1 1 ...

R10 :SEC_5_2_AGRI_INCOME'data.frame':

4196 obs. of:
24 variables:$ REC_TYPE: int

11 11 11 11 11 11 11 11 11 11 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 10 10 9 8 8 8 ...

$ PSU

: int

41 41 42 42 25 28 18 10 10 10 ...

$ A0

: int

96872 96872 97152 97152 96779 97521 96612 96053 96053 96053 ...

$ SNUMBER : int

10 10 2 3 5 4 7 1 1 2 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

20

$ COL_2X: int

1 1 1 1 1 1 2 1 1 1 ...

$ COL_4X: int

1 4 4 4 4 1 1 1 4 4 ...

$ COL_5X: int

NA NA NA NA NA NA NA 1 NA NA ...

$ COL_6X: int

1 NA NA NA NA 1 1 2 NA NA ...

$ COL_7X: int

NA NA NA NA 30 10 NA 0 NA NA ...

$ COL_8X: int

350 20 NA NA 45 200 300 1300 NA 40 ...

$ COL_8X1 : int

8750 850 NA NA 1800 5000 7000 40000 1000 1500 ...

$ COL_9X

5600 150 NA NA 300 4340 4500 21750 NA 500 ...

: int$ COL_10X : int

200 NA NA NA NA 200 300 13000 NA NA ...

$ COL_10X1: chr"000005000" "$ COL_11X : int

150 NA NA NA NA NA NA 120 NA NA ...

$ COL_12X : int

NA NA NA NA NA NA NA NA NA NA ...

$ COL_13X : int

1800 NA NA NA NA 340 1200 1500 NA NA ...

R11 :

" "

SEC_5_2_IS_AGRI_INCOME'data.frame':

20540 obs. of

" "

" ...

:
12 variables:$ REC_TYPE: int

10 10 10 10 10 10 10 10 10 10 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A096732 ...

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_AGRICULTURAL_INCOME: int

2 2 2 2 2 2 2 2 2 2 ...

R12 :SEC_5_3_IS_OTHER_AGRI_INCOME'data.frame':

20540 obs. of:

12 variables:$ REC_TYPE: int

12 12 12 12 12 12 12 12 12 12 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

21

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A096732 ...

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_OTHER_AGRRI_INCOME: int

2 2 2 2 2 1 1 1 2 2 ...

R13 :SEC_5_3_OTHER_AGRI_INCOME:'data.frame':

3968 obs. of$ REC_TYPE: int

13 13 13 13 13 13 13 13 13 13 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 41 41 41 41 43 43 ...

$ A0

: int

96375 96375 96375 96375 96872 96872 96872 96872 97612 97612 ...

$ SNUMBER: int

6 7 9 9 1 8 10 10 1 2 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ SER_NO_SEC_5_3: int

2 1 1 1 1 1 1 1 1 1 ...

$ SEASONAL_CROP : int

7 5 5 7 2 4 2 4 1 2 ...

$ ACRES_5_3: int

NA NA NA NA NA NA NA NA NA 1 ...

$ ROOTS_5_3: int

NA NA NA NA NA NA NA NA 2 2 ...

$ PERCHS_5_3: int

NA NA NA NA NA NA NA NA NA NA ...

$ OUTPUT_5_3: int

NA NA 10000 1800 800 480 1600 1800 4000 1120 ...

$ INPUT_5_3: int

600 2000 2000 800 NA NA NA NA 2000 NA ...

$ FERTILIZES: int

NA NA NA NA NA NA NA NA NA NA ...

R14 :19 variables:

SEC_5_4_IS_NON_AGRI_INCOME:'data.frame':

20540 obs. of

12 variables:$ REC_TYPE: int

14 14 14 14 14 14 14 14 14 14 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

22

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_NON_AGRI_INCOME: int

2 2 1 1 1 1 1 1 2 1 ...

R15 :SEC_5_4_NON_AGRI_INCOME'data.frame':

5700 obs. of:
16 variables:$ REC_TYPE: int

15 15 15 15 15 15 15 15 15 15 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 40 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96732 96732 ...

$ SNUMBER: int

3 3 4 4 5 6 7 9 1 10 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ SERIAL_5_4: int

1 4 1 3 3 1 1 1 1 1 ...

$ NON_AGRI

2 2 4 4 4 4 4 4 9 4 ...

: int$ OUTPUT_5_4: chr"000045000" "000054000" "000036000" "000048000" ...

$ INPUT_5_4 : chr"000035000" "000030000" "000024000" "000035000" ...

$ SUBSIDIES : int

NA NA NA NA NA NA NA NA NA NA ...

R16 :SEC_5_5_1_IS_OTHER_INCOME'data.frame':

20540 obs. of:

12 variables:$ REC_TYPE: int

16 16 16 16 16 16 16 16 16 16 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

23

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_OTHER_INCOME: int

1 1 1 1 1 2 2 1 1 1 ...

R17 :SEC_5_5_1_OTHER_INCOME'data.frame':

12602 obs. of:
24 variables:$ REC_TYPE: int

17 17 17 17 17 17 17 17 17 17 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 40 40 40 ...

$ A096732 ...

: int

96375 96375 96375 96375 96375 96375 96375 96732 96732$ SNUMBER: int

1 2 3 4 5 9 10 1 1 5 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ SERIAL_5_5_1: int

1 1 1 2 2 1 1 1 2 6 ...

$ PENSION: int

NA NA NA NA NA NA 20800 NA NA NA ...

$ DISABILITY_AND_RELIEF: int

NA NA NA NA NA NA NA NA NA NA ...

$ PROPERTY_RENTS: int

NA NA NA NA NA NA NA NA NA NA ...

$ SAMURDHI: int

NA NA 300 NA NA 900 NA 550 NA NA ...

$ DIVIDENDS: int

NA NA NA NA NA NA NA NA NA NA ...

$ ELDER: int

NA NA NA NA NA NA NA NA NA NA ...

$ SCHOLAR: int

NA NA NA NA NA NA NA NA NA NA ...

$ SC_LUNCH: int

NA NA NA NA NA NA NA NA NA NA ...

$ THREEPOSHA: int

NA NA NA NA NA NA NA NA NA NA ...

$ OTHER_INCOME: int

NA NA NA NA NA NA NA NA NA NA ...

$ INCOME_FORIGN: int

NA NA NA NA NA NA NA NA NA 22000 ...

$ INCOME_LOCAL: int

20000 300000 NA 24000 18000 NA NA NA 36000 NA ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

24

R18 :

SEC_5_5_2_IS_WINDFALL_INCOME:'data.frame':

20540 obs. of$ REC_TYPE: int

18 18 18 18 18 18 18 18 18 18 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_WINDFALL_INCOME: int

2 2 2 2 2 2 1 1 2 2 ...

R19 :12 variables:

SEC_5_5_2_WINDFALL_INCOME'data.frame':

8575 obs. of:

22 variables:$ REC_TYPE: int

19 19 19 19 19 19 19 19 19 19 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 42 42 42 42 42 42 43 43 ...

$ A097612 ...

: int

96375 96375 97152 97152 97152 97152 97152 97152 97612$ SNUMBER: int

7 9 2 4 5 7 8 9 1 4 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ PERSON_5_5_2: int

1 1 3 1 1 2 1 1 2 1 ...

$ LOANS: int

NA NA NA NA NA 100000 NA 250000 NA NA ...

$ PAWNING_SELLING: int

60000 12000 50000 50000 25000 57000 NA 125000 70000 NA ...

$ DEPOSITS_PENSIONS_EPF: int

NA NA NA NA NA NA NA NA NA NA ...

$ WELFARE_SOCITY: int

NA NA NA NA NA NA NA NA NA NA ...

$ SEETTU_DEBITS: int

NA NA 24000 NA 60000 NA 100000 NA NA 20000 ...

$ MEDICAL: int

NA NA NA NA NA NA NA NA NA NA ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

25

$ INSUARANCE: int

NA NA NA NA NA NA NA NA NA NA ...

$ LOTTERY: int

NA NA NA NA NA NA NA NA NA NA ...

$ FOODALLOWENCE: int

NA NA NA NA NA NA NA NA NA NA ...

$ DIASTER: int

NA NA NA NA NA NA NA NA NA NA ...

R20 :SEC_6_B_DEBTNESS'data.frame':

20540 obs. of:
29 variables:$ REC_TYPE: int

21 21 21 21 21 21 21 21 21 21 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ BANKS: int

2 2 2 2 2 2 2 2 2 2 ...

$ BANK_AMOUNT: int

NA NA NA NA NA NA NA NA NA NA ...

$ FINANCE: int

2 2 2 2 2 2 2 2 2 2 ...

$ FINANCE_AMOUNT: int

NA NA NA NA NA NA NA NA NA NA ...

$ EMPLOYER: int

2 2 2 2 2 2 2 2 2 2 ...

$ EMPLOYER_AMOUNT: int

NA NA NA NA NA NA NA NA NA NA ...

$ LENDER: int

1 2 1 2 2 2 2 2 2 2 ...

$ LENDER_AMOUNT: int

2450 NA 150000 NA NA NA NA NA NA NA ...

$ CREDIT_CARDS: int

2 2 2 2 2 2 2 2 2 2 ...

$ CREDIT_CARDS_AMOUNT: int

NA NA NA NA NA NA NA NA NA NA ...

$ RETAIL_SHOPS

2 2 1 2 2 2 2 2 2 2 ...

: int$ RETAIL_SHOP_AMOUNT : int

NA NA 3000 NA NA NA NA NA NA NA ...

$ PAWNING: int

2 2 2 2 2 2 1 1 2 2 ...

$ PAWNING_AMOUNT: int

NA NA NA NA NA NA 200000 12000 NA NA ...

$ INSTALMENT_GOODS: int

2 2 2 2 2 2 2 2 2 2 ...

$ INSTALEMENT_AMOUNT : int

NA NA NA NA NA NA NA NA NA NA ...

$ OTHER_DEBTS: int

2 2 2 2 2 2 2 2 2 2 ...

$ OTHER_AMOUNT: int

NA NA NA NA NA NA NA NA NA NA ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

26

R21 :

SEC_6A_DURABLE_GOODS:'data.frame':

20540 obs. of

36 variables:$ REC_TYPE: int

20 20 20 20 20 20 20 20 20 20 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ RADIO: int

1 1 2 1 1 1 1 1 1 1 ...

$ TV

: int

2 1 1 1 1 1 2 1 1 1 ...

$ VCD

: int

2 2 1 1 2 1 2 2 2 2 ...

$ SEWING_MECHINE: int

2 1 1 1 1 2 1 1 1 2 ...

$ WASHING_MECHINE : int

2 1 2 1 2 2 1 2 1 2 ...

$ FRIDGE: int

2 1 1 1 1 1 1 1 1 2 ...

$ COOKERT: int

2 2 1 1 1 1 1 1 1 2 ...

$ ELECTRIC_FANS: int

2 2 1 1 1 1 2 2 1 2 ...

$ TELEPHONE: int

2 1 2 2 2 1 2 2 2 2 ...

$ TELEPHONE_MOBILE: int

1 2 1 1 1 1 1 1 1 2 ...

$ COMPUTERS: int

2 2 2 2 2 2 2 1 1 2 ...

$ CAMERA: int

2 2 2 2 2 2 2 2 2 2 ...

$ BICYCLE: int

2 1 2 2 2 2 1 1 2 2 ...

$ MOTOR_BICYCLE: int

2 2 2 2 2 2 2 1 2 2 ...

$ THREE_WHEELER: int

2 2 2 2 2 2 2 2 2 2 ...

$ MOTOR_CAR_VAN: int

2 2 2 2 2 2 2 2 2 2 ...

$ BUS_LORRY: int

2 2 2 2 2 2 2 2 2 2 ...

$ TRACTOR_2_WHEEL : int

2 2 2 2 2 2 2 2 2 2 ...

$ TRACTOR_4_WHEEL : int

2 2 2 2 2 2 2 2 2 2 ...

$ PESTICIDER: int

2 2 2 2 2 2 2 2 2 2 ...

$ THRESHERS: int

2 2 2 2 2 2 2 2 2 2 ...

$ WATERPUMPS: int

2 2 2 2 2 2 2 2 2 2 ...

$ MECHINE: int

2 2 2 2 2 2 2 2 2 2 ...

$ BOATS: int

2 2 2 2 2 2 2 2 2 2 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

27

$ FISHING_NETS

R22 :

: int

2 2 2 2 2 2 2 2 2 2 ...

SEC_7_BASIC_FACILITIES'data.frame'::

20540 obs. of

48 variables:$ REC_TYPE: int

22 22 22 22 22 22 22 22 22 22 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A096732 ...

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ BUS_HALT: int

0 0 0 0 0 0 0 0 0 0 ...

$ BUS_HALT_TIME: int

5 5 5 5 5 5 5 1 5 5 ...

$ PRE_SCHOOL: int

0 0 0 0 0 0 0 0 0 0 ...

$ PRE_SCHOOL_TIME: int

5 5 5 5 5 5 5 5 5 5 ...

$ PRIMERY_SCHOOL: int

0 0 0 0 0 0 0 0 0 0 ...

$ PRIMERY_SCHOOL_TIME: int

5 5 5 5 5 5 5 5 5 5 ...

$ SECONDERY_SCHOOL: int

0 0 0 0 0 0 0 0 0 0 ...

$ SEC_SCHOOL_TIME: int

5 5 5 5 5 5 5 5 5 10 ...

$ HOSPITAL: int

4 4 4 4 4 4 4 4 4 0 ...

$ HOSPITAL_TIME: int

15 15 15 15 15 15 15 15 15 10 ...

$ MATRENITY_HOME: int

4 4 4 4 4 4 4 4 4 0 ...

$ MATERNITY_HOME_TIME: int

15 15 15 15 15 15 15 15 15 10 ...

$ GOV_DISPENSARZ: int

4 4 4 4 4 4 4 4 4 0 ...

$ GOV_DISPENSARY_TIME: int

15 15 15 15 15 15 15 15 15 10 ...

$ PRIVATE_DISPENSARY: int

0 0 0 0 0 0 0 0 0 0 ...

$ PRIVATE_DISPENSARY_TIME: int

10 10 10 10 15 10 10 10 10 10 ...

$ MATERNITY_CLINIC: int

0 0 0 0 0 0 0 0 0 0 ...

$ MATERNITY_CLINIC_TIME: int

10 10 10 10 10 10 10 10 10 10 ...

$ DMO

: int

4 4 4 4 4 4 4 4 4 0 ...

$ DMO_TIME: int

15 15 15 15 15 15 15 15 15 10 ...

$ MCUCPC: int

3 3 3 3 3 3 3 3 3 0 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

28

$ MCUCPC_TIME: int

15 15 15 15 15 15 15 15 15 10 ...

$ DS_OFFICE: int

4 4 4 4 4 4 4 4 4 0 ...

$ DS_OFFICE_TIME: int

20 20 20 20 20 20 20 20 20 10 ...

$ GN_OFFICE: int

1 1 1 1 1 1 1 1 1 0 ...

$ GN_OFFICE_TIME: int

10 10 10 10 10 10 10 10 10 3 ...

$ POST_OFFICE: int

0 0 0 0 0 0 0 0 0 0 ...

$ POST_OFFICE_TIME: int

5 5 5 5 5 5 5 5 5 10 ...

$ BANK: int

4 4 4 4 4 4 4 0 4 0 ...

$ BANK_TIME: int

15 15 15 15 15 15 15 15 15 5 ...

$ AGRI_OFFICE: int

8 8 8 8 8 8 8 8 8 1 ...

$ AGRI_OFFICE_TIME: int

40 40 40 40 40 40 40 40 40 15 ...

$ IS_POWER_LINES_NEAR: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_TEL_LINES_NEAR: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_WATER_SERVICE_NEAR: int

1 1 1 1 1 1 1 1 1 1 ...

$ ATM_CARD: int

2 2 2 2 2 2 1 1 1 2 ...

$ ATMCARDAMOUNT: int

NA NA NA NA NA NA 15000 10000 40000 NA ...

R23 :SEC_8_HOUSING'data.frame'::

20540 obs. of

38 variables:$ REC_TYPE: int

23 23 23 23 23 23 23 23 23 23 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A096732 ...

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ STRUCTURE: int

1 2 1 1 1 1 1 1 2 9 ...

$ BED_ROOMS: int

2 3 1 5 3 2 2 2 5 1 ...

$ AREA: int

4 6 3 6 4 5 6 5 7 2 ...

$ WALLS: int

1 1 3 1 1 1 1 5 1 6 ...

$ FLOOR: int

1 1 1 1 1 6 6 1 1 3 ...

$ ROOF: int

1 2 2 2 2 3 3 1 2 5 ...

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29

$ OWNERSHIP: int

1 1 1 1 1 1 1 2 1 1 ...

$ DRINKING_WATER: int

4 4 4 4 4 4 4 1 1 6 ...

$ OWN_WATER: int

1 1 1 1 1 1 1 1 1 2 ...

$ WATER_DISTANCE

: chr

"

$ WATER_SUFFICENCY: int

1 1 1 1 1 1 1 1 1 1 ...

$ OTHER_WATER_SUFFICENCY: int

1 1 1 1 1 1 1 1 1 1 ...

$ TIOILET_USE: int

1 1 3 1 3 1 1 3 1 1 ...

$ TOILET_TYPE: int

1 1 1 1 1 1 1 1 1 1 ...

$ GARBAGE_DUMPING: int

1 1 1 1 1 1 3 1 2 2 ...

$ LITE_SOURCE: int

2 2 2 2 2 2 2 2 2 2 ...

$ COOKING_FUEL: int

1 1 1 2 1 1 1 1 2 1 ...

$ IS_COLLECT_FIREWOOD: int

2 2 1 2 1 2 1 1 2 2 ...

$ FIRE_WOOD_OWN: int

NA NA 3 NA 3 NA 1 1 NA NA ...

$ OTHER_DISTANCE: int

NA NA 500 NA 200 NA NA NA NA NA ...

$ NATURAL_CALAMITY: int

2 2 2 2 2 2 2 2 2 2 ...

$ FLOODING: int

NA NA NA NA NA NA NA NA NA NA ...

$ DROUGHT: int

NA NA NA NA NA NA NA NA NA NA ...

$ LAND_SLIDES: int

NA NA NA NA NA NA NA NA NA NA ...

$ WILDANIMALS: int

NA NA NA NA NA NA NA NA NA NA ...

$ WINDS: int

NA NA NA NA NA NA NA NA NA NA ...

$ OTHER_CALAMITY: int

NA NA NA NA NA NA NA NA NA NA ...

R24 :SEC_9_LAND_ANIMAL

" "

" "

" "

" ...

:

'data.frame':

20540 obs. of$ REC_TYPE: int

24 24 24 24 24 24 24 24 24 24 ...

$ DISTRICT: int

92 92 92 92 92 92 92 92 92 92 ...

$ SECTOR: int

2 2 2 2 2 2 2 2 2 2 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

12 12 12 12 12 12 12 12 12 12 ...

$ PSU

: int

39 39 39 39 39 39 39 39 39 40 ...

$ A0

: int

96375 96375 96375 96375 96375 96375 96375 96375 96375 96732 ...

$ SNUMBER: int

1 2 3 4 5 6 7 9 10 1 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ IS_AGRILAND_OWNER: int

1 1 1 1 1 1 1 1 1 2 ...

$ PADDY_OWN_ACR

NA NA NA NA NA NA NA NA NA NA ...

: int

40 variables:

Sri Lanka HIES 2012/13 Manual (Version 1.1)

30

$ PADDY_OWN_RT: int

NA NA NA NA NA NA NA NA NA NA ...

$ PADDY_OWN_PERCH: int

NA NA NA NA NA NA NA NA NA NA ...

$ PADDY_OTHER_ACR: int

NA NA NA NA NA NA NA NA NA NA ...

$ PADDY_OTHER_RT: int

NA NA NA NA NA NA NA NA NA NA ...

$ PADDY_OTHER_PERCH: int

NA NA NA NA NA NA NA NA NA NA ...

$ LAND_OWN_ACR: int

NA NA NA NA NA NA NA NA NA NA ...

$ LAND_OWN_RT: int

NA NA NA NA NA NA NA NA NA NA ...

$ LAND_OWN_PERCH: int

NA 9 5 NA NA NA 25 NA 13 NA ...

$ LAND_OTHER_ACR: int

NA NA NA NA NA NA NA NA NA NA ...

$ LAND_OTHER_RT: int

NA NA NA NA NA NA NA NA NA NA ...

$ LAND_OTHER_PERCH : int

NA NA NA NA NA NA NA NA NA NA ...

$ HOME_OWN_ACR: int

NA NA NA NA NA NA NA NA NA NA ...

$ HOME_OWN_RT: int

NA NA NA NA NA NA NA NA NA NA ...

$ HOME_OWN_PERCH: int

10 9 5 10 10 5 15 4 10 NA ...

$ HOME_OTHER_ACR: int

NA NA NA NA NA NA NA NA NA NA ...

$ HOME_OTHER_RT: int

NA NA NA NA NA NA NA NA NA NA ...

$ HOME_OTHER_PERCH : int

NA NA NA NA NA NA NA NA NA NA ...

$ COWS_BUFFALOWS: int

2 2 2 2 2 2 2 2 2 2 ...

$ COWS_COUNT: int

NA NA NA NA NA NA NA NA NA NA ...

$ GOATS_SHEEPS: int

2 2 2 2 2 2 1 1 2 2 ...

$ GOAT_COUNT: int

NA NA NA NA NA NA 2 2 NA NA ...

$ PIGS: int

2 2 2 2 2 2 2 2 2 2 ...

$ PIGS_COUNT: int

NA NA NA NA NA NA NA NA NA NA ...

$ CHICKENS: int

2 2 2 2 2 1 2 1 2 2 ...

$ CHICKEN_COUNT: int

NA NA NA NA NA 1 NA 3 NA NA ...

$ OTHER_ANIMALS: int

2 2 2 2 2 2 2 2 2 2 ...

$ OTHER_COUNT: int

NA NA NA NA NA NA NA NA NA NA ...

R25 :WEIGHT_INCOME_EXPENDITURE'data.frame':

20540 obs. of:

14 variables:$ REC_TYPE: int

25 25 25 25 25 25 25 25 25 25 ...

$ DISTRICT: int

11 11 11 11 11 11 11 11 11 11 ...

$ SECTOR: int

1 1 1 1 1 1 1 1 1 1 ...

$ DS

: int

10 10 10 10 10 10 10 10 10 10 ...

$ MONTH: int

7 7 7 7 7 7 7 7 7 7 ...

$ PSU

: int

1 1 1 1 1 1 1 1 1 2 ...

$ A0

: int

10002 10002 10002 10002 10002 10002 10002 10002 10002 10615 ...

Sri Lanka HIES 2012/13 Manual (Version 1.1)

31

$ SNUMBER : int

1 3 4 5 6 7 8 9 10 2 ...

$ HHNO: int

1 1 1 1 1 1 1 1 1 1 ...

$ NHH

: int

1 1 1 1 1 1 1 1 1 1 ...

$ RESULT: int

1 1 1 1 1 1 1 1 1 1 ...

$ HExpPM: num

27597 13131 40234 51286 26063 ...

$ HIncPM: num

28900 19893 16192 63047 32233 ...

$ Weight: num

256 256 256 256 256 ...

Some variables, such as QUANTITY, VALUE, are treated as character, however, theyshould be converted to numeric.

> R04<-outfiles[[04]]> R04$QUANTITY<-as.integer(R04$QUANTITY)> R04$VALUE<-as.integer(R04$VALUE)> R04$INKIND_VALUE<-as.integer(R04$INKIND_VALUE)> outfiles[[04]]<-R04> R05<-outfiles[[05]]> R05$NF_QUANTITY<-as.integer(R05$NF_QUANTITY)> R05$NF_VALUE<-as.integer(R05$NF_VALUE)> outfiles[[05]]<-R05> R10<-outfiles[[10]]> R10$COL_10X1<-as.integer(R10$COL_10X1)> outfiles[[10]]<-R10> R15<-outfiles[[15]]> R15$OUTPUT_5_4<-as.integer(R15$OUTPUT_5_4)> R15$INPUT_5_4<-as.integer(R15$INPUT_5_4)> outfiles[[15]]<-R15> R23<-outfiles[[23]]> R23$WATER_DISTANCE<-as.integer(R23$WATER_DISTANCE)> outfiles[[23]]<-R23

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32

ID: Household identifier# The combination of A0, SNUMBER and HHNO becomes a unique household identifier.
> d<-outfiles[[25]]> dim(d)[1] 20540

14> head(d)REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO NHH RESULTHExpPM

1

25

11

1 10

7

1 10002

1

1

1

1

27597.48

2

25

11

1 10

7

1 10002

3

1

1

1

13130.57

3

25

11

1 10

7

1 10002

4

1

1

1

40234.20

4

25

11

1 10

7

1 10002

5

1

1

1

51285.67

5

25

11

1 10

7

1 10002

6

1

1

1

26063.48

6

25

11

1 10

7

1 100027

1

1

1 126262.00

HIncPM

Weight

1 28900.00 256.313
2 19892.86 256.313
3 16191.67 256.313
4 63046.66 256.313
5 32233.33 256.313
6 13400.00 256.313> d$ID<-d$A0*1000+d$SNUMBER*10+d$HHNO> length(unique(d$ID))[1] 20540# Generated the variable of ID consisted of the next three items for all data files;
A0 (Block identification No.)SNUMBER (Sample No.)
HHNO (Household serial No.)> outfiles.old<-outfiles> for(j in 1:25){+ d<-outfiles[[j]]+ d$ID<- d$A0*1000+d$SNUMBER*10+d$HHNO+ outfiles[[j]]<-d+ }Sri Lanka HIES 2012/13 Manual (Version 1.1)

33

> d<-outfiles[[25]]> dim(d)[1] 20540

15> colnames(d)[1] "REC_TYPE" "DISTRICT" "SECTOR""DS""MONTH""PSU"[7] "A0""SNUMBER""HHNO""NHH""RESULT""HExpPM"[13] "HIncPM""Weight""ID"> d<-d[,c(15,1:14)]> d<-d[order(d$ID),]> head(d)ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO NHH

1 10002011

25

11

1 10

7

1 100021

1

1

2 10002031

25

11

1 10

7

1 100023

1

1

3 10002041

25

11

1 10

7

1 100024

1

1

4 10002051

25

11

1 10

7

1 1000251

1

5 10002061

25

11

1 10

7

1 1000261

1

6 10002071

25

11

1 10

7

1 10002

7

1

1

HExpPM

HIncPMRESULT

Weight1

127597.48 28900.00 256.313

2

113130.57 19892.86 256.313

3

140234.20 16191.67 256.313

4151285.67 63046.66 256.313

5126063.48 32233.33 256.313

61 126262.00 13400.00 256.313> outfiles[[25]]<-d

PID: Individual identifier
Generated the variable of individual identifier PID consisted of ID and person number for R01,
R02, R03, R06, R08, R10, R13, R15, R17 and R19.
The person serial number in the above data files is the 12-th variable for each data frame despite
the various variable names.

> for(j in 1:25){+ if(j==1) cat("The 12-th variable name in each data file\n")+ cat(Rnames[j],": ",format(colnames(outfiles[[j]])[12],width=25),": ",filenames[j],"\n")+ }Sri Lanka HIES 2012/13 Manual (Version 1.1)

34

The 12-th variable name in each data fileR01 :PERSON_SERIAL_NO:

SEC_1_DEMOGRAPHIC

R02 :R2_PERSON_SERIAL:

SEC_2_SCHOOL_EDUCATION

R03 :R3_PERSON_SERIAL2:

SEC_3_HEALTH

R04 :

CODE

:

SEC_4_1_FOOD_EXP

R05 :NF_CODE:

SEC_4_2_NONFOOD

R06 :

COL_2

:

SEC_4_3_BOARDERS

R07 :IS_BOARDERS_SERVENTS:SEC_4_3_IS_BOADERS

R08 :SERIAL_NO_SEC_1:

SEC_5_1_EMP_INCOME

R09 :IS_EMPLOYMENT_INCOME:

SEC_5_1_IS_EMP_INCOME

R10 :COL_2X:

SEC_5_2_AGRI_INCOME

R11 :IS_AGRICULTURAL_INCOME:

SEC_5_2_IS_AGRI_INCOME

R12 :IS_OTHER_AGRRI_INCOME:

SEC_5_3_IS_OTHER_AGRI_INCOME

R13 :SER_NO_SEC_5_3:

SEC_5_3_OTHER_AGRI_INCOME

R14 :IS_NON_AGRI_INCOME:

SEC_5_4_IS_NON_AGRI_INCOME

R15 :SERIAL_5_4:

SEC_5_4_NON_AGRI_INCOME

R16 :IS_OTHER_INCOME:

SEC_5_5_1_IS_OTHER_INCOME

R17 :SERIAL_5_5_1:

SEC_5_5_1_OTHER_INCOME

R18 :IS_WINDFALL_INCOME:

SEC_5_5_2_IS_WINDFALL_INCOMER19 :PERSON_5_5_2:

SEC_5_5_2_WINDFALL_INCOME

R20 :

BANKS

:

SEC_6_B_DEBTNESS

R21 :

RADIO

:

SEC_6A_DURABLE_GOODS

R22 :BUS_HALT:

SEC_7_BASIC_FACILITIESR23 :STRUCTURE:

SEC_8_HOUSING

R24 :IS_AGRILAND_OWNER:

SEC_9_LAND_ANIMAL

R25 :HExpPM:

WEIGHT_INCOME_EXPENDITURE> ind.files<-c(1,2,3,6,8,10,13,15,17,19)> for(j in ind.files){+ d<-outfiles[[j]]+ d["PID"]<-paste(d$ID,formatC(d[,12],width=2,flag="0"),sep="")+ outfiles[[j]]<-d+ }Sri Lanka HIES 2012/13 Manual (Version 1.1)

35

Appended weight in R25 to all data files> R25<-outfiles[[25]]> for(j in 1:24){+ d<-outfiles[[j]]+ d<-merge(d,R25[,c("Weight","ID")],by="ID",all.x=T)+ outfiles[[j]]<-d+ }# Number of rows and columns of each data file
> for(j in 1:25){+ cat(Rnames[j],": ",format(filenames[j],width=30),": ",+ format(nrow(outfiles[[j]]),width=6),",",+ format(ncol(outfiles[[j]]),width=4),"\n")
+ }
R01 :

SEC_1_DEMOGRAPHIC:

89742 ,

30

R02 :SEC_2_SCHOOL_EDUCATION:

22029 ,

25

R03 :SEC_3_HEALTH:

80534 ,

30

R04 :SEC_4_1_FOOD_EXP:

815186 ,

17

R05 :

SEC_4_2_NONFOOD:

612085 ,

17

R06 :

SEC_4_3_BOARDERS

:

260 ,

28

R07 :

SEC_4_3_IS_BOADERS:

20540 ,

14

R08 :SEC_5_1_EMP_INCOME:

18594 ,

19

R09 :SEC_5_1_IS_EMP_INCOME:

20540 ,

14

R10 :SEC_5_2_AGRI_INCOME:

4196 ,

27

R11 :SEC_5_2_IS_AGRI_INCOME:

20540 ,

14

R12 :SEC_5_3_IS_OTHER_AGRI_INCOME:

20540 ,

14

R13 :SEC_5_3_OTHER_AGRI_INCOME:

3968 ,

22

R14 :SEC_5_4_IS_NON_AGRI_INCOME:

20540 ,

14

R15 :SEC_5_4_NON_AGRI_INCOME:

5700 ,

19

R16 :SEC_5_5_1_IS_OTHER_INCOME:

20540 ,

14

R17 :SEC_5_5_1_OTHER_INCOME:

12602 ,

27

R18 :SEC_5_5_2_IS_WINDFALL_INCOME:

20540 ,

14

R19 :SEC_5_5_2_WINDFALL_INCOME:

8575 ,

25

R20 :SEC_6_B_DEBTNESS:

20540 ,

31

R21 :SEC_6A_DURABLE_GOODS:

20540 ,

38

R22 :SEC_7_BASIC_FACILITIES:

20540 ,

50

Sri Lanka HIES 2012/13 Manual (Version 1.1)

36

R23 :

SEC_8_HOUSING:

20540 ,

40

R24 :SEC_9_LAND_ANIMAL:

20540 ,42

R25 :WEIGHT_INCOME_EXPENDITURE:

20540 ,

15[1] "csv.names" "filenames" "outfiles""Rnames"> ls()> save.image("G:\\SriLanka2012\\Working\\Sri Lanka HIES 2012 outfiles_20170825.RData")

Sri Lanka HIES 2012/13 Manual (Version 1.1)

37

5. Data Check
5.1 Summary of each variable
SUMMARY OF EACH DATA FRAME> for(j in 1:25){+ cat("#### ",Rnames[j]," ################################################\n")+ print(summary(outfiles[[j]]))+ cat("\n\n")
+ }
####

R01 ################################################
ID
REC_TYPE
DISTRICTSECTOR
DS
Min.
:10002011
Min.
:1
Min.
:11.00Min.
:1.000Min.
:10
1st Qu.:25633081
1st Qu.:1
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :44433051
Median :1
Median :32.00
Median :2.000
Median :10
Mean:49443204
Mean
:1
Mean
:40.32Mean:1.837Mean:10
3rd Qu.:72464051
3rd Qu.:1
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:1
Max.
:92.00Max.
:3.000
Max.
:10
NA's
:898
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:0.000
1st Qu.: 4.000
1st Qu.: 26.00
1st Qu.:25544
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 54.00
Median :44356
Median : 5.000
Median :1.000
Mean: 6.669
Mean: 68.75
Mean:49396Mean: 5.482Mean:1.006
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72497
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:9.000
NA's
:898
NHH
RESULTPERSON_SERIAL_NO RELATIONSHIP
SEX
Min.
:1.000Min.
:1.000Min.
: 0.000
Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.: 1.000
1st Qu.:2.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median : 3.000
Median :3.000
Median :2.000
Mean:1.012Mean:1.194Mean: 4.604
Mean:2.643Mean:1.518
3rd Qu.:1.000
3rd Qu.:1.000
3rd Qu.: 4.000
3rd Qu.:3.000
3rd Qu.:2.000
Max.
:4.000Max.
:6.000Max.
:47.000
Max.
:9.000Max.
:2.000
NA's
:1284
NA's
:4624
NA's
:4625
BIRTH_YEARBIRTH_MONTHAGEETHNICITYRELIGION
Min.
: 0.0
Min.
: 0.000Min.
: 0.00Min.
:1.000Min.
:1.000
1st Qu.:34.0
1st Qu.: 3.000
1st Qu.:14.00
1st Qu.:1.000
1st Qu.:1.000
Median :64.0
Median : 6.000
Median :31.00
Median :1.000
Median :1.000
Mean
:56.3
Mean
: 6.444Mean:32.56Mean:1.646Mean:1.681
3rd Qu.:82.0
3rd Qu.: 9.000
3rd Qu.:49.00
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:99.0
Max.
:99.000Max.
:99.00Max.
:9.000Max.
:9.000
NA's
:9184
NA's
:9243
NA's
:9174
NA's
:9164
NA's
:9164
MARITAL_STATUS
CURR_EDUCEDUCATION
IS_ACTIVE
MAIN_ACTIVITY
Min.
:1.000Min.
:1.000Min.
: 0.000
Min.
:1.000Min.
:1.00
1st Qu.:1.000
1st Qu.:2.000
1st Qu.: 5.000
1st Qu.:1.000
1st Qu.:2.00
Median :2.000
Median :9.000
Median :10.000
Median :2.000
Median :3.00
Mean:1.657Mean:7.024Mean: 8.673
Mean:1.522Mean:3.03
3rd Qu.:2.000
3rd Qu.:9.000
3rd Qu.:11.000
3rd Qu.:2.000
3rd Qu.:3.00
Max.
:5.000Max.
:9.000Max.
:19.000
Max.
:2.000
Max.
:9.00
NA's
:9195
NA's
:13142NA's:16077NA's:30452NA's:58529MAIN_OCCUPATION
INDUSTRYEMPLOYMENT_STATUS
PID
Weight
Min.
: 110
Min.
:
3
Min.
:1.00
Length:89742
Min.
: 3.423
1st Qu.:5112
1st Qu.: 1500
1st Qu.:3.00
Class :character
1st Qu.:141.821

Sri Lanka HIES 2012/13 Manual (Version 1.1)

38

Median :6440
Mean
:6347
3rd Qu.:9111
Max.
:9999
NA's
:61414

Median :41003
Mean:382033rd Qu.:64191
Max.
:99999NA's:61412

Median :3.00
Mean
:3.46
3rd Qu.:5.00
Max.
:6.00
NA's
:61387Mode:character

Median :240.742
Mean:246.669
3rd Qu.:332.646
Max.
:752.308
NA's
:4662

Remarks:
The records without RESULT=1 should be dropped.
> d<-outfiles[[1]]> d<-subset(d,RESULT==1)> dim(d)[1] 85080
30> sum(is.na(d$ID))[1] 0> sum(is.na(d$Weight))[1] 0> outfiles.old<-outfiles> outfiles[[1]]<-d> save.image("G:\\SriLanka2012\\Working\\Sri Lanka HIES 2012 outfiles_20170825.RData")####

R02 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:2
Min.
:11.00Min.
:1.000
1st Qu.:27660021
1st Qu.:2
1st Qu.:21.00
1st Qu.:1.000
Median :46296051
Median :2
Median :33.00
Median :2.000
Mean:50358939
Mean
:2
Mean
:41.15Mean:1.848
3rd Qu.:72309091
3rd Qu.:2
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:2
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.691
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 52.00
Mean: 66.52
3rd Qu.: 86.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.011
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

GRADE_THIS_YEAR
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.661
3rd Qu.:10.000
Max.
:19.000
NA's
:4157

A0
Min.
:10002
1st Qu.:27660
Median :46296
Mean:50359
3rd Qu.:72309
Max.
:98655DSMin.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 5.501
3rd Qu.: 8.000
Max.
:10.000

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.005
3rd Qu.:1.000
Max.
:3.000R2_PERSON_SERIAL R2_SCHOOL_EDUCATION TYPE_OF_SCHOOL
Min.
: 1.000
Min.
:1.000Min.
:1.000
1st Qu.: 3.000
1st Qu.:1.000
1st Qu.:1.000
Median : 4.000
Median :1.000
Median :1.000
Mean: 3.819
Mean:1.341Mean:1.0543rd Qu.: 4.000
3rd Qu.:1.000
3rd Qu.:1.000
Max.
:16.000
Max.
:3.000Max.
:3.000
NA's
:7
NA's
:4156
GRADE_LAST_YEARDISTANCE
TRANSPORT_MEDIUM TIME_TO_SCHOOL
Min.
: 0.000Min.
: 0.000
Min.
:1.000Min.
: 0.00
1st Qu.: 3.000
1st Qu.: 0.000
1st Qu.:1.000
1st Qu.: 10.00
Median : 6.000
Median : 2.000
Median :2.000
Median : 15.00
Mean: 6.974
Mean: 3.363
Mean:2.592Mean: 20.93
3rd Qu.:10.000
3rd Qu.: 4.000
3rd Qu.:4.000
3rd Qu.: 30.00
Max.
:19.000Max.
:80.000
Max.
:9.000Max.
:205.00
NA's
:4158
NA's
:4177
NA's
:4180
NA's
:4197

Sri Lanka HIES 2012/13 Manual (Version 1.1)

39

NOSCHOOLING_REASON REASON_NOT_GOING WHEN_STOP_SCHOOLING
PID
Min.
:1.000Min.
:1.000Min.
: 98
Length:22029
1st Qu.:7.000
1st Qu.:3.000
1st Qu.:2009
Class :character
Median :7.000
Median :5.000
Median :2010
Mode :character
Mean:6.701Mean:5.521Mean:2009
3rd Qu.:7.000
3rd Qu.:7.000
3rd Qu.:2011Max.
:9.000Max.
:9.000
Max.
:2013
NA's
:21241
NA's:18785NA's:19113WeightMin.
: 3.423
1st Qu.:139.511
Median :229.890
Mean:241.851
3rd Qu.:329.545
Max.
:752.308####

R03 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:3
Min.
:11.0
Min.
:1.000
1st Qu.:25507031
1st Qu.:3
1st Qu.:13.0
1st Qu.:1.000
Median :44615071
Median :3
Median :32.0
Median :2.000
Mean:49472703
Mean
:3
Mean
:40.4
Mean
:1.8343rd Qu.:72464018
3rd Qu.:3
3rd Qu.:61.0
3rd Qu.:2.000
Max.
:98655101
Max.
:3
Max.
:92.0
Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.672
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 26.00
Median : 54.00
Mean: 68.43
3rd Qu.: 90.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0133rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

A0
Min.
:10002
1st Qu.:25507
Median :44615
Mean:494733rd Qu.:72464
Max.
:98655DSMin.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.478
3rd Qu.: 8.000
Max.
:10.000

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.006
3rd Qu.:1.000
Max.
:4.000R3_PERSON_SERIAL2 ATTEND_GOVT_HOSPITAL3 REASON_HOSPITAL4
Min.
: 1.000
Min.
:1.000Min.
:1.00
1st Qu.: 1.000
1st Qu.:2.000
1st Qu.:1.00
Median : 3.000
Median :2.000
Median :1.00
Mean: 2.801Mean:1.831Mean:1.42
3rd Qu.: 4.000
3rd Qu.:2.000
3rd Qu.:1.00
Max.
:16.000
Max.
:2.000
Max.
:9.00
NA's
:7
NA's
:66908ATTEND_PRIVATE_HOSPITAL5 REASON_FOR_WHAT6 IS_STAY_GOVHOSPITAL7 REASON_STAY8
Min.
:1.000
Min.
:1.0
Min.
:1.000Min.
:1.00
1st Qu.:2.000
1st Qu.:1.0
1st Qu.:2.000
1st Qu.:1.00
Median :2.000
Median :1.0
Median :2.000
Median :1.00
Mean:1.856
Mean
:1.3
Mean
:1.916Mean:2.29
3rd Qu.:2.000
3rd Qu.:1.0
3rd Qu.:2.000
3rd Qu.:3.00
Max.
:2.000
Max.
:9.0
Max.
:2.000
Max.
:9.00
NA's
:10
NA's
:68972NA's:7NA's:73771
STAY_IN_PRIVTE_HOSPITAL9 REASON_FOR_STAY10 IS_ILL_DISABLE11 WHAT_ILL_DISABLE12
Min.
:1.000
Min.
:1.00
Min.
:1.000Min.
: 0.0
1st Qu.:2.000
1st Qu.:1.00
1st Qu.:2.000
1st Qu.: 2.0
Median :2.000
Median :1.00
Median :2.000
Median : 3.0
Mean:1.992
Mean
:2.21
Mean
:1.861
Mean:16.5

Sri Lanka HIES 2012/13 Manual (Version 1.1)

40

3rd Qu.:2.000
3rd Qu.:3.00
3rd Qu.:2.000
3rd Qu.:10.0
Max.
:2.000
Max.
:9.00
Max.
:2.000
Max.
:99.0
NA's
:10
NA's
:79902
NA's
:15
NA's
:69341
IS_EMPL_REASON13 DURATION_YEARS14 DURATION_MONTHS14 IS_ABSENT_ACT15 DAYS_ABSENT16
Min.
:0.00
Min.
: 0.0
Min.
: 0.00
Min.
:0.00
Min.
: 0.00
1st Qu.:2.00
1st Qu.: 3.0
1st Qu.: 0.00
1st Qu.:2.00
1st Qu.:10.00
Median :2.00
Median : 5.0
Median : 0.00
Median :2.00
Median :30.00
Mean
:1.92
Mean
: 7.9
Mean
: 1.81
Mean
:1.84
Mean
:20.833rd Qu.:2.00
3rd Qu.:10.0
3rd Qu.: 3.00
3rd Qu.:2.00
3rd Qu.:30.00
Max.
:4.00
Max.
:82.0
Max.
:14.00
Max.
:4.00
Max.
:99.00NA's:69385NA's:69428NA's:69766NA's:69413NA's:78792
PID
WeightLength:80534
Min.
: 3.423Class :character
1st Qu.:141.821
Mode :character
Median :240.742
Mean:246.853
3rd Qu.:332.646
Max.
:752.308####

R04 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:4
Min.
:11.00Min.
:1.000
1st Qu.:24888041
1st Qu.:4
1st Qu.:13.00
1st Qu.:1.000
Median :44071041
Median :4Median :32.00
Median :2.000
Mean:48821202
Mean
:4
Mean
:39.78Mean:1.817
3rd Qu.:72497071
3rd Qu.:4
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:4
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.684
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 26.00
Median : 54.00
Mean: 69.76
3rd Qu.: 92.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0143rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

INKIND_VALUE:740568
0000020: 10700
0000010: 8661
0000030: 6281
0000040: 5350
0000060: 3559
(Other): 40067

WeightMin.
: 3.423
1st Qu.:156.054
Median :242.991
Mean:251.404
3rd Qu.:352.161
Max.
:752.308

A0
Min.
:10002
1st Qu.:24888
Median :44071
Mean:48821
3rd Qu.:72497
Max.
:98655CODE
Min.
: 101.0
1st Qu.: 416.0
Median :1102.0
Mean: 894.9
3rd Qu.:1120.0
Max.
:1924.0
NA's
:1

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.465
3rd Qu.: 8.000
Max.
:10.000

QUANTITY:148635
000250 : 80367
000500 : 80331
000100 : 64082
000050 : 42681
001000 : 33521(Other):365569

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.006
3rd Qu.:1.000
Max.
:4.000VALUE
0000020: 67157
0000030: 59499
0000010: 54538
0000040: 52986
0000060: 39574
0000050: 34717(Other):506715

Sri Lanka HIES 2012/13 Manual (Version 1.1)

41

####

R05 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:5
Min.
:11.00Min.
:1.000
1st Qu.:24953051
1st Qu.:5
1st Qu.:13.00
1st Qu.:1.000
Median :44146071
Median :5Median :32.00
Median :2.000
Mean:48800454
Mean
:5
Mean
:39.71Mean:1.8143rd Qu.:72464011
3rd Qu.:5
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:5
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.659
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 54.00
Mean: 69.18
3rd Qu.: 92.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0133rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

NF_INKIND_VALUE
Min.
:
0
1st Qu.:350
Median :800
Mean: 3118
3rd Qu.: 2500
Max.
:8000000NA's:563125

A0
Min.
:10002
1st Qu.:24953
Median :44146
Mean:48800
3rd Qu.:72464
Max.
:98655

NF_CODEMin.
:2001
1st Qu.:2204
Median :2602
Mean
:2647
3rd Qu.:3016
Max.
:3509

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.449
3rd Qu.: 8.000
Max.
:10.000

NF_QUANTITY:386468
000001 : 88297
000002 : 60983
000003 : 19022
000004 : 15721
000005 : 7720
(Other): 33874

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.006
3rd Qu.:1.000
Max.
:4.000

NF_VALUE
0000200: 31627
0000100: 31267
0000500: 24609
0000300: 23487
0001000: 20563
0000150: 18101(Other):462431

WeightMin.
: 3.423
1st Qu.:153.131
Median :242.792
Mean:249.779
3rd Qu.:352.161
Max.
:752.308####

R06 ################################################
ID
REC_TYPE
DISTRICTSECTORMin.
:11149011
Min.
:7
Min.
:11.00Min.
:1.000
1st Qu.:19571094
1st Qu.:7
1st Qu.:12.00
1st Qu.:1.000
Median :33055081
Median :7
Median :22.00
Median :1.000
Mean:42254578
Mean
:7
Mean
:33.59Mean:1.335
3rd Qu.:72463044
3rd Qu.:7
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98645081
Max.
:7
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.023
3rd Qu.: 9.000
Max.
:12.000
NHH
Min.
:1
1st Qu.:1
Median :1

PSU
Min.
: 2.00
1st Qu.: 26.75
Median : 65.00
Mean: 79.33
3rd Qu.:105.00
Max.
:296.00

RESULTMin.
:1
1st Qu.:1
Median :1

A0
Min.
:11149
1st Qu.:19571
Median :33055
Mean:42255
3rd Qu.:72463
Max.
:98645

COL_2
Min.
: 1.000
1st Qu.: 4.000
Median : 5.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

SNUMBERMin.
: 1.000
1st Qu.: 2.750
Median : 5.000
Mean: 4.958
3rd Qu.: 7.000
Max.
:10.000

COL_3
Min.
:
0
1st Qu.: 140
Median : 300

HHNO
Min.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

COL_4
Min.
:
0.0
1st Qu.: 80.0
Median : 150.0

Sri Lanka HIES 2012/13 Manual (Version 1.1)

42

Mean
:1
3rd Qu.:1
Max.
:1

Mean
:1
3rd Qu.:1
Max.
:1

COL_5
Min.
: 100
1st Qu.: 200
Median : 325
Mean
: 518
3rd Qu.: 750
Max.
:2500
NA's
:201
COL_10Min.
:1200
1st Qu.:4000
Median :5000
Mean
:4756
3rd Qu.:5500
Max.
:9000
NA's
:140
COL_15Min.
: 250
1st Qu.: 1200
Median : 3400
Mean
: 8357
3rd Qu.: 7400
Max.
:76000
NA's
:241

Mean
: 5.054
3rd Qu.: 6.000
Max.
:14.000

Mean
: 647
Mean
: 437.5
3rd Qu.: 650
3rd Qu.: 350.0
Max.
:6000
Max.
:2300.0
NA's
:179
NA's
:244
COL_6
COL_7
COL_8
COL_9
Min.
: 100
Min.
: 20.0Min.
: 60.0Min.
: 100
1st Qu.:1188
1st Qu.: 170.2
1st Qu.: 300.0
1st Qu.: 500
Median :1875
Median : 398.0
Median : 500.0
Median : 1125
Mean
:1975
Mean
: 802.6
Mean: 947.6
Mean: 1738
3rd Qu.:2000
3rd Qu.:1000.0
3rd Qu.:1090.0
3rd Qu.: 2375
Max.
:5000
Max.
:5550.0Max.
:6500.0Max.
:10000
NA's
:254
NA's
:126
NA's
:122
NA's
:210
COL_11
COL_12
COL_13
COL_14
Min.
: 110
Min.
: 140
Min.
:
26
Min.
: 375
1st Qu.: 3000
1st Qu.: 1194
1st Qu.: 275
1st Qu.: 1500
Median : 5000
Median : 3000
Median : 500
Median : 2650
Mean
: 6101Mean: 3740Mean: 1147
Mean: 3239
3rd Qu.: 8000
3rd Qu.: 5000
3rd Qu.: 1500
3rd Qu.: 4850
Max.
:37000Max.
:20000Max.
:14500Max.
:25000
NA's
:128
NA's
:177
NA's
:169
NA's
:148
PID
WeightLength:260
Min.
: 15.68
Class :character
1st Qu.: 88.14
Mode :character
Median :165.97
Mean:218.08
3rd Qu.:354.24
Max.
:752.31####

