(Version 1.1)

Users’ Manual for Handling Resampled Micro Data of
Sri Lanka Household Income and Expenditure Survey
HIES 2009/10

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

1

History of revision of the manualVersion 1.1 in March 2015
- Revised based on the discussion during the Sixth International Workshop on Analysis of
Micro Data of Official Statistics in December 2014.
- Revised errors in the resampled micro data.

First draft version 1.0 in September 2014

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2

CONTENTS

HIES 2009/10
1. About this Manual

Page 4

2. Outline of the survey

5

3. Data and metadata provided

7

4. Data import12Strategy to import micro data into RImport CSV files to R

ID: Household identifier

PID: Individual identifierImport weight data by district and psu

Append weight to all data files

5. Data check5.1 Summary of each variable

22

5.2 Frequency table of categorical variables

44

5.3 Primary key and foreign key of each data set

61

5.4 Sample allocation

76

5.5 Population estimates

79

6. Household income
6.1 Definition of household income

80

6.2 Process of estimating household income

82

6.3 Generating the variable of monthly monetary income

88

6.4 Creating data set of household income at household level

97

6.5 Non-monetary income (income in-kind)100

6.6 Creating individual-level income data file107

6.7 Income receivers’ income

112

7. Household expenditure
7.1 Definition of household expenditure121

7.2 Process of estimating household expenditure

123

7.3 Converting to monthly expenditure124

7.4 Estimating household expenditure by 4-digit item

127

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7.5 Household expenditure by subgroup137

7.6 Creating data set on household expenditure by subgroup at household level

139

8. Resampling method

144

9. R scripts for some tables of the final report

155

10. Agricultural land160

11. Employment

165

12. Demography
12.1 Ethnicity

173

12.3 Religion

179

12.4 Marriage

181

12.5 Family type

185

13. References

202

Attachments:
1. HIES 2009/10 Questionnaire203

2. HIES 2009/10 Data dictionary

235

3. Districts Map

278

4. Administrative structure, and urban, rural and estate279

5. Classification of Industry (4-digit)283

6. Classification of occupation (4-digit)

289

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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 2009/10.
Survey year

Data files

Contents

HIES 2009/10

26 files

R01 to R24: HIES 2009/10 Questionnaire
R25, R26: Summary files compiled by the author

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""R26_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" "R26.80"

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 Statistical Use of MicroData, 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 September 2014 by;
Hiroshige Furuta
Visiting Senior Research Fellow, Sinfonica

Acknowledgements
Special thanks to Mr. K.M.R. Wickramasinghe and Ms. Dilhanie Deepawansa, DCS, Sri Lanka, who
assisted my work of compiling the manuals by properly answering to my queries via email.

Sri Lanka HIES 2009/10 Manual (Version 1.1)5

2. Outline of HIES 2009/10
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 seven topics have been introduced;
Education, Health, Durable goods, Access to facilities, Debts, Housing,
Agriculture holdings and livestock
Data collection

Direct personal interview
The data collection in the field is done in twelve monthly rounds from July 2009
to June 2010.

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

Geographically, whole country excluding Mannar, Kilinochchi and Mullaithivu
districts in the Northern province as post liberation settlements of internallydisplaced persons.
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 2001
List of buildings prepared for Census 2001

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HIES 2009/10 will be the last HIES sampled from this sampling frame.
Sample design

Two stage stratified random sample design
Strata: district (22) and sector (3)Psu: census blocks. 2,500 psu were selected.
List of buildings in each selected psu was updated about one month prior to thescheduled 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, 19,958 households responded to the survey.

Data processing

According to the delegates from Sri Lanka for the International Workshop,“Data cleaning is done in districts and head office concurrently with the fieldwork using an online and interactive computer data editing and cleaning
program, which reports errors in the data as identified according to theconditions pre-prepared and the corrections are made referring to the hardquestionnaire 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 report

Based on the 100% data

For more detail, please refer to “1. Introduction” of the survey report.

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3.

Data and metadata provided by NSO

Two following micro data and metadata were provided to Sinfonica by Mr. S. A. S. Bandulasena,
Director of ICT Division, DCS upon request in June 2014.

Type

Filename

Description

Included inthismanualData
HIES-2009-10-100%-Data.txt (66.0M)

Text data file with multi layouts;
In total, 1,844,869 lines; 24 record types

Folder: HIES-2009-10-100%-

The next 24 data files in CSV format

Data_CSV Format[1] "HIES-2009-10-100%-Data-SEC_1_DEMOGRAPHIC.CSV"[2] "HIES-2009-10-100%-Data-SEC_2_SCHOOL_EDUCATION.CSV"[3] "HIES-2009-10-100%-Data-SEC_3_HEALTH.CSV"[4] "HIES-2009-10-100%-Data-SEC_4_1_FOOD_EXP.CSV"[5] "HIES-2009-10-100%-Data-SEC_4_2_NONFOOD.CSV"[6] "HIES-2009-10-100%-Data-SEC_4_3_BOARDERS.CSV"[7] "HIES-2009-10-100%-Data-SEC_4_3_IS_BOADERS.CSV"[8] "HIES-2009-10-100%-Data-SEC_5_1_EMP_INCOME.CSV"[9] "HIES-2009-10-100%-Data-SEC_5_1_IS_EMP_INCOME.CSV"[10] "HIES-2009-10-100%-Data-SEC_5_2_AGRI_INCOME.CSV"[11] "HIES-2009-10-100%-Data-SEC_5_2_IS_AGRI_INCOME.CSV"[12] "HIES-2009-10-100%-Data-SEC_5_3_IS_OTHER_AGRI_INCOME.CSV"[13] "HIES-2009-10-100%-Data-SEC_5_3_OTHER_AGRI_INCOME.CSV"[14] "HIES-2009-10-100%-Data-SEC_5_4_IS_NON_AGRI_INCOME.CSV"[15] "HIES-2009-10-100%-Data-SEC_5_4_NON_AGRI_INCOME.CSV"[16] "HIES-2009-10-100%-Data-SEC_5_5_1_IS_OTHER_INCOME.CSV"[17] "HIES-2009-10-100%-Data-SEC_5_5_1_OTHER_INCOME.CSV"[18] "HIES-2009-10-100%-Data-SEC_5_5_2_IS_WINDFALL_INCOME.CSV"[19] "HIES-2009-10-100%-Data-SEC_5_5_2_WINDFALL_INCOME.CSV"[20] "HIES-2009-10-100%-Data-SEC_6_B_DEBTNESS.CSV"[21] "HIES-2009-10-100%-Data-SEC_6A_DURABLE_GOODS.CSV"[22] "HIES-2009-10-100%-Data-SEC_7_BASIC_FACILITIES.CSV"[23] "HIES-2009-10-100%-Data-SEC_8_HOUSING.CSV"

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Yes

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[24] "HIES-2009-10-100%-Data-SEC_9_LAND_ANIMAL.CSV"

HIES-2009-10-100%-Factor File.xls

Weight data by district and psu,

Yes2,269 records
Metadata

HIES-2009_10-Questionnaire.pdf

32 pages

HIES-2009-10-Data Layout.pdf

Note: The original file provided was for

Yes

HIES 2006/07. The revised Data Layout aswell as the next Data Dictionary were
provided upon request.
25 pages
Sri Lanka HIES-2009-10-Data Layout

Data dictionary/Codebook

with codes.pdf

65 pages

Yes

Example:ICI_OCodesSriLanka.xlsx

4-digit codes of industry and occupation
classification

Remarks:
Enumerator’s manual is only given in Sinhala and Tamil, not in English.

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Yes

9



Changes from the previous HIES 2006/07



The questionnaire of HIES 2009/10 is almost the same as HIES 2006/07.
Changes are shown as below. Most of them are changes of response categories.

Section

Item

Changes from HIES 2006/07

Section 1

Col. 9 & 10

Contents of col. 9 and col. 10 were swapped.

Section 2

Target“Aged 5-19” from “Aged 5-20”

Col. 9 & 10

Number of response categories increased.

Section 5.5.2

Col. 6, 8 & 9Contents were revised.

Section 8

5 Tenure

Response categories

7A

Response categories

Availabilityof toilet

Remarks: Change of the target age of education section
According to the delegates from Sri Lanka for the International Workshop,“Since the school education tabulations are made on certain age bands, this has never been an issue.”

Geographical coverage
The next three districts were covered by HIES 2009 in addition to 19 districts in HIES 2006.
41

Jaffna43

Vavuniya

53

Trincomalee

Sri Lanka HIES 2009/10 Manual (Version 1.1)

10References

Homepage of DCS
http://www.statistics.gov.lk/page.asp?page=Income%20and%20Expenditure(Accessed on 9 July 2014)

It provides the preliminary report and the final report of HIES 2009/10.

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

IHSN Survey Catalog:

Sri Lanka - Household Income and Expenditure Survey 2009-2010
http://catalog.ihsn.org/index.php/catalog/3246(Accessed on 9 July 2014)
The below materials are available on the web.

Note: The file of “Data Dictionary.pdf” was also mistaken. It was for HIES 2006/07.

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4.

Data Import

Strategy to import micro data into R
Two types of data set were provided. One was a text file with multi layouts. There were 24 types ofrecords, as same as HIES 2006. The other was a set of 24 CSV files, which were already split andgiven variable names.
Weight data by district and psu was prepared separately.

1) To import CSV data files into R, in order to make use of the variable names prepared by NSO.

2) To combine the top nine variables of each data file and generate the variable 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 import weight data into R and append weight to all data files.

*******************************************************************************

Import CSV files to R
# CSV files to be imported> list.files()[1] "HIES-2009-10-100%-Data-SEC_1_DEMOGRAPHIC.CSV"[2] "HIES-2009-10-100%-Data-SEC_2_SCHOOL_EDUCATION.CSV":[23] "HIES-2009-10-100%-Data-SEC_8_HOUSING.CSV"[24] "HIES-2009-10-100%-Data-SEC_9_LAND_ANIMAL.CSV"# Names of data frames in R
# using the sequential number in alphabetical order of CSV file names> (Rnames<-paste("R",formatC(1:24,width=2,flag="0"),sep=""))[1] "R01" "R02" "R03" "R04" "R05" "R06" "R07" "R08" "R09" "R10" "R11" "R12"

Sri Lanka HIES 2009/10 Manual (Version 1.1)

13[13] "R13" "R14" "R15" "R16" "R17" "R18" "R19" "R20" "R21" "R22" "R23" "R24"# Imported 24 CSV files to R and stored R data frames in the list hies2009.
> for(j in 1:24){ hies2009<-c(hies2009,list(read.csv(list.files()[j]))) }> dim(hies2009)# List of the number of rows and columns of the created data frames> file.names<-list.files()> for(j in 1:24){+ cat(Rnames[j],":",formatC(nrow(hies2009[[j]]),width=7),",",+ formatC(ncol(hies2009[[j]]),width=3),":",+ sub(".CSV","",sub("HIES-2009-10-100%-Data-","",file.names))[j],"\n",sep="")
+ }
R01:

85443, 25:

SEC_1_DEMOGRAPHIC

R02:

20853, 20:

SEC_2_SCHOOL_EDUCATION

R03:

80866, 22:

SEC_3_HEALTH

R04: 798046, 14:

SEC_4_1_FOOD_EXP

R05: 568665, 14:

SEC_4_2_NONFOOD

R06:240, 24:

SEC_4_3_BOARDERS

R07:

19958, 11:

SEC_4_3_IS_BOADERS

R08:

18369, 15:

SEC_5_1_EMP_INCOME

R09:

19958, 11:

SEC_5_1_IS_EMP_INCOME

R10:4278, 18:

SEC_5_2_AGRI_INCOME

R11:

19958, 11:

SEC_5_2_IS_AGRI_INCOME

R12:

19958, 11:

SEC_5_3_IS_OTHER_AGRI_INCOME

R13:

4196, 17:

SEC_5_3_OTHER_AGRI_INCOME

R14:

19958, 11:

SEC_5_4_IS_NON_AGRI_INCOME

R15:

5528, 14:

SEC_5_4_NON_AGRI_INCOME

R16:

19958, 11:

SEC_5_5_1_IS_OTHER_INCOME

R17:

10735, 19:

SEC_5_5_1_OTHER_INCOME

R18:

19958, 11:

SEC_5_5_2_IS_WINDFALL_INCOME

R19:

8154, 18:

SEC_5_5_2_WINDFALL_INCOME

R20:

19958, 26:

SEC_6_B_DEBTNESS

R21:19958, 33:

SEC_6A_DURABLE_GOODS

R22:19958, 45:

SEC_7_BASIC_FACILITIES

R23:

19958, 37:

SEC_8_HOUSING

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R24:

19958, 38:

SEC_9_LAND_ANIMAL# Saved hies2009> hies2009.old<-hies2009

ID: Household identifier
Generated the variable of ID consisted of the next 9 items for all data files;
DISTRICT(2), SECTOR(1), DSD(2), MONTH(2), PSU(3), SAMPLE_N(2), SERIAL_NO(1),NHH(1) and RESULT(1)> for(j in 1:24){+ d<-hies2009[[j]]+ d["ID"]<-as.character(d$DISTRICT*10^13+d$SECTOR*10^12+d$DSD*10^10+d$MONTH*10^8++ d$PSU*10^5+d$SAMPLE_N*10^3+d$SERIAL_NO*10^2+d$NHH*10+d$RESULT)+ hies2009[[j]]<-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.

> ind.files<-c(1,2,3,6,8,10,13,15,17,19)> for(j in ind.files){+ d<-hies2009[[j]]+ d["PID"]<-paste(d$ID,formatC(d[,11],width=2,flag="0"),sep="")+ hies2009[[j]]<-d+ }Import weight data by district and psu
Opened the weight file "HIES-2009-10-100%-Factor File.xls", and saved as “wt2009.csv”.

Imported “wt2009.csv”to R as data frame “wt”.

> wt<-read.csv("wt2009.csv")> dim(wt)

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15[1] 2269

3> colnames(wt)[1] "District.code" "PSU.Number""Weight"> colnames(wt)<-c("district","psu","WT")> head(wt)district psu

WT

1

11

1

41.420922

11

2 224.96960

311

3 211.77130

411

4 131.90840

511

5 160.39880

611

6 475.23530

Append weight to all data files> for(j in 1:24){+ d<-hies2009[[j]]+ d<-merge(d,wt,by.x=c("DISTRICT","PSU"),by.y=c("district","psu"))+ hies2009[[j]]<-d+ }# Number of rows and columns of each data file
> for(j in 1:24){+ cat(Rnames[j],":",formatC(nrow(hies2009[[j]]),width=7),",",+ formatC(ncol(hies2009[[j]]),width=3),": ",+ sub(".CSV","",sub("HIES-2009-10-100%-Data-","",file.names))[j],"\n",sep="")+ }R01: 85443, 28: SEC_1_DEMOGRAPHIC
R02: 20853, 23: SEC_2_SCHOOL_EDUCATION
R03: 80866, 25: SEC_3_HEALTH
R04: 798046, 16: SEC_4_1_FOOD_EXP
R05: 568665, 16: SEC_4_2_NONFOOD
R06:240, 27: SEC_4_3_BOARDERS
R07: 19958, 13: SEC_4_3_IS_BOADERS
R08: 18369, 18: SEC_5_1_EMP_INCOME
R09: 19958, 13: SEC_5_1_IS_EMP_INCOME
R10: 4278, 21: SEC_5_2_AGRI_INCOME
R11: 19958, 13: SEC_5_2_IS_AGRI_INCOME
R12: 19958, 13: SEC_5_3_IS_OTHER_AGRI_INCOME
R13: 4196, 20: SEC_5_3_OTHER_AGRI_INCOME
R14: 19958, 13: SEC_5_4_IS_NON_AGRI_INCOME
R15: 5528, 17: SEC_5_4_NON_AGRI_INCOME
R16: 19958, 13: SEC_5_5_1_IS_OTHER_INCOME
R17: 10735, 22: SEC_5_5_1_OTHER_INCOME
R18: 19958, 13: SEC_5_5_2_IS_WINDFALL_INCOME

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R19:
R20:
R21:
R22:
R23:
R24:

8154, 21:
19958, 28:
19958, 35:
19958, 47:
19958, 39:
19958, 40:

SEC_5_5_2_WINDFALL_INCOME
SEC_6_B_DEBTNESSSEC_6A_DURABLE_GOODS
SEC_7_BASIC_FACILITIESSEC_8_HOUSING
SEC_9_LAND_ANIMAL# Example: R01> head(R01)
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT PERSON_SERIAL_NO
1

11

1

1

1

0

7

1

1

1

1

1

2

11

1

1

1

0

7

1

1

1

1

2

3

11

1

1

1

0

7

1

1

1

1

3

4

11

1

1

1

0

7

1

1

1

1

4

5

11

1

1

1

0

7

1

1

1

1

5

6

11

1

1

1

0

7

1

1

1

1

6

RELATIONSHIP SEX_LIVING BIRTH_YEAR BIRTH_MONTH AGE ETHNICITY RELIGION CURR_EDUCATION
1

1

2

32

5 77

2

4

7

2

3

1

56

10 52

2

4

7

3

3

1

61

10 47

2

4

7

4

3

2

67

12 41

2

4

7

5

5

1

2

11

6

2

4

2

6

5

2

7

3

2

2

4

NA

EDUCATION MARITAL_STATUS MAIN_ACTIVITY MAIN_OCCUPATION INDUSTRY EMPLOYMENT_STATUS
1

19

3

5

NA

NA

NA

2

7

2

5

NA

NA

NA

3

8

1

2

NA

NA

NA

4

10

5

1

7433

1810

5

5

1

NA

NA

NA

NA

NA

6

NA

NA

NA

NA

NA

NA

ID

PID

WT

1 111000700101111 11100070010111101 41.42092
2 111000700101111 11100070010111102 41.42092
3 111000700101111 11100070010111103 41.42092
4 111000700101111 11100070010111104 41.42092
5 111000700101111 11100070010111105 41.42092
6 111000700101111 11100070010111106 41.42092

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17

# Saved hies2009> hies2009.old1<-hies2009

Remarks: R01
Out of 85,443, R01 included 4,571 records with PERSON_NO over 40, which were not regarded ashousehold members.


Omitted 4,571 records with PERSON_NO over 40.
After omitting such records, the number of records of R01 became 80,872. This is the final
number of household members.

> R01<-hies2009[[1]]> dim(R01)[1] 85443

28> addmargins(table(R01$PERSON_SERIAL_NO))
1

2

3

4

5

6

7

8

9

10

11

12

13

19958 19056 16470 12321 7156

3354

1417

582

264

138

71

43

21

14

15

16

17

41

42

43

44

45

46

47

48

49

12

7

1

1 3588

746

182

37

11

4

1

1

1

Sum
85443
> R01.old<-R01> R01<-subset(R01,PERSON_SERIAL_NO<41)> dim(R01)[1] 80872

28*********************************************************************************

Remarks: Objectives of recording persons with person number over 40
According to the delegates from Sri Lanka for the International Workshop,“Sri Lanka is a country with a large number of economic migrators and they bring the highest
foreign income to the country. HIES records their migration status and relationship th the household
and the questionnaire has a place to record the income the household received from such abroad
resources or from other local remittances. This two information is highly helpful and necessary inmaking a consistent household in between demography and economy.”

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18

Characteristics of persons omitted from R01, for reference

Persons with PERSON_SERIAL_NO > 40 are not regarded as the household members in HIES
because their usual residences are not in the household.
# Number of persons omitted from R01> R01.omit<-subset(R01.old,PERSON_SERIAL_NO>40)> dim(R01.omit)[1] 4571

28

For persons with person number > 40, the variables from BIRTH_YEAR to EMPLOYMENT_STATUS are
empty as in the questionnaire, and the variable SEX_LIVING is read as USUAL_RESIDENCE.

> R01.omit<-R01.omit[-14:-25]> colnames(R01.omit)[13]<-"USUAL_RESIDENCE"# Number of persons omitted by relationship to the household head
> addmargins(table(R01.omit$RELATIONSHIP,useNA="ifany"))
1

2

3

3 1346 2336

45

97 755

9 <NA> Sum
10

24 4571

Note: Three person are household heads. However, it is confirmed that there is a household headin the remaining household.
> R01.omit[!is.na(R01.omit$RELATIONSHIP)&R01.omit$RELATIONSHIP==1,"PID"][1] "91200080140111141" "91200080140111142" "91200080140111143"> R01.old[R01.old$ID=="912000801401111",c("ID","PERSON_SERIAL_NO","RELATIONSHIP",+ "SEX_LIVING","AGE","MARITAL_STATUS")]ID PERSON_SERIAL_NO RELATIONSHIP SEX_LIVING AGE MARITAL_STATUS
78833 912000801401111

1

1

1

60

2

78834 912000801401111

41

1

NA

NA

NA

78835 912000801401111

42

1

NA

NA

NA

78836 912000801401111

43

1

NA

NA

NA

# Number of persons omitted by usual residence> t<-addmargins(table(R01.omit$USUAL_RESIDENCE,useNA="ifany"))

Sri Lanka HIES 2009/10 Manual (Version 1.1)

19

> names(t)<-c("In the country","Abroad","<NA>","Sum")> t
In the country

Abroad

<NA>

Sum

3153

1413

5

4571

*************************************************************************

Remarks: R03 Health
The number of records of R03 is 80,866, which is 6 less than R01, and the variable of person numberin R03 has 3 NA.


Summary:
1) Omitted the records with person number=NA, because all data are NA.
2) 11 records with the next PID are missing as compared with R01.

File

RecordTO-DO

R03

person number = NA (3)To drop

R03

As compared with PIDs of R01, there are

The next are PIDs of missing records of R03;

11 PIDs without a corresponding PID in"22100040510211107", "51200110330611106" ,R03.

"53200110270311101", "53200110270311102","53200110270311103", "53200110270611102","53200110270611103", "53200110270611105","53200110270611106", "82200030470211101","91200060900411103"# Frequency of person number in R03> R03<-hies2009[[3]]> dim(R03)[1] 8086625> addmargins(table(R03$R3_PERSON_SERIAL,useNA="ifany"))
1

2

3

4

5

6

7

8

9

10

11

12

13

19957 19054 16467 12321 7155

3353

1416

582

264

138

71

43

21

14

15

16

17 <NA>

12

7

1

1

Sum

3 80866> R03[is.na(R03$R3_PERSON_SERIAL),c(24,11:22)]
PID R3_PERSON_SERIAL DID_ATTEND_HOSPITAL REASON_HOSPITALSri Lanka HIES 2009/10 Manual (Version 1.1)

2055805 532001102703111NA

NA

NA

NA

55806 532001102703111NA

NA

NA

NA

55807 532001102703111NA

NA

NA

NA

IS_STAY_HOSPITAL REASON_STAY IS_ILL_DISABLE WHAT_ILL_DISABLE IS_EMPL_REASON
55805

NA

NA

NA

NA

NA

55806

NA

NA

NA

NA

NA

55807

NA

NA

NA

NA

NA

DURATION_YEARS DURATION_MONTHS IS_ABSENT_ACT DAYS_ABSENT
55805

NA

NA

NA

NA

55806

NA

NA

NA

NA

55807

NA

NA

NA

NA

# Omitted the above 3 records.
> R03.old<-R03> R03<-R03[!is.na(R03$R3_PERSON_SERIAL),]> dim(R03)[1] 80863



25

The difference of PID between R01 and R03 is as follows.

> PID.e<-setdiff(R01$PID,R03$PID)> PID.e[1] "22100040510211107" "51200110330611106" "53200110270311101"[4] "53200110270311102" "53200110270311103" "53200110270611102"[7] "53200110270611103" "53200110270611105" "53200110270611106"[10] "82200030470211101" "91200060900411103"

Un-weighted and weighted number of households and household members

Un-weighted number

Weighted number

Number of households

19,9585,079,362

Number of household members

80,872

20,337,761> dim(R01)

Sri Lanka HIES 2009/10 Manual (Version 1.1)

21[1] 80872

28> sum(R01$WT)[1] 20337761> R23<-hies2009[[23]]> dim(R23)[1] 19958

39> sum(R23$WT)[1] 5079362# Updated R01 and R03, and saved hies2009> hies2009[[1]]<-R01> hies2009[[3]]<-R03> hies2009.old2<-hies2009

Sri Lanka HIES 2009/10 Manual (Version 1.1)

22

5 Data Check
5.1 Summary of each variable
SUMMARY OF EACH DATA FRAME> for(j in 1:24){+ cat("#### ",Rnames[j]," ################################################\n")+ print(summary(hies2009[[j]]))+ cat("\n\n")
+ }
####

R01 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.0 Min. : 1.00 Min. :1 Min. :1.000
1st Qu.:13.0 1st Qu.: 28.00
1st Qu.:1
1st Qu.:1.000
Median :32.0 Median : 55.00 Median :1 Median :2.000
Mean :40.4 Mean : 68.77 Mean :1 Mean :1.813
3rd Qu.:61.0
3rd Qu.: 90.00
3rd Qu.:1
3rd Qu.:2.000
Max. :92.0 Max. :300.00 Max. :1 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.512
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.448
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:4.000PERSON_SERIAL_NO RELATIONSHIP
Min.
: 1.00Min.
:1.000
1st Qu.: 2.00
1st Qu.:2.000
Median : 3.00
Median :3.000
Mean: 2.88Mean:2.661
3rd Qu.: 4.00
3rd Qu.:3.000
Max.
:17.00Max.
:9.000

SEX_LIVING
Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.5243rd Qu.:2.000
Max.
:2.000BIRTH_MONTH
AGE
Min.
: 1.000Min.
: 1.00
1st Qu.: 3.000 1st Qu.:15.00
Median : 6.000
Median :30.00
Mean: 6.373Mean:32.50
3rd Qu.: 9.000 3rd Qu.:48.25
Max.
:12.000Max.
:99.00NA's :442
NA's
:1316
CURR_EDUCATIONEDUCATION
Min.
:1.000Min.
: 1.000
1st Qu.:2.000 1st Qu.: 6.000
Median :7.000 Median :10.000
Mean:5.648Mean: 8.804
3rd Qu.:7.000 3rd Qu.:11.000
Max.
:9.000Max.
:19.000
NA's
:4209
NA's
:8692
MAIN_OCCUPATION
INDUSTRY

DSD
Min. :0
1st Qu.:0
Median :0
Mean :0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.015
3rd Qu.:1.000
Max.
:4.000BIRTH_YEAR
Min.
: 1.00
1st Qu.:46.00
Median :67.00
Mean:60.56
3rd Qu.:84.00
Max.
:99.00
NA's:1536
ETHNICITYRELIGION
Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000
Mean:1.627Mean:1.685
3rd Qu.:2.000 3rd Qu.:2.000
Max.
:9.000Max.
:9.000
NA's
:11
NA's
:9
MARITAL_STATUS MAIN_ACTIVITY
Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000
Median :2.000 Median :3.000
Mean:1.773Mean:2.541
3rd Qu.:2.000 3rd Qu.:4.000
Max.
:5.000Max.
:9.000NA's:14178NA's:14197EMPLOYMENT_STATUSID

Sri Lanka HIES 2009/10 Manual (Version 1.1)

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

23

Min.
: 110
Min.
: 111
Min.
:1.00
1st Qu.:4116
1st Qu.: 434
1st Qu.:3.00
Median :6153
Median :4521
Median :3.00
Mean
:6149
Mean
:3987
Mean
:3.44
3rd Qu.:8323
3rd Qu.:6519
3rd Qu.:5.00
Max.
:9411
Max.
:9900
Max. :9.00
NA's:53209NA's:53218NA's:53176PID
WT
Length:80872
Min.
:
3.467
Class :character
1st Qu.: 84.095
Mode :character
Median : 249.479
Mean: 251.481
3rd Qu.: 377.676
Max. :1555.993

Length:80872
Class :character
Mode :character

Remarks: R01
1. The range of the variable AGE is from 1 to 99, and there are 1,316 NA. The reason might bethat the age 0 is represented as NA (blank in CSV file) because the question states “Age as atlast birthday”. Most of persons with AGE=NA born in the year 2008, 2009 or 2010.
> table(subset(R01,is.na(AGE))$BIRTH_YEAR,useNA="ifany")
8
164

9

10

28

58

91 <NA>991 140

1

1

2

17

Note: According to the delegates from Sri Lanka for the International Workshop, “Blank represents‘Not reported’ and 0 is used when a member is less than one year old.” However, blank code of age
variable is found in the provided CSV and Text files.

2. The number of missing values of the variables taking into consideration the target age groups

Variable

Target respondent

Number of NA

CURR_EDUCATION

Aged 3 and over

20

EDUCATION

Aged 5 and over

1,538

MARITAL_STATUS

Aged 10 and over8

MAIN_ACTIVITY

Aged 10 and over26

MAIN_OCCUPATION

MAIN_ACTIVITY=1 (Employed)

34

INDUSTRY

MAIN_ACTIVITY=1 (Employed)

43

EMPLOYMENT_STATUS

MAIN_ACTIVITY=1 (Employed)

6

Sri Lanka HIES 2009/10 Manual (Version 1.1)

24

> nrow(subset(R01,AGE>=3&is.na(CURR_EDUCATION)))[1] 20> nrow(subset(R01,AGE>=5&is.na(EDUCATION)))[1] 1538> nrow(subset(R01,AGE>=10&is.na(MARITAL_STATUS)))[1] 8> nrow(subset(R01,AGE>=10&is.na(MAIN_ACTIVITY)))[1] 26> nrow(subset(R01,MAIN_ACTIVITY==1&is.na(MAIN_OCCUPATION)))[1] 34> nrow(subset(R01,MAIN_ACTIVITY==1&is.na(INDUSTRY)))[1] 43> nrow(subset(R01,MAIN_ACTIVITY==1&is.na(EMPLOYMENT_STATUS)))
[1] 6

####

R02 ################################################
DISTRICTPSU
REC_TYPESECTOR
DSD
Min.
:11.0
Min.
: 1.00
Min.
:2
Min.
:1.00
Min.
:0
1st Qu.:13.0
1st Qu.: 27.00
1st Qu.:2
1st Qu.:1.00
1st Qu.:0
Median :33.0
Median : 53.00
Median :2
Median :2.00
Median :0
Mean
:41.7
Mean
: 65.95
Mean :2 Mean :1.82 Mean :0
3rd Qu.:61.0
3rd Qu.: 85.00
3rd Qu.:2
3rd Qu.:2.00
3rd Qu.:0
Max.
:92.0
Max.
:300.00
Max.
:2
Max.
:3.00
Max.
:0
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.509
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.442
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000NHH
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

R2_PERSON_SERIAL R2_SCHOOL_EDUCATION GRADE_THIS_YEAR GRADE_LAST_YEAR
Min.
: 1.000
Min.
:1.000Min.
: 1.000Min.
: 1.000
1st Qu.: 3.000
1st Qu.:1.000
1st Qu.: 4.000
1st Qu.: 3.000
Median : 4.000
Median :1.000
Median : 7.000
Median : 6.000
Mean: 3.941Mean:1.307Mean: 6.689Mean: 7.013
3rd Qu.: 5.000
3rd Qu.:1.000
3rd Qu.:10.000 3rd Qu.:10.000
Max.
:15.000
Max.
:9.000Max.
:19.000Max.
:19.000
NA's
:6
NA's
:3574
NA's
:3636
DISTANCE
TRANSPORT_MEDIUM TIME_TO_SCHOOL NOSCHOOLING_REASON
Min. : 1.000 Min. :1.000
Min. : 1.00 Min. :1.000
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.: 10.00
1st Qu.:7.000
Median : 2.000 Median :2.000
Median : 15.00 Median :7.000
Mean : 4.593 Mean :2.465
Mean : 20.45 Mean :6.611
3rd Qu.: 5.000
3rd Qu.:4.000
3rd Qu.: 30.00
3rd Qu.:7.000

Sri Lanka HIES 2009/10 Manual (Version 1.1)

25

Max. :80.000 Max. :9.000
Max. :150.00 Max. :9.000
NA's :9026
NA's :3598
NA's :3614
NA's :20111
REASON_NOT_GOING WHEN_STOP_SCHOOLING
ID
PID
Min.
:1.000Min.
:
1
Length:20853
Length:20853
1st Qu.:3.000
1st Qu.:2006
Class :character Class :character
Median :6.000
Median :2007
Mode :character
Mode :character
Mean:5.537Mean:1997
3rd Qu.:7.000
3rd Qu.:2008
Max.
:9.000
Max.
:2078
NA's
:18043NA's:18292
WT
Min.
:
3.467
1st Qu.: 82.206
Median : 241.497
Mean: 247.548
3rd Qu.: 373.299
Max.
:1555.993

Remarks: R02
The target of R02 is persons aged 5-19 years. The number of persons aged 5-19 of R02 whomatched with the corresponding record in R01 is 20,847. The records with the following PID are
missing.
> nrow(merge(R02,subset(R01,AGE>=5&AGE<=19),by="PID"))[1] 20847# Household members aged 5-19 in R01 who have no corresponding records in R02;
> setdiff(subset(R01,AGE>=5&AGE<=19)$PID,R02$PID)[1] "11200111110211104" "12200021280211104" "13200080260511103"[4] "13200090360411103" "13200090410811106" "13200090410811107"[7] "13200100540811103" "13200100540911103" "13300100580911103"[10] "13200120760411103" "13200120770111103" "13200120770611104"[13] "22100040510211107" "22200050541011105" "31200070070311105"[16] "52200110390411103" "53200110270111102" "53200110270411103"[19] "53200110270511103" "53100120290711107" "71200120430311103"[22] "82200030470211102" "91200080160611102" "91300080170611103"[25] "91200090200911103" "91200090201011104" "91300070040911105"[28] "91200060900111104" "91200060900311103"

Sri Lanka HIES 2009/10 Manual (Version 1.1)

26

####

R03 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.0 Min. : 1.00 Min. :3 Min. :1.000
1st Qu.:13.0
1st Qu.: 28.00
1st Qu.:3
1st Qu.:1.000
Median :32.0 Median : 55.00 Median :3 Median :2.000
Mean :40.4 Mean : 68.77 Mean :3 Mean :1.813
3rd Qu.:61.0
3rd Qu.: 90.00
3rd Qu.:3
3rd Qu.:2.000
Max.
:92.0
Max. :300.00 Max. :3 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.512
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.448
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:4.000DSD
Min. :0
1st Qu.:0
Median :0
Mean :0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.015
3rd Qu.:1.000
Max.
:4.000RESULT
Min. :1
1st Qu.:1
Median :1
Mean :1
3rd Qu.:1
Max.
:1

R3_PERSON_SERIAL DID_ATTEND_HOSPITAL REASON_HOSPITAL IS_STAY_HOSPITAL
Min.
: 1.000
Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.: 2.000
1st Qu.:1.000
1st Qu.:1.00
1st Qu.:2.000
Median : 3.000
Median :2.000
Median :1.00
Median :2.000
Mean: 2.879
Mean:1.703
Mean
:1.24
Mean
:1.896
3rd Qu.: 4.000
3rd Qu.:2.000
3rd Qu.:1.00
3rd Qu.:2.000
Max. :17.000 Max. :4.000
Max. :9.00
Max. :9.000
NA's :38
NA's :56848 NA's :51
REASON_STAY
IS_ILL_DISABLE WHAT_ILL_DISABLE IS_EMPL_REASON
Min.
:1.00
Min.
:1.000
Min.
: 1.0
Min.
:1.00
1st Qu.:1.00
1st Qu.:2.000 1st Qu.: 2.0
1st Qu.:2.00
Median :1.00
Median :2.000 Median : 4.0
Median :2.00
Mean
:2.11
Mean
:1.856
Mean
: 6.8
Mean
:1.93
3rd Qu.:3.00
3rd Qu.:2.000 3rd Qu.:11.0
3rd Qu.:2.00
Max.
:9.00
Max.
:2.000
Max.
:51.0
Max.
:4.00
NA's
:72476
NA's
:57
NA's
:69233NA's:69309DURATION_YEARS DURATION_MONTHS IS_ABSENT_ACT
DAYS_ABSENTMin.
: 1.00Min.
: 1.00
Min.
:1.00
Min.
: 1.0
1st Qu.: 3.00
1st Qu.: 2.00
1st Qu.:2.00
1st Qu.: 7.0
Median : 5.00
Median : 5.00
Median :2.00
Median :25.0
Mean: 8.15Mean: 4.69
Mean
:1.81
Mean
:19.5
3rd Qu.:10.00
3rd Qu.: 6.00
3rd Qu.:2.00
3rd Qu.:30.0
Max.
:75.00Max.
:26.00
Max.
:2.00
Max.
:90.0
NA's
:70163NA's:76785NA's:69404NA's:78755ID
PID
WT
Length:80863
Length:80863
Min.
:
3.467
Class :character
Class :character
1st Qu.: 84.095
Mode :character
Mode :character
Median : 249.479
Mean : 251.483
3rd Qu.: 377.676
Max. :1555.993####

R04 ################################################
DISTRICTPSU
REC_TYPESECTORMin.
:11.00Min.
: 1.00Min.
:4Min.
:1.000
1st Qu.:13.00 1st Qu.: 27.00 1st Qu.:4 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :4Median :2.000

DSD
Min.
:0
1st Qu.:0
Median :0

Sri Lanka HIES 2009/10 Manual (Version 1.1)

27

Mean
:40.12
3rd Qu.:61.00
Max.
:92.00Mean: 69.01
3rd Qu.: 90.00
Max.
:300.00

Mean:4
3rd Qu.:4
Max.
:4

Mean
:1.8033rd Qu.:2.000
Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.514
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min. : 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.441
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000CODE
Min.
: 101.0
1st Qu.: 415.0
Median :1102.0
Mean: 904.4
3rd Qu.:1120.0
Max.
:1924.0

Mean:0
3rd Qu.:0
Max.
:0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.016
3rd Qu.:1.000
Max.
:4.000QUANTITY
VALUE
Min.
:
1.0
Min.
:
1.00
1st Qu.:50.0
1st Qu.:20.00
Median : 200.0
Median :40.00
Mean: 596.3
Mean
:
83.87
3rd Qu.: 500.0
3rd Qu.:90.00
Max.
:50000.0Max.
:30000.00
NA's:145525
ID
WT
Length:798046
Min.
:
3.467
Class :character
1st Qu.: 87.500
Mode :character
Median : 258.978
Mean: 255.198
3rd Qu.: 381.289
Max. :1555.993

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

INKIND_VALUE
Min.
:
1.0
1st Qu.: 10.0
Median : 30.0
Mean
: 94.9
3rd Qu.: 80.0
Max.
:6000.0
NA's:728119####

R05 ################################################
DISTRICTPSU
REC_TYPESECTOR
DSD
Min.
:11.00Min.
: 1.00
Min.
:5
Min.
:1.0
Min.
:0
1st Qu.:13.00 1st Qu.: 26.00 1st Qu.:5 1st Qu.:1.0 1st Qu.:0
Median :32.00
Median : 54.00
Median :5Median :2.0
Median :0
Mean:40.27
Mean: 67.32
Mean
:5
Mean
:1.8
Mean
:0
3rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:5 3rd Qu.:2.0 3rd Qu.:0
Max.
:92.00Max.
:300.00
Max.
:5
Max.
:3.0
Max.
:0
MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.528
3rd Qu.:10.000
Max.
:12.000
NF_CODEMin.
:2001
1st Qu.:2202
Median :2605
Mean :2647
3rd Qu.:3013
Max. :3509
ID

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.427
3rd Qu.: 8.000
Max. :10.000

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

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.015
3rd Qu.:1.000
Max.
:4.000NF_QUANTITY
Min.
:
1.0
1st Qu.:1.0
Median :2.0
Mean : 323.5
3rd Qu.:4.0
Max. :75000.0
NA's :354022
WT

NF_VALUEMin.
:
1
1st Qu.:100
Median :276
Mean : 1736
3rd Qu.:800
Max. :7000000
NA's :1

NF_INKIND_VALUE
Min. :
1
1st Qu.:300
Median :650
Mean : 2694
3rd Qu.: 2000
Max. :4000000
NA's :525297

Sri Lanka HIES 2009/10 Manual (Version 1.1)

RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max. :1

28

Length:568665
Class :character
Mode :character

Min. :
3.467
1st Qu.: 84.460
Median : 253.296
Mean: 252.226
3rd Qu.: 377.893
Max. :1555.993####

R06 ################################################
DISTRICTPSU
REC_TYPESECTOR
DSD
Min.
:11.00
Min.
: 1.0
Min.
:7
Min.
:1.00
Min.
:0
1st Qu.:11.00
1st Qu.: 29.0
1st Qu.:7
1st Qu.:1.00
1st Qu.:0
Median :13.00
Median : 67.0
Median :7
Median :1.00
Median :0
Mean:28.56
Mean: 83.8
Mean
:7
Mean
:1.45
Mean
:0
3rd Qu.:33.00
3rd Qu.:130.2
3rd Qu.:7
3rd Qu.:2.00
3rd Qu.:0
Max.
:92.00Max.
:271.0Max. :7 Max. :3.00 Max. :0
MONTH
Min. : 1.000
1st Qu.: 3.000
Median : 7.000
Mean : 6.287
3rd Qu.: 9.000
Max. :12.000

SAMPLE_N
Min. : 1.000
1st Qu.: 2.000
Median : 5.000
Mean : 5.021
3rd Qu.: 7.000
Max. :10.000

SERIAL_NO
NHH
Min. :1 Min. :1
1st Qu.:1
1st Qu.:1
Median :1 Median :1
Mean :1 Mean :1
3rd Qu.:1
3rd Qu.:1
Max. :1 Max. :1

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

COL_2
Min.
: 2.000
1st Qu.: 3.000
Median : 4.000
Mean : 4.642
3rd Qu.: 6.000
Max. :12.000

COL_3
COL_4
COL_5
Min. : 100.0 Min. : 50.0 Min. : 240.0
1st Qu.: 200.0
1st Qu.: 53.0
1st Qu.: 812.5
Median : 500.0 Median : 150.0 Median : 1500.0
Mean : 774.7 Mean : 330.8 Mean : 2127.0
3rd Qu.:1000.0
3rd Qu.: 500.0
3rd Qu.: 3000.0
Max. :4320.0 Max. :1500.0 Max. :14500.0
NA's :167
NA's :228
NA's :134
COL_6
COL_7
COL_8
COL_9
Min.
:
50.0
Min.
: 200
Min.
: 38.0Min.
: 50
1st Qu.: 200.0
1st Qu.: 625
1st Qu.: 200.0
1st Qu.: 200
Median : 350.0
Median :3500
Median : 300.0
Median : 500
Mean: 788.5
Mean:3400 Mean
: 538.4
Mean: 750
3rd Qu.: 500.0 3rd Qu.:6000 3rd Qu.: 600.0 3rd Qu.:1050
Max.
:14000.0
Max.
:7000
Max.
:4500.0
Max.
:3500
NA's
:201
NA's
:232
NA's
:131
NA's
:133
COL_10
COL_11
COL_12
COL_13
COL_14
Min.
: 100
Min.
: 250
Min.
: 500
Min.
: 500
Min.
:
75
1st Qu.: 200 1st Qu.: 1625 1st Qu.:1500 1st Qu.: 2000 1st Qu.: 1000
Median : 720
Median : 4750
Median :4000
Median : 3000
Median : 2000
Mean
:1209
Mean
: 8657
Mean
:3314
Mean
: 3635Mean: 30033rd Qu.:1625 3rd Qu.: 9410 3rd Qu.:4500 3rd Qu.: 5000 3rd Qu.: 4450
Max.
:5000
Max.
:55000
Max.
:8000
Max.
:12000 Max. :12837
NA's :204
NA's :221
NA's :165
NA's :126
NA's :182
COL_15
ID
PID
WT
Min.
: 100.0
Length:240
Length:240
Min.
:
7.483
1st Qu.: 200.0
Class :character
Class :character
1st Qu.: 74.156
Median : 500.0
Mode :character
Mode :character
Median : 228.936
Mean: 918.6
Mean: 242.918
3rd Qu.:1000.0
3rd Qu.: 366.536

Sri Lanka HIES 2009/10 Manual (Version 1.1)

29

Max.
NA's

:4500.0:208

Max.