R07 ################################################
ID
REC_TYPE
DISTRICTSECTOR
DS
Min.
:10002011
Min.
:6
Min.
:11.00Min.
:1.000Min.
:10
1st Qu.:25778068
1st Qu.:6
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :45297056
Median :6
Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:6
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:6
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:6
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT IS_BOARDERS_SERVENTS
WeightMin.
:1.000Min.
:1Min.
:1.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.:2.000
1st Qu.:152.327
Median :1.000
Median :1
Median :2.000
Median :242.792
Mean:1.014
Mean:1
Mean:1.991
Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.:2.000
3rd Qu.:346.846
Max.
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:1Max.
:2.000Max.
:752.308

####

R08
ID

################################################
REC_TYPE
DISTRICTSECTOR

Sri Lanka HIES 2012/13 Manual (Version 1.1)

DS

43

Min.
:10002011
1st Qu.:23742051
Median :42192091
Mean:47512339
3rd Qu.:71292058
Max.
:98655101

Min.
:9
1st Qu.:9Median :9Mean:9
3rd Qu.:9
Max.
:9

Min.
:11.00
1st Qu.:13.00
Median :32.00
Mean:38.68
3rd Qu.:61.00
Max.
:92.00Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.895
3rd Qu.:2.000
Max.
:3.000Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.736
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 26.00
Median : 57.00
Mean: 71.94
3rd Qu.: 96.00
Max.
:300.00

A0
Min.
:10002
1st Qu.:23742
Median :42192
Mean:47512
3rd Qu.:71292
Max.
:98655NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.012
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

ALLOWENCES
Min.
:
0
1st Qu.: 2200
Median : 5000
Mean
: 6849
3rd Qu.: 8126
Max.
:80000NA's:15928

BONUS
Min.
:
229
1st Qu.: 4500
Median : 10000
Mean: 26272
3rd Qu.: 25000
Max.
:850000
NA's:17573

PRI_SECWAGES_SALARIES
Min.
:1.000Min.
:
0
1st Qu.:1.000
1st Qu.: 9500
Median :1.000
Median : 15000
Mean:1.012Mean: 17104
3rd Qu.:1.000
3rd Qu.: 20000
Max.
:2.000Max.
:900000
NA's
:27
NA's
:174
PID
WeightLength:18594
Min.
: 3.423Class :character
1st Qu.:138.773
Mode :character
Median :229.890
Mean:240.599
3rd Qu.:329.545
Max.
:752.308

SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.474
3rd Qu.: 8.000
Max.
:10.000

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.006
3rd Qu.:1.000
Max.
:4.000SERIAL_NO_SEC_1
Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.044
3rd Qu.: 3.000
Max.
:15.000####

R09 ################################################
ID
REC_TYPE
DISTRICTSECTOR
DS
Min.
:10002011
Min.
:8
Min.
:11.00Min.
:1.000Min.
:10
1st Qu.:25778068
1st Qu.:8
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :45297056
Median :8Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:8
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:8
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:8
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT IS_EMPLOYMENT_INCOME
WeightMin.
:1.000Min.
:1Min.
:1.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.:1.000
1st Qu.:152.327
Median :1.000
Median :1
Median :1.000
Median :242.792
Mean:1.014
Mean:1
Mean:1.371
Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:4.000Max.
:1Max.
:2.000Max.
:752.308

Sri Lanka HIES 2012/13 Manual (Version 1.1)

44

####

R10 ################################################
ID
REC_TYPE
DISTRICTSECTORMin.
:10935101
Min.
:11
Min.
:11.00Min.
:1.000
1st Qu.:43726784
1st Qu.:11
1st Qu.:32.00
1st Qu.:2.000
Median :69869096
Median :11
Median :61.00
Median :2.000
Mean:63490838
Mean
:11
Mean
:53.67Mean:1.995
3rd Qu.:83468026
3rd Qu.:11
3rd Qu.:72.00
3rd Qu.:2.000
Max.
:98655091
Max.
:11
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.655
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 22.00
Median : 43.00
Mean: 49.46
3rd Qu.: 69.00
Max.
:290.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:2.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

A0
Min.
:10935
1st Qu.:43727
Median :69869
Mean:63491
3rd Qu.:83468
Max.
:98655SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.458
3rd Qu.: 8.000
Max.
:10.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10
HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0033rd Qu.:1.000
Max.
:2.000

COL_2XMin.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.291
3rd Qu.: 1.000
Max.
:14.000

COL_4X
COL_5X
Min.
:1.000Min.
: 0.000
1st Qu.:1.000
1st Qu.: 1.000
Median :1.000
Median : 2.000
Mean:2.199Mean: 2.349
3rd Qu.:4.000
3rd Qu.: 3.000
Max.
:9.000Max.
:40.000
NA's
:23
NA's
:1928
COL_6X
COL_7X
COL_8X
COL_8X1
COL_9X
Min.
:0.00
Min.
: 0.0
Min.
:
0
Min.
:
0
Min.
:
3
1st Qu.:1.00
1st Qu.: 8.0
1st Qu.:557
1st Qu.: 18480
1st Qu.: 8000
Median :2.00
Median :20.0
Median :1500
Median : 45000
Median : 20000
Mean
:1.79
Mean
:15.3
Mean
:
14544
Mean
: 91066
Mean: 42451
3rd Qu.:2.00
3rd Qu.:20.0
3rd Qu.:4000
3rd Qu.: 110000
3rd Qu.: 50000
Max.
:6.00
Max.
:50.0
Max.
:40026000
Max.
:1900000
Max.
:881000
NA's
:2162
NA's
:3728
NA's
:201
NA's
:164
NA's
:323
COL_10X
COL_10X1
COL_11X
COL_12X
Min.
:
1.0
:1269
Min.
:
0.0
Min.
:
0
1st Qu.: 160.0
000015000: 72
1st Qu.:150.0
1st Qu.:800
Median : 400.0
000012000: 66
Median :300.0
Median :2267
Mean: 921.6000010000: 50
Mean
:
766.5
Mean
:
7445
3rd Qu.: 720.0
000018000: 47
3rd Qu.:500.0
3rd Qu.:6350
Max.
:700000.0
000030000: 45
Max.
:350000.0
Max.
:1400000
NA's:1164(Other) :2647
NA's
:2247
NA's
:3616
COL_13X
PID
WeightMin.
:
58
Length:4196
Min.
: 3.423
1st Qu.: 1500
Class :character
1st Qu.:205.950
Median : 3400
Mode :character
Median :273.365
Mean: 8082
Mean:283.235
3rd Qu.: 8600
3rd Qu.:359.603
Max.
:288000Max.
:752.308
NA's
:2125

####

R11 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:10
Min.
:11.00Min.
:1.000
1st Qu.:25778068
1st Qu.:10
1st Qu.:13.00
1st Qu.:1.000

Sri Lanka HIES 2012/13 Manual (Version 1.1)

DS
Min.
:10
1st Qu.:10

45Median :45297056
Median :10
Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:10
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:10
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:10
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT IS_AGRICULTURAL_INCOME
WeightMin.
:1.000Min.
:1Min.
:0.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.:2.000
1st Qu.:152.327
Median :1.000
Median :1
Median :2.000
Median :242.792
Mean:1.014
Mean:1
Mean:1.829Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:4.000Max.
:1Max.
:2.000Max.
:752.308####

R12 ################################################
ID
REC_TYPE
DISTRICTSECTOR
DS
Min.
:10002011
Min.
:12
Min.
:11.00Min.
:1.000Min.
:10
1st Qu.:25778068
1st Qu.:12
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :45297056
Median :12
Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:12
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:12
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:12
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT IS_OTHER_AGRRI_INCOME
WeightMin.
:1.000Min.
:1Min.
:1.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.:2.000
1st Qu.:152.327
Median :1.000
Median :1
Median :2.000
Median :242.792
Mean:1.014
Mean:1
Mean:1.836
Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:4.000Max.
:1Max.
:2.000Max.
:752.308####

R13 ################################################
ID
REC_TYPE
DISTRICTSECTORMin.
:10935051
Min.
:13
Min.
:11.00Min.
:1.000
1st Qu.:35364048
1st Qu.:13
1st Qu.:23.00
1st Qu.:2.000
Median :46283556
Median :13
Median :33.00
Median :2.000
Mean:55206839
Mean
:13
Mean
:45.95Mean:1.972
3rd Qu.:77322018
3rd Qu.:13
3rd Qu.:62.00
3rd Qu.:2.000
Max.
:98655101
Max.
:13
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000

PSU
Min.
: 1.00
1st Qu.: 22.00

A0Min.
:10935
1st Qu.:35364

SNUMBERMin.
: 1.00
1st Qu.: 3.00

Sri Lanka HIES 2012/13 Manual (Version 1.1)

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10
HHNO
Min.
:1.000
1st Qu.:1.000

46Median : 7.000
Mean: 6.684
3rd Qu.:10.000
Max.
:12.000

Median : 49.00
Mean: 58.50
3rd Qu.: 83.25
Max.
:293.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.006
3rd Qu.:1.000
Max.
:2.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1Max.
:1Median :46284
Mean:55207
3rd Qu.:77322
Max.
:98655Median : 5.00
Mean
: 5.46
3rd Qu.: 8.00
Max.
:10.00Median :1.000
Mean:1.002
3rd Qu.:1.000
Max.
:2.000

SER_NO_SEC_5_3
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.327
3rd Qu.:1.000
Max.
:9.000SEASONAL_CROPACRES_5_3
Min.
: 1.000Min.
: 0.000
1st Qu.: 1.000
1st Qu.: 1.000
Median : 2.000
Median : 1.000
Mean: 6.883
Mean: 2.015
3rd Qu.: 4.000
3rd Qu.: 2.000
Max.
:99.000Max.
:32.000
NA's
:10
NA's
:2897
OUTPUT_5_3
INPUT_5_3
FERTILIZES
Min.
:
0
Min.
:
0
Min.
: 100
1st Qu.:2100
1st Qu.:600
1st Qu.: 600
Median :7000
Median :2000
Median : 1500
Mean
: 29489
Mean: 14223
Mean: 3871
3rd Qu.: 20000
3rd Qu.:7800
3rd Qu.: 3900
Max.
:5200000
Max.
:1159000
Max.
:85000
NA's
:169
NA's
:1274
NA's
:3714

ROOTS_5_3
PERCHS_5_3
Min.
:0.000Min.
: 0.00
1st Qu.:1.000
1st Qu.:10.00
Median :2.000
Median :20.00
Mean:1.758Mean:16.67
3rd Qu.:2.000
3rd Qu.:20.00
Max.
:7.000Max.
:40.00
NA's
:2269
NA's
:3354
PID
WeightLength:3968
Min.
: 3.619
Class :character
1st Qu.:211.994
Mode :character
Median :284.643
Mean:290.161
3rd Qu.:376.154
Max.
:752.308####

R14 ################################################
ID
REC_TYPE
DISTRICTSECTOR
DS
Min.
:10002011
Min.
:14
Min.
:11.00Min.
:1.000Min.
:10
1st Qu.:25778068
1st Qu.:14
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :45297056
Median :14
Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:14
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:14
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:14
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT IS_NON_AGRI_INCOME
WeightMin.
:1.000Min.
:1Min.
:1.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.:2.000
1st Qu.:152.327
Median :1.000
Median :1
Median :2.000
Median :242.792
Mean:1.014
Mean:1
Mean:1.753
Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:4.000Max.
:1Max.
:2.000Max.
:752.308

####

R15

################################################

Sri Lanka HIES 2012/13 Manual (Version 1.1)

47

ID
Min.
:10002011
1st Qu.:22065064
Median :42844081
Mean:46751611
3rd Qu.:71879329
Max.
:98598101

REC_TYPEMin.
:15
1st Qu.:15
Median :15
Mean:15
3rd Qu.:15
Max.
:15

DISTRICTMin.
:11
1st Qu.:12
Median :32
Mean:38
3rd Qu.:61
Max.
:92

SECTORMin.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.702
3rd Qu.:2.000
Max.
:3.000DSMin.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.589
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 27.00
Median : 57.00
Mean: 73.96
3rd Qu.:100.25
Max.
:300.00

A0
Min.
:10002
1st Qu.:22065
Median :42844
Mean:46752
3rd Qu.:71879
Max.
:98598NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0133rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

INPUT_5_4: 896
000010000: 179
000005000: 166
000015000: 149
000020000: 134
000006000: 119(Other) :4057

SUBSIDIESMin.
:
0
1st Qu.: 4000
Median : 9375
Mean
: 29246
3rd Qu.: 21800
Max.
:216000
NA's:5667

NON_AGRIOUTPUT_5_4
Min.
:0.000000030000: 285
1st Qu.:4.000
000015000: 255
Median :4.000
000020000: 247
Mean:4.764000025000: 205
3rd Qu.:5.000
000010000: 204Max.
:9.000000050000: 166
NA's:20(Other) :4338
PID
WeightLength:5700
Min.
: 3.423Class :character
1st Qu.:156.054
Mode :character
Median :251.816
Mean:252.800
3rd Qu.:353.722
Max.
:752.308

SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.425
3rd Qu.: 8.000
Max.
:10.000

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:3.000SERIAL_5_4
Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.674
3rd Qu.: 2.000
Max.
:14.000####

R16 ################################################
ID
REC_TYPE
DISTRICTSECTOR
DS
Min.
:10002011
Min.
:16
Min.
:11.00Min.
:1.000Min.
:10
1st Qu.:25778068
1st Qu.:16
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :45297056
Median :16
Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:16
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:16
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:16
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT IS_OTHER_INCOME
WeightMin.
:1.000Min.
:1Min.
:0.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.:1.000
1st Qu.:152.327
Median :1.000
Median :1
Median :1.000
Median :242.792
Mean:1.014
Mean:1
Mean:1.483
Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:4.000Max.
:1Max.
:2.000Max.
:752.308

Sri Lanka HIES 2012/13 Manual (Version 1.1)

48

####

R17 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002051
Min.
:17
Min.
:11.00Min.
:1.00
1st Qu.:31422074
1st Qu.:17
1st Qu.:21.00
1st Qu.:2.00
Median :49466051
Median :17
Median :41.00
Median :2.00
Mean:52186970
Mean
:17
Mean
:42.79Mean:1.83
3rd Qu.:72912034
3rd Qu.:17
3rd Qu.:61.00
3rd Qu.:2.00
Max.
:98655101
Max.
:17
Max.
:92.00
Max.
:3.00
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.603
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 24.00
Median : 49.00
Mean: 62.57
3rd Qu.: 82.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.012
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

PROPERTY_RENTS
Min.
: 200
1st Qu.: 5000
Median : 10000
Mean: 20263
3rd Qu.: 20000
Max.
:700000
NA's:11749
SC_LUNCH
Min.
: 20.0
1st Qu.: 350.0
Median : 400.0
Mean: 673.9
3rd Qu.: 700.0Max.
:9140.0
NA's:11213
INCOME_LOCAL
Min.
:
100
1st Qu.: 12000
Median : 43500
Mean: 95634
3rd Qu.: 120000
Max.
:4200000NA's:10528A0
Min.
:10002
1st Qu.:31422
Median :49466
Mean:52187
3rd Qu.:72912
Max.
:98655DSMin.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

SNUMBERMin.
: 1.00
1st Qu.: 3.00
Median : 5.00
Mean
: 5.443rd Qu.: 8.00
Max.
:10.00

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0043rd Qu.:1.000
Max.
:3.000SERIAL_5_5_1
Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.855
3rd Qu.: 2.000
Max.
:41.000

PENSIONDISABILITY_AND_RELIEF
Min.
:
1
Min.
:
0
1st Qu.: 12000
1st Qu.: 250
Median : 15000
Median : 500
Mean: 18647
Mean: 15643rd Qu.: 21498
3rd Qu.: 1038
Max.
:550000
Max.
:36000NA's:10904NA's:12462
SAMURDHI
DIVIDENDSELDERSCHOLAR
Min.
:
50.0
Min.
:
50
Min.
: 100.0Min.
: 175
1st Qu.: 360.0
1st Qu.: 2000
1st Qu.: 250.0
1st Qu.: 500
Median : 550.0
Median : 7500
Median : 300.0
Median : 1025
Mean
: 711.9
Mean: 14539Mean: 724.3
Mean: 23333rd Qu.: 750.0
3rd Qu.: 18000
3rd Qu.: 1000.0
3rd Qu.: 2512
Max.
:30000.0Max.
:275000
Max.
:12000.0
Max.
:20000
NA's
:8873
NA's
:12311NA's:11994NA's:12446
THREEPOSHA
OTHER_INCOME
INCOME_FORIGN
Min.
: 41.0
Min.
:
5
Min.
:
450
1st Qu.: 160.0
1st Qu.:5000
1st Qu.: 30000
Median : 244.0
Median : 15500
Median : 80000
Mean: 341.2
Mean: 62520Mean: 167367
3rd Qu.: 300.0
3rd Qu.: 60000
3rd Qu.: 200000
Max.
:4000.0
Max.
:2500000
Max.
:6000000NA's:11887NA's:11153NA's:10957PID
WeightLength:12602
Min.
: 3.423Class :character
1st Qu.:141.329
Mode :character
Median :229.890
Mean:242.829
3rd Qu.:329.545
Max.
:752.308####

R18 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:18
Min.
:11.00Min.
:1.000

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Min.

DS
:10

49

1st Qu.:25778068
1st Qu.:18
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :45297056
Median :18
Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:18
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:18
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:18
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT IS_WINDFALL_INCOME
WeightMin.
:1.000Min.
:1Min.
:1.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.:1.000
1st Qu.:152.327
Median :1.000
Median :1
Median :2.000
Median :242.792
Mean:1.014
Mean:1
Mean:1.611
Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:4.000Max.
:1Max.
:2.000Max.
:752.308####

R19 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002041
Min.
:19
Min.
:11.00Min.
:1.000
1st Qu.:25876076
1st Qu.:19
1st Qu.:13.00
1st Qu.:2.000
Median :44383061
Median :19
Median :32.00
Median :2.000
Mean:49761478
Mean
:19
Mean
:40.39Mean:1.896
3rd Qu.:72585056
3rd Qu.:19
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:19
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.726
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 52.00
Mean: 66.28
3rd Qu.: 86.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.016
3rd Qu.:1.000
Max.
:3.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

A0
Min.
:10002
1st Qu.:25876
Median :44383
Mean:49761
3rd Qu.:72585
Max.
:98655SNUMBERMin.
: 1.00
1st Qu.: 3.00
Median : 5.00
Mean
: 5.443rd Qu.: 8.00
Max.
:10.00PERSON_5_5_2
Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.563
3rd Qu.: 2.000
Max.
:12.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10
HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:3.000LOANSPAWNING_SELLING
Min.
:
7
Min.
:
400
1st Qu.: 25000
1st Qu.:20000
Median : 54000
Median :50000
Mean: 168221
Mean: 108462
3rd Qu.: 150000
3rd Qu.: 120000
Max.
:9000000
Max.
:12000000
NA's
:6394
NA's
:2414
DEPOSITS_PENSIONS_EPF WELFARE_SOCITYSEETTU_DEBITSMEDICALMin.
: 1000
Min.
:
600
Min.
:
500
Min.
:
200
1st Qu.: 15000
1st Qu.: 10000
1st Qu.: 10000
1st Qu.: 4250
Median : 50000
Median : 20000
Median : 20000
Median : 10000
Mean: 171495
Mean: 35659
Mean: 48373
Mean: 30711
3rd Qu.: 150000
3rd Qu.: 40000
3rd Qu.: 50000
3rd Qu.: 36250
Max.
:5000000
Max.
:275000
Max.
:9000000
Max.
:300000
NA's
:7949
NA's
:8420
NA's
:7295
NA's
:8489
INSUARANCELOTTERY
FOODALLOWENCE
DIASTER
Min.
:
500
Min.
:
20
Min.
:
150
Min.
:
600

Sri Lanka HIES 2012/13 Manual (Version 1.1)

50

1st Qu.: 5000
Median : 24000
Mean: 131095
3rd Qu.: 100000
Max.
:2000000
NA's
:8490
PID
Length:8575
Class :character
Mode :character

1st Qu.:500
1st Qu.: 1000
Median : 1470
Median : 3000
Mean
: 11649
Mean: 15291
3rd Qu.: 5000
3rd Qu.: 8000
Max.
:250000
Max.
:606988
NA's
:8447
NA's
:8362
WeightMin.
: 3.423
1st Qu.:156.120
Median :242.792
Mean:247.610
3rd Qu.:332.646
Max.
:752.308

1st Qu.: 2500
Median : 14400
Mean: 39821
3rd Qu.: 17000
Max.
:325000
NA's
:8542

####

R20 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:21
Min.
:11.00Min.
:1.000
1st Qu.:25778068
1st Qu.:21
1st Qu.:13.00
1st Qu.:1.000
Median :45297056
Median :21
Median :33.00
Median :2.000
Mean:49924702
Mean
:21
Mean
:40.85Mean:1.838
3rd Qu.:72971078
3rd Qu.:21
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:21
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.671
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 53.00
Mean: 68.16
3rd Qu.: 90.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0143rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

A0
Min.
:10002
1st Qu.:25778
Median :45297
Mean:49925
3rd Qu.:72971
Max.
:98655SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.466
3rd Qu.: 8.000
Max.
:10.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

BANKS
Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.732
3rd Qu.:2.000
Max.
:2.000

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:4.000BANK_AMOUNTFINANCE
Min.
:
1
Min.
:1.000
1st Qu.: 25000
1st Qu.:2.000
Median : 65000
Median :2.000
Mean: 236798
Mean:1.9243rd Qu.: 200000
3rd Qu.:2.000
Max.
:9240000
Max.
:2.000NA's:15042
FINANCE_AMOUNTEMPLOYEREMPLOYER_AMOUNTLENDERLENDER_AMOUNTMin.
: 1500Min.
:1.000
Min.
:
500
Min.
:1.000Min.
:
400
1st Qu.: 60000
1st Qu.:2.000
1st Qu.: 15000
1st Qu.:2.000
1st Qu.: 10000
Median : 180000
Median :2.000
Median : 60000
Median :2.000
Median : 25000
Mean: 362954
Mean:1.928Mean: 168691
Mean:1.922
Mean: 91879
3rd Qu.: 400000
3rd Qu.:2.000
3rd Qu.: 150000
3rd Qu.:2.000
3rd Qu.: 83500
Max.
:8000000
Max.
:2.000Max.
:9260000
Max.
:2.000Max.
:7000000
NA's:18972NA's:1NA's:19071NA's:1NA's:18928CREDIT_CARDSCREDIT_CARDS_AMOUNT RETAIL_SHOPSRETAIL_SHOP_AMOUNTPAWNINGMin.
:1.000
Min.
:
1000
Min.
:1.000
Min.
:
100
Min.
:1.000
1st Qu.:2.000
1st Qu.: 14250
1st Qu.:2.000
1st Qu.: 2000
1st Qu.:1.000
Median :2.000
Median : 35000
Median :2.000
Median : 4000
Median :2.000
Mean:1.986
Mean: 79835Mean:1.841
Mean: 6408Mean:1.606
3rd Qu.:2.000
3rd Qu.: 77500
3rd Qu.:2.000
3rd Qu.: 8000
3rd Qu.:2.000
Max.
:2.000Max.
:3500000
Max.
:2.000Max.
:500000
Max.
:2.000

Sri Lanka HIES 2012/13 Manual (Version 1.1)

51

PAWNING_AMOUNT
Min.
:
700
1st Qu.: 30000
Median : 75000
Mean: 137762
3rd Qu.: 153460
Max.
:8500000NA's:12440OTHER_AMOUNTMin.
:
2
1st Qu.: 8750
Median : 20000
Mean: 75194
3rd Qu.: 50000
Max.
:3200000NA's:20088NA's:20258NA's:1NA's:17268INSTALMENT_GOODS INSTALEMENT_AMOUNT OTHER_DEBTS
Min.
:1.000
Min.
:
150
Min.
:1.000
1st Qu.:2.000
1st Qu.: 4000
1st Qu.:2.000
Median :2.000
Median : 10000
Median :2.000
Mean:1.936Mean: 22453
Mean:1.978
3rd Qu.:2.000
3rd Qu.: 25000
3rd Qu.:2.000
Max.
:2.000Max.
:600000
Max.
:2.000NA's:1NA's:19229NA's:6WeightMin.
: 3.423
1st Qu.:152.327
Median :242.792
Mean:249.336
3rd Qu.:346.846
Max.
:752.308

R21 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:20
Min.
:11.00Min.
:1.000
1st Qu.:25778068
1st Qu.:20
1st Qu.:13.00
1st Qu.:1.000
Median :45297056
Median :20
Median :33.00
Median :2.000
Mean:49924702
Mean
:20
Mean
:40.85Mean:1.838
3rd Qu.:72971078
3rd Qu.:20
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:20
Max.
:92.00Max.
:3.000

NA's

:1

####

MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.671
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 53.00
Mean: 68.16
3rd Qu.: 90.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0143rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

A0
Min.
:10002
1st Qu.:25778
Median :45297
Mean:49925
3rd Qu.:72971
Max.
:98655SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.466
3rd Qu.: 8.000
Max.
:10.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10
HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:4.000

RADIO
TV
VCD
Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median :2.000
Mean:1.307Mean:1.194Mean:1.577
3rd Qu.:2.000
3rd Qu.:1.000
3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000
NA's
:1
NA's
:2
NA's
:3
SEWING_MECHINE WASHING_MECHINEFRIDGE
COOKERT
ELECTRIC_FANS
Min.
:1.0
Min.
:1.000Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.:1.0
1st Qu.:2.000
1st Qu.:1.000
1st Qu.:1.00
1st Qu.:1.000
Median :2.0
Median :2.000
Median :2.000
Median :2.00
Median :1.000
Mean
:1.6
Mean
:1.832Mean:1.554
Mean
:1.57
Mean
:1.4343rd Qu.:2.0
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.00
3rd Qu.:2.000
Max.
:2.0
Max.
:2.000Max.
:2.000
Max.
:2.00
Max.
:2.000
NA's
:2
NA's
:1
NA's
:1
NA's
:1
NA's
:1
TELEPHONE
TELEPHONE_MOBILECOMPUTERSCAMERA
BICYCLEMin.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:1.000
Median :2.000
Median :1.000
Median :2.000
Median :2.000
Median :2.000

Sri Lanka HIES 2012/13 Manual (Version 1.1)

52

Mean
:1.648Mean:1.192Mean:1.821Mean:1.897Mean:1.617
3rd Qu.:2.000
3rd Qu.:1.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000
NA's
:6
NA's
:2
NA's
:4
NA's
:14
NA's
:3
MOTOR_BICYCLE
THREE_WHEELER MOTOR_CAR_VAN
BUS_LORRY
TRACTOR_2_WHEEL
Min.
:1.000
Min.
:1.0
Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.:1.000
1st Qu.:2.0
1st Qu.:2.000
1st Qu.:2.00
1st Qu.:2.000
Median :2.000
Median :2.0
Median :2.000
Median :2.00
Median :2.000
Mean:1.706
Mean
:1.9
Mean
:1.931
Mean
:1.98
Mean
:1.9743rd Qu.:2.000
3rd Qu.:2.0
3rd Qu.:2.000
3rd Qu.:2.00
3rd Qu.:2.000
Max.
:2.000
Max.
:2.0
Max.
:2.000
Max.
:2.00
Max.
:2.000
NA's
:1
NA's
:1
NA's
:2
NA's
:2
NA's
:3
TRACTOR_4_WHEELPESTICIDER
THRESHERSWATERPUMPSMECHINEMin.
:1.000
Min.
:1.00
Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.:2.000
1st Qu.:2.00
1st Qu.:2.000
1st Qu.:2.00
1st Qu.:2.000
Median :2.000
Median :2.00
Median :2.000
Median :2.00
Median :2.000
Mean:1.989
Mean
:1.97
Mean
:1.996
Mean
:1.98
Mean
:1.998
3rd Qu.:2.000
3rd Qu.:2.00
3rd Qu.:2.000
3rd Qu.:2.00
3rd Qu.:2.000
Max.
:2.000
Max.
:2.00
Max.
:2.000
Max.
:2.00
Max.
:2.000
NA's
:3
NA's
:3
NA's
:3
NA's
:3
NA's
:4
BOATS
FISHING_NETS
WeightMin.
:1.000Min.
:1.000Min.
: 3.423
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:152.327
Median :2.000
Median :2.000
Median :242.792
Mean:1.993Mean:1.989Mean:249.336
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:2.000Max.
:2.000Max.
:752.308
NA's
:3
NA's
:7

####

R22 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:22
Min.
:11.00Min.
:1.000
1st Qu.:25778068
1st Qu.:22
1st Qu.:13.00
1st Qu.:1.000
Median :45297056
Median :22
Median :33.00
Median :2.000
Mean:49924702
Mean
:22
Mean
:40.85Mean:1.838
3rd Qu.:72971078
3rd Qu.:22
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:22
Max.
:92.00Max.
:3.000SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.466
3rd Qu.: 8.000
Max.
:10.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.671
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 53.00
Mean: 68.16
3rd Qu.: 90.00
Max.
:300.00

A0
Min.
:10002
1st Qu.:25778
Median :45297
Mean:49925
3rd Qu.:72971
Max.
:98655NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0143rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

PRE_SCHOOL_TIMEPRIMERY_SCHOOL

BUS_HALT_TIME
PRE_SCHOOL
Min.
: 0.00Min.
: 0.0000
1st Qu.: 5.00
1st Qu.: 0.0000
Median : 10.00
Median : 0.0000
Mean: 11.37
Mean: 0.5372
3rd Qu.: 15.00
3rd Qu.: 1.0000
Max.
:410.00Max.
:21.0000
NA's
:45
NA's
:50
PRIMERY_SCHOOL_TIME SECONDERY_SCHOOL

BUS_HALTMin.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.7382
3rd Qu.: 1.0000
Max.
:30.0000

Sri Lanka HIES 2012/13 Manual (Version 1.1)

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:4.00053

Min.
: 0.00Min.
: 0.000Min.
: 0.00Min.
: 0.000
1st Qu.: 5.00
1st Qu.: 0.000
1st Qu.: 10.00
1st Qu.: 0.000
Median : 10.00
Median : 1.000
Median : 14.00
Median : 2.000
Mean: 12.11Mean: 1.063
Mean: 15.43
Mean: 2.468
3rd Qu.: 15.00
3rd Qu.: 2.000
3rd Qu.: 20.00
3rd Qu.: 3.000
Max.
:300.00Max.
:31.000
Max.
:160.00Max.
:72.000
NA's
:54
NA's
:298
NA's
:300
NA's
:56
SEC_SCHOOL_TIMEHOSPITALHOSPITAL_TIME
MATRENITY_HOME
Min.
: 0.00Min.
: 0.000Min.
: 0.00Min.
: 0.000
1st Qu.: 10.00
1st Qu.: 2.000
1st Qu.: 20.00
1st Qu.: 2.000
Median : 20.00
Median : 5.000
Median : 30.00
Median : 4.000
Mean: 21.39
Mean: 6.497
Mean: 33.69
Mean: 5.134
3rd Qu.: 30.00
3rd Qu.: 8.000
3rd Qu.: 45.00
3rd Qu.: 7.000
Max.
:210.00Max.
:56.000Max.
:210.00Max.
:65.000
NA's
:61
NA's
:89
NA's
:92
NA's
:1314
MATERNITY_HOME_TIME GOV_DISPENSARZ
GOV_DISPENSARY_TIME PRIVATE_DISPENSARY
Min.
: 0.00Min.
: 0.000Min.
: 0.00Min.
: 0.000
1st Qu.: 15.00
1st Qu.: 1.000
1st Qu.: 15.00
1st Qu.: 0.000
Median : 25.00
Median : 3.000
Median : 20.00
Median : 1.000
Mean: 29.33
Mean: 4.073
Mean: 26.37
Mean: 2.446
3rd Qu.: 40.00
3rd Qu.: 5.000
3rd Qu.: 30.00
3rd Qu.: 3.000Max.
:200.00Max.
:99.000
Max.
:200.00Max.
:53.000
NA's
:1317
NA's
:1206
NA's
:1207
NA's
:575
PRIVATE_DISPENSARY_TIME MATERNITY_CLINIC MATERNITY_CLINIC_TIME
DMO
Min.
: 0.00Min.
: 0.000Min.
: 0.00Min.
: 0.000
1st Qu.: 10.00
1st Qu.: 0.000
1st Qu.: 10.00
1st Qu.: 2.000
Median : 15.00
Median : 1.000
Median : 15.00
Median : 5.000
Mean: 19.82
Mean: 2.528
Mean: 20.62Mean: 6.403
3rd Qu.: 25.00
3rd Qu.: 3.000
3rd Qu.: 25.00
3rd Qu.: 9.000
Max.
:900.00Max.
:82.000Max.
:250.00Max.
:90.000
NA's
:577
NA's
:174
NA's
:177
NA's
:165
DMO_TIME
MCUCPCMCUCPC_TIME
DS_OFFICE
DS_OFFICE_TIME
Min.
: 0.00Min.
: 0.000Min.
: 0.00Min.
: 0.000Min.
: 0.00
1st Qu.: 18.00
1st Qu.: 2.000
1st Qu.: 20.00
1st Qu.: 3.000
1st Qu.: 20.00
Median : 30.00
Median : 5.000
Median : 30.00
Median : 5.000
Median : 30.00
Mean: 33.55
Mean: 7.167
Mean: 35.67
Mean: 7.577
Mean: 37.05
3rd Qu.: 45.00
3rd Qu.: 9.000
3rd Qu.: 45.00
3rd Qu.:10.000
3rd Qu.: 45.00
Max.
:290.00Max.
:70.000Max.
:290.00Max.
:70.000Max.
:290.00
NA's
:169
NA's
:45
NA's
:49
NA's
:43
NA's
:46
GN_OFFICE
GN_OFFICE_TIME
POST_OFFICEPOST_OFFICE_TIME
BANK
Min.
: 0.0000Min.
: 0.00Min.
: 0.000Min.
: 0.00Min.
: 0.000
1st Qu.: 0.0000
1st Qu.: 5.00
1st Qu.: 0.000
1st Qu.: 10.00
1st Qu.: 1.000
Median : 0.0000
Median : 10.00
Median : 1.000
Median : 15.00
Median : 2.000
Mean: 0.8041
Mean: 13.18
Mean: 1.721
Mean: 17.89
Mean: 3.563
3rd Qu.: 1.0000
3rd Qu.: 15.00
3rd Qu.: 2.000
3rd Qu.: 20.00
3rd Qu.: 5.000
Max.
:60.0000Max.
:240.00Max.
:54.000
Max.
:200.00Max.
:50.000
NA's
:48
NA's
:53
NA's
:45
NA's
:48
NA's
:51
BANK_TIMEAGRI_OFFICEAGRI_OFFICE_TIME IS_POWER_LINES_NEAR
Min.
: 0.00Min.
: 0.00Min.
: 0.00Min.
:1.000
1st Qu.: 10.00
1st Qu.: 3.00
1st Qu.: 20.00
1st Qu.:1.000
Median : 20.00
Median : 5.00
Median : 30.00
Median :1.000
Mean: 24.18
Mean: 6.53Mean: 35.03
Mean:1.0433rd Qu.: 30.00
3rd Qu.: 8.00
3rd Qu.: 45.00
3rd Qu.:1.000
Max.
:200.00Max.
:83.00Max.
:290.00
Max.
:2.000
NA's
:54
NA's
:158
NA's
:160
NA's
:5
IS_TEL_LINES_NEAR IS_WATER_SERVICE_NEAR
ATM_CARD
ATMCARDAMOUNT

Sri Lanka HIES 2012/13 Manual (Version 1.1)

54

Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.212
3rd Qu.:1.000
Max.
:2.000NA's:5WeightMin.
: 3.423
1st Qu.:152.327
Median :242.792
Mean:249.336
3rd Qu.:346.846
Max.
:752.308

Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.37
3rd Qu.:2.00
Max.
:2.00
NA's
:6

Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.815
3rd Qu.:2.000
Max.
:2.000
NA's
:16

Min.
:
0
1st Qu.: 10000
Median : 20000
Mean: 29333
3rd Qu.: 30000
Max.
:9999999
NA's:17252####

R23 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:23
Min.
:11.00Min.
:1.000
1st Qu.:25778068
1st Qu.:23
1st Qu.:13.00
1st Qu.:1.000
Median :45297056
Median :23
Median :33.00
Median :2.000
Mean:49924702
Mean
:23
Mean
:40.85Mean:1.838
3rd Qu.:72971078
3rd Qu.:23
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:23
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.671
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 53.00
Mean: 68.16
3rd Qu.: 90.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0143rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

WALLS
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:2.1943rd Qu.:3.000
Max.
:9.000NA's:5
OWN_WATER
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.217
3rd Qu.:1.000

FLOOR
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.597
3rd Qu.:2.000
Max.
:9.000
NA's:4
WATER_DISTANCE:16193
0100
: 898
0050
: 831
0200
: 448
0010
: 337

A0
Min.
:10002
1st Qu.:25778
Median :45297
Mean:49925
3rd Qu.:72971
Max.
:98655SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.466
3rd Qu.: 8.000
Max.
:10.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

STRUCTURE
Min.
: 0.000
1st Qu.: 1.000
Median : 1.000
Mean: 2.126
3rd Qu.: 1.000
Max.
:99.000

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:4.000

BED_ROOMS
AREA
Min.
:0.000Min.
:1.000
1st Qu.:2.000
1st Qu.:3.000
Median :2.000
Median :4.000
Mean:2.312Mean:4.219
3rd Qu.:3.000
3rd Qu.:5.000
Max.
:9.000Max.
:9.000
NA's
:54
NA's
:5
ROOF
OWNERSHIPDRINKING_WATER
Min.
:1.000Min.
: 1.000Min.
: 1.00
1st Qu.:1.000
1st Qu.: 1.000
1st Qu.: 1.00
Median :2.000
Median : 1.000
Median : 4.00
Mean
:2.029Mean: 3.837
Mean: 4.38
3rd Qu.:2.000
3rd Qu.: 2.000
3rd Qu.: 5.00
Max.
:9.000Max.
:99.000Max.
:99.00
NA's
:5
NA's
:5
NA's
:2
WATER_SUFFICENCY OTHER_WATER_SUFFICENCY
Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
Median :1.000
Median :1.000
Mean:1.069Mean:1.108
3rd Qu.:1.000
3rd Qu.:1.000

Sri Lanka HIES 2012/13 Manual (Version 1.1)

55

Max.
:2.000
0500
: 297
Max.
:2.000Max.
:2.000NA's:28(Other): 1536
NA's
:18
NA's
:29
TIOILET_USE
TOILET_TYPEGARBAGE_DUMPING LITE_SOURCECOOKING_FUEL
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:1.000
Median :3.000
Median :1.000
Median :2.000
Median :2.000
Median :1.000
Mean:2.275Mean:1.123Mean:2.246Mean:1.906Mean:1.3143rd Qu.:3.000
3rd Qu.:1.000
3rd Qu.:3.000
3rd Qu.:2.000
3rd Qu.:1.000
Max.
:7.000Max.
:9.000Max.
:9.000Max.
:9.000Max.
:9.000
NA's
:8
NA's
:246
NA's
:11
NA's
:1
NA's
:18
IS_COLLECT_FIREWOOD FIRE_WOOD_OWN
OTHER_DISTANCE
NATURAL_CALAMITY
FLOODING
Min.
:1.000Min.
:1.000
Min.
:
1.0
Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.: 100.0
1st Qu.:2.000
1st Qu.:1.000
Median :1.000
Median :1.000
Median : 200.0
Median :2.000
Median :2.000
Mean:1.322Mean:1.607
Mean: 482.3
Mean:1.909Mean:1.566
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.: 500.0
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:2.000Max.
:3.000Max.
:9000.0
Max.
:2.000Max.
:2.000
NA's
:8
NA's
:6729
NA's
:14858NA's:7NA's:18673DROUGHTLAND_SLIDESWILDANIMALSWINDSOTHER_CALAMITY
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:2.000
Median :2.000
Median :2.000
Median :2.000
Median :2.000
Median :2.000
Mean:1.677Mean:1.974Mean:1.731Mean:1.949Mean:1.965
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000NA's:18673NA's:18673NA's:18673NA's:18673NA's:18673WeightMin.
: 3.423
1st Qu.:152.327
Median :242.792
Mean:249.336
3rd Qu.:346.846
Max.
:752.308####

R24 ################################################
ID
REC_TYPE
DISTRICTSECTOR
Min.
:10002011
Min.
:24
Min.
:11.00Min.
:1.000
1st Qu.:25778068
1st Qu.:24
1st Qu.:13.00
1st Qu.:1.000
Median :45297056
Median :24
Median :33.00
Median :2.000
Mean:49924702
Mean
:24
Mean
:40.85Mean:1.838
3rd Qu.:72971078
3rd Qu.:24
3rd Qu.:61.00
3rd Qu.:2.000
Max.
:98655101
Max.
:24
Max.
:92.00Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.671
3rd Qu.:10.000
Max.
:12.000

PSU
Min.
: 1.00
1st Qu.: 25.00
Median : 53.00
Mean: 68.16
3rd Qu.: 90.00
Max.
:300.00

NHH
Min.
:1.000
1st Qu.:1.000

RESULTMin.
:1
1st Qu.:1

A0
Min.
:10002
1st Qu.:25778
Median :45297
Mean:49925
3rd Qu.:72971
Max.
:98655SNUMBERMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.466
3rd Qu.: 8.000
Max.
:10.000

DS
Min.
:10
1st Qu.:10
Median :10
Mean:10
3rd Qu.:10
Max.
:10

IS_AGRILAND_OWNER PADDY_OWN_ACR
Min.
:1.000Min.
: 0.000
1st Qu.:1.000
1st Qu.: 1.000

Sri Lanka HIES 2012/13 Manual (Version 1.1)

HHNO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:4.000PADDY_OWN_RT
Min.
:0.000
1st Qu.:1.000

56Median :1.000
Mean:1.0143rd Qu.:1.000
Max.
:4.000Median : 2.000
Median :2.000
Mean: 2.528
Mean:1.8433rd Qu.: 2.000
3rd Qu.:2.000
Max.
:312.000
Max.
:6.000NA's:18787NA's:18980PADDY_OWN_PERCH PADDY_OTHER_ACR
PADDY_OTHER_RT PADDY_OTHER_PERCH LAND_OWN_ACR
Min.
: 0.00Min.
: 0.000
Min.
:0.000Min.
: 0.00Min.
: 0.000
1st Qu.:10.00
1st Qu.: 1.000
1st Qu.:2.000
1st Qu.:10.00
1st Qu.: 1.000
Median :20.00
Median : 2.000
Median :2.000
Median :20.00
Median : 1.000
Mean:16.94Mean: 2.808
Mean:1.868Mean:16.25Mean: 2.729
3rd Qu.:20.00
3rd Qu.: 2.000
3rd Qu.:2.000
3rd Qu.:20.00
3rd Qu.: 2.000
Max.
:80.00Max.
:485.000
Max.
:6.000Max.
:32.00Max.
:505.000NA's:20272NA's:19424NA's:19618NA's:20404NA's:19281
LAND_OWN_RT
LAND_OWN_PERCH LAND_OTHER_ACR
LAND_OTHER_RT
LAND_OTHER_PERCH
Min.
:0.000Min.
: 0.00Min.
: 0.000
Min.
:0.000Min.
: 0.00
1st Qu.:1.000
1st Qu.:10.00
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.: 8.00
Median :2.000
Median :15.00
Median : 1.000
Median :2.000
Median :15.00
Mean:1.769Mean:16.37Mean: 2.436
Mean:1.734Mean:15.11
3rd Qu.:2.000
3rd Qu.:20.00
3rd Qu.: 2.000
3rd Qu.:2.000
3rd Qu.:20.00
Max.
:5.000Max.
:90.00Max.
:222.000
Max.
:3.000Max.
:45.00NA's:19143NA's:19651
NA's:19973NA's:19976NA's:20331
HOME_OWN_ACR
HOME_OWN_RT
HOME_OWN_PERCH HOME_OTHER_ACR
HOME_OTHER_RT
Min.
: 0.000
Min.
:0.000Min.
: 0.00Min.
: 0.000
Min.
:0.000
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.: 9.00
1st Qu.: 1.000
1st Qu.:1.000
Median : 1.000
Median :2.000
Median :14.00
Median : 1.000
Median :2.000
Mean: 1.584
Mean:1.598Mean:14.91Mean: 2.704
Mean:1.682
3rd Qu.: 2.000
3rd Qu.:2.000
3rd Qu.:20.00
3rd Qu.: 2.000
3rd Qu.:2.000
Max.
:150.000
Max.
:4.000Max.
:80.00Max.
:503.000
Max.
:3.000NA's:18549NA's:15378NA's:11202NA's:20047
NA's:19405
HOME_OTHER_PERCH COWS_BUFFALOWSCOWS_COUNTGOATS_SHEEPSGOAT_COUNT
Min.
: 0.00Min.
:1.000Min.
:1.000Min.
:1.000Min.
:0.000
1st Qu.:10.00
1st Qu.:2.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:1.000
Median :15.00
Median :2.000
Median :1.000
Median :2.000
Median :1.000
Mean:15.94Mean:1.956Mean:1.384Mean:1.982Mean:1.359
3rd Qu.:20.00
3rd Qu.:2.000
3rd Qu.:1.000
3rd Qu.:2.000
3rd Qu.:2.000
Max.
:60.00Max.
:2.000Max.
:8.000Max.
:2.000Max.
:5.000NA's:19664
NA's
:28
NA's
:19624
NA's
:83
NA's
:20167
PIGS
PIGS_COUNTCHICKENS
CHICKEN_COUNTOTHER_ANIMALS
Min.
:1.000Min.
:0.000Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.:2.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:1.00
1st Qu.:2.000
Median :2.000
Median :1.000
Median :2.000
Median :2.00
Median :2.000
Mean:1.998Mean:1.456Mean:1.926
Mean
:1.77
Mean
:1.998
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.:2.00
3rd Qu.:2.000
Max.
:2.000Max.
:3.000Max.
:2.000
Max.
:8.00
Max.
:2.000
NA's
:82
NA's
:20483
NA's
:81
NA's
:19008
NA's:87
OTHER_COUNT
WeightMin.
:1.000Min.
: 3.423
1st Qu.:1.000
1st Qu.:152.327
Median :1.000
Median :242.792
Mean:1.588Mean:249.336
3rd Qu.:2.000
3rd Qu.:346.846
Max.
:5.000Max.
:752.308
NA's:20489####R25Median :1
Mean:1
3rd Qu.:1Max.
:1Median :1.000
Mean:1.1833rd Qu.:1.000
Max.
:3.000################################################

Sri Lanka HIES 2012/13 Manual (Version 1.1)

57

ID
REC_TYPE
DISTRICTSECTOR
DS
Min.
:10002011
Min.
:25
Min.
:11.00Min.
:1.000Min.
:10
1st Qu.:25778068
1st Qu.:25
1st Qu.:13.00
1st Qu.:1.000
1st Qu.:10
Median :45297056
Median :25
Median :33.00
Median :2.000
Median :10
Mean:49924702
Mean
:25
Mean
:40.85Mean:1.838Mean:10
3rd Qu.:72971078
3rd Qu.:25
3rd Qu.:61.00
3rd Qu.:2.000
3rd Qu.:10Max.
:98655101
Max.
:25
Max.
:92.00Max.
:3.000
Max.
:10
MONTH
PSU
A0
SNUMBER
HHNO
Min.
: 1.000Min.
: 1.00Min.
:10002Min.
: 1.000
Min.
:1.000
1st Qu.: 4.000
1st Qu.: 25.00
1st Qu.:25778
1st Qu.: 3.000
1st Qu.:1.000
Median : 7.000
Median : 53.00
Median :45297
Median : 5.000
Median :1.000
Mean: 6.671
Mean: 68.16
Mean:49925Mean: 5.466
Mean:1.007
3rd Qu.:10.000
3rd Qu.: 90.00
3rd Qu.:72971
3rd Qu.: 8.000
3rd Qu.:1.000
Max.
:12.000
Max.
:300.00
Max.
:98655Max.
:10.000
Max.
:4.000NHH
RESULT
HExpPM
HIncPM
WeightMin.
:1.000
Min.
:1
Min.
:
1411
Min.
:
129
Min.
: 3.423
1st Qu.:1.000
1st Qu.:1
1st Qu.: 20370
1st Qu.: 18600
1st Qu.:152.327
Median :1.000
Median :1
Median : 30710
Median : 30337
Median :242.792
Mean:1.014
Mean:1
Mean: 41424
Mean: 45200
Mean:249.336
3rd Qu.:1.000
3rd Qu.:1
3rd Qu.: 47532
3rd Qu.: 50000
3rd Qu.:346.846
Max.
:4.000Max.
:1
Max.
:1175382
Max.
:4983000Max.
:752.308
serialMin.
:
1
1st Qu.: 5136
Median :10270
Mean:10270
3rd Qu.:15405
Max.
:20540

Sri Lanka HIES 2012/13 Manual (Version 1.1)

58

5.2 Frequency table of categorical variables
Data check of categorical variables> for(j in 1:25){+ cat(c("\n\n","#### ", Rnames[j],+ "##################################################"),"\n\n")+ print(colnames(outfiles[[j]]))
+ }

####

R01 ##################################################[1] "ID"[5] "DS"[9] "SNUMBER"[13] "PERSON_SERIAL_NO"[17] "BIRTH_MONTH"[21] "MARITAL_STATUS"[25] "MAIN_ACTIVITY"[29] "PID"####"DISTRICT"
"PSU"
"NHH"
"SEX"
"ETHNICITY""EDUCATION""INDUSTRY""SECTOR""A0""RESULT""BIRTH_YEAR""RELIGION""IS_ACTIVE""EMPLOYMENT_STATUS"

R02 ##################################################[1] "ID"[4] "SECTOR"[7] "PSU"[10] "HHNO"[13] "R2_PERSON_SERIAL"[16] "GRADE_THIS_YEAR"[19] "TRANSPORT_MEDIUM"[22] "REASON_NOT_GOING"[25] "Weight"####"REC_TYPE""MONTH""HHNO""RELATIONSHIP""AGE""CURR_EDUC""MAIN_OCCUPATION""Weight""REC_TYPE""DISTRICT""DS""MONTH""A0""SNUMBER""NHH""RESULT""R2_SCHOOL_EDUCATION" "TYPE_OF_SCHOOL""GRADE_LAST_YEAR""DISTANCE""TIME_TO_SCHOOL""NOSCHOOLING_REASON""WHEN_STOP_SCHOOLING" "PID"

R03 ##################################################[1] "ID""REC_TYPE""DISTRICT"[4] "SECTOR""DS""MONTH"[7] "PSU""A0""SNUMBER"[10] "HHNO""NHH""RESULT"[13] "R3_PERSON_SERIAL2""ATTEND_GOVT_HOSPITAL3""REASON_HOSPITAL4"[16] "ATTEND_PRIVATE_HOSPITAL5" "REASON_FOR_WHAT6""IS_STAY_GOVHOSPITAL7"[19] "REASON_STAY8""STAY_IN_PRIVTE_HOSPITAL9" "REASON_FOR_STAY10"[22] "IS_ILL_DISABLE11""WHAT_ILL_DISABLE12""IS_EMPL_REASON13"[25] "DURATION_YEARS14""DURATION_MONTHS14""IS_ABSENT_ACT15"[28] "DAYS_ABSENT16""PID""Weight"####

R04 ##################################################[1] "ID"[6] "MONTH"[11] "NHH""REC_TYPE""PSU""RESULT""DISTRICT""A0""CODE""SECTOR""SNUMBER""QUANTITY"

Sri Lanka HIES 2012/13 Manual (Version 1.1)

"DS"
"HHNO""VALUE"59[16] "INKIND_VALUE" "Weight"####

R05 ##################################################[1] "ID"[5] "DS"[9] "SNUMBER"[13] "NF_CODE"[17] "Weight"####"DISTRICT"
"PSU"
"NHH"
"NF_VALUE""REC_TYPE" "DISTRICT" "SECTOR""SNUMBER" "HHNO""NHH""COL_5""COL_6""COL_7""COL_12""COL_13""COL_14""DISTRICT"
"PSU"
"NHH"
"WAGES_SALARIES""Weight""SECTOR""A0""RESULT""ALLOWENCES"

R09 ##################################################"DISTRICT""MONTH""SNUMBER""RESULT"R10 ##################################################[1] "ID""REC_TYPE" "DISTRICT" "SECTOR"[8] "A0""SNUMBER" "HHNO""NHH"[15] "COL_5X""COL_6X""COL_7X""COL_8X"[22] "COL_10X1" "COL_11X" "COL_12X" "COL_13X"

####

"PSU"
"COL_3""COL_10""Weight""DISTRICT""MONTH""SNUMBER""RESULT"[1] "ID""REC_TYPE"[4] "SECTOR""DS"[7] "PSU""A0"[10] "HHNO""NHH"[13] "IS_EMPLOYMENT_INCOME" "Weight"####"MONTH""COL_2""COL_9""PID"

R08 ##################################################[1] "ID""REC_TYPE"[5] "DS""MONTH"[9] "SNUMBER""HHNO"[13] "SERIAL_NO_SEC_1" "PRI_SEC"[17] "BONUS"
"PID"

####

"DS"
"RESULT""COL_8""COL_15"

R07 ##################################################[1] "ID""REC_TYPE"[4] "SECTOR""DS"[7] "PSU""A0"[10] "HHNO""NHH"[13] "IS_BOARDERS_SERVENTS" "Weight"####"SECTOR""A0""RESULT""NF_INKIND_VALUE"

R06 ##################################################[1] "ID"[8] "A0"[15] "COL_4"[22] "COL_11"####"REC_TYPE""MONTH""HHNO""NF_QUANTITY""DS""RESULT""COL_8X1""PID""MONTH""COL_2X""COL_9X""Weight"

R11 ##################################################

Sri Lanka HIES 2012/13 Manual (Version 1.1)

"PSU"
"COL_4X""COL_10X"

60[1] "ID""REC_TYPE"[4] "SECTOR""DS"[7] "PSU""A0"[10] "HHNO""NHH"[13] "IS_AGRICULTURAL_INCOME" "Weight"####"DISTRICT""MONTH""SNUMBER""RESULT"R12 ##################################################[1] "ID""REC_TYPE"[4] "SECTOR""DS"[7] "PSU""A0"[10] "HHNO""NHH"[13] "IS_OTHER_AGRRI_INCOME" "Weight"####"DISTRICT""MONTH""SNUMBER""RESULT"R13 ##################################################[1] "ID""REC_TYPE"[5] "DS""MONTH"[9] "SNUMBER""HHNO"[13] "SER_NO_SEC_5_3" "SEASONAL_CROP"[17] "PERCHS_5_3""OUTPUT_5_3"[21] "PID""Weight"####"DISTRICT"
"PSU"
"NHH"
"ACRES_5_3""INPUT_5_3"

R14 ##################################################[1] "ID""REC_TYPE"[5] "DS""MONTH"[9] "SNUMBER""HHNO"[13] "IS_NON_AGRI_INCOME" "Weight"####"DISTRICT""SECTOR""SNUMBER""HHNO""OUTPUT_5_4" "INPUT_5_4""SECTOR""A0""RESULT"

"DS"
"NHH"
"SUBSIDIES"

R16 ##################################################[1] "ID""REC_TYPE"[5] "DS""MONTH"[9] "SNUMBER""HHNO"[13] "IS_OTHER_INCOME" "Weight"####"DISTRICT"
"PSU"
"NHH"

R15 ##################################################[1] "ID""REC_TYPE"[7] "PSU""A0"[13] "SERIAL_5_4" "NON_AGRI"[19] "Weight"####"SECTOR""A0""RESULT""ROOTS_5_3""FERTILIZES""DISTRICT"
"PSU"
"NHH"

"SECTOR""A0""RESULT"R17 ##################################################[1] "ID"[4] "SECTOR"[7] "PSU""REC_TYPE"
"DS"
"A0"

"DISTRICT""MONTH""SNUMBER"

Sri Lanka HIES 2012/13 Manual (Version 1.1)

"MONTH""RESULT"
"PID"

61

[10] "HHNO"[13] "SERIAL_5_5_1"[16] "PROPERTY_RENTS"[19] "ELDER"[22] "THREEPOSHA"[25] "INCOME_LOCAL"

####

"NHH"
"PENSION""SAMURDHI""SCHOLAR""OTHER_INCOME""PID"

R18 ##################################################[1] "ID""REC_TYPE"[5] "DS""MONTH"[9] "SNUMBER""HHNO"[13] "IS_WINDFALL_INCOME" "Weight"####"SECTOR""A0""RESULT""DISTRICT""MONTH""SNUMBER""RESULT""PAWNING_SELLING""SEETTU_DEBITS""LOTTERY""PID"

R20 ##################################################[1] "ID""REC_TYPE"[4] "SECTOR""DS"[7] "PSU""A0"[10] "HHNO""NHH"[13] "BANKS""BANK_AMOUNT"[16] "FINANCE_AMOUNT""EMPLOYER"[19] "LENDER""LENDER_AMOUNT"[22] "CREDIT_CARDS_AMOUNT" "RETAIL_SHOPS"[25] "PAWNING""PAWNING_AMOUNT"[28] "INSTALEMENT_AMOUNT" "OTHER_DEBTS"[31] "Weight"####"DISTRICT"
"PSU"
"NHH"

R19 ##################################################[1] "ID""REC_TYPE"[4] "SECTOR""DS"[7] "PSU""A0"[10] "HHNO""NHH"[13] "PERSON_5_5_2""LOANS"[16] "DEPOSITS_PENSIONS_EPF" "WELFARE_SOCITY"[19] "MEDICAL""INSUARANCE"[22] "FOODALLOWENCE""DIASTER"[25] "Weight"####"RESULT""DISABILITY_AND_RELIEF""DIVIDENDS""SC_LUNCH""INCOME_FORIGN""Weight""DISTRICT""MONTH""SNUMBER""RESULT""FINANCE""EMPLOYER_AMOUNT""CREDIT_CARDS""RETAIL_SHOP_AMOUNT""INSTALMENT_GOODS""OTHER_AMOUNT"R21 ##################################################[1] "ID"[5] "DS"[9] "SNUMBER"[13] "RADIO"[17] "WASHING_MECHINE"[21] "TELEPHONE"[25] "BICYCLE"[29] "BUS_LORRY"[33] "THRESHERS"[37] "FISHING_NETS""REC_TYPE""DISTRICT""MONTH""PSU""HHNO"
"NHH"
"TV"
"VCD"
"FRIDGE""COOKERT""TELEPHONE_MOBILE" "COMPUTERS""MOTOR_BICYCLE""THREE_WHEELER""TRACTOR_2_WHEEL" "TRACTOR_4_WHEEL""WATERPUMPS""MECHINE""Weight""SECTOR""A0""RESULT""SEWING_MECHINE""ELECTRIC_FANS""CAMERA""MOTOR_CAR_VAN""PESTICIDER""BOATS"

Sri Lanka HIES 2012/13 Manual (Version 1.1)

62

####

R22 ##################################################[1] "ID""REC_TYPE"[4] "SECTOR""DS"[7] "PSU""A0"[10] "HHNO""NHH"[13] "BUS_HALT""BUS_HALT_TIME"[16] "PRE_SCHOOL_TIME""PRIMERY_SCHOOL"[19] "SECONDERY_SCHOOL""SEC_SCHOOL_TIME"[22] "HOSPITAL_TIME""MATRENITY_HOME"[25] "GOV_DISPENSARZ""GOV_DISPENSARY_TIME"[28] "PRIVATE_DISPENSARY_TIME" "MATERNITY_CLINIC"[31] "DMO""DMO_TIME"[34] "MCUCPC_TIME""DS_OFFICE"[37] "GN_OFFICE""GN_OFFICE_TIME"[40] "POST_OFFICE_TIME""BANK"[43] "AGRI_OFFICE""AGRI_OFFICE_TIME"[46] "IS_TEL_LINES_NEAR""IS_WATER_SERVICE_NEAR"[49] "ATMCARDAMOUNT""Weight"####

R23 ##################################################[1] "ID"[4] "SECTOR"[7] "PSU"[10] "HHNO"[13] "STRUCTURE"[16] "WALLS"[19] "OWNERSHIP"[22] "WATER_DISTANCE"[25] "TIOILET_USE"[28] "LITE_SOURCE"[31] "FIRE_WOOD_OWN"[34] "FLOODING"[37] "WILDANIMALS"[40] "Weight"####"DISTRICT""MONTH""SNUMBER""RESULT""PRE_SCHOOL""PRIMERY_SCHOOL_TIME""HOSPITAL""MATERNITY_HOME_TIME""PRIVATE_DISPENSARY""MATERNITY_CLINIC_TIME""MCUCPC""DS_OFFICE_TIME""POST_OFFICE""BANK_TIME""IS_POWER_LINES_NEAR""ATM_CARD""REC_TYPE"
"DS"
"A0"
"NHH"
"BED_ROOMS""FLOOR""DRINKING_WATER""WATER_SUFFICENCY""TOILET_TYPE""COOKING_FUEL""OTHER_DISTANCE""DROUGHT""WINDS""DISTRICT""MONTH""SNUMBER""RESULT""AREA""ROOF""OWN_WATER""OTHER_WATER_SUFFICENCY""GARBAGE_DUMPING""IS_COLLECT_FIREWOOD""NATURAL_CALAMITY""LAND_SLIDES""OTHER_CALAMITY"

R24 ##################################################[1] "ID""REC_TYPE"[5] "DS""MONTH"[9] "SNUMBER""HHNO"[13] "IS_AGRILAND_OWNER" "PADDY_OWN_ACR"[17] "PADDY_OTHER_ACR""PADDY_OTHER_RT"[21] "LAND_OWN_RT""LAND_OWN_PERCH"[25] "LAND_OTHER_PERCH" "HOME_OWN_ACR"[29] "HOME_OTHER_ACR""HOME_OTHER_RT"[33] "COWS_COUNT""GOATS_SHEEPS"[37] "PIGS_COUNT""CHICKENS"[41] "OTHER_COUNT""Weight""DISTRICT""SECTOR"
"PSU"
"A0"
"NHH"
"RESULT""PADDY_OWN_RT""PADDY_OWN_PERCH""PADDY_OTHER_PERCH" "LAND_OWN_ACR""LAND_OTHER_ACR""LAND_OTHER_RT""HOME_OWN_RT""HOME_OWN_PERCH""HOME_OTHER_PERCH" "COWS_BUFFALOWS""GOAT_COUNT""PIGS""CHICKEN_COUNT""OTHER_ANIMALS"

Sri Lanka HIES 2012/13 Manual (Version 1.1)

63

####

R25 ##################################################[1] "ID"[8] "A0"[15] "Weight">"REC_TYPE" "DISTRICT" "SECTOR""SNUMBER" "HHNO""NHH""serial""DS""RESULT""MONTH""HExpPM""PSU""HIncPM"> # file.names: Rnames[j]> # file.list: outfiles[[j]]> # list of column numbers of categorical variables> check.list<-list()> check.list[[1]]<-c(14:15,19:25,28)> check.list[[2]]<-c(14:17,19,21:22)> check.list[[3]]<-c(14:24,27)> check.list[[4]]<-c()> check.list[[5]]<-c()> check.list[[6]]<-c()> check.list[[7]]<-c(13)> check.list[[8]]<-c(14)> check.list[[9]]<-c(13)> check.list[[10]]<-c(14)> check.list[[11]]<-c(13)> check.list[[12]]<-c(13)> check.list[[13]]<-c(14)> check.list[[14]]<-c(13)> check.list[[15]]<-c(14)> check.list[[16]]<-c(13)> check.list[[17]]<-c()> check.list[[18]]<-c(13)> check.list[[19]]<-c()> check.list[[20]]<-c(seq(13,29,by=2))> check.list[[21]]<-c(13:37)> check.list[[22]]<-c(45:48)> check.list[[23]]<-c(13:21,23:31,33:39)> check.list[[24]]<-c(13,32:41)> check.list[[25]]<-c()> for(j in 1:25){+ if(length(check.list[[j]])==0){ next }+ cat(c("\n\n","#### FREQUENCY OF VARIABLES IN", Rnames[j],+ "##################################################"),"\n\n")+ for(k in check.list[[j]]){+ variable.name<-colnames(outfiles[[j]])[k]+ cat(c("-----",variable.name,"-------------------------------------------------------"))+ print(table(outfiles[[j]][k],useNA="ifany"))+ }}#### FREQUENCY OF VARIABLES IN R01 ##################################################----- RELATIONSHIP ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
20583 16207 34329 2006 11578
124
135
156 4624

Sri Lanka HIES 2012/13 Manual (Version 1.1)

64----- SEX ------------------------------------------------------1
2 <NA>
41022 44095 4625----- ETHNICITY ------------------------------------------------------1
2
3
4
5
6
9 <NA>
52506 13615 5424 8689
212
121
11 9164----- RELIGION ------------------------------------------------------12
3
4
9 <NA>
49095 15345 8998 7118
22 9164----- MARITAL_STATUS ------------------------------------------------------12
3
45 <NA>
35940 38256 5248
239
864 9195----- CURR_EDUC ------------------------------------------------------1
2
3
4
5
6
9 <NA>
2213 17914
298
634
662
225 54654 13142----- EDUCATION ------------------------------------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
1609 2265 3013 3101 3995 4926 3383 3910 5166 4061 15255 7059 3901
13
14
15
16
17
18
19 <NA>
7208
209 1281
256
18
33 3016 16077----- IS_ACTIVE ------------------------------------------------------1
2 <NA>
28337 30953 30452----- MAIN_ACTIVITY ------------------------------------------------------12
3
4
9 <NA>
2197 5924 15792 6488
812 58529----- EMPLOYMENT_STATUS ------------------------------------------------------123456 <NA>
3407 1338 13566
533 7889 1622 61387#### FREQUENCY OF VARIABLES IN R02 ##################################################----- R2_SCHOOL_EDUCATION ------------------------------------------------------1
2
3 <NA>
17875
788 3359
7----- TYPE_OF_SCHOOL ------------------------------------------------------1
2
3 <NA>
17198
389
286 4156----- GRADE_THIS_YEAR ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
14
19 <NA>
1367 1592 1535 1486 1481 1504 1432 1321 1358 1353 1467 1040 851
26
59 4157----- GRADE_LAST_YEAR ------------------------------------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
19
65 1670 1542 1488 1487 1490 1427 1326 1351 1353 1449 1065 827
30
10 1291<NA>4158----- TRANSPORT_MEDIUM ------------------------------------------------------1
2
3
4
5
6
9 <NA>
7634 2011 2259 2298 3441 142
64 4180----- NOSCHOOLING_REASON ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
4
21
5
67
2
6
621
62 21241----- REASON_NOT_GOING ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
15
383
431
45
924
369
435
642 18785

Sri Lanka HIES 2012/13 Manual (Version 1.1)

65#### FREQUENCY OF VARIABLES IN R03 ##################################################----- ATTEND_GOVT_HOSPITAL3 ------------------------------------------------------1
2 <NA>
13626 66901
7----- REASON_HOSPITAL4 ------------------------------------------------------1
2
3
4
5
6
9 <NA>
11516
504 1073
176
811
275 66908----- ATTEND_PRIVATE_HOSPITAL5 ------------------------------------------------------1
2 <NA>
11565 68959
10----- REASON_FOR_WHAT6 ------------------------------------------------------123459 <NA>
10281
399
581
53
58
190 68972----- IS_STAY_GOVHOSPITAL7 ------------------------------------------------------1
2 <NA>
6764 73763
7----- REASON_STAY8 ------------------------------------------------------1
2
3
4
5
6
7
8
9 <NA>
3976
622
724
771
101
204
1
1
363 73771----- STAY_IN_PRIVTE_HOSPITAL9 ------------------------------------------------------1
2 <NA>
631 79893
10----- REASON_FOR_STAY10 ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
379
55
103
39
9
8
1
38 79902----- IS_ILL_DISABLE11 ------------------------------------------------------1
2 <NA>
11205 69314
15----- WHAT_ILL_DISABLE12 ------------------------------------------------------0
1
2
3
4
5
6
7
8
9
10
11
12
1
902 2676 2031 1237
163
172
257
231
151
600
309
86
13
14
15
16
99 <NA>
291
151
264
303 1368 69341----- IS_EMPL_REASON13 ------------------------------------------------------0
1
24 <NA>
8
896 10243
2 69385----- IS_ABSENT_ACT15 ------------------------------------------------------0
1
24 <NA>
40 1745 9334
2 69413#### FREQUENCY OF VARIABLES IN R07 ##################################################----- IS_BOARDERS_SERVENTS ------------------------------------------------------1
2
177 20363#### FREQUENCY OF VARIABLES IN R08 ##################################################----- PRI_SEC ------------------------------------------------------1
2 <NA>
18353
214
27

Sri Lanka HIES 2012/13 Manual (Version 1.1)

66#### FREQUENCY OF VARIABLES IN R09 ##################################################----- IS_EMPLOYMENT_INCOME ------------------------------------------------------1
212914 7626#### FREQUENCY OF VARIABLES IN R10 ##################################################----- COL_4X ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
2759
88 82 700 364
67
21
92
23

#### FREQUENCY OF VARIABLES IN R11 ##################################################----- IS_AGRICULTURAL_INCOME ------------------------------------------------------0
1
2
4 3496 17040#### FREQUENCY OF VARIABLES IN R12 ##################################################----- IS_OTHER_AGRRI_INCOME ------------------------------------------------------1
23362 17178#### FREQUENCY OF VARIABLES IN R13 ##################################################----- SEASONAL_CROP ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
16
19
24
99 <NA>
1119 1006 469 409
54 246 160 280
40
14
1
4
1 155
10

#### FREQUENCY OF VARIABLES IN R14 ##################################################----- IS_NON_AGRI_INCOME ------------------------------------------------------1
25065 15475#### FREQUENCY OF VARIABLES IN R15 ##################################################----- NON_AGRI ------------------------------------------------------0
1
2
3
4
5
6
7
9 <NA>
1 162 962 134 2139 1024 122
30 110620#### FREQUENCY OF VARIABLES IN R16 ##################################################----- IS_OTHER_INCOME ------------------------------------------------------0
1
2
1 10623 9916

Sri Lanka HIES 2012/13 Manual (Version 1.1)

67#### FREQUENCY OF VARIABLES IN R18 ##################################################----- IS_WINDFALL_INCOME ------------------------------------------------------1
27991 12549#### FREQUENCY OF VARIABLES IN R20 ##################################################----- BANKS ------------------------------------------------------1
25498 15042----- FINANCE ------------------------------------------------------1
21569 18971----- EMPLOYER ------------------------------------------------------1
2 <NA>
1469 19070
1----- LENDER ------------------------------------------------------1
2 <NA>
1612 18927
1----- CREDIT_CARDS ------------------------------------------------------1
2
289 20251----- RETAIL_SHOPS ------------------------------------------------------1
2 <NA>
3272 17267
1----- PAWNING ------------------------------------------------------1
2 <NA>
8100 12439
1----- INSTALMENT_GOODS ------------------------------------------------------1
2 <NA>
1311 19228
1----- OTHER_DEBTS ------------------------------------------------------1
2 <NA>
451 20083
6#### FREQUENCY OF VARIABLES IN R21 ##################################################----- RADIO ------------------------------------------------------1
2 <NA>
14226 6313
1----- TV ------------------------------------------------------1
2 <NA>
16555 3983
2----- VCD ------------------------------------------------------1
2 <NA>
8692 11845
3----- SEWING_MECHINE ------------------------------------------------------1
2 <NA>
8219 12319
2----- WASHING_MECHINE ------------------------------------------------------1
2 <NA>

Sri Lanka HIES 2012/13 Manual (Version 1.1)

683445 17094
1----- FRIDGE ------------------------------------------------------1
2 <NA>
9159 11380
1----- COOKERT ------------------------------------------------------1
2 <NA>
8827 11712
1----- ELECTRIC_FANS ------------------------------------------------------1
2 <NA>
11634 8905
1----- TELEPHONE ------------------------------------------------------1
2 <NA>
7238 13296
6----- TELEPHONE_MOBILE ------------------------------------------------------1
2 <NA>
16600 3938
2----- COMPUTERS ------------------------------------------------------1
2 <NA>
3682 16854
4----- CAMERA ------------------------------------------------------1
2 <NA>
2116 18410
14----- BICYCLE ------------------------------------------------------1
2 <NA>
7875 12662
3----- MOTOR_BICYCLE ------------------------------------------------------1
2 <NA>
6048 14491
1----- THREE_WHEELER ------------------------------------------------------1
2 <NA>
2046 18493
1----- MOTOR_CAR_VAN ------------------------------------------------------1
2 <NA>
1408 19130
2----- BUS_LORRY ------------------------------------------------------1
2 <NA>
404 20134
2----- TRACTOR_2_WHEEL ------------------------------------------------------1
2 <NA>
529 20008
3----- TRACTOR_4_WHEEL ------------------------------------------------------1
2 <NA>
219 20318
3----- PESTICIDER ------------------------------------------------------1
2 <NA>
624 19913
3----- THRESHERS ------------------------------------------------------1
2 <NA>
87 204503----- WATERPUMPS ------------------------------------------------------1
2 <NA>
420 20117
3----- MECHINE ------------------------------------------------------1
2 <NA>
38 204984----- BOATS -------------------------------------------------------

Sri Lanka HIES 2012/13 Manual (Version 1.1)

69

1
2 <NA>
145 20392
3----- FISHING_NETS ------------------------------------------------------1
2 <NA>
220 20313
7#### FREQUENCY OF VARIABLES IN R22 ##################################################----- IS_POWER_LINES_NEAR ------------------------------------------------------1
2 <NA>
19648
887
5----- IS_TEL_LINES_NEAR ------------------------------------------------------1
2 <NA>
16184 4351
5----- IS_WATER_SERVICE_NEAR ------------------------------------------------------1
2 <NA>
12935 7599
6----- ATM_CARD ------------------------------------------------------1
2 <NA>
3800 16724
16#### FREQUENCY OF VARIABLES IN R23 ##################################################----- STRUCTURE ------------------------------------------------------0
1
2
3
4
5
6
7
8
9
99
1 16833 1342
82
302
103
16
187 1210
387
77----- BED_ROOMS ------------------------------------------------------0
1
2
3
4
5
6
7
8
9 <NA>
483 4280 7385 5785 1994 421 110
19
6
3
54
----- AREA ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
955 2588 3715 4224 4050 3301 1452 250
5----- WALLS ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
10377
637 7288
761
764
460
161
87
5
----- FLOOR ------------------------------------------------------1
2
3
4
5
6
9 <NA>
15303 2535 1292
16
129 1171904----- ROOF ------------------------------------------------------1
2
3
4
5
6
9 <NA>
9389 7312 768 397 2333 311
25
5----- OWNERSHIP ------------------------------------------------------1
2
3
4
5
6
7
8
9
99 <NA>
12485 3399
930
179 1796
243
883
29
215
376
5
----- DRINKING_WATER ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
99 <NA>
6570 2681 728 4961 1384 483 1267 715 124 1377
17
73 1582----- OWN_WATER ------------------------------------------------------1
2 <NA>
16061 4451
28----- WATER_SUFFICENCY ------------------------------------------------------1
2 <NA>
19104 1418
18----- OTHER_WATER_SUFFICENCY -------------------------------------------------------

Sri Lanka HIES 2012/13 Manual (Version 1.1)

70

1
2 <NA>
18306 2205
29----- TIOILET_USE ------------------------------------------------------1
2
3
4
5
6
7 <NA>
8931 393 9293 1079 450 144 242
8----- TOILET_TYPE ------------------------------------------------------12
3
4
9 <NA>
19017
633
267
336
41
246
----- GARBAGE_DUMPING ------------------------------------------------------123459 <NA>
5056 8305 5713 1001 199 255
11----- LITE_SOURCE ------------------------------------------------------123479 <NA>
2177 18184
161
6
1
10
1
----- COOKING_FUEL ------------------------------------------------------123459 <NA>
15803 4043
432
53
36
155
18
----- IS_COLLECT_FIREWOOD ------------------------------------------------------1
2 <NA>
13927 6605
8----- FIRE_WOOD_OWN ------------------------------------------------------1
2
3 <NA>
8088 3063 2660 6729----- NATURAL_CALAMITY ------------------------------------------------------1
2 <NA>
1867 18666
7----- FLOODING ------------------------------------------------------1
2 <NA>
810 1057 18673----- DROUGHT ------------------------------------------------------1
2 <NA>
603 1264 18673----- LAND_SLIDES ------------------------------------------------------1
2 <NA>48 1819 18673----- WILDANIMALS ------------------------------------------------------1
2 <NA>
503 1364 18673----- WINDS ------------------------------------------------------1
2 <NA>
95 1772 18673----- OTHER_CALAMITY ------------------------------------------------------1
2 <NA>
65 1802 18673#### FREQUENCY OF VARIABLES IN R24 ##################################################----- IS_AGRILAND_OWNER ------------------------------------------------------1
2
316772 3767
1----- COWS_BUFFALOWS ------------------------------------------------------1
2 <NA>
908 19604
28----- COWS_COUNT ------------------------------------------------------123458 <NA>

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693
125
76
19
1
2 19624----- GOATS_SHEEPS ------------------------------------------------------1
2 <NA>
364 20093
83----- GOAT_COUNT ------------------------------------------------------0
12345 <NA>
1
278
62
24
7
1 20167----- PIGS ------------------------------------------------------1
2 <NA>
47 20411
82----- PIGS_COUNT ------------------------------------------------------0
1
2
3 <NA>
134
17
5 20483----- CHICKENS ------------------------------------------------------1
2 <NA>
1524 18935
81----- CHICKEN_COUNT ------------------------------------------------------1
2
3
4
5
6
7
8 <NA>
719
547
191
65
2
4
1
3 19008----- OTHER_ANIMALS ------------------------------------------------------1
2 <NA>
40 20413
87----- OTHER_COUNT ------------------------------------------------------1
2
35 <NA>
30
16
3
2 20489

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5.3 Primary key and foreign key of each data set

Background: Relationship between data sets
The questionnaire has 9 sections and the micro data is split into 25 data files. Among 25 data sets,
R01 and R25 are the most basic. R25 provides the list of all sample households and R01 provides thelist of all household members. The next table shows the primary key and the foreign key of each data
set.

File

Primary key

No. ofmissing

No. ofForeign key

duplication

isolation

R01

PID

PID

R02

PID

PID

R03

PID

PID

R04

ID + CODE(101-1924)

R05

ID + NF_CODE(2001-3509)

1

ID
2

ID

R06

PID

PID

R07

ID

ID

R08

PID + PRI_SEC (1-2)

R09

ID

R10

27

PID
ID

23

PID + COL_4X
(1-9)

PID

R11

ID

ID

R12

ID

ID

R13

PID + SEASONAL_CROP(1-10, 99)

R14

ID

R15
R16

PID + NON_AGRI(1-6, 9)
ID

R17

PID

PID

R18

ID

ID

R19

PID

PID

R20

ID

ID

R21

ID

ID

R22

ID

ID

R23

ID

ID

R24

ID

ID

R25

ID

ID

10

1

PID
ID

20

1

PID
ID

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73

Figure: Example of relationship between data sets
R01

Primary key →

ID
PID
data

R23
Foreign key

Primary key →R08

Primary key →

ID
data

R01

ID
Foreign key
PID
PRI_SEC
data

Primary key →Sri Lanka HIES 2012/13 Manual (Version 1.1)

ID
PID
data

74



Primary key: UniquenessIn case of the primary key = ID
Primary key should be unique. There should be no duplicated key.
The uniqueness of the primary key was satisfied for all data set.

> file.no<-c(7,9,11,12,14,16,18,20:25)> for(j in file.no){+ n<-sum(duplicated(outfiles[[j]]$ID))+ cat("Number of duplicated IDs in",Rnames[j],"= ",n,"\n")+ }Number of duplicated IDs in R07 = 0
Number of duplicated IDs in R09 = 0
Number of duplicated IDs in R11 = 0
Number of duplicated IDs in R12 = 0
Number of duplicated IDs in R14 = 0
Number of duplicated IDs in R16 = 0
Number of duplicated IDs in R18 = 0
Number of duplicated IDs in R20 = 0
Number of duplicated IDs in R21 = 0
Number of duplicated IDs in R22 = 0
Number of duplicated IDs in R23 = 0
Number of duplicated IDs in R24 = 0
Number of duplicated IDs in R25 = 0

In case of the primary key = PID
Primary key should be unique. There should be no duplicated key.
The uniqueness of the primary key is satisfied for all data sets.

> file.no<-c(1,2,3,6,17,19)> for(j in file.no){+ n<-sum(duplicated(outfiles[[j]]$PID))+ cat("Number of duplicated PIDs of",Rnames[j],"= ",n,"\n")+ }Number of duplicated PIDs of R01 = 0
Number of duplicated PIDs of R02 = 0
Number of duplicated PIDs of R03 = 0
Number of duplicated PIDs of R06 = 0
Number of duplicated PIDs of R17 = 0
Number of duplicated PIDs of R19 = 0

In case that the primary key is a combination of variables


Generated the function to verify the uniqueness of the primary key> verify.unique<-function(j,k1,k2){+ # j: file no.
+ # k1: ID or PID+ # k2: unit variable (maximun nchar=4)+ d<-outfiles[[j]]+ key1<-d[,k1]+ key2<-d[,k2]+ d$pkey<-paste(key1,formatC(key2,width=4,flag="0"),sep="")+ n<-sum(duplicated(d$pkey))+ cat("Number of duplicated combination",k1,"+",k2," in",Rnames[j],"= ",n,"\n")Sri Lanka HIES 2012/13 Manual (Version 1.1)

75

+ if(n>0){+ pkey.e<-d$pkey[duplicated(d$pkey)]+ id.e<-d[duplicated(d$pkey),"ID"]+ for(k in 1:n){+ cat("Duplicated primary key:",pkey.e[k],"\n")+ cat("Records with the above primary key","\n")+ print(d[d$pkey==pkey.e[k],])+ } # end for+ } # end if+ }The results are as follows;> verify.unique(4,"ID","CODE")
Number of duplicated combination ID + CODEin R04 =0> verify.unique(5,"ID","NF_CODE")
Number of duplicated combination ID + NF_CODE in R05 = 2
Duplicated primary key: 193170213102
Records with the above primary key
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU
A0 SNUMBER HHNO NHH
96463 19317021
5
12
2 10
3 185 19317
2
1196464 19317021
5
12
2 10
3 185 19317
2
1
1
RESULT NF_CODE NF_QUANTITY NF_VALUE NF_INKIND_VALUE Weight
96463
1
3102
5
600
NA 406.632
96464
1
3102
2
1600
NA 406.632
pkey
96463 193170213102
96464 193170213102
Duplicated primary key: 454451012417
Records with the above primary key
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU
A0 SNUMBER HHNO NHH
320074 45445101
5
33
2 10
8 11 45445
101
1320075 45445101
5
33
2 10
8 11 45445
101
1
RESULT NF_CODE NF_QUANTITY NF_VALUE NF_INKIND_VALUE Weight32007412417
NA65
NA 251.816
320075
1
2417
NA
500
NA 251.816
pkey
320074 454451012417
320075 454451012417> verify.unique(8,"PID","PRI_SEC")
Number of duplicated combination PID + PRI_SEC

in R08 =0> verify.unique(10,"PID","COL_4X")
Number of duplicated combination PID + COL_4X

in R10 =0> verify.unique(13,"PID","SEASONAL_CROP")
Number of duplicated combination PID + SEASONAL_CROP in R13 = 1
Duplicated primary key: 1278906101 NA
Records with the above primary key
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU
A0 SNUMBER HHNO NHH
65 12789061
13
11
2 10
10 92 12789
61
1
66 12789061
13
11
2 10
10 92 12789
6
1
1
RESULT SER_NO_SEC_5_3 SEASONAL_CROP ACRES_5_3 ROOTS_5_3 PERCHS_5_3
65
1
1
NA
NA
2
NA
66
1
1
NA
NA
2
NA
OUTPUT_5_3 INPUT_5_3 FERTILIZESPID Weight
pkey
65
NA
NA
NA 1278906101 181.709 1278906101 NA
66
450
NA
NA 1278906101 181.709 1278906101 NA> verify.unique(15,"PID","NON_AGRI")
Number of duplicated combination PID + NON_AGRI in R15 = 1
Duplicated primary key: 83173071010009
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Records with the above primary key
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU
A0 SNUMBER HHNO NHH
4931 83173071
15
72
2 10
9 14 83173
71
14932 83173071
15
72
2 10
9 14 83173
71
1
RESULT SERIAL_5_4 NON_AGRI OUTPUT_5_4 INPUT_5_4 SUBSIDIES
PID
4931
1
1
9
5000
NA
NA 8317307101
4932
1
1
9
4500
NA
NA 8317307101
Weightpkey
4931 170.145 83173071010009
4932 170.145 83173071010009



Summary

FileRecordTo Do;All

Records with missing Key2

Drop

R05

ID=19317021 and NF_CODE=3102

AS IS

ID=45445101 and NF_CODE=2417
R13

PID=1278906101 andDrop

SEASONAL_CROP=NA
R15

PID=8317307101 and NON_AGRI=9

AS IS# Dropped the records with primary key=NA.
> outfiles.old2<-outfiles> d<-outfiles[[4]]> dim(d)[1] 815186
17> d<-d[!is.na(d$CODE),]> dim(d)[1] 815185
17> outfiles[[4]]<-d> d<-outfiles[[8]]> dim(d)[1] 18594
19> d<-d[!is.na(d$PRI_SEC),]> dim(d)[1] 18567
19> outfiles[[8]]<-d> d<-outfiles[[10]]> dim(d)[1] 419627> d<-d[!is.na(d$COL_4X),]> dim(d)[1] 417327> outfiles[[10]]<-d> d<-outfiles[[13]]> dim(d)[1] 396822> d<-d[!is.na(d$SEASONAL_CROP),]> dim(d)[1] 3958
22> outfiles[[13]]<-d
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Remarks

77> d<-outfiles[[15]]> dim(d)[1] 5700
19> d<-d[!is.na(d$NON_AGRI),]> dim(d)[1] 5680
19> outfiles[[15]]<-d# R13: Resolved already.
> nrow(d[d$PID==1278906101,])
[1] 0



Foreign key: Referential integrity

In case of the foreign key = ID
A corresponding ID should exist in R25.


The referential integrity was satisfied for all data sets.

> R25<-outfiles[[25]]> id.all<-R25$ID> file.no<-c(4,5,7,9,11,12,14,16,18,20:25)> for(j in file.no){+ n<-sum(!is.element(outfiles[[j]]$ID,id.all))+ cat("Number of isolated IDs of",Rnames[j],"= ",n,"\n")+ }Number of isolated IDs of R04 =0

Number of isolated IDs of R05 =0

Number of isolated IDs of R07 =0

Number of isolated IDs of R09 =0

Number of isolated IDs of R11 =0

Number of isolated IDs of R12 =0

Number of isolated IDs of R14 =0

Number of isolated IDs of R16 =0

Number of isolated IDs of R18 =0

Number of isolated IDs of R20 =0

Number of isolated IDs of R21 =0

Number of isolated IDs of R22 =0

Number of isolated IDs of R23 =0

Number of isolated IDs of R24 =0

Number of isolated IDs of R25 =0

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In case of the foreign key = PID
A corresponding PID should exist in R01.
If not, all records within the household with the isolated foreign key PID as well as all household
members within the household should be displayed, in order to make it easy to identify the causesof the errors and revise the errors.
The referential integrity was satisfied for all, as follows.

> R01<-outfiles[[1]]> pid.all<-R01$PID> file.no<-c(1:3,6,8,10,13,15,17,19)> for(j in file.no){+ n<-sum(!is.element(outfiles[[j]]$PID,pid.all))+ cat("Number of isolated PIDs of",Rnames[j],"= ",n,"\n")+ if(n>0){+ d<-outfiles[[j]]+ pid.e<-d[!is.element(d$PID,pid.all),"PID"]+ id.e<-substr(pid.e,1,15)+ for(k in 1:n){+ cat("Isolated foreign key: PID=",pid.e[k],"\n")+ cat("Records of the household with ID=",id.e[k],"\n")+ print(d[d$ID==id.e[k],])+ cat("Records of household members with ID=",id.e[k],"\n")+ print(R01[R01$ID==id.e[k],])
+ }
+ }
+ }
Number of isolated PIDs of R01 =0

Number of isolated PIDs of R02 =0

Number of isolated PIDs of R03 =0

Number of isolated PIDs of R06 =0

Number of isolated PIDs of R08 =0

Number of isolated PIDs of R10 =0

Number of isolated PIDs of R13 =0

Number of isolated PIDs of R15 =0

Number of isolated PIDs of R17 =0

Number of isolated PIDs of R19 =0

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# Number of records and variables of data files> for(j in 1:25){+ cat(Rnames[j],":",formatC(nrow(outfiles[[j]]),width=7),",",+ formatC(ncol(outfiles[[j]]),width=3),":",+ filenames[j],"\n",sep="")
+ }
R01:

85080, 30:

SEC_1_DEMOGRAPHIC

R02:

22029, 25:

SEC_2_SCHOOL_EDUCATION

R03:

80534, 30:

SEC_3_HEALTH

R04: 815185, 17:

SEC_4_1_FOOD_EXP

R05: 612085, 17:

SEC_4_2_NONFOOD

R06:260, 28:

SEC_4_3_BOARDERS

R07:

20540, 14:

SEC_4_3_IS_BOADERS

R08:

18567, 19:

SEC_5_1_EMP_INCOME

R09:

20540, 14:

SEC_5_1_IS_EMP_INCOME

R10:4173, 27:

SEC_5_2_AGRI_INCOME

R11:

20540, 14:

SEC_5_2_IS_AGRI_INCOME

R12:

20540, 14:

SEC_5_3_IS_OTHER_AGRI_INCOME

R13:

3958, 22:

SEC_5_3_OTHER_AGRI_INCOME

R14:

20540, 14:

SEC_5_4_IS_NON_AGRI_INCOME

R15:5680, 19:

SEC_5_4_NON_AGRI_INCOME

R16:

20540, 14:

SEC_5_5_1_IS_OTHER_INCOME

R17:

12602, 27:

SEC_5_5_1_OTHER_INCOME

R18:

20540, 14:

SEC_5_5_2_IS_WINDFALL_INCOME

R19:

8575, 25:

SEC_5_5_2_WINDFALL_INCOME

R20:

20540, 31:

SEC_6_B_DEBTNESS

R21:20540, 38:

SEC_6A_DURABLE_GOODS

R22:20540, 50:

SEC_7_BASIC_FACILITIES

R23:20540, 40:

SEC_8_HOUSING

R24:

20540, 42:

SEC_9_LAND_ANIMAL

R25:

20540, 15:

WEIGHT_INCOME_EXPENDITURE

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Relationship between R01 and R02



The members of R02 belong R01. Therefore, R02 was merged with R01 in order to get
demographic information.

> d<-merge(outfiles[[01]],outfiles[[02]][,13:24],by="PID",all.y=T)> dim(d)[1] 22029
41> colnames(d)[1] "PID""ID""REC_TYPE"[4] "DISTRICT""SECTOR""DS"[7] "MONTH"
"PSU"
"A0"
[10] "SNUMBER""HHNO""NHH"[13] "RESULT""PERSON_SERIAL_NO""RELATIONSHIP"[16] "SEX""BIRTH_YEAR""BIRTH_MONTH"[19] "AGE""ETHNICITY""RELIGION"[22] "MARITAL_STATUS""CURR_EDUC""EDUCATION"[25] "IS_ACTIVE""MAIN_ACTIVITY""MAIN_OCCUPATION"[28] "INDUSTRY""EMPLOYMENT_STATUS""Weight"[31] "R2_PERSON_SERIAL""R2_SCHOOL_EDUCATION" "TYPE_OF_SCHOOL"[34] "GRADE_THIS_YEAR""GRADE_LAST_YEAR""DISTANCE"[37] "TRANSPORT_MEDIUM""TIME_TO_SCHOOL""NOSCHOOLING_REASON"[40] "REASON_NOT_GOING""WHEN_STOP_SCHOOLING"

The target of R02 is persons aged 5-19 years old. The next results are within allowable
range.

> range(d$AGE)[1] 4 20

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5.4 Sample allocation


Strata of HIES 2012 are district (25) and sector (3).
PSUs are the census blocks selected for the survey. Here, we assume that the variable of A0 is
a block identification number.

> R25<-outfiles[[25]]> R25.old<-R25> length(unique(R25$A0))[1] 2507

Generated data set PSU at psu level consist of DISTRICT, SECTOR and A0.

> PSU<-R25[!duplicated(R25$A0),c("MONTH","DISTRICT","SECTOR","A0")]> dim(PSU)[1] 2507
4> head(PSU)
MONTH DISTRICT SECTOR
A0
1
7
11
1 100021211
1
11
1 10022581
10
11
1 100411739
4
11
1 10061996
12
11
1 100822021
6
11
1 10103

Number of psu selected by district and sector;> dist.name<-c("11 Colombo","12 Gampaha","13 Kalutara","21 Kandy","22 Matale",+ "23 Nuwara eliya","31 Galle","32 Matara","33 Hambantota","41 Jaffna","42 Mannar",+ "43 Vavuniya","44 Mullaitivu","45 Kilinochchi","51 Batticaloa","52 Ampara",+ "53 Trincomalee","61 Kurunegala","62 Puttalama","71 Anuradhapura","72 Polonnaruwa",+ "81 Badulla","82 Moneragala","91 Ratnapura","92 Kegalle")> t<-addmargins(table(PSU$DISTRICT,PSU$SECTOR))> rownames(t)<-c(dist.name,"Sri Lanka")> colnames(t)<-c("Urban","Rural","Estate","Total")> t

11 Colombo
12 Gampaha
13 Kalutara
21 Kandy
22 Matale
23 Nuwara eliya
31 Galle
32 Matara
33 Hambantota
41 Jaffna
42 Mannar
43 Vavuniya
44 Mullaitivu
45 Kilinochchi
51 Batticaloa
52 Ampara
53 Trincomalee
61 Kurunegala
62 Puttalama
71 Anuradhapura
72 Polonnaruwa
81 Badulla

Urban Rural Estate Total
183
100
12
295
72
169
5
246
32
107
21
160
27
73
22
122
15
41
10
66
15
41
42
98
37
98
14
149
32
87
15
134
16
71
1
88
25
50
0
75
12
22
0
34
11
21
0
32
0
32
0
32
0
40
0
40
32
50
0
82
31
57
0
88
18
39
0
57
15
112
8
135
17
56
3
76
16
66
3
85
0
67
3
70
15
47
23
85
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82 Moneragala
91 Ratnapura
92 Kegalle
Sri Lanka

0
20
8
649

63
60
60
1629

10
20
17
229

73
100
85
2507

>



SSU: 10 housing units were selected for each psu.

 Generated ssuid as the combination of A0 and SNUMBER> R25$ssuid<-R25$A0*100+R25$SNUMBER> length(unique(R25$ssuid))[1] 20413# Number of SSU within PSU> addmargins(table(tapply(R25$ssuid,R25$A0,function(x) length(unique(x)))))
3
4
5
6
7
8
9
10 Sum2
4 56 203 525 643 666 408 2507
# Number of household within SSU> table(tapply(R25$ID,R25$ssuid,length))
12
3
420294 11261

According to the final report of the survey, samples were allocated as in the table 1.1 and 1.2.

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83

The numbers of sample collected were as follows;# The number of samples by district and survey month> t1<-addmargins(table(R25$DISTRICT,R25$MONTH))> rownames(t1)<-c(dist.name,"Sri Lanka")> t1[c(26,1:25),]
1
2
3
4
5
6
7
8
9 10 11 12 Sum
Sri Lanka
1793 1648 1616 1120 1746 1646 1995 1784 1652 1906 1773 1861 20540
11 Colombo
173 163 186 116 160 165 208 196 169 214 185 231 2166
12 Gampaha
163 166 151 115 138 182 185 161 158 193 149 187 1948
13 Kalutara
113 100 91 61 122 100 111 127 92 100 118 109 1244
21 Kandy
69
75 90 49 77 82 104 81 92 90 77 97 983
22 Matale54
44 45 35 47 46 68 45 45 63 48 64 604
23 Nuwara eliya 54
65 67 31 80 63 66 82 65 63 79 76 791
31 Galle
93 107 102 78 102 104 136 104 105 142 106 120 1299
32 Matara118
91 99 49 112 93 95 116 93 91 112 79 1148
33 Hambantota
68
61 60 49 58 56 78 54 52 79 55 65 735
41 Jaffna
68
53 48 19 63 61 51 68 51 50 65 46 643
42 Mannar18
26 27 19 22 19 26 18 25 27 27 36 290
43 Vavuniya
27
18 20 12 37 13 16 27 30 29 27 26 282
44 Mullaitivu
33
20 16
9 24 18 27 25 18 26
26 21 263
45 Kilinochchi
27
23 34
7 40 25 23 36 34 21
32 23 325
51 Batticaloa
73
52 38 50 61 62 67 51 52 73
63 56 698
52 Ampara
66
61 36 74 57 73 76 63 70 54
47 62 739
53 Trincomalee41
42 45 27 43 45 43 30 34 54
50 48 502
61 Kurunegala
102
92 93 77 85 98 125 85 89 113
89 109 1157
62 Puttalama
33
59 53 38 62 51 63 57 54 65
56 63 654
71 Anuradhapura 89
69 62 27 69 40 88 62 63 50 78 46 743
72 Polonnaruwa
55
34 39 34 36 37 41 66 46 44 49 45 526
81 Badulla73
65 57 26 79 57 73 62 56 53 67 63 731
82 Moneragala
53
42 39 35 49 36 64 44 46 68 51 49 576
91 Ratnapura
69
65 64 49 62 65 88 71 65 84 65 78 825
92 Kegalle
61
55 54 34 61 55 73 53 48 60 52 62 668# The number of samples by sector and survey month> t2<-addmargins(table(R25$SECTOR,R25$MONTH))> rownames(t2)<-c("Urban","Rural","Estate","Sri Lanka")> t2[c(4,1:3),]
1
2
3
4
5
6
7
8
9 10 11 12 Sum
Sri Lanka 1793 1648 1616 1120 1746 1646 1995 1784 1652 1906 1773 1861 20540
Urban
477 448 390 284 391 423 515 423 455 495 408 463 5172
Rural
1190 1102 1060 760 1220 1081 1278 1148 1042 1224 1180 1230 13515
Estate126
98 166 76 135 142 202 213 155 187 185 168 1853
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Remarks:
According to the delegates from Sri Lanka for the International Workshop 2014,“HIES is conducted on a week base questionnaire in 52 weeks grouped in 12 consecutive monthly
rounds to capture the seasonal variation of household income, consumption and expenditure and living
conditions.”