:1488.380

R07 ################################################
DISTRICTPSU
REC_TYPESECTORMin.
:11.00Min.
: 1.00Min.
:6Min.
:1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:6 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :6
Median :2.000
Mean:40.82
Mean: 68.22
Mean
:6
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:6 3rd Qu.:2.000
Max.
:92.00Max.
:300.00
Max.
:6
Max.
:3.000DSD
Min.
:01st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max.
:0

####

MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000

IS_BOARDERS_SERVENTS
ID
Min. :1.000
Length:19958
1st Qu.:2.000
Class :character
Median :2.000
Mode :character
Mean:1.991
3rd Qu.:2.000
Max.
:2.000
NA's
:4

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

WT
Min. : 3.467
1st Qu.: 85.574
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R08 ################################################
DISTRICTPSU
REC_TYPESECTORMin.
:11.00Min.
: 1.00Min.
:9Min.
:1.000
1st Qu.:13.00 1st Qu.: 29.00 1st Qu.:9 1st Qu.:1.000
Median :31.00
Median : 56.00
Median :9Median :2.000
Mean:38.96
Mean: 71.11
Mean
:9
Mean
:1.887
3rd Qu.:61.00 3rd Qu.: 93.00 3rd Qu.:9 3rd Qu.:2.000
Max.
:92.00Max.
:300.00
Max.
:9
Max.
:3.000DSD
Min.
:01st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max.
:0

MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.536
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.468
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.016
3rd Qu.:1.000
Max.
:4.000SERIAL_NO_SEC_1
Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.096
3rd Qu.: 3.000
Max.
:14.000

PRI_SECMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.011
3rd Qu.:1.000
Max.
:2.000NA's:9

WAGES_SALARIESMin.
:
1
1st Qu.: 6300
Median : 10000
Mean: 12877
3rd Qu.: 16000
Max.
:600000
NA's:128

ALLOWENCES
Min.
:
30
1st Qu.: 2000
Median : 4000
Mean
: 5021
3rd Qu.: 5500
Max.
:95000NA's:15598

Sri Lanka HIES 2009/10 Manual (Version 1.1)

RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

30

BONUS
Min.
:
6
1st Qu.: 2500
Median : 5000
Mean
: 17393
3rd Qu.: 15000
Max.
:600000
NA's:17179ID
Length:18369
Class :character
Mode :character

PID
Length:18369
Class :character
Mode :character

WT
Min.
:
3.467
1st Qu.: 77.523
Median : 236.602
Mean : 244.958
3rd Qu.: 373.210
Max.
:1555.993####

R09 ################################################
DISTRICTPSU
REC_TYPESECTORMin.
:11.00Min.
: 1.00Min.
:8Min.
:1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:8 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :8Median :2.000
Mean:40.82
Mean: 68.22
Mean
:8
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:8 3rd Qu.:2.000
Max.
:92.00Max.
:300.00
Max.
:8
Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min. : 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.008
3rd Qu.:1.000
Max.
:4.000

IS_EMPLOYMENT_INCOME
ID
Min.
:1.000Length:19958
1st Qu.:1.000
Class :character
Median :1.000
Mode :character
Mean:1.362
3rd Qu.:2.000
Max.
:2.000
NA's
:1

DSD
Min.
:0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max.
:0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULT
Min. :1
1st Qu.:1
Median :1
Mean :1
3rd Qu.:1
Max.
:1

WT
Min.
:
3.467
1st Qu.: 85.574
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R10 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :11 Min. :1.000
1st Qu.:33.00 1st Qu.: 20.00 1st Qu.:11 1st Qu.:2.000
Median :61.00
Median : 41.00
Median :11
Median :2.000
Mean:57.02Mean: 46.74
Mean
:11
Mean
:2.001
3rd Qu.:81.00 3rd Qu.: 65.00 3rd Qu.:11 3rd Qu.:2.000
Max. :92.00 Max. :288.00 Max. :11 Max. :3.000
MONTH
Min. : 1.000
1st Qu.: 4.000
Median : 7.000
Mean : 6.668
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min. : 1.00
1st Qu.: 3.00
Median : 5.00
Mean : 5.46
3rd Qu.: 8.00
Max.
:10.00DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean :1.002
3rd Qu.:1.000
Max.
:2.000NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean :1.005
3rd Qu.:1.000
Max.
:2.000

SER_NO_SEC_5_2 SEAS_CROPS_CODE
ACR_5_2
Min. :1.000 Min. :1.000 Min. : 1.000

RT_5_2
Min. :1.00

Sri Lanka HIES 2009/10 Manual (Version 1.1)

RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

31

1st Qu.:1.000
Median :1.000
Mean :1.283
3rd Qu.:1.000
Max. :8.000

1st Qu.:1.000 1st Qu.: 1.000 1st Qu.:1.00
Median :1.000 Median : 1.000 Median :2.00
Mean :2.211 Mean : 2.057 Mean :1.81
3rd Qu.:4.000 3rd Qu.: 2.000 3rd Qu.:2.00
Max. :9.000 Max. :40.000 Max. :8.00
NA's:11
NA's :2027
NA's :2125
P
OUTPUT_5_2
HH_CONSUMPTIONINPUT_5_2
Min. : 1.00 Min. :50 Min. :10 Min. :10
1st Qu.:10.00 1st Qu.: 17000 1st Qu.: 3000 1st Qu.: 6000
Median :20.00
Median : 40000
Median : 12000 Median : 14850
Mean :17.06 Mean : 77518 Mean : 15430 Mean : 29865
3rd Qu.:20.00 3rd Qu.: 92625 3rd Qu.: 22580 3rd Qu.: 34200
Max. :39.00 Max. :1512000 Max. :250000 Max. :974000
NA's
:3844
NA's :134
NA's :735
NA's :361
ID
PID
WT
Length:4278
Length:4278
Min. : 3.732
Class :character
Class :character
1st Qu.: 249.934
Mode :character
Mode :character Median : 322.222
Mean : 322.967
3rd Qu.: 395.029
Max. :1555.993####

R11 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :10 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:10 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :10
Median :2.000
Mean:40.82
Mean: 68.22
Mean
:10
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:10 3rd Qu.:2.000
Max. :92.00 Max. :300.00 Max. :10 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000

IS_AGRICULTURAL_INCOME
ID
Min.
:1.000Length:19958
1st Qu.:2.000
Class :character
Median :2.000
Mode :character
Mean:1.828
3rd Qu.:2.000
Max.
:2.000NA's:4

DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

WT
Min.
:
3.467
1st Qu.: 85.574
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R12 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :12 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:12 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :12
Median :2.000

DSD
Min. :0
1st Qu.:0
Median :0

Sri Lanka HIES 2009/10 Manual (Version 1.1)

32

Mean
:40.82
3rd Qu.:61.00
Max. :92.00

Mean: 68.22
3rd Qu.: 89.00
Max. :300.00

Mean
:12
3rd Qu.:12
Max. :12

MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000

IS_OTHER_AGRRI_INCOME
ID
Min. :1.000
Length:19958
1st Qu.:2.000
Class :character
Median :2.000
Mode :character
Mean:1.827
3rd Qu.:2.000
Max.
:2.000
NA's
:3

Mean
:1.8233rd Qu.:2.000
Max. :3.000

Mean:0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

WT
Min. : 3.467
1st Qu.: 85.574
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R13 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :13 Min. :1.000
1st Qu.:22.00 1st Qu.: 24.00 1st Qu.:13 1st Qu.:2.000
Median :33.00
Median : 50.00
Median :13
Median :2.000
Mean:48.68Mean: 57.46
Mean
:13
Mean
:1.956
3rd Qu.:81.00 3rd Qu.: 79.00 3rd Qu.:13 3rd Qu.:2.000
Max.
:92.00 Max. :288.00 Max. :13 Max. :3.000

DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.482
3rd Qu.: 9.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.437
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean:1.002
3rd Qu.:1.000
Max.
:2.000NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:2.000

SER_NO_SEC_5_3
Min. : 1.000
1st Qu.: 1.000
Median : 1.000
Mean : 1.316
3rd Qu.: 1.000
Max. :13.000

SEASONAL_CROP
Min. : 1.000
1st Qu.: 2.000
Median : 2.000
Mean : 3.898
3rd Qu.: 4.000
Max. :19.000
NA's:4
OUTPUT_5_3
Min.
:
50
1st Qu.: 1200
Median : 4500
Mean
: 24679
3rd Qu.: 15000
Max. :2250000
NA's
:111
WT

ACRES_5_3
Min. : 1.000
1st Qu.: 1.000
Median : 1.000
Mean : 2.347
3rd Qu.: 2.000
Max. :52.000
NA's :3183
INPUT_5_3
Min.
:12
1st Qu.:400
Median : 1500
Mean
: 12608
3rd Qu.: 5662
Max. :1400000
NA's
:1358

ROOTS_5_3
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.741
3rd Qu.:2.000
Max. :5.000
NA's :2486
ID
Length:4196
Class :character
Mode :character

PERCHS_5_3
Min.
: 1.0
1st Qu.:10.0
Median :20.0
Mean
:17.1
3rd Qu.:20.0
Max. :38.0
NA's
:3637
PID

Sri Lanka HIES 2009/10 Manual (Version 1.1)

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

33

Length:4196
Class :character
Mode :character

Min.
:
3.502
1st Qu.: 239.957
Median : 336.859
Mean: 314.812
3rd Qu.: 413.665
Max. :1467.255####

R14 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :14 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:14 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :14
Median :2.000
Mean:40.82
Mean: 68.22
Mean
:14
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:14 3rd Qu.:2.000
Max. :92.00 Max. :300.00 Max. :14 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000

IS_NON_AGRI_INCOME
ID
Min.
:1.000Length:19958
1st Qu.:2.000
Class :character
Median :2.000
Mode :character
Mean:1.755
3rd Qu.:2.000
Max.
:2.000NA's:2

DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

WT
Min. : 3.467
1st Qu.: 85.574
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R15 ################################################
DISTRICTPSU
REC_TYPESECTORMin.
:11.00 Min. : 1.00 Min. :15 Min. :1.000
1st Qu.:12.00 1st Qu.: 29.00 1st Qu.:15 1st Qu.:1.000
Median :32.00
Median : 58.00
Median :15
Median :2.000
Mean:38.27
Mean: 73.15
Mean :15 Mean :1.709
3rd Qu.:61.00 3rd Qu.:100.00 3rd Qu.:15 3rd Qu.:2.000
Max. :92.00 Max. :300.00 Max. :15 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.484
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.393
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.005
3rd Qu.:1.000
Max.
:3.000SERIAL_5_4
Min.
: 1.000
1st Qu.: 1.000

NON_AGRI
Min.
:1.000
1st Qu.:4.000

OUTPUT_5_4
Min.
:9
1st Qu.:
10000

DSD
Min. :0
1st Qu.:0
Median :0
Mean :0
3rd Qu.:0
Max. :0

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

INPUT_5_4
Min. :
25
1st Qu.:4600

Sri Lanka HIES 2009/10 Manual (Version 1.1)

34Median : 1.000
Mean: 1.721
3rd Qu.: 2.000
Max. :13.000

Median :4.000
Median :22800
Median :13500
Mean:4.317
Mean:
148736
Mean:
1356033rd Qu.:5.000 3rd Qu.:
56000 3rd Qu.:
45000
Max. :7.000 Max. :180000000 Max. :143680000
NA's
:6
NA's
:91
NA's
:792
ID
PID
WT
Length:5528
Length:5528
Min. : 3.467
Class :character
Class :character
1st Qu.: 78.632
Mode :character
Mode :character
Median : 244.886
Mean : 249.758
3rd Qu.: 382.143
Max. :1555.993####

R16 ################################################
DISTRICTPSU
REC_TYPESECTOR
DSD
Min. :11.00 Min. : 1.00 Min. :16 Min. :1.000 Min. :0
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:16 1st Qu.:1.000 1st Qu.:0
Median :32.00
Median : 55.00
Median :16
Median :2.000
Median :0
Mean:40.82
Mean: 68.22
Mean
:16
Mean
:1.823Mean:0
3rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:16 3rd Qu.:2.000 3rd Qu.:0
Max. :92.00 Max. :300.00 Max. :16 Max. :3.000 Max. :0
MONTH
SAMPLE_N
SERIAL_NO
NHH
RESULTMin.
: 1.000Min.
: 1.000
Min.
:1.000Min.
:1.000Min.
:1
1st Qu.: 3.000
1st Qu.: 3.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1
Median : 6.000
Median : 5.000
Median :1.000
Median :1.000
Median :1
Mean: 6.506
Mean: 5.451
Mean:1.008Mean:1.017Mean:1
3rd Qu.:10.000
3rd Qu.: 8.000
3rd Qu.:1.000
3rd Qu.:1.000
3rd Qu.:1
Max.
:12.000Max.
:10.000
Max.
:4.000Max.
:4.000 Max. :1
IS_OTHER_INCOME
ID
WT
Min.
:1.000Length:19958
Min.
:
3.467
1st Qu.:1.000 Class :character 1st Qu.: 85.574
Median :2.000
Mode :character
Median : 254.568
Mean:1.508Mean: 254.503
3rd Qu.:2.000
3rd Qu.: 381.873
Max.
:2.000Max.
:1555.993####

R17 ################################################
DISTRICTPSU
REC_TYPESECTORMin.
:11.00Min.
: 1.00
Min.
:17
Min.
:1.00
1st Qu.:21.00
1st Qu.: 26.00
1st Qu.:17
1st Qu.:1.00
Median :32.00
Median : 53.00
Median :17
Median :2.00
Mean:40.86
Mean: 64.33
Mean
:17
Mean
:1.78
3rd Qu.:61.00
3rd Qu.: 85.00
3rd Qu.:17
3rd Qu.:2.00
Max.
:92.00Max.
:300.00
Max.
:17
Max.
:3.00
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.546
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.422
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.007
3rd Qu.:1.000
Max.
:3.000DSD
Min.
:01st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max.
:0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000

Sri Lanka HIES 2009/10 Manual (Version 1.1)

RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

35

SERIAL_5_5_1
Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.415
3rd Qu.: 1.000
Max.
:16.000
SAMURDHI
Min.
:
61.0
1st Qu.: 340.0
Median : 360.0
Mean : 513.1
3rd Qu.: 600.0
Max. :14400.0
NA's
:6138
LOCAL
Min.
:
200
1st Qu.: 8000
Median : 24000
Mean: 62412
3rd Qu.: 72000
Max.
:2400000
NA's
:9195

PENSIONDISABILITY_AND_RELIEF PROPERTY_RENTS
Min.
:
100
Min.
: 100
Min.
:
150
1st Qu.: 9031
1st Qu.: 100
1st Qu.: 3000
Median : 12000
Median : 200
Median : 6000
Mean
: 13471
Mean: 1046
Mean : 14048
3rd Qu.: 17000
3rd Qu.: 500
3rd Qu.: 13575
Max.
:150000
Max.
:35000Max.
:300000
NA's
:9120
NA's
:10185
NA's:9957
DIVIDENDSOTHERABROAD
Min.
:
8
Min.
:
100
Min.
:
608
1st Qu.: 750 1st Qu.: 2200 1st Qu.: 20000
Median : 4500
Median :5000
Median : 60000
Mean : 11127 Mean : 12688 Mean : 129946
3rd Qu.: 11750 3rd Qu.: 12000 3rd Qu.: 156000
Max. :200000 Max. :2100000 Max. :2400000
NA's:10437NA's :8798
NA's :9235
ID
PID
WT
Length:10735
Length:10735
Min.
:
3.467
Class :character Class :character 1st Qu.: 85.321
Mode :character
Mode :character
Median : 252.779
Mean: 252.606
3rd Qu.: 378.331
Max.
:1555.993####

R18 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :18 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:18 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :18
Median :2.000
Mean:40.82 Mean : 68.22 Mean :18 Mean :1.823
3rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:18 3rd Qu.:2.000
Max. :92.00 Max. :300.00 Max. :18 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000

IS_WINDFALL_INCOME
ID
Min.
:1.000Length:19958
1st Qu.:1.000
Class :character
Median :2.000
Mode :character
Mean:1.619
3rd Qu.:2.000
Max.
:2.000NA's:4

DSD
Min. :0
1st Qu.:0
Median :0
Mean :0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

WT
Min.
: 3.467
1st Qu.: 85.574
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R19 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00
Min.
: 1.0
Min.
:19
Min.
:1.000Min.

Sri Lanka HIES 2009/10 Manual (Version 1.1)

DSD
:0

36

1st Qu.:13.00
Median :32.00
Mean:41.19
3rd Qu.:62.00
Max.
:92.00

1st Qu.: 25.0
Median : 50.0
Mean
: 63.7
3rd Qu.: 84.0
Max.
:299.0

1st Qu.:19
Median :19
Mean:19
3rd Qu.:19
Max.
:19

MONTH
Min.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.618
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.479
3rd Qu.: 8.000
Max.
:10.000

1st Qu.:1.000
Median :2.000
Mean :1.856
3rd Qu.:2.000
Max.
:3.000SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:3.000

1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max.
:0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0143rd Qu.:1.000
Max.
:3.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max. :1

PERSON_5_5_2
LOANS
PAWNING_SELLING DEPOSITS_PENSIONS_EPF
Min. : 1.000 Min. :500 Min. :500 Min. :100
1st Qu.: 1.000
1st Qu.: 15000
1st Qu.: 12000
1st Qu.: 10000
Median : 1.000
Median : 40000
Median : 30000
Median : 25000
Mean : 1.561 Mean : 91659 Mean : 59650 Mean : 107267
3rd Qu.: 2.000
3rd Qu.: 100000 3rd Qu.: 60000 3rd Qu.: 100000
Max.
:41.000 Max. :3200000 Max. :7500000 Max. :2400000
NA's:1
NA's :5704
NA's :2341
NA's :7662
LOTTERYSITTU_DEBTS
COMPENSATIONOTHER_WINDFALL
Min.
:
500
Min.
:
100
Min.
:
700
Min.
:
20
1st Qu.: 7000
1st Qu.: 10000
1st Qu.:3500
1st Qu.:475
Median : 12000
Median : 20000
Median : 15000
Median : 1000
Mean
: 24199
Mean: 31181Mean: 90120
Mean: 18255
3rd Qu.: 30000
3rd Qu.: 36000
3rd Qu.: 37400
3rd Qu.: 5000
Max.
:435000
Max.
:500000
Max.
:3696000
Max.
:300000
NA's
:7953
NA's
:6866
NA's
:8067
NA's
:7986
ID
PID
WT
Length:8154
Length:8154
Min. : 3.467
Class :character
Class :character
1st Qu.: 98.296
Mode :character
Mode :character
Median : 257.853
Mean : 253.494
3rd Qu.: 376.335
Max. :1555.993####

R20 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :21 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:21 1st Qu.:1.000
Median :32.00 Median : 55.00
Median :21
Median :2.000
Mean:40.82
Mean: 68.22
Mean
:21
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:21 3rd Qu.:2.000
Max.
:92.00Max.
:300.00
Max.
:21
Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000

Sri Lanka HIES 2009/10 Manual (Version 1.1)

RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

37

BANKS
BANK_AMOUNTFINANCEFINANCE_AMOUNT
Min.
:1.000
Min.
:
300
Min.
:1.000Min.
:
2
1st Qu.:2.000 1st Qu.: 16000 1st Qu.:2.000 1st Qu.: 35000
Median :2.000
Median : 40000
Median :2.000
Median : 105000
Mean :1.755
Mean: 145711
Mean:1.955Mean: 270153
3rd Qu.:2.000
3rd Qu.: 100000
3rd Qu.:2.000
3rd Qu.: 300000
Max.
:2.000Max.
:9999999
Max.
:2.000Max.
:4000000
NA's
:3
NA's
:15066NA's:5NA's:19062EMPLOYEREMPLOYER_AMOUNTLENDERLENDER_AMOUNT
Min.
:1.000
Min.
:
150
Min.
:1.000Min.
:100
1st Qu.:2.000
1st Qu.: 10000
1st Qu.:2.000
1st Qu.:6638
Median :2.000
Median : 41000
Median :2.000
Median : 20000
Mean:1.922Mean: 114723
Mean:1.914Mean: 66158
3rd Qu.:2.000
3rd Qu.: 113695
3rd Qu.:2.000
3rd Qu.: 50000
Max.
:2.000Max.
:4000000
Max.
:2.000Max.
:3000000
NA's
:4
NA's
:18409NA's:4NA's:18248RETAIL_SHOPS RETAIL_SHOP_AMOUNTPAWNINGPAWNING_AMOUNT
Min.
:1.000
Min.
:
50
Min.
:1.000Min.
:160
1st Qu.:2.000 1st Qu.: 1500
1st Qu.:1.000 1st Qu.: 15000
Median :2.000 Median : 3000
Median :2.000 Median : 33800
Mean :1.837 Mean : 4789
Mean :1.663 Mean : 68584
3rd Qu.:2.000 3rd Qu.: 6000
3rd Qu.:2.000 3rd Qu.: 75000
Max.
:2.000Max.
:150000
Max.
:2.000 Max. :3500000
NA's
:5
NA's
:16698NA's:3NA's:13234INSTALMENT_GOODS INSTALEMENT_AMOUNT OTHER_DEBTS
OTHER_AMOUNT
Min.
:1.000
Min.
:
250
Min.
:1.000Min.
:
250
1st Qu.:2.000
1st Qu.:4000
1st Qu.:2.000
1st Qu.: 6000
Median :2.000
Median : 10000
Median :2.000
Median : 15000
Mean:1.957
Mean: 21225
Mean:1.973Mean: 38442
3rd Qu.:2.000
3rd Qu.: 25000
3rd Qu.:2.000
3rd Qu.: 35500
Max.
:2.000Max.
:1000000
Max.
:2.000Max.
:800000
NA's
:4
NA's
:19096NA's:4NA's:19427
ID
WT
Length:19958
Min.
:
3.467
Class :character
1st Qu.: 85.574
Mode :character
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max. :1555.993####

R21 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :20 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:20 1st Qu.:1.000
Median :32.00
Median : 55.00 Median :20 Median :2.000
Mean:40.82
Mean: 68.22
Mean
:20
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:20 3rd Qu.:2.000
Max.
:92.00Max.
:300.00
Max.
:20
Max.
:3.000MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000

DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000

Sri Lanka HIES 2009/10 Manual (Version 1.1)

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

38

Mean
: 6.506
3rd Qu.:10.000
Max.
:12.000

Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

Mean:1.008
3rd Qu.:1.000
Max.
:4.000Mean:1.017
3rd Qu.:1.000
Max.
:4.000

RADIO
TV
VCD
SEWING_MECHINE
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000
Median :1.000
Median :2.000
Median :2.000
Mean:1.263Mean:1.212Mean:1.638Mean:1.592
3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000
NA's
:1
NA's
:2
NA's
:2
NA's
:1
WASHING_MECHINEFRIDGE
COOKERT
ELECTRIC_FANS
Min.
:1.000Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
Median :2.000
Median :2.000
Median :2.00
Median :1.000
Mean:1.865Mean:1.604
Mean
:1.59
Mean
:1.4633rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:2.000
Max.
:2.000Max.
:2.000
Max.
:2.00
Max.
:2.000
NA's
:1
NA's
:1
NA's
:1
NA's
:1
TELEPHONE
TELEPHONE_MOBILE COMPUTERSBICYCLEMin.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000
1st Qu.:2.000 1st Qu.:1.000
Median :2.000
Median :1.000
Median :2.000
Median :2.000
Mean:1.545Mean:1.396Mean:1.876Mean:1.586
3rd Qu.:2.000 3rd Qu.:2.000
3rd Qu.:2.000 3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000
NA's
:4
NA's
:1
NA's
:2
NA's
:1
MOTOR_BICYCLE THREE_WHEELER
MOTOR_CAR_VAN
BUS_LORRY
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :2.000
Median :2.000
Median :2.000
Median :2.000
Mean :1.757 Mean
:1.941Mean:1.945Mean:1.985
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000
NA's
:1
NA's
:1
NA's
:1
NA's
:1
TRACTOR_2_WHEEL TRACTOR_4_WHEEL PESTICIDER
PADDY_BLOWER
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :2.000
Median :2.000
Median :2.000
Median :2.000
Mean:1.974Mean:1.994Mean:1.968Mean:1.997
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000
NA's
:2
NA's
:2
NA's
:2
NA's
:2
WATER_PUMPSBOATSFISHING_NETS
ID
Min. :1.000 Min. :1.000 Min. :1.00 Length:19958
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00 Class :character
Median :2.000 Median :2.000 Median :2.00 Mode :character
Mean:1.979Mean:1.986Mean:1.98
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.00
Max.
:2.000Max.
:2.000
Max.
:2.00
NA's
:2
NA's
:4
NA's
:4
WT
Min.
:
3.467
1st Qu.: 85.574
Median : 254.568

Sri Lanka HIES 2009/10 Manual (Version 1.1)

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

39

Mean
: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R22 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :22 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:22 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :22
Median :2.000
Mean:40.82
Mean: 68.22
Mean
:22
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:22 3rd Qu.:2.000
Max. :92.00 Max. :300.00 Max. :22 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

BUS_HALTBUS_HALT_TIME
PRE_SCHOOL
PRE_SCHOOL_TIME
Min. : 1.000 Min. : 1.00 Min. : 1.000 Min. : 1.00
1st Qu.: 1.000
1st Qu.: 5.00
1st Qu.: 1.000
1st Qu.: 5.00
Median : 1.000 Median : 10.00 Median : 1.000 Median : 10.00
Mean: 1.764
Mean: 10.81Mean: 1.613 Mean : 11.48
3rd Qu.: 2.000 3rd Qu.: 15.00 3rd Qu.: 2.000 3rd Qu.: 15.00
Max. :60.000 Max. :150.00 Max. :15.000 Max. :150.00
NA's :14350
NA's :52
NA's :14063
NA's :56
PRIMERY_SCHOOLPRIMERY_SCHOOL_TIME SECONDERY_SCHOOL SEC_SCHOOL_TIME
Min.
: 1.000Min.
: 1.00Min.
: 1.000Min.
: 1.00
1st Qu.: 1.000
1st Qu.: 10.00
1st Qu.: 1.000
1st Qu.: 10.00
Median : 1.000
Median : 10.00
Median : 2.000
Median : 15.00
Mean: 1.899
Mean: 14.48Mean: 3.491
Mean: 20.84
3rd Qu.: 2.000
3rd Qu.: 20.00
3rd Qu.: 4.000
3rd Qu.: 30.00
Max.
:20.000
Max.
:120.00Max.
:45.000Max.
:180.00
NA's :10456
NA's :525
NA's
:6286
NA's
:38
HOSPITALHOSPITAL_TIME
MATRENITY_HOME MATERNITY_HOME_TIME
Min. : 1.000 Min. : 1.00 Min. : 1.000 Min. : 1.0
1st Qu.: 3.000
1st Qu.: 20.00
1st Qu.: 2.000
1st Qu.: 15.0
Median : 5.000 Median : 30.00 Median : 4.000 Median : 25.0
Mean : 6.701 Mean : 32.98 Mean : 5.609 Mean : 29.1
3rd Qu.: 9.000
3rd Qu.: 40.00
3rd Qu.: 7.000
3rd Qu.: 40.0
Max.
:63.000
Max.
:190.00Max.
:80.000 Max. :190.0
NA's :1722
NA's :18
NA's :3565
NA's :915
GOV_DISPENSARZ GOV_DISPENSARY_TIME PRIVATE_DISPENSARY
Min.
: 1.000Min.
: 1.00Min.
: 1.000
1st Qu.: 2.000
1st Qu.: 15.00
1st Qu.: 1.000
Median : 3.000
Median : 20.00
Median : 2.000
Mean: 4.317
Mean: 25.36
Mean: 3.799
3rd Qu.: 5.000
3rd Qu.: 30.00
3rd Qu.: 5.000
Max.
:45.000
Max.
:190.00Max.
:42.000
NA's
:4123
NA's
:858
NA's
:8204
PRIVATE_DISPENSARY_TIME MATERNITY_CLINIC MATERNITY_CLINIC_TIME

Sri Lanka HIES 2009/10 Manual (Version 1.1)

40

Min. : 1.00
Min. : 1.000 Min. : 1.00
1st Qu.: 10.00
1st Qu.: 1.000 1st Qu.: 10.00
Median : 15.00
Median : 2.000 Median : 15.00
Mean: 19.07
Mean: 3.476
Mean: 20.88
3rd Qu.: 25.00
3rd Qu.: 4.000 3rd Qu.: 30.00
Max. :190.00
Max. :40.000 Max. :240.00
NA's :199
NA's :6083
NA's :153
DMO
DMO_TIME
MCUCPCMCUCPC_TIME
Min. : 1.000 Min. : 1.00 Min. : 1.000 Min. : 1.0
1st Qu.: 2.000
1st Qu.: 20.00
1st Qu.: 3.000
1st Qu.: 18.0
Median : 5.000 Median : 30.00 Median : 5.000 Median : 30.0
Mean : 6.649 Mean : 32.51 Mean : 7.362 Mean : 33.9
3rd Qu.: 9.000
3rd Qu.: 40.00
3rd Qu.:10.000
3rd Qu.: 45.0
Max. :50.000 Max. :240.00 Max. :90.000 Max. :240.0
NA's :2093
NA's :151
NA's :1852
NA's :11
DS_OFFICE
DS_OFFICE_TIME
GN_OFFICE
GN_OFFICE_TIME
Min. : 1.000 Min. : 1.00 Min. : 1.000 Min. : 1.00
1st Qu.: 3.000
1st Qu.: 20.00
1st Qu.: 1.000 1st Qu.: 5.00
Median : 5.000 Median : 30.00 Median : 1.000 Median : 10.00
Mean : 7.828 Mean : 35.53 Mean : 1.869 Mean : 12.43
3rd Qu.:10.000 3rd Qu.: 45.00 3rd Qu.: 2.000 3rd Qu.: 15.00
Max.
:90.000
Max. :300.00 Max. :30.000 Max. :150.00
NA's :1706
NA's :14
NA's :12911
NA's :81
POST_OFFICEPOST_OFFICE_TIME
BANK
BANK_TIME
Min. : 1.000 Min. : 1.00 Min. : 1.0 Min. : 1.00
1st Qu.: 1.000 1st Qu.: 10.00 1st Qu.: 2.0 1st Qu.: 10.00
Median : 2.000 Median : 15.00 Median : 3.0 Median : 20.00
Mean : 2.502 Mean : 17.14 Mean : 4.6 Mean : 23.56
3rd Qu.: 3.000 3rd Qu.: 20.00 3rd Qu.: 6.0 3rd Qu.: 30.00
Max. :30.000 Max. :180.00 Max. :40.0 Max. :200.00
NA's :7723
NA's :33
NA's :5070 NA's :35
AGRI_OFFICEAGRI_OFFICE_TIME IS_POWER_LINES_NEAR IS_TEL_LINES_NEAR
Min.
: 1.000Min.
: 1.00Min. :1.000
Min.
:1.00
1st Qu.: 3.000
1st Qu.: 20.00
1st Qu.:1.000
1st Qu.:1.00
Median : 5.000
Median : 30.00
Median :1.000
Median :1.00
Mean: 7.241
Mean: 35.78Mean:1.055Mean:1.19
3rd Qu.:10.000
3rd Qu.: 45.00
3rd Qu.:1.000
3rd Qu.:1.00
Max.
:52.000
Max.
:200.00
Max.
:2.000
Max.
:2.00
NA's
:1310
NA's
:201
IS_WATER_SERVICE_NEAR
ID
WTMin. :1.000
Length:19958
Min. : 3.467
1st Qu.:1.000
Class :character 1st Qu.: 85.574
Median :1.000
Mode :character
Median : 254.568
Mean:1.342
Mean: 254.503
3rd Qu.:2.000
3rd Qu.: 381.873
Max.
:2.000Max.
:1555.993####

R23 ################################################
DISTRICTPSU
REC_TYPESECTOR
Min. :11.00 Min. : 1.00 Min. :23 Min. :1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:23 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :23 Median :2.000
Mean:40.82
Mean: 68.22
Mean
:23
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:23 3rd Qu.:2.000

DSD
Min. :0
1st Qu.:0
Median :0
Mean:0
3rd Qu.:0

Sri Lanka HIES 2009/10 Manual (Version 1.1)

41

Max.

:92.00MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

Max.

:300.00

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

Max.

:23

Max.

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000:3.000

Max.

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000:0

RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

STRUCTURE
BED_ROOMS
AREA
WALLS
Min.
:1.000
Min.
:1.00
Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:1.000
Median :1.000
Median :2.00
Median :4.000
Median :1.000
Mean :1.358 Mean :2.32 Mean :3.669 Mean
:2.0733rd Qu.:1.000 3rd Qu.:3.00 3rd Qu.:5.000 3rd Qu.:3.000
Max.
:9.000
Max.
:9.00
Max.
:5.000Max.
:9.000
NA's
:3
NA's
:480
NA's
:4
NA's
:4
FLOOR
ROOF
OWNERSHIPDRINKING_WATER
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000
Median :2.000
Median :1.000
Median :5.000
Mean:1.438Mean:1.881Mean:2.176 Mean
:3.636
3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:5.000
Max.
:9.000Max.
:9.000Max.
:9.000Max.
:9.000
NA's
:3
NA's
:4
NA's
:5
NA's
:6
OWN_WATER
WATER_DISTANCE WATER_SUFFICENCY OTHER_WATER_SUFFICENCY
Min.
:1.000
Min.
:
1.0
Min.
:1.00
Min.
:1.000
1st Qu.:1.000 1st Qu.: 25.0 1st Qu.:1.00
1st Qu.:1.000
Median :1.000
Median : 100.0
Median :1.00
Median :1.000
Mean:1.232Mean: 185.4
Mean
:1.07
Mean
:1.106
3rd Qu.:1.000 3rd Qu.: 200.0 3rd Qu.:1.00
3rd Qu.:1.000
Max.
:2.000Max.
:4500.0
Max.
:2.00
Max.
:2.000
NA's
:36
NA's
:15441
NA's
:26
NA's
:35
TIOILET_USE
TOILET_TYPEGARBAGE_DUMPING LITE_SOURCE
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
Median :1.000 Median :1.000
Median :2.000
Median :2.000
Mean:1.186Mean:1.272Mean:2.348Mean:1.889
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:2.000
Max.
:4.000Max.
:9.000Max.
:9.000Max.
:9.000NA's :7
NA's
:514
NA's
:6
NA's
:3
COOKING_FUEL
IS_COLLECT_FIREWOOD FIRE_WOOD_OWN
OTHER_DISTANCE
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:
1.0
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:1.000
1st Qu.: 100.0
Median :1.000
Median :1.000
Median :1.000
Median : 200.0
Mean:1.327Mean:1.364Mean:1.703Mean: 487.1
3rd Qu.:1.000
3rd Qu.:2.000
3rd Qu.:2.000
3rd Qu.: 500.0
Max.
:9.000Max.
:2.000Max.
:3.000Max.
:9000.0
NA's
:9
NA's
:8
NA's
:7271
NA's
:14273
NATURAL_CALAMITY
FLOODING
DROUGHTLAND_SLIDES
Min.
:1.000Min.
:1.000
Min.
:1.00
Min.
:1.000
1st Qu.:2.000
1st Qu.:1.000 1st Qu.:1.00
1st Qu.:2.000
Median :2.000
Median :2.000 Median :2.00
Median :2.000
Mean:1.937Mean:1.555
Mean
:1.74
Mean
:1.956
3rd Qu.:2.000
3rd Qu.:2.000 3rd Qu.:2.00
3rd Qu.:2.000

Sri Lanka HIES 2009/10 Manual (Version 1.1)

42

Max.
:2.000Max.
:2.000
Max.
:2.00
Max.
:2.000
NA's
:13
NA's
:18696NA's:18706NA's:18706
CIVIL_UNREST
WILD_ANIMALOTHER_CALAMITY
ID
Min.
:1.000Min.
:1.000Min.
:1.000Length:19958
1st Qu.:2.000
1st Qu.:2.000
1st Qu.:2.000
Class :character
Median :2.000
Median :2.000
Median :2.000
Mode :character
Mean:1.992Mean:1.765Mean:1.881
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000NA's:18706NA's:18706NA's:18706
WT
Min.
:
3.467
1st Qu.: 85.574
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max.
:1555.993####

R24 ################################################
DISTRICTPSU
REC_TYPESECTORMin.
:11.00Min.
: 1.00
Min.
:24
Min.
:1.000
1st Qu.:13.00 1st Qu.: 28.00 1st Qu.:24 1st Qu.:1.000
Median :32.00
Median : 55.00
Median :24
Median :2.000
Mean:40.82
Mean: 68.22
Mean
:24
Mean
:1.8233rd Qu.:61.00 3rd Qu.: 89.00 3rd Qu.:24 3rd Qu.:2.000
Max. :92.00 Max. :300.00 Max. :24 Max. :3.000
MONTH
Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.506
3rd Qu.:10.000
Max.
:12.000

SAMPLE_N
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.451
3rd Qu.: 8.000
Max.
:10.000

SERIAL_NO
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.008
3rd Qu.:1.000
Max.
:4.000DSD
Min.
:01st Qu.:0
Median :0
Mean:0
3rd Qu.:0
Max. :0

NHH
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.017
3rd Qu.:1.000
Max.
:4.000RESULTMin.
:1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

IS_AGRILAND_OWNER PADDY_OWN_ACR
PADDY_OWN_RT PADDY_OWN_PERCH
Min.
:1.000Min.
: 1.000
Min.
:1.000Min.
: 1
1st Qu.:1.000
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.:15
Median :1.000
Median : 2.000
Median :2.000
Median :20
Mean:1.172Mean: 2.642Mean:1.837Mean:19
3rd Qu.:1.000
3rd Qu.: 2.000
3rd Qu.:2.000
3rd Qu.:24
Max.
:3.000Max.
:802.000
Max.
:3.000
Max.
:38
NA's
:1
NA's
:18443NA's:18520NA's:19750PADDY_OTHER_ACR PADDY_OTHER_RT PADDY_OTHER_PERCH LAND_OWN_ACR
Min.
: 1.00Min.
:1.000Min.
: 1.00Min.
: 1.000
1st Qu.: 1.00 1st Qu.:1.000
1st Qu.:14.50
1st Qu.: 1.000
Median : 2.00
Median :2.000
Median :20.00
Median : 1.000
Mean: 2.17Mean:1.849Mean:19.56Mean: 2.599
3rd Qu.: 2.00
3rd Qu.:2.000
3rd Qu.:26.00
3rd Qu.: 2.000
Max.
:80.00Max.
:3.000Max.
:35.00Max.
:111.000
NA's:18853NA's:18894NA's:19826NA's:18848
LAND_OWN_RT
LAND_OWN_PERCH LAND_OTHER_ACR LAND_OTHER_RT
Min.
:1.000Min.
: 1.00Min.
: 1.000 Min.
:1.000
1st Qu.:1.000 1st Qu.:10.00 1st Qu.: 1.000 1st Qu.:1.000

Sri Lanka HIES 2009/10 Manual (Version 1.1)

43Median :2.000 Median :15.00 Median : 1.000 Median :2.000
Mean:1.783Mean:16.16Mean: 2.445
Mean:1.815
3rd Qu.:2.000 3rd Qu.:20.00 3rd Qu.: 2.000 3rd Qu.:2.000
Max.
:5.000Max.
:40.00Max.
:50.000
Max.
:3.000NA's:18858NA's:19130NA's:19347NA's:19374
LAND_OTHER_PERCH HOME_OWN_ACR
HOME_OWN_RT
HOME_OWN_PERCH
Min. : 1.00
Min. : 1.000 Min.
:1.000Min.
: 1.0
1st Qu.:10.00
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.: 7.0
Median :18.00
Median : 1.000
Median :2.000
Median :11.0
Mean:16.43Mean: 1.644
Mean:1.628Mean:13.5
3rd Qu.:20.00
3rd Qu.: 2.000
3rd Qu.:2.000
3rd Qu.:20.0
Max.
:38.00Max.
:25.000
Max.
:4.000Max.
:50.0NA's :19768
NA's :18011
NA's :15510 NA's
:10037
HOME_OTHER_ACR
HOME_OTHER_RT
HOME_OTHER_PERCH COWS_BUFFALOWS
Min.
: 1.000
Min.
:1.000
Min.
: 1.0
Min.
:1.000
1st Qu.: 1.000
1st Qu.:1.000
1st Qu.:10.0
1st Qu.:2.000
Median : 1.000
Median :2.000
Median :15.0
Median :2.000
Mean: 1.788
Mean:1.692
Mean
:15.8
Mean
:1.9543rd Qu.: 2.000
3rd Qu.:2.000
3rd Qu.:20.0
3rd Qu.:2.000
Max.
:35.000
Max.
:3.000
Max.
:50.0
Max.
:2.000NA's :19373
NA's :18738 NA's :18955
NA's:9
COWS_COUNTGOATS_SHEEPSGOAT_COUNT
PIGS
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
Median :1.000
Median :2.000
Median :1.000
Median :2.000
Mean:1.331Mean:1.983Mean:1.409Mean:1.998
3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:8.000Max.
:2.000Max.
:8.000Max.
:2.000NA's:19045NA's:9NA's:19621NA's:9
PIGS_COUNTCHICKENS
CHICKEN_COUNT OTHER_ANIMALS
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
Median :1.000
Median :2.000
Median :2.000
Median :2.000
Mean:1.386Mean:1.921Mean:1.806Mean:1.996
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:4.000Max.
:2.000Max.
:8.000Max.
:2.000NA's:19914NA's:9NA's:18381
NA's
:8
ID
WT
Length:19958
Min.
:
3.467
Class :character
1st Qu.: 85.574
Mode :character
Median : 254.568
Mean: 254.503
3rd Qu.: 381.873
Max. :1555.993

Sri Lanka HIES 2009/10 Manual (Version 1.1)

44

5.2 Frequency table of categorical variables
Data check of categorical variables> # file.names: Rnames[j]> # file.list: hies2009[[j]]> # list of column numbers of categorical variables> check.list<-list()> check.list[[1]]<-c(11:13,17:22,25)> check.list[[2]]<-c(11:14,16:19)> check.list[[3]]<-c(11:18,21)> check.list[[4]]<-c()> check.list[[5]]<-c()> check.list[[6]]<-c(11)> check.list[[7]]<-c(11)> check.list[[8]]<-c(11,12)> check.list[[9]]<-c(11)> check.list[[10]]<-c(11,12)> check.list[[11]]<-c(11)> check.list[[12]]<-c(11)> check.list[[13]]<-c(11,12)> check.list[[14]]<-c(11)> check.list[[15]]<-c(11,12)> check.list[[16]]<-c(11)> check.list[[17]]<-c(11)> check.list[[18]]<-c(11)> check.list[[19]]<-c(11)> check.list[[20]]<-c(seq(11,25,by=2))> check.list[[21]]<-c(11:33)> check.list[[22]]<-c(43:45)> check.list[[23]]<-c(11:19,21:29,31:37)> check.list[[24]]<-c(11,30,32,34,36,38)> for(j in 1:24){+ 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(hies2009[[j]])[k]+ cat(c("-----",variable.name,"-------------------------------------------------------"))+ print(table(hies2009[[j]][k],useNA="ifany"))+ }}#### FREQUENCY OF VARIABLES IN R01 ##################################################----- PERSON_SERIAL_NO ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
19958 19056 16470 12321 7156 3354 1417
582
264
138
71
43
21
14
15
16
17
12
7
1
1
----- RELATIONSHIP ------------------------------------------------------1
2
3
4
5
6
7
9
19959 14223 32929 1555 11849 148
95 114

Sri Lanka HIES 2009/10 Manual (Version 1.1)

45----- SEX_LIVING ------------------------------------------------------1
238485 42387----- ETHNICITY ------------------------------------------------------1
2
3
4
5
6
9 <NA>
55037 11781 3953 9713
231
83
63
11
----- RELIGION ------------------------------------------------------12
3
4
9 <NA>
50341 13146 9946 7424
69----- CURR_EDUCATION ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
2102 17342
290
681
433
250 55564
1 4209----- EDUCATION ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
2144 3117 3192 4019 5169 3614 4093 5553 4061 14967 7029 3766 6571
14
15
16
17
19 <NA>184 1051
279
67 3304 8692----- MARITAL_STATUS ------------------------------------------------------12
3
45 <NA>
22784 37794 5275
157
684 14178----- MAIN_ACTIVITY ------------------------------------------------------1
2
3
4
5
6
9 <NA>
27693 5157 12355 14627 6433
1 409 14197----- EMPLOYMENT_STATUS ------------------------------------------------------1
2
3
4
5
6
9 <NA>
3414 1191 13437 542 7562 1549
1 53176

Remarks: R01
The number of RELATIONSHIP=1 is 19,959, one larger than the number of households.
As for PID= “91200080140511102”, RELATIONSHIP should be read as 2 and SEX_LIVING
should be read as 2.
> table(subset(R01,RELATIONSHIP==1)$PERSON_SERIAL_NO)
1
2
19958
1
> R01[R01$RELATIONSHIP==1&R01$PERSON_SERIAL_NO==2,"ID"][1] "912000801405111"> R01[R01$ID=="912000801405111",c(11:13,16,21,22,26,27)]
PERSON_SERIAL_NO RELATIONSHIP SEX_LIVING AGE MARITAL_STATUS
78847
1
1
1 33
2
78848
2
1
1 28
2
78849
3
3
1 11
1
78850
4
3
2
5
NA
MAIN_ACTIVITY
ID
PID
78847
1 912000801405111 91200080140511101
78848
4 912000801405111 91200080140511102
78849
3 912000801405111 91200080140511103
78850
NA 912000801405111 91200080140511104



Revised the record with PID="91200080140511102"> R01[R01$PID=="91200080140511102","RELATIONSHIP"]<-2> R01[R01$PID=="91200080140511102","SEX_LIVING"]<-2> R01[R01$PID=="91200080140511102",]
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT

Sri Lanka HIES 2009/10 Manual (Version 1.1)

46

78848

91 14
1
2 0
8
5
1
1
1
PERSON_SERIAL_NO RELATIONSHIP SEX_LIVING BIRTH_YEAR BIRTH_MONTH AGE
78848
2
2
2
80
11 28
ETHNICITY RELIGION CURR_EDUCATION EDUCATION MARITAL_STATUS MAIN_ACTIVITY
78848
1
1
7
10
2
4
MAIN_OCCUPATION INDUSTRY EMPLOYMENT_STATUS
ID
PID
78848
NA
NA
NA 912000801405111 91200080140511102
WT
78848 440.1271# Updated R01 in hies2009> hies2009[[1]]<-R01#### FREQUENCY OF VARIABLES IN R02 ##################################################----- R2_PERSON_SERIAL ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 14 1530 1451 7590 6279 3305 1307 496 207
98
47
19
10
6
6
2
----- R2_SCHOOL_EDUCATION ------------------------------------------------------12
3
69 <NA>
17277
742 2826116----- GRADE_THIS_YEAR ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 14 19
1276 1509 1449 1465 1413 1422 1359 1371 1316 1398 1537 930 755
43 36
<NA>
3574
----- GRADE_LAST_YEAR ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 14 191553 1454 1464 1411 1416 1369 1357 1324 1409 1507 955 732
22 21 1223
<NA>
3636
----- TRANSPORT_MEDIUM ------------------------------------------------------1
2
3
4
5
6
9 <NA>
8445 1696 1553 2003 3448
67
43 3598----- TIME_TO_SCHOOL ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
12
13
14 15 16106 112 162
83 148870
87 155
10 3830 124
14
5 3098
1
17 18
20
21
22
23
25 26
27
28
29
30
33 35 36
12 17 2663
2
7
4 612
4
1
8
2 2329
1 256
3
39 40
44
45
50
55
60 65
70
75
80
85
90 95 100
2 478
2 633
75
16 488
9
27
48
30
2 112
2
8
105 110 120 150 <NA>3235
1 3614----- NOSCHOOLING_REASON ------------------------------------------------------1
2
3
4
6
7
9 <NA>
8
32
5
57
10
570
60 20111----- REASON_NOT_GOING ------------------------------------------------------1
2
3
4
5
6
7
8
9 <NA>
11
482
311
55
25 1065327
180
354 18043