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5.5 Population estimates

The estimated number of total households and total household members are as follows;

Un-weighted and weighted number of households and household members

Un-weighted number Weighted number
Number of households

20,5405,121,354

Number of household members

85,08020,986,629

4.14

4.10

Household size
> dim(R25)[1] 20540

15> sum(R25$Weight)[1] 5121354> dim(R01)[1] 85080

30> sum(R01$Weight)[1] 20986629> nrow(R01)/nrow(R25)[1] 4.142162> sum(R01$Weight)/sum(R25$Weight)[1] 4.097867

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6. Household Income
6.1 Definition of household income Household income
According to the final report of the survey, household income is defined as follows;“The Household Income and Expenditure Survey (HIES) defines the household income as the total income
received by all the members of the household, either in cash (monetary income) or in kind (non- monetary
income) from all the sources.
The household income sources are investigated and reported under the following 7 main categories in the survey
questionnaire.
1. Wages and salaries
2. Agricultural activities (seasonal crops)
3. Agricultural activities (non-seasonal crops)
4. Non-agricultural activities
5. Other cash receipts such as pensions, dividends, rents, interest amounts received from various types of savings,
current remittances and local and foreign transfers
6. Income by chance or adhoc gains such as compensations, lottery wins etc. and sales of goods and savings.
7. Income in-kind
Obtaining income information from individuals and households is a difficult task as many people reluctant todisclose many of them and often under report. Therefore to ease the field work, which is the most challenging
activity of the survey, and to gather more accurate and reliable data, income information of the household
members were collected individually in all the 6 income sections (income in-kind collects at household level)tactically arranged in the HIES questionnaire.
The income in kind is mostly the estimated values of the household consumed items such as home grown fruits andvegetables, firewood collected etc. and estimated rental values of owner occupied housing units gathered in the
consumption expenditure section of the survey questionnaire.

An extra column has been provided at household level in the expenditure section to record
estimated values of household consumed goods and services received fully or partially free ofcharge or purchased on price concessions provided by employers etc. This information of income inkind along with the monitory income collected in the 6 income sections are aggregated andsummarized in order to estimate, average monthly household income (mean household income),
median household income, per capita household income, income receivers' household income andvarious other indexes such as, Gini coefficients, shares of household income etc. at many different
geographic and social domain levels.”
And the some results of estimated income in the final report are as follows;

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 Income receivers’ income
Definition of income receiver and income receivers’ income;“In order to obtain the income receivers income, the HIES records the household income, received from all thesources, by source and person. The income receivers income is the sum of the income values recorded in each
income section arranged according to the income source in the survey questionnaire.

If a person is less than 10 years old or aggregated total monthly income is less than Rs. 250, thenhe was not defined as an income receiver by the HIES 2012/13 and such income values were added
to the income of the heads of the respective households. It is obvious that the household income isso built on the income of the income receivers in the household and thus the total householdincome of the country is equal to the sum of the income values recorded by the total income
receivers at all of the source sections of the survey questionnaire.”

Note: It is obvious that non-monetary income is not included in income receiver’s income.
The results of income receivers in the final report are as follows;

Table 2.7: Income receivers mean and median monthly income by sector

Table 2.8: Average number of income receivers and household size by sector

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6.2. Process of estimating household income
The data sets R01, R04, R05, R08, R10, R13, R15, R17 and R19 are used for estimating household income.
The data files to be used for estimating household income;Type of income
Data Reference period
file
Monetary Wage
R08
Wages and allowances forincomeone month,
Bonus for 12 month
Agricultural
R10
12 months,(Seasonal crops)
Agricultural
R13
One month(Non-seasonal
crops)
Non-agricultural R15
One monthNonmonetary
incomeOther income

R17

Windfall
Income in kind

R19
R04
and
R05

6 items for one month,
Remittance for 12 months
12 months
One week: CODE<2000
6 months: CODE>3000 &CODE<3300
12 months: CODE>3300 &CODE<3400 |CODE>3500
One month: otherwise

Unit of records
Individual,
Occupation (Pri/Sec)
Individual,
Seasonal crop code
Individual,
Non-seasonal crop code
Individual,
Economic activity code
Individual
IndividualHousehold,
Item code

The process of estimating household income is as follows;
Preparing data files:
1. To filter out records without RESULT=1 (Completed).
(No such record was found in the given data set.)
2. To filter out records with person number >40.
(Such records were found in R01 and R19.)

3. To eliminate records of R04 and R05with duplicated item codes. Item code is unique within the household
due to the structure of the questionnaire.
(No such record was found in the given data set.)
4. To verify records with NA in the variable of person number and activity code.
Generating the variable of monthly monetary income
5. To calculate monthly income for each record, that is, for each person and for each activity in R08, R10,
R13, R15, R17 and R19.
Note: The variable of output includes the estimated total value of the total output of the product sold or tobe sold plus consumed or to be consumed. That is, output means the estimated value in cash and in kind.
6. To replace the income in each record with 0 if it is less than 0. Income by person and by activity should
be non-negative as in the HIES 2009/10.
Data
file
R08

Variable ofmonthly
incomewage.inc

Definition of monthly monetary income

R10

crop.inc

WAGES_SALARIES+ALLOWANCES+
BONUS/12(COL_8X1-COL_9X-COL_10X1)/12

R13

livestock.inc

OUTPUT_5_3－INPUT_5_3
Sri Lanka HIES 2012/13 Manual (Version 1.1)

Remarks

Replacecrop.inc=0 ifcrop.inc<0
Replacelivestock.inc=0 if

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R15

nonagri.inc

OUTPUT_5_4－INPUT_5_4

R17

other.inc

R19

windfall.inc

PENSION+DISABILITY_AND_RELIEF+
PROPERTY_RENTS+SAMURDHI+
DIVIDENTS+ELDER+SCHOLAR+SC_LUNCH+
THREEPOSHA+(OTHER_INCOME+
INCOME_FORING+INCOME_LOCAL)/12(LOANS+PAWNING_SELLING+DEPOSITS_PENSIONS_EPF+WELFARE_SOCITY+SEETTU_DEBITS+MEDICAL+INSUARANCE+LOTTERY+FOODALLOWENCE+DIASTER)/12

livestock.inc<0
Replacenonagri.inc=0 if
nonagri.inc<0Part I: Income receivers’ income
Data files to be used for estimating income receivers’ income
Income source
Data Variables to be used
Unit of records
file
Wage
R08
wage.inc
Individual,
Occupation (Pri/Sec)
Agricultural
R10crop.inc
Individual,(Seasonal crops)
Seasonal crop code
Agricultural
R13
livestock.inc
Individual,(Non-seasonalNon-seasonal crop code
crops)
Non-agricultural
R15
nonagri.inc
Individual,
Economic activity code
Other income
R17
other.inc
Individual
WindfallR19
windfall.inc
Individual
Individual
R01
AGE, RELATIONSHIP
Individual
characteristics
If a person is less than 10 years old or his/her aggregated total monthly income is less than Rs. 250, then he/she
was not defined as an income receiver by the HIES 2012/13 and such income values were added to the incomeof the heads of the respective households.
Note: It is obvious that non-monetary income is not included in income receiver’s income.

Generating individual-level income data file
7. To collapse income data files R08, R10, R13 and R15 at individual level
8. To merge all individual-level income data files R08, R10, R13, R15, R17 and R19, as well as individual
characteristics file R01 by using PID (individual identifier).
9. To generate the variable of monthly total income.

Transferring the income of a person with age less than 10 years or total income less than Rs. 250 to thehead of the respective households
10. If a person is less than 10 years old or his/her aggregated total monthly income is less than Rs. 250 and
his/her relationship is not a head of household, to add such income values to the income of the heads ofthe respective households.
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Defining the income receivers
11. To select records with age >=10 and total income >=250, or relationship=1. Household heads are qualified
as income receivers even if the income of the head is none or less than 250.
(Justification for including household head with income=0 is needed.)Remarks: The concept of income receiver is slightly vague in connection with whether a head with income <250 is regarded as an income receiver, and whether a household with no income receiver is possible.

Part II: Household income in cash and in kind at household level
Creating data file of household monetary income at household level
12. To aggregate the above monthly income by household, and create data file with household-level records
from R08, R10, R13, R15, R17 and R19.
13. To merge the above data files using key of household identifier.
14. To generate the variable of the monthly total monetary household income.

Non-monetary income (income in kind)
Consumption of own production will be captured both at consumption side in section 4 of the questionnaire
and production side in section 5. The former and the latter should be equivalent on a conceptual basis, while
they are not the same in practice. The next is the treatment for rice and the other products taken by DCS in the
HIES 2009/10.
(Justification for the different treatment is needed.)
For rice (paddy),
to compare the consumption of own production (INKIND_VALUE) of rice in section 4 with the output of
paddy consumed by the household (HH_CONSUMPTION) in section 5 at household level, and take the larger
one as in-kind income of rice.
15. To aggregate (INKIND_VALUE*(30/7)) of R04 with CODE=<4 by household.
16. To aggregate (HH_CONSUMPTION/12) of R10 with SEAS_CROPS_CODE=1 by household.
17. To take the larger one out of the above two as monthly monetary income of rice (inkind.rice) at household
level.
For other products,
to take the consumption of own production (INKIND_VALUE of R04 and NF_INKIND_VALUE of R05) in
section 4 as in-kind income.
18. To convert INKIND_VALUE of R04 and NF_INKIND_VALUE of R05 to monthly variable (inkind04
and inkind05).
19. To aggregate the above by household.
Data
file

Variable ofmonthly income

Definition

R04

inkind.rice

R04

inkind04

max (a, b)
where,a) Sum of (INKIND_VALUE*(30/7)) of R04 with
CODE=<4 within the householdb) Sum of (HH_CONSUMPTION/12) of R10 with
SEAS_CROPS_CODE=1 within the household
Sum of INKIND_VALUE*(30/7) (CODE>=5) within
the household
Sri Lanka HIES 2012/13 Manual (Version 1.1)

Created data
file athousehold
level

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R05

inkind05

Sum of the followings within the household; NF_INKIND_VALUE/6
if NF_CODE>3000 & NF_CODE<3300,
 NF_INKIND_VALUE/12
if (NF_CODE>3300 & NF_CODE<3400) or
NF_CODE>3500,
 NF_INKIND_VALUE otherwise

Generating the variables of non-monetary income and total income at household level
20. To merge the above three files using ID (household identifier).
21. To generate the variable of non-monetary income by adding inkind.rice, inkind04 and inkind05.
22. To generate the variable of monthly total income by adding monetary income and non-monetary income.

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6.3 Generating the variable of monthly monetary income> R08<- outfiles[[08]]> R10<- outfiles[[10]]> R13<- outfiles[[13]]> R15<- outfiles[[15]]> R17<- outfiles[[17]]> R19<- outfiles[[19]]> R04<- outfiles[[04]]> R05<- outfiles[[05]] Checked that the following unit variables have no missing value NA.
File
Unit Variable
Item
R08

PRI_SEC

1 Main occupation

Errors27NAs

2 Secondary occupation
R10

COL_4X

1 Paddy

23NAs

2 Chillies
3 Onions
4 Vegetables
5 Cereals
6 Yams
7 Tobacco
9 Other
R13

SEASONAL_CROP

1 Tea, Rubber
2 Coconuts

10NAs and 6 out-ofrange

3 Coffee, Pepper Betel etc
4 Banana / Fruits
5 Meat6 Fish7 Eggs8 Milk
9 Other food items
10 Horticulture
99 Other
R15

NON_AGRI

1 Mining & Quarrying.
2 Manufacturing
3Construction
4 Trade5 Transport
6 Guest house, restaurants, bars/hotels etc
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20NAs and 31 out-ofrange

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9 Other services
R17

SERIAL_5_5_1

Person number 1-40

R19

PERSON_5_5_2

Person number 1-40

one records withperson number=41
None> addmargins(table(R08$PRI_SEC,useNA="ifany"))
1

2

18353

214

<NA>

Sum

27 18594> addmargins(table(R10$COL_4X,useNA="ifany"))
1
2
3
4
5
6
7
9 <NA> Sum2759
88 82 700 364
67
2192
23 4196> addmargins(table(R13$SEASONAL_CROP,useNA="ifany"))
1
2
3
4
5
6
7
8
9
10
16
1119 1006 469 409
54 246 160 280
40
14
1

19
4

24
1

99 <NA> Sum
155
10 3968

11
2

41
Sum
1 12602> addmargins(table(R15$NON_AGRI,useNA="ifany") )
0
1
2
3
4
5
6
7
9 <NA> Sum
1 162 962 134 2139 1024 122
30 1106
20 5700> addmargins(table(R17$SERIAL_5_5_1,useNA="ifany"))
1
2
3
4
5
6
7
8
9
8160 1612 1009
847
544
272
103
35
12

10
5

> addmargins(table(R19$PERSON_5_5_2,useNA="ifany"))
1
2
3
4
5
6
7
8
9
11
12 Sum5215 2435 581 222
81
24
9
4
2
1
1 8575# Removed records with errors by setting drop=1> R08$drop<-ifelse(is.na(R08$PRI_SEC),1,0)> R10$drop<-ifelse(is.na(R10$COL_4X),1,0)> R13$drop<-ifelse(is.na(R13$SEASONAL_CROP),1,0)> R13$drop<-ifelse(R13$SEASONAL_CROP>10&R13$SEASONAL_CROP<99,1,R13$drop)> R15$drop<-ifelse(is.na(R15$NON_AGRI),1,0)> R15$drop<-ifelse(R15$NON_AGRI==0| R15$NON_AGRI==7,1,R13$drop)> R17$drop<-ifelse(R17$SERIAL_5_5_1>40,1,0)> R19$drop<-ifelse(R19$PERSON_5_5_2>40,1,0)# Replaced NA of the income variables with 0 in the above data sets
> R08[is.na(R08)]<-0> R10[is.na(R10)]<-0> R13[is.na(R13)]<-0> R15[is.na(R15)]<-0> R17[is.na(R17)]<-0

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> R19[is.na(R19)]<-0Generated the monthly income variable for each data set.

== R08: Employment income ==> R08$wage.inc<-R08$WAGES_SALARIES+R08$ALLOWENCES+R08$BONUS/12> head(R08[c(18,14:17,21)])
PID PRI_SEC WAGES_SALARIES ALLOWENCES BONUS wage.inc
1 1000201103
1
15000
0
0
15000
2 1000203101
1
16800
0
0
16800
3 1000205104
1
14400
0
0
14400
4 1000206103
1
12000
0
0
12000
5 1000208101
1
14665
5850
0
20515
6 1000209101
1
12000
0
0
12000

== R10: Agricultural income (Paddy, other seasonal crops) ==> R10$crop.inc<-(R10$COL_8X1-R10$COL_9X-R10$COL_10X1)/12> table(R10$crop.inc<0)
FALSE

TRUE

3183

1013

> R10$crop.inc<-ifelse(R10$crop.inc<0,0,R10$crop.inc)> head(R10[c(26,14,19,20,22,29)])
PID COL_4X COL_8X1 COL_9X COL_10X1 crop.inc
1 1093510101
928800 13000
0 1316.6667
2 1112701101
4
3200
700
0 208.3333
3 1112701101
62400
00 200.0000
4 1119010101
1
16380
6300
16380
0.00005 1202302103
4
10000
5200
0 400.0000
6 1202303101
45000
00 416.6667== R13: Other agricultural activities/ Livestocks ==> R13$livestock.inc<-R13$OUTPUT_5_3-R13$INPUT_5_3> table(R13$livestock.inc<0)
FALSE

TRUE

3879

89

> R13["livestock.inc"]<-ifelse(R13$livestock.inc<0,0,R13$livestock.inc)> head(R13[,c(21,14,18,19,24)])
PID SEASONAL_CROP OUTPUT_5_3 INPUT_5_3 livestock.inc
1 1093505103

8

48000

22000

26000

2 1105802101

7

3600

1200

2400

3 1105802101

4

400

0

400

4 1112701101

2

900

420

480

5 1112701101

4

1200

0

1200

6 1113804101

2

4000

0

4000

== R15: Non-agricultural activities ==> R15$nonagri.inc<-R15$OUTPUT_5_4-R15$INPUT_5_4> table(R15$nonagri.inc<0)
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FALSE
5581

TRUE
119

> R15$nonagri.inc<-ifelse(R15$nonagri.inc<0,0,R15$nonagri.inc)> head(R15[c(18,14:16,21)])
PID NON_AGRI OUTPUT_5_4 INPUT_5_4 nonagri.inc
1 1000201102

2

18000

7500

10500

2 1000204102

4

14000

6500

7500

3 1000205101

4

9600

0

9600

4 1000205102

4

4800

0

4800

5 1000206103

4

10000

4000

6000

6 1000207101

5

22400

14000

8400

== R17: Other income ==> R17$other.inc<-rowSums(R17[,14:22])+rowSums(R17[,23:25])/12> head(R17[c(26,14:25,29)])
PID PENSION DISABILITY_AND_RELIEF PROPERTY_RENTS SAMURDHI DIVIDENDS ELDER SCHOLAR
1 1000205102
0
0
0
0
0
0
0
2 1000206104
0
0
0
0
0
0
0
3 1000209104
0
0
0
0
0
0
0
4 1000210103
0
0
0
0
0
0
0
5 1002201101
0
0
0
0
0
0
0
6 1002202104
0
0
0
0
0
0
0
SC_LUNCH THREEPOSHA OTHER_INCOME INCOME_FORIGN INCOME_LOCAL other.inc
1
0
0
0
30000
0 2500.000
2
600
0
0
0
0
600.000
3
600
0
0
0
0
600.000
4
600
0
0
0
0
600.000
5
0
0
6000
0
0
500.000
6000032000 2666.667== R19: Windfall income ==> R19$windfall.inc<-rowSums(R19[,14:23])/12> head(R19[c(24,14:23,27)])
PID LOANS PAWNING_SELLING DEPOSITS_PENSIONS_EPF WELFARE_SOCITY SEETTU_DEBITS MEDICAL
1 1000204101
0
35000
0
0
7500
0
2 1000205104 150000
0
0
0
0
0
3 1000205105
0200000000
0
4 1000206101
01000000000
5 1000208101 200000
0
0
0
0
0
6 1000208102
0
14000
0
0
0
0
INSUARANCE LOTTERY FOODALLOWENCE DIASTER windfall.inc
1
0
0
0
0
3541.6672000012500.000
30000
16666.667
4
0
600
0
0
8383.333
50000
16666.667600001166.667# Updated outfiles> outfiles.old1<-outfiles> outfiles[[8]]<-R08> outfiles[[10]]<-R10> outfiles[[13]]<-R13> outfiles[[15]]<-R15
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> outfiles[[17]]<-R17> outfiles[[19]]<-R19

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# For reference:
Sum of monthly income in each data set.

File

No. ofVariable

Sum of

Sum of the

Weighted

recordofthevariableaveragesmonthly

variable(weighted)

incomeR08

18,594 wage.inc

335,408,533

82,612,870,981

16,131.1R10

4,196 crop.inc

14,162,048

3,709,104,607

724.2

R13

3,968 livestock.inc

76,676,818

21,513,323,868

4,200.7R155,700 nonagri.inc

155,670,335

40,488,764,787

7,905.9

R17

12,602 other.inc

105,050,749

26,759,575,669

5,225.1

R198,575 windfall.inc

102,481,079

24,423,182,615

4,768.9

Total

789,449,561

199,506,822,526

38,955.9# Un-weighted sum of the variables> t<-NULL> t<-c(t,sum(subset(R08,drop==0)$wage.inc))> t<-c(t,sum(subset(R10,drop==0)$crop.inc))> t<-c(t,sum(subset(R13,drop==0)$livestock.inc))> t<-c(t,sum(subset(R15,drop==0)$nonagri.inc))> t<-c(t,sum(subset(R17,drop==0)$other.inc))> t<-c(t,sum(subset(R19,drop==0)$windfall.inc))> t<-c(t,sum(t))> names(t)<-c("wage","crop","livestock","nonagri","other","windfall","Total")> t
wage
crop livestock
nonagriother windfall
Total
335408533 14162048 76676818 155670335 105050749 102481079 789449561# Weighted sum of the variables> t<-with(subset(R08,drop==0),sum(wage.inc*Weight))> t<-c(t,with(subset(R10,drop==0),sum(crop.inc*Weight)))> t<-c(t,with(subset(R13,drop==0),sum(livestock.inc*Weight)))> t<-c(t,with(subset(R15,drop==0),sum(nonagri.inc*Weight)))> t<-c(t,with(subset(R17,drop==0),sum(other.inc*Weight)))> t<-c(t,with(subset(R19,drop==0),sum(windfall.inc*Weight)))> t<-c(t,sum(t))> names(t)<-c("wage","crop","livestock","nonagri","other","windfall","Total")
> t
wage
crop
livestock
nonagriotherwindfallTotal
82612870981
3709104607 21513323868 40488764787 26759575669 24423182615 199506822526# Weighted average income per household (monetary income)> round(t/sum(R25$Weight),1)
wage
crop livestock
nonagriother windfall
16131.1
724.2
4200.77905.95225.14768.9Total
38955.9

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99*********************************************************************# R08: Average monthly income per household from paid employment> t<-addmargins(with(subset(R08,drop==0),tapply(wage.inc*Weight,PRI_SEC,sum)/sum(R25$Weight)))> names(t)<-c("Main occupation","Secondary occupation","Total")> round(t[c(3,1,2)],1)
Total
Main occupation Secondary occupation
16131.1
16028.6
102.4# R10: Average monthly income per household from agricultural activities> t<-addmargins(with(subset(R10,drop==0),tapply(crop.inc*Weight,COL_4X,sum)/sum(R25$Weight)))> names(t)<-c("1 Paddy","2 Chillies","3 Onions","4 Vegetables","5 Cereals",+ "6 Yams","7 Tobacco","9 Other","Total")> round(t[c(9,1:8)],1)Total
1 Paddy
2 Chillies
3 Onions 4 Vegetables
5 Cereals
6 Yams
724.2
427.6
15.1
31.7
149.0
68.3
10.0
7 Tobacco
9 Other
8.1
14.4# R13: Average monthly income per household from other agricultural activities> t<-addmargins(with(subset(R13,drop==0),tapply(livestock.inc*Weight,+ SEASONAL_CROP,sum)/sum(R25$Weight)))> names(t)<-c("1 Tea, Rubber","2 Coconuts","3 Coffee, Pepper Betel etc",+ "4 Banana / Fruits","5 Meat","6 Fish","7 Eggs","8 Milk","9 Other food items",+ "10 Horticulture","99 Other","Total")> df<-data.frame(Code=names(t),Average=round(t,1),row.names=NULL)> df[c(12,1:11),]
Code Average
12
Total 4200.7
1
1 Tea, Rubber 1371.2
2
2 Coconuts
747.5
3 3 Coffee, Pepper Betel etc
671.5
4
4 Banana / Fruits
503.7
5
5 Meat93.7
6
6 Fish198.7
7
7 Eggs45.8
88 Milk
293.6
99 Other food items
48.4
10
10 Horticulture
12.3
11
99 Other214.3# R15: Average monthly income per household from non-agricultural activities> t<-addmargins(with(subset(R15,drop==0),tapply(nonagri.inc*Weight,+ NON_AGRI,sum)/sum(R25$Weight)))> names(t)<-c("1 Mining & Quarrying","2 Manufacturing","3 Construction","4 Trade",+ "5 Transport","6 Guest house, restaurants, bars/hotels etc","9 Other services",+ "Total")> df<-data.frame(Code=names(t),Average=round(t,1),row.names=NULL)> df[c(8,1:7),]
Code Average
8
Total 7905.9
1
1 Mining & Quarrying
228.6
2
2 Manufacturing 1628.7
3
3 Construction
322.6
4
4 Trade 2976.1
55 Transport 1271.3
6 6 Guest house, restaurants, bars/hotels etc
147.6
7
9 Other services 1331.0

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# R17: Average monthly income per household from other cash receipt> r17.inc<-colSums(subset(R17,drop==0)[,14:22]*subset(R17,drop==0)$Weight)/sum(R25$Weight)> r17.inc<-c(r17.inc,+ colSums(subset(R17,drop==0)[,23:25]*subset(R17,drop==0)$Weight)/sum(R25$Weight)/12)> r17.inc<-c(r17.inc,sum(r17.inc))> names(r17.inc)[13]<-"Total"> round(r17.inc,1)
PENSION DISABILITY_AND_RELIEF
PROPERTY_RENTS
SAMURDHI
1591.1
12.0
791.4
135.9
DIVIDENDSELDERSCHOLAR
SC_LUNCH
231.1
19.7
19.2
31.1
THREEPOSHA
OTHER_INCOME
INCOME_FORIGNINCOME_LOCAL
9.8
356.1
1135.6892.2
Total
5225.1# R19: Average monthly income per household by chance or ad hoc gains> r19.inc<-colSums(subset(R19,drop==0)[,14:23]*subset(R19,drop==0)$Weight)/sum(R25$Weight)/12> r19.inc<-c(r19.inc,sum(r19.inc))> names(r19.inc)[11]<-"Total"> round(r19.inc,1)
LOANS
PAWNING_SELLING DEPOSITS_PENSIONS_EPF
WELFARE_SOCITY
1429.42598.4
425.6
25.2
SEETTU_DEBITSMEDICALINSUARANCELOTTERY
236.9
11.7
25.3
4.4
FOODALLOWENCE
DIASTER
Total
8.9
3.2
4768.9

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6.4 Creating individual-level income data file

Strategy for estimating income receivers’ income;1) To collapse income data files R08, R10, R13 and R15 at individual level2) To merge all individual-level income data files R08, R10, R13, R15, R17 and R19 by using PID.
3) To generate the variable of monthly total income4) If a person is less than 10 years old or his/her aggregated total monthly income is less thanRs. 250, to add such income values to the income of the heads of the respective households.

Data files to be used for estimating income receivers’ income
Income source

Data

Variables to be used

Unit of records

wage.inc

Individual,

file
Wage

R08

Occupation (Pri/Sec)
Agricultural

R10crop.inc(Seasonal crops)
Agricultural

Individual,
Seasonal crop code

R13

livestock.inc

Individual,
Non-seasonal crop code

(Non-seasonal
crops)
Non-agricultural

R15

nonagri.inc

Individual,
Economic activity code

Other income

R17

other.inc

Individual

WindfallR19

windfall.inc

Individual

Individual

R01

AGE, RELATIONSHIP

Individual

characteristics



Defined function CollapseBy2.

> CollapseBy2<-function(df=df,var=var,fac=fac,ID=NULL){+ # To aggregate variables with the same factors in data frame+ # df: data frame+ # var: vector of variables to be aggregated (in position number or variable name)+ # fac: factor variables (in position number or variable name)+ # ID: combinations of factor levels to be matched, if specified+ #+ if(is.numeric(var)){ var<-colnames(df)[var] } # 変数位置は変数名に変換+ if(is.numeric(fac)){ fac<-colnames(df)[fac] } # 同上
+ d<-data.frame(df[,fac]) # factor 変数からなるデーターフレーム+ colnames(d)<-fac+ if(missing(ID)) { ID<- subset(d,!duplicated(d)) } # ID のデフォルト設定+ y<-data.frame(ID,row.names=NULL) # ID からなるデータフレーム+ colnames(y)<-fac+ for(j in var){+ t<-aggregate(df[,j],d,sum,na.rm=T)+ colnames(t)<-c(fac,j)+ y<-merge(y,t,by=fac,all.x=T)+ }+ y[is.na(y)]<-0
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+ return (y)+ }Extracted ID, PID, WT, AGE, MONTH, BIRTH_YEAR, BIRTH_MONTH and
RELATIONSHIP from R01, and created data frame pinc.

> R01<-outfiles[[01]]> pinc<-R01[R01$RESULT==1,c("ID","PID","Weight","AGE","MONTH","BIRTH_YEAR","BIRTH_MONTH",+ "RELATIONSHIP","MARITAL_STATUS")]> dim(pinc)[1] 85080
9> head(pinc)ID
PID Weight AGE MONTH BIRTH_YEAR BIRTH_MONTH RELATIONSHIP MARITAL_STATUS
1 10002011 1000201106 256.313 10
7
2
7
5
1
2 10002011 1000201104 256.313 21
7
90
9
5
2
3 10002011 1000201102 256.313 58
7
53
8
2
2
4 10002011 1000201103 256.313 24
7
87
8
3
2
5 10002011 1000201105 256.313
2
7
10
3
5
1
6 10002011 1000201107 256.313
7
7
5
6
5
1
> table(is.na(pinc$AGE))
FALSE TRUE
80525 4555
Compute AGE from birth day if AGE is NA.
# Survey period: July 2012 to June 2013
> pinc$s<-ifelse(pinc$MONTH>=7,201200+pinc$MONTH,201300+pinc$MONTH)# Birth year: 1914 to 2013
> pinc$b<-ifelse(pinc$BIRTH_YEAR<=13,(2000+pinc$BIRTH_YEAR)*100+pinc$BIRTH_MONTH,+ (1900+ pinc$BIRTH_YEAR)*100+pinc$BIRTH_MONTH)> pinc$age<-floor((pinc$s-pinc$b)/100)> table(is.na(pinc$AGE),is.na(pinc$age))
FALSE TRUE
FALSE 80456
69TRUE
0 4555

Replaced AGE with age if age is not NA.
> pinc$AGE<-ifelse(is.na(pinc$AGE)&!is.na(pinc$age),pinc$age,pinc$AGE)> table(is.na(pinc$AGE))
FALSE TRUE
80525 4555



The variable of AGE is missing in 4,555 records. Here, we assume that their age is less than
10 years old.

> pinc.old<-pinc> pinc<-pinc[,c(1:4,8)]> head(pinc)
ID

PID

Weight AGE RELATIONSHIP

1 10002011 1000201106 256.313

10

52 10002011 1000201104 256.313

21

5

3 10002011 1000201102 256.313

58

2

4 10002011 1000201103 256.313

24

3

5 10002011 1000201105 256.313

25

6 10002011 1000201107 256.313

75> dim(pinc)[1] 85080

5
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

Merged pinc with individual-level R08 with varables of PID and wage.inc.

> nrow(subset(R08,drop==0))[1] 18567> t<-CollapseBy2(subset(R08,drop==0),var="wage.inc",fac="PID")> dim(t)[1] 18354

2> head(t)PID wage.inc
1 1000201103

15000

2 1000203101

16800

3 1000205104

14400

4 1000206103

12000

5 1000208101

20515

6 1000209101

12000> pinc<-merge(pinc,t,by="PID",all.x=T)> head(pinc)
PID

ID

Weight AGE RELATIONSHIP wage.inc

1 1000201101 10002011 256.313

61

1

NA

2 1000201102 10002011 256.313

58

2

NA

3 1000201103 10002011 256.313

24

3

15000

4 1000201104 10002011 256.313

21

5

NA

5 1000201105 10002011 256.313

2

5

NA

6 1000201106 10002011 256.313

10

5

NA

> nrow(subset(R10,drop==0))[1] 4173> pinc<-merge(pinc, CollapseBy2(subset(R10,drop==0),var="crop.inc",fac="PID"), by="PID",all.x=T)> head(pinc[!is.na(pinc$crop.inc)&pinc$crop.inc > 0,])
PID

ID

Weight AGE RELATIONSHIP wage.inc

crop.inc1126 1093510101 10935101 211.99454

1NA 1316.6667

1700 1112701101 11127011 211.994

71

1

NA

408.3333

2945 1202302103 12023021 158.995

41

3

NA

400.0000

2950 1202303101 12023031 158.995

61

1

NA

416.6667

3033 1205501101 12055011 158.99575

1NA 3000.0000

3206 1211203101 12112031 181.709

57

1

NA

665.8333> table(pinc$crop.inc>0,useNA="ifany")
FALSE

TRUE

<NA>

1013

2621 81446> pinc<-merge(pinc, CollapseBy2(subset(R13,drop==0),var="livestock.inc",fac="PID"),+ by="PID",all.x=T)> pinc<-merge(pinc, CollapseBy2(subset(R15,drop==0),var="nonagri.inc",fac="PID"),+ by="PID",all.x=T)> pinc<-merge(pinc, CollapseBy2(subset(R17,drop==0),var="other.inc",fac="PID"),
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+ by="PID",all.x=T)> pinc<-merge(pinc, CollapseBy2(subset(R19,drop==0),var="windfall.inc",fac="PID"),+ by="PID",all.x=T)> dim(pinc)[1] 85080

11> head(pinc)
PID

ID

Weight AGE RELATIONSHIP wage.inc crop.inc livestock.inc

1 1000201101 10002011 256.313

61

1

NA

NA

NA

2 1000201102 10002011 256.313

58

2

NA

NA

NA

3 1000201103 10002011 256.313

24

3

15000

NA

NA

4 1000201104 10002011 256.313

21

5

NA

NA

NA

5 1000201105 10002011 256.313

2

5

NA

NA

NA

6 1000201106 10002011 256.313

10

5

NA

NA

NA

nonagri.inc other.inc windfall.inc
1

NA

NA

NA

2

10500

NA

NA

3

NA

NA

NA

4

NA

NA

NA

5

NA

NA

NA

6

NA

NA

NA



Replaced NA in income variables with 0> df<-pinc[,6:11]> df[is.na(df)]<-0> pinc<-cbind(pinc[,1:5],df)> colnames(pinc)[4:11]<-c("age","relation","wage","crop","livestock","nonagri",+ "other","windfall") Generated the variable ttinc, total monthly income.
> pinc["ttinc"]<-rowSums(pinc[,6:11])> head(pinc)
PID
ID Weight age relation wage crop livestock nonagri other1 1000201101 10002011 256.313 61
1
0
0
0
0
0
2 1000201102 10002011 256.313 58
20
0
0 10500
0
3 1000201103 10002011 256.313 24
3 15000
000
0
4 1000201104 10002011 256.313 21
5
0
0
0
0
0
5 1000201105 10002011 256.313 2
5
0
0
0
0
0
6 1000201106 10002011 256.313 10
5
0
0
0
0
0
windfall ttinc
100
20 10500
30 15000
4
0
0
5
0
0
6
0
0

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

Remarks:

Out of 20,540 household heads, the number of those having less than 250 income is 1,996.
> dim(pinc)[1] 85080

12> nrow(subset(pinc,relation==1))[1] 20540> addmargins(table(subset(pinc,relation==1)$ttinc<250,useNA="ifany"))
FALSE

TRUE

Sum

18544

1996 20540# For reference:> colSums(pinc[,6:12],na.rm=T)
wage
crop livestock nonagri
other windfall
ttinc
335408533 14162048 76676818 155670335 105050749 102481079 789449561> pinc.old<-pinc

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6.5 Income receivers’ incomeGrouped household members considering income receivers as follows;

flag
1
2
3
4

Conditions
Household headttinc>=250 & age>=10
ttinc=00<ttinc<250
ttinc>=250 & age<10

Income receiver

To transfer his/her ttinc to household head

> pinc$flag<-0> pinc$flag<-ifelse(pinc$relation==1,1,pinc$flag)> pinc$flag<-ifelse(pinc$ttinc>=250&pinc$age>=10&pinc$flag!=1,1,pinc$flag)> pinc$flag<-ifelse(pinc$ttinc==0&pinc$flag==0,2,pinc$flag)> pinc$flag<-ifelse(pinc$ttinc>0&pinc$ttinc<250&pinc$flag==0,3,pinc$flag)> pinc$flag<-ifelse(pinc$ttinc>=250&pinc$age<10&pinc$flag==0,4,pinc$flag)> addmargins(table(pinc$flag,useNA="ifan"))12
34 <NA> Sum
36515 46969 746 841
9 85080> addmargins(table(pinc$ttinc>0,is.na(pinc$flag),useNA="ifany"))
FALSE TRUESum
FALSE 48840
8 48848
TRUE 36231
1 36232
Sum 85071
9 85080Out of 9 persons with flag=NA, 8 persons should be regarded as flag=2 because theirttinc are not positive.

> pinc$flag<-ifelse(is.na(pinc$flag)&pinc$ttinc==0,2,pinc$flag)> addmargins(table(pinc$ttinc>0,is.na(pinc$flag),useNA="ifany"))
FALSE TRUESum
FALSE 48848
0 48848
TRUE 36231
1 36232
Sum 85079
1 85080> addmargins(table(pinc$flag,useNA="ifan"))12
34 <NA> Sum
36515 46977 746 841
1 85080
The person with flag=NA should be regarded as flag=1 because she is spouse and her
ttinc>250.

> pinc[is.na(pinc$flag),]
PID
ID Weight age relation wage crop livestock nonagri
56916 6444301102 64443011 237.743 NA
2 40000000other windfall ttinc flag
56916
00 40000 NA> head(R01[R01$ID==64443011,c(1,29,14:15,18,21)])ID
PID RELATIONSHIP SEX AGE MARITAL_STATUS
59558 64443011 6444301103
3 2 12
159559 64443011 6444301104
3 2 9
159560 64443011 6444301102
2 2 NA2
59561 64443011 6444301105
4 1 84259562 64443011 6444301106
4 2 90259563 64443011 6444301101
1 1 NA2> addmargins(table(pinc$flag,useNA="ifan"))
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1
2
36516 46977



3
746

4 Sum
841 85080

Transferred the income of persons with flag=3 or 4 to that of the respective household heads.

# list of PID with flag=3 or 4
> pida<-subset(pinc,flag>=3)$PID> length(pida)[1] 1587> head(pida)[1] "1000209104" "1018406105" "1044802104" "1091604104" "1092607102"[6] "1148606105"> table(substr(pida,9,10))
02 03 04 05 06 07 08
196 408 487 273 133 58 19

09 10 117 5 1

# list of PID of the respective household heads> pidb<-paste(substr(pida,1,nchar(pida)-2),"01",sep="")> head(pidb)[1] "1000209101" "1018406101" "1044802101" "1091604101" "1092607101"[6] "1148606101"> pinc["ttinc.before"]<-pinc$ttinc> pinc.old2<-pinc# Transfer of income ---- Took time to conduct this scripts!!
> for(j in 1:length(pida)){+ a<-pida[j]+ b<-pidb[j]+ pinc[pinc$PID==b,"ttinc"]<-pinc[pinc$PID==b,"ttinc"]+pinc[pinc$PID==a,"ttinc"]+ pinc[pinc$PID==a,"ttinc"]<-0+ }> sum(pinc$ttinc)[1] 789449561> sum(pinc$ttinc.before)[1] 789449561# Example of transfer> head(pinc[pinc$flag==3,c(1:5,12:14)])PID
ID Weight age relation ttinc flag ttinc.before
742 1044802104 10448021 329.545 27
30
3244.00000
1087 1092607102 10926071 211.994 35
2
0
3
83.33333
2173 1148606105 11486061 288.352 750380.00000
2852 1195308103 11953081 329.545 0303240.00000
3061 1205509102 12055091 158.995 20
303
83.33333
3875 1235510102 12355101 211.994 20
20
3244.00000> head(pinc[pinc$ID==10448021,c(1:5,12:14)])
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PID
ID Weight age relation ttinc flag ttinc.before
739 1044802101 10448021 329.545 63
1 47441
4500
740 1044802102 10448021 329.545 6120
20741 1044802103 10448021 329.545 35
3 20000
1
20000
742 1044802104 10448021 329.545 27
3
0
3
244
743 1044802105 10448021 329.545 28
5020744 1044802106 10448021 329.545 34
50
20> head(pinc[pinc$flag==4,c(1:5,12:14)])PID
ID Weight age relation ttinc flag ttinc.before
41 1000209104 10002091 256.313 550
4600.0000
326 1018406105 10184061 329.545 1
5048583.3333
1045 1091604104 10916041 256.313 830
4800.0000
2177 1148608103 11486081 288.352 6
30
4500.0000
4487 1262602105 12626021 17.421 3
5
04
416.6667
4488 1262602106 12626021 17.421 1
5
04
416.6667> head(pinc[pinc$ID==10002091,c(1:5,12:14)])PID
ID Weight age relation ttinc flag ttinc.before
38 1000209101 10002091 256.313 20
1 12600
1
12000
39 1000209102 10002091 256.313 43
5 5000
1
5000
40 1000209103 10002091 256.313 49
5 10000
1
10000
41 1000209104 10002091 256.313 5
5
0
4
600
42 1000209105 10002091 256.313 2
5
0
2
0
NA
<NA>
NA
NA NA
NA
NA NA
NA



Created data set of income receivers consisted of persons with flag=1.

> receivers<-subset(pinc,flag==1)> dim(receivers)[1] 36516
14# Estimated number of income receivers> sum(receivers$Weight)[1] 9038718# Average number of income receivers per household> nrow(receivers)/nrow(R25)[1] 1.777799> sum(receivers$Weight)/sum(R25$Weight)[1] 1.764908# Average monthly income of income receivers> sum(receivers$ttinc*receivers$ Weight)/sum(receivers$ Weight)[1] 22072.47# Total amount of monthly income> sum(receivers$ttinc)
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109[1] 789449561> sum(receivers$ttinc*receivers$ Weight)[1] 199506822526



Summary;
Un-weighted

Weighted

estimates

estimates

Final report

Household size

4.14

4.10

3.9

Average number of income receivers per

1.78

1.76

1.8

36,516

9,038,718

householdTotal number of income receivers

Income receivers’ mean monthly income

Total amount of monthly income

22,072.47

789,449561

Number of persons whose income were

199,506,822,526

1,587

transferred to the household head
Number of income receivers with ttinc<250

1,935> nrow(subset(receivers,ttinc<250))[1] 1935# Saved outfiles, receivers and pinc
> outfiles.old<-outfiles> receivers.old<-receivers> pinc.old<-pincSum of ttinc and ttinc.brfore in pinc are the same.

> colSums(pinc[,c(6:12,14)])
wage
crop
livestock335408533
14162048
76676818
ttinc ttinc.before
789449561
789449561

nonagri
155670335

other105050749

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windfall102481079

25,963

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Sum of ttinc in receivers is the same as that of pinc.
Remarks: For instance, not all values of crop are not transferred to that of data frame receivers.

> colSums(receivers[,c(6:12,14)])
wage
crop
livestock335408533
14158791
76576668
ttinc ttinc.before
789449561
788193106

nonagri
155643331

other104036801

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6.6 Creating data set of household monetary income at household level Source of income
According to the final report, household income is estimated in two ways;“Income is received in two main ways, either in cash or in kind. The first is identified asmonetary and the second is identified as non-monetary. Income from wages and salaries,
agricultural activities (seasonal and non-seasonal crops), non-agricultural activities, andother cash income (which includes pension payments, disability payments, Samurdi, local
and foreign transfers, windfall income such as lottery wins, compensations etc.) are
identified as monetary income. The non-monetary income is the estimated value of goods
and services received in kind and consumed within the survey reference period. This value
is formed by home grown fruits and vegetables, firewood, home consumed quantities of the
product of agricultural activities, and other goods or concession values received from
employer or other parties. Estimated rental value of owner occupied housing units or freely
occupied housing units are also included under non-monetary income.”

Strategy to estimate monetary income:To collapse individual-level data file pinc at household level, and create data frame hhinc withvariables of monthly household income by income source.
> CollapseBy2<-function(df=df,var=var,fac=fac,ID=NULL){+ # To aggregate variables with the same factors in data frame+ # df: data frame+ # var: vector of variables to be aggregated (in position number or variable name)+ # fac: factor variables (in position number or variable name)+ # ID: combinations of factor levels to be matched, if specified+ #+ if(is.numeric(var)){ var<-colnames(df)[var] } # 変数位置は変数名に変換+ if(is.numeric(fac)){ fac<-colnames(df)[fac] } # 同上
+ d<-data.frame(df[,fac]) # factor 変数からなるデーターフレーム+ colnames(d)<-fac+ if(missing(ID)) { ID<- subset(d,!duplicated(d)) } # ID のデフォルト設定+ y<-data.frame(ID,row.names=NULL) # ID からなるデータフレーム+ colnames(y)<-fac+ for(j in var){+ t<-aggregate(df[,j],d,sum,na.rm=T)+ colnames(t)<-c(fac,j)+ y<-merge(y,t,by=fac,all.x=T)+ }Sri Lanka HIES 2012/13 Manual (Version 1.1)

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+ y[is.na(y)]<-0+ return (y)+ }>hhinc<CollapseBy2(pinc,var=c("wage","crop","livestock","nonagri","other","windfall","ttinc"),fac="ID")> dim(hhinc)[1] 20540
8> head(hhinc)
ID wage crop livestock nonagri other windfall
ttinc
1 10002011 15000
0
0
10500
0
0.000 25500.00
2 10002031 16800
00000.000 16800.00
3 10002041
0
0
0
7500
0 3541.667 11041.67
4 10002051 14400
0014400 2500 29166.667 60466.67
5 10002061 12000
006000 600 8383.333 26983.33
6 10002071
0
0
0
8400
0
0.000 8400.00# Total amount of monthly monetary household income by income source> colSums(hhinc[,2:8])
wage
crop livestock nonagri
other windfall
ttinc
335408533 14162048 76676818 155670335 105050749 102481079 789449561# Example== Individual-level data set ==> pinc[pinc$ID==10002011,c(1:2,6:12)]
PID
ID wage crop livestock nonagri other windfall ttinc
1 1000201101 10002011
0
0
0
0
0
0
0
2 1000201102 10002011
00
0 1050000 10500
3 1000201103 10002011 15000
00000 15000
4 1000201104 10002011
0
0
0
0
0
0
0
5 1000201105 10002011
0
0
0
0
0
0
0
6 1000201106 10002011
0
0
0
0
0
0
0
7 1000201107 10002011
0
0
0
0
0
0
0
== Collapsed to household-level data set ==> hhinc[hhinc$ID==10002011,]
ID wage crop livestock nonagri other windfall ttinc
1 10002011 15000
00 10500
00 25500# Saved hhinc> hhinc.old<-hhinc

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6.7 Non-monetary income (income in-kind) at household-level

The variables of monthly in-kind income to be generated;

Data

Variable ofDefinition

Remarks

file
R04

monthly income
inkind.ricemax (rice.a, rice.b)

Followed2009

where,

procedurerice.a: Sum of(INKIND_VALUE*(30/7)) of R04 with

CODE=<109 within the household
R04

inkind04

INKIND_VALUE*(30/7)

R05

inkind05

NF_INKIND_VALUE/6(CODE>=110)

if NF_CODE>3000 & NF_CODE<3300,
NF_INKIND_VALUE/12
if (NF_CODE>3300 & NF_CODE<3400) or
R05

imputed.rent

NF_INKIND_VALUE & NF_CODE=2001> dim(R04)[1] 815184

19> dim(R05)[1] 612085



19

Generated variable rice.a from R04> head(R04[!is.na(R04$INKIND_VALUE)&R04$CODE<110,c(1,13:18)])
ID CODE QUANTITY VALUE INKIND_VALUE Weight m.value
3009 10184051 101

2000

140

105 329.545

600.03746 10225071 105

3000

215

70 288.352

921.4

6612 10472011 105

7000

525

525 288.352 2250.0

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7000

490

70 288.352 2100.011344 11002071 103

100

16

16 181.709

68.6

22271 11612011 101

2000

120

120 329.545

514.3> d<-subset(R04, !is.na(R04$INKIND_VALUE)& CODE<110)> dim(d)[1] 2927

19> t<-tapply(d$INKIND_VALUE*(30/7),d$ID,sum,na.rm=T)> rice1<-data.frame(ID=names(t),rice.a=as.vector(t))> dim(rice1)[1] 2491

2> table(rice1$rice.a>0)

TRUE
2491
> head(rice1)
ID

rice.a1 10184051 450.00000
2 10225071 300.00000
3 10472011 2250.00000
4 10472061 300.00000
5 11002071

68.57143

6 11612011 514.28571



Generated variable rice.b from R10> head(R10[R10$COL_4X==1,c(1,14,18:22,26,28)])
ID COL_4X COL_8X COL_8X1 COL_9X COL_10X COL_10X1

PID drop

4 11190101

1

468

16380

6300

468

16380 1119010101

0

8 12055101

1

1040

30160

8000

1040

30160 1205510102

0

10 12186031

1

300

15000 10000

100

5000 1218603101

0

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11 12206061

1

1760

44000 23290

00 1220606101

0

12 12217051

1

200

15000 13350

200

15000 1221705103

0

13 12217091

1

200

10000

200

0 1221709101

0

3850

> t2<-tapply(d2$COL_10X1/12,d2$ID,sum,na.rm=T)> rice2<-data.frame(ID=names(t2),rice.b=as.vector(t2))> dim(rice2)[1] 2733

2> head(rice2)
ID

rice.b

1 11190101 1365.0000
2 12055101 2513.3333
3 12186031 416.6667
4 12206061

0.00005 12217051 1250.0000
6 12217091



0.0000Generated the variable of inkind.rice after comparing rice.a and rice.b> inkind<-R25[c("ID","Weight")]> dim(inkind)[1] 20540

2> inkind<-merge(inkind,rice1,all=T)> inkind<-merge(inkind,rice2,all=T)> dim(inkind)[1] 20540

4> inkind[is.na(inkind)]<-0> inkind["inkind.rice"]<-pmax(inkind$rice.a,inkind$rice.b)> head(inkind[inkind$rice.a>0&inkind$rice.b>0,])
ID Weight
877

rice.arice.b inkind.rice12411011 158.995 514.2857 1375.000

1375.000

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887

12423031 141.329 257.1429 5546.667

5546.667

929

12476031 181.709 1285.7143 1305.000

1305.000

932

12476061 181.709 3321.4286 2600.000

3321.429

1022 12666041 181.709 2357.1429 1265.000

2357.143

1095 12764051 141.329 2700.0000 1260.417

2700.000

Remarks:
There are 3,294 sample households with rice.a>0 and/or ric.b>0. The value of rice.a is bigger thanthat of rice.b in 2,135 households.

> d<-subset(inkind,rice.a>0|rice.b>0)> dim(d)[1] 3294

5> addmargins(table(d$rice.a>d$rice.b,useNA="ifany"))
FALSE

TRUE

1159

2135 3294

Sum

Generated the variable inkind04 and inkind05== R04 ==> d<-subset(R04, !is.na(R04$INKIND_VALUE)& CODE>=110)> dim(d)[1] 71690

19> t<-tapply(d$INKIND_VALUE*(30/7),d$ID,sum,na.rm=T)> inkind04<-data.frame(ID=names(t),inkind04=as.vector(t))> dim(inkind04)[1] 15901

2> head(inkind04)

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ID

inkind04

1 10002031

42.85714

2 10082021

85.71429

3 10082061 9000.00000
4 10103051 642.85714
5 10103061 3000.00000
6 10103091 900.00000> table(inkind04$inkind04>0)
TRUE
15901

== R05 ==> d<-subset(R05,!is.na(NF_INKIND_VALUE))> dim(d)[1] 48960

19> d["inkind05"]<-ifelse(d$NF_CODE!=2001,d$NF_INKIND_VALUE,0)> d["inkind05"]<-ifelse(d$NF_CODE>3000&d$NF_CODE<3300,+ d$inkind05/6,d$inkind05)> d["inkind05"]<-ifelse(d$NF_CODE>3300&d$NF_CODE<3400|d$NF_CODE>3500,+ d$inkind05/12,d$inkind05)> d$imputed.rent<-ifelse(d$NF_CODE==2001,d$NF_INKIND_VALUE,0)> dim(d)[1] 48960

21> head(d[,c(1,13,15:16,20:21)])
ID NF_CODE NF_VALUE NF_INKIND_VALUE inkind05 imputed.rent
1 10002011

2001

2500

2500

0

2500

5 10002011

2105

250

250

250

0

17 10002011

3005

300

300

50

0

18 10002011

3062

600

600

100

0

19 10002011

3101

3000

3000

500

0

21 10002031

2001

2500

2500

0

2500

> inkind05<-CollapseBy2(d,var=c("inkind05","imputed.rent"),fac="ID")

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> head(inkind05)
ID inkind05 imputed.rent
1 10002011

900

2500

2 10002031

550

2500

3 10002041

2650

2500

4 10002051

80

2500

5 10002061

250

5000

6 10002071

0

5000



Merged inkind, inkind04 and inkind05.

> inkind<-merge(inkind,inkind04,all=T)> inkind<-merge(inkind,inkind05,all=T)> dim(inkind)[1] 20540

8> head(inkind)
ID Weight rice.a rice.b inkind.rice inkind04 inkind05 imputed.rent
1 10002011 256.313

0

0

0

NA

900

2500

2 10002031 256.313

00

0 42.85714

550

2500

3 10002041 256.313

0

0

0

NA

2650

2500

4 10002051 256.313

0

0

0

NA

80

2500

5 10002061 256.313

0

0

0

NA

250

5000

6 10002071 256.313

0

0

0

NA

0

5000



Generated the variable of inkind (total in-kind income) and nonmonetary(total nonmonetary income)> inkind[is.na(inkind)]<-0> inkind["inkind"]<-rowSums(inkind[,5:7])> inkind["nonmonetary"]<-rowSums(inkind[,5:8])> head(inkind[inkind$inkind.rice>0,])
ID Weight

rice.a rice.b inkind.rice

inkind04 inkind05

70 10184051 329.545 450.00000

0450.00000 1157.14286 650.0000

88 10225071 288.352 300.00000

0

300.00000 4114.28571

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164 10472011 288.352 2250.00000

0 2250.00000

0.00000

0.0000169 10472061 288.352 300.00000

0

300.00000

0.00000

0.0000269 11002071 181.709

68.57143068.57143

77.14286 333.3333

389 11190101 158.995

0.00000

imputed.rent

1365 1365.00000 1928.57143

inkind nonmonetary

70

8000 2257.1429

10257.14388

30000 4414.2857

34414.2861647000 2250.0000

9250.000

1697000 300.0000

7300.000

269

6000 479.0476

6479.048

389

8000 3293.5714

11293.571



0.0000Merged hhinc and inkind, and generated the variable of total income.

> hhinc.old<-hhinc> inkind.old<-inkind> colnames(hhinc)[8]<-"monetary"> hhinc<-merge(hhinc,inkind)> hhinc["ttinc"]<-hhinc$monetary+hhinc$nonmonetary> head(hhinc[hhinc$inkind.rice>0,])ID

wage crop livestock nonagri other windfall monetary

ttinc

70 10184051

0

0

0

0 25000025000 35257.14

88 10225071

0

0

0

0

0

0

0 34414.29

164 10472011

0

0

0

35000

0

0

35000 44250.00

169 10472061

0

0

0

2000 1000

03000 10300.00

269 11002071 30000

0

0

45280

0

0

75280 81759.05

389 11190101 20360

0

26000

0

0

0

46360 57653.57

Weightrice.a rice.b inkind.rice

inkind04 inkind05 imputed.rent

70 329.545 450.00000

0450.00000 1157.14286 650.0000

8000

88 288.352 300.00000

0

300.00000 4114.28571

0.000030000

164 288.352 2250.00000

02250.00000

0.00000

0.00007000

169 288.352 300.00000

0

300.00000

0.00000

0.00007000

269 181.709

68.57143068.57143

77.14286 333.3333

6000

389 158.995

0.00000

1365

1365.00000 1928.57143

0.0000

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inkind nonmonetary
70 2257.1429

10257.143

88 4414.2857

34414.286

164 2250.0000

9250.000169

300.0000

7300.000

269

479.0476

6479.048

389 3293.5714

11293.571# Average monthly household income per household by income source> t<-round(colSums(hhinc[,c(2:9,11:18)]*hhinc[,"Weight"])/sum(hhinc$Weight))> t[c(1:7,9:16,8)]
wage

crop

livestock

nonagriotherwindfall

16131

724

4201

7906

5225

4769

monetaryrice.arice.binkind.rice

inkind04

inkind05

38956

302

205

383

1395

825

imputed.rent

inkindnonmonetary

ttinc

4197

2603

6800

45756

# Saved hhinc> outfiles.old<-outfiles> hhinc.old<-hhinc

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

Summary:
Average monthly household income per household by income source is as the next;

My estimates

Final report ofGAP

the survey (Table 2.12)
Total income

45,75645,878-122

Monetary income

38,95639,300-344

Wages/Salaries

16,131

16,134-3

Agricultural activities

4,925

5,213

-288

Non-agricultural activities

7,906

7,990

-84

Other income

5,225

5,230

-5

Windfall income

4,769

4,733

+36

Non-monetary income

6,800

6,578

+222

In-kind income

2,603

2,381

+222

Imputed rent

4,197

4,197

0

For reference:

Un-weighted

Weighted

estimates

estimates

Total number of households
Total amount of monthly monetary income

20,5405,121,354

789,449,561

199,506,822,526

38,43538,956135,910,441

34,825,712,390

6,617

6,800

Average monthly monetary income per household
Total amount of monthly non-monetary income
Average monthly non-monetary income per household> nrow(R25)[1] 20540> sum(R25$Weight)

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[1] 5121354> sum(hhinc$monetary)[1] 789449561> sum(hhinc$monetary*hhinc$Weight)[1] 199506822526> sum(hhinc$monetary)/nrow(R25)[1] 38434.74> sum(hhinc$monetary*hhinc$Weight)/sum(R25$Weight)[1] 38955.88> sum(hhinc$nonmonetary)[1] 135910441> sum(hhinc$nonmonetary*hhinc$Weight)[1] 34825712390> sum(hhinc$nonmonetary)/nrow(R25)[1] 6616.867> sum(hhinc$nonmonetary*hhinc$Weight)/sum(R25$Weight)[1] 6800.099

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6.7 The variable of HIncPM in R25


The variable of monthly household income at household level is available in the data set R25,
and it is compatible with the survey report.

> weighted.mean(R25$HIncPM,R25$Weight)[1] 45878.08

The differences between HIncPM and the sum from income sources.

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7. Household Expenditure
7.1 Definition of household expenditure

According to the final report of the survey, the definition of household expenditure is as follows;“Household expenditure data were collected in three major sections of the survey questionnaire.
1. Expenditure on food.
2. Expenditure on non-food.
3. Expenditure incurred by boarders and domestic servants.

Under food expenditure, all the food items consumed by the household during the reference period(one week) were collected. For non-food expenditure, all non-food items and services purchased by
the household during the given reference period were collected. Personal expenditure of boarders
and domestic servants who live in the household is reported according to the related expendituregroup in a separate section of the schedule.

To obtain more accurate data, the expenditure sections of the schedule were divided in to 35
sub-groups. Of that total 19 sub-groups are included under the section of expenditure on food suchas cereals , prepared food, vegetables , fish , meat etc. and the rest of 16 sub-groups are included
under the section of non-food expenditure. i.e. housing, fuel and light, health, durable goods etc.

To gather more reliable information on expenditure, food expenditure was collected for 7
consecutive days from each household selected in the sample. But non-food expenditure wascollected for different reference periods varying from one month to twelve months.”

Remarks: Expenditure on food and non-food
In the survey report, expenditure items are divided into food and non-food as follows;
Food: sum of sub group 01 to 18
Non-food: sum of sub group 19 to 35
Sub group 19 (Liquor, drugs and tobacco) is included in non-food, regardless of the structure of thequestionnaire.
The food ratio is also calculated based on the above classification.

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The next table of the final report shows the result of average monthly household expenditure;

Remarks:
Reasons why boarders’ expenditures are captured in the separate questionnaire
According to the delegates from Sri Lanka for the International Workshop 2014,“If boarders partake food with the household, he is treated as a household member. As he is not a
blood relative or dependent, his outside expenditure is not known to the survey respondent who is apermanent member. Therefore, in order to avert an underestimation of the true household
expenditure, he is given a separate section in the HIES questionnaire to report his personal
expenditure made in addition to the facilities he enjoys in the household he actually being amember.”Remarks: Process of estimating household expenditure
According to the delegates from Sri Lanka for the International Workshop,“Household expenditure is calculated for a month at Item level using the following,

Item expenditure per month = Item expenditure x 30 / Reference period in days

And the Item expenditure are summed up to household expenditure.”

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7.2 Process of estimating household expenditureThree data sets R04, R05 and R06 are used for estimating household expenditure.

The data files to be used for estimating household expenditure;
Data

Description

Sub group

Reference period

R04

Expenditure on food01 to 19

7 days

R05

Expenditure on non food

20 to 29 and 34

1 month

30 to 32

6 months

33 and 35

12 months

Remarks

file

R06

Expenditure incurred by boarders

4-digit code*

col.3

Food purchased outside02

7 days

0220

col.4

Fuel

21

1 month

2112

col.5

Non-durable goods

32

1 month**

3220

col.6

Household services

29

1 month

2910

col.7

Personal care22

1 month

2221

col.8

Transport & communication25

1 month

2510

col.9

Recreation27

1 month2720

col.10Boarding fees paid

20

1 month2004

col.11

Sent to family

35

1 month3510

col.12

Savings provident funds

34

1 month3411

col.13

Miscellaneous

34

1 month3412

col.14

Clothing

30

6 months3050

col.15

Durable goods

33

12 months**

3340

Note *: The 4-digit codes of col. 3 to col. 15 are set so as not to overlap the codes of R04 and R05,
and used for estimating the mean household expenditure by item.
**: The reference period of “32 Durable household goods” of R05 is 6 months.

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The process of estimating household expenditure is as follows;

Preparing data files: R04, R05 and R06
1. To filter out records without RESULT=1 (Completed).
(No such record was found in the given data set.)> R04<-outfiles[[04]]> R05<-outfiles[[05]]> R06<-outfiles[[06]]> sum(R04$RESULT!=1)[1] 0> sum(R05$RESULT!=1)[1] 0> sum(R06$RESULT!=1)[1] 0

2. R04 and R05 have no duplicated item code within the household.
(No such record was found in the given data set.)> sum(duplicated(R04$ID*10000+R04$CODE))[1] 0> sum(duplicated(R05$ID*10000+R05$CODE))[1] 0

3. Col. 3 to col. 15 of R06 correspond to the 4-digit item codes in the above table.

Converting to monthly expenditure
4. To convert VALUE of R04, NF_VALUE of R05 and COL_3 to COL_15 of R06 to monthly
expenditure taking into consideration of the reference period.
5. To use the multiplier 30/7 when converting weekly to monthly.
6. To generate the variable of subgroups of expenditure items.

Estimating household expenditure by 4-digit item
7. To generate the average monthly household expenditure by item from R04 and R05.
8. To generate the average monthly household expenditure of COL_3 to COL_15 in R06.
9. To add the summaries of R06 to the results of 6 under the codes given in 3.

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Creating data on household expenditure at household level
10. To aggregate VALUE of R04 and NF_VALUE of R05 by household and by item subgroup, andcreate data file with records of household and variables of subgroup.
11. To aggregate COL_3 to COL_15 of R06 by household, and create data file with records ofhousehold.
12. To merge the above data files using household identifier.
13. To add COL_3 to COL_15 to the respective subgroups.
14. To generate the variables of total household expenditure, expenditure on food and non-food.

R04

R05

4&5

R06

Convert to monthly expenditure, and 6

generate subgroup
8

Average monthly hh exp

7

Average monthly hh exp by 4-digit

9

Average household exp by subgroup under 4-digit codes allocated to col 3-15

10hh-level data framewith subgroup variables

10

hh-level data framewith subgroup variables

12

Merge data files using household ID

13

Add col 3-15 to respective subgroups

by col 3-15

11hh-level data frame withvariables of col 3-15

14 Generate variables of food, non-food and total exp

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7.3. Converting to monthly expenditure### R04: FOOD_EXP ###

The variable VALUE and INKIND_VALUE;VALUE: goods and services consumed in cash and in kind, that is, both bought from themarket and homegrown/ freely received.
INKIND_VALUE: a part of VALUE.
Therefore, used only the variable VALUE for estimation of household expenditure.
Generated the variable of monthly expenditure, m.value.

> R04$m.value<-round(R04$VALUE*30/7,1)> head(R04[,c(1,13:18)])
ID CODE QUANTITY VALUE INKIND_VALUE Weight m.value
1 10002011 105

6000

420

NA 256.313 1800.0

2 10002011 207

45

600

NA 256.313 2571.4

3 10002011 209

990

NA 256.313

385.7

4 10002011 211

2100

NA 256.313

428.6

5 10002011 302

500

90

NA 256.313

385.7

6 10002011 425

250

30

NA 256.313

128.6



Generated the variable sg of subgroups of 4-digit expenditure item (CODE). Subgroup code is
the top-2-digit of 4-digit expenditure item code.

> R04$sg<-floor(R04$CODE/100)> head(R04[,c(1,13:19)])
ID CODE QUANTITY VALUE INKIND_VALUE Weight m.value sg
1 10002011 105

6000

420

NA 256.313 1800.0 1

2 10002011 207

45

600

NA 256.313 2571.4 2

3 10002011 209

990

NA 256.313

385.7 24 10002011 211

2100

NA 256.313

428.6 25 10002011 302

500

90

NA 256.313

385.7 36 10002011 425

250

30

NA 256.313

128.6 4

# Number of records in R04 by subgroup> addmargins(table(R04$sg,useNA="ifany"))
1
2
3
4
5
6

7

8

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52594 38467 31621 147534 27357
9230 30188 26564 9854 19348
11
12
13
14
15
16
17
18
19 <NA>
253338 2358 23681 19579 20845 27751 25208 28104 21564
1
Sum815186

Remarks

The record with empty CODE, QUANTY and VALUE should be dropped.
> R04[is.na(R04$sg),]
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU
A0 SNUMBER HHNO
505060 58103021
4
45
2 10
10 12 58103
2
1
NHH RESULT CODE QUANTITY VALUE INKIND_VALUE Weight m.value sg
505060 1
1 NA
NANA
NA 84.667
NA NA> R04<-R04[!is.na(R04$sg),]> dim(R04)[1] 815185
19> R04<-R04[!is.na(R04$sg),]> dim(R04)[1] 815185
19> outfiles[[04]]<-R04### R05: NONFOOD ###

The variable NF_VALUE and NF_INKIND_VALUE;
NF_VALUE: goods and services consumed in cash and in kind, that is, both bought fromthe market and freely received/received as a gift.
NF_INKIND_VALUE: a part of NF_VALUE.
Therefore, used only the variable NF_VALUE for estimation of household expenditure.

Generated the variable of sg, subgroup of expenditure item

> R05["sg"]<-floor(R05$NF_CODE/100)> head(R05[,c(1,13:18)])
ID NF_CODE NF_QUANTITY NF_VALUE NF_INKIND_VALUE

Weight sg

1 10002011

2001

NA

2500

2500 256.313 20

2 10002011

2003

NA

350

NA 256.313 20

3 10002011

2101

NA

600

NA 256.313 21

4 10002011

2103

2250

300

NA 256.313 21

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5 10002011

2105

NA

250

250 256.313 21

6 10002011

2109

8

40

NA 256.313 21# Number of records in R05 by subgroup> addmargins(table(R05$sg,useNA="ifany"))
20

21

22

23

24

25

26

27

28

31965

76116

96170

17251

49700

24773

24442

16107

37629

32

33

34

35

Sum

33421

7986

24881



29

30

31

3949 115882

27600

24213 612085

Generated the variable of m.value, monthly expenditure.

> R05["m.value"]<-R05$NF_VALUE> R05["m.value"]<-ifelse(R05$sg>=30&R05$sg<=32,round(R05$m.value/6,1),R05$m.value)> R05["m.value"]<-ifelse(R05$sg==33|R05$sg==35,round(R05$m.value/12,1),R05$m.value)> head(R05[,c(1,13:19)])
ID NF_CODE NF_QUANTITY NF_VALUE NF_INKIND_VALUE

Weight sg m.value

1 10002011

2001

NA

2500

2500 256.313 202500

2 10002011

2003

NA

350

NA 256.313 20350

3 10002011

2101

NA

600

NA 256.313 21600

4 10002011

2103

2250

300

NA 256.313 21300

5 10002011

2105

NA

250

250 256.313 21250

6 10002011

2109

8

40

NA 256.313 21

40> outfiles[[05]]<-R05> dim(R05)[1] 612085

19### R06: BOARDERS ###

Converted to the monthly expenditure, and generated data frame of boarder.

> dim(R06)[1] 260 28> boarder<-R06

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> boarder[is.na(boarder)]<-0> boarder$COL_3<-round(boarder$COL_3*30/7,1)> boarder$COL_14<-round(boarder$COL_14/6,1)> boarder$COL_15<-round(boarder$COL_15/12,1)> head(boarder)
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO NHH RESULT COL_2

1 11149011

7

11

2 10

7

4 11149

1

1

1

1

4

2 11672041

7

11

1 10

5 257 11672

4

1

1

1

2

3 11799021

7

11

1 10

12 140 11799

2

1

1

1

6

4 12303031

7

11

2 10

10 89 12303

3

1

1

1

3

5 12705061

7

11

2 10

9 66 12705

6

1

1

1

5

6 12705081

7

11

2 10

9 66 12705

8

1

1

1

3

COL_3 COL_4 COL_5 COL_6 COL_7 COL_8 COL_9 COL_10 COL_11 COL_12 COL_13 COL_14
1 3673.5

0

0

0

0

600

0

3000

0

0

0

80.8

2

0.0

0

0

0

500

0

0

0

4000

0

0

0.0

3

0.0

0

0

0

0

0

0

0

5000

5000

0

0.0

4

0.0

0

0

0

0

0

0

0

5000

0

0

0.0

5

0.0

0

0

0

0

0

0

0

0

0

0 194.4

6 1836.7

0

0

0

200

500

0

0

0

0

0 111.1

COL_15

PID Weight1

9.7 1114901104 254.392

2

0.0 1167204102 461.363

3

0.0 1179902106 329.545

4

0.0 1230303103 158.995

5

0.0 1270506105 141.329

6

0.0 1270508103 141.329> n.boarder<-rep(0,13) # Number of records with expenditure >0> av.boarder<-rep(0,13) # Monthly expenditure per household> (NHH<-sum(outfiles[[25]]$Weight))[1] 5121354> for(j in 1:13){+ n.boarder[j]<-nrow(subset(boarder,boarder[,j+13]>0))+ av.boarder[j]<-sum(boarder[,j+13]*boarder$Weight)/NHH+ }> n.boarder

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[1] 77 15 59

6 134 138 50 120 132 83 91 112 19> round(av.boarder,2)[1] 47.66 0.32 1.23 0.34 3.54 4.78 1.81 17.73 35.70 13.66 3.91 0.38 0.05

> for(j in 1:13){+ if(j==1) cat("Variable: hhexp , No. of records \n")+ cat(colnames(boarder)[j+13],":",format(round(av.boarder[j],2),width=8),+ ", ",format(n.boarder[j],width=5),"\n")+ }Variable: hhexp , No. of records
COL_3 :

47.66 ,

77

COL_4 :

0.32 ,

15

COL_5 :

1.23 ,

59

COL_6 :

0.34 ,

6

COL_7 :

3.54 ,

134

COL_8 :

4.78 ,

138

COL_9 :

1.81 ,

50

COL_10 :17.73 ,

120

COL_11 :35.7 ,

132

COL_12 :13.66 ,

83

COL_13 :3.91 ,

91

COL_14 :0.38 ,

112

COL_15 :0.05 ,

19

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7.4. Estimating household expenditure by 4-digit item

Created data frame with variables of 4-digit item code and average monthly expenditure

per household.
Generated monthly household expenditure on food by item from R04(Correspond to Table 5.2 of the final report)> (NHH<-sum(outfiles[[25]]$Weight))[1] 5121354> food.item<-by(R04,R04$CODE,function(df) round(sum(df$m.value*df$Weight)/NHH,2))> length(food.item)[1] 280> food.byitem<-data.frame(code=as.integer(names(food.item)),+ m.value=as.vector(food.item))> head(food.byitem)
code m.value
1 101 405.89
2 102

59.75

3 103 413.01
4 104

24.88

5 105 405.39
6 106



91.38

Generated monthly household expenditure on non-food by item from R5(Correspond to Table 5.4 of the final report)> nfood.byitem<-data.frame(code=as.integer(names(nfood.item)),+ m.value=as.vector(nfood.item))> head(nfood.byitem)
code m.value
1 2001 4491.60
2 200224.70

3 2003 150.70
4 2101 759.46
5 2102

3.65

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6 2103



93.16

Binded two data frames of food.byitem and nfood.byitem together, and generated

hhexp.byitem.
> hhexp.byitem<-rbind(food.byitem,nfood.byitem)> dim(hhexp.byitem)[1] 4902Remarks: Checked out-of-range codes in hhexp.byitem> hhexp.byitem$code[1] 101 102 103 104 105 106 107 108 109 110 111 112 113 114[15] 115 116 117 118 119 120 121 122 129 140 201 202 203 204[29] 205 206 207 208 209 210 211 212 213 214 215 216 219 301[43] 302 303 304 305 306 307 308 309 319 401 402 403 404 405[57] 406 407 408 409 410 411 412 413 414 415 416 417 418 419[71] 420 421 422 423 424 425 426 427 428 429 430 431 432 433[85] 434 439 441 442 443 444 445 446 447 448 449 450 459 501[99] 502 503 504 505 506 507 508 509 601 602 603 604 605 609[113] 701 702 703 704 705 706 707 708 709 710 711 712 713 714[127] 715 716 717 718 719 720 721 722 723 801 802 803 804 805[141] 806 807 808 809 810 811 812 813 814 815 816 817 819 901[155] 902 909 1001 1002 1003 1101 1102 1103 1104 1105 1106 1107 1108 1109
[169] 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123
[183] 1129 1201 1202 1203 1204 1205 1206 1209 1301 1302 1303 1304 1305 1306
[197] 1307 1308 1309 1310 1311 1312 1319 1401 1402 1403 1404 1409 1501 1502
[211] 1503 1504 1509 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611
[225] 1612 1613 1614 1615 1616 1619 1651 1652 1653 1654 1659 1701 1702 1703
[239] 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1719 1801 1802 1803
[253] 1804 1805 1806 1807 1808 1809 1810 1811 1812 1819 1901 1902 1903 1904
[267] 1905 1906 1907 1908 1909 1910 1911 1912 1919 1920 1921 1922 1923 1924
[281] 2001 2002 2003 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111
[295] 2119 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213
[309] 2214 2219 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2319 2401
[323] 2402 2403 2404 2405 2406 2407 2409 2411 2412 2413 2414 2415 2416 2417
[337] 2418 2419 2501 2502 2503 2504 2505 2509 2601 2602 2603 2604 2605 2606
[351] 2607 2608 2609 2610 2611 2612 2619 2701 2702 2703 2704 2705 2706 2707
[365] 2708 2709 2710 2711 2712 2719 2801 2802 2803 2804 2805 2809 2901 2902
[379] 2903 2904 2905 2906 2909 3001 3002 3003 3004 3005 3006 3007 3008 3009
[393] 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023
[407] 3024 3029 3041 3042 3043 3049 3061 3062 3101 3102 3103 3104 3105 3106
[421] 3107 3109 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212
[435] 3213 3214 3219 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311
[449] 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325
[463] 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3339 3401 3402 3403
[477] 3404 3405 3406 3407 3408 3419 3501 3502 3503 3504 3505 3506 3507 3509

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>

The code=140 in hhexp.byitem are out of range, as compared with the codes on the questionnaire;

Omitted the records with the above code of 140 in hhexp.byitem and R04.

> head(R04[R04$CODE==140,])
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU
600945 71418021

4

61

2 10

7

NHH RESULT CODE QUANTITY VALUE INKIND_VALUE
600945

1

1

140

10000

530

A0 SNUMBER HHNO

5 714182

1

Weight m.value sg

NA 617.269

2271.41> head(R04[R04$ID==71418021,])
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO

600944 71418021

4

61

2 10

7

5 714182

1600945 71418021

4

61

2 10

7

5 714182

1600946 71418021

4

61

2 10

7

5 714182

1600947 71418021

4

61

2 10

7

5 714182

1600948 71418021

4

61

2 10

7

5 714182

1600949 71418021

4

61

2 10

7

5 71418

2

1NHH RESULT CODE QUANTITY VALUE INKIND_VALUE

Weight m.value sg

600944

1

1

107

2000

100

NA 617.269

428.6

1

600945

1

1

140

10000

530

NA 617.269

2271.41600946

1

1

201

1800

270

NA 617.269

1157.12600947

1

1

302

150

25

NA 617.269

107.1

3

600948

1

1

401

500

40

NA 617.269

171.4

4

600949

1

1

402

1000

65

NA 617.269

278.6

4

> hhexp.byitem.old<-hhexp.byitem> hhexp.byitem<-subset(hhexp.byitem,code!=140)> dim(hhexp.byitem)[1] 4892> R04.old<-R04> R04<-subset(R04,CODE!=140)> dim(R04)[1] 815184

19> outfiles[[4]]<-R04

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

Generated data frame exp.boarder from boarder> n.boarder<-rep(0,13) # Number of records with expenditure >0> av.boarder<-rep(0,13) # Monthly expenditure per household> (NHH<-sum(outfiles[[25]]$Weight))[1] 5121354> for(j in 1:13){+ n.boarder[j]<-nrow(subset(boarder,boarder[,j+13]>0))+ av.boarder[j]<-sum(boarder[,j+13]*boarder$Weight)/NHH+ }> n.boarder
[1]

77

15

59

6 134 138

50 120 132

83

91 11219

3.54

1.81 17.73 35.70 13.66> round(av.boarder,2)[1] 47.66

0.32

1.23

0.34

4.78

3.91

0.38

0.05

# Corresponding 4-digit code, which never overlap the codes of R04 and R05> code.boarder<-c(0220,2112,3220,2910,2221,2510,2720,2004,3510,3411,3412,3050,3340)> for(j in 1:13){+ if(j==1) cat("Variable:

hhexp , No. of records , 4-digit code \n")+ cat(colnames(boarder)[j+13],":",format(round(av.boarder[j],2),width=8),+ ", ",format(n.boarder[j],width=14), ", ",format(code.boarder[j],width=14),"\n")+ }Variable:hhexp , No. of records , 4-digit code

COL_3 :

47.66 ,

77 ,220

COL_4 :

0.32 ,

15 ,2112

COL_5 :

1.23 ,

59 ,3220

COL_6 :

0.34 ,

6 ,2910

COL_7 :

3.54 ,

134 ,

2221

COL_8 :

4.78 ,

138 ,

2510

COL_9 :

1.81 ,50 ,2720

COL_10 :17.73 ,

120 ,

2004

COL_11 :35.7 ,

132 ,

3510

COL_12 :13.66 ,

83 ,

3411

COL_13 :3.91 ,

91 ,3412

COL_14 :0.38 ,

112 ,

3050

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138

COL_15 :0.05 ,

19 ,3340# Generated data frame exp.boarder> exp.boarder<-data.frame(code=code.boarder,col= colnames(boarder)[14:26],+ m.value=round(av.boarder,2),nhh=n.boarder)> exp.boardercode
1

col m.value nhh

220 COL_3

47.66

77

2 2112 COL_4

0.32

15

3 3220 COL_5

1.23

59

4 2910 COL_6

0.34

6

5 2221 COL_7

3.54 134

6 2510 COL_8

4.78 138

7 2720 COL_9

1.81

8 2004 COL_10

17.73 120

9 3510 COL_11

35.70 132

10 3411 COL_12

13.66

11 3412 COL_13

3.91 91

12 3050 COL_14

0.38 112

13 3340 COL_15

0.05 19

50

83

Binded hhexp.byitem and exp.boarder> df<-rbind(hhexp.byitem,exp.boarder[,c(1,3)])> hhexp.byitem<-df[order(df$code),]> dim(hhexp.byitem)[1] 502

2# Average monthly expenditure per household> sum(is.na(hhexp.byitem$m.value))[1] 6> hhexp.byitem[is.na(hhexp.byitem)]<-0> dim(hhexp.byitem)[1] 5022> sum(hhexp.byitem[,"m.value"])

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139

[1] 40006.01> sum(hhexp.byitem[hhexp.byitem$code<1900,"m.value"])[1] 15337.1> sum(hhexp.byitem[hhexp.byitem$code>=1900,"m.value"])[1] 24668.91

 Made table on average monthly expenditure per household by 4-digit code
> for(j in 1:502){+ cat(format(hhexp.byitem[j,1],width=4),":", format(hhexp.byitem[j,2],width=8),"¥n")+}

Sri Lanka HIES 2012/13 Manual (Version 1.1)

140

101 :

405.89

214 :

6.35

422 :

13.5

603 :

13.68

102 :

59.75

215 :

10.72

423 :

12.89

604 :

13.31

103 :

413.01

216 :

9.99

424 :

2.88

605 :

14.11

104 :

24.88

219 :

80.42

425 :

15.5

609 :

6.62

105 :

405.39

220 :

47.66

426 :

5.75

701 :

136.09

106 :

91.38

301 :

10.77

427 :

8.59

702 :

24.67

107 :

697.37302 :359.33

428 :

1.64

703 :

12.79

108 :

5.34

303 :

4.54

429 :

15.95

704 :

87.99

109 :

30.43

304 :

31.24

430 :

8.56

705 :

100.46

110 :

71.65

305 :

38.08

431 :

3.15

706 :

160.44

111 :

428.4

306 :

7.2

432 :

8.09

707 :

59.59

112 :

13.44

307 :

9.42

433 :

10.97

708 :

120.41

113 :

6.49

308 :

10.18

434 :

30.5

709 :

20.31

114 :

4.43

309 :

72.97

439 :

11.39

710 :

106.98

115 :

10.32

319 :

8.42

441 :

74.87

711 :

11.29

116 :

2.91

401 :

20.69

442 :

56.1

712 :

30.63

117 :

82.22

402 :

93.29

443 :

21.73

713 :

77.92

118 :

56.36

403 :

44.58

444 :

0

714 :

120.21

119 :

6.17

404 :

34.16

445 :

7.79

715 :

4.94

120 :

0.2

405 :

3.24

446 :

13.87

716 :

103.27

121 :

0.9

406 :

57.97

447 :

2.46

717 :

27.25

122 :

1.26

407 :

22.09

448 :

10.57

718 :

48.4

129 :

10.21

408 :

27.66

449 :

5.99

719 :

7.5

201 :

462.17

409 :

71.51

450 :

6.67

720 :

14.47

202 :

18.55

410 :

158.27

459 :

35.71

721 :

41.23

203 :

13.78

411 :

72.66

501 :

49.73

722 :

0.35

204 :

122.71

412 :

47.26

502 :

26.38

723 :

113.02

205 :

64.81

413 :

59.02

503 :

206.08801 :246.94206 :147.18

414 :

1.9

504 :

11.57

802 :

51.22

207 :

21.41

415 :

29.4

505 :

33.5

803 :

17.79

208 :

45.5

416 :

40.41

506 :

4.23

804 :

17.89

209 :

27.38

417 :

7.05

507 :

2.12

805 :

15.94

210 :

40.07

418 :

28.36

508 :

0.26

806 :

3.88

211 :

200.49

419 :

20.68

509 :

3.43

807 :

66.46

212 :

336.74

420 :

18.54

601 :

509.71

808 :

1.72

213 :

153.75

421 :

19.05

602 :

111.79

809 :

0.75

Sri Lanka HIES 2012/13 Manual (Version 1.1)

141

810 :

70.12

1122 :

01604 :

50.01

1804 :

10.53

811 :

58.3

1123 :

2.06

1605 :

11.59

1805 :

0.74

812 :

3.92

1129 :

2.78

1606 :

20.28

1806 :

49.6

813 :

28.36

1201 :

6.47

1607 :

92.49

1807 :

72.88

814 :

4.8

1202 :

10.23

1608 :

32.31

1808 :

3.16

815 :

0.5

1203 :

8.1

1609 :

31.31

1809 :

0.69

816 :

9.17

1204 :

0.64

1610 :

10.09

1810 :

5.04

817 :

0.45

1205 :

0.6

1611 :

5.16

1811 :

8.81

819 :

57.31

1206 :

0.8

1612 :

14.72

1812 :

5.94

901 :

159.7

1209 :

1.8

1613 :

4.66

1819 :

7.26

902 :

0.46

1301 :

25.55

1614 :

4.77

1901 :

19.37

909 :

0.03

1302 :

1.92

1615 :

12.19

1902 :

263.141001 :

962.721303 :

2.03

1616 :

6.16

1903 :

28.46

1002 :

2.74

1304 :

41.13

1619 :

15.6

1904 :

25.75

1003 :

0.02

1305 :

83.6

1651 :

10.53

1905 :

5.95

1101 :

53.69

1306 :

8.52

1652 :

3.76

1906 :

0.27

1102 :

183.511307 :

1035.771653 :

7.32

1907 :

0.82

1103 :

98.65

1308 :

111.9

1654 :

1.65

1908 :

3.18

1104 :

205.391309 :

18.73

1659 :

0.37

1909 :

2.99

1105 :

01310 :

22.37

1701 :

10.02

1910 :

137.591106 :

64.47

1311 :

16.53

1702 :

61.52

1911 :

3.51

1107 :

94.69

1312 :

9.31

1703 :

17.08

1912 :

21.66

1108 :

78.09

1319 :

11.3

1704 :

4.41

1919 :

0.33

1109 :

113.971401 :

342.661705 :

289.1

1920 :

47.57

1110 :

103.041402 :

27.61

1706 :

3.91

1921 :

37.21

1111 :

51.94

1403 :

5.39

1707 :

13.24

1922 :

20.07

1112 :

18.22

1404 :

0.58

1708 :

44.09

1923 :

4.45

1113 :

15.22

1409 :

3.47

1709 :

9.91

1924 :

82.56

1114 :

22.04

1501 :01710 :

3.17

2001 :

4491.61115 :

21.82

1502 :

11.86

1711 :

3.57

2002 :

24.7

1116 :

16.76

1503 :

3.9

1712 :

95.03

2003 :

150.7

1117 :

31.93

1504 :

1.1

1713 :

0.76

2004 :

17.73

1118 :

20.7

1509 :

0.21

1719 :

27.21

2101 :

759.461119 :

21.12

1601 :

77.82

1801 :

281.922102 :

3.65

1120 :

61.46

1602 :

15.5

1802 :

14.33

2103 :

93.16

1121 :

38.97

1603 :

33.02

1803 :

26.21

2104 :

110.49

Sri Lanka HIES 2012/13 Manual (Version 1.1)

1422105 :

02401 :

37.31

2619 :

56.56

3008 :

47.92

2106 :

369.662402 :

623.152701 :

11.81

3009 :

97.6

2107 :

24.96

2403 :

35.04

2702 :

62.22

3010 :

13.38

2108 :

98.8

2404 :

45.43

2703 :

48.6

3011 :

11.02

2109 :

18.7

2405 :

158.3

2704 :

222.393012 :

16.1

2110 :

3.62

2406 :

327.9

2705 :

3.9

3013 :

24.63

2111 :

9.61

2407 :

68.51

2706 :

4.38

3014 :

13.67

2112 :

0.32

2409 :

11.91

2707 :

13.5

3015 :

21.42

2119 :

0.79

2411 :

02708 :

27.32

3016 :

8.06

2201 :

172.052412 :

246.332709 :

14.06

3017 :

2.16

2202 :

71.08

2413 :

24.71

2710 :

56.58

3018 :

4.23

2203 :

29.48

2414 :

15.97

2711 :

20.16

3019 :

70.74

2204 :

19.79

2415 :

179.472712 :

17.09

3020 :

25.67

2205 :

34.95

2416 :

356.812719 :

11.71

3021 :

12.39

2206 :

48.13

2417 :

74.38

2720 :

1.81

3022 :

17.02

2207 :

19.44

2418 :

245.842801 :

182.143023 :

5.83

2208 :

42419 :

40.06

2802 :

102.543024 :

14.21

2209 :

42.12

2501 :

3.12

2803 :

12.09

3029 :

11.31

2210 :

6.26

2502 :

260.942804 :

16.23

3041 :

20.21

2211 :

45.14

2503 :

567.452805 :

3.77

3042 :

12.94

2212 :

2.88

2504 :

17.19

2809 :

1.61

3043 :

11.98

2213 :

98.52

2505 :

40.35

2901 :

5.89

3049 :

1.67

2214 :

19.45

2509 :

2.52

2902 :

16.98

3050 :

0.38

2219 :

3.72

2510 :

4.78

2903 :

108.423061 :

4.11

2221 :

3.54

2601 :

236.062904 :

38.57

3062 :

5.33

2301 :

594.362602 :

24.13

2905 :

2.1

3101 :

98.61

2302 :

25.45

2603 :

20.44

2906 :

0.8

3102 :

58.01

2303 :

78.56

2604 :

39.37

2909 :

17.22

3103 :

14.27

2304 :

118.912605 :

41.21

2910 :

0.34

3104 :

28.02

2305 :

325.212606 :

76.9

3001 :

138.023105 :

4.45

2306 :

357.152607 :

646.883002 :

29.3

3106 :

3.14

2307 :

15.23

2608 :

21.81

3003 :

106.143107 :

4.08

2308 :

5.07

2609 :

130.013004 :

79.1

3109 :

1.87

2309 :

17.06

2610 :

74.08

3005 :

66.26

3201 :

11.83

2310 :

4.15

2611 :

57.35

3006 :

32.13

3202 :

18.76

2319 :

24.75

2612 :

22.93

3007 :

51.26

3203 :

17.23

Sri Lanka HIES 2012/13 Manual (Version 1.1)

1433204 :

10.15

3324 :

2.01

3205 :

1.43

3325 :

75.15

3206 :

3.52

3326 :

108.353207 :

2.12

3327 :

269.933208 :

22.3

3328 :

0.44

3209 :

4.25

3329 :

3.16

3210 :

6.32

3330 :

1.41

3211 :

33.31

3331 :

25.79

3212 :

2.44

3332 :

59.82

3213 :

9.02

3333 :

10.68

3214 :

2.35

3334 :

6.95

3219 :

7.93

3335 :

82.73

3220 :

1.23

3339 :

14.08

3301 :

14.15

3340 :

0.05

3302 :

103401 :

439.553303 :

0.54

3402 :

72.4

3304 :

8.26

3403 :

209.083305 :

22.52

3404 :

643.6

3306 :

20.64

3405 :

2650.153307 :

10.88

3406 :

190.393308 :

15.82

3407 :

58.45

3309 :

11.36

3408 :

38.48

3310 :

1.65

3411 :

13.66

3311 :

0.17

3412 :

3.91

3312 :

4.43

3419 :

155.233313 :

2.98

3501 :

577.283314 :

2.37

3502 :

198

3315 :

1.83

3503 :

15.98

3316 :

23.35

3504 :

131.583317 :

1.83

3505 :

806.933318 :

7.07

3506 :

356.943319 :

5.9

3507 :

810.563320 :

3.88

3509 :

114.553321 :

4.6

3510 :

35.7

3322 :

30.98

3323 :

9.19

Sri Lanka HIES 2012/13 Manual (Version 1.1)

144

7.5. Household expenditure by subgroup### R04: hhexp1 ###Aggregated the variable of m.value, monthly household expenditure by ID (household) and sg,
and created household-level data frame hhexp with variables of total monthly expenditure by
subgroup.

> t<-tapply(R04$m.value,list(R04$ID,R04$sg),sum,na.rm=T)> dim(t)[1] 20540

19> head(t)
1

2

3

7

8

9

NA 3364.3

NANA 1050

NA

NA

NA

NA

10002041 1191.5 6600.0 792.9 1075.7 338.6 2057.1 3450.0857.2

NA 1050NANA 1200

10002011 1800.0 3385.7 385.7
10002031

NA 7585.7

45385.8 428.6

NA

NA

NA

10002051 2764.3 2031.5 407.2 1157.1
10002061 1950.0

497.1 385.7

10002071 3085.7 3021.4 222.9
11 12

NA 1071.4 2250.1

600.0 278.6

964.2 235.7 4285.7 1821.4
14

1610002011 1731.7 NA

NA 385.7 450.0

NA10002031

NA

17

NA
18

19

342.9 3471.4 1285.7

42.9

NA

NA

NA

10002041 1654.6 NA 2040.0 385.7 792.9

NA

NA

85.7

NA

10002051 1581.7 NA

NA

150.0

NA

587.1

NA

274.3

NA

NA

10002071 1581.7 NA 3857.1 364.3 900.0 1050.0 3857.2 1157.1

NA

385.7 385.7 677.1
NA

94.3 450.0

NA

NA 1500

NA

10002061 1508.8 NANA10

NA 1714.3 1178.5 235.7 1050

15

NA NA

13

6

> t[is.na(t)]<-0> colnames(t)<-paste("g",1:19,sep="")> hhexp<-data.frame(ID=rownames(t),t)> head(hhexp)
ID

g1

g2

g3

g7

g8

g9

0.0 3364.3

0.0

0.0 1050

0.0

0.0

0.0

0.0

10002041 10002041 1191.5 6600.0 792.9 1075.7 338.6 2057.1 3450.0857.2

0.0 1050

0.0

0.0 1200

10002011 10002011 1800.0 3385.7 385.7
10002031 10002031

0.0 7585.7

0.0

g4

385.8 428.6
0.010002051 10002051 2764.3 2031.5 407.2 1157.1
10002061 10002061 1950.0

497.1 385.7

g5

0.0

g6

0.0 1071.4 2250.1

600.0 278.6

g10

0

0.0 1714.3 1178.5 235.7 1050

Sri Lanka HIES 2012/13 Manual (Version 1.1)

145

10002071 10002071 3085.7 3021.4 222.9
g11 g12g13

g14

964.2 235.7 4285.7 1821.4

g15

g16

g17

g18

0.0

g19

10002011 1731.7

0

0.0 385.7 450.0

0.010002031

0

0.0

0.0

42.9

0.0

0.0

0.0

10002041 1654.6

0 2040.0 385.7 792.9

0.0

0.0

85.7

0.0

10002051 1581.7

0

0.0

150.0

0.0

587.1

10002061 1508.8

0

0.0

274.3

0.0

0.0

10002071 1581.7

0 3857.1 364.3 900.0 1050.0 3857.2 1157.1

0.0

0.0

0.0

385.7 385.7 677.1
0.0

94.3 450.0

0.0 1500

342.9 3471.4 1285.7> hhexp1<-hhexp### R05: hhexp2 ###Aggregated the variable of m.value, monthly household expenditure by ID (household) and sg,
and created household-level data frame hhexp2 with variables of total monthly expenditure by
subgroup.

> t<-tapply(R05$m.value,list(R05$ID,R05$sg),sum,na.rm=T)> dim(t)[1] 20540

16> head(t)
20

21

22

23

24

25

26 27

28 29

10002011 2850 1190

272

20

200 2800

160 NA

945 NA

10002031 2650

420

602

430

NA 1400

NA NA

NA NA

10002041 3500 2850

668

NA

30

32

33

150.0 500.0 43.3

NA

NA

NA

NA

2650 1500 2200 NA 1045 NA

908.4 125.0

NA

625.0

10002051 2950 3420 1116 8000

960 3200 1000 NA 1300 NA

916.7 233.4

NA

41.7

10002061 5150 2850

353

3000

41.7 141.7

NANA

10002071 5150 2370

907 3750 49820

350

NA 1356 NA 1024 NANA

31800 1060 NA 1340 NA 1083.4 250.0 66.7 31583.4

34 3510002011

NA NA

10002031

NA NA

10002041

1800 NA

10002051 13500 NA
10002061

1580 NA

10002071

610 NA

Sri Lanka HIES 2012/13 Manual (Version 1.1)

146

> t[is.na(t)]<-0> colnames(t)<-paste("g",20:35,sep="")> hhexp2<-data.frame(ID=rownames(t),t)> head(hhexp2)
ID

g20

g21

g22

g23

g24

10002011 10002011 2850 1190

272

20

10002031 10002031 2650

420

602

10002041 10002041 3500 2850

668

g26 g27

g28 g29

200 2800

160

0

945

0

430

0 1400

0

0

0

0

0

2650 1500 2200

0 104510002051 10002051 2950 3420 1116 8000

960 3200 1000

10002061 10002061 5150 2850

353

3000

10002071 10002071 5150 2370

907 3750 49820

g33

g34 g35

10002011

0.0

0

0

10002031

0.0

0

0

10002041

625.0

1800

0

10002051

41.7 13500

010002061

0.0

1580

0

10002071 31583.4

610

0

350

g25

g30

g31

g32

150.0 500.0 43.3
0.0

0.0

0.0

0

908.4 125.0

0.0

0 13000916.7 233.4

0.0

0 1356

0 1024041.7 141.7

0.0

800 1060

0 13400 1083.4 250.0 66.7### R06: hhexp3 ###Collapsed to household level and created data frame hhexp3.

> CollapseBy2<-function(df=df,var=var,fac=fac,ID=NULL){+ # To aggregate variables with the same factors in data frame+ # df: data frame+ # var: vector of variables to be aggregated (in position number or variable name)+ # fac: factor variables (in position number or variable name)+ # ID: combinations of factor levels to be matched, if specified+ #+ if(is.numeric(var)){ var<-colnames(df)[var] }+ if(is.numeric(fac)){ fac<-colnames(df)[fac] }+ d<-data.frame(df[,fac])+ colnames(d)<-fac+ if(missing(ID)) { ID<- subset(d,!duplicated(d)) }+ y<-data.frame(ID,row.names=NULL)

Sri Lanka HIES 2012/13 Manual (Version 1.1)

147

+ colnames(y)<-fac+ for(j in var){+ t<-aggregate(df[,j],d,sum,na.rm=T)+ colnames(t)<-c(fac,j)+ y<-merge(y,t,by=fac,all.x=T)+ }+ y[is.na(y)]<-0+ return (y)+ }> bd.ID<-unique(boarder$ID)> length(bd.ID)[1] 178> hhexp3<-CollapseBy2(boarder,var=c(14:26),fac="ID",ID=bd.ID)> dim(hhexp3)[1] 17814> head(hhexp3)
ID

COL_3 COL_4 COL_5 COL_6 COL_7 COL_8 COL_9 COL_10 COL_11 COL_12 COL_13

1 11149011 3673.5

0

0

0

0

600

0

3000

0

0

0

2 11672041

0.0

0

0

0

500

0

0

0

4000

0

0

3 11799021

0.0

0

0

0

0

0

0

0

5000

5000

0

4 12303031

0.0

0

0

0

0

0

0

0

5000

0

0

5 12705061

0.0

0

0

0

0

0

0

0

0

0

0

6 12705081 1836.7

0

0

0

200

500

0

0

0

0

0

COL_14 COL_15
1

80.8

9.7

2

0.0

0.0

3

0.0

0.0

4

0.0

0.0

5

194.4

0.0

6

111.1

0.0

Sri Lanka HIES 2012/13 Manual (Version 1.1)

148### Merged R04, R05 and R06 ###Merged R04, R05 and R06 by using ID> hhexp<-merge(hhexp1,hhexp2)> dim(hhexp)[1] 20540

36> hhexp<-merge(hhexp,hhexp3,all.x=TRUE)> dim(hhexp)[1] 20540



49

Replaced NA with 0> hhexp[is.na(hhexp)]<-0Appended weight> R25<-outfiles[[25]]> hhexp<-merge(hhexp,R25[c("ID","Weight")])> colnames(hhexp)[1] "ID"

"g1"

"g2"

"g3"

"g4"

"g5"

"g6"

"g7"

[9] "g8"

"g9"

"g10"

"g11"

"g12"

"g13"

"g14"

"g15"

[17] "g16"

"g17"

"g18"

"g19"

"g20"

"g21"

"g22"

"g23"

[25] "g24"

"g25"

"g26"

"g27"

"g28"

"g29"

"g30"

"g31"

[33] "g32"

"g33"

"g34"

"g35"

"COL_3""COL_4""COL_5""COL_6"[41] "COL_7""COL_8""COL_9""COL_10" "COL_11" "COL_12" "COL_13" "COL_14"[49] "COL_15" "Weight"

Added COL_3 to COL_15 to the respective subgroups.