Sri Lanka HIES 2009/10 Manual (Version 1.1)

47#### FREQUENCY OF VARIABLES IN R03 ##################################################----- R3_PERSON_SERIAL ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
19957 19054 16467 12321 7155 3353 1416
582
264
138
71
43
21
14
15
16
17
12
7
1
1
----- DID_ATTEND_HOSPITAL ------------------------------------------------------1
2
4 <NA>
24039 56785
138----- REASON_HOSPITAL ------------------------------------------------------123459 <NA>
21897
655
924
127
123
289 56848----- IS_STAY_HOSPITAL ------------------------------------------------------1
2
3
9 <NA>
8397 72413
1
151----- REASON_STAY ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
5354
592
873
861
130
240
1
336 72476----- IS_ILL_DISABLE ------------------------------------------------------1
2 <NA>
11644 69162
57----- WHAT_ILL_DISABLE ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
1041 2500 1899 1389
237
142
295
255
631
332
150
486
174
14
15
19
20
51 <NA>372
296 1429
1
1 69233----- IS_EMPL_REASON ------------------------------------------------------1
2
4 <NA>
852 10701
1 69309----- IS_ABSENT_ACT ------------------------------------------------------1
2 <NA>
2136 9323 69404#### FREQUENCY OF VARIABLES IN R06 ##################################################----- COL_2 ------------------------------------------------------2 3 4 5 6 7 8 9 10 12
23 40 58 45 45 21 3 2 2 1#### FREQUENCY OF VARIABLES IN R07 ##################################################----- IS_BOARDERS_SERVENTS ------------------------------------------------------1
2 <NA>
187 19767
4

Sri Lanka HIES 2009/10 Manual (Version 1.1)

48#### FREQUENCY OF VARIABLES IN R08 ##################################################----- SERIAL_NO_SEC_1 ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 14
8674 4050 3062 1446 559 310 150 61
31
10
8
3
4
1
----- PRI_SEC ------------------------------------------------------1
2 <NA>
18155
205
9#### FREQUENCY OF VARIABLES IN R09 ##################################################----- IS_EMPLOYMENT_INCOME ------------------------------------------------------1
2 <NA>
12739 7218
1#### FREQUENCY OF VARIABLES IN R10 ##################################################----- SER_NO_SEC_5_2 ------------------------------------------------------1
2
3
4
5
6
7
8
3480 515 198
53
23
5
3
1
----- SEAS_CROPS_CODE ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
2782 122
76 695 410
81
23 78
11

#### FREQUENCY OF VARIABLES IN R11 ##################################################----- IS_AGRICULTURAL_INCOME ------------------------------------------------------1
2 <NA>
3425 16529
4#### FREQUENCY OF VARIABLES IN R12 ##################################################----- IS_OTHER_AGRRI_INCOME ------------------------------------------------------1
2 <NA>
3449 16506
3#### FREQUENCY OF VARIABLES IN R13 ##################################################----- SER_NO_SEC_5_3 ------------------------------------------------------1
2
3
4
5
6
7
8
10
13
3343 581 164
54 35
11
4
2
1
1
----- SEASONAL_CROP ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
19 <NA>989 1355 424 384 68 329 141 216
53
11 222
4

Sri Lanka HIES 2009/10 Manual (Version 1.1)

49#### FREQUENCY OF VARIABLES IN R14 ##################################################----- IS_NON_AGRI_INCOME ------------------------------------------------------1
2 <NA>
4889 15067
2#### FREQUENCY OF VARIABLES IN R15 ##################################################----- SERIAL_5_4 ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13
3375 1129 578 251 103
54
21
7
5
2
1
1
1
----- NON_AGRI ------------------------------------------------------1
2
3
4
5
6
7 <NA>
126 980 152 2282 797 114 1071
6#### FREQUENCY OF VARIABLES IN R16 ##################################################----- IS_OTHER_INCOME ------------------------------------------------------1
29826 10132#### FREQUENCY OF VARIABLES IN R17 ##################################################----- SERIAL_5_5_1 ------------------------------------------------------1
2
3
4
5
6
7
8
9
10
11
12
13 16
8408 1343 431 232 181
81
31 12
5
5
3
1
1
1

#### FREQUENCY OF VARIABLES IN R18 ##################################################----- IS_WINDFALL_INCOME ------------------------------------------------------1
2 <NA>
7611 12343
4#### FREQUENCY OF VARIABLES IN R19 ##################################################----- PERSON_5_5_2 ------------------------------------------------------1
2
3
4
5
6
7
8
9
11
41 <NA>
5073 2206 517 228
69
37
11
9
1
1
1
1

#### FREQUENCY OF VARIABLES IN R20 ##################################################----- BANKS -------------------------------------------------------

Sri Lanka HIES 2009/10 Manual (Version 1.1)

50

1
2 <NA>
4897 15058
3----- FINANCE ------------------------------------------------------1
2 <NA>
896 19057
5----- EMPLOYER ------------------------------------------------------1
2 <NA>
1552 18402
4----- LENDER ------------------------------------------------------1
2 <NA>
1710 18244
4----- RETAIL_SHOPS ------------------------------------------------------1
2 <NA>
3262 16691
5----- PAWNING ------------------------------------------------------1
2 <NA>
6731 13224
3----- INSTALMENT_GOODS ------------------------------------------------------1
2 <NA>
862 19092
4----- OTHER_DEBTS ------------------------------------------------------1
2 <NA>
533 194214#### FREQUENCY OF VARIABLES IN R21 ##################################################----- RADIO ------------------------------------------------------1
2 <NA>
14705 5252
1----- TV ------------------------------------------------------1
2 <NA>
15735 42212----- VCD ------------------------------------------------------1
2 <NA>
7225 12731
2----- SEWING_MECHINE ------------------------------------------------------1
2 <NA>
8143 11814
1----- WASHING_MECHINE ------------------------------------------------------1
2 <NA>
2685 17272
1----- FRIDGE ------------------------------------------------------1
2 <NA>
7909 12048
1----- COOKERT ------------------------------------------------------1
2 <NA>
8180 11777
1----- ELECTRIC_FANS ------------------------------------------------------1
2 <NA>

Sri Lanka HIES 2009/10 Manual (Version 1.1)

5110717 92401----- TELEPHONE ------------------------------------------------------1
2 <NA>
9073 10881
4----- TELEPHONE_MOBILE ------------------------------------------------------1
2 <NA>
12060 7897
1----- COMPUTERS ------------------------------------------------------1
2 <NA>
2465 17491
2----- BICYCLE ------------------------------------------------------1
2 <NA>
8272 11685
1----- MOTOR_BICYCLE ------------------------------------------------------1
2 <NA>
4846 15111
1----- THREE_WHEELER ------------------------------------------------------1
2 <NA>
1179 187781----- MOTOR_CAR_VAN ------------------------------------------------------1
2 <NA>
1092 18865
1----- BUS_LORRY ------------------------------------------------------1
2 <NA>
306 196511----- TRACTOR_2_WHEEL ------------------------------------------------------1
2 <NA>
517 19439
2----- TRACTOR_4_WHEEL ------------------------------------------------------1
2 <NA>
123 19833
2----- PESTICIDER ------------------------------------------------------1
2 <NA>
648 19308
2----- PADDY_BLOWER ------------------------------------------------------1
2 <NA>
59 198972----- WATER_PUMPS ------------------------------------------------------1
2 <NA>
410 19546
2----- BOATS ------------------------------------------------------1
2 <NA>
287 19667
4----- FISHING_NETS ------------------------------------------------------1
2 <NA>
406 19548
4#### FREQUENCY OF VARIABLES IN R22 ##################################################

Sri Lanka HIES 2009/10 Manual (Version 1.1)

52----- IS_POWER_LINES_NEAR ------------------------------------------------------1
218857 1101----- IS_TEL_LINES_NEAR ------------------------------------------------------1
216172 3786----- IS_WATER_SERVICE_NEAR ------------------------------------------------------1
2
13134 6824#### FREQUENCY OF VARIABLES IN R23 ##################################################----- STRUCTURE ------------------------------------------------------123459 <NA>
17078
519
788 1401126
433----- BED_ROOMS ------------------------------------------------------1
2
3
4
5
6
7
8
9 <NA>
4702 7033 5293 1907 412
97
17 10
7 480
----- AREA ------------------------------------------------------12
3
45 <NA>
968 2979 4411 4934 6662
4----- WALLS ------------------------------------------------------1
2
3
4
5
6
7
9 <NA>
11609
829 5226
673
795
486
94
242
4
----- FLOOR ------------------------------------------------------12
3
4
9 <NA>
16132 1832 1503
1487
3----- ROOF ------------------------------------------------------123459 <NA>
9778 6440 711 2555 426
444----- OWNERSHIP ------------------------------------------------------1
2
3
4
5
6
7
8
9 <NA>
11539 3812 1096
294 1650
225
833
182
322
5
----- DRINKING_WATER ------------------------------------------------------1
2
3
4
5
6
7
8
9 <NA>
5783 2476 647 693 7448 1247 1183 178 297
6----- OWN_WATER ------------------------------------------------------1
2 <NA>
15308 4614
36----- WATER_SUFFICENCY ------------------------------------------------------1
2 <NA>
18540 1392
26----- OTHER_WATER_SUFFICENCY ------------------------------------------------------1
2 <NA>
17814 2109
35----- TIOILET_USE ------------------------------------------------------1
2
34 <NA>
17641 1418
392
500
7
----- TOILET_TYPE -------------------------------------------------------

Sri Lanka HIES 2009/10 Manual (Version 1.1)

53

1
2
3
9 <NA>
14941 3812
676
15
514
----- GARBAGE_DUMPING ------------------------------------------------------123459 <NA>
4631 10062 1111 2602 1389
157
6----- LITE_SOURCE ------------------------------------------------------12
3
4
9 <NA>
2516 17148
286
3
2
3
----- COOKING_FUEL ------------------------------------------------------123459 <NA>
15310 3712
692
57
41
137
9
----- IS_COLLECT_FIREWOOD ------------------------------------------------------1
2 <NA>
12692 7258
8----- FIRE_WOOD_OWN ------------------------------------------------------1
2
3 <NA>
6924 2610 3153 7271----- NATURAL_CALAMITY ------------------------------------------------------1
2 <NA>
1260 18685
13----- FLOODING ------------------------------------------------------1
2 <NA>
562
700 18696----- DROUGHT ------------------------------------------------------1
2 <NA>
326
926 18706----- LAND_SLIDES ------------------------------------------------------1
2 <NA>
55 1197 18706----- CIVIL_UNREST ------------------------------------------------------1
2 <NA>
10 1242 18706----- WILD_ANIMAL ------------------------------------------------------1
2 <NA>
294
958 18706----- OTHER_CALAMITY ------------------------------------------------------1
2 <NA>
149 1103 18706#### FREQUENCY OF VARIABLES IN R24 ##################################################----- IS_AGRILAND_OWNER ------------------------------------------------------1
2
3 <NA>
16524 3432
11----- COWS_BUFFALOWS ------------------------------------------------------1
2 <NA>
913 19036
9----- GOATS_SHEEPS ------------------------------------------------------1
2 <NA>

Sri Lanka HIES 2009/10 Manual (Version 1.1)

54

336 19613
9----- PIGS ------------------------------------------------------1
2 <NA>
43 199069----- CHICKENS ------------------------------------------------------1
2 <NA>
1577 18372
9----- OTHER_ANIMALS ------------------------------------------------------1
2 <NA>
72 19878
8

Sri Lanka HIES 2009/10 Manual (Version 1.1)

55



Summary:
Problems found in Chapter 4, Chapter 5.1 and Chapter 5.2

File

Problems

TO-DO

R01

Person number >40 (4,571)

Dropped, as discussed in Chapter 4

R01

Number of RELATIONSHIP=1 (Head)

Already discussed in Chapter 5.1

is 19,959, larger than that ofhouseholds.
R03

Person number = NA (3)Dropped, as discussed in Chapter 4.

R08

PRI_SEC = NA (9)

To omit the 5 records with the next PID, because all income
variables were NA.
12200031460311102, 22300030450911101,
51100110340711104, 62200070080111107,
92200050720911101



To read NA as 1 (Primary) in the 4 records with the next PID,
because wages were non-zero and no primary occupation was
found.
53200110270211106, 53200110270611101,
53200110270811101, 53200110270911101

R10

SENONAS_CROPS_CODE = NA (11)

To omit the record with the next PID, because all income
variables were NA.
33200120420811101



To read NA as 9 (Other) in the 10 records with the next PID ,because outputs were non-zero.
13200110720411101, 53200100210111101,
61200061140611101, 61200061140611102,
61200120570111101, 91200080100511101,
91200080160511103, 91200080160911101,
91200090230611101, 91200110380711101

R13

SEASONAL_CROP = NA (4)

To omit the 2 records with the next PID, because all income
variables were NA.
91200030681011102, 91200030681011103



To read NA as 19 (Other) in the 2 records with the next PID ,because outputs were non-zero.
31100080270311102, 91200080140511102

Sri Lanka HIES 2009/10 Manual (Version 1.1)

56

R15



NON_AGRI = NA (6)To omit the 3 records with the next PID, because all income
variables were NA.
32100031030411101, 61200100340711101,
61200100340711101



To read NA as 19 (Other) , in the 3 records with the next PID
because outputs were non-zero.
12200021280211102, 31100070130511101,
43100010270411101

R19

Person number > 40 (1)To drop

R19

Person number = NA (1)To drop== R08 ==> R08<-hies2009.old2[[8]]> table(R08$PRI_SEC,useNA="ifany")
118155

2 <NA>
205

9> R08[is.na(R08$PRI_SEC),c("PID","PRI_SEC","WAGES_SALARIES","ALLOWENCES","BONUS")]PID PRI_SEC WAGES_SALARIES ALLOWENCES BONUS
3032

12200031460311102

NA

NA

NA

NA

7075

22300030450911101

NA

NA

NA

NA

11814 51100110340711104

NA

NA

NA

NA

12981 53200110270211106

NA

5500

NA

NA

12982 53200110270611101

NA

10000

NA

NA

12983 53200110270811101

NA

6500

NA

NA

12984 53200110270911101

NA

6000

NA

NA

14702 62200070080111107

NA

NA

NA

NA

18216 92200050720911101

NA

NA

NA

NA

> pid1<-c("12200031460311102", "22300030450911101", "51100110340711104",+ "62200070080111107", "92200050720911101")> R08<-subset(R08,!is.na(R08$PRI_SEC)|!is.element(R08$PID,pid1))> dim(R08)[1] 18364

18> pid2<-c("53200110270211106", "53200110270611101", "53200110270811101", "53200110270911101")> R08["PRI_SEC"]<-ifelse(is.element(R08$PID,pid2)&is.na(R08$PRI_SEC),1,R08$PRI_SEC)

Sri Lanka HIES 2009/10 Manual (Version 1.1)

57

> table(R08$PRI_SEC,useNA="ifany")
1

2

18159

205

== R10 ==> R10<-hies2009.old2[[10]]> dim(R10)[1] 4278

21> table(R10$SEAS_CROPS_CODE,useNA="ifany")
1

2

3

2782

122

76

4

5

6

7

9 <NA>

695 410

81

23

78

11

> R10[is.na(R10$SEAS_CROPS_CODE),c(20,12:18)]
PID SEAS_CROPS_CODE ACR_5_2 RT_5_2

P OUTPUT_5_2 HH_CONSUMPTION INPUT_5_2

257 13200110720411101

NA

2

NA NA

55000

18000

49000

1121 33200120420811101

NA

1

NA NA

NA

NA

NA

1566 53200100210111101

NA

2

2 NA

80000

15000

37000

1711 61200061140611101

NA

NA

2 NA

13200

3900

9300

1712 61200061140611102

NA

NA

2 NA

30000

150

8830

1989 61200120570111101

NA

NA

2 NA

21000

21000

7000

3974 91200080100511101

NA

NA

1 NA

16000

16000

4000

3991 91200080160511103

NA

NA

2 NA

9225

9225

6350

3993 91200080160911101

NA

1

NA NA

2800001000

1597003995 91200090230611101

NA

NA

2 NA

7000

NA

5000

4041 91200110380711101

NA

NA

1 NA

1800

1800

175

> pid1<-c("33200120420811101")> R10<-subset(R10,!is.element(R10$PID,pid1)|!is.na(R10$SEAS_CROPS_CODE))> dim(R10)[1] 4277

21> pid2<-c("13200110720411101", "53200100210111101", "61200061140611101", "61200061140611102",+ "61200120570111101", "91200080100511101", "91200080160511103", "91200080160911101",+ "91200090230611101", "91200110380711101")> R10["SEAS_CROPS_CODE"]<-ifelse(is.element(R10$PID,pid2)&is.na(R10$SEAS_CROPS_CODE),+ 9,R10$SEAS_CROPS_CODE)> table(R10$SEAS_CROPS_CODE,useNA="ifany")
1

2

3

4

5

6

7

9

Sri Lanka HIES 2009/10 Manual (Version 1.1)

58

2782

122

76

695 410

81

23

88

== R13 ==> R13<-hies2009.old2[[13]]> dim(R13)[1] 4196

20> table(R13$SEASONAL_CROP,useNA="ifany")
1

2

3

4

5

6

989 1355 424

384

68

329

7

8

9

10

19 <NA>141 216

53

11

222

4

> R13[is.na(R13$SEASONAL_CROP),c(19,12:17)]
PID SEASONAL_CROP ACRES_5_3 ROOTS_5_3 PERCHS_5_3 OUTPUT_5_3 INPUT_5_3
1269 31100080270311102

NA

1

NA

NA

6000

1000

3510 91200080140511102

NA

NA

NA

30

2000

NA

3716 91200030681011102

NA

NA

NA

NA

NA

NA

3717 91200030681011103

NA

NA

NA

NA

NA

NA

> R13<-R13[R13$PID!="91200030681011102",]> R13<-R13[R13$PID!="91200030681011103",]> dim(R13)[1] 419420> R13[R13$PID=="31100080270311102","SEASONAL_CROP"]<-19> R13[R13$PID=="91200080140511102","SEASONAL_CROP"]<-19== R15==> R15<-hies2009.old2[[15]]> dim(R15)[1] 5528

17> table(R15$NON_AGRI,useNA="ifany")
1
126

2

3

4

5

980 152 2282 797

6

7 <NA>

114 1071

6> R15[is.na(R15$NON_AGRI),c(16,12:14)]
PID NON_AGRI OUTPUT_5_4 INPUT_5_4
913 12200021280211102

NA

10300

2300

2433 31100070130511101

NA

3000002070002763 32100031030411101

NA

NA

NA

Sri Lanka HIES 2009/10 Manual (Version 1.1)

59

3486 43100010270411101

NA

2628500

1578500

4076 61200100340711101

NA

NA

NA

4078 61200100340711101

NA

NA

NA

> pid1<-c("32100031030411101", "61200100340711101", "61200100340711101")> R15<-subset(R15,!is.element(R15$PID,pid1)|!is.na(R15$NON_AGRI))> dim(R15)[1] 552517> pid2<-c("12200021280211102", "31100070130511101", "43100010270411101")> R15["NON_AGRI"]<-ifelse(is.element(R15$PID,pid2)&is.na(R15$NON_AGRI),7,R15$NON_AGRI)== R19 ==> R19<-hies2009.old2[[19]]> dim(R19)[1] 8154

21> table(R19$PERSON_5_5_2,useNA="ifany")
1

2

3

4

5

6

7

8

9

11

41 <NA>

5073 2206 517

228

69

37

11

9

1

1

1

1

> R19[is.na(R19$PERSON_5_5_2),c(20,11:18)]
PID PERSON_5_5_2 LOANS PAWNING_SELLING DEPOSITS_PENSIONS_EPF LOTTERY
5393 531001102508111NA

NA

NA

NA

NA

NA

SITTU_DEBTS COMPENSATION OTHER_WINDFALL
5393

NA

NA

NA

> R19<-R19[R19$PID!="531001102508111NA",]> R19[R19$PERSON_5_5_2>40,c(20,11:18)]
PID PERSON_5_5_2
6764 72200110270711141

LOANS PAWNING_SELLING DEPOSITS_PENSIONS_EPF LOTTERY

41 150000

NA

SITTU_DEBTS COMPENSATION OTHER_WINDFALL
6764

NA

NA

NA

> R19<-R19[R19$PERSON_5_5_2<=40,]> dim(R19)[1] 8152

21# Updated R03, R08, R10, R13, R15 and R19 in hies2009> hies2009[[3]]<-R03> hies2009[[8]]<-R08

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NA

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> hies2009[[10]]<-R10> hies2009[[13]]<-R13> hies2009[[15]]<-R15> hies2009[[19]]<-R19# Saved hies2009> hies2009.old3<-hies2009

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5.3 Primary key and foreign key of each data set

Background: Relationship between data sets
The questionnaire has 24 sections and the micro data is split into 24 data files. Among 24 data sets,
R01 and R23 are the most basic. R23 provide 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

No. ofPrimary key

Foreign key

duplication

isolation

R01

PID

PID

R02

PID

PID

R03

PID

PID

R04

ID + CODE (101-1924)

ID

R05

ID + NF_CODE

R06

PID

PID

R07

ID

ID

R08

PID + PRI_SEC (1-2)

PID

R09

ID

ID

R10

(2001-3509)

PID + SEAS_CROPS_CODE

(1-9)

1

ID

PID

R11

ID

ID

R12

ID

ID

R13

PID + SEASONAL_CROP (1-19)

PID

R14

ID

ID

R15

PID + NON_AGRI (1-7)

PID

R16

ID

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

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Figure: Example of relationship between data sets
R01
ID
PID
data

Primary key →R23
Foreign key

Primary key →R08

Primary key →



R01

ID
PID
Foreign key
PRI_SEC
data

Primary key →Primary key: UniquenessIn case of the primary key = ID
Primary key should be unique. There should be no duplicated key.


ID
data

The uniqueness of the primary key was satisfied for all data set.

> file.no<-c(7,9,11,12,14,16,18,20:24)> for(j in file.no){+ n<-sum(duplicated(hies2009[[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

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PID
data

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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

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(hies2009[[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)+ key1<-hies2009[[j]][,k1]+ key2<-hies2009[[j]][,k2]+ pkey<-paste(key1,formatC(key2,width=4,flag="0"),sep="")+ n<-sum(duplicated(hies2009[[j]]$pkey))

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+ cat("Number of duplicated combination",k1,"+",k2," in",Rnames[j],"= ",n,"\n")+ }The uniqueness of the primary key was satisfied for all data set.

> verify.unique(4,"ID","CODE")
Number of duplicated combination ID + CODE in R04 = 0> verify.unique(5,"ID","NF_CODE")
Number of duplicated combination ID + NF_CODE

in R05 =0

Number of duplicated combination PID + PRI_SEC in R08 =0> verify.unique(8,"PID","PRI_SEC")> verify.unique(10,"PID","SEAS_CROPS_CODE")
Number of duplicated combination PID + SEAS_CROPS_CODE

in R10 =0> verify.unique(13,"PID","SEASONAL_CROP")
Number of duplicated combination PID + SEASONAL_CROP in R13 => verify.unique(15,"PID","NON_AGRI")
Number of duplicated combination PID + NON_AGRI

in R15 = 0

Foreign key: Referential integrity

In case of the foreign key = ID
A corresponding ID should exist in R23.


The referential integrity was satisfied for all data sets.

> R23<-hies2009[[23]]> id.all<-R23$ID> file.no<-c(4,5,7,9,11,12,14,16,18,20:24)> for(j in file.no){+ n<-sum(!is.element(hies2009[[j]]$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

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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

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 R02, R06, R08, R10 and R13. However, there is
one isolated foreign key PID in R03, one in R15, one in R17 and two in R19, as follows.

> R01<-hies2009[[1]]> pid.all<-R01$PID> file.no<-c(1:3,6,8,10,13,15,17,19)> for(j in file.no){+ n<-sum(!is.element(hies2009[[j]]$PID,pid.all))+ cat("Number of isolated PIDs of",Rnames[j],"= ",n,"\n")+ if(n>0){+ d<-hies2009[[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],])Sri Lanka HIES 2009/10 Manual (Version 1.1)

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+ 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 = 1
Isolated foreign key: PID= 82200030470211106
Records of the household with ID= 822000304702111
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT R3_PERSON_SERIAL
73770

82

47

3

2

0

3

2

1

1

1

2

73771

82

47

3

2

0

3

2

1

1

1

3

73772

82

47

3

2

0

3

2

1

1

1

4

73773

82

47

3

2

0

3

2

1

1

1

5

73774

82

47

3

2

0

3

2

1

1

1

6

DID_ATTEND_HOSPITAL REASON_HOSPITAL IS_STAY_HOSPITAL REASON_STAY IS_ILL_DISABLE
73770

2

NA

2

NA

2

73771

2

NA

2

NA

2

73772

2

NA

2

NA

2

73773

2

NA

2

NA

2

73774

1

1

1

5

2

WHAT_ILL_DISABLE IS_EMPL_REASON DURATION_YEARS DURATION_MONTHS IS_ABSENT_ACT DAYS_ABSENT
73770

NA

NA

NA

NA

NA

NA

73771

NA

NA

NA

NA

NA

NA

73772

NA

NA

NA

NA

NA

NA

73773

NA

NA

NA

NA

NA

NA

73774

NA

NA

NA

NA

NA

NA

ID

PID

WT

73770 822000304702111 82200030470211102 174.0506
73771 822000304702111 82200030470211103 174.0506
73772 822000304702111 82200030470211104 174.0506
73773 822000304702111 82200030470211105 174.0506
73774 822000304702111 82200030470211106 174.0506
Records of household members with ID= 822000304702111
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT PERSON_SERIAL_NO

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77979

82

47

1

2

0

3

2

1

1

1

1

77980

82

47

1

2

0

3

2

1

1

1

2

77981

82

47

1

2

0

3

2

1

1

1

3

77982

82

47

1

2

0

3

2

1

1

1

4

77983

82

47

1

2

0

3

2

1

1

1

5

RELATIONSHIP SEX_LIVING BIRTH_YEAR BIRTH_MONTH AGE ETHNICITY RELIGION CURR_EDUCATION
77979

1

2

72

10 37

1

1

7

77980

3

1

95

12 14

1

1

2

77981

3

2

98

2 12

1

1

2

77982

3

1

NA

4

9

1

1

2

77983

3

2

8

9

1

1

1

NA

EDUCATION MARITAL_STATUS MAIN_ACTIVITY MAIN_OCCUPATION INDUSTRY EMPLOYMENT_STATUS
77979

5

2

1

6111

111

5

77980

9

1

NA

NA

NA

NA

77981

6

1

NA

NA

NA

NA

77982

4

NA

NA

NA

NA

NA

77983

NA

NA

NA

NA

NA

NA

ID

PID

WT

77979 822000304702111 82200030470211101 174.0506
77980 822000304702111 82200030470211102 174.0506
77981 822000304702111 82200030470211103 174.0506
77982 822000304702111 82200030470211104 174.0506
77983 822000304702111 82200030470211105 174.0506
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 = 1
Isolated foreign key: PID= 53100110250111104
Records of the household with ID= 531001102501111
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT SERIAL_5_4 NON_AGRI
3867

53 25

15

OUTPUT_5_4 INPUT_5_4
3867

10000

1

0

11

1

ID

1
PID

1

1

4

WT

4000 531001102501111 53100110250111104 49.91331

Records of household members with ID= 531001102501111
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT PERSON_SERIAL_NO

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58913

53

25

1

1

0

11

1

1

1

1

1

58914

53

25

1

1

0

11

1

1

1

1

2

58915

53

25

1

1

0

11

1

1

1

1

3

RELATIONSHIP SEX_LIVING BIRTH_YEAR BIRTH_MONTH AGE ETHNICITY RELIGION CURR_EDUCATION
58913

1

1

53

4 56

1

1

7

58914

2

2

61

6 48

1

1

7

58915

3

2

87

2 22

1

1

7

EDUCATION MARITAL_STATUS MAIN_ACTIVITY MAIN_OCCUPATION INDUSTRY EMPLOYMENT_STATUS
58913

8

2

1

6152

601

3

58914

8

2

1

1412

5211

5

58915

11

1

2

NA

NA

NA

ID

PID

WT

58913 531001102501111 53100110250111101 49.91331
58914 531001102501111 53100110250111102 49.91331
58915 531001102501111 53100110250111103 49.91331
Number of isolated PIDs of R17 = 1
Isolated foreign key: PID= 52100100190711105
Records of the household with ID= 521001001907111
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT SERIAL_5_5_1 PENSION
7017

52 19

17

1

0

10

7

1

1

1

5

DISABILITY_AND_RELIEF PROPERTY_RENTS SAMURDHI DIVIDENDS OTHER ABROAD LOCAL
7017

NA

NA

PID

NA

NA

NA 120000

NAID

NA 521001001907111

WT

7017 52100100190711105 41.44451
Records of household members with ID= 521001001907111
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT PERSON_SERIAL_NO
55279

52

19

1

1

0

10

7

1

1

1

1

55280

52

19

1

1

0

10

7

1

1

1

2

55281

52

19

1

1

0

10

7

1

1

1

3

55282

52

19

1

1

0

10

7

1

1

1

4

RELATIONSHIP SEX_LIVING BIRTH_YEAR BIRTH_MONTH AGE ETHNICITY RELIGION CURR_EDUCATION
55279

1

2

69

5 40

2

2

7

55280

3

2

95

8 13

2

2

2

55281

3

2

98

11 10

2

2

2

55282

3

1

NA

12

2

2

2

8

EDUCATION MARITAL_STATUS MAIN_ACTIVITY MAIN_OCCUPATION INDUSTRY EMPLOYMENT_STATUS

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55279

12

2

4

NA

NA

NA

55280

8

1

3

NA

NA

NA

55281

5

1

3

NA

NA

NA

55282

3

NA

NA

NA

NA

NA

ID

PID

WT

55279 521001001907111 52100100190711101 41.44451
55280 521001001907111 52100100190711102 41.44451
55281 521001001907111 52100100190711103 41.44451
55282 521001001907111 52100100190711104 41.44451
Number of isolated PIDs of R19 = 0

Summary:
Revised the next records.

File

RecordTo be read as;

Justification

R03

PID=82200030470211106,

PID=82200030470211101,

Among 1 to 5 of person number in

R3_PERSON_SERIAL=6

R3_PERSON_SERIAL=1

R01, person number=2 to 5 havealready counterparts.

R15

PID=82200030470211106

PID=82200030470211102

Income is non-agri.inc.

& NON_AGRI=4,& NON_AGRI=4,

While the husband (person

SERIAL_5_4=4

SERIAL_5_4=2

number=1) is a private sector
employee, the wife (person
number=2) is an own account worker.

R17

PID=52100100190711105,

PID=52100100190711101,

Married female head has person

SERIAL_5_5_1=5

SERIAL_5_5_1=1

number=1, and her children haveperson number=2 to 4.
Income may be remittance fromabroad by her husband.

# Revised the three records.
> R03<-hies2009.old3[[3]]> R15<-hies2009.old3[[15]]> R17<-hies2009.old3[[17]]> R03.old<-R03

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> R03[R03$PID=="82200030470211106","R3_PERSON_SERIAL"]<-1> R03[R03$PID=="82200030470211106","PID"]<-"82200030470211101"> hies2009[[3]]<-R03> R15.old<-R15> R15[R15$PID=="53100110250111104","SERIAL_5_4"]<-2> R15[R15$PID=="53100110250111104","PID"]<-"53100110250111102"> hies2009[[15]]<-R15> R17.old<-R17> R17[R17$PID=="52100100190711105","SERIAL_5_5_1"]<-2> R17[R17$PID=="52100100190711105","PID"]<-"52100100190711101"> hies2009[[17]]<-R17# Saved hies2009> hies2009.old4<-hies2009# Number of records and variables of data files> file.names<-list.files()> for(j in 1:24){+ cat(Rnames[j],":",formatC(nrow(hies2009[[j]]),width=7),",",+ formatC(ncol(hies2009[[j]]),width=3),":",+ sub(".CSV","",sub("HIES-2009-10-100%-Data-","",file.names))[j],"\n",sep="")
+ }
R01:

80872, 28:

SEC_1_DEMOGRAPHIC

R02:

20853, 23:

SEC_2_SCHOOL_EDUCATION

R03:

80862, 25:

SEC_3_HEALTH

R04: 798046, 16:

SEC_4_1_FOOD_EXP

R05: 568665, 16:

SEC_4_2_NONFOOD

R06:240, 27:

SEC_4_3_BOARDERS

R07:

19958, 13:

SEC_4_3_IS_BOADERS

R08:

18364, 18:

SEC_5_1_EMP_INCOME

R09:

19958, 13:

SEC_5_1_IS_EMP_INCOME

R10:4277, 21:

SEC_5_2_AGRI_INCOME

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R11:

19958, 13:

SEC_5_2_IS_AGRI_INCOME

R12:

19958, 13:

SEC_5_3_IS_OTHER_AGRI_INCOME

R13:

4194, 20:

SEC_5_3_OTHER_AGRI_INCOME

R14:

19958, 13:

SEC_5_4_IS_NON_AGRI_INCOME

R15:5525, 17:

SEC_5_4_NON_AGRI_INCOME

R16:

19958, 13:

SEC_5_5_1_IS_OTHER_INCOME

R17:

10735, 22:

SEC_5_5_1_OTHER_INCOME

R18:

19958, 13:

SEC_5_5_2_IS_WINDFALL_INCOME

R19:

8152, 21:

SEC_5_5_2_WINDFALL_INCOME

R20:

19958, 28:

SEC_6_B_DEBTNESS

R21:19958, 35:

SEC_6A_DURABLE_GOODS

R22:19958, 47:

SEC_7_BASIC_FACILITIES

R23:

19958, 39:

SEC_8_HOUSING

R24:

19958, 40:

SEC_9_LAND_ANIMAL

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Relationship among R01, R02 and R03


R01 excluding person number > 40 is the data of all household members.



The target of R02 is persons aged 5-19 years old. There are 29 missing records in R02, as
discussed in Chapter 4.
PIDs of R02 should have corresponding records in subset of R01 with age 5-19.

# Household members in R02 who have no corresponding records in R01;
> R01s<-subset(R01,AGE>=5&AGE<=19)> setdiff(R02$PID,R01s$PID)[1] "53200110270211105" "53200110270411101" "53200110270511101"[4] "71200120430311101" "82200030470211105" "91200060900311101"

PID="53200110270211105" should be dropped, because age is 4.

> R02[R02$ID=="532001102702111",c("PID","R2_SCHOOL_EDUCATION","GRADE_THIS_YEAR")]PID R2_SCHOOL_EDUCATION GRADE_THIS_YEAR
14189 53200110270211103

1

614190 53200110270211104

1

414191 53200110270211105

1

14> R01[R01$ID=="532001102702111",c("PID","RELATIONSHIP","SEX_LIVING","AGE","EDUCATION")]PID RELATIONSHIP SEX_LIVING AGE EDUCATION
59001 53200110270211101

1

1

48

NA

59002 53200110270211102

2

2

39

10

59003 53200110270211103

3

1

10

5

59004 53200110270211104

3

2

9

3

59005 53200110270211105

3

2

4

19

59006 53200110270211106

6

2

44

2



For PID="53200110270211105", person number should be read as 3, because only it belongs to
the target.

> R02[R02$ID=="532001102704111",c("PID","R2_SCHOOL_EDUCATION","GRADE_THIS_YEAR")]PID R2_SCHOOL_EDUCATION GRADE_THIS_YEAR
14192 53200110270411101

2NA> R01[R01$ID=="532001102704111",c("PID","RELATIONSHIP","SEX_LIVING","AGE","EDUCATION")]PID RELATIONSHIP SEX_LIVING AGE EDUCATION
59010 53200110270411101

1

2

33

12

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7359011 53200110270411102

2

1

38

12

59012 53200110270411103

3

1

5

19

59013 53200110270411104

3

1

2

NA



For PID="53200110270511101", person number should be read as 3, because only it belongs to
the target.

> R02[R02$ID=="532001102705111",c("PID","R2_SCHOOL_EDUCATION","GRADE_THIS_YEAR")]PID R2_SCHOOL_EDUCATION GRADE_THIS_YEAR
14193 53200110270511101

1

11> R01[R01$ID=="532001102705111",c("PID","RELATIONSHIP","SEX_LIVING","AGE","EDUCATION")]PID RELATIONSHIP SEX_LIVING AGE EDUCATION
59014 53200110270511101

1

1

42

10

59015 53200110270511102

2

2

47

6

59016 53200110270511103

3

1

16

10

59017 53200110270511104

3

1

1

6



For PID="71200120430311101", person number should be read as 3, because only it belongs to
the target.

> R02[R02$ID=="712001204303111",c("PID","R2_SCHOOL_EDUCATION","GRADE_THIS_YEAR")]PID R2_SCHOOL_EDUCATION GRADE_THIS_YEAR
16917 71200120430311101

1

8> R01[R01$ID=="712001204303111",c("PID","RELATIONSHIP","SEX_LIVING","AGE","EDUCATION")]PID RELATIONSHIP SEX_LIVING AGE EDUCATION
69536 71200120430311101

1

1

45

2

69537 71200120430311102

2

2

46

10

69538 71200120430311103

3

2

13

8



As for PID="82200030470211105", person numbers in R02 are shifted one by one.

> R02[R02$ID=="822000304702111",c("PID","R2_SCHOOL_EDUCATION","GRADE_THIS_YEAR")]PID R2_SCHOOL_EDUCATION GRADE_THIS_YEAR
19076 82200030470211103

1

1019077 82200030470211104

1

719078 82200030470211105

1

5> R01[R01$ID=="822000304702111",c("PID","RELATIONSHIP","SEX_LIVING","AGE","EDUCATION")]PID RELATIONSHIP SEX_LIVING AGE EDUCATION

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7477979 82200030470211101

1

2

37

5

77980 82200030470211102

3

1 14

9

77981 82200030470211103

3

2 12

6

77982 82200030470211104

3

1

9

4

77983 82200030470211105

3

2

1

NA



For PID="91200060900311101", person number should be read as 3, because only it belongs to
the target.

> R02[R02$ID=="912000609003111",c("PID","R2_SCHOOL_EDUCATION","GRADE_THIS_YEAR")]PID R2_SCHOOL_EDUCATION GRADE_THIS_YEAR
20105 91200060900311101

1

13> R01[R01$ID=="912000609003111",c("PID","RELATIONSHIP","SEX_LIVING","AGE","EDUCATION")]PID RELATIONSHIP SEX_LIVING AGE EDUCATION
82095 91200060900311101

1

1

52

19

82096 91200060900311102

2

2

50

10

82097 91200060900311103

3

1 17

12



Summary:
The next records in R02 should be omitted or revised.

PID in R02

TO-DO"53200110270211105"

The record should be omitted.

"53200110270411101"

R2_PERSON_SERIAL should be read as 3.

"53200110270511101"

R2_PERSON_SERIAL should be read as 3.

"71200120430311101"

R2_PERSON_SERIAL should be read as 3.

"82200030470211105"

In ID="822000304702111",
R2_PERSON_SERIAL =3 should be read as 2.
R2_PERSON_SERIAL =4 should be read as 3.
R2_PERSON_SERIAL =5 should be read as 4.

"91200060900311101"

R2_PERSON_SERIAL should be read as 3.

# Deleted> R02.old<-R02> R02<-R02[R02$PID!="53200110270211105",]> dim(R02)[1] 20852

23

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# Revised> R02[R02$PID=="53200110270411101","R2_PERSON_SERIAL"]<-3> R02[R02$PID=="53200110270511101","R2_PERSON_SERIAL"]<-3> R02[R02$PID=="71200120430311101","R2_PERSON_SERIAL"]<-3> R02[R02$PID=="82200030470211103","R2_PERSON_SERIAL"]<-2> R02[R02$PID=="82200030470211104","R2_PERSON_SERIAL"]<-3> R02[R02$PID=="82200030470211105","R2_PERSON_SERIAL"]<-4> R02[R02$PID=="91200060900311101","R2_PERSON_SERIAL"]<-3# Updated PID because the variable of R2_PERSON_SERIAL was revised.
> R02.old2<-R02> R02["PID"]<-paste(R02$ID,formatC(R02$ R2_PERSON_SERIAL,width=2,flag="0"),sep="")# Confirmed again> setdiff(R02$PID,R01s$PID)
character(0)# Updated R02 in hies2009 and saved hies2009.
> hies2009[[2]]<-R02> hies2009.old5<-hies2009

The target of R03 is all household members. As discussed in Chapter 4, there are 11 missing
records in R03.
PIDs of R03 should have corresponding records in R01.



This referential integrity was satisfied.

> setdiff(R03$PID,R01$PID)
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5.4 Sample allocation
Strata of HIES are district (22) and sector (3).
The number of psu selected is as follows;> R23.old<-R23



Generated psu identifier including district, sector and psu.

> R23["psuid"]<-substr(R23$ID,1,10)Generated data set PSU at psu level consist of DISTRICT, SECTOR and psuid.

> PSU<-R23[!duplicated(R23$psuid),c("DISTRICT","SECTOR","psuid")]> dim(PSU)[1] 2269
3> head(PSU)
DISTRICT SECTOR
psuid
1
11
1 1110007001
11
11
2 1120007010
21
11
1 1110003100
29
11
1 1110010101
37
11
1 1110010102
45
11
1 1110010103



Number of psu selected by district and sector;> t<-addmargins(table(PSU$DISTRICT,PSU$SECTOR))> dist.name<-c("Colombo","Gampaha","Kalutara","Kandy","Matale","Nuwara eliya",+ "Galle","Matara","Hambantota","Jaffna","Vavuniya","Batticaloa","Ampara",+ "Trincomalee","Kurunegala","Puttalama","Anuradhapura","Polonnaruwa",+ "Badulla","Moneragala","Ratnapura","Kegalle")> t<-addmargins(table(PSU$DISTRICT,PSU$SECTOR))> rownames(t)<-c(dist.name,"Sri Lanka")> colnames(t)<-c("Urban","Rural","Estate","Total")> tColomboGampahaKalutara
Kandy
MataleNuwara eliya
Galle
MataraHambantota
JaffnaVavuniya
Batticaloa
AmparaTrincomalee
Kurunegala
Puttalama
Anuradhapura
Polonnaruwa
BadullaUrban Rural Estate Total
166 117
10 29358 1393 200
44 10417 165
25
72
22
119
11
41
10
62
10
29
31
70
44 10410 1584483
10 137
21
67
2
90
19
17
0
36
16
22
0
38
35
53
0
88
32
54
0
86
17
44
0
61
13 102
5 120
17
66
4
87
15
66
2
83
0
57
2
59
12
45
24
81

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Moneragala
Ratnapura
KegalleSri Lanka

0
14
7
620

52
59
59
1452

7
21
17
197

59
94
83
2269

According to the final report of the survey, samples were allocated as in the table 1.1 and 1.2.

The numbers of sample collected were as follows;# The number of samples by district and survey month> district.code<-unique(R23$DISTRICT)> district.code[1] 11 12 13 21 22 23 31 32 33 41 43 51 52 53 61 62 71 72 81 82 91 92> dist.name<-c("Colombo","Gampaha","Kalutara","Kandy","Matale","Nuwara eliya",

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+ "Galle","Matara","Hambantota","Jaffna","Vavuniya","Batticaloa","Ampara",+ "Trincomalee","Kurunegala","Puttalama","Anuradhapura","Polonnaruwa",+ "Badulla","Moneragala","Ratnapura","Kegalle")> length(dist.name)[1] 22> t1<-addmargins(table(R23$DISTRICT,R23$MONTH))> rownames(t1)<-c(dist.name,"Sri Lanka")> t1[c(23,1:22),]
1
2
3
4
5
6
7
8
9
10
11
12
Sum
Sri Lanka
1643 1700 1821 1520 1718 1599 1716 1484 1573 1736 1705 1743 19958
Colombo
196 194 166 187 173 165 262 201 200 222 209 229 2404
Gampaha126 146 164 113 161 164 171 123 160
93 156 131 1708
Kalutara
65 147 128 101 111 131 158 147
73 13196
89 1377Kandy
82 74 86 79 72 63 108 87 72 112 111 64 1010
Matale37 49 48 43 49 48 40 54 47 45 60 67 587
Nuwara eliya 56 52 76 46 57 52 52 76 32 10 56 50 615
Galle
124 118 145
92 12973 160 138 123 115 145 118 1480
Matara92 96 100 88 100 91 123 104 103 120 101 111 1229
Hambantota
59 59 65 58 60 65 85 58 63 90 62 80 804
Jaffna59 43 44 54 45 47
00000 41 333
Vavuniya
34 38 36 36 25 28
0
0 19 39 17 56 328
Batticaloa
95 65 81 66 84 84
00 83 80 67 73 778
Ampara68 67 65 87 53 94
0
0 93 91 64 90 772
Trincomalee
57 66 56 50 58 61
00 38 59 47 73 565
Kurunegala
92
91 129
60
92
65
87
98
92
87
87
86 1066
Puttalama
51 82 92 45 105 44 68 73 42 65 69 44 780
Anuradhapura 73 56 54 60 55 64 83 53 58 72 53 69 750
Polonnaruwa
38 47 41 37 42 33 56 37 49 55 41 49 525
Badulla45 60
64 49 73 65 75 57 58 71 60 66 743
Moneragala
44 41 48 43 41 34 31 40 38 48 54 30 492
Ratnapura
69 73 65 74 61 75 90 75 67 80 69 73 871
Kegalle81 36 68 52 72
53
67
63
63
51
81
54
741

# The number of samples by sector and survey month> t2<-addmargins(table(R23$SECTOR,R23$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 1643 1700 1821 1520 1718 1599 1716 1484 1573 1736 1705 1743 19958
Urban
445 439 453 464 420 429 441 360 433 462 394 533 5273
Rural
1080 1088 1192 975 1138 1061 1087 996 1036 1130 1125 1041 12949
Estate118 173 176
81 160 109 188 128 104 144 186 169 1736

Remarks:
According to the delegates from Sri Lanka for the International Workshop,“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

19,9585,079,362

Number of household members

80,872

20,337,761

4.05

4.00

Household size
> dim(R01)[1] 80872

28> sum(R01$WT)[1] 20337761> R23<-hies2009[[23]]> dim(R23)[1] 19958

39> sum(R23$WT)[1] 5079362> nrow(R01)/nrow(R23)[1] 4.052109> sum(R01$WT)/sum(R23$WT)[1] 4.003999

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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
(nonmonetary income) from all the sources. The household income sources are investigated andreported 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 regular cash receipts such as pensions, dividends, rents, interest amounts received fromvarious types of savings, current remittances and local and foreign transfers
6. Irregular gains or windfall income such as compensations, lottery wins etc. and sales of goods andsavings.
7. Income in-kind

Obtaining income information from individuals and households is a difficult task as many people
reluctant to disclose 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
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 and vegetables, firewood collected etc. andestimated rental values of owner occupied housing units gathered in the consumption expendituresection 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 of charge or purchased on price concessions provided by employers etc. This
information of income in-kind along with the monitory income collected in the 6 income sections areaggregated and summarized in order to estimate, average monthly household income (mean income),
median income, per capita income, income receivers' income and various other indexes such as, Gini
coefficients, shares of 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 receiver’s income, the HIES records the household income, received
from all the sources, by source and person. The Income receiver’s 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 his aggregated total monthly income is less than Rs. 200, thenhe was not defined as an income receiver by the HIES 2009/10 and such income values were added tothe income of the heads of the respective households. It is obvious that the household income is sobuilt on the income of the income receivers in the household and thus the total household income 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;

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Table 2.9: Average number of income receivers and household size by sector

Table 2.10: Income receivers mean and median monthly income by sector

6.2. Process of estimating household income
The data sets R01, R04, R05, R08, R10, R13, R15, R17 and R19 are used for estimating householdincome.