> hhexp$g2<-hhexp$g2+hhexp$COL_3> hhexp$g21<-hhexp$g21+hhexp$COL_4> hhexp$g32<-hhexp$g32+hhexp$COL_5> hhexp$g29<-hhexp$g29+hhexp$COL_6> hhexp$g22<-hhexp$g22+hhexp$COL_7> hhexp$g25<-hhexp$g25+hhexp$COL_8> hhexp$g27<-hhexp$g27+hhexp$COL_9> hhexp$g20<-hhexp$g20+hhexp$COL_10> hhexp$g35<-hhexp$g35+hhexp$COL_11> hhexp$g34<-hhexp$g34+hhexp$COL_12> hhexp$g34<-hhexp$g34+hhexp$COL_13

Sri Lanka HIES 2012/13 Manual (Version 1.1)

149

> hhexp$g30<-hhexp$g30+hhexp$COL_14> hhexp$g33<-hhexp$g33+hhexp$COL_15

Generated the variable of monthly total expenditure> hhexp["ttexp"]<-rowSums(hhexp[,2:36])> hhexp["food"]<-rowSums(hhexp[,c(2:19)])> hhexp["nfood"]<-rowSums(hhexp[,c(20:36)])# Average monthly household expenditure> sum(hhexp$ttexp*hhexp$Weight)/sum(hhexp$Weight)[1] 41765.19> sum(hhexp$food*hhexp$Weight)/sum(hhexp$Weight)[1] 15907.8> sum(hhexp$nfood*hhexp$Weight)/sum(hhexp$Weight)[1] 25857.39

Summary:
My estimatesFinalreportGAPof the survey
Total monthly household expenditure

41,76541,444+321 (0.8%)

Food

15,90815,651+257 (1.6%)

Non-food

25,857

25,793+64 (0.2%)

Sri Lanka HIES 2012/13 Manual (Version 1.1)

150Household expenditure on food by subgroup> df<-hhexp> av.food<-rep(0,19)> for(j in 1:18){+ av.food[j+1]<-sum(df[,j+1]*df$Weight)/sum(hhexp$Weight)+ }> av.food[1]<-sum(av.food[2:19])> names(av.food)<-c("All food items","Cereals","Prepared food","Pulses","Vegitables",+ "Yams and other","Meat","Fish","Dried fish","Eggs","Coconuts","Condiments",+ "Other foods","Milk and milk foods","Fats and oils","Sugar, juggery & treacle",+ "Fruits","Confectionery & short eats","Beverages")> for(j in 1:19){+ cat(format(names(av.food)[j],width=28),format(round(av.food[j]),width=6),"\n")+ }All food items

15908

Cereals

2829

Prepared food

1810

Pulses552

Vegitables

1279

Yams and other

337

Meat

669

Fish

1430

Dried fish

656

Eggs

160

Coconuts

965

Condiments

1416

Other foods

29

Milk and milk foods

1389

Fats and oils

380

Sugar, juggery & treacle

476

Fruits461

Confectionery & short eats

583

Beverages

487

Sri Lanka HIES 2012/13 Manual (Version 1.1)

151Household expenditure on non-food by subgroup> df<-hhexp> av.nfood<-rep(0,18)> for(j in 1:17){+ av.nfood[j+1]<-sum(df[,j+19]*df$Weight)/sum(hhexp$Weight)+ }> av.nfood[1]<-sum(av.nfood[2:18])> names(av.nfood)<-c("Total non-food","Liquor,Drugs & Tabacco","Housing",+ "Fuel & Light","Personal care","Health","Transport","Communication","Education",+ "Cultural activities & Entertainment","Non-durable household goods",+ "Household services","Clothing & Textile","Footwear","Durable household goods",+ "Long durable household goods","Other expenses","Other adhoc expenses")> for(j in 1:18){+ cat(format(names(av.nfood)[j],width=35),format(round(av.nfood[j]),width=6),"\n")+ }Total non-food

25857

Liquor,Drugs & Tabacco705

Housing4685

Fuel & Light

1754

Personal care

621

Health1566

Transport

3419

Communication

896

Education

1448

Cultural activities & Entertainment516

Non-durable household goods

318

Household services

190

Clothing & Textile

976

Footwear212

Durable household goods

154

Long durable household goods

875

Other expenses

4475

Other adhoc expenses

3048

Sri Lanka HIES 2012/13 Manual (Version 1.1)

152#### CAUSE OF ERRORS ####
Comparison with the total household expenditure in R25, household summary file> R25<-outfiles[[25]]> dim(R25)[1] 20540

15> head(R25)
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO NHH

1 10002011

25

11

1 10

7

1 100021

1

1

2 10002031

25

11

1 10

7

1 100023

1

1

3 10002041

25

11

1 10

7

1 100024

1

1

4 10002051

25

11

1 10

7

1 1000251

1

5 10002061

25

11

1 10

7

1 1000261

1

6 10002071

25

11

1 10

7

1 10002

7

1

1

RESULT

HExpPM

HIncPM Weight1

1 27597.48 28900.00 256.313

2

1 13130.57 19892.86 256.313

3

1 40234.20 16191.67 256.313

41 51285.67 63046.66 256.313

5

1 26063.48 32233.33 256.313

61 126262.00 13400.00 256.313> weighted.mean(R25$HExpPM,R25$Weight)[1] 41444.11

R25 has the household-level variable of total household expenditure, HExpPM, and its
weighted mean is the same as the survey report.

Compared my estimates of hhexp with HExpPM at household level.

> table(hhexp$ID==R25$ID)
TRUE
20540
> addmargins(table(abs(hhexp$ttexp-R25$HExpPM)<1))
FALSE TRUE

Sum

13891 6649 20540

Sri Lanka HIES 2012/13 Manual (Version 1.1)

153



Generated the variable of differences between hhexp and HExpPM.

> hhexp$ttR25<-R25$HExpPM> hhexp$diff<-hhexp$ttexp-hhexp$ttR25



Example of the differences> range(hhexp$diff)[1]-8572 4381080> hhexp[hhexp$diff>4000000,]
ID

g1

g2

g3

g4

g5

g6

g7

g8

g9

17257 83130011 4383900 1028.6 827.1 2207.1 642.9 1071.4 1650 771.4 347.1
g10 g11 g12 g13
17257 1028.6 1586

0

g14

g15

g16

g17

g18 g19 g20 g21

0 471.4 707.1 214.3 1114.3 257.1

g22 g23 g24 g25 g26 g27 g28 g2917257 2175 1000 500 1700 6580 450 200

g30

0 3160 3595

g31 g32 g33 g34

0 1550.1 766.7 370

g35

0 8960 583.3

COL_3 COL_4 COL_5 COL_6 COL_7 COL_8 COL_9 COL_10 COL_11 COL_12
17257

0

0

0

0

0

COL_13 COL_14 COL_15 Weight
17257

0

0

0
ttexp

0

0

0

food

nfood

0
ttR25

0 255.218 4429415 4397824 31590.1 48334.87

diff17257 4381080> d<-subset(R04,ID==83130011)> dim(d)[1] 44 19> head(d)ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO

693405 83130011

4

72

2 10

11 27 83130

1

1693406 83130011

4

72

2 10

11 27 83130

1

1693407 83130011

4

72

2 10

11 27 83130

1

1693408 83130011

4

72

2 10

11 27 83130

1

1693409 83130011

4

72

2 10

11 27 83130

1

1693410 83130011

4

72

2 10

11 27 83130

1

1

NHH RESULT CODE QUANTITYVALUE INKIND_VALUE Weight

Sri Lanka HIES 2012/13 Manual (Version 1.1)

m.value sg

154693405

1

1 107

10500

630

630 255.218

2700.0 16934061

1 111

NA 1022250

NA 255.218 4381071.4

693407

1

1 118

100

30

NA 255.218

128.6 1693408

1

1 201

900

140

NA 255.218

600.0 2693409

1

1 205

10

100

NA 255.218

428.6 2693410

1

1 302

1100

168

NA 255.218

720.0 3

1

Remarks: The value of 1,022,250 in code=111 (Wheat flour) seems to be an outlier, and thevariable QUANTITY is NA.


The number of records with VALUE=NA is 4, while that with QUANTITY=NA is 148, 638.

> sum(is.na(R04$VALUE))[1] 4> R04[is.na(R04$VALUE),]ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO

22133 11586081

4

11

1 10

6 282 11586

8182930 15802031

4

11

1 10

12 159 15802

3

1320947 37178051

4

23

3 10

10 35 37178

5

1812760 98450051

4

92

2 10

11 38 98450

51

NHH RESULT CODE QUANTITY VALUE INKIND_VALUE Weight m.value sg
22133

1

1 444

2

NA

50 256.313

NA 4

82930

1

1 1105100

NANA 288.352

NA 113209471

1 15011000

NANA 289.311

NA 158127601

1 1122NANA

10 366.407

NA 11> sum(is.na(R04$QUANTITY))[1] 148638# Examples of the differences> hhexp[hhexp$diff< -8000,]
ID

g1

g2

g3

g4

g5

g6

g7 g8

g9

12620 58309071 11250 3428.6 1898.6 1842.8 385.7 8571.4 17142.8 0 822.9
g10

g11 g12

g13

g14

g15 g16

g17

Sri Lanka HIES 2012/13 Manual (Version 1.1)

g18 g19g20

15512620 3000 3235.8

0 20678.6 3085.7 1714.3

g21 g22 g23 g24 g25
12620 5525 2180

0 1307.1 1885.7

g26 g27 g28 g29

0 1820 400 10570

0 10000

g30 g31 g32 g33

0 2085 200 2051.6

0 66.7

g34

0 21100

g35 COL_3 COL_4 COL_5 COL_6 COL_7 COL_8 COL_9 COL_10 COL_11 COL_12
12620 166.7

0

0

0

0

0

0

0

0

0

0

COL_13 COL_14 COL_15 Weight ttexp food nfood ttR25 diff
12620

0

0

0 84.667 136415 80250 56165 144987 -8572# Sum of food expenditure> d1<-R04[R04$ID==58309071,]> range(d1$CODE)[1] 106 1809> sum(d1$VALUE)*30/7[1] 80250# Sum of non-food expenditure> d2<-R05[R05$ID==58309071,]> dim(d2)[1] 33 19> sum(d2$m.value)[1] 56165> d2[,c(1,13:15,17:19)]
ID NF_CODE NF_QUANTITY NF_VALUE Weight sg m.value
384829 58309071

2001

NA

10000 84.667 20 10000.0 # House rent

384830 58309071

2103

15000

1500 84.667 21 1500.0384831 58309071

2104

80

4000 84.667 21 4000.0

384832 58309071

2109

5

25 84.667 21

25.0384833 58309071

2201

NA

200 84.667 22200.0384834 58309071

2202

NA

320 84.667 22

320.0384835 58309071

2203

NA

340 84.667 22340.0384836 58309071

2206

NA

550 84.667 22550.0384837 58309071

2209

NA

70 84.667 22

70.0384838 58309071

2212

NA

500 84.667 22500.0384839 58309071

2213

NA

200 84.667 22200.0384840 58309071

2411

NA

1820 84.667 24 1820.0

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384841 58309071

2503

NA

400 84.667 25

400.0384842 58309071

2601

NA

620 84.667 26

620.0384843 58309071

2603

NA

450 84.667 26

450.0384844 58309071

2607

NA

9500 84.667 26 9500.0 # Tuition fees

384845 58309071

2801

NA

1920 84.667 28 1920.0

384846 58309071

2802

NA

165 84.667 28

165.0384847 58309071

2902

NA

200 84.667 29

200.0384848 58309071

3001

2

2400 84.667 30

400.0384849 58309071

3003

2

2400 84.667 30

400.0384850 58309071

3007

2

2000 84.667 30

333.3384851 58309071

3009

4

2600 84.667 30

433.3

384852 58309071

3013

10

600 84.667 30

100.0384853 58309071

3015

2

640 84.667 30

106.7384854 58309071

3016

4

200 84.667 30

33.3384855 58309071

3018

4

120 84.667 30

20.0384856 58309071

3042

7500

900 84.667 30

150.0384857 58309071

3061

NA

450 84.667 30

75.0384858 58309071

3208

4

400 84.667 32

66.7384859 58309071

3403

NA

3500 84.667 34 3500.0

384860 58309071

3405

NA

17600 84.667 34 17600.0 # Payment for debits

384861 58309071

3504

NA

2000 84.667 35

166.7

Summary

The variable of HExpPM in R25 is compatible with the final report.



There are inconsistencies between HExpPM and the sum of R04 and R05 at household
level.

> save.image("G:\\SriLanka2012\\Working\\hhexp_20170826.RData")>

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8. Resampling method

Strategies for 80% resampling1) To order R25 by the variables of DISTRICT, SECTOR, PSU and HIncPM, andappend the variable sn, the serial number of records to R25.
2) To select 80% of R25, by dropping 20% households using systematic selection
with the interval=5 and a random start number, andto create the vector ID.res, a set of ID selected for resampled data set.
3) For each data set, to select records with ID.res.
4) To append the adjust the weight WT as the next;
Adjusted weight = original weight / 0.8
5) For each data set, to rename R data frame “xxx” as “xxx.80”.
6) Export R data frames to CSV files with the name “xxx_80.csv”.

######################################################################################## Number of records and variables of each data frame> for(j in 1:25){+ cat(Rnames[j],":",nrow(outfiles[[j]]),":",ncol(outfiles[[j]]),"\n")+ }R01 : 85080 : 30
R02 : 22029 : 25
R03 : 80534 : 30
R04 : 815185 : 17
R05 : 612085 : 17
R06 : 260 : 28
R07 : 20540 : 14
R08 : 18567 : 19
R09 : 20540 : 14
R10 : 4173 : 27
R11 : 20540 : 14

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R12 : 20540 : 14
R13 : 3958 : 22
R14 : 20540 : 14
R15 : 5680 : 19
R16 : 20540 : 14
R17 : 12602 : 27
R18 : 20540 : 14
R19 : 8575 : 25
R20 : 20540 : 31
R21 : 20540 : 38
R22 : 20540 : 50
R23 : 20540 : 40
R24 : 20540 : 42
R25 : 20540 : 15

# Variables of R25> colnames(R25)[1] "ID""REC_TYPE" "DISTRICT" "SECTOR""DS""MONTH"[7] "PSU""A0""SNUMBER" "HHNO""NHH""RESULT"[13] "HExpPM""HIncPM""Weight"

Ordered R25 by the variables of DISTRICT, SECTOR, PSU and HIncPM, and append thevariable sn, the serial number of records to R25.

> d<-outfiles[[25]]> d<-d[order(d$DISTRICT,d$SECTOR,d$PSU,d$HIncPM),]> d$sn<-seq(1,nrow(d))

 Selected 80% of hhinc, by dropping 20% households using systematic selection with theinterval=5 and a random start number.
> int<-5> (st<-

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sample(1:5,1)) [1]1> d$flag<-ifelse(d$sn%%int==(st-1),0,1)> head(d)ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO NHH RESULT

6 10002071

25

11

1 10

7

1 10002

7

1

1

1

3 10002041

25

11

1 10

7

1 10002

4

1

1

1

2 10002031

25

11

1 10

7

1 10002

3

1

1

1

1 10002011

25

11

1 10

7

1 10002

1

1

1

1

8 10002091

25

11

1 10

7

1 10002

9

1

1

1

25

11

1 10

7

1 10002

6

1

1

1

5 10002061
HExpPM

HIncPM Weight sn flag

6 126262.00 13400.00 256.313 1

1

3 40234.20 16191.67 256.313 21

2 13130.57 19892.86 256.313 3

1

1 27597.48 28900.00 256.313 41

8 23754.48 30100.00 256.313 505 26063.48 32233.33 256.313 6

1> table(d$flag)
014108 16432> nrow(subset(d,flag==1))/nrow(d)[1] 0.8> ID.res<-subset(d,flag==1)$ID> length(ID.res)[1] 16432> head(ID.res)[1] 10002071 10002041 10002031 10002011 10002061 10002081

 The number of resampled households is 16,432.
 For each data set, select records with ID.res and confirmed the resampling rate.

> outfiles.res<-outfiles> for(j in 1:25){+ d<-outfiles[[j]]+ d<-subset(d,is.element(d$ID,ID.res))+ outfiles.res[[j]]<-d+ }Sri Lanka HIES 2012/13 Manual (Version 1.1)

160

# Resampled rates> for(j in 1:25) cat(Rnames[j],round(nrow(outfiles.res[[j]])/nrow(outfiles[[j]])*100,1),"\n")R01 80.1
R02 79.8
R03 80.1
R04 80
R05 79.9
R06 76.5
R07 80R08 80.7
R09 80R10 79R11 80R12 80R13 79.9
R14 80R15 80.2
R16 80R17 79.9
R18 80R19 79.1
R20 80R21 80R22 80R23 80R24 80R25 80

Appended the adjusted weight WT, and renamed R data frame “xxx” as “xxx.80”.

> outfiles.resold<-outfiles.res> for(j in 1:25){+ d<-outfiles.res[[j]]+ d$WT<-d$Weight/0.8+ outfiles.res[[j]]<-d

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+ }

> head(outfiles.res[[25]])
ID REC_TYPE DISTRICT SECTOR DS MONTH PSU

A0 SNUMBER HHNO NHH RESULT

1 10002011

25

11

1 10

7

1 10002

1

1

1

1

2 10002031

25

11

1 10

7

1 10002

3

1

1

1

3 10002041

25

11

1 10

7

1 10002

4

1

1

1

4 10002051

25

11

1 10

7

1 10002

5

1

1

1

5 10002061

25

11

1 10

7

1 10002

6

1

1

1

6 10002071

25

11

1 10

7

1 10002

7

1

1

1

HIncPM Weight

WT

HExpPM

1 27597.48 28900.00 256.313 320.3912
2 13130.57 19892.86 256.313 320.3912
3 40234.20 16191.67 256.313 320.3912
4 51285.67 63046.66 256.313 320.3912
5 26063.48 32233.33 256.313 320.3912
6 126262.00 13400.00 256.313 320.3912> Rnames.res<-paste(Rnames,".80",sep="")> Rnames.res[1] "R01.80" "R02.80" "R03.80" "R04.80" "R05.80" "R06.80" "R07.80" "R08.80"[9] "R09.80" "R10.80" "R11.80" "R12.80" "R13.80" "R14.80" "R15.80" "R16.80"[17] "R17.80" "R18.80" "R19.80" "R20.80" "R21.80" "R22.80" "R23.80" "R24.80"[25] "R25.80"



Exported the resampled R data frames to CSV files with the name “xxx_80.txt”.

> CSVnames.res<-paste(Rnames,"_80.csv",sep="")> head(CSVnames.res)[1] "R01_80.csv" "R02_80.csv" "R03_80.csv" "R04_80.csv" "R05_80.csv"[6] "R06_80.csv"> for(j in 1:25){+ cmd<-paste("write.csv(outfiles.res[[",j,"]],'",CSVnames.res[j],"',+ row.names=F)",sep="")+ eval(parse(text=cmd))

Sri Lanka HIES 2012/13 Manual (Version 1.1)

162

+ }
> list.files()[1] "R01_80.csv" "R02_80.csv" "R03_80.csv" "R04_80.csv" "R05_80.csv"[6] "R06_80.csv" "R07_80.csv" "R08_80.csv" "R09_80.csv" "R10_80.csv"[11] "R11_80.csv" "R12_80.csv" "R13_80.csv" "R14_80.csv" "R15_80.csv"[16] "R16_80.csv" "R17_80.csv" "R18_80.csv" "R19_80.csv" "R20_80.csv"[21] "R21_80.csv" "R22_80.csv" "R23_80.csv" "R24_80.csv" "R25_80.csv"# Number of records and variables in resampled data files> for(j in 1:25){+ cat(format(Rnames.res[j],width=10),":",format(CSVnames.res[j],width=13),":",+ format(nrow(outfiles.res[[j]]),width=6),":",+ format(ncol(outfiles.res[[j]]),width=3),"\n")+ }R01.80: R01_80.csv: 68152 : 31

R02.80: R02_80.csv: 17579 : 26

R03.80: R03_80.csv: 64520 : 31

R04.80: R04_80.csv: 652437 : 18

R05.80: R05_80.csv: 489125 : 18

R06.80: R06_80.csv:

R07.80: R07_80.csv: 16432 : 15

R08.80: R08_80.csv: 14978 : 20

R09.80: R09_80.csv: 16432 : 15

R10.80: R10_80.csv:

R11.80: R11_80.csv: 16432 : 15

R12.80: R12_80.csv: 16432 : 15

R13.80: R13_80.csv:

R14.80: R14_80.csv: 16432 : 15

R15.80: R15_80.csv:

R16.80: R16_80.csv: 16432 : 15

R17.80: R17_80.csv: 10068 : 28

R18.80: R18_80.csv: 16432 : 15

R19.80: R19_80.csv:

R20.80: R20_80.csv: 16432 : 32

199 : 29

3296 : 28

3164 : 234557 : 206779 : 26

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R21.80: R21_80.csv: 16432 : 39

R22.80: R22_80.csv: 16432 : 51

R23.80: R23_80.csv: 16432 : 41

R24.80: R24_80.csv: 16432 : 43

R25.80: R25_80.csv: 16432 : 16

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Attachments
1. HIES 2012/13 Questionnaire
2. HIES 2012/13 Data Dictionary
3. HIES 2012/13 Final Buletin
4. Excerpt of Introduction from HIES 2012/13 Final Report

Sri Lanka HIES 2012/13 Manual (Version 1.1)

165(For office use)
Month

CONFIDENTIAL
The information collected in the survey will be strictly
confidential accordiing to the survey ordinance &individual level information will not be divulged to any
person or agency

SectorDistrict

DS Division

MRCB No. (A0 )PSU Number
SSU Number
Household Number

HOUSEHOLD INCOME AND EXPENDITURE SURVEY) 2012/13
NATIONAL HOUSEHOLD SAMPLE SURVEY PROGRAMMESURVEY SCHEDULE

Department of Census & Statisics
Ministry of Finance and Planning
Sri Lanka

Sri Lanka HIES 2012/13 Manual (Version 1.1)

166

1
IDENTIFICATION INFORMATIONS
1'

Address (Location)()2'

Province ()3'District ()4'

DS Division ()5'

Name of MC/UC ()'

10'

11'

12'

^if urban sector only & Ward No
6'

GN Division (-7'

Name of Village (-Number ()Survey Month

SectorPSU Number

District

DS Division

SSU Number

Household
Number

MRCB No: (AO)

Name()
13'

Number of Households in this unit (^lf estate sector only &14'

Result()C. B. No ()15'

Name of the Head of the Household

16'

Interviewers Name ()(If rural sector only&
8'

9

Name of Estate :)** Result CodeCompleted1

Deferred2
3

Not competent respondent at home
RefusedHousehold is temporarilyclosedHousehold is demolished/Vacant
Other (Specify&

4

Signature ()Date()

5
6
9

17'

Supervising Officers Name ()Signature:-

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Date ()

167

2

Codes for Section lCol. 3 Relationship to head of thehousehold
Head of the household
Wife/Husband
Son/Daughter
Parents
Other Relative
Domestic Servants
Boarder
Other

Col. 10 - Attendance at School or Other
Educational Institution
1
2
3
4
5
6
7
9

Col. 4 - Sex
Male
Female

1
2

1
2
3
4
5
6
9

Col. 8 - Religion
Buddhist
Hindu
Islam
Roman Catholic / Other Christian
Other

1
2
3
4
5
6
9

Col. 11 - Level of Education

Col. 7 - Ethnicity
SinhalaSri Lanka Tamil
Indian Tamil
Sri Lanka Moors
Malay
BurgherOther

Pre school
SchoolUniversity
Other educational institution
Vocational/Technical institution
Pending results G.C.E.(O.L/A.L)
Does not attend

Col. 09 - Marital Status

1
2
3
4
9

Studding in Grade 1
Passed Grade 1
Passed Grade 2
Passed Grade 3
Passed Grade 4
Passed Grade 5
Passed Grade 6
Passed Grade 7
Passed Grade 8
Passed Grade 9
Passed Grade 10
Passed G.C.E.(O/L) or equivalent
Passed Grade 12
Passed G.C.E.(A / L)a or equivalent
Passed GAQ / GSQ
Passed Degree
Passed post Graduate / Diploma
PHD
Special Education unit
No Schooling

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Never Married
MarriedWidowedDivorced
Separated

1
2
3
4
5

Col. 13 - Current Activity
00
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19

Looking for a job
Student
Household work
Unable/Too old to work
Other

1
2
3
4
9

Col. 16 - Employment status
Government employee
Semi government employee
Private sector employee
Employee
Own account worker
Contributing family worker

1
2
3
4
5
6

168

3

Section 1 - Demographic Characteristics

Name the person who are member of this household andusually live else where in the country or abroad
41

Did you invalive in an
economic activity during last
week Yes - 1 ---> Goto
Col. 14 , No - 2

Main activity involved atpresent only for the persons ,who were marked Code 2 inCol. 12

8

9

10

11

12

13

Employment Status at
the main occupation

Level of Education(5 years and over )7

Main Industry **Attendance at school or other
Educationl institution( 3 years and over )6

For employed persons only
(Column 12 Code =1)
Main Occupation *Marital Status

5

Religion

month

4

year

3

Ethnicity

2

For Persons 15 years and overAge as at last birthday (years)

1
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15

sex

Serial Number

Name of all persons who usually live in thishousehold

Relation ship to head of thehouseholdDate of Birth(year and month )

14

15

16

Usual residence
Inthecountry )1
Abroad ) 2
SerialNumber

42
43
44
45

Sri Lanka HIES 2012/13 Manual (Version 1.1)

* Main Occupation(Describe )* * Main Industry(Describe)

169

4

Codes for Section 2
For Column 5 and 6

Column 10
01

Grade
Grade
Grade
Grade
Grade
Grade
Grade
Grade

1
2
3
4
5
6
7
8

Grade

9

09

Grade

10

10

School is too far away
Financial problems
Had to help house keeping activities / Family business
Disability / Illness
Civil disturbances
Not willing to attend /poor academic progress
Incompletion of 5 years at the beginning of the school year
Other ( specify)

02
03
04
05
06
07
08

1
2
3
4
5
6
7
9

11

Grade 11
Grade 12
Grade 13
Special Education Unite

14

Not relevant

19

12Column 11

13

8 Column

Further schooling not available or too far away

1

Financial problems2

Had to help house keeping activities / Family business3

Disability / Illness

4

Walk

1

Civil disturbance5

Bicycle2

Not willing to attend / poor academic progress

6

Motor bicycle / Three Wheel / car

3

Pending results (G.C.E.(O/L) / G.C.E.(A/L) )7

School hiring Van / Bus

4

By Bus5Completed G.C.E.(A/L) / Grade 13
Other ( specify)

9

By Train

6

Other Specify )9'

Sri Lanka HIES 2012/13 Manual (Version 1.1)

8

this household(as given in col. 2 of sec. 1)

12
3

Distance from home toschool ( km ) if less than
1km Mark "0"Mode of travel to school( Main Code )Time taken to schoolfor traveling (Minutes )Go to Col. 4

Never attended school2Go to Col. 10

Attend school in the past3
Go to Col. 11

For Person = Code 1 in Col. 3

4
5
6
7
8
9

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Person =Code 3 inCol. 2
Person = Code 3 in Col.3

Which year did
you leave school(Not suitable topersons given
Code 7 in Col. 11)

Grade ( previous year)1

Why did you leaveschool(Give Code)

Grade (current year ) Currently attending school

Why did you neverattend school(Give Code)

1- Government
2 - Private
3- International

Type of school

 School Education (Code )Name of persons ( age 5-15 years ) who usually live inSerial number as given incolumn 1 of section 1

170

5

Section 2 - School Education (for persons aged 5 - 20 years)

10
11
12

171

6

Codes for Section 3

Column 4 & 6

Column 12

Treatment for illness

1

Heart Conditions / Diseases

01

Treatment for injury

2

Blood pressure

02

Medical checkup / Consultation

3

Diabetics

03

Immunization

4

Asthma04

Treatment for infectious diseases (injections etc.)

5

Epilepsy

05

Other ( Specify )9Cancer06

Stomach diseases / Gastritis

07

Diseases related to Eyes

08

Diseases related to Ear (Auditory problem )09

Arthritis

10

Column 8 & 10
Treatment for illness

1

Mental retardation

11

Treatment for injury

2

Hemorrhoids

12

Operation / Surgery

3

Catarrh

13

Child delivery

4

Severer headache

14

Treatment for infectious diseases

5

Naturally Disabled

15

An accident

6

Disabled by an accident

16

Other (Specify )9Other (Specify)99

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Name of all persons who usually
live in this household(as given in Col. 2 of Sec. 1)

1

Serial number as given in column
1 of section 1

Did you visit Private
hospital or Medical /Health Centre, Yes -1,
No -2 ---.> Go to Col. 9
For what kind of treatment( write the relevant code )Did you stay at a
Government hospital as an inpatient? Yes - 1
No - 2 ----> Go to Col. 9
Reason for staying( write the relevant code )Did you stay at a Private
hospital as an in patientYes-1,
No, 2 ---> Go to Col. 11
Reason for staying( write the relevant code )Do you suffer from Chronic illness /Disability Yes - 1,
No - 2---> Go to Sec. 04
What chronic illness / Disability do
you suffer from (write the relevant
code)
Have your job effected / caused foryour illness / Disability
Yes - 1, No - 2

How long have you
been suffering fromyour illness /Disability

3
4
5
6
7
8
9
10
11
12
13
14

01.

02.

03.

04.

05.

06.

07.

08.

09.

10.

11.

12.

13.

14.

15.

16.

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Did you have to stop doing yourusual activities because of thisillness /Disability Yes -1,
No -2 --> Goto Sec. 04
If so haw many days did you stop
your usual activities during last
month

Month

During the last 12 months in patient treatment

year

For what kind of treatment( write the relevant code )2

During last month for out patient treatment
Did you visit Government
hospital or Medical / HealthCenter Yes - 1,
No-2 ---> Go to Col. 5

1727

Section 3 - Health

15
16

173

8

Section 4 - Expenditure
4.1 Weekly Consumptions on Food & Drink
Consumed
Item

Code

1

1. Cereals
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.

2

3

0101
0102
0103
0104
0105
0106
0107
0108
0109
0110
0111
0112
0113
0114
0115
0116
0117
0118
0119
0120
0121
0122
0129

grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams
grams

0201
0202

grams

Qty.

Value
(Rs.)

4

5

*Home grown /ItemFreely received
(Rs.)
6

Code

1

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

Buns / Spanch cake
Hoppers
String Hoppers
Pittu
Roti / Parata / kotthu
Thosa / Itly
Kottu
Rice ( Meat & Vegetables )Rice (Fish / Egg / Vegetables)
Rice ( Vegetables )Meat CurryFish Curry
Vegetable Curry
Other

3.

Pulses

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

Grame
Masoor Dhal
Watana Dhal
Green gram
Gram
Red Kawpi
White Kawpi
Soya
Soya meet
Other

2

3

0204
0205
0206
0207
0208
0209
0210
0211
0212
0213
0214
0215
0216
0219

numbernumbernumbernumbernumbernumbernumbernumbernumbernumber

0301
0302
0303
0304
0305
0306
0307
0308
0309
0319

grams
grams
grams
grams
grams
grams
grams
grams
grams
grams

4.

Vegetable

1.

Ash Plantain

03

02

1.
2.

Bread - Normal
Bread - Specify

3.

Roespaan
0203
grams
2.
Brinjal*The estimated value of item which are consumed from home grown / freely received should be included in col.6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(Rs.)

* Home grown/
Freely received
(Rs.)

4

5

6

Unit

01

Rice - (white Kekulu - normal )Rice - (white Kekulu Samba)
Rice - ( Red Kekulu - normal )Rice - ( Red Kekulu Samba )Rice -Samba
Rice - Nadu Red
Rice - Nadu White
Rice - Basmathi
Rice - Other
Rice flour
Wheat flour
Kurakkan flour
Ulundu flour
Maize
Samaposha
Triposha
Noodles / Pasta
Papadum
Infant Cerial food / Malt etc.
Barly
Sago
Corn flakes
Other ( specify )2. Prepared food

Unit

Consumed

04
0401

grams

0402

grams

174

9
Consumed
Item

Code

Consumed

Value
(Rs.)

* Home grown /Freely received
(Rs.)

Item

Qty.
4

5

6

1

Unit

Code

1

2

3

2

3

3.

Ladies fingers

0403

grams

34.

4.

Better gold

0404

grams

35.

Mushroom

0433

grams

Jack immatured (polos)

0434

5.

Thuba karivila

0405

grams

36.

6.

Long beans

0406

grams

Other (Vegetable)

0439

7.

Sanke gourd

0407

grams

8.

Watakolu

0408

grams

1.

Mukunuwanna

0441

bundles9.

Cucumber

0409

grams

2.

Gotukola0442

bundles

10.

Beans

0410

grams

3.

Kankun0443

bundles

11.

Carrot

0411

grams

4.

Kathurumurunga

0444

bundles

12.

Beetroot

0412

grams

5.

Nivithi0445

bundles

13.

Cabbage

0413

grams

6.

Thampala

0446

bundles

14.

Cauliflower

0414

grams

7.

Sarana0447

bundles

15.

Tomatoes

0415

grams

8

Kohola leaves

0448

bundles

16.

Leeks

0416

grams

9.

Onion leaves

0449

grams

17.

Knolkhol

0417

grams

10.

Cabbage leaves

0450

grams

18.

Malu miris

0418

grams

11.

Other leaves

0459

19.

Dambala

0419

grams

20.

Raddish

0420

grams

21.

Drumstick

0421

grams

22.

Cucumber

0422

grams

1.

Jack & Jack seed

0501

24.

Kekiri

0423

grams

2.

Bread fruit

0502

number

25.
26.
27.

Ash pumpkin
Thalana batu
Thobbatu

0424
0425
0426

grams
grams
grams

3.
4.
5.

Potatoes
Sweet potato
Mannioc

0503
0504
0505

grams
grams
grams

28.
29.
30.
31.
32.

Kohila yams
Nelum Ala / lotus roots
Kehel muwa
Abarailla
Mango

0427
0428
0429
0430
0431

grams
grams
grams
grams
grams

6.
7.
8.
9.

KirialaInnalaPotato chips &Other Yams

0506
0507
0508
0509

grams
grams
grams
grams

33.

Casue
0432
grams
* The estimated value of item which are consumed from home grown / freely received should be included in col.6

Leafy Vegetables

5. Yam & Other

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(Rs.)

* Home grown /Freely received
(Rs.)

4

5

6

Unit

05

175

10
Consumed
Item

Code

1

2

Value
(Rs.)

* Home grown /Freely received
(Rs.)

Item

Qty.
4

5

6

1

Unit

3

Consumed
Code

2

3

0721
0722
0723

grams
grams
grams

21.
22.
23.

Linna
CopparaSaman tin

8.

Dried Fish

grams
grams

1.
2.

Sprats
Keeramin

0801
0802

grams
grams

0609

grams

Balaya
Seer

0701
0702

grams
grams

3.
4.
5.
6.
7.
8.

SalayaHurllaThalapath
Seer
Katta
Koduwa

0803
0804
0805
0806
0807
0808

grams
grams
grams
grams
grams
grams

3.

Shark

0703

grams

9.

Anjila

0809

grams

4.

Paraw

0704

grams

10.

Balaya

0810

grams

5.

Thalapath

0705

grams

11.

Shark / Keelan

0811

grams

6.

Kelawalla

0706

grams

12.

Paraw

0812

grams

7.

Mullet

0707

grams

13.

Anguluwa

0813

grams

8.
9.
10.
11.
12.
13.
14.
15.
16.
17.

Other large fish
SpratsHurullaKaralla / Katuwalla
Kumbala / Angila
Salaya / Sudaya
Other small fish
Lula
Theppli / Telapiya / Korali
Other fresh water fish

0708
0709
0710
0711
0712
0713
0714
0715
0716
0717

grams
grams
grams
grams
grams
grams
grams
grams
grams
grams

14.
15.
16.
17.

PrawnsCuttle fish
Fresh water fish
jadi

0814
0815
0816
0817

grams
grams
grams
grams

18.

Other

0819

grams

9.

Eggs

1.

Hen eggs

0901

number18.

Prawns

0718

grams

2.

Quail eggs

0902

number

19.

Crabs

0719

grams

20.

Cuttle fish

0720

grams

3.

Other eggs

0909

number

6.

Meat

06

1.
2.

Chicken
Beef

0601
0602

grams
grams

3.

Mutton

0603

grams

4.
5.

Pork
Sausages / Meat balls

0604
0605

6.

Other

7.

Fish

1.
2.

07

* The estimated value of item which are consumed from home grown / freely received should be included in col.6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(Rs.)

* Home grown /Freely received
(Rs.)

4

5

6

Unit

08

09

176

11
Consumed
Item

Code

1

10. Coconuts

2

10
1001
1002
1003

*Home grown /Freely received
(Rs.)

Item

Qty.

Value
(Rs.)

4

5

6

1

Unit

3

12.

Other food

1.
2.
3.
4.
5.
6.
7.

Sauce
Marmite / Vegimite
Soup cubes /packLime pickle
Chutney
Canned fruit
Others

Code

2

Nuts
Coconuts milk powder
Coconuts milk tin / bottle

11.

Condiments

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.

Dried chillie
1101
grams
Chilly powder
1102
grams
Red onions
1103
grams
Bombe onions
1104
grams
Garlic1105
grams
13. Milk and Milk foods 13
Moldive fish
1106
grams
1.
Pepper1107
grams
Cow milk
1301
2.
Turmeric / Turmeric powder
1108
grams
Goat milk
1302
3.
Curry powder ( sarakku )
1109
grams
Sterilized milk
1303
4.
Green chillies
1110
grams
Curd
1304
Lime
1111
grams
5.
Yoghurt / Moru
1305
6.
Cumin seeds
1112
Condensed milk
1306
7.
Fennel seeds
1113
Milk powder
1307
8.
Coriander
1114
Infant milk powder
1308
9.
Mathe seeds
1115
Butter1309
10. Margarine
Mustard1116
1310
11. Cheese
Goraka1117
1311
12. Milk packets (liquid)
Tamarind
1118
1312
Cinnamon1119
13. Other
1319
Salt
1120
Rampe / curry leaves
1121
14. Fats and Oils
14
Ginger1122
Vinegar1123
1.
Coconuts oil
1401
2.
Other
1129
Vegetable oil
1402* The estimated value of item which are consumed from home grown / freely received should be included in col.6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(Rs.)

*Home grown /Freely received
(Rs.)

4

5

6

Unit

3

12

1.
2.
3.

11

numbergrams
m.l

Consumed

1201
1202
1203
1204
1205
1206
1209

(1 bottle = 750 ml)
ml
ml
ml

ml
ml
ml
ml
ml
ml
ml

(1 bottle = 750 ml)
ml
ml

177

12
Consumed
Item

3.
4.

5.

Code

2

3

Sesame oil
Vegetable oil
Other

1403
1404
1409

ml
ml
ml

15. Sugar , Juggery & Treacle

Value
(Rs.)

* Home grown /Freely received
(Rs.)

Item

Qty.
4

5

6

1

Unit

1

15

Consumed
Code

2

3

Dried Fruits

1.
2.

Sugar
Juggery

1501
1502

grams
grams

1.
2.
3.
4.

3.

Treacle

1503

ml

5.

4.
5.

Honey
Other

1504
1509

16.

Fruit

1.

BananaDates
cause nuts
Ground nuts
Plums

1651
1652
1653
1654

grams
grams
grams
grams

Other

1659

grams

17. Confectionery & short eats

17

16
Embul

1601

number

1.

Jam

1701

2.

Anamaalu

1602

number2.

Ice -Cream

1702

3.

Koli kuttu

1603

number3.

Chocolates

1703

grams

4.

Seeni kesal

1604

number4.

Toffees1704

number

5.

Other

1605

number5.

Biscuits

1705

grams

grams

6.

Pineapple

1606

number6.

Jelly packets

1706

7.

Papaya1607

number7.

Snacks

1707

grams

8.

Mangoes1608

number

8.

Cake

1708

grams
number

9.

Apple

1609

number9.

Kawum / Kokis etc.

1709

10.

avacardo

1610

number10.

Maskette / Kaiu dodoil etc.

1710

grams

11.

Wood apple

1611

number11.

Thala bola / Rulan etc.

1711

number12.

Orange1612

number13.

King coconut / Kurumba

1613

number12.

Cutlets / Patis / Wade /pastry etc.

1712

number

14.

Guwawa

1614

grams

15.

Grapes

1615

grams

13.

Palmyrah Products

1713

number

16.

Melan

1616

grams

Others

1719

17.

14.
Others1619* The estimated value of item which are consumed from home grown / freely received should be included in col.6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(Rs.)

*Home grown /Freely received
(Rs.)

4

5

6

Unit

178

13
Consumed
Item

1

Code

2

*Home grown /Freely received
(Rs.)

Item

Qty.

Value
(Rs.)

4

5

6

1

Unit

3

18. Beverages (non alcoholic ) 18

Consumed
Code

2

3

11.

Cigars1911

numberBeedi
Pipe tobacco
Betel leaves

1912
1919
1920

numbernumber1.

Tea dust / Leaves

1801

grams

12.
13.
14.

2.

Coffee powder / seeds

1802

grams

15.

Arecanuts

1921

3.

Soft drink

1803

16.

Tobacco chewing

1922

4.

Fruit drinks / Cordial

1804

17.

Chnuam1923

5.

Saruwath

1805

number18.

Bulath vita / Saravita

1924

6.

Milk tea / Nest cafe

1806

number7.

Plant tea

1807

number8.

Milk coffee

1808

number9.

Coffee1809

number10.

Ice packets /1810

number

11.
12.

Gruel

1811

number

Water bottle

1812

number

13.

Others1819

19.

Liquor , Drugs &Tobacco

19

1.

Toddy

1901

2.

1902

3.

ArrackKasippu

4.

Beer / Stout

1904

5.

Whisky / Brandy

1905

6.

Gin

1906

7.

Wine

1907

8.

Other Liquor

1908

9.

Drugs (Ganja , Heroin )

1909

10.

Cigarettes
1910
number* The estimated value of item which are consumed from home grown / freely received should be included in col.6

1903

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(Rs.)

* Home grown /Freely received
(Rs.)

4

5

6

Unit

numbernumber

179

14
4. 2

Household expenditure on Housing , Fuel & Light, Non- durable goods , Services & Consumer durable for main Household .
* Main household

Item

1

1.

2.

2.2

Code

2

Qty.

Value
(Rs.)

** Freely
received /Received
as a gift
(Rs.)

4

5

6

Unit

3

* Main household

Housing(Average per month )Rent ( Estimated rent ofowner occupied house should
be included in col. 5 & col. 6

2001

Taxes
Water bills

Fuel & Light( Average per month)Electricity
Solar power
Kerosene oil
Fire wood (purchased )Fire wood ( free )L..P. Gas

2101
2102
2103
2104
2105
2106

Fuel & Light(last month)

Normal bulbs
Energy saving bulbs
Matches
Candles

2107
2108
2109
2110

numbernumbernumbernumberBatteries(used for lighting purposes )2111

number

20

Item

Value
(Rs.)

3

4

5

6

(last month)

22

Code

1

3.

Qty.

** Freely
received /Received
as a gift(Rs.)

2

Personal care expense

Toilet Soap / shampoo packet/
shampoo bottle / liquid soap

2201

2002

Tooth past

2202

2003

Tooth brush
Cosmetics powder
perfumes
Face cream / Lotions
Hair oil , Cream and hair dye
Lipstick / Nail polishShaving equipments
Artificial jewel
Sanitary towel
Sesum oil (Body apply)
Beauty culture service
Hair cut / Hair dressing &Shaving charges
Other

2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213

21

ml
kg
grams

4.

Health Expenses

2214
2219

(last month)

Fees to private medical
practitioners ( Included cost of
medicine )Ayurvedic consultation fees

Others2119*Main household - including all members who usually live in this household and exclude servants and boarders

Unit( Included cost of medicine )

2301
2302

** The estimated value of items which are received freely / fringe benefit / relief payment / donation/ as a gift should be included in col. 6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

23

180

15
*Main household*Main household

Item

1

2

Consultation fees to specialist

2303

Payments to medical laboratories fortest analysis. etc.

2304

Payment to private hospitals andNursing home

2305

Purchased of medical andpharmaceutical products
Spectacles
Hearing aids
Scan / C.T. , Ultra sounds
X-Ray
Others

5.

Code

Qty.

Value
(Rs.)

**Freelyreceived/
Received
as a gift
(Rs.)

4

5

6

Unit

3

Item

1

6.

2319

Transport ( last month)
Transport fees
24
Train ( other than Schooling )2401

Bus ( other than Schooling )2402

Van ( other than Schooling )Taxi car ( other than Schooling )

2403
2404

Three wheeler ( other than Schooling )Chargers for Schooling &pre Schooling childShips air lines
Others

2405
2406
2407
2409

Maintenance of private Vehicle
Petrol2411

2

DeesalOil
Other fuelTyre /Tube & Other
Service charges of spare parts
accessories
LisionInsuwarance
Other

2306
2307
2308
2309
2310

Code

Communication(last month )

2416
2417
2418
2419

25

Postal & Telegraph charges
Telephone charges (Domestic)( Average per month )Telephone charges ( Mobil )Telephone charges (Taken
from out side )E-mail / Internet charge
Other

7.

2412
2413
2414
2415

2501
2502
2503
2504
2505
2509

Education(Last month )26

Exercise books /Stationeries

2601

News papers / Magazines

2602

School text books

2603*Main household - including all members who usually live in this household and exclude servants and boarders** The estimated value of items which are received freely / fringe benefit / relief payment / donation/ as a gift should be included in col. 6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Unit

3

Qty.

Value
(Rs.)

4

5

** Freely
received/
Received
as a gift
(Rs.)
6

181

16
*Main household

Item

Code

1

8.

2

School facility fees (Government )2604

School fees (Privet )School fees (International )Tuition fees

2605
2606
2607

Boarding fees

2608

Fees for higher education (External
degree / post graduate degree,
Diploma etc.

2609

Vocational Training

2610

Fees for pre school
Fees for examination
Others

2611
2612
2619

Qty.

Value
(Rs.)

**Freelyreceived/
Received
as a gift
(Rs.)

4

5

6

Unit

3

*Main household

Item

1

27

Cinema , Drama , Video films
CD/ DVD/VCD
Books / News papers / Magazines
Lotteries / Bettings
Excursion / pligrimayes
Arts /Music / Dancing

9.

SportsPets / Aquarium
Toys
Maintenance of Radio recovers,
Television & Video deck
Sacred goods

2702
2703
2704
2705
2706
2707
2708
2709
2710

Non-durable
Household goods(Last month )

2711
2712
2719

28

Washing Soap
Washing powder
Mosquito coils
Detergents
Insecticide's( for home use )
Other

10.
2701

2

Cable / Satellite / T.V.
payments
Astrology /Other

Recreation ,entertainment& Cultural activities(Last month)

CodeHousehold Services( last month )

2801
2802
2803
2804
2805
2809

29

Laundry Charges
Grinding Charges
Wagers to Servants /Chauffeurs
Charges for Drivers

2901
2902
2903
2904

Charges for Day care Centers

2905

Charges for elders home2906

Payments for other household
services

2909*Main household - including all members who usually live in this household and exclude servants and boarders** The estimated value of items which are received freely / fringe benefit / relief payment / donation/ as a gift should be included in col. 6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Unit

3

Qty.

Value
(Rs.)

4

5

**Freelyreceived/
Received
as a gift
(Rs.)
6

182

17
*Main household
Item

Code

1

11.

Clothing & Textiles( Last 6 month )

2

Qty.

Value
(Rs.)

**Freelyreceived/
Received
as a gift
(Rs.)

4

5

6

Unit

3

*Main household
Item

Code

1

2

Materials purchased
in meters ( last 6 months)

30

Trousers ( gents )Trousers ( Ladies )ShirtsSari

For clothing
For school uniform
For cuttens
Other

3041
3042
3043
3049

numbernumbernumber

3004
3005
3006

numbernumbernumber

Tailoring charges

T-Shirts ( gents )T-Shirts ( Ladies )( Last 6 month )For clothing

3061

Sarongs / Vetties
Frocks
Skirts / Blouses
Salwars
Banians
Men 's Under wears

3007
3008
3009
3010
3011
3012

numbernumbernumbernumbernumbernumberFor School uniform

3062

Women 's Under wears /12.

3(1 m = 100 cm )

3001
3002
3003

numbernumbernumbernumberFoot wear & Other
Personal effect(Last 6 month )
31
Shoes

3101

numberSandals / Slippers

3102

numberUmbrellas
Hand bags / Traveling bags
Hats / Helmets
Rain coats
Repair charges for shoes ,Bag 's , umbrellas etc.
Others

3103
3104
3105
3106

numbernumbernumbernumber

3107

number

3109

number

3013

number

3014
3015
3016
3017
3018
3019
3020
3021
3022

numbernumbernumbernumbernumbernumbernumbernumbernumber

Pillow case3023

numberGlass ware

3201

numberMosquito nets

3024

numberPlastic ware3202

numberAluminium ware

3203

numberClay ware / pots

3204

numberpetticoats / Underskirt
Housecoat / Kimonas / Night dresses
Brassieres
Socks / Stockings
Ties
Handkerchiefs
Children 's dresses
School uniform
Towels
Bed sheet

Other

3029

number13.

Durable household
Goods ( Last 6 month )32*Main household - including all members who usually live in this household and exclude servants and boarders** The estimated value of items which are received freely / fringe benefit / relief payment / donation/ as a gift should be included in col. 6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(Rs.)

**Freelyreceived/
Received
as a gift
(Rs.)

4

5

6

Unit

183

18
*Main household
Item

Code

2

3

Cane ware
CutleryLamp / Lamp shade
Brooms / Brushes / ekels
Mats / pillows
Rugs / Carpets
Mettress
Torches
Thermos flasks
Twines / Rope
Other

3205
3206
3207
3208
3209
3210
3211
3312
3213
3214
3219

numbernumbernumbernumbernumbernumbernumbernumbernumbernumbernumber

3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313

numbernumbernumbernumbernumbernumbernumbernumbernumbernumbernumbernumbernumber

14. Durable household goods( Last 12 month )33
Chairs - Plastic
Chairs - wood
Chairs - steel

Qty.

Value
(Rs.)

**Freelyreceived /Received
as a gift
(Rs.)

4

5

6

Unit

1

*Main household
Item

Code

1
Fans
Sewing machines
Radio / CD,DVD,
Record players& Tape recorders
Television / Video decks
Washing machines
Musical Instruments
Mo. cycles , Bicycles, scooter
Three wheeler
Motor cars/ Vans
Prams/ Go carts / Tricycles
Gardening tools & equipments
Cameras & Projectors
Computers
Jewelleries

TablesSettees
Almyrahs - wood
Almyrahs - steel
Beds
Other furniture
Clocks/ wrist watches
Cookers (Kerosene )Cookers (Electric)
Cookers (Gas)
Mixing machines , Grinders,3314
numberBeaters
Irons
3315
numberRefrigerators
3316
numberOven (Gas / Electric )3317
number*Main household - including all members who usually live in this household and exclude servants and boarders

Telephones / Cellular phones
Cookers , Toasters ,Hot plates / Heaters etc.
Durable goods purchased ininstallments
other

15.

Other expenses( last month )Provident fund /W. & O. P. fundContribution to trade unions,Thriff Societies / Eldrly
Societies etc.