The data files to be used for estimating household income;Type of income

Data

Reference period

Unit of records

Wages and allowances for

Individual,

one month,

Occupation (Pri/Sec)

file
Monetary

Wage

R08

incomeBonus for 12 month
Agricultural

R10

12 months,

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Individual,

83(Seasonal crops)
Agricultural

Seasonal crop code
R13

One month(Non-seasonal

Individual,
Non-seasonal crop code

crops)
Non-agricultural

R15

One monthIndividual,
Economic activity code

Other income

R17

6 items for one month,Individual

Remittance for 12 months
WindfallR19

12 months

Individual

Income in kind

R04

One week: CODE<2000

Household,monetaryand6 months: CODE>3000 &Item code

incomeR05

CODE<3300

Non-

12 months: CODE>3300 &CODE<3400 |CODE>3500
One month: otherwise

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. They are not regarded as household members.
(One record was found in R19.)

3. R04 and R05 have no duplicated item code within the household due to the structure of thequestionnaire.
(No such record was found in the given data set.)

Generating the variable of monthly monetary income
4. To calculate monthly income for each record, that is, for each person and for each activity in R08,
R10, R13, R15, R17 and R19.

The variable of output includes the estimated total value of thetotal output of the product sold or to be sold plus consumed or to be consumed.

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5. To replace the income in each record with 0 if it is less than 0.

Data

Variable of

filemonthly income

R08

wage.inc

Definition of monthly monetary income

Remarks

WAGES_SALARIES+ALLOWANCES+
BONUS/12R10

R13

crop.inclivestock.inc(OUTPUT_5_2－HH_CONSUMPTION－

Replace crop.inc=0 if

INPUT_5_2)/12

crop.inc<0

OUTPUT_5_3－INPUT_5_3

Replace livestock.inc=0
if livestock.inc<0

R15

nonagri.inc

OUTPUT_5_4－INPUT_5_4

Replace nonagri.inc=0 if
nonagri.inc<0R17

other.inc

PENSION+DISABILITY_AND_RELIEF+
PROPERTY_RENTS+SAMURDHI+
DIVIDENTS+OTHER+(ABROAD+LOCAL)/12

R19

windfall.inc(LOANS+PAWNING_SELLING+DEPOSITS_PENSIONS_EPF+
INCOME_WELFARE+SITTU_DEBTS+COMPENSATION+OTHER_LOTTERY)/12

Part I: Household income in cash and in kind at household level
Creating data file of household income at household level
6. To aggregate the above monthly income by household, and create data file with household-level
records from R08, R10, R13, R15, R17 and R19.

7. To merge the above data files using key of household identifier.

8. 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

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questionnaire and production side in section 5. The former and the latter should be equivalent in theconcept, while they are not the same in practice. The next is the treatment for rice and the otherproducts taken by DCS.

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, andtake the larger one as in-kind income of rice (inkind.income).

9. To aggregate (INKIND_VALUE*(30/7)) of R04 with CODE=<4 by household.
10. To aggregate (HH_CONSUMPTION/12) of R10 with SEAS_CROPS_CODE=1 by household.
11. To take the larger one out of the above two as monthly monetary income of rice (inkind.rice) athousehold level.

Remarks:
According to the delegates from Sri Lanka for the International Workshop,“Larger value of freely received rice value and paddy income in kind (household consumption valueof the paddy production) is added to the household income as paddy income in kind.”

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.

12. To convert INKIND_VALUE of R04 and NF_INKIND_VALUE of R05 to monthly variable(inkind04 and inkind05).
13. To aggregate the above by household.

Data

Variable of

filemonthly income

R04

inkind.rice

Definitionmax (rice.a, rice.b)
where,
rice.a: Sum of (INKIND_VALUE*(30/7)) of R04 with
CODE=<104 within the household
rice.b: Sum of (HH_CONSUMPTION/12) of R10 with
SEAS_CROPS_CODE=1 within the household

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Remarks

86

R04

inkind04

Sum of INKIND_VALUE*(30/7) (CODE>=105)
within the household

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
14. To generate the variable of non-monetary income by adding inkind.rice, inkind04 and inkind05.
15. To generate the variable of monthly total income by adding monetary income and non-monetary
income.

Part II: Income receivers’ income
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(Non-seasonal

Individual,
Non-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

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If a person is less than 10 years old or his/her aggregated total monthly income is less than Rs. 200,
then he/she was not defined as an income receiver by the HIES 2009/10 and such income values wereadded to the income of 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
16. To collapse income data files R08, R10, R13 and R15 at individual level
17. 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.
18. To generate the variable of monthly total income.

Transferring the income of persons with age less than 10 years or income less than Rs. 200 to thehead of the respective households
19. If a person is less than 10 years old or his/her aggregated total monthly income is less than Rs.
200 and his/her relationship is not a head of household, to add such income values to the incomeof the heads of the respective households.

Defining the income receivers
20. To select records with age >=10 and total income >=200, or relationship=1. Household heads are
qualified as income receivers even if the income of the head is none or less than 200.

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6.3 Generating the variable of monthly monetary income> hies2009<-hies2009.old5> R08<-hies2009[[08]]> R10<-hies2009[[10]]> R13<-hies2009[[13]]> R15<-hies2009[[15]]> R17<-hies2009[[17]]> R19<-hies2009[[19]]> R04<-hies2009[[04]]> R05<-hies2009[[05]]> hies2009.i1<-hies2009

Confirmed that the following unit variables have no missing value NA.

File

Unit Variable

R08

PRI_SEC

1 Main occupation
2 Secondary occupation

R10

SEAS_CROPS_CODE

1 Paddy
2 Chillies
3 Onions
4 Vegetables
5 Cereals
6 Yams
7 Tobacco
9 Other

R13

SEASONAL_CROP

1 Tea , Rubber
2 Coconuts
3 Coffee, Pepper Betel etc
4 Banana / Fruits
5 Meat6 Fish7 Eggs8 Milk
9 Other food items
10 Horticulture
19 Other

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R15

NON_AGRI

1 Mining & Quarrying.
2 Manufacturing
3 Construction
4 Trade5 Transport
6 Guest house, restaurants, bars/hotels etc
7 Other services

R17

SERIAL_5_5_1

Person number 1-40

R19

PERSON_5_5_2

Person number 1-40

> table(R08$PRI_SEC,useNA="ifany")
1

2

18159

205

> table(R10$SEAS_CROPS_CODE,useNA="ifany")
1

2

3

2782

122

76

4

5

6

7

9

695 410

81

23

88

> table(R13$SEASONAL_CROP,useNA="ifany")
1

2

3

4

5

6

989 1355 424

384

68

329

7

8

9

10

19

141 216

53

11

224

> table(R15$NON_AGRI,useNA="ifany")
1
126

2

3

4

5

980 152 2282 797

67

114 1074> table(R17$SERIAL_5_5_1,useNA="ifany")
1238408 1344 431

4

5

6

7

8

9

10

11

12

13

16

232 180

81

31

12

5

5

3

1

1

1

> table(R19$PERSON_5_5_2,useNA="ifany")
1

2

3

4

5

6

7

8

9

11

5073 2206 517

228

69

37

11

9

1

1

# 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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90

> R19[is.na(R19)]<-0Generated the monthly income variable for each data set.

== R08 ==> R08["wage.inc"]<-R08[13]+R08[14]+R08[15]/12> head(R08[c(17,12:15,19)])
PID PRI_SEC WAGES_SALARIES ALLOWENCES BONUS wage.inc
1 11100070010111107

1

7500

0

0 7500.000

2 11100070010211101

1

15000

0

0 15000.000

3 11100070010411101

1

7500

1500

2500 9208.333

4 11100070010511107

1

9000

0

0 9000.000

5 11100070010511108

1

11500

0

0 11500.000

6 11100070010711101

1

6000

0

0 6000.000== R10 ==> R10["crop.inc"]<-(R10[,16]-R10[,17]-R10[,18])/12> table(R10$crop.inc<0)
FALSE TRUE
3262 1015> R10["crop.inc"]<-ifelse(R10$crop.inc<0,0,R10$crop.inc)> head(R10[c(20,12,16:18,22)])
PID SEAS_CROPS_CODE OUTPUT_5_2 HH_CONSUMPTION INPUT_5_2
1 11200070110111103

1

30000

30000

2 11200111110311101

4

4000

800

3 11200111110411102

2

1100

400

0

58.33333

4 11200111150411101

1

25000

25000

16460

0.00000

5 11200070120311101

1

40000

40000

15000

0.00000

6 11200070120511101

1

91000

20000

41000 2500.00000== R13 ==> R13["livestock.inc"]<-R13[,16]-R13[,17]> table(R13$livestock.inc<0)
FALSE TRUE

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14850

crop.inc0.00000

650 212.50000

91

4150

44

> R13["livestock.inc"]<-ifelse(R13$livestock.inc<0,0,R13$livestock.inc)> head(R13[c(19,12,16,17,21)])
PID SEASONAL_CROP OUTPUT_5_3 INPUT_5_3 livestock.inc
1 11100101010211101

63850002600001250002 11200070110111103

2

2200

0

2200

3 11200111100211101

2

650

60

590

4 11200111110311101

3

4000

750

3250

5 11200111110311102

7

1200

0

1200

6 11200111110411103

2

12500

6000

6500

== R15 ==> R15["nonagri.inc"]<-R15[,13]-R15[,14]> table(R15$nonagri.inc<0)

FALSE TRUE
5463

62

> R15["nonagri.inc"]<-ifelse(R15$nonagri.inc<0,0,R15$nonagri.inc)> head(R15[c(16,12:14,18)])
PID NON_AGRI OUTPUT_5_4 INPUT_5_4 nonagri.inc
1 11100070010111104

7

2500

0

2500

2 11100070010211105

7

28000

10000

18000

3 11100070010311101

7

18000

0

18000

4 11100070010611101

5

20000

5000

15000

5 11100070010611102

5

12000

7000

5000

6 11100070010811101

5

25000

11250

13750

== R17 ==> R17["other.inc"]<-rowSums(R17[,12:17])+(R17[,18]+R17[,19])/12> head(R17[c(21,12:19,23)])
PID PENSION DISABILITY_AND_RELIEF PROPERTY_RENTS SAMURDHI DIVIDENDS OTHER
1 11100070010111101

0

400

0

240

0

0

2 11100070010111102

0

400

0

0

0

0

3 11100070010511102

0

0

0

600

0

0

4 11100070010511113

10000

0

0

0

0

0

5 11100070010611103

7000

0

0

0

0

0

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6 11200070100111101

0

0

22000

0

0

ABROAD LOCAL other.inc
1

0

0

640

2

0

0

400

3

0

0

600

4

0

0

10000

5

0

0

7000

6

0 15000

23250== R19 ==> R19["windfall.inc"]<-rowSums(R19[,12:18])/12> head(R19[c(20,12:18,22)])
PID LOANS PAWNING_SELLING DEPOSITS_PENSIONS_EPF LOTTERY SITTU_DEBTS
1 11200070100222101

0

25000

0

0

0

2 11200070100511103

0

75000

0

0

0

3 11200070100611101

0

50000

0

0

0

4 11200070100711101

0

50000

0

0

0

5 11100031000211101

0

30000

0

0

0

6 11100031000211102

0

50000

0

0

0

COMPENSATION OTHER_WINDFALL windfall.inc
1002083.3332006250.0003004166.667

4004166.6675002500.0006004166.667# Updated hies2009> hies2009[[8]]<-R08> hies2009[[10]]<-R10> hies2009[[13]]<-R13> hies2009[[15]]<-R15> hies2009[[17]]<-R17> hies2009[[19]]<-R19# Saved hies2009

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> hies2009.i2<-hies2009# For reference:
Sum of monthly income in each data set.
File

No. ofVariable of

Sum of the

Sum of the variable

Weighted

records

monthly

variable(weighted)

average income

income(un-weighted)

per household

R08

18,364wage.inc250,526,008

62,284,504,589

12,262.3

R10

4,277

crop.inc

13,284,307

3,916,717,127

771.1

R13

4,194

livestock.inc

65,899,155

20,208,439,835

3,978.5

R15

5,525

nonagri.inc

175,260,468

46,762,170,361

9,206.3

R17

10,735other.inc

87,765,278

21,359,560,030

4,205.2

R19

8,152

windfall.inc

56,655,468

14,142,357,917

2,784.3

Total

649,390,684

168,673,749,859

33,207.7# Un-weighted sum of the variables> t<-c(t,sum(R10$crop.inc))> t<-c(t,sum(R13$livestock.inc))> t<-c(t,sum(R15$nonagri.inc))> t<-c(t,sum(R17$other.inc))> t<-c(t,sum(R19$windfall.inc))> t<-c(t,sum(t))> names(t)<-c("wage","crop","livestock","nonagri","other","windfall","Total")
> t
wage

crop livestock

nonagriother250526008 13284307 65899155 175260468

87765278

windfallTotal

56655468 649390684# Weighted sum of the variables> t<-sum(R08$wage.inc*R08$WT)> t<-c(t,sum(R10$crop.inc*R10$WT))> t<-c(t,sum(R13$livestock.inc*R13$WT))> t<-c(t,sum(R15$nonagri.inc*R15$WT))> t<-c(t,sum(R17$other.inc*R17$WT))

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> t<-c(t,sum(R19$windfall.inc*R19$WT))> t<-c(t,sum(t))> names(t)<-c("wage","crop","livestock","nonagri","other","windfall","Total")> t
wage
62284504589

crop

livestock

nonagriother3916717127 20208439835 46762170361

21359560030

windfallTotal

14142357917 168673749859# Weighted average income per household> round(t/sum(R23$WT),1)
wage
12262.3

crop livestock

nonagriotherwindfall

Total

771.1

9206.34205.22784.333207.7

3978.5*********************************************************************# R08: Average monthly income per household from paid employment> t<-addmargins(tapply(R08$wage.inc*R08$WT,R08$PRI_SEC,sum)/sum(R23$WT))> names(t)<-c("Main occupation","Secondary occupation","Total")> round(t[c(3,1,2)],1)
Total

Main occupation Secondary occupation

12262.3

12199.4

62.8# R10: Average monthly income per household from agricultural activities> t<-addmargins(tapply(R10$crop.inc*R10$WT,R10$SEAS_CROPS_CODE,sum)/sum(R23$WT))> 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

771.1

499.7

27.3

7 Tobacco

9 Other

8.0

14.4

3 Onions 4 Vegetables
14.5

5 Cereals

6 Yams

63.8

19.0

124.5

# R13: Average monthly income per household from other agricultural activities> t<-addmargins(tapply(R13$livestock.inc*R13$WT,R13$SEASONAL_CROP,sum)/sum(R23$WT))> names(t)<-c("1 Tea, Rubber","2 Coconuts","3 Coffee, Pepper Betel etc",

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+ "4 Banana / Fruits","5 Meat","6 Fish","7 Eggs","8 Milk","9 Other food items",+ "10 Horticulture","19 Other","Total")> df<-data.frame(Code=names(t),Average=round(t,1),row.names=NULL)> df[c(12,1:11),]
Code Average
12

Total

3978.51

1 Tea, Rubber

1548.72

2 Coconuts

938.8

3

3 Coffee, Pepper Betel etc

380.5

4

4 Banana / Fruits

365.9

5

5 Meat166.0

6

6 Fish177.0

77 Eggs21.2

8

8 Milk122.8

99 Other food items

53.2

10

10 Horticulture

22.1

11

19 Other

182.5# R15: Average monthly income per household from non-agricultural activities> t<-addmargins(tapply(R15$nonagri.inc*R15$WT,R15$NON_AGRI,sum)/sum(R23$WT))> names(t)<-c("1 Mining & Quarrying","2 Manufacturing","3 Construction","4 Trade",+ "5 Transport","6 Guest house, restaurants, bars/hotels etc","7 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

9206.3

1

1 Mining & Quarrying

140.5

2

2 Manufacturing1179.33

3 Construction

2931.744 Trade2532.955 Transport

1318.46 6 Guest house, restaurants, bars/hotels etc

171.9

7

931.7

7 Other services# R17: Average monthly income per household from other cash receipt

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> r17.inc<-colSums(R17[,12:17]*R17$WT)/sum(R23$WT)> r17.inc<-c(r17.inc,colSums(R17[,18:19]*R17$WT)/sum(R23$WT)/12)> r17.inc<-c(r17.inc,sum(r17.inc))> names(r17.inc)[9]<-"Total"> round(r17.inc,1)
PENSION DISABILITY_AND_RELIEF

PROPERTY_RENTS

SAMURDHI

1058.3

33.0

501.0

127.4

DIVIDENDSOTHERABROAD

LOCAL

157.5

1103.4

758.3

466.1

Total
4205.2# R19: Average monthly income per household by chance or ad hoc gains> r19.inc<-colSums(R19[,12:18]*R19$WT)/sum(R23$WT)/12> r19.inc<-c(r19.inc,sum(r19.inc))> names(r19.inc)[8]<-"Total"> round(r19.inc,1)
LOANS

PAWNING_SELLING DEPOSITS_PENSIONS_EPF

LOTTERY

1005.41311.2

228.5

24.1

SITTU_DEBTS

COMPENSATIONOTHER_WINDFALL

Total

163.4

37.5

14.1

2784.3

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6.4 Creating data set of household monetary income at household level
Strategy:
To collapse data files R08, R10, R13, R15, R17 and R19 at household level, and create data frame
hhinc with variables of monthly household income by income source.
Generated function ind2hh# Converting individual-level data frame to household-level data frame# df: individual-level data frame# variables: to be aggregated by household# hhid: household identifier> ind2hh<-function(df,variables,hhid){+ n<-length(variables)+ d<-data.frame(ID=unique(hhid))+ for(j in 1:n){+ t<-tapply(df[,variables[j]],hhid,sum,na.rm=T)+ d2<-data.frame(ID=names(t),x=as.vector(t))+ colnames(d2)[2]<-variables[j]+ d<-merge(d,d2,by="ID",all=T)+ }+ return(d)+ }# Example: usage of ind2hh== Individual-level data set ==> head(R08[order(R08$ID),c("ID","WAGES_SALARIES","ALLOWENCES","BONUS","wage.inc")])ID WAGES_SALARIES ALLOWENCES BONUS wage.inc
475 111000114803111

25800

4000 10000 30633.33

476 111000114804111

14560

2500 10000 17893.33

477 111000114804111

15000

478 111000114805111

40000

5000 15000 46250.00

479 111000114807111

24560

5000 10000 30393.33

480 111000114808111

16380

0

0

0 15000.00

5000 16796.67== Collapsed to household-level data set ==> head(ind2hh(R08,c("WAGES_SALARIES","ALLOWENCES","BONUS","wage.inc"),R08$ID))
ID WAGES_SALARIES ALLOWENCES BONUS wage.inc

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1 111000114803111

25800

4000 10000 30633.33

2 111000114804111

29560

2500 10000 32893.33

3 111000114805111

40000

5000 15000 46250.00

4 111000114807111

24560

5000 10000 30393.33

5 111000114808111

16380

0

5000 16796.67

6 111000115501111

13000

0

0 13000.00

Generated household-level data frame with variables of ID and wage.inc.

> hhinc<-R23[c("ID","WT")]> hhinc<-merge(hhinc,ind2hh(R08,"wage.inc",R08$ID),all=T)> head(hhinc)ID

WT wage.inc

1 111000114801111 30.4971

NA

2 111000114802111 30.4971

NA

3 111000114803111 30.4971 30633.33
4 111000114804111 30.4971 32893.33
5 111000114805111 30.4971 46250.00
6 111000114807111 30.4971 30393.33> dim(hhinc)[1] 19958

3Merged hhinc with household-level data frame with variables of ID and crop.inc, and so on.

> hhinc<-merge(hhinc,ind2hh(R10,"crop.inc",R10$ID),all=T)> hhinc<-merge(hhinc,ind2hh(R13,"livestock.inc",R13$ID),all=T)> hhinc<-merge(hhinc,ind2hh(R15,"nonagri.inc",R15$ID),all=T)> hhinc<-merge(hhinc,ind2hh(R17,"other.inc",R17$ID),all=T)> hhinc<-merge(hhinc,ind2hh(R19,"windfall.inc",R19$ID),all=T)> dim(hhinc)[1] 19958

8> colnames(hhinc)<-c("ID","WT","wage","crop","livestock","nonagri","other","windfall")> hhinc[is.na(hhinc)]<-0> hhinc["monetary.tt"]<-rowSums(hhinc[,3:8])> head(hhinc)ID

WT

1 111000114801111 30.4971

wage crop livestock nonagri other windfall monetary.tt
0.00

0

0

60000

0

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99

2 111000114802111 30.4971

0.00

0

0

3 111000114803111 30.4971 30633.33

0

4 111000114804111 30.4971 32893.33

80000

0

0

80000.00

00 32825063458.33

0

0

0

0

0

32893.33

5 111000114805111 30.4971 46250.00

000 6125052375.00

6 111000114807111 30.4971 30393.33

0

0

0

0

30393.33

0# Saved hies2009> hies2009.i3<-hies2009

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6.5 Non-monetary income (income in-kind)
The variables to be generated;
Data

Variable of

filemonthly income

R04

inkind.rice

Definitionmax (rice.a, rice.b)
where,
rice.a: Sum of (INKIND_VALUE*(30/7)) of R04 with
CODE=<104 within the household
rice.b: Sum of (HH_CONSUMPTION/12) of R10 with
SEAS_CROPS_CODE=1 within the household

R04

inkind04

INKIND_VALUE*(30/7) (CODE>=105)

R05

inkind05

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> R04<-hies2009[[4]]> R05<-hies2009[[5]]> dim(R04)[1] 798046

16> dim(R05)[1] 568665


16

Generated variable rice.a from R04> d<-subset(R04,CODE<=104)> dim(d)[1] 25533

16> table(is.na(d$INKIND_VALUE))
FALSE TRUE
2738 22795> t<-tapply(d$INKIND_VALUE*(30/7),d$ID,sum,na.rm=T)> rice1<-data.frame(ID=names(t),rice.a=as.vector(t))> table(rice1$rice.a>0)
FALSE TRUE

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17095 2375

Generated variable rice.b from R10> d2<-subset(R10,SEAS_CROPS_CODE==1)> dim(d2)[1] 2782

22> length(unique(d2$ID))[1] 2741> t2<-tapply(d2$HH_CONSUMPTION/12,d2$ID,sum,na.rm=T)> rice2<-data.frame(ID=names(t2),rice.b=as.vector(t2))> dim(rice2)[1] 2741

2> head(rice2)
ID

rice.b

1 111000424205111 1666.6667
2 111000526709111 375.0000
3 111000701601111 1416.6667
4 111000701610111 333.3333
5 112000116209111

0.00006 112000220507111 2083.3333

Generated the variable of inkind.rice after comparing rice.a and rice.b> inkind<-R23[c("ID","WT")]> inkind<-merge(inkind,rice1,all=T)> inkind<-merge(inkind,rice2,all=T)> dim(inkind)[1] 19958

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

WT

rice.arice.b inkind.rice

330 111000424205111 696.6240 2185.714 1666.6667

2185.714

637 111000701610111 495.3275

900.000 333.3333

900.000

1466 112000220507111 221.5858 2914.286 2083.3333

2914.286

1535 112000321806111 244.8862 3741.429 1050.0000

3741.429

1536 112000321808111 244.8862 2400.000 1260.0000

2400.000

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1021607 112000424106111 305.7838 1542.857 4783.3333

4783.333

Remarks:
There are 3,277 sample households with rice.a>0 and/or ric.b>0. The value of rice.a is bigger than thatof rice.b in 1,989 households.
> d<-subset(inkind,rice.a>0|rice.b>0)> dim(d)[1] 32775> table(d$rice.a>d$rice.b)
FALSE TRUE
1288 1989

10000
0

5000

d$rice.b

15000

20000

> plot(d$rice.a,d$rice.b)

0

2000

4000

6000

8000

10000

d$rice.aGenerated the variable inkind04 and inkind05== R04 ==> d<-subset(R04,CODE>=105)> dim(d)[1] 772513

16> t<-tapply(d$INKIND_VALUE*(30/7),d$ID,sum,na.rm=T)

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> inkind04<-data.frame(ID=names(t),inkind04=as.vector(t))> dim(inkind04)[1] 19958

2> table(inkind04$inkind04>0)
FALSE TRUE
5072 14886== R05 ==> d<-subset(R05,!is.na(NF_INKIND_VALUE))> dim(d)[1] 43368

16> d["inkind05"]<-d$NF_INKIND_VALUE> d["inkind05"]<-ifelse(d$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)> t<-tapply(d$inkind05,d$ID,sum,na.rm=T)> inkind05<-data.frame(ID=names(t),inkind05=as.vector(t))> dim(inkind05)[1] 19509

2

Merged inkind, inkind04 and inkind05.

> inkind<-merge(inkind,inkind04,all=T)> inkind<-merge(inkind,inkind05,all=T)> dim(inkind)[1] 19958

7

Generated the variable of total non-monetary income> inkind[is.na(inkind)]<-0> inkind["inkind.tt"]<-rowSums(inkind[,5:7])> head(inkind[inkind$inkind.rice>0,])ID

WT

rice.arice.b inkind.rice

inkind04 inkind05 inkind.tt

37

111000116807111 509.68350

338.5714

0.000

338.5714

42.85714

73

111000117405111 342.63580 2721.4286

0.000

2721.4286 26940.00000

51067 80728.429

0.000

428.5714

0.00000

9000

9428.571

330 111000424205111 696.62400 2185.7143 1666.667

2185.7143

171.42857

5000

7357.143

220 111000310003111 34.93164

428.5714

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8000

8381.429

104

427 111000526601111 406.64470

257.1429

0.000

257.1429

814.28571

440 111000526709111 448.65360

0.0000375.000

375.0000

1714.28571



500

1571.429

50000 52089.286

Merged hhinc and inkind, and generated the variable of total income.

> hhinc.old<-hhinc> inkind.old<-inkind> hhinc<-merge(hhinc,inkind[,-2])> hhinc["ttinc"]<-hhinc$monetary.tt+hhinc$inkind.tt> head(hhinc[hhinc$inkind.rice>0,])
ID

WT

wage

crop livestock nonagri other windfall monetary.tt

37111000116807111 509.68350 45000

0.000

0

0 12000

0.000

57000

73

111000117405111 342.63580 34500

0.000

0

0

0

0.000

34500

220 111000310003111 34.93164 23000

0.000

0

0

0 3500.000

26500

330 111000424205111 696.62400 30000 2666.667

000 4583.333

37250

427 111000526601111 406.64470 10000

0.000

0

0

0

0.000

10000

440 111000526709111 448.65360 40000 3375.000

150000

0

0

0.000

193375rice.arice.b inkind.rice

inkind04 inkind05 inkind.tt
42.85714

37

338.5714

0.000

338.5714

73

2721.4286

0.000

2721.4286 26940.00000

51067 80728.429 115228.43

220 428.5714

0.000

428.5714

0.00000

9000

9428.571 35928.57

330 2185.7143 1666.667

2185.7143

171.42857

5000

7357.143 44607.14

427 257.1429

257.1429

814.28571

500

1571.429 11571.43

440

0.000

0.0000 375.000

375.0000 1714.28571

8000

ttinc

8381.429 65381.43

50000 52089.286 245464.29# Average monthly household income per household by income source> round(colSums(hhinc[,3:16]*hhinc[,"WT"])/sum(hhinc$WT))
wage

crop

livestock

nonagri

other

12262

771

3979

9206

4205

2784

33208

rice.b inkind.rice

inkind04

inkind05

inkind.tt

ttinc

1147

3955

5579

38786

rice.a
368

279

477

windfall monetary.tt# Un-weighted total amount of monthly income> colSums(hhinc[,3:16])
wage

crop

livestock

nonagriotherwindfall monetary.tt

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105250526008

13284307

65899155

175260468

87765278

56655468

649390684

rice.arice.b inkind.rice

inkind04

inkind05

inkind.tt

ttinc

5861344433984822580031

81692825

111808875

761199559

7536018# Saved hies2009> hies2009.i4<-hies2009> hhinc.old2<-hhincSummary:
Average monthly household income per household by income source
My estimates

Final report ofthe survey*

Total income

38,78636,451

Monetary income

33,20830,803

Wages/Salaries

12,262

12,434Agricultural activities

4,750

4,832

Non-agricultural activities

9,206

6,477

Other income

4,205

4,252

Windfall income

2,784

2,808

Non-monetary income

5,579

5,648

Note: * The figures of the final report came from Mr. KMR Wickramasinghe, DCS.

For reference:

Total number of households
Total amount of monthly monetary income
Average monthly monetary income per household

Un-weighted

Weighted

estimates

estimates

19,9585,079,362

649,390,684

168,673,749,859

32,53833,208> sum(hhinc$monetary.tt)[1] 649390684> sum(hhinc$monetary.tt*hhinc$WT)[1] 168673749859

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> mean(hhinc$monetary.tt)[1] 32537.86> sum(hhinc$monetary.tt*hhinc$WT)/sum(hhinc$WT)[1] 33207.66****************************************************************************************************************************************************************************************

Caution: Errors in the distributed data file R15
Through discussion with Mr. KMR Wickramasinghe on the possible causes of large discrepancy of
income from non-agricultural activities in the above summary table, the next fact was found.
The following revisions were made during the primary tabulation and the final tabulation in DCS,
but it was not reflected on the distributed data file R15 (Section 5.4 Income from non-agriculturalactivities).

Case (DISTRICT/ PSU/

Revisions made in DCS

SAMPLE_N/ NHH/ SERIAL_5_4
Case 12/38/7/1/2

Output 1,680,000 →Case 12/188/1/1/1

Output 3,450,000 →

345,000

Case 61/72/1/1/4

Output 3,500,000 →

350,000

Case 61/93/6/1/1

Output 180,000,000 →

15,000,000

Input 143,680,000 →

12,680,000

Output 60,000,000 →

5,000,000

Input 50,000,000 →

4,000,000

Case 91/54/3/1/1

Case 53/25/1/1/4

168,000

The record was deleted because the household size of case53/25/1/1 is three.

The above revisions were made on the resampled data set.

See Chapter 8 Resampling method.

****************************************************************************************************************************************************************************************

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6.6 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 than Rs.
200, 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(Non-seasonal

Individual,
Non-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


Defined function col2ind.

> col2ind<-function(df,variables,pid){+ # df: data frame at individual and some unit level+ # variables: vector of variable names to be applied+ # indid: individual identifier (grouping factor)+ # FUN: sum, na.rm=T+ n<-length(variables)+ d<-data.frame(PID=unique(pid))+ for(j in 1:n){

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+ t<-tapply(df[,variables[j]],pid,sum,na.rm=T)+ d2<-data.frame(PID=names(t),x=as.vector(t))+ colnames(d2)[2]<-variables[j]+ d<-merge(d,d2,by="PID",all=T)+ }+ return(d)+ }# Example: usage of col2ind> head(R08[R08$PRI_SEC==2,"PID"])[1] "11200111150911102" "11200070120711101" "11100070130911102" "11300121410611101"[5] "11100011560811101" "11100011700711101"== BEFORE ==> R08[R08$PID=="11200111150911102",+ c("PID","PRI_SEC","WAGES_SALARIES","ALLOWENCES","BONUS","wage.inc")]
PID PRI_SEC WAGES_SALARIES ALLOWENCES BONUS wage.inc
170 11200111150911102

1

20045

4500

0

24545

171 11200111150911102

2

12000

0

0

12000

> df<-col2ind(R08,c("WAGES_SALARIES","ALLOWENCES","BONUS","wage.inc"),R08$PID)== AFTER ==> df[df$PID=="11200111150911102",]
PID WAGES_SALARIES ALLOWENCES BONUS wage.inc
2245 11200111150911102



32045

4500

0

36545

Extracted ID, PID, WT, AGE and RELATIONSHIP from R01, and created data frame pinc.

> pinc<-R01[c("ID","PID","WT","AGE","RELATIONSHIP")]> head(pinc)ID

PID

WT AGE RELATIONSHIP

1 111000700101111 11100070010111101 41.42092 77

1

2 111000700101111 11100070010111102 41.42092 52

3

3 111000700101111 11100070010111103 41.42092 47

3

4 111000700101111 11100070010111104 41.42092 41

3

5 111000700101111 11100070010111105 41.42092

656 111000700101111 11100070010111106 41.42092

2

5> dim(pinc)

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[1] 80872



5

Merged pinc with individual-level R08 with variables of PID and wage.inc.

> pinc<-merge(pinc,col2ind(R08,"wage.inc",R08$PID),all.x=T)> head(pinc[!is.na(pinc$wage.inc),])PID

ID

WT AGE RELATIONSHIP wage.inc

13 11100011480311103 111000114803111 30.4971 36

3 30633.33

19 11100011480411101 111000114804111 30.4971 42

1 17893.33

20 11100011480411102 111000114804111 30.4971 18

3 15000.00

24 11100011480511103 111000114805111 30.4971 28

3 46250.00

25 11100011480711101 111000114807111 30.4971 29

1 30393.33

28 11100011480811101 111000114808111 30.4971 29

1 16796.67> sum(pinc$wage.inc,na.rm=T)[1] 250526008

Merged pinc with individual-level R10 with variables of PID and crop.inc and so on.

> pinc<-merge(pinc,col2ind(R10,"crop.inc",R10$PID),all.x=T)> sum(pinc$crop.inc,na.rm=T)[1] 17244970> pinc<-merge(pinc,col2ind(R13,"livestock.inc",R13$PID),all.x=T)> sum(pinc$livestock.inc,na.rm=T)[1] 65899155> pinc<-merge(pinc,col2ind(R15,"nonagri.inc",R15$PID),all.x=T)> sum(pinc$nonagri.inc,na.rm=T)[1] 175260468> pinc<-merge(pinc,R17[c("PID","other.inc")],all.x=T)> pinc<-merge(pinc,R19[c("PID","windfall.inc")],all.x=T)> dim(pinc)[1] 80872

11> head(pinc)PID

ID

WT AGE RELATIONSHIP wage.inc crop.inc

1 11100011480111101 111000114801111 30.4971

44

1

NA

NA

2 11100011480111102 111000114801111 30.4971

41

2

NA

NA

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3 11100011480111103 111000114801111 30.4971

21

3

NA

NA

4 11100011480111104 111000114801111 30.4971

16

3

NA

NA

5 11100011480111105 111000114801111 30.4971

67

4

NA

NA

6 11100011480211101 111000114802111 30.4971

36

1

NA

NA

livestock.inc nonagri.inc other.inc windfall.inc
1

NA

60000

NA

NA

2

NA

NA

NA

NA

3

NA

NA

NA

NA

4

NA

NA

NA

NA

5

NA

NA

NA

NA

6

NA

80000

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

WT age relation wage crop livestock

1 11100011480111101 111000114801111 30.4971

44

1

0

0

0

2 11100011480111102 111000114801111 30.4971

41

2

0

0

0

3 11100011480111103 111000114801111 30.4971

21

3

0

0

0

4 11100011480111104 111000114801111 30.4971

16

3

0

0

0

5 11100011480111105 111000114801111 30.4971

67

4

0

0

0

6 11100011480211101 111000114802111 30.4971

36

1

0

0

0

nonagri other windfall ttinc
1

60000

0

0 60000

2

0

0

0

0

3

0

0

0

0

4

0

0

0

0

5

0

0

0

0

6

80000

0

0 80000

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Remarks:
Out of 19,958 household heads, the number of those having less than 200 income is 1,832.
> addmargins(table(subset(pinc,relation==1)$ttinc<200,useNA="ifany"))
FALSE TRUE

Sum

18049 1909 19958# Saved hies2009 and pinc
> hies2009.i5<-hies2009> pinc.old<-pinc# For reference:> colSums(pinc[,6:12],na.rm=T)
wage

crop livestock

nonagriother250526008 13284307 65899155 175260468

87765278

windfallttinc

56655468 649390684

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6.7 Income receivers’ income***** About AGE *****The variable AGE of R01 has many NA. It is necessary to distinguish persons with age < 10 andpersons with age >= 10 in order to define the income receivers.
Firstly, to calculate the age using birth year and birth month if AGE = NA.
Secondly, to estimate age >= 10 unless MARITAL_STATUS =NA.
Lastly, to determine age < 10 considering all persons’ information within the household.
# Generated the variable age> d<-R01> d["smonth"]<-d$MONTH> d["syear"]<-2009+ifelse(d$MONTH<=6,1,0)> d["bmonth"]<-d$BIRTH_MONTH> d["byear"]<-2000+d$BIRTH_YEAR-ifelse(d$BIRTH_YEAR>10,100,0)> d["m"]<-(d$syear-d$byear)*12+(d$smonth-d$bmonth)> d["age"]<-ifelse(is.na(d$AGE),floor(d$m/12),d$AGE)> head(d[is.na(d$AGE),+ c("PID","AGE","smonth","syear","bmonth","byear","m","age","MARITAL_STATUS")])PID AGE smonth syear bmonth byear

m age MARITAL_STATUS

1411100070010211107 NA

7 2009

10

2008

9

0

NA

34

11100070010511112 NA

7 2009

12

2008

7

0

NA

138 11100101010411105 NA

10 2009

4

2009

6

0

NA

184 11100101020711104 NA

10 2009

10

2009

0

0

NA

261 11200101040811105 NA

10 2009

6

2009

4

0

NA

267 11200101040911104 NA

10 2009

11

2008 110NA# 19 NAs found in the variable age> table(is.na(d$age),useNA="ifany")
FALSE TRUE
80853

19

> head(d[is.na(d$age),c("PID","AGE","smonth","syear","bmonth","byear","m","age",+ "MARITAL_STATUS")])PID AGE smonth syear bmonth byear

m age MARITAL_STATUS

15553 12200090430211101 NA

9 2009

NA

NA NA

NA

2

15554 12200090430211102 NA

9 2009

NA

NA NA

NA

2

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11315581 12200090430911103 NA

9 2009NA2009 NA

NA

NA

16167 12100060580711101 NA

6 2010

NA

NA NA

NA

2

16910 12200110780911103 NA

11 2009

NA

NA NA

NA

2

20181 13200061600711104 NA

6 2010

NA

NA NA

NA

2



Defined the variable age10:
0 if age < 10
1 if age >= 10> d["age10"]<-ifelse(d$age>=10|(is.na(d$age)&!is.na(d$MARITAL_STATUS)),1,0)> table(d$age10,useNA="ifany")0

1 <NA>

14181 66687

4> d[is.na(d$age10),c("PID","AGE","smonth","syear","bmonth","byear","m","age",+ "MARITAL_STATUS")]PID AGE smonth syear bmonth byear

m age MARITAL_STATUS

15581 12200090430911103 NA

9 2009NA2009 NA

NA

NA

31556 23300110400411105 NA

11 2009

NA

2009 NA

NA

NA

36931 31200100370711103 NA

10 2009

8

NA NA

NA

NA

52641 51200110330611106 NA

11 2009

NA

NA NA

NA

NA



The top two persons are less than 10 years old, because the birth year is 2009.
The bottom two persons are also considered as age < 10 considering the all household members,
as follows;> d[d$ID=="312001003707111",c(27,12,13,19,21,29:35)]
PID RELATIONSHIP SEX_LIVING CURR_EDUCATION MARITAL_STATUS
36929 31200100370711101

1

2

7

3

36930 31200100370711102

3

2

7

2

36931 31200100370711103

5

2

NA

NA

smonth syear bmonth byear

m age age10

36929

10

2009

7 1955 651

54

1

36930

10

2009

7 1981 339

28

1

36931

10

2009

8

NA

NA

NA

NA

> d[d$ID=="512001103306111",c(27,12,13,19,21,29:35)]
PID RELATIONSHIP SEX_LIVING CURR_EDUCATION MARITAL_STATUS
52636 51200110330611101

1

1

7

2

52637 51200110330611102

2

2

7

2

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52638 51200110330611103

3

1

1

NA

52639 51200110330611104

3

1

NA

NA

52640 51200110330611105

4

2

7

3

52641 51200110330611106

5

2

NA

NA

smonth syear bmonth byear

m age age10

52636

11

2009

8 1969 483

40

1

52637

11

2009

5 1984 306

25

1

52638

11

2009

10 2005

49

4

0

52639

11

2009

6 2009

5

0

0

52640

11

2009

7 1963 556

46

1

52641

11

2009

NA

NA

NA

NA

NA

# Replaced age10 = NA with age10 = 0.
> d[is.na(d$age10),"age10"]<-0> table(d$age10,useNA="ifany")0114185 66687# Appended age10 to pinc
> pinc<-merge(pinc,d[c("PID","age10")])***********************************************************

Figure: Grouping persons by relationship to household head, age and income
inc = 0Non-head0 < inc < 200

inc >=200

age < 10
flag0

flag1

age >= 10
flag2
Head



flag4

flag3

Added flag0=1 to persons in pinc satisfying the next conditions;
1) RELATIONSHIP is not household head.
2) ttinc=0

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> pinc["flag0"]<-ifelse(pinc$relation!=1&pinc$ttinc==0,1,0)> addmargins(table(pinc$flag0==1,useNA="ifany"))
FALSE TRUE

Sum

35655 45217 80872

Added flag1=1 to persons in pinc satisfying the next conditions;
1) RELATIONSHIP is not household head.
2) AGE <10 or ttinc <2003) ttinc>0> pinc["flag1"]<-ifelse(pinc$relation!=1&(pinc$age10==0|pinc$ttinc<200)&+ pinc$ttinc>0,1,0)> addmargins(table(pinc$flag1==1,useNA="ifany"))
FALSE TRUE
80635

Sum

237 80872> head(pinc[pinc$flag1==1,c("PID","ID","age","relation","ttinc","flag0","flag1")])
PID
29

ID age relation

ttinc flag0 flag111100011480811102 111000114808111

5

38500.00000

01

685 11100021950811103 111000219508111

2

3 10500.00000

01

941 11100031000511103 111000310005111 283170.00000

012617 11100070050411104 111000700504111 28

583.33333013120 11100070260611102 111000702606111 29

283.33333

014905 11100111180711104 111001111807111 81

4150.00000

01Transferred the income of persons with flag=1 to that of the respective household heads.