2

3

3318
3319
3320

numbernumbernumber3321

number

3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333

numbernumbernumbernumber3334

numbernumbernumbernumbernumbernumbernumbernumber3335
3339

number

34
3401

3402

** The estimated value of items which are received freely / fringe benefit / relief payment / donation/ as a gift should be included in col. 6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Qty.

Value
(R.s)

**Freelyreceived /Received
as a gift
(Rs.)

4

5

6

Unit

184

19

*Main household
Item

1

Code

2

Insurance / Agrahara

3403

Other savings ( including seettu )3404

Payment for debits

3405

Money lending
Income tax

3406
3407

Other

3409

Qty.

Value
(Rs.)

**Freelyreceived /Received
as a gift
(Rs.)

4

5

6

Unit

3

16. Other adhoc ( rarely)
expenses( last 12 month )35
Expenditure on weddings/funerals for family members

3501

Social activities / Ceremonies

3502

Litigation

3503

Gift, Donation, Similar transfers

3504

Maintenance & Repairing(Houses)
Purchased properties House

3505
3506

Build a new building

3507

Other

3509

.*Main household - including all members who usually live in this household and exclude servants and boarders** The estimated value of items which are received freely / fringe benefit / relief payment / donation/ as a gift should be included in col. 6

Sri Lanka HIES 2012/13 Manual (Version 1.1)

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20
4.3 Expenditure on Housing, Fuel & Light, Non-durable goods, Services & Consuer durables for boarders & domestic servants.
Are there Boarders & / or Domestic Servants in this house hold?

Yes

Recreation, Entertainments,
Education& Cultural activites etc.

Boarding fees paid

Amount sent tofamily / parents

Amount transfered as savings,
provident funds, W & O P, taxes,
insurance etc.

Miscellaneous

Clothing & Textiles

Durable household goods

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

3

4

5

6

7

8

9

10

11

12

13

14

15

1.
2.
3.
4.
5'
6'
7'
If any boarder or domestic servant given in column 1 of section 1, not spent any of the above Please note down here.

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Last
12
month

Transport & Communicaton

Rs.

Last 6month

Personal care & Health expenses

Last monthHousehold services(Last month)

Non durable household goods

2 Go to Section 5

2

Fuel & Light

1

No

Food purchased outside the household(Last month)

Name of boarders & domestic servants who has spentmoney on any of the following columns 3 - 15

Serial number as given in Column 1 ofsection 1

Last
week

1

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21

Section 5 - Income
5.1 Income from paid employments / During last 4 weeks / Last calender month
Did any of the household members (usually live) engage as an employee during last 4 weeks / last calender month?

1

Yes

No

2

 Go to Section 5.2

including boarders & domestic servants who were paid employees
during last four weeks / last calendar month

1
1.
2.
3.
4.
5.
6.
7.
8.

Column 1 of section 1

List all hosuehold members (usually live in this household)

Serial number as given in

Last calendar month
Principal / Secondary occupation

2

3

Wages /
Main

1Salaries

Last calendar 12 months

Tips,
Commissions,
Overtime pay etc.

Bonus, Arrears Payment

Secondary 2

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

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Rs.

Rs.

Rs.

4

5

6

187

22
5'2 Income from agricultural activities - (Paddy, Other seasonal crops)
Did any of the household members cultivate paddy, other seasonal crops as an employer or own account worker for sale and / or household consumption during last cultivation year?

have engaged Non- agricultural activities as
employers or own account works.

1

No

Cultivated Area

2

Paddy / Other

small cultivationseasonal crops

like home2of this household from
Value of output(Rs.)*Cost ofF orConsumption

For Sale

Quantity

Value

Quantity

Quantity

Value

(Kg)

(Rs.)

(Kg)

(Kg)

(Rs.)

11

12

13

garden(Describe)

cultivation)

3

A

R

P

4

5

6

7

Quantity

Value

(Kg)

(Rs.)

8

(Rs.)

9

10

3.
4.
5.
6.
7.
8.
9.
10.

** Last Cultivation year

Column no
Iinput

1
2
3
4
5
6
7
9

Fertilizer
Other
subsides forlastcultivation
year

production ( out put )

input

2.

Paddy
Chillies
OnionsVegetables
Cereals
Yams
TobaccoOther

Only for paddy
currently available

Consumption quantity

1.

+ Code for column 3

 Go to Section 5.3** Last cultivation year

cultivated to+ Codeincluding boarders & domestic servants who

1(Suitable to area

1 of section 1

List all household members (usually live)

Serial number as given in column

Yes

Last two cultivation seasons(Yala / Maha or Maha / Yala)*

Hired Labor
Seeds
Fertilizer
Chemicals
Transport / Tractor
Total

Sri Lanka HIES 2012/13 Manual (Version 1.1)

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23

5'3 Income from other agricultural activities
Did any of the household members engage in other agricultural activities / Livestock as an employer or own account worker for sale during last calendar 12 months?
Yes

1

No

2

 Go to Section 5.4

Last calendar month / Average per month1

Cultivated area
(Suitable to area cultivated tosmall cultivation like homegarden cultivation)

Agricultural product(Describe)

2

3+ CodeList all household members (usually live) including boarders &domestic servants who have engaged Non- agricultural activities as
employers or own account works.

Serialnumberas givenincolumn1ofsection 1

A

R

P

4

5

6

7

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Column no
Input+ Codes for column*

Hired laborTea, Rubber

01

Fish

06

Coconuts

02

Eggs

07

Seeds

Coffee, Pepper Betel etc

03

Milk

08

Fertilizer

Banana / Fruits

04

Other food items

09

Chemicals

Meat

05

Horticulture

10

Transport / Tractor charges

99

Other

Other
Not necessary for cultivated area under the codes from 05 - 10 in column 4.

Total

Sri Lanka HIES 2012/13 Manual (Version 1.1)

input

Fertilizer &Othersubsidies

(Rs.)

(Rs.)

(Rs.)

8

9

10

Value ofoutput* Cost of

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24
5. 4 Income Form Non - Agricultural activities
Did any of the household members engage in any non - agricultural as an employer or own account worker for sale during last calendar month?
1

List all household members (usually live) including boarders &domestic servants who have engaged Non - agricultral activities
as employers or own account workers.
1

Serial number
as given incolumn 1 ofsection 1

Last calendar month

Economic activity(Describe)2

 Go to Section 5.5.1

2

No

3

+ CodeYes

Value of output(Rs.)

Value of input(Rs.)

Subsidies

4

5

6

7

1'
2'
3'
4'
5'
6'
7'
8'
9'
10'
Column no+ Codes for col. no 3

Input

*
Mining & Quarrying
Manufacturing
Construction
TradeTransport
Guest house, restaurants, bars, hotels etc
Other sercices

1
2
3
4

InputsFuel & Light
Transport
Hired labor
Other services

5
6
9

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Total

(Rs.)

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25

5'5'1 Income from other cash receipt during last calendar month / last calendar 12 months
1

Yes

2

No

 Go to Section 5.5.2

List all household members (usually live)
including boards & servabts who have received
any other cash receipt.

Serial number asgiven incolumn 1

Income from last calendar month1

Pension
Payment

Disability/ reglief
payments

Rent fromproperties/ boarding
fees etc.

Samurdhi

Rs.

Rs.

Rs.

Rs.

3

4

5

6

2

Last calendar 12 month

Elderly
payment

Educational&
Scholarships

Schoolfood
program

Triposha
food
program

Other
Income

Rs.

Rs.

Rs.

Rs.

Rs.

7

8

9

10

11

Dividends/ Interests

Current remittance& transfers
Outside thecountry

Within the
country

Rs.

Rs.

Rs.

12

13

14

Foods andother
cominadations

Disaster relif
Assistona

1'
2'
3'
4'
5'
6'
7'

5'5'2

Income by chance or adhoc gains during last calendar 12 months

List all household members (usually live)
excluding boarders & servants who havereceived income by chance or rarely.

1

1

2

No

 Go to Section 6

Serial number asgiven in column 1

Yes
Loans takenfrom banks /moneylenders, etc(*including
credit cards).

Sale ofassests(Land,
house,
jewellary)Withdrawals
from savings,bank deposits,
Grativity,
Provident fund

Incomereceives frombirths, deaths,
marriages /welfaresociety, etc

Seettu /Repaymentsof loans
given

Health
medicalaids

Compensation
Insurance etc

Other lottery& otheradhoc gains

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

Rs.

2

3

4

5

6

7

8

9

10

11

12

1'
2'
3'
4'
5'
6'
7'
* If you purchase goods / Items using credts card , plase report the list

Sri Lanka HIES 2012/13 Manual (Version 1.1)

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26

Section 6A - Inventory of durable goods
Items

Code

Household Equipments
Radio / Cassette player

5001

Television

5002

V.C.D. / D.V.D.

5003

Sewing machines

5004

Washing machines

5005

Refrigerators

5006

Cookers (Gas, Kerosene, Electric)

5007

Electric fans

5008

Telephone (Domestic)

5009

Telephone (Mobile)

5010

Personal Computers

5011

Camera / Video camera

5012

For Transport purpose
Bicycles5013

Motor cycles / Scooters

5014

Three wheelers

5015

Motor cars / Vans

5016

Bus / Lorry / Tipper

5017

Agricultural Equipments
Tractors (Two wheel)

5018

Tractors (Four wheel)

5019

Sprayers

5020

Threshers

5021

Water pumps (Only for agricultural purpose)

5022
5023

Fishing Equipments
Boats

5024

Fishing nets

5025

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Have - 1
Have not - 2

192

27
Section6 B - Debts (Excluding boarders & servants)

Have any of the household members

Debited todebited to the followings

Amount to pay

Yes - 1  Go to Col. 3(Rs.)

No - 2

1

2

1. Banks (Government / Private)
2. Finance Companies / Leasing Companies
3. Own place of work
(Departments, Boards, Private Companies etc)

4. Money Lenders

5. Prepayment for credit cards
6. Retail outlets

7. Sales of assets (Land, houses, jewelries etc.)

8. Durable goods purchased in installments

9. Other (Specify)

Sri Lanka HIES 2012/13 Manual (Version 1.1)

3

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28

Section 7 - Access to Primary facilities
Section 7.1

SerialNo.

Place of facilities

Distance from yourhouse to this closest
facility(Km)

Time takenfrom yourhouse to thisclosest facility(Minutes)

SerialNo.

Place of facilities

01

Bus Halt (nearest)

09

Clinic (Maternity / Infancy)02

Pre school / Montessori

10

M. O. H. Office03

Primary School

11

M.C. / U. C. / P. S.

04

Maha Vidyalaya / M.M.V. / National school

12

Divisional Secretariat office05

Base Hospital / Teaching Hospital / District
Hospital

13

G. N. Office

06

Maternity Home

14

Post office / Sub post office07

Government Dispensary

15

Bank (Govt. / Private)08

Private Dispensary

16

Agrarian Service Center

Distance fromyour house tothis closest
facility(Km)

Section 7. 2
Yes

1.

Do you have electricity supply (main line) nearby your area

2.

Do you have telephone facilities in your area

3.

Do you have pipe borne line (main line) nearby your area

Section 7. 3
1.

(a)

(b)

Did you use ATM Cards for cash transactions?

Yes

1

No

2

 Go to (b)How much money did you withdrow via ATM Card?

Sri Lanka HIES 2012/13 Manual (Version 1.1)

NoTime takenfrom yourhouse to thisclosest facility(Minutes)

194

29

Section 8 Housing Information

1.

Type of Structure

3.

4.

Floor

Single House - Single storeyed

1

Cement1

Single House - Double storeyed2

Teraso / Tile

2

Single House - More than two storeys

3

Mud

3

Attached house / Annex

4

Wood

4

Flat
Condominium/ Luxury apartments
Twin house

5
6
7
8
9
99

Line room / Row house
Slum / Shanty
Other (Specify)

2.

(B)

(C)

Number of bed rooms
Total floor area (Sq. feet)

Sand
Concrete
Other ( Specify)
Roof

5
6
9

Tile
Asbestos

1
2

Concrete

3

Metal sheet

4

Taka ram

5

Less than 100

1

Cadjan / Palmyrah / Straw

6

100 - less than 250

2

Other (Specify)

9250 - less than 500

3500 - less than 750

4750 - less than 1000

51000 - less than 1500

6

Constructed / Purchased by an occupant

1

1500 - less than 3000

7

In herited

2

3000 & more

9

Freely received / Received as a gift

3Compensated4

Rent free (Employer / Other)5

Relief payment (Employer / Other)6

Principal materials of Construction(A) Wall5.

Tenure

Brick

1

Rent

7

Cabook

2

Lease

8

Cement block3

Encroached9

Pressed soil block4

Other (Specify)

99

Mud

5

Plank / Metal sheet
Cadjan / Palmyrah

6

Other (Specify)

97

Sri Lanka HIES 2012/13 Manual (Version 1.1)

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30

6. A. Main source of drinking water

Other

Well

No toilet to Housing unit but sharing with another unit
Public toilet
Not using toilets

Protected well within premises
Protected well outside premises
Unprotected well
Main tap line
Tap in side home
Tap with in unite / premises (main line)
Tap out side premises ( main line)

5
6
7

1
2
3

B. Type of Toilet

45
6Connected with water seal
Not Connected with water seal
Pour Flash
Pit

1
2
3
4

Other (Specify)

9

Other
Project in village

7

Tube well

8

8.

BowserRiver/ Tank / Streams

Collected by garbage truck

Rainey water

9
10
11

Dumped within premises

1
2
3

Bottled water
Other (Specify)

12
99

Process for fertilizer
Dumped / Throw away out side premises
Other (Specify)

45
9Disposal of Garbage

BurnedB. Distance to take source of drinking water
MetersWithin premises
Outside premises

9. Principal Type of Lighting
Kerosene
Electricity

1
2

Solar energy
Generator / Battery
Gas
Other (Specify)

C. was there enough water to drink, bath & wash during last year

1. Drink
2. Bath / wash

Yes
1
1

No
2
2

7.A Availability of toilet
Within unit

10.

Exclusive for the household
Sharing with another household
Outside unit

1
2

Exclusive for the household
Sharing with another household

3
4

1
2
3
4
5
9

Principal Type of cooking fuelFire wood
Gas
Kerosene
Electricity
Saw dust / Paddy husk
Other (Specify)

Sri Lanka HIES 2012/13 Manual (Version 1.1)

1
2
3
4
5
9

196

31

11. (A) Did any of the household member collect fire wood during last month?

Yes
No

1
2

 Go to Q. 12(B). Distance & place of collecting firewood
Meter
Own land1

ForestOther land23

12. (A) Was this housing unit effected by any natural disaster during last year?
Yes
No

1
2

 Go to Section 9(B) Nature of disaster
1. Flood
2. Drought
3. Earth slip
4. Attacked by wild animals
5. storm
6. Other (specify)

Yes
1
1
1
1
1
1

No
2
2
2
2
2
2

Sri Lanka HIES 2012/13 Manual (Version 1.1)

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32

Section 9 - Agricultural Holdings & Livestock
1. Does any member's of your household own any agricultural land / lands?

Yes

1

No



2

Go to Question 3

2. Land area

Cultivated
Owned(Rent, lease, joined own, and etc.)
1
A

2

R

P

A

R

P

40 Purches =1 Rude
04 Rudes = 1 A.

1. Paddy land
2. High land
3. Land area with occupied housing units

3. Livestock (Owned)

Category

1. Cattle / Buffaloes
2. Goats / Sheep
3. Swine (Pigs)
4. Poultry
9. OtherYes - 1
No - 2*Number oflivestock (code)

1

2


(1)

5 or less than 5(2) 6 to 10(3) 11 to 50(4) More than 50

Sri Lanka HIES 2012/13 Manual (Version 1.1)

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33

Administration Data
Interviewers Name

First

Second

Third

Forth

1. Date2. Questions completed section

3. Time taken to fill the section

Minutes

Minutes

4. Problem, Ideas and Suggestion

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Minutes

Minutes

199

34

Sri Lanka HIES 2012/13 Manual (Version 1.1)

HIES-2012_13DD_With_Exp_Inc_Wgt.lst

7/07/2015
HIES2012-13
HIES2012_DICT200

G:\04072015from pen\HIES2012_13 - to be uploaded - Copy\HIES-2012_13DD_With_Exp_Inc_Wgt.dcf
Last Modified: 7/07/2015 1:56:04 PM-------------------------------------------------------------------------------------------------------------Level Label
Level Name
Type
Rec
Record Label
Record Name
Value Req Max Len-------------------------------------------------------------------------------------------------------------HIES2012_13 Questionnaire
HIES2012_QUEST
Section - 1 - Demography
SEC_1_DEMOGRAPHIC
01
No
40
50
Section - 2 - School Education
SEC_2_SCHOOL_EDUCATION
02
No
40
42
Section - 3 - Health
SEC_3_HEALTH
03
No
40
43
Section - 4.1 - Food expenditure
SEC_4_1_FOOD_EXP
04
No 24046
Section - 4.2 - Non food
SEC_4_2_NONFOOD
05
No 17546
Section - 4.3 - Is Boaders Servent
SEC_4_3_IS_BOADERS
06
No
1
23
Section - 4.3 - Boaders Servent
SEC_4_3_BOARDERS
07
No
40 102
Section - 5.1 - Is employment Income
SEC_5_1_IS_EMP_INCOME
08
No
1
23
Section - 5.1 - Employment Income
SEC_5_1_EMP_INCOME
09
No
40
45
Section - 5.2 - Is aggricultural Income
SEC_5_2_IS_AGRI_INCOME
10
No
1
23
Section - 5.2 - Aggricultural Income
SEC_5_2_AGRI_INCOME
11
No
40
99
Section - 5.3 - Is other aggricultural income
SEC_5_3_IS_OTHER_AGRI_INCOME
12
No
1
23
Section - 5.3 - Other aggricultural income
SEC_5_3_OTHER_AGRI_INCOME
13
No
40
59
Section - 5.4 - Is non_agri income
SEC_5_4_IS_NON_AGRI_INCOME
14
No
1
23
Section - 5.4 - Non agri income
SEC_5_4_NON_AGRI_INCOME
15
No
40
52
section - 5.5.1 - Is other income
SEC_5_5_1_IS_OTHER_INCOME
16
No
1
23
Section - 5.5.1 - Other income
SEC_5_5_1_OTHER_INCOME
17
No
40
97
Section - 5.5.2 - Is windfall income
SEC_5_5_2_IS_WINDFALL_INCOME
18
No
1
23
Section - 5.5.2 - Windfall income
SEC_5_5_2_WINDFALL_INCOME
19
No
40 104
Section - 6A - Durable goods
SEC_6A_DURABLE_GOODS
20
No
1
47
Section - 6B - Debtness
SEC_6_B_DEBTNESS
21
No
1
94
Section - 7 - Basic facilities
SEC_7_BASIC_FACILITIES
22
No
1 113
Section - 8 - Housing
SEC_8_HOUSING
23
No
1
58
Section - 9 - Land and animal
SEC_9_LAND_ANIMAL
24
No
1
69
Household Income and Expenditure
HOUSEHOLD_INC_AND_EXPEN
25
No
1
47

Sri Lanka HIES 2012/13 Manual (Version 1.1)
- 1 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst

7/07/2015

Level: HIES2012_13 Questionnaire
Record: Section - 1 - Demography-------------------------------------------------------------------------------------------------------------201
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S1-1Person
PERSON_SERIAL_NO
23
2
N
I
1
0
No Yes
1:45(na)
S1-3Relationship
RELATIONSHIP
25
1
N
I
1
0
No Yes
1 Head2 Spouse
3 Son/Daughter
4 Parents
5 Other Ralative
6 Domestic Servent
7 Boarders
9 Other(m)
S1-4Sex
SEX
26
1
N
I
1
0
No Yes
1 Male2 Female(na)
S1-5Birth Year
BIRTH_YEAR
27
2
N
I
1
0
No Yes
0:99(na)
S1-5Birth Month
BIRTH_MONTH
29
2
N
I
1
0
No Yes
1:12(na)
S1-6Age
AGE
31
2
N
I
1
0
No Yes
0:99(na)
S1-7Race
ETHNICITY
33
1
N
I
1
0
No Yes1 Sinhala
2 Sri Lanka Tamil
3 Indian Tamil
4 Sri Lanka Moors
5 Malay
6 Burger
9 Other(na)
S1-8Reliqion
RELIGION
34
1
N
I
1
0
No YesSri Lanka HIES 2012/13 Manual (Version 1.1)
1 Buddhist- 2 -HIES-2012_13DD_With_Exp_Inc_Wgt.lst
2
3
4
9

Hindu
Islam
Roman Ctholic/ChristianOther

7/07/2015
202(na)
S1-9Marital
MARITAL_STATUS
1 Never Married
2 Married
3 Widowed
4 Divorced
5 Separated(na)
S1-10Current Education
CURR_EDUC
1 Pre school
2 School
3 University
4 Other educational institution
5 Vocational/ Technical institution
6 Pending results G.C.E. (O.L/A.L)
9 Does not attend(na)
S1-11education
EDUCATION
0
Studingin Grade 1
1
Passed Grade 1
2
Passeed Grade2
3
Passed Grade 3
4
Passed Grade 4
5
Passed Grade 5
6
Passed Grade 6
7
Passed Grade 78Passed Grade 89Passed Grade 910 Passed Grade 10
11 Passe G.C .E (O/L) or Equivalent
12 Passed Grade 12
13 Passed G.C.E.(A/L) or Equivalent
14 Passed GAQ/GSQ
15 Passed Degree
16 Passed Post Graduate Dgree/Diploma
17 Doctorate
18 Special Education
19 No Schooling(na)
S1-12Is Active
IS_ACTIVE
1:2(na)
S1-13Main Activity
MAIN_ACTIVITY
1 Seeking Job
2 Student
3 Household work
4 Unable
9 Other(na)
S1-14Main occupation
MAIN_OCCUPATION
0:9999(m)(na)
S1-15Industry
INDUSTRY
0:9999(m)(na)
S1-16Emp status
EMPLOYMENT_STATUS
1 Government employee
2 Semi government employee
3 Private sector employee
4 Employer
5 Own account worker
6 Unpaid family worker
(m)

35

1

N

I

1

0

No

Yes

36

1

N

I

1

0

No

Yes

37

2

N

I

1

0

No

Yes

39

1

N

I

1

0

No

Yes

40

1

N

I

1

0

No

Yes

41

4

N

I

1

0

No

Yes

45

5

N

I

1

0

No

Yes

50

1

N

I

1

0

No

Yes

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Level: HIES2012_13 Questionnaire
Record: Section - 2 - School Education-------------------------------------------------------------------------------------------------------------203
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S2-2 Sr. No
R2_PERSON_SERIAL
23
2
N
I
1
0
No Yes
1:40(na)
S2-3 School Education
R2_SCHOOL_EDUCATION
25
1
N
I
1
0
No Yes1 Currently attending school
2 Never attend school
3 Attend school in the past(na)
S2-4Type of School
TYPE_OF_SCHOOL
26
1
N
I
1
0
No Yes
1 Government
2 Private
3 International(na)
S2-5CurrentGrade
GRADE_THIS_YEAR
27
2
N
I
1
0
No Yes1
Grade 1
2
Grade 2
3
Grade 3
4
Grade 4
5
Grade 5
6
Grade 6
7
Grade 7
8
Grade 89Grade 910 Grade 10
11 Grade 11
12 Grade 12
13 Grade 13
14 Special Education
19 Not relevant(na)
S2-6GradePreviouse
GRADE_LAST_YEAR
29
2
N
I
1
0
No Yes1
Grade 1
2
Grade 2
3
Grade 3
4
Grade 4
5
Grade 5
Sri Lanka HIES 2012/13 Manual (Version 1.1)
6
Grade 6- 4 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst
7
8
9
10
11
12
13
14
19

Grade 7
Grade 8Grade 9Grade 10
Grade 11
Grade 12
Grade 13
Special Education
Not relevant

7/07/2015
204(na)
S2-7 DistanceToSchool
DISTANCE
0:99
DistanceToSchool(na)
S2-8Transport
TRANSPORT_MEDIUM
1 Walk
2 Bicycle
3 Motor bicycle / Three-wheeler/Car
4 School hiring Van/ Bus
5 By Bus
6 By Train
9 Other (Specify)(na)
S2-9TimeTo School
TIME_TO_SCHOOL
0:120 TimeTo School(na)
S2-10WhyNeverSchooling
NOSCHOOLING_REASON
1 School too far away
2 Financial problems
3 Had to help house keeping activities /Family Business4 Disability / Illness
5 Civil disturbance
6 Not willing to attend / Poor academic progress
7 Not Completed Age
9 Other (specify)(na)
S2-11 ReasonsNotGoingSchool
REASON_NOT_GOING
1 Further schooling not available or too far away
2 Finalcial Problems
3 Had to help house keeping activities / Family business4 Disability / Illness
5 Not willing to attend / Poor academic progress
6 Pending results ( G.C.E. (O/L) / G.C.E. (A/L)
7 Complete G.C.E.(A/L)
9 Other (Specify)(na)S2-12WhenStopSchool
WHEN_STOP_SCHOOLING
0:9999 WhenStopSchool
(na)

31

2

N

I

1

0

No

Yes

33

1

N

I

1

0

No

Yes

34

3

N

I

1

0

No

Yes

37

1

N

I

1

0

No

Yes

38

1

N

I

1

0

No

Yes

39

4

N

I

1

0

No

Yes

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Level: HIES2012_13 Questionnaire
Record: Section - 3 - Health-------------------------------------------------------------------------------------------------------------205
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S3-2 Person Sr.
R3_PERSON_SERIAL2
23
2
N
I
1
0
No Yes
1:40(na)
S3-3 Attend Govt.hospital
ATTEND_GOVT_HOSPITAL3
25
1
N
I
1
0
No Yes
1:2
Did attend hospital(na)
S3-4 Reason for attending
REASON_HOSPITAL4
26
1
N
I
1
0
No Yes1 Treatment for illness
2 Treatment for injury
3 Medical checkup/ Consultation
4 Immunization
5 Treatment for infectious diseases(Injection etc)
9 Other (specify)(na)S3-5 Attend Private Hospital
ATTEND_PRIVATE_HOSPITAL
27
1
N
I
1
0
No Yes
1:2(na)
S3-6 Reason for what
REASON_FOR_WHAT6
28
1
N
I
1
0
No Yes1 Treatment for illness
2 Treatment for injury
3 Medical checkup/ Consultation
4 Immunization
5 Treatment for infectious diseases(Injection etc)
9 Other (specify)(na)S3-7 Stay in Govt. hospital
IS_STAY_GOVHOSPITAL7
29
1
N
I
1
0
No Yes
1:2(na)
S3-8 Reason for hospitalise
REASON_STAY8
30
1
N
I
1
0
No Yes1 Treatment for illness
2 Treatment for injury
3 Operation / Surgey
4 Child delivery
5 Treatment for infectious diseases
6 An accident
9 Other (specify)
Sri Lanka HIES 2012/13 Manual (Version 1.1)
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- 6 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst
S3-9 Stay in Privte Hospital
1:2(na)
S3-10 Reason for Stay
1 Treatment for illness
2 Treatment for injury
3 Operation / Surgey
4 Child delivery
5 Treatment for infectious diseases
6 An accident
9 Other (specify)(na)
S3-11 Is ill or dissable
1:2(na)
S3-12 Illnee or dissability
1Heart Conditions /Diseases
2
Blood pressure
3
Diabetics
4
Asthma5
Epilepsy
6
Cancer7
Stomach diseases / Gastritis
8
Diseases related to Eyes
9
Diseases related to ear
10 Arthritis
11 Mental retardation
12 Haemorrhoids
13 Catarrh
14 Severe headache
15 Naturally Disabled
16 Disabled by an accident
99 Other(na)
S3-13 Is employmet caused
1:2(na)
S3-14 Duration of illness disa
0:99
Duration of illness disa(na)S3-14 Months duration
0:12 Months duration(na)
S3-15 Did absent for normal
1:2(na)
S3-16 Duration absent days
0:99 Duration absent days(na)

7/07/2015
STAY_IN_PRIVTE_HOSPITAL
206

31

1

N

I

1

0

No

Yes

REASON_FOR_STAY10

32

1

N

I

1

0

No

Yes

IS_ILL_DISABLE11

33

1

N

I

1

0

No

Yes

WHAT_ILL_DISABLE12

34

2

N

I

1

0

No

Yes

IS_EMPL_REASON13

36

1

N

I

1

0

No

Yes

DURATION_YEARS14

37

2

N

I

1

0

No

Yes

DURATION_MONTHS14

39

2

N

I

1

0

No

Yes

IS_ABSENT_ACT15

41

1

N

I

1

0

No

Yes

DAYS_ABSENT16

42

2

N

I

1

0

No

Yes

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Level: HIES2012_13 Questionnaire
Record: Section - 4.1 - Food expenditure-------------------------------------------------------------------------------------------------------------207
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S41-2. Code
CODE
23
4
N
I
1
0
No Yes101:1924
S41-4. Quantity
QUANTITY
27
6
N
I
1
0
No Yes0:999999(na)
S41-5. Value
VALUE
33
7
N
I
1
0
No Yes0:9999999(na)
S41-6. Inkind value
INKIND_VALUE
40
7
N
I
1
0
No Yes0:9999999(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 4.2 - Non food-------------------------------------------------------------------------------------------------------------208
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S42-2. CodeNF_CODE
23
4
N
I
1
0
No Yes2001:3509(na)
S42-4. Quantity
NF_QUANTITY
27
6
N
I
1
0
No Yes0:999999(na)
S42-5. ValueNF_VALUE
33
7
N
I
1
0
No Yes0:9999999(na)
S42-6. Inkind value
NF_INKIND_VALUE
40
7
N
I
1
0
No Yes0:9999999(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 4.3 - Is Boaders Servent-------------------------------------------------------------------------------------------------------------209
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S43-Is Boarders and Domestic Servents
IS_BOARDERS_SERVENTS
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 4.3 - Boaders Servent-------------------------------------------------------------------------------------------------------------210
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S43-2 Person
COL_2
23
2
N
I
1
0
No Yes
1:40 SerNo(na)
S43-3 Food
COL_3
25
6
N
I
1
0
No Yes2:70000(na)
S43-4 Fuel and lite
COL_4
31
6
N
I
1
0
No Yes2:999999(na)
S43-5 Non durable goods
COL_5
37
6
N
I
1
0
No Yes
10:999999(na)
S43-6 HH services
COL_6
43
6
N
I
1
0
No Yes
10:999999(na)
S43-7 Personal effects
COL_7
49
6
N
I
1
0
No Yes
10:999999(na)
S43-8 Transport
COL_8
55
6
N
I
1
0
No Yes
10:999999(na)
S43-9 Ent., Edu and cult
COL_9
61
6
N
I
1
0
No Yes
10:999999(na)
S43-10 Bording fees
COL_1067
6NI
1
0No Yes
10:999999(na)
S43-11 Send to family
COL_1173
6NI
10
No Yes100:999999(na)
S43-12 Savings etc
COL_1279
6NI
10
No Yes50:999999(na)
S43-13 Other Expensus
COL_1385
6NI
10
No Yes
10:999999(na)
Sri
Lanka HIES 2012/13 Manual (Version
S43-14 Clothing
COL_1491 1.1) 6NI
10No Yes- 11 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst
10:999999(na)
S43-15 Durable goods
10:999999(na)

7/07/2015
211
COL_1597

Sri Lanka HIES 2012/13 Manual (Version 1.1)
- 12 -

6

N

I

1

0

No

Yes

HIES-2012_13DD_With_Exp_Inc_Wgt.lst

7/07/2015

Level: HIES2012_13 Questionnaire
Record: Section - 5.1 - Is employment Income-------------------------------------------------------------------------------------------------------------212
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S51-Is employment income
IS_EMPLOYMENT_INCOME
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 5.1 - Employment Income-------------------------------------------------------------------------------------------------------------213
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S51-2Scr.No
SERIAL_NO_SEC_1
23
2
N
I
1
0
No Yes
1:40(na)
S51-3Principal/Second
PRI_SEC
25
1
N
I
1
0
No Yes1
Main2
Secondary
1:2(na)
S51-4 WagesSalary
WAGES_SALARIES
26
6
N
I
1
0
No Yes
50:300000(na)
S51-5 Allowences
ALLOWENCES
32
7
N
I
1
0
No Yes100:100000(na)
S51-6 Bonus
BONUS
39
7
N
I
1
0
No Yes
10:250000(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 5.2 - Is aggricultural Income-------------------------------------------------------------------------------------------------------------214
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S52-Is agricultural income
IS_AGRICULTURAL_INCOME
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 5.2 - Aggricultural Income-------------------------------------------------------------------------------------------------------------215
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S52-2Person
COL_2X
23
2
NI
1
0
No Yes
1:40(na)
S52-4Code
COL_4X
25
1
N
I
1
0
No Yes1 Paddy
2 Chillies
3 Onions
4 Vegetables
5 Cereals
6 Yams
7 Tobacco
9 Other(na)
S52-5Acres
COL_5X
26
3
NI
1
0
No Yes0:25(na)
S52-6Roots
COL_6X
29
1
N
I
10
No Yes0:3(na)
S52-7Perchs
COL_7X
30
2
NI
1
0
No Yes0:39(na)
S52-8Quantity
COL_8X
32
8NI
1
0
No Yes0:99999999(na)
S52-8Output value
COL_8X1
40
9
N
I
1
0
No Yes50:1000000(na)
S52-9Expenses
COL_9X
49
9N
I
1
0
No Yes0:999999999(na)
S52-10Consumtion_quantity
COL_10X
58
8
N
I
1
0
No Yes0:99999999(na)
S52-10Consumtion_Value
COL_10X1
66
9
N
I
1
0
No Yes0:999999999
Sri Lanka HIES 2012/13 Manual (Version 1.1)
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HIES-2012_13DD_With_Exp_Inc_Wgt.lst
S52-11Eating
0:99999999(na)
S52-12To sell
0:99999999(na)
S52-13Fertilizer
0:999999999(na)

7/07/2015
COL_11X

75

8

N

I

1

0

No

Yes

COL_12X

83

8

N

I

1

0

No

Yes

COL_13X

91

9

N

I

1

0

No

Yes

216

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Level: HIES2012_13 Questionnaire
Record: Section - 5.3 - Is other aggricultural income-------------------------------------------------------------------------------------------------------------217
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S53-Is other agrri income
IS_OTHER_AGRRI_INCOME
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 5.3 - Other aggricultural income-------------------------------------------------------------------------------------------------------------218
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S53-2Person
SER_NO_SEC_5_3
23
2
N
I
1
0
No Yes
1:40(na)
S53-4Other crop code
SEASONAL_CROP
25
2
N
I
1
0
No Yes1
Tea , Rubber
2
Coconuts
3
Coffee, Pepper Betel etc
4Banana / Fruits
5
Meat
6
Fish
7
Eggs
8
Milk
9
Other food items
10 Horticulture
99 Other(na)
S53-5Acrs
ACRES_5_3
27
3
N
I
1
0
No Yes0:25(na)
S53-6RootsROOTS_5_3
30
1
N
I
1
0
No Yes0:3(na)
S53-7Perchs
PERCHS_5_3
31
2
N
I
1
0
No Yes0:39(na)
S53-8Output value
OUTPUT_5_3
33
9
N
I
1
0
No Yes50:1000000(na)
S53-9Input value
INPUT_5_3
42
9
N
I
1
0
No Yes50:1000000(na)
S53-10Fertilizer
FERTILIZES
51
9
N
I
1
0
No Yes50:1000000(na)
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Level: HIES2012_13 Questionnaire
Record: Section - 5.4 - Is non_agri income-------------------------------------------------------------------------------------------------------------219
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S54-Is non agri income
IS_NON_AGRI_INCOME
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)

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HIES-2012_13DD_With_Exp_Inc_Wgt.lst

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Level: HIES2012_13 Questionnaire
Record: Section - 5.4 - Non agri income-------------------------------------------------------------------------------------------------------------220
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S54-2 Person
SERIAL_5_4
23
2
N
I
1
0
No Yes
1:40(na)
S54-4 Non agri code
NON_AGRI
25
1
N
I
1
0
No Yes1 Mining & Quarrying.
2 Manufacturing
3 Construction
4 Trade5 Transport
6 Guest house, restaurants, bars/hotels etc
9 Other services(na)S54-5 Output value
OUTPUT_5_4
26
9
N
I
1
0
No Yes
50:500000(na)
S54-6 Input value
INPUT_5_4
35
9
N
I
1
0
No Yes
50:500000(na)
S54-7subsidies
SUBSIDIES
44
9
N
I
1
0
No Yes0:999999999(na)

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Level: HIES2012_13 Questionnaire
Record: section - 5.5.1 - Is other income-------------------------------------------------------------------------------------------------------------221
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S551-Is Other Income
IS_OTHER_INCOME
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)

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HIES-2012_13DD_With_Exp_Inc_Wgt.lst

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Level: HIES2012_13 Questionnaire
Record: Section - 5.5.1 - Other income-------------------------------------------------------------------------------------------------------------222
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S551-2Person
SERIAL_5_5_1
23
2
N
I
1
0
No Yes
1:40(na)
S551-3Pention
PENSION
25
6
N
I
1
0
No Yes100:60000(na)
S551-4Disability
DISABILITY_AND_RELIEF
31
5
N
I
1
0
No Yes
50:7000(na)
S551-5Rent
PROPERTY_RENTS
36
7
N
I
1
0
No Yes50:250000(na)
S551-6Samurdhi
SAMURDHI
43
5
N
I
1
0
No Yes50:99999(na)
S551-7Profits
DIVIDENDS
48
7
N
I
1
0
No Yes
10:50000(na)
S551-8Elder Salary
ELDER
55
5
N
I
1
0
No Yes
10:50000(na)
S551-9Sch.ship
SCHOLAR
60
7
N
I
1
0
No Yes100:700000(na)
S551-10School Lunch
SC_LUNCH
67
5
N
I
1
0
No Yes50:99999(na)
S551-11Threeposha
THREEPOSHA
72
5
N
I
1
0
No Yes0:99999(na)
S551-12Other Income
OTHER_INCOME
77
7
N
I
1
0
No Yes0:9999999(na)
S551-13Income_forign
INCOME_FORIGN
84
7
N
I
1
0
No Yes0:9999999(na)
Sri
Lanka HIES 2012/13 Manual (Version
S551-14Income_Local
INCOME_LOCAL
91 1.1) 7NI
10No Yes- 23 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst

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0:9999999
(na)

223

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HIES-2012_13DD_With_Exp_Inc_Wgt.lst

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Level: HIES2012_13 Questionnaire
Record: Section - 5.5.2 - Is windfall income-------------------------------------------------------------------------------------------------------------224
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S552-Is windfall income
IS_WINDFALL_INCOME
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)

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Level: HIES2012_13 Questionnaire
Record: Section - 5.5.2 - Windfall income-------------------------------------------------------------------------------------------------------------225
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S552-2Person
PERSON_5_5_2
23
2
N
I
1
0
No Yes
1:40(na)
S552-3Loans/Credit Cards
LOANS
25
8
N
I
1
0
No Yes100:1000000(na)
S552-4Selling pawning
PAWNING_SELLING
33
8
N
I
1
0
No Yes500:500000(na)
S552-5Deposits
DEPOSITS_PENSIONS_EPF
41
8
N
I
1
0
No Yes100:600000(na)
S552-6WelfareSocity
WELFARE_SOCITY
49
8
N
I
1
0
No Yes0:99999999(na)
S552-7Seettu
SEETTU_DEBITS
57
8
N
I
1
0
No Yes0:99999999(na)
S552-8Medical
MEDICAL
65
8
N
I
1
0
No Yes0:99999999(na)
S552-9Insuarance
INSUARANCE
73
8
N
I
1
0
No Yes0:99999999(na)
S552-10Lottery
LOTTERY
81
8
N
I
1
0
No Yes0:99999999(na)
S552-11FoodAllowence
FOODALLOWENCE
89
8
N
I
1
0
No Yes0:99999999(na)
S552-12Diaster
DIASTER
97
8
N
I
1
0
No Yes0:99999999(na)

Sri Lanka HIES 2012/13 Manual (Version 1.1)
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Level: HIES2012_13 Questionnaire
Record: Section - 6A - Durable goods-------------------------------------------------------------------------------------------------------------226
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S6A-Radio
RADIO
23
1
N
I
1
0
No Yes
1:2 Radio / Cassette player(na)
S6A-TV
TV
24
1
N
I
1
0
No Yes
1:2 Television(na)
S6A-VCD
VCD
25
1
N
I
1
0
No Yes
1:2 V.C.D. / D.V.D.
(na)
S6A-Sewing Mechine
SEWING_MECHINE
26
1
N
I
1
0
No Yes
1:2 Sewing machines(na)
S6A-Washing mechine
WASHING_MECHINE
27
1
N
I
1
0
No Yes
1:2 Washing machines(na)
S6A-Fridge
FRIDGE28
1NI
10No Yes
1:2 Refrigerators(na)
S6A-Cookers
COOKERT
29
1
N
I
1
0
No Yes
1:2 Cookers ( Gas, Kerosene, Electric)(na)
S6A-Electric Fans
ELECTRIC_FANS
30
1
N
I
1
0
No Yes
1:2 Electric fans(na)
S6A-Telephone
TELEPHONE
31
1
N
I
1
0
No Yes
1:2 Telephone (Domestic)(na)
S6A-Telephone Mobile
TELEPHONE_MOBILE
32
1
N
I
1
0
No Yes
1:2 Telephone (mobile)(na)
S6A-Computers
COMPUTERS
33
1
N
I
1
0
No Yes
1:2 Personal Computers(na)
S6A-Camera
CAMERA34
1
N
I
10
No Yes
1:2 Camera(na)
Sri
Lanka HIES 2012/13 Manual (Version
S6A-Bicycle
BICYCLE
35 1.1) 1NI
10No Yes- 27 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst
1:2 Bicycles(na)
S6A-Motor Bicycle
1:2 Motor cycles / Scooters(na)
S6A-Three wheeler
1:2 Three wheelers(na)
S6A-Motor car van
1:2 Motor cars / Vans(na)
S6A-Bus lorry
1:2 Bus / Lorry(na)
S6A-Tractor 2 wheel
1:2 Tractors (Two wheel)(na)
S6A-Tractor 4 wheel
1:2 Tractors (Four wheel)(na)
S6A-Pesticider
1:2 Sprayers(na)
S6A-Threshers
1:2 Threshers(na)
S6A-Water Pumps
1:2 Water pumps(na)
S6A-Mechine
1:2(na)
S6A-Boats
1:2 Boats(na)
S6A-Fishing nets
1:2 Fishing nets
(na)

7/07/2015
227
MOTOR_BICYCLE

36

1

N

I

1

0

No

Yes

THREE_WHEELER

37

1

N

I

1

0

No

Yes

MOTOR_CAR_VAN

38

1

N

I

1

0

No

Yes

BUS_LORRY

39

1

N

I

1

0

No

Yes

TRACTOR_2_WHEEL

40

1

N

I

1

0

No

Yes

TRACTOR_4_WHEEL

41

1

N

I

1

0

No

Yes

PESTICIDER

42

1

N

I

1

0

No

Yes

THRESHERS

43

1

N

I

1

0

No

Yes

WATERPUMPS

44

1

N

I

1

0

No

Yes

MECHINE

45

1

N

I

1

0

No

Yes

BOATS

46

1

N

I

1

0

No

Yes

FISHING_NETS

47

1

N

I

1

0

No

Yes

Sri Lanka HIES 2012/13 Manual (Version 1.1)
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Level: HIES2012_13 Questionnaire
Record: Section - 6B - Debtness-------------------------------------------------------------------------------------------------------------228
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S6B-Banks
BANKS
23
1
N
I
1
0
No Yes
1:2 Banks(na)
S6B-Bank amount
BANK_AMOUNT
24
7
N
I
1
0
No Yes100:1000000 Bank Amount(na)
S6B-Finance
FINANCE
31
1
N
I
1
0
No Yes
1:2 Finance(na)
S6B-Finance amount
FINANCE_AMOUNT
32
7
N
I
1
0
No Yes100:1000000 Finance Amount(na)
S6B-Employer
EMPLOYER
39
1
N
I
1
0
No Yes
1:2 Own Place of work(na)
S6B-Employer amountEMPLOYER_AMOUNT
40
7
N
I
1
0
No Yes100:300000 Employer amount(na)
S6B-Lender
LENDER
47
1
N
I
10
No Yes
1:2 Money Lenders(na)
S6B-Lender amount
LENDER_AMOUNT
48
7
N
I
1
0
No Yes100:100000 Money Lenders Amount(na)
S6B-Credit Cards
CREDIT_CARDS
55
1
N
I
1
0
No Yes
1:2(na)
S6B-Credit Cards Amount
CREDIT_CARDS_AMOUNT
56
7
N
I
1
0
No Yes100:1000000(na)
S6B-Retail shops
RETAIL_SHOPS
63
1
N
I
1
0
No Yes
1:2 Retail outlets(na)
S6B-Retail shop amount
RETAIL_SHOP_AMOUNT
64
7
N
I
1
0
No Yes100:9999999 Retail outlets Amount(na)
Sri
Lanka HIES 2012/13 Manual (Version
S6B-Pawning
PAWNING
71 1.1) 1NI
10No Yes- 29 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst

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1:2 Pawning(na)
229
S6B-Pawning amountPAWNING_AMOUNT
100:100000 Pawning Amount(na)
S6B-Instalment goods
INSTALMENT_GOODS
1:2 Durable goods purchased in instalments(na)
S6B-Instalement amount
INSTALEMENT_AMOUNT
100:100000 Instalement amount(na)
S6B-Other
OTHER_DEBTS
1:2 Other(na)
S6B-other amount
OTHER_AMOUNT
100:100000 Other mount
(na)

72

7

N

I

1

0

No

Yes

79

1

N

I

1

0

No

Yes

80

7

N

I

1

0

No

Yes

87

1

N

I

1

0

No

Yes

88

7

N

I

1

0

No

Yes

Sri Lanka HIES 2012/13 Manual (Version 1.1)
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HIES-2012_13DD_With_Exp_Inc_Wgt.lst

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Level: HIES2012_13 Questionnaire
Record: Section - 7 - Basic facilities-------------------------------------------------------------------------------------------------------------230
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S7-Bus halt
BUS_HALT
23
2
N
I
1
0
No Yes0:99 Bus Halt
(na)
S7-Bus halt time
BUS_HALT_TIME
25
3
N
I
1
0
No Yes1:120 BHTime(na)
S7-Pre school
PRE_SCHOOL
28
2
N
I
1
0
No Yes0:99 Pre School(na)
S7-Pre school time
PRE_SCHOOL_TIME
30
3
N
I
1
0
No Yes1:120 Pre SchTime(na)
S7-Primery school
PRIMERY_SCHOOL
33
2
N
I
1
0
No Yes0:99 Primary School(na)
S7-Primery school time
PRIMERY_SCHOOL_TIME
35
3
N
I
1
0
No Yes1:120 Primary SchoolTime(na)
S7-Secondery school
SECONDERY_SCHOOL
38
2
N
I
1
0
No Yes0:99 Secondery School(na)
S7-Sec school time
SEC_SCHOOL_TIME
40
3
N
I
1
0
No Yes1:120 SeconderySchoolTime(na)
S7-Hospital
HOSPITAL
43
2
N
I
1
0
No Yes0:99 Base Hospital(na)
S7-Hospital time
HOSPITAL_TIME
45
3
N
I
1
0
No Yes1:120 Base Hospital Time(na)
S7-Matrenity home
MATRENITY_HOME
48
2
N
I
1
0
No Yes0:99 Maternity Home(na)
S7-Maternity home time
MATERNITY_HOME_TIME
50
3
N
I
1
0
No Yes1:120 Maternity HomeTime(na)
Sri
Lanka HIES 2012/13 Manual (Version
S7-Gov dispensary
GOV_DISPENSARZ
53 1.1) 2NI
10No Yes- 31 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst
0:99 Government Dispensary(na)
S7-Gov dispensary time
1:120 Government DispensaryTime(na)
S7-Private dispensary
0:99 Private Dispensary(na)S7-Private dispensary time
1:120 Private DispensaryTime(na)
S7-Maternity clinic
0:99 MatenityClinic(na)
S7-Maternity clinic time
1:120 MaternityclinicTime(na)
S7-DMO
0:99 DMO(na)
S7-DMO time
1:120 DMOTime(na)
S7-MCUCPC
0:99 MCUCPC(na)
S7-MCUCPC time
1:120 MCUCPCTime(na)
S7-DS office
0:99 DS Office(na)
S7-DS office time
1:120 DSOfficeTime(na)
S7-GN office
0:99 GNOffice(na)
S7-GN office time
1:120 GNOfficeTime(na)
S7-Post office
0:99 Post office(na)
S7-Post office time
1:120 PostOfficeTime(na)
S7-Bank
0:99 Bank(na)
S7-Bank time
1:120 BankTime(na)
S7-Agri office
0:99 AgriOffice(na)
S7-Agri office time
1:120 AgriOfficeTime(na)
S7-Is power lines near
1 Yes
2 No
(na)
S7-Is tel lines near
1 Yes
2 No
(na)
S7-Is water service near
1 Yes
2 No
(na)
S7-ATM Card
1 Yes
2 No
(na)
S7-ATMCardAmount
0:9999999(na)