# list of PID with flag=1> pida<-pinc[pinc$flag1==1,"PID"]> length(pida)[1] 237> head(pida)[1] "11100011480811102" "11100021950811103" "11100031000511103" "11100070050411104"[5] "11100070260611102" "11100111180711104"> table(substr(pida,16,17))
02 03 04 05 06 07 08

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110 48 35 27 1052# list of PID of the respective household heads> pidb<-paste(substr(pida,1,nchar(pida)-2),"01",sep="")> head(pidb)[1] "11100011480811101" "11100021950811101" "11100031000511101"[4] "11100070050411101" "11100070260611101" "11100111180711101"> pinc["ttinc.before"]<-pinc$ttinc> pinc.old2<-pinc# Transfer of income> for(j in 1:length(pida)){+ a<-pida[j]+ b<-pidb[j]+ pinc[pinc$PID==b,"ttinc"]<-pinc[pinc$PID==b,"ttinc"]+pinc[pinc$PID==a,"ttinc"]+ pinc[pinc$PID==a,"ttinc"]<-0+ }# Example of transfer> pinc[pinc$ID=="111000114808111",c("PID","age","relation","ttinc","ttinc.before","flag0","flag1")]

PID age relation

ttinc ttinc.before flag0 flag1

28 11100011480811101

29

1 25296.67

16796.67

00

29 11100011480811102

5

38500.00

0

1

0.00

> pinc[pinc$ID=="111000310005111",c("PID","age","relation","ttinc","ttinc.before","flag0","flag1")]

PID age relation ttinc ttinc.before flag0 flag1
939 11100031000511101 58

1 5170940 11100031000511102 373

941 11100031000511103 28
942 11100031000511104

9

5000

0

0

0

0

1

0

3

0

170

0

1

5

0

0

1

0

> pinc.old3<-pinc

Added flag2=1 to persons in pinc satisfying the next conditions;
AGE >=10 and ttinc>=200

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> pinc["flag2"]<-ifelse(pinc$age10==1&pinc$ttinc>=200,1,0)> addmargins(table(pinc$flag2,useNA="ifany"))
0

1

Sum

47354 33518 80872> head(pinc)
PID

ID

WT age relation wage crop livestock nonagri other1 11100011480111101 111000114801111 30.4971

44

1

0

0

0

60000

0

2 11100011480111102 111000114801111 30.4971

41

2

0

0

0

0

0

3 11100011480111103 111000114801111 30.4971

21

3

0

0

0

0

0

4 11100011480111104 111000114801111 30.4971

16

3

0

0

0

0

0

5 11100011480111105 111000114801111 30.4971

67

4

0

0

0

0

0

6 11100011480211101 111000114802111 30.4971

36

1

0

0

0

80000

0

windfall ttinc age10 flag0 flag1 ttinc.before flag2
10 60000

1

0

0

60000

1

2

0

0

1

1

0

0

0

3

0

0

1

1

0

0

0

4

0

0

1

1

0

0

0

5

0

0

1

1

0

0

0

6

0 80000

1

0

0

80000

1



Added flag3=1 to household heads with income of 0 < ttinc < 200.

> pinc["flag3"]<-ifelse(pinc$relation==1&pinc$ttinc>0&pinc$ttinc<200,1,0)> table(pinc$flag3,useNA="ifany")
0

1

80773

99



Added flag4=1 to household heads without income.

> pinc["flag4"]<-ifelse(pinc$relation==1&pinc$ttinc==0,1,0)> table(pinc$flag4,useNA="ifany")
0179071 1801

Remarks:

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Number of household heads without income> nrow(subset(pinc,relation==1&flag4==1))[1] 1801Created data set of income receivers consisted of persons with flag2=1 and flag3=1.

> receivers<-subset(pinc,flag2==1|flag3==1)> dim(receivers)[1] 33617

19# Estimated number of income receivers> sum(receivers$WT)[1] 8469695# Average number of income receivers per household> nrow(receivers)/nrow(R23)[1] 1.684387> sum(receivers$WT)/sum(R23$WT)[1] 1.667472# Average monthly income of income receivers> sum(receivers$ttinc*receivers$WT)/sum(receivers$WT)[1] 19914.97# Total amount of monthly income> sum(receivers$ttinc)[1] 649390684> sum(receivers$ttinc*receivers$WT)[1] 168673749859

Summary;
Un-weighted

Weighted

estimates

estimates

Final report

Household size

4.05

4.00

4.0

Average number of income receivers per

1.68

1.67

1.8

33,6178,469,695

householdTotal number of income receivers

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Income receivers’ average monthly income

19,915

Total amount of monthly income

649,390,684

Number of persons whose income were

20,427

168,673,749,859

237transferred to the household head
Number of income receivers with ttinc<20099> nrow(subset(receivers,relation==1&ttinc<200))[1] 99# Saved hies2009, receivers and pinc
> hies2009.i6<-hies2009> receivers.old<-receivers> pinc.old4<-pincSum of ttinc and ttinc.brfore in pinc are the same.

> colSums(pinc[,6:19],na.rm=T)



wage

crop

livestock

nonagriotherwindfallttinc

250526008

13284307

65899155

175260468

87765278

56655468

649390684

age10

flag0

flag1 ttinc.before

flag2

flag3

flag4

66687

45217

33518

99

1801

237

649390684

Sum of ttinc in receivers is the same as that of pinc..

> colSums(receivers[,6:19],na.rm=T)
wage

crop

livestock

nonagriotherwindfallttinc

250525880

13279031

65893405

175254923

87683520

56634281

649390684

age10

flag0

flag1 ttinc.before

flag2

flag3

flag4

33617

0

33518

99

0

0

649271039

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Remarks:
According to the delegates from Sri Lanka for the International Workshop,“Household income is calculated at each source at individual level, and minors and minor income
receivers are omitted adding their income to the head of the household, and the income receivers
income sum up to household income.”

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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.
I. Expenditure on food.
II. Expenditure on non food.
III. 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 anddomestic servants who live in the household is reported according to the related expenditure group in
a separate section of the schedule.

To obtain more accurate date, the first two sections are divided in to 35 sub groups. Of that total 19
sub groups are included under the section of expenditure on food such as cereals , prepared food,vegetables , fish , meat etc. and the rest of 16 sub groups are included under the section of non foodexpenditure. 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 was collected for different
reference periods varying from one month to twelve months.”

Remarks:
Definition of 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,“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 a member.”

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

Clothing

30

6 months

3050

col.6

Non-durable goods

32

1 month

3220

col.7

Household services

29

1 month

2910

col.8

Personal care22

1 month

2221

col.9

Transport & communication25

1 month2510

col.10Recreation27

1 month2720

col.11

Durable goods

33

12 months

3340

col.12

Boarding fees paid

20

1 month2004

col.13Sent to family

35

1 month3510

col.14

Savings provident funds

34

1 month3411

col.15

Miscellaneous

34

1 month3412

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.)

2. R04 and R05 have no duplicated item code within the household.
(No such record was found in the given data set.)

3. Col. 3 to col. 15 of R06 correspond to the 4-digit item codes in the above table.

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Note: The 4-digit codes of col. 3 to col.15 are confirmed with Mr. KMR Wickramasinghe, DCS. The
summaries of R06 made per month should be added to the household expenditure under the codes
given above.

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.

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.

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.

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> R04["m.value"]<-R04$VALUE*30/7

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(15,11:13,18,19)])
ID CODE QUANTITY VALUE

m.value sg

1 111000700101111 110550

625.71429 11

2 111000700101111 110720

1564.28571 11

3 111000700101111 110850

18

77.14286 11

4 111000700101111 1109

100

38 162.85714 11

5 111000700101111 1110

50

730.00000 11

6 111000700101111 111702085.71429 11# Number of records in R04 by subgroup> addmargins(table(R04$sg,useNA="ifany"))
1
46688
13
23599

2

3

4

5

6

40308 29843 142784

26324

8001

17

18

14

15

16

19087 20581

26171

25178 28152

7

8

9

31020 26658

8551

19

10

11

12

18725 249031

2433

Sum

24912 798046### 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(15,11:13,18)])
ID NF_CODE NF_QUANTITY NF_VALUE sg
1 111000700101111

2001

0

5000 20

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2 111000700101111

2002

0

25 20

3 111000700101111

2003

0

160 204 111000700101111

2101

0

800 215 111000700101111

2106

6250

750 21

6 111000700101111

2109

2

7 21

# Number of records in R05 by subgroup> addmargins(table(R05$sg,useNA="ifany"))
20
32271
32
28868



21

22

23

76684 85336

17397

33

34

7783 24913

24

2537526 22146

35

26

27

28

22220 12813

36522

29

30

31

3874 115389

22286

Sum

22637 568665

Generated the variable of m.value, monthly expenditure.

> R05["m.value"]<-R05$NF_VALUE> R05["m.value"]<-ifelse(R05$sg>=30&R05$sg<=32,R05$m.value/6,R05$m.value)> R05["m.value"]<-ifelse(R05$sg==33|R05$sg==35,R05$m.value/12,R05$m.value)> head(R05[c(15,11:13,18,19)])
ID NF_CODE NF_QUANTITY NF_VALUE sg m.value
1 111000700101111

2001

0

5000 205000

2 111000700101111

2002

0

25 20

25

3 111000700101111

2003

0

160 20160

4 111000700101111

2101

0

800 21800

5 111000700101111

2106

6250

750 21750

6 111000700101111

2109

2

7 21

7

### R06: BOARDERS ###

Converted the variables of COL_3, COL_5 and COL_11 of R06 to the monthly expenditure, andgenerated data frame boarder. Variables of other col. are originally monthly.

> boarder<-R06> boarder[is.na(boarder)]<-0> boarder$COL_3<-boarder$COL_3*30/7

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> boarder$COL_5<-boarder$COL_5/6> boarder$COL_11<-boarder$COL_11/12> head(boarder)
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT COL_2

COL_3

1

11 113

7

2

0

11

1

1

1

1

4

0.000

2

11 117

7

2

0

11

1

1

1

1

3 2571.429

3

11 117

7

2

0

11

1

1

1

1

4 2571.429

4

11 143

7

1

0

12

4

1

1

1

4

0.000

5

11 145

7

1

0

12

2

1

1

1

3

0.000

6

11 146

7

1

0

12

9

1

1

1

2

0.000

COL_4 COL_5 COL_6 COL_7 COL_8 COL_9 COL_10 COL_11 COL_12 COL_13 COL_14 COL_15
1

0

0

0

0

0

0

0

0

0

8000

8000

0

2

0

40

0

0

650

500

0

0

1125

0

0

4500

3

0

50

0

0

650

400

0

0

1125

0

0

4500

4

0

0

0

0

0

0

0

0

0

2500

0

0

5

0

0

0

0

0

0

0

0

0

5000

0

0

6

0

0

0

0

0

0

0

0

0

2000

0

0

ID

PID

WT

1 112001111301111 11200111130111104 210.42070
2 112001111701111 11200111170111103 269.07250
3 112001111701111 11200111170111104 269.07250
4 111001214304111 11100121430411104 436.39860
5 111001214502111 11100121450211103 192.48930
6 111001214609111 11100121460911102

23.52299

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(R23$WT))[1] 5079362> food.item<-by(R04,R04$CODE,function(df) sum(df$m.value*df$WT)/NHH)> length(food.item)

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[1] 254> food.byitem<-data.frame(code=as.integer(names(food.item)),+ m.value=as.vector(food.item))> head(food.byitem)code

m.value

1

101 975.214735

2

102 478.137585

3

103 839.872898

4104

5.152743510544.646871

6

106 189.258824

Generated monthly household expenditure on non-food by item from R5(Correspond to Table 5.4 of the final report)> nfood.item<-by(R05,R05$NF_CODE,function(df) sum(df$m.value*df$WT)/NHH)> length(nfood.item)[1] 183> nfood.byitem<-data.frame(code=as.integer(names(nfood.item)),+ m.value=as.vector(nfood.item))> head(nfood.byitem)code

m.value

1 2001 3333.036891
2 2002

13.390113

3 200399.752748

4 2101531.639208

5 2102

9.055037

6 210376.389799

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] 437

2

Remarks: Out-of-range codes in hhexp.byitem

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The below codes in hhexp.byitem are out of range, as compared with the codes on the questionnaire;Codemonthly value

NumberofID

records
0440

01

512001103205111

2112

0.01

1

712000406910111

2506

0.11

2

132001207702111

2510

0

1

612001205701111



Omitted the above codes in hhexp.byitem.

> code.err<-c(0440,2112,2506,2510)> hhexp.byitem.old<-hhexp.byitem> hhexp.byitem<-hhexp.byitem[!is.element(hhexp.byitem$code,code.err),]> dim(hhexp.byitem)[1] 433

2

Generated data frame exp.boarder from boarder> colnames(boarder)[1] "DISTRICT""PSU""REC_TYPE" "SECTOR""DSD""MONTH""SAMPLE_N"[8] "SERIAL_NO" "NHH""RESULT""COL_2""COL_3""COL_4""COL_5"[15] "COL_6""COL_7""COL_8""COL_9""COL_10""COL_11""COL_12"[22] "COL_13""COL_14""COL_15"

"ID"

"PID"

"WT"

> n.boarder<-rep(0,13)# number of household-level records with expenditure >0> av.boarder<-rep(0,13)# Monthly expenditure per household> for(j in 1:13){+ n.boarder[j]<-nrow(subset(hhexp,hhexp[,j+36]>0))+ av.boarder[j]<-sum(hhexp[,j+36]*hhexp$WT)/NHH+ }> n.boarder[1] 50 8 80 26

8 81 77 26 17 48 99 50 30> av.boarder> av.boarder[1] 15.1680377 0.2669037

2.1111812[8]

9.9697279 20.4474491 8.9468985 1.4301532

1.9453115 1.1599937

1.5709600 0.4395956 2.9206370

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4.2698707

130# Monthly average expenditure per household and the number of household with non-zero expenditure
by item code
> for(j in 1:13){+ cat(colnames(hhexp)[j+36],":",format(round(av.boarder[j],2),width=8),+ format(n.boarder[j],width=8),"\n")+ }COL_3 :

15.17

50

COL_4 :

0.27

8

COL_5 :

2.11

80

COL_6 :

1.57

26

COL_7 :

0.448

COL_8 :

2.92

81

COL_9 :

4.27

77

COL_10 :1.95

26

COL_11 :1.16

17

COL_12 :9.97

48

COL_13 :

20.45

99

COL_14 :8.95

50

COL_15 :1.43

30# Corresponding 4-digit code, which never overlap the codes of R04 and R05> code.boarder<-c(0220,2112,3050,3220,2910,2221,2510,2720,3340,2004,3510,3411,3412)# Generated data frame exp.boarder> exp.boarder<-data.frame(code=code.boarder,col= colnames(boarder)[12:24],+ m.value=av.boarder,nhh=n.boarder)> head(exp.boarder)
code
1

col

m.value nhh

220 COL_3 15.1680377 50

2 2112 COL_4

0.2669037

8

3 3050 COL_5

2.1111812 80

4 3220 COL_6

1.5709600 26

5 2910 COL_7

0.4395956

6 2221 COL_8

2.9206370 81

8

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

Binded hhexp.byitem and exp.boarder> df<-rbind(hhexp.byitem,exp.boarder[,c(1,3)])> hhexp.byitem<-df[order(df$code),]> dim(hhexp.byitem)[1] 4462# Average monthly expenditure per household> sum(hhexp.byitem[,"m.value"])[1] 31435.24> sum(hhexp.byitem[hhexp.byitem$code<1900,"m.value"])[1] 13282.47> sum(hhexp.byitem[hhexp.byitem$code>=1900,"m.value"])[1] 18152.77

Made table on average monthly expenditure per household by 4-digit code

> for(j in 1:446){+ cat(format(hhexp.byitem[j,1],width=4),":",format(round(hhexp.byitem[j,2],2),width=8),"¥n")+}

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Table

Averagemonthly

expenditure by 4-digit code

101 :975.21

302 :

32.44

430 :

21.66

102 :

478.14

303 :

35.95

439 :

16.3

103 :

839.87

304 :

12.73

441 :

63.14

104 :

5.15

305 :

8.97

442 :

41.1

105 :

44.65

306 :

51.97

443 :

16.78

106 :

189.26

309 :

9.56

444 :

12.8

107 :

9.21

401 :

17.84

445 :

5.88

108 :

4.45

402 :

73.89

446 :

5.04

109 :

3.02

403 :

29.25

447 :

3.67

110 :

11.38

404 :

23.43

448 :

5.57

111 :

54.38

405 :

46.48

449 :

4.41

112 :

39.47

406 :

17.74

450 :

4.82

113 :

4.26

407 :

21.34

459 :

29.59

114 :

0.07

408 :

54.24

501 :

42.04

115 :

0.73

409 :

130.86

502 :

18.15

116 :

0.89

410 :

59.97

503 :

173.94

119 :

8.82

411 :

39.78

504 :

9.74

201 :

425.98

412 :

52.22

505 :

25.33

202 :

13.51

413 :

23.44

506 :

3.4

203 :

7.24

414 :

38.06

507 :

2.25

204 :

84.57

415 :

6.94

509 :

2.36

205 :

41.95

416 :

22.68

601 :

357.29

206 :

115.5

417 :

13.06

602 :

116.52

207 :

16.41

418 :

13.86

603 :

16.37

208 :

28.67

419 :

18.75

604 :

11.86

209 :

19.05

420 :

9.53

605 :

9.23

210 :

152.48

421 :

10.01

609 :

5.56

211 :

262.56

422 :

1.57

701 :

115.05212 :132.94

423 :

12.87

702 :

14.11

213 :

8.28

424 :

7.59

703 :

11.29

214 :

12.03

425 :

1.85

704 :

74.35

215 :

8.56

426 :

10.51

705 :

66.73

219 :

79.29

427 :

4.95

706 :

128.42

220 :

15.17

428 :

6.28

707 :

46.96

301 :

395.51

429 :

6.47

708 :

106.04

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133

709 :

19.23

1103 :

89.01

1308 :

71.65

710 :

97.38

1104 :

182.281309 :

16.82

711 :

7.99

1105 :

71.92

1310 :

24.13

712 :

23.37

1106 :

85.08

1311 :

11.26

713 :

73.92

1107 :

46.24

1312 :

6.6

714 :

111.181108 :

62.86

1319 :

11.16

715 :

4.2

1109 :

104.231401 :

293.49

716 :

86.71

1110 :

88.44

1402 :

21.92

717 :

22.14

1111 :

40.25

1403 :

3.16

718 :

35.48

1112 :

14.32

1404 :

0.44

719 :

6.53

1113 :

11.2

1409 :

4.5

720 :

12.78

1114 :

17.37

1501 :

452.09

721 :

98.84

1115 :

17.32

1502 :

9.58

801 :

184.621116 :

12.37

1503 :

2.92

802 :

27.49

1117 :

24.07

1504 :

0.58

803 :

13.01

1118 :

16.83

1509 :

0.78

804 :

10.38

1119 :

15.67

1601 :

163.15

805 :

3.69

1120 :

60.69

1602 :

13.5

806 :

49.89

1121 :

31.55

1603 :

72.34

807 :

0.78

1122 :

9.63

1604 :

26.28

808 :

0.26

1123 :

1.21

1605 :

32.09

809 :

62.32

1129 :

1.44

1606 :

8.36

810 :

47.77

1201 :

5.21

1607 :

4.62

811 :

3.02

1202 :

10.36

1608 :

11.76

812 :

24.86

1203 :

7.02

1609 :

5.84

813 :

4.48

1204 :

0.91

1610 :

9.1

814 :

0.51

1205 :

0.74

1619 :

20.2

815 :

8.04

1206 :

0.63

1651 :

7.4

816 :

0.13

1209 :

1.4

1652 :

3.54

819 :

51.02

1301 :

20.66

1653 :

6.11

901 :

134.271302 :

0.57

1654 :

1.25

909 :

0.18

1303 :

2.14

1659 :

0.34

1001 :

737.321304 :

34.7

1701 :

9.06

1002 :

0.99

1305 :

47.03

1702 :

52.1

1101 :

46.41

1306 :

7.12

1703 :

17.08

1102 :

158.961307 :

784.611704 :

4.49

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1341705 :

214.06

1920 :

47.88

2303 :

51.75

1706 :

3.27

1921 :

35.48

2304 :

74.24

1707 :

10.06

1922 :

24.52

2305 :

194.431708 :

41.75

1923 :

4.77

2306 :

192.261709 :

13.32

1924 :

34.68

2307 :

8.43

1710 :

5.56

2001 :

3333.04

2309 :

15.94

1711 :

4.05

2002 :

13.39

2401 :

571.291712 :

69.37

2003 :

99.75

2402 :

31.67

1713 :

0.97

2004 :

9.97

2403 :

114.151719 :

25.79

2101 :

531.642404 :

212.411801 :

227.2

2102 :

9.06

2405 :

28.64

1802 :

12.4

2103 :

76.39

2409 :

9.87

1803 :

17.36

2104 :

104.512411 :

743.03

1804 :

9.06

2105 :

224.072412 :

22.73

1805 :

0.51

2106 :

217.592413 :

131.251806 :

42.54

2107 :

20.08

2414 :

246.481807 :

64.74

2108 :

58.16

2415 :

187.2

1808 :

2.6

2109 :

16.47

2419 :

18.57

1809 :

1.34

2110 :

2.8

2501 :

4.41

1810 :

2.99

2111 :

15.69

2502 :

345.361811 :

5.84

2112 :

0.27

2503 :

382.721812 :

1.9

2119 :

1.31

2504 :

8.98

1819 :

5.36

2201 :

134.072505 :

12.78

1901 :

16.41

2202 :

56.19

2509 :

0.65

1902 :

235.912203 :

20.92

2510 :

4.27

1903 :

42.92

2204 :

15.79

2601 :

184.591904 :

15.68

2205 :

23.66

2602 :

19.21

1905 :

10.08

2206 :

30.69

2603 :

10.94

1906 :

2.43

2207 :

17.29

2604 :

18.25

1907 :

0.41

2208 :

2.74

2605 :

74.38

1908 :

1.4

2209 :

34.36

2606 :

458.381909 :

6.98

2210 :

82.55

2607 :

28.39

1910 :

159.332219 :

2.52

2608 :

96.55

1911 :

3.89

2221 :

2.92

2609 :

56.13

1912 :

21.52

2301 :

454.392610 :

34.74

1919 :

0.27

2302 :

16.52

2619 :

36.32

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1352701 :

7.37

3013 :

6.53

3212 :

1.55

2702 :

42.63

3014 :

1.54

3213 :

3.53

2703 :

50.05

3015 :

3.31

3214 :

0.86

2704 :

225.643016 :

50.87

3219 :

2.45

2705 :

4.32

3017 :

16.09

3220 :

1.57

2706 :

9.51

3018 :

10.62

3301 :

20.33

2707 :

10.57

3019 :

15.42

3302 :

7.03

2708 :

25.32

3020 :

5.28

3303 :

35.05

2709 :

12.91

3021 :

11.23

3304 :

28.45

2719 :

14.15

3029 :

10.57

3305 :

16.25

2720 :

1.95

3041 :

9.86

3306 :

13.27

2801 :

163.173042 :

11.2

3307 :

2.57

2802 :

73.17

3043 :

11.73

3308 :

4.73

2803 :

12.7

3049 :

3.3

3309 :

1.84

2804 :

11.95

3050 :

2.11

3310 :

2.34

2805 :

1.73

3061 :

6.43

3311 :

21.01

2809 :

0.89

3062 :

7.69

3312 :

5.71

2901 :

3.24

3101 :

76.31

3313 :

7.42

2902 :

16.17

3102 :

44.2

3314 :

6.89

2903 :

70.49

3103 :

7.45

3315 :

28.66

2904 :

1.17

3104 :

16.1

3316 :

6.15

2905 :

0.12

3105 :

1.64

3317 :

1.47

2909 :

6.89

3106 :

0.56

3318 :

5.1

2910 :

0.44

3107 :

0.96

3319 :

85.08

3001 :

109.213109 :

0.61

3320 :

212.483002 :

87.95

3201 :

7.53

3321 :

3.04

3003 :

63.23

3202 :

10.72

3322 :

2.52

3004 :

60.3

3203 :

9.42

3323 :

1.56

3005 :

44.17

3204 :

6.84

3324 :

26.42

3006 :

48.11

3205 :

0.59

3325 :

52.35

3007 :

90.83

3206 :

1.16

3326 :

19.81

3008 :

9.05

3207 :

0.69

3327 :

4.79

3009 :

11.96

3208 :

17.62

3329 :

28.61

3010 :

20.63

3209 :

1.4

3339 :

53.89

3011 :

12.58

3210 :

2.97

3340 :

1.16

3012 :

15.53

3211 :

17.48

3401 :

382.94

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1363402 :

70.55

3403 :

139.313404 :

487.333405 :

1607.83406 :

66.24

3409 :

238.233411 :

8.95

3412 :

1.43

3501 :

400.993502 :

158.733503 :

14.07

3504 :

87.17

3505 :

711.573506 :

253.2

3507 :

24.78

3509 :

90.26

3510 :

20.45

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7.5. Household expenditure by subgroupGenerated the variable of subgroup sg in hhexp.byitem> hhexp.byitem["sg"]<-floor(hhexp.byitem$code/100)

Monthly expenditure per household on food by subgroup> d<-subset(hhexp.byitem,code<1900)> t<-addmargins(tapply(d$m.value,d$sg,sum))[c(19,1:18)]> names(t)<-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(t)[j],width=28),format(round(t[j]),width=6),"\n")All food items

13282

Cereals

2669

Prepared food1424

Pulses547

Vegitables

1006

Yams and other

277

Meat

517

Fish

1163

Dried fish

492

Eggs

134

Coconuts

738

Condiments

1209

Other foods

26

Milk and milk foods

1038

Fats and oils

324

Sugar, juggery & treacle

466

Fruits386

Confectionery & short eats

471

Beverages

394

Monthly expenditure per household on non-food by subgroup> d<-subset(hhexp.byitem,code>=1900)> t<-addmargins(tapply(d$m.value,d$sg,sum))[c(18,1:17)]

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> names(t)<-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(t)[j],width=35),format(round(t[j]),width=6),"\n")Total non-food18153

Liquor,Drugs & Tabacco

665

Housing3456

Fuel & Light

1278

Personal care

424

Health1008

Transport

2317

Communication

759

Education

1018

Cultural activities & Entertainment404

Non-durable household goods

264

Household services

99

Clothing & Textile

757

Footwear148

Durable household goods

86

Long durable household goods

706

Other expenses

3003

Other adhoc expenses

1761

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7.6. Creating data set on household expenditure by subgroup at household levelDropped the next records with out-of-range codes in R04 and R05.

File

Code

monthly value

NumberofID

records
R04

0440

0

1

512001103205111

R05

2112

0.01

1

712000406910111

2506

0.11

2

132001207702111

2510

0

1

612001205701111> R04.old<-R04> R04<-subset(R04,!(ID=="512001103205111"&CODE==440))> dim(R04)[1] 798045

19> R05.old<-R05> R05<-subset(R05,!(ID=="712000406910111"&NF_CODE==2112))> R05<-subset(R05,!(ID=="132001207702111"&NF_CODE==2506))> R05<-subset(R05,!(ID=="612001205701111"&NF_CODE==2510))> dim(R05)[1] 568661

19### R04 ###Aggregated the variable of m.value, monthly household expenditure by ID (household) and sg,
and created household-level data frame hhexp1 with variables of total monthly expenditure by
subgroup.

> t<-tapply(R04$m.value,list(R04$ID,R04$sg),sum)> dim(t)[1] 19958

19> t[is.na(t)]<-0> colnames(t)<-paste("g",1:19,sep="")> hhexp1<-data.frame(ID=rownames(t),t,row.names=NULL)> head(hhexp1)
ID

g1

g2

g3

g4

g5

g6

g7

1 111000114801111 3300.000 1080.0000 385.7143 621.4286 342.8571 942.8571 2850.000

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2 111000114802111 2558.571 2931.4286 685.7143 672.8571 248.5714

0.0000 3000.000

3 111000114803111 4478.571 2121.4286 964.2857 1371.4286 642.8571 1457.1429 2614.286
4 111000114804111 1821.429

698.5714 441.4286 557.1429 231.4286

0.0000 1200.000

5 111000114805111 2708.571

912.8571 2172.8571 1255.7143 300.0000

0.0000 3985.714

6 111000114807111 2091.429

578.5714 120.0000 754.2857 107.1429

0.0000 1200.000

g8

g9

g10

g11 g12

g13

g14

g15

g16

1

514.2857

0.0000 750.0000 4928.571

0 2657.143 771.4286 557.1429 107.1429

2

424.2857

0.0000 642.8571 4384.286

0 3021.429 621.4286 535.7143 857.1429

3 1234.2857

0.0000 750.0000 2905.714

0 1114.286 642.8571 471.4286 342.8571

4

488.5714 167.1429 535.7143 2442.857

0 1114.286 600.0000 385.7143 278.5714

5

385.7143

0.0000 642.8571 3214.286

0 1692.857 685.7143 385.7143 1842.8571

6300.0000

0.0000 642.8571 3278.571

0 1328.571 685.7143 394.2857 544.2857

g17
1

g18

g19

0.0000 900.0000 2700

2 1054.2857 428.5714

0

3 1367.1429 428.5714

04

407.1429 257.14290

5300.0000 535.7143

06

655.7143 407.1429

0> hies2009[[04]]<-R04### R05 ###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)> dim(t)[1] 19958

16> t[is.na(t)]<-0> colnames(t)<-paste("g",20:35,sep="")> hhexp2<-data.frame(ID=rownames(t),t,row.names=NULL)> head(hhexp2)
ID

g20 g21

g22

g23 g24g25 g26 g27 g28 g29

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g30

g31

141

1 111000114801111 6558 1387 1540 1200 1000 1000
2 111000114802111 4620 2220 1065

0 2700 1750 1130

3 111000114803111 4238 2275

730

650 1500

4 111000114804111 2650 996

640

0 360

0

0 1200 1897

6 111000114807111 3650 976

0 650

g32

g33

g34

1 31.66667

0.0

0 333.33333

2 35.00000

0.0

0 83.33333

3 15.00000

0.0

960 41.66667

4 10.83333

0.0

320 41.66667

0 1891.6667 158.33333

0 480

0 4607.5000 891.66667

0 480

0 756.6667 233.33333

0 310

0 479.1667 63.33333

0 200 400

0 928.3333 416.66667

950 560

5 111000114805111 13100 2254 1060
810

0 200 5100500 260

0 370

0 1595.8333 498.33333

g35

5 15.00000 812.5 1500 125.00000
6 15.83333

0.0

750 20.83333> hies2009[[05]]<-R05### R06 ###Collapsed boarder at individual level to household level and created data frame hhexp3.

> hhexp3<-data.frame()> bd.ID<-unique(boarder$ID)> hhexp3<-bd.ID[order(bd.ID)]> for(j in 3:15){+ t<-as.vector(tapply(boarder[,j+9],boarder$ID,sum))+ hhexp3<-data.frame(hhexp3,t)+ colnames(hhexp3)[ncol(hhexp3)]<-paste("COL_",j,sep= "")+ }> colnames(hhexp3)[1]<- "ID"> dim(hhexp3)[1] 187 14> head(hhexp3)
ID COL_3 COL_4

COL_5 COL_6 COL_7 COL_8 COL_9 COL_10 COL_11

1 111000117002111

00 166.6667

0

0

0

0

0

0

2 111000117004111

000.0000

0

0

300

0

0

0

3 111000117306111

000.0000

300

0

0

0

0

0

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4 111000117307111

00 633.3333

0

0

150

0

0

0

5 111000117405111

000.0000

0

0

0

0

0

0

6 111000118002111

000.0000

0

0

0

0

0

0

COL_12 COL_13 COL_14 COL_15
1

0

5000

0

0

2

0

0

2000

0

3

0

3000

0

0

4

0

3000

0

0

5

0

0

0

0

6

0

0

0

0

### Merged R04, R05 and R06 at household level ###

Merged hhexp1, hhexp2 and hhexp3 by using ID> hhexp<-merge(hhexp1,hhexp2)> dim(hhexp)[1] 19958

36> hhexp<-merge(hhexp,hhexp3,all.x=TRUE)> dim(hhexp)[1] 19958



49

Replaced NA with 0> hhexp[is.na(hhexp)]<-0> hhexp.old<-hhexp

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$g30<-hhexp$g30+hhexp$COL_5> hhexp$g32<-hhexp$g32+hhexp$COL_6> hhexp$g29<-hhexp$g29+hhexp$COL_7> hhexp$g22<-hhexp$g22+hhexp$COL_8> hhexp$g25<-hhexp$g25+hhexp$COL_9> hhexp$g27<-hhexp$g27+hhexp$COL_10> hhexp$g33<-hhexp$g33+hhexp$COL_11> hhexp$g20<-hhexp$g20+hhexp$COL_12

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> hhexp$g35<-hhexp$g35+hhexp$COL_13> hhexp$g34<-hhexp$g34+hhexp$COL_14> hhexp$g34<-hhexp$g34+hhexp$COL_15

Appended weight to hhexp> hhexp<-merge(hhexp,R23[c("ID","WT")])> colnames(hhexp)[1] "ID"

"g1"

"g2"

"g3"

"g4"

"g5"

"g6"

"g7"

"g8"

[10] "g9"

"g10"

"g11"

"g12"

"g13"

"g14"

"g15"

"g16"

"g17"

[19] "g18"

"g19"

"g20"

"g21"

"g22"

"g23"

"g24"

"g25"

"g26"

[28] "g27"

"g28"

"g29"

"g30"

"g31"

"g32"

"g33"

"g34"

"g35"

[37] "COL_3""COL_4" "COL_5" "COL_6""COL_7" "COL_8" "COL_9""COL_10" "COL_11"[46] "COL_12" "COL_13" "COL_14" "COL_15" "WT"

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$ttexp*hhexp$WT)/sum(hhexp$WT)[1] 31435.24> sum(hhexp$food*hhexp$WT)/sum(hhexp$WT)[1] 13282.47> sum(hhexp$nfood*hhexp$WT)/sum(hhexp$WT)[1] 18152.77

Summary:
My estimates

Final report ofthe survey

Total monthly household expenditure

31,43531,331

Food

13,282

13,267

Non-food

18,15318,064

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8. Resampling method
Strategies for 80% resampling1) To create R25 from hhinc and hhexp as well as R26 from pinc as the next.
2) To order hhinc by strata (district and sector), psu and the amount of total householdincome ttinc, and append the variable sn, the serial number of records to hhinc.
3) To select 80% of hhinc, by dropping 20% households using systematic selection with theinterval=5 and a random start number, andto create the vector ID.res, a set of ID selected for resampled data set.
4) For each data set, to select records with ID.res.
5) To adjust the weight as the next;
Adjusted weight = original weight / 0.8
6) For each data set, to replace WT with the adjusted weight, and rename R data frame “xxx”
as “xxx.80”.
7) Export R data frames to CSV files with the name “xxx_80.CSV”.



Data dictionary of R25:

Summary of household income and expenditure at household level
Number of records: 19,958
Variable

Description

Remarks

ID

Household identifier

WT

Weightttinc

Monthly total household income= monetary + inkind

monetary

Monthly total monetary income= wage + crop + livestock +nonagri + other + widfall

wage

Monthly income from paid employments

cropMonthly income from agricultural activities

livestockMonthly income from other agricultural activities

nonagriMonthly income from non-agricultural activities

other

Monthly income from other cash receipt

windfallMonthly income by chance or ad hoc gains

inkindMonthly total non-monetary income

ttexp

Monthly total household expenditure= food + nfood

food

Monthly household expenditure on food= Sum of g1 to g18

nfoodMonthly household expenditure on non-food= Sum of g19 to g35

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g1 to g35

Monthly household expenditure by subgroup

COL_3 to COL_15 are already
added to the respective subgroups

COL_3

to

Monthly household expenditure for boarders

COL_15



Data dictionary of R26:

Summary of household income at individual level
Number of records: 80,872
Variable

Description

Remarks

ID

Household identifier

PID

Individual identifier

WT

WeightreceiverIs an income receiver?

relation

Relationship to the household headage10

Is age over 10 years old?

ttinc

Monthly total income after transferredtransferred

Is income transferred to the head?

1. Yes 0. No

befincMonthly total income before transferred= wage + crop + livestock +1. Yes 0. No

1. age >= 10

0. age < 10nonagri + other + widfall
wage

Monthly income from paid employments

cropMonthly income from agricultural activities

livestockMonthly income from other agricultural activities

nonagriMonthly income from non-agricultural activities

other

Monthly income from other cash receipt

windfallMonthly income by chance or ad hoc gains****************************************************************== R25 ==> hhinc<-hhinc.old2> colnames(hhinc)[1] "ID""WT""wage""crop""livestock"

Sri Lanka HIES 2009/10 Manual (Version 1.1)

"nonagri"146[7] "other""windfall""monetary.tt" "rice.a"[13] "inkind04""inkind05""inkind.tt""rice.b""inkind.rice""ttinc"> R25<-hhinc[,c(1,2,16,9,3:8,15)]> colnames(R25)[4]<-"monetary"> colnames(R25)[11]<-"inkind"> head(R25)ID

WT

ttinc monetary

wage crop livestock nonagri other windfall

1 111000114801111 30.4971 66850.00 60000.00

0.00

0

0

60000

0

0

2 111000114802111 30.4971 84500.00 80000.00

0.00

0

0

80000

0

0

3 111000114803111 30.4971 67458.33 63458.33 30633.33

00

0 32825

0

4 111000114804111 30.4971 35393.33 32893.33 32893.33

0

0

0

0

0

5 111000114805111 30.4971 64875.00 52375.00 46250.00

000 61250

6 111000114807111 30.4971 33893.33 30393.33 30393.33

0

0

0

0

0

inkind
1

6850

2

4500

3

4000

4

2500

5

12500

6

3500

> colnames(hhexp)[1] "ID"

"g1"

"g2"

"g3"

"g4"

"g5"

"g6"

"g7"

"g8"

[10] "g9"

"g10"

"g11"

"g12"

"g13"

"g14"

"g15"

"g16"

"g17"

[19] "g18"

"g19"

"g20"

"g21"

"g22"

"g23"

"g24"

"g25"

"g26"

[28] "g27"

"g28"

"g29"

"g30"

"g31"

"g32"

"g33"

"g34"

"g35"

[37] "COL_3""COL_4" "COL_5" "COL_6""COL_7" "COL_8" "COL_9"[46] "COL_12" "COL_13" "COL_14" "COL_15" "WT""ttexp" "food""COL_10" "COL_11""nfood"> R25.old<-R25> R25<-merge(R25,hhexp[,c(1,51:53,2:49)],by="ID")> dim(R25)[1] 19958

62> colnames(R25)[1] "ID""WT""ttinc""monetary""wage""crop""livestock"[8] "nonagri""other""windfall""inkind""ttexp""food""nfood"[15] "g1"

"g2"

"g3"

"g4"

"g5"

"g6"

"g7"

[22] "g8"

"g9"

"g10"

"g11"

"g12"

"g13"

"g14"

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147[29] "g15"

"g16"

"g17"

"g18"

"g19"

"g20"

"g21"

[36] "g22"

"g23"

"g24"

"g25"

"g26"

"g27"

"g28"

[43] "g29"

"g30"

"g31"

"g32"

"g33"

"g34"

"g35"

[50] "COL_3""COL_4""COL_5""COL_6""COL_7""COL_8""COL_9"[57] "COL_10""COL_11""COL_12""COL_13""COL_14""COL_15"> R25.old2<-R25== R26 ==> pinc<-pinc.old4> colnames(pinc)[1] "PID"

"ID"

"WT"

"age"

"relation"[6] "wage""crop""livestock""nonagri""other"[11] "windfall""ttinc""age10""flag0""flag1"[16] "ttinc.before" "flag2""flag3""flag4"> R26<-pinc[,c(1:3,5,13,12,16,6:11,15,17,18)]> colnames(R26)[1] "PID""ID"[6] "ttinc"[11] "nonagri""WT""relation""age10""ttinc.before" "wage""crop""livestock""other""flag1""flag2""windfall"[16] "flag3"> colnames(R26)[7]<-"befinc"> R26["receiver"]<-ifelse(R26$flag2==1|R26$flag3==1,1,0)> R26["transferred"]<-R26$flag1> colnames(R26)[1] "PID"

"ID"

"WT"

"relation""age10""ttinc"[7] "befinc""wage""crop""livestock""nonagri""other"[13] "windfall""flag1""flag2""flag3""receiver""transferred""receiver""relation""age10""wage""crop""livestock"> R26.old<-R26> R26<-R26[,c(1:3,17,4:6,18,7:13)]> colnames(R26)[1] "PID"

"ID"

"WT"

[7] "ttinc""transferred" "befinc"[13] "nonagri""other""windfall"> dim(R26)[1] 80872

15

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> table(R26$receiver)0147255 33617> table(R26$transferred)
0

1

80635

237

> R26.old2<-R26# Appended R25 and R26 to hies2009.
> hies2009<-hies2009.old5> hies2009<-c(hies2009,list(R25,R26))> Rnames<-c(Rnames,"R25","R26")> hies2009.old6<-hies2009



Ordered hhinc by strata (province and sector), psu and the amount of household income ttinc.

> d<-hhinc> d["psuid"]<-substr(d$ID,1,10)> d<-d[order(d$psuid,d$ttinc),c("ID","psuid","ttinc")]> d["sn"]<-seq(1:nrow(d))> head(d)
ID

psuid

ttinc sn

9 111000114810111 1110001148 21700.00

1

7 111000114808111 1110001148 28796.67

2

6 111000114807111 1110001148 33893.33

3

4 111000114804111 1110001148 35393.33

45 111000114805111 1110001148 64875.00

5

1 111000114801111 1110001148 66850.00

6

Selected 80% of hhinc, by dropping 20% households using systematic selection with theinterval=5 and a random start number.

> int<-5> (st<-sample(1:5,1))[1] 5> d["flag"]<-ifelse(d$sn%%int==(st-1),0,1)

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> head(d)
ID

psuid

ttinc sn flag

9 111000114810111 1110001148 21700.00

1

1

7 111000114808111 1110001148 28796.67

2

1

6 111000114807111 1110001148 33893.33

3

1

4 111000114804111 1110001148 35393.33

40

5 111000114805111 1110001148 64875.00

5

1

1 111000114801111 1110001148 66850.00

6

1> table(d$flag)
013991 15967> nrow(subset(d,flag==1))/nrow(d)[1] 0.8000301> ID.res<-subset(d,flag==1)$ID> length(ID.res)[1] 15967



The number of resampled households is 15,967.

For each data set, select records with ID.res and confirmed the resampling rate.

> hies2009res<-hies2009> for(j in 1:26){+ d<-hies2009[[j]]+ d<-subset(d,is.element(d$ID,ID.res))+ hies2009res[[j]]<-d+ }# The share of resampled households (%)> for(j in 1:26)
cat(paste(Rnames[j],round(nrow(hies2009res[[j]])/nrow(hies2009[[j]])*100,1)),"\n")R01 80R02 79.9
R03 80R04 80
R05 80R06 76.7

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R07 80R08 79.7
R09 80R10 80.5
R11 80R12 80R13 80.2
R14 80R15 81.1
R16 80R17 79.4
R18 80R19 79.9
R20 80R21 80R22 80R23 80R24 80R25 80R26 80

Replaced the original weight WT with the adjusted weight wt, and renamed R data frame “xxx”
as “xxx.80”.

> hies2009res.old<-hies2009res> for(j in 1:26){+ d<-hies2009res[[j]]+ d["wt"]<-d$WT/0.8+ d<-d[,colnames(d)!="WT"]+ hies2009res[[j]]<-d+ }> hies2009res.old2<-hies2009res> 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" "R09.80"[10] "R10.80" "R11.80" "R12.80" "R13.80" "R14.80" "R15.80" "R16.80" "R17.80" "R18.80"

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151[19] "R19.80" "R20.80" "R21.80" "R22.80" "R23.80" "R24.80" "R25.80" "R26.80"

Exported the resampled R data frames to CSV files with the name “xxx_80.CSV”.

> 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" "R06_80.csv"> for(j in 1:26){+ cmd<-paste("write.csv(hies2009res[[",j,"]],'",CSVnames.res[j],"',row.names=F)",sep="")+ eval(parse(text=cmd))+ }> list.files()[1] "R01_80.csv" "R02_80.csv" "R03_80.csv" "R04_80.csv" "R05_80.csv" "R06_80.csv"[7] "R07_80.csv" "R08_80.csv" "R09_80.csv" "R10_80.csv" "R11_80.csv" "R12_80.csv"[13] "R13_80.csv" "R14_80.csv" "R15_80.csv" "R16_80.csv" "R17_80.csv" "R18_80.csv"[19] "R19_80.csv" "R20_80.csv" "R21_80.csv" "R22_80.csv" "R23_80.csv" "R24_80.csv"[25] "R25_80.csv" "R26_80.csv"# Number of records and variables in resampled data files> for(j in 1:26){+ cat(format(Rnames.res[j],width=10),":",format(CSVnames.res[j],width=13),":",+ format(nrow(hies2009res[[j]]),width=6),":",format(ncol(hies2009res[[j]]),width=3),"\n")+ }R01.80: R01_80.csv:

64679 :

28

R02.80: R02_80.csv:

16668 :

23

R03.80: R03_80.csv:

64674 :

25

R04.80: R04_80.csv: 638090 :16

R05.80: R05_80.csv: 454963 :16

R06.80: R06_80.csv

:

184 :

27

R07.80: R07_80.csv:

15967 :

13

R08.80: R08_80.csv:

14635 :

18

R09.80: R09_80.csv:

15967 :

13

R10.80: R10_80.csv:

3443 :

21

R11.80: R11_80.csv:

15967 :

13

R12.80: R12_80.csv:

15967 :

13

R13.80: R13_80.csv:

3362 :

20

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R14.80: R14_80.csv:

15967 :

13

R15.80: R15_80.csv:

4479 :

17

R16.80: R16_80.csv:

15967 :

13

R17.80: R17_80.csv:

8523 :

22

R18.80: R18_80.csv:

15967 :

13

R19.80: R19_80.csv:

6514 :

21

R20.80: R20_80.csv:

15967 :

28

R21.80: R21_80.csv:

15967 :

35

R22.80: R22_80.csv:

15967 :

47

R23.80: R23_80.csv:

15967 :

39

R24.80: R24_80.csv:

15967 :

40

R25.80: R25_80.csv:

15967 :

62

R26.80: R26_80.csv:

64679 :

15********************************************************************************************

Revision of resampled micro data regarding errors found in R15********************************************************************************************The data files of R15.80, R25.80 and R26.80 were revised as follows.

Revision of R15.80, Income from non-agricultural activities
PID

OUTPUT(old)

OUTPUT(rev)

INPUT(old)12100090380711102

1,680,000

168,000

150,000

150,000

12100051880111101

3,450,000

345,000

300,000

300,000

61200120720111104

3,500,000

350,000

35,00035,00061200030930611101

180,000,000

15,000,000

143,680,000

12,680,000

91300010540311101

60,000,000

5,000,000

50,000,000

4,000,000> d<-R15.80> fix(d)> d[d$PID=="12100090380711102","OUTPUT_5_4"]<-168000> d[d$PID=="12100051880111101","OUTPUT_5_4"]<-345000> d[d$PID=="61200120720111104","OUTPUT_5_4"]<-350000> d[d$PID=="61200030930611101","OUTPUT_5_4"]<-15000000> d[d$PID=="61200030930611101","INPUT_5_4"]<- 12680000> d[d$PID=="91300010540311101","OUTPUT_5_4"]<-5000000> d[d$PID=="91300010540311101","INPUT_5_4"]<- 4000000

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INPUT(rev)153> R15.80.old<-R15.80> R15.80<-d

Revision of R26.80, summary of household income at individual level
PID

nonagri(old)nonagri(rev)befinc(old)befinc(rev)ttinc(old)ttinc(rev)12100090380711102

1,530,000

18,0001,530,000

18,00012100051880111101

3,150,000

50,0003,166,667

66,66761200120720111104

3,465,000

315,000

3,545,863

395,86361200030930611101

36,320,000

2,320,000

36,679,000

2,679,000

91300010540311101

1e+07

1,000,000

10,630,000

1,630,000> d<-R26.80> fix(d)> d[d$PID=="12100090380711102","nonagri"]<-18000> d[d$PID=="12100051880111101","nonagri"]<-50000> d[d$PID=="61200120720111104","nonagri"]<-315000> d[d$PID=="61200030930611101","nonagri"]<-2320000> d[d$PID=="91300010540311101","nonagri"]<-1000000> d[d$PID=="12100090380711102","befinc"]<-18000> d[d$PID=="12100051880111101","befinc"]<-66667> d[d$PID=="61200120720111104","befinc"]<-395863> d[d$PID=="61200030930611101","befinc"]<-2679000> d[d$PID=="91300010540311101","befinc"]<-1630000> d[d$PID=="12100090380711102","ttinc"]<-18000> d[d$PID=="12100051880111101","ttinc"]<-66667> d[d$PID=="61200120720111104","ttinc"]<-395863> d[d$PID=="61200030930611101","ttinc"]<-2679000> d[d$PID=="91300010540311101","ttinc"]<-1630000> fix(d)> R26.80.old<-R26.80> R26.80<-d

Revision of R25.80, summary of household income and expenditure at household levelID

ttinc(old)ttinc(rev)monetary(old)monetary(rev)nonagri(old)nonagri(rev)121000903807111

1,600,418

88,4181,595,418

83,4181,560,000

48,000121000518801111

3,171,667

71,667

3,166,667

66,6673,150,000

50,000612001207201111

3,570,198

420,198

3,555,863

405,8633,475,000

325,000

612000309306111

36,731,136

2,731,136

36,707,000

2,707,000

36,320,000

2,320,000

913000105403111

10,693,837

1,693,837

10,680,000

1,680,000

1e+07

1,000,000

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> d<-R25.80> d[d$ID=="121000903807111","nonagri"]<-48000> d[d$ID=="121000518801111","nonagri"]<-50000> d[d$ID=="612001207201111","nonagri"]<-325000> d[d$ID=="612000309306111","nonagri"]<-2320000> d[d$ID=="913000105403111","nonagri"]<-1000000> d[d$ID=="121000903807111","monetary"]<-83418> d[d$ID=="121000518801111","monetary"]<-66667> d[d$ID=="612001207201111","monetary"]<-405863> d[d$ID=="612000309306111","monetary"]<-2707000> d[d$ID=="913000105403111","monetary"]<-1680000> d[d$ID=="121000903807111","ttinc"]<-88418> d[d$ID=="121000518801111","ttinc"]<-71667> d[d$ID=="612001207201111","ttinc"]<-420198> d[d$ID=="612000309306111","ttinc"]<-2731136> d[d$ID=="913000105403111","ttinc"]<-1693837> fix(d)> R25.80.old<-R25.80> R25.80<-d> hies2009res.old<-hies2009res> hies2009res[[15]]<-R15.80> hies2009res[[25]]<-R25.80> hies2009res[[26]]<-R26.80# Exported CSV files> write.csv(R15.80,"R15_80.csv",row.names=F)> write.csv(R25.80,"R25_80.csv",row.names=F)> write.csv(R26.80,"R26_80.csv",row.names=F)

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9. R Scripts for some of the Additional Tables of the Final Report
Table A1: Household population by sex and sector> t1<-addmargins(tapply(R01$WT,list(R01$SECTOR,R01$SEX_LIVING),sum))> colnames(t1)<-c("Male","Female","Total")> rownames(t1)<-c("Urban","Rural","Estate","Sri Lanka")> round(t1[c(4,1:3),c(3,1:2)]/1000,1)
Total

Male

FemaleSri Lanka 20337.8 9645.8 10692.0
Urban

3021.9 1440.1

1581.8Rural

16289.7 7717.6

8572.1Estate1026.1 488.0

538.1

Table A1: Household population by sex and province> province<-floor(R01$DISTRICT/10)> table(province)
province
1

2

3

4

5

6

7

8

9

22748 8812 14332 2894 8737

7090

4887

4885

6487

> prov.name<-c("Western","Central","Southern","Northern",+ "Eastern","North-western","North-central","Uva","Sabaragamuwa")> t2<-addmargins(tapply(R01$WT,list(province,R01$SEX_LIVING),sum))> colnames(t2)<-c("Male","Female","Total")> rownames(t2)<-c(prov.name,"Sri Lanka")> round(t2[c(10,1:9),c(3,1:2)]/1000,1)
Total

Male

FemaleSri Lanka

20337.8 9645.8 10692.0

Western5983.2 2823.4

3159.9Central

2641.7 1222.4

1419.3

Southern

2525.3 1193.5

1331.8

Northern

715.6 338.2

377.4

Eastern

1499.5 729.1

770.4

North-western 2419.8 1160.4

1259.4

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North-central 1237.6 593.5

644.1

Uva

699.4

1330.2 630.8

Table A1: Household population by sex and district> dist.name<-c("Colombo","Gampaha","Kalutara","Kandy","Matale","Nuwara eliya",+ "Galle","Matara","Hambantota","Jaffna","Vavuniya","Batticaloa","Ampara",+ "Trincomalee","Kurunegala","Puttalama","Anuradhapura","Polonnaruwa",+ "Badulla","Moneragala","Ratnapura","Kegalle")> length(dist.name)[1] 22> t3<-addmargins(tapply(R01$WT,list(R01$DISTRICT,R01$SEX_LIVING),sum))> colnames(t3)<-c("Male","Female","Total")> rownames(t3)<-c(dist.name,"Sri Lanka")> round(t3[c(23,1:22),c(3,1:2)]/1000,1)
Total

Male

FemaleSri Lanka

20337.8 9645.8 10692.0

Colombo2404.5 1167.6

1236.9Gampaha2386.0 1108.9

1277.1

Kalutara

1192.8

546.9

645.9

Kandy

1386.9

630.5

756.4

Matale

485.3

229.0

256.3

Nuwara eliya

769.5

362.8

406.7

Galle

1098.8

508.6

590.2

Matara

834.8

387.4

447.4

Hambantota

591.7

297.5

294.2

Jaffna

545.8

256.2

289.6

Vavuniya

169.9

82.0

87.8

Batticaloa

538.3

259.3

279.0

Ampara

618.4

299.8

318.6

Trincomalee

342.8

170.0

172.8

Kurunegala

1585.7

763.1

822.6

Puttalama

834.1

397.3

436.8

Anuradhapura

824.7

399.1

425.6

Polonnaruwa

413.0

194.5

218.5

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Badulla

853.9

396.9

457.1

Moneragala

476.3

233.9

242.4

Ratnapura

1138.2

552.8

585.4

Kegalle

846.5

401.6

444.9

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Table A2: Household population of head of the household by sex and sector> df<-subset(R01,RELATIONSHIP==1)> dim(df)[1] 19959

28> t1<-addmargins(tapply(df$WT,list(df$SECTOR,df$SEX_LIVING),sum))> colnames(t1)<-c("Male","Female","Total")> rownames(t1)<-c("Urban","Rural","Estate","Sri Lanka")> round(t1[c(4,1:3),c(3,1:2)]/1000,1)
Total

Male Female

Sri Lanka 5079.8 3899.9 1179.9
Urban

711.2

517.6 193.5

Rural

4123.1 3197.8 925.3

Estate

245.5

184.4

61.1

Table A3: Percentage distribution of population by selected age groups and sector

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Table A34: Percentage distribution of households by principal type of cooking fuel by sector> t1<-tapply(R23$WT,list(R23$SECTOR,R23$COOKING_FUEL),sum)> t1[is.na(t1)]<-0> colnames(t1)<-c("Firewood","Gas","Kerosene","Electricity",+ "Saw dust/Paddy husk","Other")> rownames(t1)<-c("Urban","Rural","Estate")> round(t1/1000,1)
Firewood

Gas Kerosene Electricity Saw dust/Paddy husk Other

Urban

278.8 341.4

81.2

3.8

1.6

3.9

Rural

3573.5 467.2

44.0

6.0

4.2

27.1

Estate

238.8

1.4

0.5

0.0

0.8

3.7

> t2<-cbind(t1[,1:3],Other=rowSums(t1[,4:6]))> round(t2/1000,1)
Firewood

Gas Kerosene Other

Urban

278.8 341.4

81.2

Rural

3573.5 467.2

44.0 37.3

Estate

238.8

1.4

3.7

9.2

1.3

> t3<-addmargins(t2)[c(4,1:3),c(5,1:4)]> round(t3/t3[,1]*100,1)
Sum Firewood Gas Kerosene Other
Sum

100

80.6 16.0

2.5

0.9

Urban 100

39.2 48.0

11.4

1.3

Rural 100

86.7 11.3

1.1

0.9

Estate 100

97.3 1.5

0.6

0.5> round(t1[c(4,1:3),c(7,1:6)]/1000,1)

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10 Agricultural land*******************************************
Remarks: Unit of area in yard-pound system (UK)
Unit of length and area for measuring land in yard-pound system (UK)
Unit

Definition

Remarks

rod/pole/

1 rod = 5.5 yard = 16.5 feet = 5.0292 m

Typical length of a mediaeval

perch

ox-goadchain

1 chain = 4 rods = 66 feet = 20.1168 mfurlong

1 furlong = 10 chains = 40 rods = 660 feet = 201.168 m

Distance a team of oxen could
plough without restingmile

1 mile = 8 furlong

square rod/

1 square rod = 30.25 square yards = 25.293 m2

square perch/
perch
rood

1 rood = 1 furlong x 1 rod = 40 square rods = 1,012 m2

acre

1 acre = 1 furlong x 1 chain = 4 roods = 160 square rods

Amount of land tillable by one= 4,840 square yards = 4,046.85 m2

man behind one ox in one day

hide/hyde

1 hide = 120 acre = 485.622 m2

square mile

1 square mile = 8 furlong x 80 chain = 640 acres= 2.59 km2

According to the delegates from Sri Lanka for the International Workshop,“Metric area is not used for measuring land area in Sri Lanka.
Conversion of it in office is as the land area in perches is given by
Perches + Rood x 40 + Acres x 160 “**************************************************************

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161== R24: Agricultural holdings and livestock==
Out of 19,958 households, 16,524 responded that some household members own agricultural land, but
it seems that only few reported the area owned and cultivated.
> addmargins(table(R24$IS_AGRILAND_OWNER,useNA="ifany"))1

216524 3432

3 <NA>
1Sum

1 19958# Number of households reported non-NA for each question of area
> for(j in 12:29){+ n<-sum(!is.na(R24[,j]))+ cat("Number of non-NAs in",colnames(R24)[j],"= ",n,"\n")+ }Number of non-NAs in PADDY_OWN_ACR = 1515
Number of non-NAs in PADDY_OWN_RT =1438

Number of non-NAs in PADDY_OWN_PERCH = 208
Number of non-NAs in PADDY_OTHER_ACR = 1105
Number of non-NAs in PADDY_OTHER_RT =1064

Number of non-NAs in PADDY_OTHER_PERCH = 132
Number of non-NAs in LAND_OWN_ACR =1110

Number of non-NAs in LAND_OWN_RT = 1100
Number of non-NAs in LAND_OWN_PERCH =828

Number of non-NAs in LAND_OTHER_ACR =611

Number of non-NAs in LAND_OTHER_RT = 584
Number of non-NAs in LAND_OTHER_PERCH =Number of non-NAs in HOME_OWN_ACR =

190

1947

Number of non-NAs in HOME_OWN_RT = 4448
Number of non-NAs in HOME_OWN_PERCH =9921

Number of non-NAs in HOME_OTHER_ACR =585

Number of non-NAs in HOME_OTHER_RT = 1220
Number of non-NAs in HOME_OTHER_PERCH =



1003

Generated variables area1 to area6 in square meters and counted number of households with nonzero area, as follows;

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Owned

CultivatedVariable

No. of non-zero

Variable

No. of non-zero

1. Paddy land

area1

2,548

area2

1,875

2. High land

area3

2,693

area4

1,215

area5

15,249

area6

2,507

3.

Land

area

with

occupied housing units> R24.old<-R24> R24[is.na(R24)]<-0> variables<-c("area1","area2","area3","area4","area5","area6")> for(j in 1:6){+ R24[variables[j]]<-R24[,3*j+9]*4046.85+R24[,3*j+10]*1011.7125+R24[,3*j+11]*25.293+ }> nrow(subset(R24,area1>0))[1] 2548> nrow(subset(R24,area2>0))[1] 1875> nrow(subset(R24,area3>0))[1] 2693> nrow(subset(R24,area4>0))[1] 1215> nrow(subset(R24,area5>0))[1] 15249> nrow(subset(R24,area6>0))[1] 2507> nrow(subset(R24,area1+area2+area3+area4+area5+area6>0))[1] 16521# Average area per household (m2)
> round(colSums(R24[,41:46]*R24$WT)/sum(R24$WT),1)
area1

area2 area3

area4

area5 area61307.9699.8 815.6

424.2 1448.4 410.5

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# Average area per household conditional on household with non-zero area
> for(j in 1:6){+ d<-subset(R24,R24[,variables[j]]>0)+ cat(variables[j],":")+ cat(round(sum(d[,j+40]*d$WT)/sum(d$WT),1),"\n")+ }area1 :8155.5
area2 :5836.2
area3 :5246.7
area4 :5687.2
area5 :1841.4
area6 :2723.7



Average area per household (m2)
Owned
Average area

Cultivated

Average area

Average area

Average area

conditional on

conditional on

non-zero

non-zero

1. Paddy land

1,307.9

8,155.5

699.8

5,836.2

2. High land

815.6

5,246.7424.2

5,687.2

3. Land area with

1,448.4

1,841.4

410.5

2,723.7

occupied housing units

Comparison between R10 and R24== R10: Income from agricultural activities ==> R10.old<-R10> R10[is.na(R10)]<-0Generated the variable area in square meter.

> R10["area"]<-R10$ACR_5_2*4046.85+R10$RT_5_2*1011.7125+R10$P*25.293

Generated R10p as a subset of R10 who have cultivated paddy as employers or own accountworkers.

> R10p<-subset(R10,SEAS_CROPS_CODE==1)

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> dim(R10p)[1] 2782

22> table(R10p$area>0)
FALSE TRUE
38 2744# Average cultivated area of paddy per household> round(sum(R10p$area*R10p$WT)/sum(R24$WT),1)[1] 1068.9

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11. Employment
R01 and R08 include information on employment.

== R01 ==Number of persons by main activity and employment status
Main activity

Un-weighted

Weighted

number of

number ofof

persons

persons

employed

Proportion

Proportion

Persons aged 10 and over

66,668

16,763,675

100.0

1 Employed

27,685

7,052,450

42.1

1 Government employee

3,410

837,349

11.9

2 Semi government employee

1,190

314,081

4.5

3 Private sector employee

13,4353,260,912

46.2

540

129,328

1.8

5 Own account worker

7,558

2,042,996

29.0

6 Unpaid family worker

1,545

466,243

6.6

Unknown

7

234

0.0

2 Unemployed

5,155

1,222,140

7.3

3 Student

12,3403,091,97818.4

4 Household work

14,623

3,662,052

21.8

5 Unable/ Too old to work

6,429

1,626,131

9.7

9 Other409

102,665

0.6

Unknown

27

6,259

0.0

4 Employer> d1<-subset(R01,AGE>=10)> nrow(d1)[1] 66668> addmargins(table(d1$MAIN_ACTIVITY,useNA="ifany"))
1

2

3

4

5

6

9

27685 5155 12340 14623 6429

1

409

<NA>

Sum

26 66668> t<-tapply(d1$WT,d1$MAIN_ACTIVITY,sum,na.rm=T)> t<-c(sum(d1$WT),t)

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100.0

166

> round(t)116763675

2

37052450 1222140

3091978

4

5

6

9

3662052 1626131

349

102316> round(t/t[1]*100,1)
1

2

100.0 42.1

3

4

5

6

9

7.3 18.4 21.8

9.7

0.0

0.6

> d2<-subset(d1,MAIN_ACTIVITY==1)> nrow(d2)[1] 27685> addmargins(table(d2$EMPLOYMENT_STATUS,useNA="ifany"))
1

2

3

4

3410 1190 13435

5

6

9

540 7558

1545

1

<NA>

Sum

6 27685> t<-tapply(d2$WT,d2$EMPLOYMENT_STATUS,sum,na.rm=T)> t<-c(sum(d2$WT),t)> round(t)
1

2

3

4

7052450 837349 314081 3260912

5

6

9

129328 2042996

466243234> round(t/t[1]*100,1)
1

2

100.0 11.9



3

4

5

6

9

4.5 46.2

1.8

29.0

6.6

0.0

For employed, main occupation and main industry are recorded both in 4-digit code.

Variable

Description

Classification

Remarks

MAIN_OCCUPATION

Main occupation

ISCO

See Attachment 6

INDUSTRY

Main industry

ISIC

See Attachment 5# Number of employed persons by major occupation> d2["occ"]<-floor(d2$MAIN_OCCUPATION/1000)> t1<-table(d2$occ,useNA="ifany")
> t1
0

1

2

3

4

5

6

7

8

9 <NA>

129 1771 1862 2834 1056 2142 5448 4060 1943 6406

34

> t1<-c(sum(t1),t1[c(2:10,1,11)])> occ.name<-c("Total","1 Manager, Senior Officials and Legislator",

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+ "2 Professionals","3 Technicians and Associate Professionals",+ "4 Clerks and Clerical Support Works","5 Services and Sales Workers",+ "6 Skilled Agricultural, Forestry and Fishery Workers",+ "7 Craft and Related Trade Workers","8 Plant and machine Operators and Assemblers",+ "9 Elementary occupations","0 Other","Unknown")> t2<-tapply(d2$WT,d2$occ,sum,na.rm=T)> t2<-c(sum(d2$WT),t2[c(2:10,1)],sum(d2$WT)-sum(t2))> df<-data.frame(Occupation=occ.name,Unweighted=as.vector(t1),+ Weighted=round(t2),Prop=round(t2/t2[1]*100,1),row.names=NULL)> df
Occupation Unweighted Weighted Prop
1

Total

27685

7052450 100.0

2

1 Manager, Senior Officials and Legislator

1771

441580

6.3

3

2 Professionals

1862

449305

6.4

4

3 Technicians and Associate Professionals

2834

6851949.7

5

4 Clerks and Clerical Support Works

1056

2768793.9

6

5 Services and Sales Workers

2142

5183047.3

7

6 Skilled Agricultural, Forestry and Fishery Workers

5448

1556740 22.1

8

7 Craft and Related Trade Workers

4060

1067496 15.1

9

8 Plant and machine Operators and Assemblers

1943

51645710

9 Elementary occupations

6406

1496287 21.2

110 Other

129

37644

0.5

12

Unknown

34

6565

0.1

# Number of employed persons by major industry> breaks<-c(111,710,1010,3510,3600,4100,4510,4910,5510,5811,6411,6810,6910,+ 7710,8411,8510,8610,9000,9411,9700,9900,9999)> d2["ind"]<-cut(d2$INDUSTRY,breaks,right=F,include.lowest=T,+ labels=c("A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P",+ "Q","R","S","T","U"))> t1<-table(d2$ind,useNA="ifany")> t1<-c(sum(t1),t1)> t2<-tapply(d2$WT,d2$ind,sum,na.rm=T)

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> round(t2)> t2[is.na(t2)]<-0> t2<-c(sum(d2$WT),t2,sum(d2$WT)-sum(t2))> ind.name<-c("Total","A Agriculture, Forestry and Fishing","B Mining and Quarrying",+ "C Manufacturing","D Electricity, gas, steam and air conditioning supply",+ "E Water Supply and Sewerage",+ "F Construction","G Wholesale and retail trade",+ "H Transportation and storage","I Accommodation and food service activities",+ "J Information and Communication","K Financial and Insurance Activities",+ "L Real estate activities","M Professional, Scientific and technical activities",+ "N Administrative and support service activities",+ "O Public administration and defense",+ "P Education","Q Human health and social work activities",+ "R Arts, entertainment and recreation","S Other service activities",+ "T Activities of households as employers for own use",+ "U Activities of extraterritorial organizations and bodies","Unknown")> df<-data.frame(Industry=ind.name,Unweighted=t1,+ Weighted=round(t2),Prop=round(t2/t2[1]*100,1),row.names=NULL)> df
Industry Unweighted Weighted

Prop

1

Total

2

A Agriculture, Forestry and Fishing3

B Mining and Quarrying

4

C Manufacturing

5

D Electricity, gas, steam and air conditioning supply

21

4779

0.1

6

E Water Supply and Sewerage

420109720

1.6

7

F Construction

23

7081

0.1

8

G Wholesale and retail trade

1646

4406886.2

9H Transportation and storage

3914

92156913.1

10I Accommodation and food service activities

537

117856

1.7

11

J Information and Communication

1539

412235

5.8

12

K Financial and Insurance Activities

585

149383

2.1

13

L Real estate activities

0

0

0.0

14

M Professional, Scientific and technical activities

2998

75934710.8

15N Administrative and support service activities

1033

2466083.5

16

O Public administration and defense

0

0

0.0

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27685 7052450 100.08123 2079547
0

29.5

0

0.0

4252 1143615

16.2

169

17

P Education

461

106273

1.5

18

Q Human health and social work activities

0

0

0.0

19

R Arts, entertainment and recreation

466

111508

1.6

20

S Other service activities

1578

422718

6.0

21

T Activities of households as employers for own use

0

0

0.0

22 U Activities of extraterritorial organizations and bodies

46

11314

0.2

23

43

8208

0.1

Unknown== R08 ==The questionnaire of R08 is designed to record at most two occupation for paid employees, that is,main occupation and secondary occupation.

If the secondary occupation is recorded, the main occupation should be recorded.
# e1: subset of R08 with PRI_SEC=1> e1<-subset(R08,PRI_SEC==1)> dim(e1)[1] 18159

18# e2: subset of R08 with PRI_SEC=2> e2<-subset(R08,PRI_SEC==2)> dim(e2)[1] 205 18

While there are 205 records of PRI_SEC=2, the number of isolated records without thecorresponding records of PRI_SEC=1 is 29.

> table(is.element(e2$PID,e1$PID))FALSE TRUE
29



176

PRI_SEC=2 of the records with the isolated PID should be read as PRI_SEC=1.

# Isolated PID

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> PID.e<-e2[!is.element(e2$PID,e1$PID),"PID"]> length(PID.e)[1] 29> head(PID.e)[1] "13200100590411102" "23200070020111101" "33200090260211105"[4] "33200110380411101" "33200110380611101" "33200110380611102"> d3<-R08> d3[is.element(d3$PID,PID.e),"PRI_SEC"]<-1> e1<-subset(d3,PRI_SEC==1)> dim(e1)[1] 1818818> e2<-subset(d3,PRI_SEC==2)> dim(e2)[1] 176 18

Created data frame emp by merging subset of d2 who are employee, e1 and e2 using PID.

> emp<-subset(d2,EMPLOYMENT_STATUS<=3)> emp<-merge(emp,e1[,c(12:15,17)],by="PID",all.x=T)> colnames(emp)[31:34]<-c("PRI_SEC1","WAGE1","ALLOWANCE1","BONUS1")> dim(emp)[1] 18035

34> emp<-merge(emp,e2[,c(12:15,17)],by="PID",all.x=T)> dim(emp)[1] 18035

38> colnames(emp)[35:38]<-c("PRI_SEC2","WAGE2","ALLOWANCE2","BONUS2")> colnames(emp)[1] "PID""DISTRICT""PSU""REC_TYPE"[5] "SECTOR""DSD""MONTH""SAMPLE_N"[9] "SERIAL_NO""NHH""RESULT""PERSON_SERIAL_NO"[13] "RELATIONSHIP""SEX_LIVING""BIRTH_YEAR""BIRTH_MONTH"[17] "AGE""ETHNICITY""RELIGION""CURR_EDUCATION"[21] "EDUCATION""MARITAL_STATUS""MAIN_ACTIVITY""MAIN_OCCUPATION"[25] "INDUSTRY""EMPLOYMENT_STATUS" "ID"

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[29] "occ""ind""PRI_SEC1""WAGE1"[33] "ALLOWANCE1""BONUS1""PRI_SEC2""WAGE2"[37] "ALLOWANCE2""BONUS2"# Generated monthly wage income from main job> emp.old<-emp> emp["WAGE1"]<-ifelse(is.na(emp$WAGE1),0,emp$WAGE1)> emp["ALLOWANCE1"]<-ifelse(is.na(emp$ALLOWANCE1),0,emp$ALLOWANCE1)> emp["BONUS1"]<-ifelse(is.na(emp$BONUS1),0,emp$BONUS1)> emp["wage.inc1"]<-emp$WAGE1+emp$ALLOWANCE1+emp$BONUS1/12> table(emp$wage.inc1==0)
FALSE TRUE
17826



209

Weighted average monthly wage income of main job by occupation of main job> emp.old2<-emp> emp<-subset(emp,wage.inc1>0)> nrow(emp)[1] 17826> t<-by(emp,emp$occ,function(df) sum(df$wage.inc1*df$WT)/sum(df$WT))> t<-as.vector(t)[c(2:10,1)]> df<-data.frame(Occupation=occ.name[2:11],Wage=round(t))> df
Occupation

Wage

1

1 Manager, Senior Officials and Legislator 37079

2

2 Professionals 23024

33 Technicians and Associate Professionals 20994

44 Clerks and Clerical Support Works 18405

5

5 Services and Sales Workers 14548

6

6 Skilled Agricultural, Forestry and Fishery Workers

7134

7

7 Craft and Related Trade Workers 11777

8

8 Plant and machine Operators and Assemblers 14298

910

9 Elementary occupations

9123

0 Other 25958

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

Weighted average monthly wage income of main job by industry of main job> t<-by(emp,emp$ind,function(df) sum(df$wage.inc1*df$WT)/sum(df$WT))> df<-data.frame(Industry=ind.name[2:22],Wage=round(as.vector(t)))> dfIndustryWage

1

A Agriculture, Forestry and Fishing

7929

2

B Mining and Quarrying

NA

3

C Manufacturing 12612

4

D Electricity, gas, steam and air conditioning supply 13379

5

E Water Supply and Sewerage 13862

6F Construction 21740

7

G Wholesale and retail trade 13236

8

H Transportation and storage 13447

9

I Accommodation and food service activities 12945

10J Information and Communication 16092

11

K Financial and Insurance Activities 26578

12

L Real estate activities

NA

13

M Professional, Scientific and technical activities 21575

14

N Administrative and support service activities 19265

15
16
17
18

O Public administration and defense

NA

P Education 19819
Q Human health and social work activities

NA

R Arts, entertainment and recreation 12973

19

S Other service activities

7258

20

T Activities of households as employers for own use

NA

21 U Activities of extraterritorial organizations and bodies 15401

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12 Demography
12.1 Ethnicity

Brief outline of ethnic groups in Sri Lanka
Ethnic group

Description

SinhalaThe Sinhalese are an ethnic group native to the island of Sri Lanka. They constitute
75% of the Sri Lanka population. The Sinhalese identity is based on language,
historic heritage and religion. The Sinhalese speaks Sinhara and are predominantly
Buddhists, although a small percentage of Sinhalese follow branches ofChristianity. The Sinhalese are mostly found in North central, Central, South, andWest Sri lanka.
According to legend Mahavamsa they are the descendants of the exiled Prince
Vijaya who arrived from East India to Sri Lanka in 543 BCE.

Sri Lanka

Sri Lankan Tamils have a very long history in Sri Lanka and have lived on theTamilisland since around the 2nd century BCE. Most modern Sri Lankan Tamils claim
descent from residents of Jaffna Kingdom, a former kingdom in the north of theisland and Vannimai chieftaincies from the east. They constitute a majority in the
Northern Province, live in significant numbers in the Eastern Province, and are in
the minority throughout the rest of the country. 70% of Sri Lankan Tamils in Sri
Lanka live in the Northern and Eastern provinces.
Although Sri Lankan Tamils are culturally and linguistically distinct, genetic
studies indicate that they are closely related to Sinhalese ethnic group in the island.
The Sri Lankan Tamils are mostly Hindus with a significant Christian population.
One-third of Sri Lankan Tamils now live outside Sri Lanka. While there wassignificant migration during the British colonial period, the civil war led to morethan 800,000 Tamils leaving Sri Lanka, and many have left the country fordestinations such as Canada, India and Europe as refugees.

Indian Tamil

Indian Tamils of Sri Lanka are Tamil people of Indian origin in Sri Lanka. They
are also known as Hill Country Tamils, Up-Country Tamils or simply Indian
Tamils. They are partly descended from workers sent from South India to Sri Lanka
in the 19th and 20th centuries to work in coffee, tea and rubber plantations. Somealso migrated on their own as merchants and as other service providers. These
Tamil-speakers mostly live in the central highlands. They are instrumental in theplantation sector economy of Sri Lanka. In general socio-economically theirstandard of living is below that of the national average. In 1948, the United National

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Party government stripped the Indian Tamils of their citizenship. Under the termsof an agreement reached between the Sri Lankan and Indian governments in the
1960s, about forty percent of the Indian Tamils were granted Sri Lankan
citizenship, and most of the remainder were repatriated to India. By the 1990s, mostIndian Tamils had received Sri Lankan citizenship.
Most of Indian Tamils are Hindus with a minority of Christians and Muslims
amongst them.
Sri Lanka

Sri Lankan Moors (commonly referred to as Muslims) are the third largest ethnic

Moorsgroup in Sri Lanka. They are native speakers of the Tamil language andpredominantly followers of Islam. While some sources describe them as a subsetof the Tamil people who had adopted Islam as their religion and spoke Tamil as
their mother tongue, which they continue to do so, other sources trace their ancestry
to Arab traders (Moors) who settled in Sri Lanka sometime between the 8th and15th centuries. The Arabic language brought by the early merchants is no longer
spoken, though many Arabic words and phrases are still commonly used.

Malay

Malays of Sri Lanka originated in Southeast Asia. Their ancestors initially came tothe country when both Sri Lanka and Indonesia were colonies of the Dutch, while
a second wave (1796–1948) came from the Malay Peninsula, when both Malaya
and Sri Lanka were in the British Empire.
Most of the early immigrants were soldiers, posted by the Dutch which later
continued by the British for colonial administration to Sri Lanka, who decided tosettle on the island. Other immigrants were convicts or members of noble houses
from Dutch East Indies (present day Indonesia) who were exiled to Sri Lanka andwho never left. The main source of a continuing Malay identity is their common
Malay language. Many Sri Lankan Malays have been celebrated as courageous
soldiers, politicians, sportsmen, lawyers, accountants and doctors.

Burgher

The Burgher people are a Eurasian ethnic group, historically from Sri Lanka,
consisting mainly of male-line descendants of European colonists from the 16th to20th centuries (mostly Portuguese, Dutch, German and British) and local women,with some minorities of Swedish, Norwegian, French and Irish. Today the native
language of the Burghers is English.
Burghers were defined as those whose father was born in Sri Lanka, with at least
one European ancestor on one's direct paternal side, regardless of the ethnic originof one's mother, or what other ethnic groups may be found on the father's side.
Because of this definition, Burghers almost always have European surnames(mostly of Portuguese, Dutch and British origin, but sometimes German, French or

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Russian).
> ethnic<-c("Sinhala","Sri Lanka Tamil","Indian Tamil","Sri Lanka Moors",> "Malay","Burgher","Other")

There are 11 NA in ETHNICITY.

> table(is.na(R01$ETHNICITY))FALSE TRUE
80861

11

# Number of persons by ethnicity> ethnic<-c("Sinhala","Sri Lanka Tamil","Indian Tamil","Sri Lanka Moors",+ "Malay","Burgher","Other")> t<-tapply(R01$WT,R01$ETHNICITY,sum,na.rm=T)> names(t)<-ethnic> round(addmargins(t))
Sinhala Sri Lanka Tamil

Indian Tamil Sri Lanka Moors

15312210

21098797794652070535

Malay

Burgher

Other

Sum

25063

17886

22007

20337043> round(addmargins(t)/sum(t)*100,1)
Sinhala Sri Lanka Tamil

Indian Tamil Sri Lanka Moors

75.3

10.4

3.8

10.2

Malay

Burgher

Other

Sum

0.1

0.1

0.1

100.0

# Number of persons by ethnicity and sector> m<-matrix(as.vector(by(R01$WT,list(R01$ETHNICITY,R01$SECTOR),sum,na.rm=T)),nrow=7)> rownames(m)<-ethnic> colnames(m)<-c("Urban","Rural","Estate")> m[is.na(m)]<-0> round(addmargins(m))
UrbanSinhalaRural EstateSum

1685902 13511797 114511 15312210

Sri Lanka Tamil 705196 1115889 288794 2109879
Indian Tamil

38964

121672 618829

779465

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Sri Lanka Moors 561630 1505006

3900 2070535

Malay

13907

11156

0

25063

Burgher

11963

5923

0

17886

Other

4273

17660

74

22007

Sum

3021834 16289102 1026107 20337043> round(prop.table(m,2)*100,1)
Urban Rural Estate
Sinhala55.8 82.9

11.2

Sri Lanka Tamil 23.3

6.9

28.1

Indian Tamil

1.3

0.7

60.3

Sri Lanka Moors 18.6

9.2

0.4

Malay

0.5

0.1

0.0

Burgher

0.4

0.0

0.0

Other

0.1

0.1

0.0

# Number of persons by ethnicity and province (unit: thousand persons)> province<-c("Western","Central","Southern","Northern","Eastern","North-western",+ "North-central","Uva","Sabaragamuwa")> t<-by(R01$WT,list(substr(R01$DISTRICT,1,1),R01$ETHNICITY),sum,na.rm=T)> m<-matrix(as.vector(t),nrow=9)> rownames(m)<-province> colnames(m)<-ethnic> m[is.na(m)]<-0> round(m/1000)
Sinhala Sri Lanka Tamil Indian Tamil Sri Lanka Moors Malay Burgher Other
Western

5049

365

36

486

19

13

15

Central

1671

174

505

286

1

1

3

Southern

2406

31

15

67

3

0

2

Northern

24

678

0

13

0

0

0

Eastern

359

567

3

566

0

4

0

North-western

1960

77

2

380

0

0

0

North-central

1117

4

0

116

0

0

0

Uva

1058

84

132

55

0

0

1

Sabaragamuwa

1667

129

87

101

1

0

0

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# Composition of ethnic groups by province (unit: %)> round(prop.table(m,1)*100,1)
Sinhala Sri Lanka Tamil Indian Tamil Sri Lanka Moors Malay Burgher Other
Western

84.4

6.1

0.6

8.1

0.3

0.2

0.3

Central

63.3

6.6

19.1

10.8

0.0

0.0

0.1

Southern

95.3

1.2

0.6

2.7

0.1

0.0

0.1

Northern

3.4

94.8

0.0

1.8

0.0

0.0

0.0

Eastern

23.9

37.8

0.2

37.8

0.0

0.3

0.0

North-western

81.0

3.2

0.1

15.7

0.0

0.0

0.0

North-central

90.3

0.3

0.0

9.4

0.0

0.0

0.0

Uva

79.5

6.3

9.9

4.1

0.0

0.0

0.1

Sabaragamuwa

84.0

6.5

4.4

5.1

0.1

0.0

0.0

# Regional distribution by ethnic groups (unit: %)> round(prop.table(m,2)*100,1)
Sinhala Sri Lanka Tamil Indian Tamil Sri Lanka Moors Malay Burgher Other
Western

33.0

17.3

4.6

23.5

77.2

70.3

70.4

Central

10.9

8.2

64.8

13.8

4.2

6.7

14.2

Southern

15.7

1.5

1.9

3.3

12.2

0.0

9.3

Northern

0.2

32.2

0.0

0.6

0.0

0.0

0.0

Eastern

2.3

26.9

0.4

27.4

0.0

22.2

0.0

North-western

12.8

3.6

0.3

18.4

0.9

0.8

1.7

North-central

7.3

0.2

0.0

5.6

1.2

0.0

0.0

Uva

6.9

4.0

16.9

2.7

0.0

0.0

4.1

Sabaragamuwa

10.9

6.1

11.1

4.9

4.3

0.0

0.3

Summary:Shinhalese are the majority ethnic group of Sri Lanka. They occupy 75.3%, followed by 10.4%
of Sri Lanka Tamil and 10.2% of Sri Lanka Moors.

The ethnic group shows a distinct geographical distribution. Sri Lanka Tamil is dominant inNorthern province and major in Eastern. Sri Lanka Moors live in Eastern and Western. Many
Indian Tamil live in Estate sector of Central and Uva provinces.

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12.2 Religion
Religion is closely related to ethnicity.

> table(is.na(R01$RELIGION))
FALSE TRUE
80863

9

> table(R01$RELIGION)
1

2

3

4

9

50341 13146 9946 7424

6

> religion<-c("Buddhism","Hindu","Islam","Catholic","Other")> t<-tapply(R01$WT,R01$RELIGION,sum,na.rm=T)> names(t)<-religion> round(addmargins(t))
Buddhism14359512

Hindu

Islam Catholic

2456608 2098303

Other

1422117Sum

539 20337079> round(addmargins(t)/sum(t)*100,1)
Buddhism

Hindu

Islam Catholic

70.6

12.1

10.3

7.0

Other

Sum

0.0

100.0

# Number of persons by religion and sector> m<-matrix(as.vector(by(R01$WT,list(R01$RELIGION,R01$SECTOR),sum,na.rm=T)),nrow=5)> rownames(m)<-religion> colnames(m)<-c("Urban","Rural","Estate")> m[is.na(m)]<-0> round(addmargins(m))
Urban

Rural EstateSum

Buddhism 1391905 12851017 116590 14359512
Hindu

5358961093447 827266

2456608

Islam

57492315147618619

2098303

Catholic

51912582942073572

1422117

16

464

60

539

Other
Sum

3021865 16289107 1026107 20337079

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> round(prop.table(m,2)*100,1)
Urban Rural Estate
Urban Rural Estate
Buddhism

46.1

78.9

11.4

Hindu

17.7

6.7

80.6

Islam

19.0

9.3

0.8

Catholic

17.2

5.1

7.2

Other

0.0

0.0

0.0

# Number of persons by ethnicity and religion> m<-matrix(as.vector(by(R01$WT,list(R01$ETHNICITY,R01$RELIGION),sum,na.rm=T)),nrow=7)> colnames(m)<-religion> rownames(m)<-ethnic> m[is.na(m)]<-0> round(addmargins(m))
Buddhism

Hindu

Islam Catholic Other

14323068

13139

7767

968071

165 15312210

Sri Lanka Tamil

25667 1746464

19258

318430602109879

Indian Tamil

5917

5192

82390

0

779465

Sri Lanka Moors

2479

4157 2041476

22408

16

2070535

Malay

753

587

21750

1674

299

25063

Burgher

291

0

0

17594

0

17886

Other

1336

6295

2830

11545

0

22007

SinhalaSum

68596514359512 2456608 2098273 1422112

Sum

539 20337043> round(addmargins(m,2)/rowSums(m)*100,1)
Buddhism Hindu Islam Catholic Other Sum
Sinhala

93.5

0.1

0.1

6.3

0.0 100

Sri Lanka Tamil

1.2

82.8

0.9

15.1

0.0 100

Indian Tamil

0.8

88.0

0.7

10.6

0.0 100

Sri Lanka Moors

0.1

0.2 98.6

1.1

0.0 100

Malay

3.0

2.3 86.8

6.7

1.2 100

Burgher

1.6

0.0

0.0

98.4

0.0 100

Other

6.1

28.6 12.9

52.5

0.0 100

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12.3 MarriagePolygamy

Islamic law allows up to four wives, and Sri Lanka has about 10% of Islamic population.
In HIES, only Muslim mails were allowed to have more than one wife. There are one such case in thedata set of HIES 2009.

Remarks:
According to the delegates from Sri Lanka for the International Workshop,“Only Muslim ethnicities can enjoy multiple wives legally in Sri Lanka, and even then it is captured
if they live in the same HIES household in the HIES.”# Number of spouses within the household> nsp<-tapply(R01$RELATIONSHIP==2,R01$ID,sum,na.rm=T)> table(nsp)
nsp
0

1

2

5736 14220

2> names(nsp)[nsp==2][1] "131000413703111" "532000201104111"> R01[R01$ID=="131000413703111",]
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT
19272

13 137

1

1

0

4

3

1

1

1

19273

13 137

1

1

0

4

3

1

1

1

19274

13 137

1

1

0

4

3

1

1

1

19275

13 137

1

1

0

4

3

1

1

1

19276

13 137

1

1

0

4

3

1

1

1

19277

13 137

1

1

0

4

3

1

1

1

19278

13 137

1

1

0

4

3

1

1

1

19279

13 137

1

1

0

4

3

1

1

1

PERSON_SERIAL_NO RELATIONSHIP SEX_LIVING BIRTH_YEAR BIRTH_MONTH AGE
19272

1

1

1

45

3 65

19273

2

2

2

47

2 63

19274

3

2

2

47

12 62

19275

4

3

1

78

12 31

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19276

5

5

2

81

11 28

19277

6

5

2

6

3

4

19278

7

5

1

9

2

1

19279

8

5

1

9

2

1

ETHNICITY RELIGION CURR_EDUCATION EDUCATION MARITAL_STATUS
19272

4

3

7

6

2

19273

4

3

7

19

2

19274

4

3

7

19

2

19275

4

3

7

7

2

19276

4

3

7

3

2

19277

4

3

1

NA

NA

19278

4

3

NA

NA

NA

19279

4

3

NA

NA

NA

MAIN_ACTIVITY MAIN_OCCUPATION INDUSTRY EMPLOYMENT_STATUS
19272

1

3424

5211

5

19273

4

NA

NA

NA

19274

4

NA

NA

NA

19275

1

7411

601

3

19276

4

NA

NA

NA

19277

NA

NA

NA

NA

19278

NA

NA

NA

NA

19279

NA

NA

NA

NA

ID

PID

WT

19272 131000413703111 13100041370311101 28.45289
19273 131000413703111 13100041370311102 28.45289
19274 131000413703111 13100041370311103 28.45289
19275 131000413703111 13100041370311104 28.45289
19276 131000413703111 13100041370311105 28.45289
19277 131000413703111 13100041370311106 28.45289
19278 131000413703111 13100041370311107 28.45289
19279 131000413703111 13100041370311108 28.45289> R01[R01$ID=="532000201104111",]
DISTRICT PSU REC_TYPE SECTOR DSD MONTH SAMPLE_N SERIAL_NO NHH RESULT
58319

53

11

1

2

0

2

4

1

1

1

58320

53

11

1

2

0

2

4

1

1

1

58321

53

11

1

2

0

2

4

1

1

1

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PERSON_SERIAL_NO RELATIONSHIP SEX_LIVING BIRTH_YEAR BIRTH_MONTH AGE
58319

1

1

1

76

3 33

58320

2

2

2

81

3 28

58321

3

2

2

5

11

4

ETHNICITY RELIGION CURR_EDUCATION EDUCATION MARITAL_STATUS
58319

4

3

7

12

2

58320

4

3

7

8

2

58321

4

3

1

NA

NA

MAIN_ACTIVITY MAIN_OCCUPATION INDUSTRY EMPLOYMENT_STATUS
58319

1

9162

161

3

58320

4

NA

NA

NA

58321

NA

NA

NA

NA

ID

PID

WT

58319 532000201104111 53200020110411101 272.5964
58320 532000201104111 53200020110411102 272.5964
58321 532000201104111 53200020110411103 272.5964

There are two cases of multiple wives within the household.
The first case might be possible, because the religion of the household is Islam.
However, the second case is coding error. RELATIONSHIP=2 of PID=” 53200020110411103” should
be read as 3.
Ethnicities and religions of married couples

Most of married couples of heads and spouses are within the same ethnic groups.

Remarks:
According to the delegates from Sri Lanka for the International Workshop,“Mixed marriages are rare and rarely found in the HIES kind of sample.”> table(R01$RELATIONSHIP)> d1<-subset(R01,RELATIONSHIP==1)[c("ID","ETHNICITY","RELIGION","SEX_LIVING")]> d2<-subset(R01,RELATIONSHIP==2)[c("ID","ETHNICITY","RELIGION","SEX_LIVING")]> d<-merge(d1,d2,by="ID")> t<-table(d$ETHNICITY.x,d$ETHNICITY.y)> rownames(t)<-ethnic

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> colnames(t)<-ethnic
> t
Head

SpouseSinhala Sri Lanka Tamil Indian Tamil Sri Lanka Moors Malay Burgher Other

Sinhala

10000

25

13

12

1

6

10

Sri Lanka Tamil

49

1926

2

7

0

0

0

Indian Tamil

8

7

585

3

0

0

0

Sri Lanka Moors

20

3

0

1478

4

0

0

Malay

5

0

1

6

32

0

0

Burgher

4

0

0

0

0

9

0

Other

2

0

0

0

0

1

4

> t<-table(d$RELIGION.x,d$RELIGION.y)> rownames(t)<-religion> colnames(t)<-religion
> t
Head

SpouseBuddhism Hindu Islam Catholic Other

Buddhism

9069

20

9

112

1

Hindu

23 2115

5

28

0

Islam

8

1 153440Catholic

87

13

3

1189

0

Other

0

0

0

1

1

SummaryHIES allows to have more than one wife. However, such cases are rare in the data set.