7/07/2015
231
GOV_DISPENSARY_TIME

55

3

N

I

1

0

No

Yes

PRIVATE_DISPENSARY

58

2

N

I

1

0

No

Yes

PRIVATE_DISPENSARY_TIME

60

3

N

I

1

0

No

Yes

MATERNITY_CLINIC

63

2

N

I

1

0

No

Yes

MATERNITY_CLINIC_TIME

65

3

N

I

1

0

No

Yes

DMO

68

2

N

I

1

0

No

Yes

DMO_TIME

70

3

N

I

1

0

No

Yes

MCUCPC

73

2

N

I

1

0

No

Yes

MCUCPC_TIME

75

3

N

I

1

0

No

Yes

DS_OFFICE

78

2

N

I

1

0

No

Yes

DS_OFFICE_TIME

80

3

N

I

1

0

No

Yes

GN_OFFICE

83

2

N

I

1

0

No

Yes

GN_OFFICE_TIME

85

3

N

I

1

0

No

Yes

POST_OFFICE

88

2

N

I

1

0

No

Yes

POST_OFFICE_TIME

90

3

N

I

1

0

No

Yes

BANK

93

2

N

I

1

0

No

Yes

BANK_TIME

95

3

N

I

1

0

No

Yes

AGRI_OFFICE

98

2

N

I

1

0

No

Yes

AGRI_OFFICE_TIME

100

3

N

I

1

0

No

Yes

IS_POWER_LINES_NEAR

103

1

N

I

1

0

No

Yes

IS_TEL_LINES_NEAR

104

1

N

I

1

0

No

Yes

IS_WATER_SERVICE_NEAR

105

1

N

I

1

0

No

Yes

ATM_CARD

106

1

N

I

1

0

No

Yes

ATMCARDAMOUNT

107

7

N

I

1

0

No

Yes

Sri Lanka HIES 2012/13 Manual (Version 1.1)
- 32 -

HIES-2012_13DD_With_Exp_Inc_Wgt.lst

7/07/2015

Level: HIES2012_13 Questionnaire
Record: Section - 8 - Housing-------------------------------------------------------------------------------------------------------------232
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)S8-1 Structure
STRUCTURE
23
2
N
I
1
0
No Yes1
Single House - Single storeyed2Single House - Double storeyed
3Single House- More than two storeys
4
Attached house / Annex
5
Flat
6
Condominium / Luxury apartments
7
Twin house8
Line room / Row house
9
Slum / Shanty
99 Other (Specify)(na)S8-2 Bed rooms
BED_ROOMS
25
1
N
I
1
0
No Yes
0:9 Bedroom(na)S8-3 Area
AREA
26
1
N
I
1
0
No Yes
1 Less than 100
2 100 - Less than 250
3 250 - less than 500
4 500 - less than 750
5 750 - less than 1000
6 1000 - less than 1500
7 1500 - less than 3000
9 3000 & more(na)
S8-4 Walls
WALLS
27
1
N
I
1
0
No Yes1 Brick
2 Cabook
3 Cement block
4 Pressed soil blocks
5 Mud
6 Plank / Metal sheet
7 Cadjan / Palmyrah
9 Other (specify)(na)S8-4 Floor
FLOOR
28
1
N
I
1
0
No YesSri Lanka HIES 2012/13 Manual (Version 1.1)
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HIES-2012_13DD_With_Exp_Inc_Wgt.lst

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2 Teraso / Tile
3 Mud
233
4 Wood5 Sand
6 Concrete
9 Other (Specify)
S8-4 Roof
ROOF
29
1
1 Tile2 Asbestos
3 Concrete
4 Metal Sheet
5 Taka ram
6 Cadjan / Palmyrah/Straw
9 Other (Specify)(na)S8-5 Ownership
OWNERSHIP30
21
Constructed/Purchased by occupant2
In herited
3
Freely received/received as gift
4Compensated5
Rent free (Employer/Other)6
Relief payment (Employer/Other)
7
Rent
8
Lease
9
Encroached
99 Other (specify)(na)
S8-6a Drinking water
DRINKING_WATER
32
2
1
Protected well within premises
2
Protected well outside premises
3
Unprotected well4
Tap in side home5
Tap with in unite
6
Tap outside premises
7
Project in village
8
Tube well
9
Bowser
10 River/Tank/Streams
11 Rainey water
12 Bottled water
99 Other (Specify)(na)
S8-6b1 Own Other water
OWN_WATER
34
1
0:2
(na)
S8-6b2 Water distance
WATER_DISTANCE
35
4
0:9999(m)
S8-6c1 Water sufficency
WATER_SUFFICENCY
39
1
1:2
(na)
S8-6c2 Other water sufficency
OTHER_WATER_SUFFICENCY
40
1
1:2
(na)
S8-7a Tioilet use
TIOILET_USE
41
1
1 Exclusive for the household
2 Sharing with another household
3 Exclusive for the household
4 Sharing with another household
5 No toilet to housing unit but sharing
6 Public toilet
7 Not using toilets(na)
S8-7b Toilet type
TOILET_TYPE
42
1
1 Connected with water seal
2 Not connected with water seal
3 Pour Flash
4 Pit
9 Other (Specify)(na)S8-8 Garbage dumping
GARBAGE_DUMPING
43
1
1 Collected by garbage truck
2 Buried / Burned
3 Process for fertilizer
4 Dumped within premises
5 Dumped / Throw away out side premises.
9 Other (specify)(na)S8-9 Lite source
LITE_SOURCE
44
1
1 Kerosene
2 Electricity
3 Solar energy
4 Generator/Battery
5 Gas
9 Other (Specify)(na)S8-10 Cooking fuel
COOKING_FUEL
45
1
Sri Lanka HIES 2012/13 Manual (Version 1.1)
1 Fire wood
- 34 -

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

N

I

1

0

No

Yes

HIES-2012_13DD_With_Exp_Inc_Wgt.lst
2
3
4
5
9

7/07/2015

Gas
Kerosene
Electricity
Saw dust / Paddy husk
Other (specify)(na)
S8-11a Is collect firewood
1 Yes
2 No
(na)
S8-11b Fire wood ownForestOther
0:3(na)
S8-11b FWD distance
0:9999(na)
S8-12a Natural calamity
1:2(na)
S8-12b1 Flooding
1:2(na)
S8-12b2 Drought
1:2(na)
S8-12b3 Land slides
1:2(na)
S8-12b4 WildAnimals
1:2(na)
S8-12b5 Winds
1:2(na)
S8-12b6 Other calamity
1:2
(na)

234

IS_COLLECT_FIREWOOD

46

1

N

I

1

0

No

Yes

FIRE_WOOD_OWN

47

1

N

I

1

0

No

Yes

OTHER_DISTANCE

48

4

N

I

1

0

No

Yes

NATURAL_CALAMITY

52

1

N

I

1

0

No

Yes

FLOODING

53

1

N

I

1

0

No

Yes

DROUGHT

54

1

N

I

1

0

No

Yes

LAND_SLIDES

55

1

N

I

1

0

No

Yes

WILDANIMALS

56

1

N

I

1

0

No

Yes

WINDS

57

1

N

I

1

0

No

Yes

OTHER_CALAMITY

58

1

N

I

1

0

No

Yes

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Level: HIES2012_13 Questionnaire
Record: Section - 9 - Land and animal-------------------------------------------------------------------------------------------------------------235
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)
S9-Is agriland owner
IS_AGRILAND_OWNER
23
1
N
I
1
0
No Yes
1 Yes
2 No
(na)
S9-Paddy own acr
PADDY_OWN_ACR
24
3
N
I
1
0
No Yes0:25 Paddy Own Acr(na)
S9-Paddy own rt
PADDY_OWN_RT
27
1
N
I
1
0
No Yes
0:3 Paddy own rt(na)
S9-Paddy own perch
PADDY_OWN_PERCH
28
2
N
I
1
0
No Yes0:39 Paddy own perch(na)
S9-Paddy other acr
PADDY_OTHER_ACR
30
3
N
I
1
0
No Yes0:25 Paddy other acr(na)
S9-Paddy other rt
PADDY_OTHER_RT
33
1
N
I
1
0
No Yes
0:3 PaddyOtherrt(na)
S9-Paddy other perch
PADDY_OTHER_PERCH
34
2
N
I
1
0
No Yes0:39 Paddy other perch(na)
S9-Land own acr
LAND_OWN_ACR
36
3
N
I
1
0
No Yes0:25 Land own acr(na)
S9-Land own rt
LAND_OWN_RT
39
1
N
I
1
0
No Yes
0:3 LandOwnrt(na)
S9-Land own perch
LAND_OWN_PERCH
40
2
N
I
1
0
No Yes0:39 Land own perch(na)
S9-Land other acr
LAND_OTHER_ACR
42
3
N
I
1
0
No Yes0:25 Land other acr(na)
S9-Land other rt
LAND_OTHER_RT
45
1
N
I
1
0
No Yes
0:3 LandOtherrt
Sri Lanka HIES 2012/13 Manual (Version 1.1)
(na)
- 36 -

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S9-Land other perch
0:39 Land other perch(na)
S9-Home own acr
0:25 HomeOwnAcr(na)
S9-Home own rt
0:3 Home own rt(na)
S9-Home own perch
0:39 Home own perch(na)
S9-Home other acr
0:25 Home other acr(na)
S9-Home other rt
0:3 Home other rt(na)
S9-Home other perch
0:39 Home other perch(na)
S9-Cows buffalows
1:2 Cows buffalows(na)
S9-Cows count1 5 or less than 5
2 6 to 10
3 11 to 50
4 More than 50
S9-Goats sheeps
1:2 Goats sheeps(m)
S9-Goat count1 5 or less than 5
2 6 to 10
3 11 to 50
4 More than 50
S9-Pigs
1:2 Pigs(na)S9-Pigs count1 5 or less than 5
2 6 to 10
3 11 to 50
4 More than 50
S9-Chickens
1:2 Chickens(na)
S9-Chicken count1 5 or less than 5
2 6 to 10
3 11 to 50
4 More than 50
S9-Other
1:2 OtherAnimal(na)
S9-Other Count
1 5 or less than 5
2 6 to 10
3 11 to 50
4 More than 50

7/07/2015
LAND_OTHER_PERCH
236

46

2

N

I

1

0

No

Yes

HOME_OWN_ACR

48

3

N

I

1

0

No

Yes

HOME_OWN_RT

51

1

N

I

1

0

No

Yes

HOME_OWN_PERCH

52

2

N

I

1

0

No

Yes

HOME_OTHER_ACR

54

3

N

I

1

0

No

Yes

HOME_OTHER_RT

57

1

N

I

1

0

No

Yes

HOME_OTHER_PERCH

58

2

N

I

1

0

No

Yes

COWS_BUFFALOWS

60

1

N

I

1

0

No

Yes

COWS_COUNT

61

1

N

I

1

0

No

Yes

GOATS_SHEEPS

62

1

N

I

1

0

No

Yes

GOAT_COUNT

63

1

N

I

1

0

No

Yes

PIGS

64

1

N

I

1

0

No

Yes

PIGS_COUNT

65

1

N

I

1

0

No

Yes

CHICKENS

66

1

N

I

1

0

No

Yes

CHICKEN_COUNT

67

1

N

I

1

0

No

Yes

OTHER_ANIMALS

68

1

N

I

1

0

No

Yes

OTHER_COUNT

69

1

N

I

1

0

No

Yes

Sri Lanka HIES 2012/13 Manual (Version 1.1)
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Level: HIES2012_13 Questionnaire
Record: Household Income and Expenditure-------------------------------------------------------------------------------------------------------------237
Data Item
Dec Zero
Item Label
Item Name
Start Len Type Type Occ Dec Char Fill-------------------------------------------------------------------------------------------------------------(record type)
1
2
AN
I
1
0
No
No
District(id) DISTRICT
3
2
N
I
1
0
No Yes
11:13 Western
21:23 Central
31:33 Southern
41:45 Northern
51:53 Eastern
61:62 North-western
71:72 North-central
81:82 Uva
91:92 Sabaragamuwa
(m)
Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes1:3(na)
DS division(id) DS
62
NI
10
No Yes
3:99
(na)
Month
(id) MONTH
8
2
N
I
1
0
No Yes1:12(na)
PSU Number(id) PSU
10
3
N
I
1
0
No Yes1:300(na)
Block Identification No(id) A0
13
5
N
I
1
0
No Yes0:99999(na)
Sample No.
(id) SNUMBER
18
2
N
I
1
0
No Yes1:10(na)Household Serial No.
(id) HHNO
20
1
N
I
1
0
No Yes
1:9(na)Number of households(id) NHH
21
1
N
I
1
0
No Yes
1:9(na)Result Code(id) RESULT
22
1
N
I
1
0
No Yes1 Completed
2 Deferred
3 Not competent respondant at home
4 Refused
5 Household is temporarily closed
6 Household is demolished / Vacant
7 Rejected
9 Other (Specify)(na)Household total Expenditure
HEXPPM
23
9N
I
1
3
No YesHousehold total Income
HINCPM
32
10
N
I
1
4
No Yes
Weighting Factor
WEIGHT
42
6
NI
1
3
No YesSri Lanka HIES 2012/13 Manual (Version 1.1)
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Primary
Linked bySecondary
Linked by238--------------------------------------------------------------------------------------------------------------

Sri Lanka HIES 2012/13 Manual (Version 1.1)
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239

HOUSEHOLD INCOME AND EXPENDITURE SURVEY - 2012/13
FINAL RESULTS
D EPARTMENT OF C ENSUS AND S TATISTICS
M INISTRY OF F INANCE AND P LANNING

Volume 11, August 2014

ISSN 2012-760X
Special points of interest:

Introduction
The Department of Census
and Statistics (DCS) conducts
the Household Income andExpenditure Survey (HIES)
under the National Household
Survey Programme. Until 1990
the HIES was conducted combining with labour force survey
named as Labour Force andSocio-Economic Survey. In
1990, the DCS for the firsttime initiated the HIES as aseparate survey and continued
once in every five years until
2006/07. As rapidly changing
economic conditions demanded
far more frequent monitoring of
the household income andspending patterns in the country, the DCS decided to conduct the HIES once in every
three years starting from2009/10.The latest survey wasin 2012/13 and it can be considered as very important milestone as it covered all 25 districts in the country since its
inception.

2012/13-HIES Average monthly household income by socioeconomic groups with 95% Confidence Intervals(CI)
Poorest 20%- Rs. 10,245(1st and 2nd decile)(95% CI :Rs. 10,173 , Rs.10,318 )Poorest 40%- Rs. 15,760(1st to 4th decile)(95% CI: Rs.15,683 , Rs.15,835)
Middle 60%- Rs. 32,595(3rd to 8th decile )(95% CI: Rs. 32,499 ,Rs. 32,689)
Richest 20%- Rs. 121,368(95% CI: Rs.118,942 ,Rs.123,794)(9th and 10th decile)

Generally the HIES isconducted over a period of
12 consecutive months tocapture seasonal variationsof income and expenditurepatterns in Sri Lanka andthe general sample size is
25,000 housing units whichis adequate to provide reliable information down to

Main objectives of the surveyi.

To measure levels and observe the changes of living
conditions of individuals and households.
ii. To estimate household income and expenditure .
iii. To compute several important poverty indicators.
iv. To provide information to calculate price indices.
v. To analyze the impact of social protection transfers.
vi. To provide information on different living standard
measurements.
Department of Census and Statistics – August 2014

district level. The HIES
2012/13 is the eighth in the
HIES series and the field workof the survey was carried out
during the period from July2012 to June 2013.
The HIES questionnaire
was revised in 2006/07 andcurrently it consists of nine
sections to collect household
information
coveringthefollowing areas;
i.
ii.
iii.
iv.

Demography
School education
HealthFood and non-food expenditurev. Incomevi. Inventory of durable
goods and debts of thehouseholdsvii. Access to facilities in theareaviii. Housing Information
ix. Agriculture holdings andLivestock

Sri Lanka HIES 2012/13 Manual (Version 1.1)

(Average household income values are arranged inascending order and thereafter divided into tengroups with equal frequencies. Such a group isdefined as a household income decile ) Average household size 3.9
 Average Number of income receivers in ahousehold 1.8

InsideIntroduction

1

Household income

2

Source of income

3Household Per capita income

3

Income receiver’s income

4

Income inequality

4-5

Household expenditure

6

Household Food expenditure

7

Household Non-food expenditure

8

240

Final Results

Household Income and Expenditure Survey – 2012/13

Page 2

Volume 11, Issue 11

Household Income
Household income refers to income received either
in cash (Monetary income) or in-kind (Non-monetary income) by all the residents in a household. This includes not
only wages and salaries but also all the other sources such as
agricultural and non-agricultural activities, other monitory
receipts such as pension, disability and relief payments,
regular rental and remittance receipts and returns from businesses or ventures , investments and any other irregulargains such as compensations, lottery wins etc.
Mean (or Average) household income is a point estimate
and it is calculated by dividing the estimated total household income in a domain by the estimated number ofhouseholds in the domain.
Median household income is the amount that divides
the household income distribution into two equal groups,
half having income above that amount, and other half having income below that amount.
The survey reveals that the average household income
per month was Rs. 45,878 in 2012/13 in Sri Lanka. In
2009/10 the average household income was reported as Rs.
36,451. Within the nearly 3 year period, the percentage increase of the household income at current price is nearly 25.9
percent.
The median household income in Sri Lanka has beenreported as Rs. 30,814 in 2012/13 showing an increase ofabout 30 percent from 2009/10.

45,87836,451
26,43330,81417220

23,746

16735

18212

27,11526,286

16,735

Income (Rs)

50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0

2006/07

2009/10
2012/13
Survey Periods
Nominal median income
Real Median Income
Nominal mean income
Real mean income

Figure 1: Average monthly real and nominal mean andmedian household income by survey periods(real values are based on 2006/07 constant prices )Department of Census and Statistics – August 2014

Table 1: Mean and median nominal household income permonth by sector, province and district - 2012/13
and 2009/10
Sector/Province/District

MeanMedian

(Rs.)

(Rs.)

(Rs.)

(Rs.)

2012/13

2009/10*

2012/13

2009/10*

Sri Lanka

45,87836,45130,81423,746Urban
Rural
Estate69,88041,47830,22047,78335,22824,16242,267
29,37624,08731,00023,126
17,366

Western
ColomboGampahaKalutara
Central
Kandy
MataleNuwara Eliya
Southern
Galle
MataraHambantota

64,152
77,72358,24850,34140,146
43,13835,00438,013
41,83439,746
41,66645,85047,11851,07048,87035,78031,89533,06330,01331,02932,514
31,37630,98036,87942,10050,07138,80736,512
28,90030,371
26,441
28,152
28,921
28,20528,22732,26730,60034,18629,821
27,511
21,41022,45018,606
21,431
23,253
21,88623,04826,406

Northern
JaffnaMannarVavuniya
Mullaitivu
Kilinochchi
Eastern
Batticaloa
AmparaTrincomalee

34,28634,78828,53543,96523,68730,64330,67625,48332,537
34,57723,712
18,91739,64023,922
22,84424,721
24,291

23,571
23,44624,20030,967
17,714
20,61422,71020,35923,42924,436

16,71014,81529,37018,03016,12919,082
19,154

North-Western
Kurunegala
Puttalam

42,756
43,62440,93535,58636,922
32,91829,34329,34329,286

20,961
20,77821,593

North-Central
Anuradhapura
Polonnaruwa
Uva
Badulla36,63235,46039,19735,63836,11935,57737,586
31,52628,71732,31329,707
29,68930,14524,22825,067

24,99325,682
22,634
19,761
20,982

Moneragala

34,804

22,161

20,686

17,226

Sabaragamuwa
Ratnapura

40,37542,42936,17341,312

27,77527,391

21,67622,154

Kegalle37,65529,342

28,524

21,122*excluding Mannar, Kilinochchi and Mullaitivu districts

Sri Lanka HIES 2012/13 Manual (Version 1.1)

241

Final Results

Household Income and Expenditure Survey – 2012/13

Page 3

Source of income
Income is received in two mainways, as in monetary or non monetary.
Income from wages and salaries, agricultural activities (seasonal and nonseasonal crops), non agricultural activities, and other cash income (which includes pension payments, disability
payments, Samurdi, local and foreigntransfers), Income by chance/adhoc
gain (windfall income) such as lottery
wins, compensations etc.) are identified
as monetary income.
The non monetary income is theestimated value of goods and services
received in kind and consumed within
the survey reference period. Estimatedrental value of owner occupied housing
units or freely occupied housing units
are included under non monetary income.
Out of the total household incomearound 86 percent of the income isreceived as monetary income in2012/13 and 2009/10. As usual majorpart of the monetary income is recorded
from wages .

Household Per capita
incomePer capita income is a measure of
average living standard of a country
or an area. Household per capita income is computed dividing the total
household income by number of household members.
In 2012/13 the household per capita income in Sri Lanka was Rs. 11,819
per month. In 2009/10 it was Rs. 7,881
and has increased by 50% by 2012/13.

Volume 11, Issue 11
Table 2: Average monthly household income by main source of income-2012/13 and 2009/10
2012/13
Mean
Share ofincome(Rs.)(%)

2009/10
Mean
Share ofincome(Rs.)(%)

Sri Lanka

45,878100.0

36,451

100.0

Monetary Income

39,30085.7

31,20985.6

16,134
5,213
7,990
5,230
4,733

35.2
11.4
17.4
11.4
10.3

12,434
5,238
6,477
4,252
2,808

34.1
14.4
17.8
11.7
7.7

6,578

14.3

5,242

14.4

2,381
4,197

5.2
9.1

2,054
3,188

5.6
8.7

Source of income

Wages/Salaries
Agricultural activities
Nonagricultural activities
Other cash income
Income by chance/adhoc gains
Non-monetary Income
Income in kind
Value of occupied housing unit

Table 3: Average household per capita income per month by sector and byprovince – 2012/13

Sector/Province

Mean per capita income(Rs.)

Sri Lanka
SectorUrban
Rural
EstateProvince
Western
CentralSouthern
Northern
Eastern
North-western
North-Central
Uva
Sabaragamuwa

Department of Census and Statistics – August 2014

Sri Lanka HIES 2012/13 Manual (Version 1.1)

Median per capita income(Rs.)

11,8197,881

17,262
10,843
7,100

10,4207,657
5,503

16,124
10,104
10,973
8,339
7,622
11,596
9,877
9,382
10,718

10,567
7,150
7,624
5,540
5,385
7,927
7,824
6,110
7,229

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Page 4

Income receiver’s income
In order to obtain the Income receiver’s income, the HIES records the household income,received from all the sources at individual level.
If a person is less than 10 years old or a person’s
total monthly income is less than Rs. 250, thenthat person is not defined as an income receiverin the HIES and such income values are added toincome of head of the respective household.
In 2012/13, the average income receiver’s
income per month for Sri Lanka was Rs. 25,963
and median income receiver’s income per month
was Rs. 16,667. The average number of income
receivers per household was 1.8 in 2012/13. Anaverage household size was 3.9 at national level.
The highest mean and median income receiver’s income was reported from Colombo
district and the lowest mean reported from Mannar and the lowest median reported from Jaffna.

Income Inequality
Income inequality is used to measure of disparities in the distribution of income in a given
society . There are many theories to explain how
income inequality is determined. It is definedover the entire population and most of inequality
measures do not depend on the mean of the distribution. Most widely used inequality measurements are Gini coefficient, quintile dispersion
ratio and share of income.

Table 4: Average monthly income receivers’ mean and median income, number of income receivers and household size by district - 2012/13
income receivers mean
income(Rs.)

incomereceivers
medianincome(Rs.)

Householdsize

No.of income receivers in
the household

Sri Lanka

25,963

16,667

3.9

1.8

ColomboGampahaKalutara
Kandy
MataleNuwara Eliya
Galle
MataraHambantota
JaffnaMannarVavuniya
Mullaitivu
Kilinochchi
Batticaloa
AmparaTrincomalee
Kurunegala
PuttalamAnuradhapura
Polonnaruwa
BadullaMoneragala
Rathnapura
Kegalle40,62031,06025,471
24,392
20,50019,631
22,59023,68025,294
18,123
15,41824,405
16,951
18,871
15,782
21,021
23,80525,72826,14021,671
22,206
21,70622,28524,09821,83024,86620,00016,70317,00015,00013,13015,577
16,00016,667
11,896
13,00017,00013,293
12,00013,00015,00017,78816,06417,75016,00015,737
14,00013,00014,672
15,221

4.0
3.9
4.0
4.0
3.7
4.2
3.8
3.8
3.9
4.2
4.2
3.9
3.8
4.2
4.1
4.1
3.9
3.7
3.7
3.7
3.8
3.9
3.7
3.7
3.8

1.9
1.9
2.0
1.8
1.7
1.9
1.8
1.8
1.8
1.9
1.9
1.8
1.4
1.6
1.6
1.6
1.5
1.7
1.6
1.6
1.8
1.7
1.6
1.8
1.7

District

Gini Coefficient
The range of the Gini coefficient index is between 0 and 1, where 0 indicates perfect equality and 1 indicates maximum
inequality. The national value of Gini coefficient for household income was 0.48 in 2012/13 and it was 0.49 in 2009/10.The highest Gini coefficient reported from Moneragala district (0.53) and the lowest reported from Mannar and Anuradhapura(0.37). Figure 2 shows the distribution pattern of Gini coefficient by districts.

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Volume 11, Issue 11

Quintile Dispersion Ratio

Figure 2: Gini coefficient of household income by district—2012/13
0.60

The simplest way to measure inequality ofincome or expenditure is by dividing the population into five groups (quintiles) from the poorest to
the richest, and computing the levels or proportions of income (or expenditure) share by each

Sri Lanka = 0.48

Gini Coefficient

0.50

Quintile :The household income values are arranged in ascending order and thereafter divide
into five groups with equal frequencies. Such agroup is defined as a income quintile.

0.40
0.30
0.20
0.10
0.00

level.
Table 5 shows the quintile dispersion ofhousehold income based on HIES 2012/13 and2009/10. A fifth of households included in thesurvey are allocated to each quintile.
The survey results indicate the slight decrease of inequality of household income from2009/10 (0.49) to 2012/13(0.48).The share of thepoorest second to fourth quintile has slightly increased in 2012/13 compared to 2009/10. The
share of fifth (Richest fifth) quintile has decreased

Districtfrom, 54.1% to 52.9% and quintile dispersion ratio has decreased from 12.0
in 2009/10 to 11.8 in 2012/13. These figures are consistent with Gini index .
The quintile dispersion ratio (The mean household income of therichest 20% (5th quintile) divided by the mean household income of the poorest 20% (1st quintile)). of household income has been change by only 0.2%
from 2009/10 to 2012/13. In general, around 47% of total household income
has allocated among 80% of total household population in the country.

Table 5: Breakdown of household income by quintile - 2012/13 and 2009/10
Household Income quintile
2012/13
Mean household Income per month (Rs.)
Share of income (%)
Cumulative share of income (%)
Cumulative % of Population

1st quintile
10,2454.5
4.5
20

2ndquintile
21,2739.3
13.7
40

3rdquintile

4thquintile

5thquintile

45,569
19.9
47.1
80

121,368
52.9
100.0
100

45,878
100.0

Upper

Overall

30,944
13.5
27.2
60

Total

Quintile Dispersion Ratio = 121,368/10,245 = 11.8
Lowest2009/10
Mean household Income per month (Rs.)
Share of income (%)
Cumulative share of income (%)
Cumulative % of Population

8,211
4.5
4.5
20

Low-mid
16,062
8.8
13.3
40

MiddleMid-upper

23,880
13.1
26.4
60

Quintile Dispersion Ratio = 98,575/8,211= 12.0
Department of Census and Statistics – August 2014

Sri Lanka HIES 2012/13 Manual (Version 1.1)

35,552
19.5
45.9
80

98,575
54.1
100.0
100

36,451100.0

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Page 6

Household Expenditure
Household expenditure was collected under three main sections;
1. Expenditure on food items
2. Expenditure on non- food items and3. Expenditure incurred by boarders and domestic
servants
To improve the quality of the data and data collection,
the HIES questionnaire imposes different reference periods fordifferent consumer items. i.e. seven consecutive days for all thefood items, one month for housing,fuel and light ,six months for
clothing, twelve months for durable goods etc.
Mean (or Average) household expenditure is a point estimate
and it is calculated by dividing the estimated total household
expenditure in a domain by the estimated number of households
in the domain.
Usually, people do not declare income accurately. Hence,
household expenditure can be used as a proxy variable to household income .

41,44440000

31,331

30000
25000

22,952

22,715

5000

30,701

10000

16,64015000

17,76924,50920000

24,49318,14435000

16,640Household Expenditure (Rs.)

45000

0
2006/07

2009/10

2012/13

Survey period
Nominal median expenditure

Real Median expenditure

Nominal mean expenditure

Real mean expenditure

Figure 3: Real (2006/07 constant price) and nominal foodand non-food mean Expenditure by survey
periodTable 6: Mean and median household nominal expenditure per month by sector, province and district 2012/13 and 2009/10
Sector/Province /District

Sri Lanka

MeanMedian(Rs.)
2012/13(Rs.)
2009/10*(Rs.)
2012/13(Rs.)
2009/10*41,444

31,33130,701

24,509Urban

58,93044,92843,82534,039Rural

38,27429,42329,01023,600Estate29,37923,98825,58020,490Western

58,29842,39944,31832,500Colombo

63,03047,291

49,22936,597

Gampaha57,06441,062

43,057

31,825

Kalutara

51,90635,54939,03628,361

Central38,98928,30829,51322,741

Kandy

41,442

29,767

31,12023,770

Matale39,222

26,52827,342

20,400

Nuwara Eliya

33,882

26,841

28,32722,245Southern

36,43028,80928,24323,137

Galle

34,87927,37027,74922,675

Matara37,63929,40827,962

23,038Hambantota

37,57330,74430,007

24,122

Northern

34,56225,656

26,414

22,171

Jaffna35,40522,72528,20921,059Mannar27,406-

22,563-

Vavuniya

44,48635,39133,50328,757-

Mullaitivu

20,581-

17,352Kilinochchi

32,992-

24,052-Eastern

30,88625,26525,93622,040Batticaloa

29,57923,50823,95920,536

Ampara31,84926,69926,757

22,68623,169Trincomalee

31,041

25,623

26,777

North -Western

37,66525,92728,906

21,212

Kurunegala

36,441

25,20128,01020,524

Puttalam

40,232

27,37630,66522,996

North -Central

33,93529,48027,21724,062Anuradhapura

31,95929,06525,57823,429

Polonnaruwa

38,25730,31530,76824,817

Uva

28,86723,547

22,953

19,548

Badulla32,381

24,87325,56820,222

From 2006/07 to 2012/13, the nominal household mean
Moneragala
22,766
21,131
18,159expenditure has steadily increased. However, real mean house hold Sabaragamuwa
32,37625,58325,469expenditure slightly decreased from 2006/07 to 2009/10 but show a
Ratnapura
29,42625,47723,968slight increased from 2009/10 to 2012/13. The survey revealed that
Kegalle36,28425,723
27,697the median household expenditure for 2012/13 was Rs.30,701 in*excluding Mannar, Kilinochchi and Mullaitivu districts
nominal value but it was Rs. 18,144 in real term.
Department of Census and Statistics – August 2014

Sri Lanka HIES 2012/13 Manual (Version 1.1)

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Household Income and Expenditure Survey - 2012/13

Page 7

Household Food Expenditure
The food ratio is computed by dividing total household Figure 4: Distribution of food ratio and non food ratio by
survey periods
food expenditure (excluding expenditure on liquor ,narcotic
drug and tobacco) by total household expenditure as given100
below;
Food ratio
80

Total household expenditure60=

The average monthly total household food expenditure
was Rs. 15,651 in 2012/13. The Figure 04 shows the distribution pattern of food and non food ratio by survey periods.
Food ratio for 2009/10 was 42.3 % and it has declined to37.8% in 2012/13

62.4%

Percentage (%)

Food ratio

Non-food ratio

Total household food expenditure

40

62.2%

57.7%
42.3%

37.6%

37.8%

20
0
2006/07

2009/10
Survey Periods

2012/13

Consumption (gram)

The Table 7 shows the average monthly per capita consumption quantities of selected food items by survey peri- Table 7: Average monthly per capita consumption quantities
of selected food items by survey period
od. It reveals that the rice, wheat flour bread (Normal), sugar and dried fish consumption quantities per person per
Unit
2012/13
2009/10
2006/07month had declined from 2009/10 to 2012/13.However, Item
Rice flour, Dhal, big onion ,chicken and egg consumption
(Qty)
(Qty)
(Qty)
has increased comparatively. There is no change in coconut
Rice
gram
8,989
9,062
8,995
consumption for three consecutive survey periods.
Rice flour
gram
194
127
83
Figure 5: Consumption pattern of selected food
Wheat Flour
gram
564
687
584
items by survey period
Bread (Normal)
gram
926
1,266
1,533
Dhal
gram
586
501
566
1800
Big onions
gram
608
582
570
1600
Sugar
gram
1,111
1,212
1,275
1400
Coconutchicken
Egg
Dried fish

1200

1000
800
600

400

Numbergram
Number
gram

7
320
3
299

7
233
2
313

7
243
3
325

200

0
2006/07

2009/10

2012/13

Survey Period

Rice flour

Wheat Flour

Bread (Normal)

chicken

When considering the consumption pattern of bread (Normal) a downward trend can be seen from 2006/07 onwards
and the wheat flour consumption too has declined from 2009/10 onwards. However, the rice flour consumption is showing an
upward trend from 2006/07 onwards. The dried fish consumption has slightly gone down while the egg and chicken consumption
have increased . Big onion consumption has also increased in last few years. (Big onions consumption in 2006/07 was 570 grams
per person per month and 608 grams in 2012/13)
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Household Income and Expenditure Survey - 2012/13

Page 8Household Non - Food Expenditure
Household Income and Expenditure
survey reveals the expenditure on housing
is the highest single expenditure group
among all the non–food expenditure groups
in Sri Lanka excluding other non-consumer
expenditure group. The estimated rental
values of owner occupied housing units
and housing units occupied without paying
any rental fee etc. are also included in thegroup of housing expenditure.

Table 8: Average monthly household expenditure by major non-food
expenditure group - 2012/13 and 2009/10
2012/13
Value

2009/10
Value

(Rs.)

(%)

(Rs.)

(%)

Total non-food

25,793100.0

18,064

100.0

Housing

4,667

18.1

3,446

19.1

Fuel & Lighting

1,755

6.8

1,278

7.1

Clothing, Textiles & Foot wear

1,194

4.6

903

5.0

Health & Personal care

2,181

8.5

1,429

7.9

Transport & Communication

4,315

16.7

3,072

17.0

Education

1,448

5.6

1,018

5.6

Cultural & entertainment

515

2.0

402

2.2

Non-durable household goods

318

1.2

264

1.5

Durable household goods

1,018

3.9

780

4.3

Other non-consumer expenditure

7,678

29.8

4,807

26.6

Liquor, Narcotic drugs & Tobacco

705

2.7

665

3.7

Item

The group "Other non consumer
expenditure" that has been reported 29.8 %share of total non food expenditure
in 2012/13 in Table 8, contains the occasional but bulk expenditure, incurred
during the one year (the last year) reference
period, on social and cultural activities
such as weddings, funerals, religious ceremonies ,payments of debts, constructions
and renewals of houses, donations,
expenditure on household services likelaundry charges, wages to servants,
charges for day carecenters etc.

Sample Surveys Division
8th floor,
Unity plaza building,Colombo 04.

Department of Census & Statistics
Department of Census and Statistics
4th,Floor,Rotunda Tower,No.109,Galle Road
Colombo 03

Phone: 011-2508689
Fax: 011-2508690
E-mail: sample.survey@statistics.gov.lk

Our web address
www.statistics.gov.lk

Department of Census and Statistics – August 2014

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247

1. Introduction
expanded beyond the collection of demographic, income
and expenditure information. It has been introduced 7
new sections to collect almost all the other household
information that helps to understand the correct living
standards of the households. Those newly introduced
areas covered by the HIES starting from the HIES
2006/07 are as follows.

The Household Income and Expenditure Survey (HIES)
is conducted by the Department of Census and Statistics(DCS) under the National Household Survey Programme(NHSP) of Sri Lanka. The DCS has completed the mostrecent HIES in 2012/13 and this publication exposes the
final information gathered in this survey. The field workof the survey was carried out during the period from July2012 to June 2013 covering all 25 districts in the country
since its inception. The HIES 2012/13 is the eighth in
the HIES series and the information in this report ispresented at national, sector, province and district levels
along with the previous survey findings where applicableto facilitate effective comparisons overtime.

2. Introduction

1.
2.
3.
4.
5.
6.
7.

1.1 History

1.2 Field operation and control oferrorsThe HIES was started to conduct as Labour Force andSocio Economic Survey (LFSES) in 1980/81 and it was
conducted for the second time in 1985/86. The LFSES,
through a single survey, provided comprehensive
information on labour force, household income andexpenditure and demographic characteristics of thehouseholds and household population in Sri Lanka. In
1990 the LFSES was separated into the under mentioned
2 individual surveys as the labour force information of
the country was more frequently needed.
1.
2.

The collection of data was done by approximately 330
field officers (Statistical Officers, Statistical Assistants)
who belong to the permanent field staffs of Department
of Census and Statistics. Each field officers is in-chargeof all statistical activities in an geographical units called
District Secretariat Divisions (DSD).These officers were
given a thorough training before they were entrustedwith the survey operations. Each administrative district
comprises with such units and there are altogether 331
DS Divisions in Sri Lanka. In each district there is a
Deputy Director (DD) /Senior Statistician (SSTN)/Statistician (STN) who in charge of the statisticalactivities in the district and one of the main functions is
to supervise the work of field staff.

Labour Force Survey (LFS)
Household Income and Expenditure Survey (HIES)

The maiden HIES was conducted in 1990/91 as aseparate survey with the view of providing information
on household income and consumption expenditure inorder to measure the levels and to observe the changes ofliving conditions in the country. The HIES informationis also used to estimate consumption needs of thecountry and to compute various other important
indicators related to poverty and price indices. Generally
the HIES surveys a sample of 25,000 housing units
throughout the country to facilitate the information be
given at district level. Data is collected at the field in
twelve consecutive monthly rounds to capture seasonalvariations in income, expenditure and consumption ofhouseholds. The data collection of the survey is done
through direct interviews using the survey questionnaire.
At the beginning the questionnaire was included thefollowing three main sections.
1.
2.
3.

School education
Health information
Inventory of durable goods
Access to infrastructure facilities
Household debts and borrowings
Housing, sanitary and disasters
Land and agriculture holdings

The main functions of the field staff for this survey(a) Update the lists of housing units of the selected
blocks (PSUs) and preparation of listing forms(9NHSP) as describe 1.3.1 under survey
methodology.
(b) Interviewing the selected households
In addition to this, the following control procedures wereadopted to control the non-sampling error and increase toaccuracy of the survey estimates.
1.

Demographic characteristics
Household expenditure (on food and non-food)Household income (monetary and non-monetary)

2.

The HIES gathers information related to demographic
characteristics of the members of the surveyed
households, expenditure on food and non-food items andincome received by each household member from all thedifferent sources in a compulsory manner. Starting fromthe HIES 2006/07, the survey questionnaire was further

3.
4.

Field supervision of listing forms and updating
operation and interviewing the data collection
done by field staff by in-charge of district
Field activity reviews by Sample Survey
Division (SSD) staff
Data entry checks, Computer edits and data
verification
Use several control sheets to do the survey
activities on time and increased the accuracy ofthe data

1
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1.3 Survey methodology

1.3.3 Sample allocation

Sample design of the survey is two stage stratified andthe Urban, Rural and the Estate sectors in each district of
the country are the selection domains thus the district isthe main domain used for the stratification. The
sampling frame is the list of housing units prepared for
the Census of Population and Housing (CPH) 2011.

Allocation of the number of PSUs or determining thesample sizes for the districts is made proportionate to thenumber of housing units and the standard deviations of
the mean household expenditure values reported in therespective districts in previous surveys (Neymann
Allocation). Sector allocation of the district sample ismade proportionate to the square root of the sizes of therespective selection domains (Urban, Rural and Estate
sectors in the district). The sample of PSUs within theselection domain is equally distributed among the 12
survey months and the monthly sample too is equallydispersed among all the weeks in the month assigning a
specific week for each PSU for the survey activities.

1.3.1 Selection of Primary Sampling Units
Primary sampling units (PSUs) are the census blocks
selected for the survey. The sampling frame, which is thecollection of all the census blocks prepared in CPH 2011
in Sri Lanka, is used for the selection of the PSUs at thefirst stage of the selection.
The PSU selection is done within all the independentselection domains that are assigned different sample size
allocations to total the targeted sample size of 2,500
PSUs. The method of selection of the PSUs at the firststage is systematic with a selection probability given toeach census block proportionate to the number ofhousing units available in the census blocks within theselection domains (PPS).

Table1.1: Sample allocation and completion by
district
District

Sri Lanka
ColomboGampahaKalutara
Kandy
MataleNuwara Eliya
Galle
MataraHambantota
JaffnaMannarVavunia
Mullaitivu
Kilinochchi
Batticaloa
AmparaTrincomalee
Kurunegala
PuttalamAnuradhapura
Polonnaruwa
BadullaMoneragala
Ratnapura
KegalleThe selected PSUs are updated to include newly built
housing units and to exclude demolished or vacatedhousing units, which are no longer considered as housing
units according to the survey definitions, to capture
variation of natural growth and to make necessary
adjustments for the same. The PSU updating operation infield is generally done less than one month prior to thesurvey and it was carried out for the 12 months startingfrom June 2012 to May 2013 to support the scheduled 12
survey months started from July 2011 to June 2013 for
the HIES 2012/13.

1.3.2 Selection of Secondary Sampling Units
Secondary Sampling Units (SSUs) or Final sampling
units (FSUs) are the housing units selected at the second
stage from the 2,500 PSUs selected at the first stage.
From each PSU, 10 SSUs (housing units) aresystematically selected giving each housing unit in the
PSU an equal probability to be selected for the survey.
The total sample of size 25,000 housing units is resulted
at the end of the sampling process and this sample
represents the whole country in different probabilities
depend on the different sample sizes allocated for theselection domains.

Housing units
Selected

Responded

25,319
3,000
2,498
1,598
1,240
660
990
1,510
1,349
890
750
350
330
330
400
820
887
590
1,350
780
850
697
850
750
1,000
850

20,411
2,156
1,940
1,230
973
595
785
1,294
1,141
731
633
287
281
263
325
697
738
501
1,146
649
743
524
715
576
822
666

Households
responded
20,540
2,166
1,948
1,244
983
604
791
1,299
1,148
735
643
290
282
263
325
698
739
502
1,157
654
743
526
731
576
825
668

Table 1.2: Sample allocation and completion by
sector
SectorHousing units

Selected
Sri Lanka

25,319Households
responded

Responded
20,411

20,540

Urban

6,597

5,117

5,172

Rural

16,315

13,456

13,515

Estate

2,293

1,838

1,853

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1.4 Estimation procedure
The estimate for the total value of the characteristic X
for the country (25 districts) could be given by,

X j be the estimate of total of any givenLetcharacteristic for jth district and this could be given by,

1 mj (u ) 1 Nh (u ) nh (u )Xj
 Xhi(u )mj(u) h (u )  1 Ph (u ) nh (u ) i (u )  1




j1

Note:
The estimation procedure given above is for the total
sample covered in 12 monthly rounds and the estimates
given in this publication is based on the sample coveredin 25 districts.

1 mj ( e ) 1 Nh ( e ) nh ( e ) Xhi(e)mj(e) h ( e )  1 Ph ( e ) nh ( e ) i ( e )  1



25

X  X j

1 mj ( r ) 1 Nh ( r ) nh ( r ) Xhi( r )mj(r) h ( r )  1 Ph ( r ) nh ( r ) i ( r )  1

1.5 Adjustment for unit non-response
The occurrence of unit non-response was determined byexamining the final result code recorded under data
control section of the survey schedule. Based on the
final result codes the households were grouped into thefollowing categories, which were used as a basis foradjusting for the unit non-response.

Where,

mj (u ) = Number of census blocks selected from theurban sector of the jth district for the survey

Ph (u ) = Selection probability of the hth census blockin the urban sector.
Category and description

Sjh (u )Ph (u )  Mjh (u ) Sjh(u )h1Resultcode

1. Schedule completed

1

2. Housing unit demolished or vacant6

3. Unable to complete schedule, refusal,
temporarily away etc.

2,3,4,5,9Sjh (u ) = Measure of size (number of housing units)of the hth census block in the urban sector of
the jth district.

Category 1 and 2
These were considered as fully accounted for as aschedule was completed to the extent required by thesituation at the time of interview and therefore no
adjustment was necessary.

Mjh(u ) = Total number of census blocks in urban
sector of the jth district.

Nh (u ) = Total number of housing units listed in theX jk (u ) 

hth census block in the urban sector.

nh (u )= Number of housing units selected from thehth census block in the urban sector.

1 mjk (u ) 1 Nh (u ) nh (u ) Xhi(u )mjk(u) h (u )  1 Ph (u ) nh (u ) i (u )  1

Category 3This is incomplete therefore a non-response adjustment
was made for this category. The assumption made here
for the adjustment of non-response was the proportionof households in category 3 is the same as thecorresponding proportion for sample households in
category 1. This assumption is applied on a block-byblock basis separately for urban, rural and estate sectors.

thXhi(u ) = The observed value for the i samplethhousehold in the h census block in theurban sector.
mj(r),Ph(r),Sjh(r),Mjh(r), Nh(r), nh(r), and Xhi(r) arecorresponding terms for the rural sector and mj(e), Ph (e),Sjh(e),Mjh(e),Nh (e),nh(e), and Xhi(e)are corresponding terms
for the estate sector.

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250

Estate Sector
Plantation areas, which are more than 20 acres of extent
and having not less than 10 residential laborers, areconsidered as estate sector.

The block weight before adjusting for unit non-responsewasW h 

1 mjk 1 Nh
mj h 1 Ph nh

Rural Sector
Residential areas, which do not belong to the Urban
sector or Estate sector described above, are considered
as Rural sector.

Where
Nh = Total number of housing units listed in block h.
nh = Number of housing units selected in block h.
The revised weight should take the form

Marital Status
The marital status recorded was the current status at thetime of the survey. A person whose marriage wasregistered is classified as married (registered). If themarriage has not been registered according to the law
but the person claimed to be married according to thecustom or repute then it is also classified as married(customary).

 n h  nh Wh  W h . 

n
h


1

2

1

Where
nh1 = Number of sample households in category 1.
nh2 = Number of sample households in category 2.
n1h = Total number of households in all the 3 categories(category 1, 2 and 3)

A person is regarded as divorced only if a divorce has
been obtained in a court of law. A person who has
obtained a judicial or legal separation in a court of law
is classified as separated (legally). On the other hand if
a person had been voluntarily separated withoutobtaining a court order then such persons fall into thesame category classified as separated (not legally)

1.6 Definitions and Concepts
Readers should aware about the frequently useddefinitions of the survey terms prior to interpret orcompare the data. The definitions of the terms
frequently used in this report are given below.
HouseholdA household may be a one-person household or a multiperson household. A one-person household is a unit
where a person lives by himself and makes separate
provision for his food, either cooking himself orpurchasing. A multi person household is a group of two
or more persons who lives together and has a common
arrangement for cooking and partaking food. Boarders
and servants who share the meals and housing facilities
with other members of the household are also
considered as members of the household.

Educational attainment
This refers to the highest academic qualification
obtained and the professional and technicalqualifications are excluded. In the case of person withno such qualifications the highest grade or class passed
in school is considered as the level of education or
educational attainment.

Head of the household
Head of a household is a person who usually resides in
the household and is acknowledged by the othermembers of the household as the head of the household.

Share of income

Share of income=

Total income of the households
or persons belong to the group
Total income of all households
or persons in all groups

Household size
Number of persons usually living in the household
including boarders and servants etc. is defined as thehousehold size.

Residential sector
The residential sector can be either Urban sector, Rural
sector or Estate sector.

Household income deciles
Income deciles are income groups whose boundaries aredefined as to get 10 percent of the total number ofhouseholds fallen into each group.

Urban Sector
Area governed by either Municipal Council (MC) or
Urban Council (UC) is considered as Urban Sector.

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251Household expenditure deciles
Expenditure deciles are defined similarly as income
deciles, but expenditure values are used instead ofincome values.

Dependency ratio
In this survey, dependency ratio is defined as follows

Gini Co-efficient
Gini Co-efficient, which is the most popular indicatorused to measure the depth of inequality of a distribution
is defined as the ratio of the area between the diagonal
and the Lorenz curve to the triangular region underneaththe diagonal.

Dependency ratio

1.7 Reliability of the estimates
The actual sample which is based for this publication islimited to 20,540 households surveyed in total 12months and the estimations given in HIES are mostly
limited to district level to preserve the reliability ofthem.

Income receiver
A person who is 10 or more years old and his or her
calculated total monthly income received from any
source is more than or equal to Rs.200 then he or she isdefined as an income receiver in this survey.

Reliability statistics calculated for the district level
estimations of mean Household expenditure (Rs. /monthly) are given below to build confidence among
advanced data users and to support future sample design
activities of similar type of surveys.

Food Ratio

Food ratio (%) =Persons aged less than 15 years
and above 60 years=
Persons aged 15 – 59 years

Household expenditure
on food and drink
X 100
Total household expenditure(food and drink and non-food)Table 1.3: Estimation, Standard error, coefficient of variation and 95% confidence intervals of mean Household
expenditure (Rs. /month) by district - 2012/13
Coefficient of
Variation (CV)Districtmean(hhexppm)

Sri Lanka

41,444459

1.1

40,54442,344

ColomboGampahaKalutara
Kandy
MataleNuwara Eliya
Galle
MataraHambantota
JaffnaMannarVavunia
Mullaitivu
Kilinochchi
Batticaloa
AmparaTrincomalee
Kurunegala
PuttalamAnuradhapura
Polonnaruwa
BadullaMoneragala
Ratnapura

63,03057,06451,90641,442
39,22233,882
34,87937,63937,57335,40527,40644,486
20,581
32,992
29,57931,84931,04136,441
40,232
31,95938,25732,381
22,76629,42636,284

2,119
1,847
2,530
1,973
3,382
1,325
1,517
1,835
1,579
1,566
2,155
4,128
1,415
5,116
1,528
1,161
1,936
1,443
2,296
1,360
2,235
1,460
1,217
1,232
2,079

3.4
3.2
4.9
4.8
8.6
3.9
4.4
4.9
4.2
4.4
7.9
9.3
6.9
15.5
5.2
3.6
6.2
4.0
5.7
4.3
5.8
4.5
5.3
4.2
5.7

58,87653,443
46,94437,57332,58931,28531,90334,04034,47632,33423,18036,391
17,80722,96026,58329,57327,24433,61135,73029,29233,87429,51820,37927,00932,206

67,18560,68556,86745,31045,85536,48037,854
41,23840,67038,47531,63352,581
23,35643,02432,574
34,12534,83739,27144,73534,62642,64135,24425,15331,84340,361

KegalleStd. Err.

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Sri Lanka HIES 2012/13 Manual (Version 1.1)

[95% Conf.]