Most of marriages are conducted within the same ethnic group.

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12.4 Family type

Family type based on the number of persons by generation

Data file for determining the family type (1)# Maximum household size is 17.
> hhsize<-tapply(R01$PID,R01$ID,length)> max(hhsize)[1] 17

Designed household-level data file consist of number of persons by relationship as well as dataof relationship, marital status, sex and age of each person.

Layout of record
ID

Number of persons by relationship

T

1

2

3

4

5

6

7

x

9

Relationship of each

Maritalpersoneach person

1

2

3

…

17

1

2

status3…

ofSex of each person

Age of each person

17

1

1

> df<-R01[,c(26:28,11:13,16,21)]> colnames(df)[4:8]<-c("no","relation","sex","age","marital")> head(df)ID

PID

WT no relation sex age marital

1 111000700101111 11100070010111101 41.42092 1

1

2

77

3

2 111000700101111 11100070010111102 41.42092 2

3

1

52

2

3 111000700101111 11100070010111103 41.42092 3

3

1

47

1

4 111000700101111 11100070010111104 41.42092 4

3

2

41

5

5 111000700101111 11100070010111105 41.42092 5

5

1

6

NA

6 111000700101111 11100070010111106 41.42092 6

5

2

2

NA



Created data frame ft: family type> ft<-data.frame(ID=names(hhsize),hhsz=hhsize,row.names=NULL)

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3

…

17

2

3

…

17

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# Number of persons by relationship> for(j in c(1:7,9)){+ d<-subset(df,relation==j)+ nj<-tapply(d$PID,d$ID,length)+ dj<-data.frame(ID=names(nj),nj)+ ft<-merge(ft,dj,by.x="ID",by.y="ID",all.x=T)+ colnames(ft)[ncol(ft)]<-paste("n",j,sep="")+ }> dim(ft)[1] 19958

10> head(ft)ID hhsz n1 n2 n3 n4 n5 n6 n7 n9
1 111000114801111

5

1 1

2

1

2 111000114802111

5

1 1

3 NA NA NA NA NA

3 111000114803111

8

1 1

1 NA 5 NA NA NA

4 111000114804111

3

1 NA

1 NA 1 NA NA NA

5 111000114805111

3

1 1

1 NA NA NA NA NA

6 111000114807111

3

1 1

1 NA NA NA NA NA> ft.old<-ft# Relationship, marital status, sex and age by person number> for(j in 1:17){+ d<-subset(df,no==j)[,c(1,5,8,6,7)]+ colnames(d)<-c("ID",paste(c("r","m","s","a"),j,sep=""))+ ft<-merge(ft,d,by.x="ID",by.y="ID",all.x=T)+ }> dim(ft)[1] 19958

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

"n2"

"n3"

"n4"

"n5"

"n6"

"n7"

"n9"

"r1"

"m1"

[13] "s1"

"a1"

"r2"

"m2"

"s2"

"a2"

"r3"

"m3"

"s3"

"a3"

"r4"

"m4"

[25] "s4"

"a4"

"r5"

"m5"

"s5"

"a5"

"r6"

"m6"

"s6"

"a6"

"r7"

"m7"

[37] "s7"

"a7"

"r8"

"m8"

"s8"

"a8"

"r9"

"m9"

"s9"

"a9"

"r10"

"m10"

[49] "s10""a10" "r11""m11""s11" "a11""a12" "r13""m13""r12" "m12" "s12"

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187[61] "s13""a13" "r14""m14""s14" "a14"[73] "s16""a16" "r17""m17""s17" "a17""r15" "m15" "s15""a15" "r16""m16"# Ordered the variables as designed> ft<-ft[,c(1:10,seq(11,75,by=4),seq(12,76,by=4),seq(13,77,by=4),seq(14,78,by=4))]> colnames(ft)[1] "ID""hhsz" "n1"

"n2"

"n3"

"n4"

"n5"

"n6"

"n7"

[13] "r3"

"r4"

"r5"

"r6"

"r7"

"r8"

"r9"

"r10" "r11""r12" "r13""r14"[25] "r15""r16" "r17"

"m1"

"m2"

"m3"

"m4"

"m5"

"m6"

"m7"

"m8"

"m9"

[37] "m10""m11" "m12""m13""m14" "m15""m16" "m17" "s1"

"s2"

"s3"

"s4"

[49] "s5"

"s6"

"s7"

"s8"

"s9"

"s10"

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

"a1"

"a2"

"a3"

"a4"

"a5"

"a6"

"a9"

"a11"

[73] "a12""a13" "a14""a15""a16" "a17"

"a7"

"n9"

"a8"

"r1"

"r2"

"a10"

> ft.old3<-ft> ft[is.na(ft)]<-0> head(ft)ID hhsz n1 n2 n3 n4 n5 n6 n7 n9 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14
1 111000114801111

5 1 1 2 1 0 0 0 0 1 2 3 3 4 0 0 0 0

0

0

0

0

0

2 111000114802111

5 1 1 3 0 0 0 0 0 1 2 3 3 3 0 0 0 0

0

0

0

0

0

3 111000114803111

8 1 1 1 0 5 0 0 0 1 2 3 5 5 5 5 5 0

0

0

0

0

0

4 111000114804111

3 1 0 1 0 1 0 0 0 1 3 5 0 0 0 0 0 0

0

0

0

0

0

5 111000114805111

3 1 1 1 0 0 0 0 0 1 2 3 0 0 0 0 0 0

0

0

0

0

0

6 111000114807111

3 1 1 1 0 0 0 0 0 1 2 3 0 0 0 0 0 0

0

0

0

0

0

r15 r16 r17 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 m12 m13 m14 m15 m16 m17 s1 s2 s3 s4 s5 s6 s7 s8
1

00

0 2 2 1 1 3 0 0 0 0

0

0

0

0

0

0

0

0 1 2 2 2 1 0 0 0

2

0

0

0 2 2 1 1 0 0 0 0 0

0

0

0

0

0

0

0

0 1 2 1 2 1 0 0 0

3

0

0

0 2 2 2 2 0 0 0 0 0

0

0

0

0

0

0

0

0 1 2 1 2 2 1 1 2

4

0

0

0 3 2 2 0 0 0 0 0 0

0

0

0

0

0

0

0

0 2 1 2 0 0 0 0 0

5

0

0

0 2 2 2 0 0 0 0 0 0

0

0

0

0

0

0

0

0 1 2 1 0 0 0 0 0

6

0

0

0 2 2 0 0 0 0 0 0 0

0

0

0

0

0

0

0

0 1 2 1 0 0 0 0 0s9 s10 s11 s12 s13 s14 s15 s16 s17 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17
1 0

0

0

0

0

0

0

0

0 44 41 21 16 67 0 0 0 0

0

0

0

0

0

0

0

0

2 0

0

0

0

0

0

0

0

0 36 31 15 12 7 0 0 0 0

0

0

0

0

0

0

0

0

3 0

0

0

0

0

0

0

0

0 65 63 36 36 9 6 6 6 0

0

0

0

0

0

0

0

0

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4 0

0

0

0

0

0

0

0

0 42 18 17 0 0 0 0 0 0

0

0

0

0

0

0

0

0

5 0

0

0

0

0

0

0

0

0 63 62 28 0 0 0 0 0 0

0

0

0

0

0

0

0

0

6 0

0

0

0

0

0

0

0

0 29 25 4 0 0 0 0 0 0

0

0

0

0

0

0

0

0

> ft.old4<-ft# Confirmed that all household heads have person number=1.
> table(ft$r1)
1
19958# Number of sample households consisted of a head’s couple only
> nrow(subset(ft,hhsz==2&n2==1))[1] 1386> 1386/19958[1] 0.06944584# Number of sample household consisted of a head’s couple and their children> nrow(subset(ft,n2==1&n3>=1&hhsz==(n1+n2+n3)))[1] 8663> 8663/19958[1] 0.4340615# Number of sample household consisted of a father and his children> nrow(subset(ft,s1==1&n2==0&n3>=1&hhsz==(n1+n2+n3)))[1] 380# Number of sample household consisted of a mother and her children> nrow(subset(ft,s1==2&n2==0&n3>=1&hhsz==(n1+n2+n3)))[1] 1720



Summary:
The share of nuclear family household in the sample household is 61%.

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

Generated variables g1 to g17 for household members. The values of g1 to g17 are as follows;

Value

Description

Definition

1

Grandparents’ generation

relation=5 & age.diff>=35

2

Parents’ generation

relation=4

3

Head’s generation

relation=1 or 2, relation=5 & abs(age.diff)<=15

4Children’s generation

relation=3, relation=5 & -14=<age.diff<=-34

5Grandchildren’s generation

relation=5 & age.diff<=-35

6

Other relative

relation=5 but not classified in the above

7

Other

relation=6, 7 or 9

where,
age.diff = age – head’s age> for(j in 1:17){+ gen<-rep(0,nrow(ft))+ relation<-ft[,j+10]+ age.diff<-ft[,j+61]-ft[,62]+ gen<-ifelse(relation==1|relation==2,3,gen)+ gen<-ifelse(relation==3,4,gen)+ gen<-ifelse(relation==4,2,gen)+ gen<-ifelse(relation==5,6,gen)+ gen<-ifelse(relation==5&age.diff>=35,1,gen)+ gen<-ifelse(relation==5&abs(age.diff)<=15,3,gen)+ gen<-ifelse(relation==5&age.diff<=-14&age.diff>=-34,4,gen)+ gen<-ifelse(relation==5&age.diff<=-35,5,gen)+ gen<-ifelse(relation>=6,7,gen)+ ft["gen"]<-gen+ colnames(ft)[ncol(ft)]<-paste("g",j,sep="")+ }> head(ft[,c(1:18,62:69,79:86)])
ID hhsz n1 n2 n3 n4 n5 n6 n7 n9 r1 r2 r3 r4 r5 r6 r7 r8 a1 a2 a3 a4 a5
1 111000114801111

5

1 1

2

1 0

0 0

0 1

2 3

3

4 0

0 0 44 41 21 16 67

2 111000114802111

5

1 1

3

0 0

0 0

0 1

2 3

3

3 0

0 0 36 31 15 127

3 111000114803111

8

1 1

1

0 5

0 0

0 1

2 3

5

5 5

5 5 65 63 36 36

9

4 111000114804111

3

1 0

1

0 1

0 0

0 1

3 5

0

0 0

0 0 42 18 17 00

5 111000114805111

3

1 1

1

0 0

0 0

0 1

2 3

0

0 0

0 0 63 62 28 00

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6 111000114807111

3

1 1

1

0 0

0 0

0 1

2 3

0

0 0

0 0 29 25

a6 a7 a8 g1 g2 g3 g4 g5 g6 g7 g8
1

0

0 0

3 3

4 4

2 0

0

0

2

0

0 0

3 3

4 4

4 0

0

0

3

6

6 6

3 3

4 4

5 5

5

5

4

0

0 0

3 4

4 0

0 0

0

0

5

0

0 0

3 3

4 0

0 0

0

0

6

0

0 0

3 3

4 0

0 0

0

0

> ft.old5<-ft

Grouping sample households based on the number of persons by generation# Defined variable pi, the number of persons in generation i> for(j in 1:7){+ ft["pi"]<-rowSums(ft[,79:95]==j)+ colnames(ft)[ncol(ft)]<-paste("p",j,sep="")+ }> head(ft[,c(1,79:87,96:102)])
ID g1 g2 g3 g4 g5 g6 g7 g8 g9 p1 p2 p3 p4 p5
1 111000114801111 3

3 4

4

2 0

0 0

0 0

1 2

2

0

2 111000114802111 3

3 4

4

4 0

0 0

0 0

0 2

3

0

3 111000114803111 3

3 4

4

5 5

5 5

0 0

0 2

2

4

4 111000114804111 3

4 4

0

0 0

0 0

0 0

0 1

2

0

5 111000114805111 3

3 4

0

0 0

0 0

0 0 0 2

1

0

6 111000114807111 3

3 4

0

0 0

0 0

0 0

1

0

0 2

# Number of persons by generation> colSums(ft[,96:102])
p1

p2

p3

p4

p5

p6

p7

169 1555 35606 35505 7052

628

357

> ft.old6<-ft

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0

191

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

Codegeneration

1

2

3

4

5

Number ofPercentage

households
All households

19,958

100.0

Total

2,593

12.9

Single

11

902

4.5

Head and spouse only

12

1,386

6.9

Head, spouse and others

13

61

0.3

Other

14

244

1.2

12,472

62.5

Total
Head – Children

21

12,271

61.5

Parents - Head

22

201

1.0

4,671

23.3

Total
Head – Children - Grandchildren

31

3,181

15.9

Head – Grandchildren

32

400

2.0

Parents – Head - Children

33

1,067

5.3

Grandparents – Parents - Head

34

5

0.0

Grandparents - Head

35

18

0.1

218

1.1

Total
Parents – Head – Children - Grandchildren

41

83

0.4

Parents – Head – Grandchildren

42

3

0.0

Grandparents – Parents – Head – Children

43

15

0.1

Grandparents – Head - Children

44

117

0.6

Grandparents – (Parents) – Head – (Children )-

50

4

0.0

Grandchildren

Summary:
The household consisted of two generations occupies 63%, followed by 23% of threegeneration household and 13% of one-generation household.

# Grouped households using p1 to p5, regardless of the value of p6 and p7.
> h<-rep(0,nrow(ft))> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4==0&ft$p5==0,14,h)

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> h<-ifelse(ft$hhsz==1,11,h)> h<-ifelse(ft$hhsz==2&ft$n2==1,12,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>=3&ft$p4==0&ft$p5==0&ft$n2==1,13,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4>0&ft$p5==0,21,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4==0&ft$p5==0,22,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4>0&ft$p5>0,31,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4==0&ft$p5>0,32,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4>0&ft$p5==0,33,h)> h<-ifelse(ft$p1>0&ft$p2>0&ft$p3>0&ft$p4==0&ft$p5==0,34,h)> h<-ifelse(ft$p1>0&ft$p2==0&ft$p3>0&ft$p4==0&ft$p5==0,35,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4>0&ft$p5>0,41,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4==0&ft$p5>0,42,h)> h<-ifelse(ft$p1>0&ft$p2>0&ft$p3>0&ft$p4>0&ft$p5==0,43,h)> h<-ifelse(ft$p1>0&ft$p2==0&ft$p3>0&ft$p4>0&ft$p5==0,44,h)> h<-ifelse(ft$p1>0&ft$p3>0&ft$p5>0,50,h)> t<-addmargins(table(h))
> t
h
11

12

13

14

21

22

31

32

33

34

35

41

42

43

902 138661

244 12271

201

3181

400

1067

5

18

83

3

15

44

50

Sum

117

4 19958> round(t/t[length(t)]*100,1)
h
11

12

13

14

21

22

31

32

33

34

35

41

42

43

4.5

6.9

0.3

1.2 61.5

1.0

15.9

2.0

5.3

0.0

0.1

0.4

0.0

0.1

44

50

Sum

0.6

0.0 100.0# Family type by sector> df<-merge(R23[,c("ID","DISTRICT","SECTOR","WT")],ft[,c("ID","hhsz","type")])> dim(df)[1] 19958

6> head(df)ID DISTRICT SECTOR

WT hhsz type

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1 111000114801111

11

1 30.4971

533

2 111000114802111

11

1 30.4971

521

3 111000114803111

11

1 30.4971

831

4 111000114804111

11

1 30.4971

321

5 111000114805111

11

1 30.4971

321

6 111000114807111

11

1 30.4971

3

21> m<-matrix(as.vector(tapply(df$WT,list(df$SECTOR,df$type),sum,na.rm=T)),+ nrow=3)> rownames(m)<-c("Urban","Rural","Estate")> colnames(m)<-c(11,12,13,14,21,22,31,32,33,34,35,41,42,43,44,50)# Weighted number of households by family type and sector> round(m)
11
Urban

12

13

14

21

26628 49928 1636 13869

4329822231

327427 118456 13692

33 34

35

41

42

37747 517 1014

2105

NA

Rural 189488 292136 8374 39872 2584633 40457 622561 81081 217751 534 2592 16166 695
Estate10539 19187
43

Urban

614 2197

1322673264

50545 6763

16066 274 102

44 50

456 4711 NA

Rural 2594 23180 543
Estate798

794 119> m[is.na(m)]<-0> round(prop.table(m,1)*100,1)
11

12

13

14

21

22

31 32 33 34 35 41 42 43 44 50

Urban 3.7 7.0 0.2 2.0 60.9 1.0 16.7 1.9 5.3 0.1 0.1 0.3

0 0.1 0.7 0

Rural 4.6 7.1 0.2 1.0 62.7 1.0 15.1 2.0 5.3 0.0 0.1 0.4

0 0.1 0.6 0

Estate 4.3 7.8 0.2 0.9 53.9 1.3 20.6 2.8 6.5 0.1 0.0 0.8

0 0.3 0.3 0# Family type by ethnicity of household head> df<-merge(df,subset(R01,RELATIONSHIP==1)[,c("ID","ETHNICITY","RELIGION")])> dim(df)[1] 19958

8> head(df)ID DISTRICT SECTOR
1 111000114801111

11

WT hhsz type ETHNICITY RELIGION

1 30.4971

5

33

1

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2012

NA

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2 111000114802111

11

1 30.4971

5

21

1

1

3 111000114803111

11

1 30.4971

8

31

1

4

4 111000114804111

11

1 30.4971

3

21

1

1

5 111000114805111

11

1 30.4971

3

21

1

4

6 111000114807111

11

1 30.4971

3

21

1

1

> m<-matrix(as.vector(tapply(df$WT,list(df$ETHNICITY,df$type),sum,na.rm=T)),+ nrow=7)> colnames(m)<-c(11,12,13,14,21,22,31,32,33,34,35,41,42,43,44,50)> rownames(m)<-c("Sinhala","Sri Lanka Tamil","Indian Tamil","Sri Lanka Moors",+ "Malay","Burgher","Other")# Weighted number of households by family type and ethnicity> round(m)11Sinhala

12

13

14

21

22

31

32

33

34

35

186422 291899 8436 46250 2423201 38790 620660 80082 214380 631 3195

Sri Lanka Tamil 20690

31691 1311 6180 320502 3976

64706 10323 24098 421

79Indian Tamil

6059

14306

319 1342

38935 4442 13396 274

161

Sri Lanka Moors 12171

21442

557 2127 298060 4645

65176 6629 19162

NA

272

Malay

516

1868

NA

39

4817

39

636

60

52

NA

NA

Burgher

798

45

NA

NA

1906

317

668

NA

476

NA

NA

Other

NA

NA

NA

NA

3861

NA

779

NA

NA

NA

NA

41 42

43

Sinhala97528 3382

44 50

14969 695 1793 19664 543

Sri Lanka Tamil 1727 NA 979 6195 NA
Indian Tamil

2508 NA 730 1567 119

Sri Lanka Moors 1067 NA 347 1187 NA
Malay

13 NA

NA

NA NA

Burgher

NA NA

NA

73 NA

Other

NA NA

NA

NA NA

> m[is.na(m)]<-0> round(prop.table(m,1)*100,1)
11

12 13

4.7

7.4 0.2 1.2 61.3 1.0 15.7 2.0

5.4 0.0 0.1 0.4

0 0.0 0.5 0.0

Sri Lanka Tamil 4.2

6.4 0.3 1.3 65.0 0.8 13.1 2.1

4.9 0.1 0.0 0.4

0 0.2 1.3 0.0

Indian Tamil

3.3

7.7 0.2 0.7 52.7 1.8 21.0 2.4

7.2 0.1 0.1 1.4

0 0.4 0.8 0.1

Sri Lanka Moors 2.8

5.0 0.1 0.5 68.9 1.1 15.1 1.5

4.4 0.0 0.1 0.2

0 0.1 0.3 0.0

Sinhala

14

21 22

31

32

33

34

35

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Malay

6.4 23.2 0.0 0.5 59.9 0.5 7.9 0.7

0.6 0.0 0.0 0.2

0 0.0 0.0 0.0

Burgher

18.6

1.1 0.0 0.0 44.5 7.4 15.6 0.0 11.1 0.0 0.0 0.0

0 0.0 1.7 0.0

Other

0.0

0.0 0.0 0.0 83.2 0.0 16.8 0.0

0 0.0 0.0 0.0

0.0 0.0 0.0 0.0# Average household size by sector and by ethnicity of household head> t<-as.vector(by(df,df$SECTOR,function(df) sum(df$hhsz*df$WT)/sum(df$WT)))> names(t)<-c("Urban","Rural","Estate")> round(t,2)
Urban

Rural Estate

4.25

3.95

4.18

> t<-as.vector(by(df,df$ETHNICITY,function(df) sum(df$hhsz*df$WT)/sum(df$WT)))> names(t)<- c("Sinhala","Sri Lanka Tamil","Indian Tamil","Sri Lanka Moors",+ "Malay","Burgher","Other")> round(t,2)
Sinhala Sri Lanka Tamil
3.87

4.29

Burgher

Other

3.74

4.05

Indian Tamil Sri Lanka Moors
4.26

4.78

Malay
3.90

Remarks:
According to the delegates from Sri Lanka for the International Workshop,“Sinhala HIES households are mostly nuclear families and others are not.”

Summary:
Average household size of Sinhala is smaller than that of Sri Lanka Tamil and Sri Lanka Moors.
Consequently, average household size is smaller in rural than in urban.

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Married couple in parents’ generation
The objective is to identify the married couple of a father and a mother within the household.
Generated the next variables in data frame ft;
npm: number of male persons with relation=4 (parents)
npf: number of female persons with relation=4
npmm: number of male persons with relation=4 and marital=2
npmf: number of female persons with relation=4 and marital=2
npc: number of married couples of parents> ft["npm"]<-0> ft["npf"]<-0> ft["npmm"]<-0> ft["npmf"]<-0> ft["npc"]<-0> for(j in 1:17){+ ft["npm"]<-ifelse(ft[,j+10]==4&ft[,j+44]==1,ft$npm+1,ft$npm)+ ft["npf"]<-ifelse(ft[,j+10]==4&ft[,j+44]==2,ft$npf+1,ft$npf)+ ft["npmm"]<-ifelse(ft[,j+10]==4&ft[,j+27]==2&ft[,j+44]==1,ft$npmm+1,ft$npmm)+ ft["npmf"]<-ifelse(ft[,j+10]==4&ft[,j+27]==2&ft[,j+44]==2,ft$npmf+1,ft$npmf)+ }The number of fathers is 385 and the number of mothers is 1170. Of which, the number ofmarried fathers is 246 and the number of married mothers is 318.
There is no household with two fathers or two mothers.

> table(ft$npm)
0

1

19573

385

> table(ft$npf)
0

118788 1170
> table(ft$npmm)
0

1

19712

246

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> table(ft$npmf)
0

1

19640

318



If there are a married father and a married mother in the household, they are estimated as amarried couple.

# Number of parents couple in the household> ft["npc"]<-ifelse(ft$npmm==1&ft$npmf==1,1,0)> table(ft$npc)
0

1

19777

181

Summary: Parents
Sex

Number of parents

Of which, married

Number of married couple
within the household

Total

1,555

564

Male

385

246

Female

1,170

318

181

Married couple in children’ generation
The objective is to identify the married couple of children within the household.
The relationship code of “spouses of children” is assumed to be 5 “other relative” in this analysis.
Generated the next variables in data frame ft;
ncm: number of male persons with relation=3
ncf: number of female persons with relation=3
ncmm: number of male persons with relation=3 and marital=2
ncmf: number of female persons with relation=3 and marital=2
ncum: number of male persons with relation=3 and marital<=1
ncuf: number of female persons with relation=3 and marital<=1n45mm: number of male person with generation=4 and marital=2 and relation=5

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n45mf: number of female person with generation=4 and marital=2 and relation=5
ncc: number of married couples of children> ft["ncm"]<-0> ft["ncf"]<-0> ft["ncmm"]<-0> ft["ncmf"]<-0> ft["ncum"]<-0> ft["ncuf"]<-0> ft["n45mm"]<-0> ft["n45mf"]<-0> ft["ncc"]<-0> for(j in 1:17){+ ft["ncm"]<-ifelse(ft[,j+10]==3&ft[,j+44]==1,ft$ncm+1,ft$ncm)+ ft["ncf"]<-ifelse(ft[,j+10]==3&ft[,j+44]==2,ft$ncf+1,ft$ncf)+ ft["ncmm"]<-ifelse(ft[,j+10]==3&ft[,j+27]==2&ft[,j+44]==1,ft$ncmm+1,ft$ncmm)+ ft["ncmf"]<-ifelse(ft[,j+10]==3&ft[,j+27]==2&ft[,j+44]==2,ft$ncmf+1,ft$ncmf)+ ft["ncum"]<-ifelse(ft[,j+10]==3&ft[,j+27]<=1&ft[,j+44]==1,ft$ncum+1,ft$ncum)+ ft["ncuf"]<-ifelse(ft[,j+10]==3&ft[,j+27]<=1&ft[,j+44]==2,ft$ncuf+1,ft$ncuf)+ ft["n45mm"]<-ifelse(ft[,j+10]==5&ft[,j+27]==2&ft[,j+44]==1&ft[,j+78]==4,ft$n45mm+1,ft$n45mm)+ ft["n45mf"]<-ifelse(ft[,j+10]==5&ft[,j+27]==2&ft[,j+44]==2&ft[,j+78]==4,ft$n45mf+1,ft$n45mf)+ }> sum(ft$ncm)[1] 17005> sum(ft$ncf)[1] 15924> sum(ft$ncum)[1] 14994> sum(ft$ncmm)[1] 1925> sum(ft$ncuf)[1] 13876> sum(ft$ncmf)[1] 1746> sum(ft$n45mm)[1] 899

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> sum(ft$n45mf)[1] 996

Defined function child.couple.

# Arguments: ncmm, ncmf, n45mm, n45mf as same as the above# Return value: number of married couples of children within the household> child.couple<-function(ncmm,ncmf,n45mm,n45mf){+ n<-0+ while(ncmm>0&n45mf>0){+ n<-n+1+ ncmm<-ncmm-1+ n45mf<-n45mf-1+ }+ while(ncmf>0&n45mm>0){+ n<-n+1+ ncmf<-ncmf-1+ n45mm<-n45mm-1+ }+ return(n)+ }Applied function child.couple for all households.

> for(j in 1:nrow(ft)){+ ft[j,"ncc"]<-child.couple(ft[j,"ncmm"],ft[j,"ncmf"],ft[j,"n45mm"],ft[j,"n45mf"])+ }# Number of married couples of children> sum(ft$ncc)[1] 1566# Frequency of number of children’s married couples within the household> table(ft$ncc)
0

1

2

3

18466 1421

68

3> ft.old7<-ft

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Summary: Children
Sex

Number of

Of which,

Of which,

generation=4 andNumber of married

childrenunmarriedmarriedmarital=2 andcouple within therelation=5

household

Total

1,566

Male

17,005

14,994

1,925

899

Female15,924

13,876

1,746

996

Characteristics of household headsSex and marital status of household heads> d<-subset(R01,RELATIONSHIP==1)> t<-table(d$SEX_LIVING,d$MARITAL_STATUS)> rownames(t)<-c("Male","Female")> colnames(t)<-c("Never","Married","Widowed","Divorced","Separated")> t
Never Married Widowed Divorced Separated
Male

249

14093

648

27

140

Female

196

1560

2737

48

260

> round(prop.table(t,1)*100,1)
Never Married Widowed Divorced Separated
Male

1.6

93.0

4.3

0.2

0.9

Female

4.1

32.5

57.0

1.0

5.4



Age and marital status of female household heads> d2<-subset(d,SEX_LIVING==2)> age10<-c(0,20,30,40,50,60,70,99)> d2["age.gr"]<-cut(d2$AGE,breaks=age10,right=F,include.lowest=T,+ labels=c("0-19","20-29","30-39","40-49","50-59","60-69","70-99"))> t2<-table(d2$age.gr,d2$MARITAL_STATUS)> colnames(t2)<- c("Never","Married","Widowed","Divorced","Separated")> t2

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Never Married Widowed Divorced Separated
0-19

9

5

0

0

1

20-29

15

199

7

3

7

30-39

11

524

84

13

47

40-49

28

481

348

12

81

50-59

54

241

712

14

79

60-69

58

84

800

5

37

70-99

21

25

785

1

8

> round(prop.table(t2,1)*100,1)
Never Married Widowed Divorced Separated
0-19

60.0

33.3

0.0

0.0

6.7

20-29

6.5

86.1

3.0

1.3

3.0

30-39

1.6

77.2

12.4

1.9

6.9

40-49

2.9

50.6

36.6

1.3

8.5

50-59

4.9

21.9

64.7

1.3

7.2

60-69

5.9

8.5

81.3

0.5

3.8

70-99

2.5

3.0

93.5

0.1

1.0

Household consisted of a mother and her unmarried children only
> d<-subset(ft,s1==2&hhsz>=2&hhsz==(1+ncum+ncuf))> nrow(d)[1] 1577

Household consisted of a father and his unmarried children only
> d<-subset(ft,s1==1&hhsz>=2&hhsz==(1+ncum+ncuf))> nrow(d)[1] 350

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13. References[1] “Household Income and Expenditure Survey – 2009/10 Final Report”, DCS, Sri Lanka, August
2011[2] “Household Income and Expenditure Survey – 2009/10 Preliminary Report”, DCS, Sri Lanka,
January 2011[3] Ms. Dilhanie Deepawansa, DCS, “Determinants of the Poverty in Western Province in Sri Lanka”,
Research - based Training Program, AY 2010, UNSIAPSri Lanka HIES 2009/10 Manual (Version 1.1)

203(For office use)
Month

CONFIDENTIAL
The information collected in this survey will bestrictly confidential according to the survey
ordinance & individual level information will
not be divulged to any person or agency.

SectorDistrict

PSU Number
SSU Number &Household Number

HOUSEHOLD INCOME AND EXPENDITURE SURVEY - 2009/10-

NATIONAL HOUSEHOLD SAMPLE SURVEY PROGRAMMESURVEY SCHEDULE
Department of Census & Statistics
Ministry of Finance and Planning
Sri Lanka
Sri Lanka HIES 2009/10 Manual (Version 1.1)

DS
Division

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1

IDENTIFICATION INFORMATIONS
1. Address (Location) :-................................................................

10

SurveyMonth............................................................................................

SectorDistrict

DS Division

2. Province :- .......................................................................................
3. District :- ...........................................................................................
4. DS Division :- ..................................................................................

11
Household
SSU
Number Number

PSU
Number

5. Name of MC / UC...........................................................................
(If urban sector only )Ward No :- ........................................................................................
6. GN Division : Number :- ................ Name :- .................................
7. Name of Village :- ...........................................................................

12

Number of Households in this unit

(If rural sector only )8. Name of Estate :- ..............................................................................
(If estate sector only )*13

Result14

Name of the Head of the Household :- .................................

9. C.B.No. :- ..........................................................................................
* * Result Code.............................................................................

Completed ................................................... 1
Deferred ...................................................... 2
Not competent respondant at home ........... 3
Refused ........................................................ 4
Household is temporarily closed ................. 5

15Interviewer's
Name :- .................................................................................

16

Supervising Officer'sName :- .................................................................................
Signature :- ........................................ Date :- .......................

Household is demolished / Vacant ............. 6* For Office Use Only

Other (Specify) ............................................ 9

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2

Codes for Section 1
Col. 3 - Relationship to head of
the household

Col. 9 -Attendance at School or Other
Educational Institution

Head of the household.............................1
Wife/ Husband .......................................2
Son/ Daughter ........................................3
Parents ....................................................4
Other Relative ........................................5
Domestic Servants .................................6
Boarder ..................................................7
Other ......................................................9

Pre school ...........................................1
School .................................................2
University ............................................3
Other educational institution ...............4
Vocational/ Technical institution ........5
Pending results G.C.E. (O.L/A.L) .........6
Does not attend ...................................7

Col. 4 - Sex
Male .......................................1
Female ....................................2

Col. 7 - Ethnicity
Sinhala................................1
Sri LankaTamil..................2
Indian Tamil.......................3
Sri Lanka Moors ................4
Malay .................................5
Burgher ..............................6
Other ..................................9Col. 8 - Religion
Buddhist ...............................1
Hindu ...................................2
Islam ....................................3
Roman Catholic/
Other Christian ..................4
Other .....................................9Col. 10 -Level of Education
Studing in Grade 1................................... 00
Passed Grade 1..........................................01
Passed Grade 2..........................................02
Passed Grade 3..........................................03
Passed Grade 4..........................................04
Passed Grade 5..........................................05
Passed Grade 6..........................................06
Passed Grade 7..........................................07
Passed Grade 8...........................................08
Passed Grade 9..........................................09
Passed Grade 10........................................10
Passed G.C.E. (O/L) or
equivalent ..................................................11
Passed Grade 12........................................12
Passed G.C.E.(A/L) .or
equivalent ..................................................13
Passed GAQ/GSQ .....................................14
Passed Degree ...........................................15
Passed Post Graduate Degree / Diploma....16
Special Education unit ...............................17
No Schooling .............................................19

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Col. 11 - Marital Status
Never Married ........................1
Married ...................................2
Widowed ................................3
Divorced .................................4
Separated ................................5

Col. 12 -Current Activity
Employed.....................................1
Unemployed.................................2
Student ........................................3Household work...........................4
Unable / Too old to work.............5
Other ...........................................9Col. 15 - Employment status
Government employee.............................1
Semi government employee ..................2
Private sector employee ........................3
Employer ................................................4
Own account worker ..............................5
Unpaid family worker...........................6

206

3

Section 1 - Demographic Characteristics
For Persons 10 years and overFor employed persons only
( Column 12 code = 1)

Date of Birth
Name of all persons who usually
live in this household

1

2

3

4

5

6

8

7

9

10

11

12

13

14

15

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
Name the persons who are member of thishousehold and usually live elsewhere in the
country or abroad

Usual residence
In the country - 1
Abroad-2

SerialNumber

*

41
42
43
44

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Main Occupation(Describe)**

Main Industry(Describe)

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4
For Column 4 and 5

Codes for Section 2
Column 9

Grade 1........................... 01
Grade 2.............................02

School too far away......................................................................1

Grade 3.............................03

Financial problems ....................................................... ...............2

Grade 4.............................04
Grade5..............................05
Grade 6.............................06
Grade 7.............................07

Had to help house keeping activities /Family business .............................................................................3
Disability / Illness .........................................................................4

Grade 8.............................08

Civil disturbance ..........................................................................5

Grade 9.............................09

Not willing to attend / Poor academic progress ............................6

Grade 10...........................10

Incompletion of 5 years at the begining of the school year..........7

Grade 11...........................11

Other (specify) .............................................................................9

Grade 12...........................12

Column 10

Grade 13...........................13
Special Education Unit....14
Not relevant ...................19

Column 7
Walk ............................................................. ..1
Bicycle ...........................................................2
Motor bicycle / Three-wheeler/Car ................3
School hiring Van/ Bus ..................................4
By Bus ............................................................5
By Train ..........................................................6
Other (Specify) ...............................................9

Further schooling not available or too far away.............................1
Finalcial Problems........................................................................2
Had to help house keeping activities / Family business.................3
Disability / Illness...........................................................................4
Civil disturbance ............................................................................5
Not willing to attend / Poor academic progress..............................6
Pending results ( G.C.E. (O/L) / G.C.E. ( A/L ) ) ..............................7
Completed G.C.E. ( A/L ) / Grade 13 ..........................................8
Other (Specify) ...........................................................................9

Sri Lanka HIES 2009/10 Manual (Version 1.1)

208

Section 2 . School Education ( for persons aged 5-19 years)
persons =code 2in col 3

For Persons = code 1 in col 3

5
For persons =code 3 in col 3

Name of persons (age 5-19 years)
who usually live in this household(as given in col.2 of sec.1 )

1

2

3

4

5

6

7

8

9

10

11

1
2
3
4
5
6
7
8
9
10
11
12

*

à . go to col. 4
Currently attending school..1..............

à
Never attended school ....2....................goto col. 9

Sri Lanka HIES 2009/10 Manual (Version 1.1)

àAttended school in the past... 3..................goto col. 10

209

6

Codes for Section 3

Column 8

Column 4
Treatment for illness......................................1

Heart Conditions /Diseases .............................01

Treatment for injury......................................2

Blood pressure ................................................02

Medical checkup/ Consultation ...................3

Diabetics ...........................................................03

Immunization ..............................................4

Asthma .............................................................04

Treatment for infectious diseases(Injections ect)............................................5

Epilepsy ............................................................05
Cancer .............................................................06

Other (specify) ..............................................9

Stomach diseases / Gastritis...............................07
Diseases related to Eyes ..................................08

Column 6

Arthritis .............................................................09

Treatment for illness ....................................1

Mental retardation............................................10

Treatment for injury .....................................2

Haemorrhoids....................................................11

Operation / Surgey........................................3

Catarrh...............................................................12Child delivery..............................................4

Severe headache.................................................13

Treatment for infectious diseases..................5

Naturally Disabled ............................................14

An accident...................................................6

Disabled by an accident ..................................15

Other (specify) .............................................9

Other ....................................................... .........19

Sri Lanka HIES 2009/10 Manual (Version 1.1)

210

7

Section 3 -Health
How long
have youbeen
sufferingfrom yourillness/
disability

Name of all persons who usually livein this household(as given in col. 2 of sec.1)

1

2

3

4

5

6

7

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Sri Lanka HIES 2009/10 Manual (Version 1.1)

8

9

10

11

12

211

Section 4 -Expenditure

8
4.1 Weekly Consumptions on Food & Drink
Consumed
Item
1
1.

Cereals

1.

Rice - (Kekulu)

2.

Rice - (Samba)

3.

Rice - (Nadu)

Code

unit

Qty.

2

3

4

Items

Consumed
Code

Unit

Qty.

6

1

2

3

4

01
8. Rotti
9. Thosa /Itly
10. Rice (meat & vegetables)
11. Rice (fish/ egg & vegetables)
12. Rice (vegetables only)
13. Curry (meat)
14. Curry (fish)
15. Curry (vegetables)
16. Other

0101 grams
0102 grams
0103 grams
0104 grams
0105 grams
0106 grams
0107 grams

4. Rice - (Other)
5. Rice flour
6. Wheat flour
7. Kurakkan flour
8. Ulundu flour
9.
Maize
10. Samaposha
11. Noodles
12. Papadam
13. Infant Cerial food/Malt etc.

0113

grams

14.

Barley

0114

15.

Sago

grams
grams

16.

Corn flakes

0115
0116

17.

Other (specify)

0119

2. Prepared foods

Value
(Rs.)
5

Home grown/
Freely received(Rs.)*

0108 grams
0109 grams
0110 grams
0111 grams
0112 grams02

3. Pulses
1.
2.
3.
4.
5.
6.
7.

grams
grams

4.

1. Ash plantain
2. Brinjal
0201 grams
3. Bandakka
0202
2. Bread (Special)
4. Bitter gourd
0203 number
3. Rospaan
5. Long beans
0204 number
4. Buns / Spanchi
6. Snake gourd
5. Hoppers
0205 number
7. Ridge gourd
6. String hoppers
0206 number
8. Sweet pumpkin
7. Pittu
0207 number* The estimated value cf items which are consumed from home grown / freely received should be included in col.6
1.

03

Dhal
Green gram
Gram
CowpeaSoya
Soyameat
Other

Vegetables

Bread (Normal)

Sri Lanka HIES 2009/10 Manual (Version 1.1)0208 number
0209 number
0210 number
0211 number
0212 number
0213
0214
0215
0219

0301
0302
0303
0304
0305
0306
0309

grams
grams
grams
grams
grams
grams

0401
0402
0403
0404
0405

grams
grams
grams
grams

grams

04

0406
0407
0408

grams
grams
grams
grams

Value
(Rs.)
5

Home grown/
Freely received
(Rs.)
*
6

212

9
Item

9. Beans
10. Carrot
11. Beetroot
12. Cabbage
13. Tomatoes
14. Leeks
15. Knolkhol
16. Capsicum
17. Dambala
18. Raddish
19. Drumstick
20. Cucumber
21. Kekiri
22. Ash pumpkin
23. Elabatu
24. Kohila yams
25. Lotus stemo
26. Plantain flower
27. Ambarella
28. Cadjunuts (raw)
29. Mushrooms
30. Jack immatured(Polos)
31. Other

Code

Unit

grams
grams
grams
grams
grams
0414 grams
0415 grams
0416 grams
0417 grams
0418 grams
0419 grams
0409
0410
0411
0412
0413

0420 grams
0421 grams
0422 grams0423 grams0424 grams
0425 grams
0426 grams
0427 grams0428 grams0429 grams
0430
0439

3. Kankun

Consumed

Home grown/
Freelyreceived *
(Rs.)

Item

4. Katurumurunga
5. Nivithi
6. Thampala
7. Sarana
8. Kohila leaves
9. Onion leaves
10. Cabbage leaves
11. Other leaves

5. Yams and Other
1.

0441 bundles
0442 bundles
0443 bundles

Code

Unit

0444
0445
0446
0447
0448
0449
0450
0459

bundlesgrams
bundlesbundlesbundles
grams
grams

05

Jak / Jak seeds

0501
0502 number

2.

Bread fruit

3.

Potatoes

4.
5.

Sweet potatos
Mannioc

6.

Kiriala0503 grams
0504 grams
0505 grams
0506 grams

7.

Innala

0507

grams

8.

Other yams

0509

grams

0601
0602

grams

6. Meat
1.

06

Chicken

2. Beef
3. Mutton

Leafy vegetables
1. Mukunuwenna
2. Gotukola

Consumed
Value
Qty.
(Rs.)

4.

Pork

5.

Sausages/ Meat balls

6.

Other

* The estimated Value of items which are consumerd from home grown / freely received should be included in col.6

Sri Lanka HIES 2009/10 Manual (Version 1.1)

grams
grams
0603
0604 grams
0605 grams
grams

0609

Qty.

Value
(Rs.)

Homegrown/
Freelyreceived *
(Rs.)

213

10
Consumed
Code Unit

Item

7. Fish
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.

07

Balaya
Seer
Mora
Paraw
Talapath
Kelawalla
MulletOther large fish
SpratsHurullaKaralla / Katuwalla
Kumbalawa / Angila
Salaya / Sudaya
Other small fish
Lula
Teppili/Telapiya/Korali
Other fresh water fish
Prawns
Crabs
Cuttle fish
Canned fish (salmon)

8. Dried fish08

0701 grams
0702 grams
0703 grams
grams
0704
0705 grams
0706 grams
0707 grams
0708 grams
0709 grams
0710 grams
0711 grams
0712 grams
0713 grams
0714 grams
0715 grams
0716 grams
0717 grams
0718 grams
0719 grams
0720 grams
0720 grams

Qty.

Value
(Rs.)

Home grown/ Freelyreceived(Rs.) *

Consumed
Item

Code

0807 grams
0808 grams
0809 grams
0810 grams
0811 grams
0812 grams
0813 grams
0814 grams
0815 grams
0816 grams
0819 grams

7. Koduwa
8. Anjila
9. Balaya
10. Shark / Keelan
11. Paraw
12. Anguluwa
13. Prawns
14. Cuttle fish
15. Fresh water fish
16. Jadi
17. Other

9. Eggs

Unit

09

1.

Hen

0901 number

2.

Others0909 number

10. Coconuts
1.

10

Nuts

2 . Coconut milk powder

1001 number
1002 grams

11. Condiments 11
1.

Dried chilllies

2. Chilly powder
1. Sprats
0801 grams
grams
3. Red onions
2.
Keeramin0802
4. Bombey onions
3.
Salaya0803 grams
5. Garlic
0804 grams
4.
Hurulla6. Maldive fish
grams
5.
Seer
0805
7. Pepper
6.
Katta
0806 grams* The estimated Value of items which are consumed from home grown / freely received should be included in col. 6

Sri Lanka HIES 2009/10 Manual (Version 1.1)

grams
1102 grams
1103 grams
1104 grams
1105 grams
1106 grams
1107 grams
1101

Qty.

Value
(Rs.)

Home grown/ Freelyreceived
*
(Rs.)

214

11
Consumed
Code Unit

Item

8. Turmeric /Turmeric powder 1108 grams
9. Curry power (sarakku)
1109 grams
10. Green chillies
1110 grams
11. Limes
1111 number
12. Cumin seeds
1112
13. Fennel seeds
1113
14. Coriander
1114
15. Mathe seeds
1115
16. Mustard
1116
17. Goraka
1117
18. Tamarind
1118
19. Cinnamon
1119
20. Salt
1120
21. Rampe/Karapincha
1121
22. Ginger
1122
1123
23. Vinegar
24. Other1129

12 .Other foods
1. Sauce
2. Marmite/Vegemite
3. Soup cubes
4. Lime pickle
5. Chutney
6. Canned fruits
7. Others

12
1201
1202
1203
1204
1205
1206

Qty.

Value
(Rs.)

Consumed

Home grown/ Freelyreceived *
(Rs.)

Code

Item

13 .Milk and Milk foods

13

1. Cow milk
2. Goat milk
3. Sterilized milk
4. Curd
5. Yoghurt / Moru
6. Condensed milk
7. Milk powder
8. Infant milk powder
9. Butter
10. Margarine
11. Cheese
12. Milk packets (liquid)
13. Other

14 . Fats and oils

1301
1302
1303

ml
ml
ml

1304
1305
1306

ml

Sugar

1209
* The estimated value of items which are consumed from home grown / freely received should be included in col.6

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Value
(Rs.)

ml
ml
ml
ml
ml
ml

14

1 Bottle = 750 ml
1401

ml

1402
1403
1404
1409

ml
ml
ml

1501
1502
1503
1504

grams

15. Sugar, Juggery & 15
Treacle1.

Qty.

1 Bottle =750 ml

1307
1308
1309
1310
1311
1312
1319

1. Coconut oil
2. Vegetable oil
3. Gingerley oil
4. Ghee
5. Other

2. Juggery
3. Treacle
4. Honey
5. Other

Units

1509

ml

grams
ml

Home grown/ Freelyreceived *
(Rs.)

215

12
Consumed
Item

16. Fruit s

Code Unit

16

1. Banana
2. Pineapple
3. Papaw
4. Mangoes
5. Apple
6. Avacado
7. Wood apple
8. Orange
9. King coconut / Kurumba
10. Grapes
11. Other

1601 number
1602 number
1603 number
1604 number
1605 number
1606 number
1607 number
1608 number
1609 number
1610 number
1619

Dates

2. Cajunuts
3. Ground nuts
4. Plums
5. Other

1651 grams
1652 grams
1653 grams
1654 grams
1659 grams

17
17. Confectionery & short eats
1.

Jam

2.
3.
4.
5.
6.

Ice -Cream
Chocolates
Toffees
Biscuits
Jelly packets

Value
(Rs.)

Consumed
Item1701 grams
1702
1703 grams
1704 number
grams
1705
1706

Code

Unit

7.

Snacks

1707

grams

8.

Cake

1708

9.

Kewum/Kokis etc.

1709

grams
number10.

Musket/Kaludodal etc.

1710

11.

Thalabola/Rulan etc.

1711

grams
number12.

Cutlets/Patis/Wade1712

numbernumberPestry etc.
13.

Palmyrah Products

1713

14.

Other

1719

18. Beverages

Dried fruits
1.

Qty.

Home grown/Freely received
*
(Rs.)

18

(non alcoholic)
1. Tea dust/leaves
2. Coffee powder/seeds
3. Soft drinks
4. Fruit drink/cordial
5. Saruwath
6. Milk tea
7. Plain tea
8. Milk coffee
9. Coffee
10. Ice packets
11. Gruel12. Botteled water

1801
1802
1803

13. Other

1819* The estimated value of items which are consumed from home grown / freely received should be included in Col. 6

Sri Lanka HIES 2009/10 Manual (Version 1.1)

1804
1805
1806
1807
1808
1809
1810
1811
1812

grams
grams

numbernumbernumbernumbernumbernumbernumbernumber

Qty.

Home grown/
Freelyreceived
Value
*
(Rs.)
(Rs.)

216

13
Consumed
Item

Code

Unit

Qty.

Value
(Rs.)

Home grown/Freely received *(Rs.)

9.Liquor,Drugs and Tobacco
19
1.

Toddy

1901

2.

Arrack1902

3.

Kassippu

1903

4.

Beer/Stout

1904

5.

Whisky/Brandy

6.

Gin

1905
1906

7.

Wine

1907

8.

Other liquor

1908

9.

Drugs (Ganja,Heroin)

10.

Cigarettes

1909
1910 number

11.

Cigars12.

Beedi1911 number
1912 number

13.

Pipe tobacco

1919

14.

Betel leaves

15.

Arecanuts

1920 number
1921 number

16.

Tobacco chewing

1922

17.

Chunam18.

Bulathvita/ Saravita

1923
1924 number* The estimated value of items which are consumed from home grown / freely received should be included in col. 6

Sri Lanka HIES 2009/10 Manual (Version 1.1)

217

14

4. 2 Household expenditure on Housing, Fuel & Light, Non-durable goods, Services & Consumer durable for main household.
∗∗∗∗Freely re∗∗ Freely
Main household
Main household
Item

Code Unit

Value
(Rs.)

Item

3. Personal care expense20
1. Housing (Average per month)

Code

Taxes

2002

Water bills

2003

21

22

Toilet Soap
Tooth paste
Tooth brush
Cosmetics powder
Perfumes
Face Cream / Lotions
Hair oil, Cream and hair dye

2201
2202
2203
2204
2205
2206

2208
2209

Electricity

2101

Lipstick / Cutex
Shaving equipments
Hair cut/ Hair dressing & Shaving

Solar power2102

charges

Kerosene oil

2103

ml

Fire wood (Purchased)

2104

kg

Fire wood (free)

2105

L.P.Gas2106 grams(Average per month)

2.2 Fuel & Light(Last month)
Normal blubs
Energy saving blubs
Matches
Candles
Batteries(used for lighting purposes)

OthersUnit( Last month)

Rent (Estimated rent of owner occupied house should be included in col. 5 2001
and col.6

2.1 Fuel & Light

Qty.

received /Received asa gift(Rs.)

2107 number
2108 number
2109 number
2110 number
2111 number
2119

Others

2207

2210
2219

23
4 . Health Expenses (Last month)
Fees to Private medical practitioners(Included cost of medicine)
Ayurvedic consultation fees
(Included cost of medicine)
Consultation fees to specialist
Payments to medical laboratories fortest analysis. etc.
Payment to private hospitals andNursing homes
Purchased of medical andpharmaceutical products
Spectacles
Others

2301
2302
2303
2304
2305
2306
2307
2309

∗ Main household - include all membes 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 2009/10 Manual (Version 1.1)

Qty.

Value
(Rs.)

ceived/Received as agift(Rs.)

218∗

Main house hold

Item

5. Transport (Last month)
Transport fees

Code

2401
Train /Bus (Other than schooling)2402
Van (Other than schooling)
Taxi car/Threewheeler (Other than schooling) 2403
Transport charges for schoolig / Preschooling 2404
2405
Ships & air lines

26

Exercise books/ StationeriesNews papers / Magazines(related to edu.)

2601
2602
2603

Other

2414

8. Recreation, entertainment &Cultural activities

2415
2419

25

Postal & Telegraph Charges
Telephone Charges (Domestic)(Average per month)
Telephone charges ( Moble )Telephone Charges ( Taken from outside)
E-Mail / Internet fees
Other

Code unit

2412

2411

Communication(Last month)

Item

2413

Maintanence of private vehicals

6.

Value
(Rs.)

School text books
2604
School facility fees (Government)
2605
School fees (private)
Tution fees
2606
2607
Boarding fees
Fees for higher education (External degree 2608
/ Post graduate degree, Diploma etc.)
Fees for professional & technical education 2609
2610
Fees for pre school

2409

Petrol & other fuelOilTyre,tube & spare parts
Repair charges of spare parts
accessaries
Licence & Insurance(Average per month)
Other

Qty.

∗

received/
Received
as a gift

7. Education (Last month)

24

Other

unit

15

∗∗ Freely

2501
2502
2503
2504
2505
2509

( Last month)

2619

27

Cinamas/ Drama / Video films
Books / News papers / Magazines
Lotteries / Bettings
Excursions / Pilgrimages
Arts / Music / Dancing
SportsPets / Aquarium
Toys
Maintanence of radio recivers, television& Video deck
Other

∗ Main household - include all membes who usually live in this household & exclude servants & 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 2009/10 Manual (Version 1.1)

2701
2702
2703
2704
2705
2706
2707
2708
2709
2719

FreelyMain house hold ∗∗received/
value
Qty.
Received(Rs.)
as a gift

219

16
Item

Code

unit

9. Non-durable household goods(Last month)28

Washing Soap
Washing powder
Mosquito Coils
Detergents
Insecticiders(for home use)
Other

Value
(Rs.)

received/
Received asa giftItme

3007 number
3008 number
3009 number

3010 number
Housecoat / Kimonas / Night dresses 3011 number
Brassieres
3012 number

2805

Ties

2809

Handkerchiefs
Children's dresses

3013 number
3014 number
3015 number
3016 number

School uniforms

3017 number

Towels3018 number
3019 number
3020 number
3021 number
3029 number

Socks / Stockings2901

Bed sheets

Grinding charges

2902

Pillow cases

Wages to Servants / Chauffers

2903

Mosquito nets

Charges for Day Care Centers

2904

other

Charges for Elders home etc

2905

Materials purchased in meters

11. Clothing & Textiles( Last 6 month )Trousers
Shirts303001 number
3002 number

Unit

Banians
Women's Under wears

Payments for other household sevices 2909

Code

Skirts / Blouses

2801
2802
2803
2804

29

Laundry Charges

Qty.

∗∗ Freely

Men's Under wears

10. Household Services(Last month)∗ Main household(Last 6 month)
For Clothing
For School Uniforms
For Curtains
Other

3041
3042
3043
3049

3003 number
Tailoring Chrges
Tea Shirts
3004 number(Last 6 month)
Sarongs / Vetties
3005 number
3061
For clothing
3006 number
Frocks
3062
For school uniform∗ Main household - include all membes who usually live in this household & exclude servants boarders.
∗∗ The estimated value of items which are received freely / fringe benefit / relief payment/ donation / as a gift should be included in col. 6
SareesSri Lanka HIES 2009/10 Manual (Version 1.1)

cm
cm
cm
cm

∗ Main household ∗∗ Freely
Qty.

Value
(Rs.)

received/
Received asa gift

220

17
Item

Code

Unit

12.Footwear & Other personal 31
effect(Last 6 months)
number

Shoes

3101

Sandals / Slippers

3102 number
3103 number

Umbrellas

3104 number
3105 number

Hand bags/ Travelling bags
Hats / Helmets
Rain coats
Repair charges for shoes, bags, umbrellas

3106 number

etc.

3107

Others3109 number

13. Durable household goods

Qty.

Value
(Rs)

∗

∗∗FreelyItem

received/
Received
as a gift

Code

Unit

3208 number
3209 number
3210 number
3211 number
3212 number

Brooms / Brushes / Ekels
Mats/ Pillows
Rugs / Carpets
Mettress
Torches
Thermos flasks
Twines / Ropes
Others3213 number
3214 number
3219 number

33
14.Durablehouseholdgoods(Last 12 month)32(Last 6 months)
Glass ware

∗ Main household

Plasticware

3201 number
3202 number

Aluminiumware

3203

Clayware / pots

3204 number

Cane ware

3205 number

Cutlery

3206 number

Other3207 number

numberChairs (Plastic / Wood)
TablesSettees
Almyrahs
Beds
Other furniture
Clocks / Wrist watches
Cookers (Kerosene / gas / Electric)
Mixingmachines,Grinders,
Beaters
Irons
Refrigerators
Fans
Sewing machines

3301

number3302 number
3303 number
3304 number
3305 number
3306 number
3307 number
3308 number
3309

number3310 number
3311 number
numbernumber
3313

3312∗ Main household - include all membes who usually live in this household & exclude servants & 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 2009/10 Manual (Version 1.1)

Main household
Qty.

Value
(Rs)

∗∗Freelyreceived/
Received
as a gif

221

18
Item

Code

Unit

∗ Main household ∗∗ Freely
Qty.

Value
(Rs.)

Item

recived/Received asa giftRadio, Record players & Tape recorders
Television / Video decks
Washing machines
Musical Instruments
Bicycles

3314 number
3315 number
3316 number
3317 number
3318 number

15. Other expenses

Code Unit

34(Last month)
Provident fund / W & O.P. fund

3401

Contribution to trade unions, Thriff

Motor cars / Vans

3319 number
3320 number

Prams / Gocarts / Tricycles

3321 number

Insurance /Agrahara

3403

Garderning tools & equipments

3322 number

Other savings (Including seettu)

3404

Cameras & Projectors

3323 number

Payment for debits

3405

Computers

Income tax

3406

Jewelleries

3324 number
3325 number
3326 number

Other

Telephones / Cellular phones

3409

Motor cycles/ Scooters

Cookers , Toasters, Hot plates,
Heaters etc.

3327 number
3329 number

Other
Durable goods purchased ininstalments

3339

Societies etc..

3402

35
6. Other adhoc (rarely) expenses(Last 12 month)
Expenditure on weddings/
funerals for family members

3501

Social activities/ Ceremonies

3502

Litigation

3503

Gift, Donation, Similar transfers

3504

Maintenance / Repairing (Houses)

3505

Purchased properties Houses

3506

Loans given

3507

Other

3509

∗ Main household - include all membes who usually live in this household & exclude servants & boarders∗∗ The estimated value of items which are received freely / fringe
benefitrelief payment/ donation
Sri Lanka
HIES/ 2009/10
Manual (Version
1.1) / as a gift should be included in col. 6∗Main household ∗∗Freely
Qty.

Value
(Rs.)

recived/Received asa giftName of boarders & domestic
servants who has spent money on anyof the following columns 3 -15

1

If any boarder or domestic servant given in column 1 of section 1, not spent any of the above Please not down here.

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Miscellaneous(Last month)

Amount transfered as savings
provident funds, W & O.P.,
taxes,donations etc.
( Last month)2

Amount sent to family/
Parents (Last month)No

Boarding fees paid
(Last month)

Recreation, Entertainments
Education & Culturalactivities etc.
Durable household goods(Last 12 months)

Yes

Transport & Communication(Last month)

Are there Boarders & / or Domestic Servants in this house hold

Personal care & Healthexpenses (Last month)

Household services.
(Last month)Non -durable household
goods (Last month)

Clothing & Textiles(Last 6 Months)

Fuel & Light(Last month)

Food purchased outside thehousehold .(Last week)

4.3

Serial number as given in
Column 1 of section 1

222

19

Expenditure on Housing, Fuel & Light, Non-durable goods, Services & Consumer durables for boarders & domestic servants.
à Go to section 5

223

20

Section 5 - Income
5.1 Income from paid employments / During last weeks / Last calendar month
Did any of the household members (usually live) engage as an employee during last 4 weeks / last calendar month

List all household members (usually live in thishousehold ) including boarders & domestic servants
who were paid employees during last four weeks /last calendar month

Serial number as given
in Column 1 of section 1

Yes

No

2 à go to section
Lastmonth

Principal /Secondary occupation
Main

1

Secondary

2

1

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Wages /
Salaries

(Rs.)

5.2
Last12months

Tips,
Commissions
Overtime pay
etc.

(Rs.)

Bonus, Arrears
Payment

(Rs.)

224

21

5.2 Income from agricultural activities - (Paddy, Other seasonal crops)* *crops as an employer or own account worker for sale and / or household consumption during
Did any of the household members cultivate paddy, other seasonallast cultivation year.
Yes
1
No 2
à 5 go to section 5.3

Code for column 3
Paddy............................................1
Chillies. ........................................2Onions ..........................................3
Vegetables....................................4
Cereals..........................................5
Yams........ ...................................'6
Tobacco........................................7
Other.... ........................................9

Serialnumberas givenincolumn 1ofsection 1**
Paddy / Other
seasonal crops( Describe)+

Cultivated
area

+

*
** Last Cultivation year-

Code

List all household members (usually live) including
boarders & domestic servants who have cultivatedpaddy & / or other seasonal crops as employers or own
account workers.

Hired Labour
Seeds

Last two cultivation seasonsFertilizer(yala / Maha or Maha / yala)

Chemicals
Transport / Tractor
Other
Total

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Value ofoutput(Rs.)

Lastcultivationyear
Value ofamountConsumedby thishousehold(Rs.)* Cost

(Rs.)

225

22

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 12 months
Yes1

go to section 5.4

Lastmonth/Averagepermonth
Agricultural product(Describe)

Code

List all household members (usually live)
Serialnumberincluding boarders & domestic servants who
as given
have engaged any other agricultural activites /inLivestock as employers or own account workers column
1 ofsection 1

àNo

Value ofOut put

Cultivated area

+
3

2

4

5

1
2
3

4
5
6
7
8

9
10'
*

Hired labour+ Codes for column
Seeds
Fish .......................................06
Fertilizer
Tea , Rubber ............................01 Eggs ..................................07
Coconuts .................................02 Milk.......................................08
Chemicals
Coffee, Pepper Betel etc.........03 Other food items....................09
Transport/ Tractor charges
Banana / Fruits ........................04 Horticulture...........................10
Meat ........................................05 Other.....................................19
Other
Total
Not necessary for cultivated area under the codes from 05-10 in column 4.
Sri Lanka HIES 2009/10 Manual (Version 1.1)

6

7

*

Cost

(Rs.)

(Rs.)

8

9

226

23

5.4 Income From Non- agricultural activities
Did any of the household members engage in any non- agricultural activities as an employer or own account worker for sale during last calendar month.
Yes
1
No; 2 à go to section 5.5.1
List all household members (usually live)
Serialincluding boarders & domestic servants who number asgiven inhave engaged Non- agricultural activities as coloumn
1employers or own account workers.
of section
1+

Codes for coloumn 3
Mining & Quarrying. ....................................1
Manufacturing.... ..........................................2
Construction. ................................................3
Trade.............................................................4Transport. .....................................................5
Guest house, restaurants, bars ,hotels etc..................................................6
Other services.. .............................................7

Economic activity(Describe)

Code

Lastcalendarmonth+*
Hired labour
InputsTransport
Other
Total
Sri Lanka HIES 2009/10 Manual (Version 1.1)

Value ofoutput
(Rs.)

*

Cost

(Rs.)

227

24

5.5.1. Income from Other cash receipt during last calendar month / last 12 month

yes

1

No 2

à

given in column 1of section 1

List all household members (usually live)
including boarders & servants who havereceived any other cash receipt.

Serial number as

Income form last calendar month
Pension
Payment

Disability/
reliefpayments

(Rs.)

(Rs.)

Serial number asgiven in colomn 1ofsection 1

5.5.2 Income by Chance or adhoc gains during last 12 months
List all household members (usually live)
excluding boarders & servants who havereceived income by chance or rarely.

Loans takenfrom banks/moneylenders etc.
(Rs.)

Last 12 monthRents from Samurdhi Dividents /properties/
Interests
boarding fees
etc.
(Rs.)

go to 5.5.2

(Rs.)

(Rs.)

Yes

1

Other

Current remittance& transfers
outsidethe country

withinthe
Country

(Rs.)

(Rs.)

(Rs.)

2

à go to section 6

No

IncomeCompersation
receives
fromSales of assets Withdrawal from
Seettu /Otherhealth /births,deaths,(Land , house, savings & bank
Repayments
lottery &marriages /insurance etc other adhoc
jewellary)deposits.
of loans
welfaregiven
gains
society, etc(Rs.)

Sri Lanka HIES 2009/10 Manual (Version 1.1)

(Rs.)

(Rs.)

(Rs.)

(Rs.)

(Rs.)

228

25

Section 6A - Inventry of durable goods
Code

Items
Household Equipments
Radio / Cassette player
Television
V.C.D. / D.V.D.
Sewing machines
Washing machines
Refrigerators
Cookers ( Gas, Kerosene, Electric)
Electric fans
Telephone (Domestic)
Telephone (mobile)
Personal Computers

5001
5002
5003
5004
5005
5006
5007
5008
5009
5010
5011

For Transport purpose
BicyclesMotor cycles / Scooters
Three wheelers
Motor cars / VansBus / Lorry

5012
5013
5014
5015
5016

Have
-1
Have not - 2

Agricultural Equipments

Tractors (Two wheel)
Tractors (Four wheel)
Sprayers
Threshers
Water pumps (Only for agricultural purpose)
Fishing Equipments
Boats
Fishing nets

5017
5018
5019
5020
5021

5022
5023
Sri Lanka HIES 2009/10 Manual (Version 1.1)

229

26

Section 6 B . Debts (Excluding boarders & Servants)
Have any of the household
members debited to the followings
Yes -1 go to col. 3
No -2

Debited to1
1Banks (Government / Private)2

Finance Companies / Leasing Companies

3

Own Place of work

2

(Departments ,Boards , Private Companies etc.)
4

Money Lenders

5

Retail outlets

6

Sales of assets (Land , houses, jewelleries etc.)7

Durable goods purchased in instalments

9

Other (Specify)

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Amount to pay
(Rs.)

3

230

Section 7.1
SerialNo.

27

Section 7 - Access to facilities
Distance from yourhouse to this closest
facility(km)

Place of Facilits

01

Bus Halt (nearest)02

Pre school / Montessori

03

Primary School04

Maha Vidyalaya/ M.M.V. / Jathika Pasala05

Base Hospital / Teaching Hospital /District Hospital

06

Maternity Home

07

Government Dispensary

08

Private Dispensary09

Clinic (Maternity / Infancy)

10

M.O.H. office

11

M.C. / U.C. / P. S.

12Divisional Secretariat office

13

G.N.Office

14

Post office / Sub post office

15

Bank (Govt. / Private)

16

Agrarian Service Center

Time taken from yourhouse to this closest
facility(Minutes)

Section -7.2

Yes No1. 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 ..............................................................................
Sri Lanka HIES 2009/10 Manual (Version 1.1)

231

28

Section 8 - Housing Information
1.

Type of Structure

Single house .................................
Flat ................................................

(B)

Floor
Cement ...................................................
Terrazo / Tile .........................................
Mud .......................................................
Other (specify) ......................................

(C)

Roof
Tile .........................................................
Asbestos .................................................
Concrete .................................................
Metal sheet .............................................
Cadjan / Palmyrah / Straw .....................
Other (specify) .......................................

5.

TenureConstructed / Purchased by an occupant ............

Attached house /Annex.................
Line room / Row house.................
Slum / Shanty ...............................
Other (specify)...............................
2.

Number of bed rooms

3.

Total floor area (sq. feet)less than 100 ......................................
100 - less than 250 ............................
250 - less than 500 ............................
500 - less than 750 ............................
750 or more than 750 ........................

Inherited ..............................................................
4.

Principal materials of Construction
wall
Brick ..................................................
Cabook ..............................................
Cement block ....................................
Pressed soil blocks ............................
Mud ...................................................
Plank / Metal sheet ...........................
Cadjan / Palmyrah ............................
Other (specify)

Freely received / Received as a gift ...................
Compensated ......................................................
Rent free (Employer / Other) .............................
Relief Payment (Employer / Other) ....................
Rent / Lease ........................................................
Encroached .........................................................
Other (specify) ....................................................

Sri Lanka HIES 2009/10 Manual (Version 1.1)

232

6. A

29
B. Type of Toilet

Main source of drinking water
Protected well within premises.........................................

Water seal .........................................................

Protected well outside premises ......................................

Pour flush .........................................................

Unprotected well ..............................................................

Pit .....................................................................

Tube well ..........................................................................

Other (specify) .................................................

Tap within unit / Premises (main line)..............................

8. Disposal of Garbage

Tap outside premises ( main line).....................................
Stream water collected & distributed by pipe lines..........

Collected by garbage truck ................................

River / Tank / Streams .....................................................

Buried / Burned .................................................

Other (specify) ..................................................................

Process for fertilizer .........................................
Dumped within premises ..................................
Dumped / Throw away out side premises.........

B.

Distance to take source of drinking water (Meters)

Other (specify) ..................................................

Within premises .............................

9. Principal Type of Lighting

Outside premises ...........................

C.

Kerosene............................................................

Was there enough water to drink, bath & wash during lastyear

Electricity ..........................................................
Solar energy ......................................................
Generator / Battery ............................................

Yes
1.

Drink

2.

Bath /washNo

Other (specify) ...............................................

10. Principal Type of cooking fuelFire wood ..........................................................

7. A. Availability of toilet

Gas .....................................................................

Exclusive for the household....................................

Kerosene ...........................................................

Sharing with another household ..............................

Electricity .........................................................

Public convenience .................................................
None........................................................................

à go to quection 8

Saw dust / Paddy husk ......................................
Other (specify) ...............................................

Sri Lanka HIES 2009/10 Manual (Version 1.1)

233

30
11. Did any of the household member collect fire wood
during last month
Yes

1

No

2

à go to Q.12

(B) Distance & place of collecting firewood(Meters)
Own land ................................

1

Forest ......................................

2
3

Other land...............................

12. (A) Was this housing unit effected by any natural disaster
during last year
Yes

1

No

2

- go to section 9
Yes No(B) Nature of disaster2. Drought .....................................................

1
1

2
2

3. Earthslip.....................................................

1

2

4. Civil disturbance........................................

1

2

5. Attacked by wild animals...........................

1
1

2
2

1. Flood ..........................................................

6. Other (specify) ..........................................

Sri Lanka HIES 2009/10 Manual (Version 1.1)

234

31

Section 9 - Agricultural Holdings & Livestock
1. Does any member / s of your household own any agricultural land / lands

Yes

1

No

2

à go to Q 3

2. Land area
Owned

Cultivated.
(Rent, lease, joined own, ande etc.)

1
A.

2

R.

A.

P.

R.

1. Paddy land
2. High land
3. Land area withoccupied housing units

3. Livestock (owned)
Category

Yes 1
No 2

Number of* livestock(code)

1

2

*

(1)

5 or less than 5

1.Cattle / Buffaloes(2)

6 to 10

2. Goats / Sheep(3)

11 to 50

3. Swine (pigs)(4)

More than 50

4. Poultry
5. Other
Sri Lanka HIES 2009/10 Manual (Version 1.1)

P.

235

Attachment 2

Sri Lanka HIES 2009/10 Data Dictionary
NOTE:
1. For data set from R02 to R24, the top 9 variables, that is, DISTRICT, SECTOR, DSD, MONTH, PSU,SAMPLE_N, SERIAL_NO, NHH and RESULT are skipped in the following sheets, because they are thesame as those in R01.

2. The next variables are added to data files.
Variable

Description

ID

Household identifier

PID

Individual identifier, if the data set includes person number

wtAdjusted weight for the resampled micro data set

3． The next item labels are revised.
File

Item label (old)Item label (revised)

R10

Roots

Roods

R13

Acrs

Acres

Roots

Roods

4. R25 and R26 are compiled by the author, as written in Chapter 6, 7 and 8. It might include errors. Please
use it at your own risk.

5. The sequence of the following sheets is by the type value, but not by the file name.

6. As for crop code in R13 (Section 5.3), it is not clear why “tea” and “rubber” are allocated in the same code 1,
regardless of totally different crops.

Sri Lanka HIES 2009/10 Manual (Version 1.1)

236-------------------------------------------------------------------------------------------------------------Level Label
Level Name
Type
Rec
Record Label
Record Name
Value Req Max Len-------------------------------------------------------------------------------------------------------------Sri Lanka HIES2009 Questionnaire
HIES2009_QUEST
Section - 1 - Demography
SEC_1_DEMOGRAPHIC
01 Yes40
43
Section - 2 - School Education
SEC_2_SCHOOL_EDUCATION
02
No
40
36
Section - 3 - Health
SEC_3_HEALTH
03
No
40
34
Section - 4.1 - Food expenditure
SEC_4_1_FOOD_EXP
04
No 24041
Section - 4.2 - Non food
SEC_4_2_NONFOOD
05
No 175
41
Section - 4.3 - Is Boaders Servent
SEC_4_3_IS_BOADERS
06
No
1
18
Section - 4.3 - Boaders Servent
SEC_4_3_BOARDERS
07
No
40
89
Section - 5.1 - Is employment Income
SEC_5_1_IS_EMP_INCOME
08
No
1
18
Section - 5.1 - Employment Income
SEC_5_1_EMP_INCOME
09
No
40
40
Section - 5.2 - Is aggricultural Income
SEC_5_2_IS_AGRI_INCOME
10
No
1
18
Section - 5.2 - Aggricultural Income
SEC_5_2_AGRI_INCOME
11
No
40
53
Section - 5.3 - Is other aggricultural income
SEC_5_3_IS_OTHER_AGRI_INCOME
12
No
1
18
Section - 5.3 - Other aggricultural income
SEC_5_3_OTHER_AGRI_INCOME
13
No
40
45
Section - 5.4 - Is non_agri income
SEC_5_4_IS_NON_AGRI_INCOME
14
No
1
18
Section - 5.4 - Non agri income
SEC_5_4_NON_AGRI_INCOME
15
No
40
38
Section - 5.5.1 - Is other income
SEC_5_5_1_IS_OTHER_INCOME
16
No
1
18
Section - 5.5.1 - Other income
SEC_5_5_1_OTHER_INCOME
17
No
40
72
Section - 5.5.2 - Is windfall income
SEC_5_5_2_IS_WINDFALL_INCOME
18
No
1
18
Section - 5.5.2 - Windfall income
SEC_5_5_2_WINDFALL_INCOME
19
No
40
75
Section - 6A - Durable goods
SEC_6A_DURABLE_GOODS
20
No
1
40
Section - 6B - Debtness
SEC_6_B_DEBTNESS
21
No
1
81
Section - 7 - Basic facilities
SEC_7_BASIC_FACILITIES
22
No
1 100
Section - 8 - Housing
SEC_8_HOUSING
23
No
1
50
Section - 9 - Land and animal
SEC_9_LAND_ANIMAL
24
No
1
63

Sri Lanka HIES 2009/10 Manual (Version 1.1)

237

R01
Record: Section - 1 - Demography-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------(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(Blank)Blank
Sector(id) SECTOR
5
1
N
I
1
0
No Yes
1:3(Blank)N
I
1
0
No YesDS division(id) DSD
62
3:99(Blank)Month(id) MONTH
8
2
N
I
1
0
No Yes
1:12(Blank)PSU Number(id) PSU
10
3
N
I
1
0
No Yes
1:300(Blank)Sample No.
(id) SAMPLE_N
13
2
N
I
1
0
No Yes
1:10(Blank)Household Serial No.
(id) SERIAL_NO
15
1
N
I
1
0
No Yes
1:9(Blank)Number of households(id) NHH
16
1
N
I
1
0
No Yes
1:9(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

238

Result Code(id) RESULT
1 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)(Blank)1PersonPERSON_SERIAL_NO
1:45(Blank)3Relationship
RELATIONSHIP
1 Head2 Spouse
3 Son/Daughter
4 Parents
5 Other Ralative
6 Domestic Servent
7 Boarders
9 Other(Blank)
4Sex
SEX_LIVING
1 Male/Local
2 Female/Abroad(Blank)5Birth Year
BIRTH_YEAR
0:99(Blank)5Birth Month
BIRTH_MONTH
1:12(Blank)
6Age
AGE
0:99
(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

17

1

N

I

1

0

No

Yes

18

2

N

I

1

0

No

Yes

20

1

N

I

1

0

No

Yes

21

1

N

I

1

0

No

Yes

22

2

N

I

1

0

No

Yes

24

2

N

I

1

0

No

Yes

26

2

N

I

1

0

No

Yes

239

7Race
1
2
3
4
5
6
9

ETHNICITY

28

1

N

I

1

0

No

Yes

RELIGION

29

1

N

I

1

0

No

Yes

CURR_EDUCATION

30

1

N

I

1

0

No

Yes

31

2

N

I

1

0

No

Yes

SinhalaSri Lanka Tamil
Indian Tamil
Sri Lanka Moors
Malay
BurgerOther(Blank)
8Reliqion
1 Buddhist
2 Hindu3 Islam
4 Roman Ctholic/Christian
9 Other(Blank)9Current Education
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)
7 Does notattend (Blank)

10Education
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
Sri Lanka HIES 2009/10 Manual (Version 1.1)
15 Passed Degree

240

16 Passed Post Graduate Dgree/Diploma
17 Special Education
19 No
Schooling(Blank)11Marital
MARITAL_STATUS
1 Never Married
2 Married
3 Widowed
4 Divorced
5 Separated(Blank)12Usual Activity
1 Employed
2 Unemployed
3 Student
4 Household work
5 Unable / Too old to work
6 Other(Blank)13Main Occupation(na)N
14Industry INDUSTRY
39
4
(na)
15Employment Status
1 Government employee
2 Semi government employee
3 Private sector employee
4 Employer
5 Own account worker
6 Unpaid family worker(Blank)

I

1

33

1

N

I

1

0

No

Yes

MAIN_ACTIVITY

34

1

N

I

1

0

No

Yes

MAIN_OCCUPATION
Yes (Blank)

35

4

N

I

1

0

No

43

1

N

I

1

0

No

0

No

Yes (Blank)

EMPLOYMENT_STATUS

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Yes

241

R02
Record: Section - 2 - School Education-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2 Sr. No
R2_PERSON_SERIAL
18
2
N
I
1
0
No
Yes 1:40(Blank)N
I
1
0
No Yes3 School Education
R2_SCHOOL_EDUCATION
20
1
1 Currently attending school
2 Never attend school
3 Attended school in thepast (Blank)
4CurrentGrade
GRADE_THIS_YEAR
21
2
N
I
1
0
No Yes
1
2
3
4
5
6
7
8
9
10
11
12
13
19

Grade 1
Grade 2
Grade 3
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8Grade 9Grade 10
Grade 11
Grade 12
Grade 13
Not relevant(Blank)5GradePreviouse GRADE_LAST_YEAR
1
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
19 Notrelevant (Blank)
6 DistanceToSchool

23

2

N

I

1

0

Sri Lanka HIES 2009/10 Manual (Version 1.1)

DISTANCE

No

Yes

25

2

N

I

1

0

No

242

Yes 0:99
DistanceToSchool(Blank)7Transport
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) (Blank)
8TimeTo School
TIME_TO_SCHOOL
0:120 TimeTo
School (Blank)
9WhyNeverSchooling
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 Incompletion of
9 Other (specify)(Blank)10 ReasonsNotGoingSchool
REASON_NOT_GOING 32
1 Further schooling not available or too far away
2 Finalcial Problems
3 Had to help house keeping activities / Family business4 Disability / Illness
5 Civil disturbance
6 Not willing to attend / Poor academic progress
7 Pending results ( G.C.E. (O/L) / G.C.E. (A/L)
8 Completed A/L
9 Other
11WhenStopSchool
WHEN_STOP_SCHOOLING
0:9999 WhenStopSchool(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

27

1

N

I

1

0

No

Yes

28

3

N

I

1

0

No

Yes

31

1

N

I

1

0

No

Yes

1

N

I

33

4

N

1

I

0

1

0

No

Yes

No

Yes

243

R03
Record: Section - 3 - Health-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2 Person Sr.
R3_PERSON_SERIAL
18
2
N
I
1
0
No Yes
1:40(Blank)3 Did attend hospital
DID_ATTEND_HOSPITAL
20
1
N
I
1
0
No Yes
1:2
Did attend hospital(Blank)N
I
1
0
No Yes4 Reason for attending
REASON_HOSPITAL
21
1
1 Treatment for illness
2 Treatment for injury
3 Medical checkup/ Consultation
4 Immunization
5 Treatment for infectious diseases(Injection etc)
9 Other (specify) (Blank)
5 Did stay in hospital
IS_STAY_HOSPITAL
22
1
N
I
1
0
No Yes
1:2(Blank)N
I
1
0
No Yes6 Reason for hospitalise
REASON_STAY
23
1
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) (Blank)
7 Is ill or dissable
IS_ILL_DISABLE
24
1
N
I
1
0
No Yes
1:2(Blank)8 Illnee or dissability
1Heart Conditions /Diseases
2
Blood pressure
3
Diabetics
Asthma4
Epilepsy
5
6
Cancer7
Stomach diseases / Gastritis
Diseases related to Eyes
8
Arthritis
9
10 Mental retardation
11 Haemorrhoids
12 Catarrh

WHAT_ILL_DISABLE

Sri Lanka HIES 2009/10 Manual (Version 1.1)

25

2

N

I

1

0

No

Yes

244

13 Severe headache
14 Naturally Disabled
15 Disabled by an accident
19 Other(Blank)9 Is employmet caused
1:2(Blank)10 Duration of illness disa
0:99
Duration of illness disa
(Blank)10 Months duration
0:12 Months duration(Blank)11 Did absent for normal
1:2(Blank)12 Duration absent days
0:99 Duration absent days
(Blank)

IS_EMPL_REASON

27

1

N

I

1

0

No

Yes

DURATION_YEARS

28

2

N

I

1

0

No

Yes

DURATION_MONTHS

30

2

N

I

1

0

No

Yes

IS_ABSENT_ACT

32

1

N

I

1

0

No

Yes

DAYS_ABSENT

33

2

N

I

1

0

No

Yes

Sri Lanka HIES 2009/10 Manual (Version 1.1)

245

R04
Record: Section - 4.1 - Food expenditure-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2. Code
CODE
18
4
N
I
1
0
No
Yes 101:1924
4. Quantity
QUANTITY
22
6
N
I
1
0
No Yes0:999999(Blank)5. ValueVALUE
28
7
N
I
1
0
No Yes0:9999999(Blank)6. Inkind value
INKIND_VALUE
35
7
N
I
1
0
No Yes0:9999999(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

246

R05
Record: Section - 4.2 - Non food-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2. CodeNF_CODE
18
4
N
I
1
0
No
Yes 2001:3509(Blank)4. Quantity
NF_QUANTITY
22
6
N
I
1
0
No Yes0:999999(Blank)5. ValueNF_VALUE
28
7
N
I
1
0
No Yes0:9999999(Blank)6. Inkind value
NF_INKIND_VALUE
35
7
N
I
1
0
No Yes0:9999999(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

247

R07
Record: Section - 4.3 - Is Boaders Servent-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is Boarders and Domestic Servents
IS_BOARDERS_SERVENTS
18
1
N
I
1
0
No Yes
1:2(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

248

R06
Record: Section - 4.3 - Boaders Servent-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2 Person
COL_2
18
2
N
I
1
0
No Yes
1:40 SerNo(Blank)
5
N
I
1
0
No
Yes
3 FoodCOL_3
20
2:70000(Blank)N
I
1
0
No Yes
4 Fuel and lite
COL_4
25
5
2:5000(Blank)5 Clothing
COL_5
30
5
N
I
1
0
No Yes
10:7000(Blank)6 Non durable goods
COL_6
35
5
N
I
1
0
No Yes
10:5000(Blank)N
I
1
0
No Yes
7 HH services
COL_7
40
5
10:5000(Blank)8 Personal effects
COL_8
45
5
N
I
1
0
No Yes
10:5000(Blank)N
I
1
0
No Yes9 Transport
COL_9
50
6
10:5000(Blank)10 Ent., Edu and cult
COL_1056
5NI
1
0No Yes
10:3000(Blank)11 Durable goods
COL_1161
6NI
10
No Yes
10:5000(Blank)12 Bording fees
10:10000(Blank)13 Send to family
100:10000(Blank)14 Savins etc
50:20000(Blank)

COL_12

67

5

N

I

1

0

No

Yes

COL_13

72

6

N

I

1

0

No

Yes

COL_14

78

6

N

I

1

0

No

Yes

Sri Lanka HIES 2009/10 Manual (Version 1.1)

249

15 Other

COL_15

10:6000(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

84

6

N

I

1

0

No

Yes

250

R09
Record: Section - 5.1 - Is employment Income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is employment income
1:2(Blank)

IS_EMPLOYMENT_INCOME

Sri Lanka HIES 2009/10 Manual (Version 1.1)

18

1

N

I

1

0

No

Yes

251

R08
Record: Section - 5.1 - Employment Income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2Scr.No
SERIAL_NO_SEC_1
18
2
N
I
1
0
No Yes
1:40(Blank)N
I
1
0
No Yes3Principal/Second
PRI_SEC201
1:2(Blank)N
I
1
0
No Yes
4 WagesSalary
WAGES_SALARIES
21
650:300000(Blank)5 Allowences
ALLOWENCES
27
7
N
I
1
0
No Yes100:100000(Blank)6 Bonus
BONUS
34
7
N
I
1
0
No
Yes
10:250000(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

252

R11
Record: Section - 5.2 - Is aggricultural Income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is agricultural income
1:2(Blank)IS AGRICULTURAL INCOME

Sri Lanka HIES 2009/10 Manual (Version 1.1)

18

1

N

I

1

0

No

Yes

253

R10
Record: Section - 5.2 - Aggricultural Income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2Person
SER_NO_SEC_5_2
18
2
N
I
1
0
No Yes
1:40(Blank)N
I
1
0
No Yes
4Code
SEAS_CROPS_CODE
20
1
1 Paddy
2 Chillies
3 Onions
4 Vegetables
5 Cereals
6 Yams
7 Tobacco
9 Other(Blank)N
I
1
0
No Yes5Acres
ACR_5_2
21
3
0:25(Blank)6RoodsRT_5_2

24

1

N

I

1

0

No

Yes

P

25

2

N

I

1

0

No

Yes

OUTPUT_5_2

27

9

N

I

1

0

No

Yes

HH_CONSUMPTION

36

9

N

I

1

0

No

Yes

INPUT_5_2

45

9

N

I

1

0

No

Yes

0:3
(Blank)7Perchs
0:39(Blank)8Output value
50:1000000(Blank)9Household consumption
10:50000(Blank)10Input value
10:1000000(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

254

R12
Record: Section - 5.3 - Is other aggricultural income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is other agrri income
1:2(Blank)IS OTHER AGRRI INCOME

Sri Lanka HIES 2009/10 Manual (Version 1.1)

18

1

N

I

1

0

No

Yes

255

R13
Record: Section - 5.3 - Other aggricultural income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2Person
SER_NO_SEC_5_3
18
2
N
I
1
0
No Yes
1:40(Blank)4Other crop code
SEASONAL_CROP
20
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
19 Other

5AcresACRES_5_3

22

3

N

I

1

0

No

Yes

ROOTS_5_3

25

1

N

I

1

0

No

Yes

PERCHS_5_3

26

2

N

I

1

0

No

Yes

OUTPUT_5_3

28

9

N

I

1

0

No

Yes

INPUT_5_3

37

9

N

I

1

0

No

Yes

0:25
(Blank)6Roods0:3(Blank)7Perchs
0:39(Blank)8Output value
50:1000000(Blank)9Input value
50:1000000(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

256

R14
Record: Section - 5.4 - Is non_agri income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is non agri income
IS_NON_AGRI_INCOME
18
1
N
I
1
0
No Yes
1:2(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

257

R15
Record: Section - 5.4 - Non agri income------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2 Person
SERIAL_5_4
18
2
N
I
1
0
No Yes
1:40(Blank)4 Non agri code
NON AGRI
20
1
N
I
1
0
No Yes1 Mining & Quarrying.
2 Manufacturing
3 Construction
4 Trade5 Transport
6 Guest house, restaurants, bars/hotels etc
7 Other services(Blank)5 Output value
OUTPUT_5_4
21
9
N
I
1
0
No Yes
50:500000(Blank)6 Input value
INPUT_5_4
30
9
N
I
1
0
No Yes
50:500000(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

258

R16
Record: Section - 5.5.1 - Is other income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is Other Income
IS_OTHER_INCOME
18
1
N
I
1
0
No Yes
1:2(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

259

R17
Record: Section - 5.5.1 - Other income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2Person
SERIAL_5_5_1
18
2
N
I
1
0
No Yes
1:40(Blank)N
I
1
0
No Yes3Pention
PENSION20
6
100:60000(Blank)N
I
1
0
No Yes
4Disability
DISABILITY_AND_RELIEF
26
5
50:7000(Blank)N
I
1
0
No Yes
5Rent
PROPERTY_RENTS
31
7
50:250000(Blank)6Samurdhi
SAMURDHI
38
5
N
I
1
0
No Yes
50:3500(Blank)
7Profits
DIVIDENDS
43
7
N
I
1
0
No Yes
10:50000(Blank)8Other
OTHER
50
7
N
I
1
0
No Yes
10:250000(Blank)9Foreign
ABROAD57
8NI
1
0
No Yes100:700000(Blank)10Local
LOCAL
65
8
N
I
1
0
No Yes
50:600000(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

260

R18
Record: Section - 5.5.2 - Is windfall income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is windfall income
IS_WINDFALL_INCOME
18
1
N
I
1
0
No Yes
1:2(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

261

R19
Record: Section - 5.5.2 - Windfall income-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------2Person
PERSON_5_5_2
18
2
N
I
1
0
No Yes
1:40(Blank)N
I
1
0
No Yes3Loans WD
LOANS
20
8
100:1000000(Blank)N
I
1
0
No Yes
4Selling pawning
PAWNING_SELLING
28
8
500:500000(Blank)N
I
1
0
No Yes5Deposits
DEPOSITS_PENSIONS_EPF
36
8
100:600000(Blank)6Income from Welfare
INCOME_WELFARE
44
8
N
I
1
0
No Yes
10:1000000(Blank)7Rcvd loans seettu
SITTU_DEBTS
52
8
N
I
1
0
No Yes100:100000(Blank)8Compenstions
COMPENSATION
60
8
N
I
1
0
No Yes100:500000(Blank)9Other Lottery_games
OTHER_LOTTERY
68
8
N
I
1
0
No Yes100:500000(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

262

R21
Record: Section - 6A - Durable goods-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Radio
RADIO
18
1
N
I
1
0
No
Yes
1:2 Radio / Cassette player(Blank)N
I
1
0
No YesTV
TV
19
1
1:2 Television(Blank)
VCD
VCD
20
1
N
I
1
0
No
Yes
1:2 V.C.D. / D.V.D.
(Blank)N
I
1
0
No YesSewing Mechine
SEWING_MECHINE
21
1
1:2 Sewing machines(Blank)Washing mechine
WASHING_MECHINE
22
1
N
I
1
0
No Yes
1:2 Washing machines(Blank)FridgeFRIDGE
23
1
NI
10
No Yes
1:2 Refrigerators(Blank)Cookers
COOKERT
24
1
N
I
1
0
No
Yes
1:2 Cookers ( Gas, Kerosene, Electric)(Blank)Electric Fans
ELECTRIC_FANS
25
1
N
I
1
0
No Yes
1:2 Electric fans(Blank)Telephone
TELEPHONE
26
1
N
I
1
0
No Yes
1:2 Telephone (Domestic)(Blank)Telephone Mobile
TELEPHONE_MOBILE
27
1
N
I
1
0
No Yes
1:2 Telephone (mobile)(Blank)
N
I
1
0
No
Yes
Computers
COMPUTERS
28
1
1:2 Personal Computers(Blank)BicycleBICYCLE
29
1
N
I
1
0
No Yes
1:2 Bicycles(Blank)Motor Bicycle
MOTOR_BICYCLE
30
1
N
I
1
0
No Yes1:2
Motor cycles / Scooters(Blank)Sri Lanka
HIES 2009/10 Manual (Version 1.1)
Three wheeler
THREE_WHEELER
31
1
N
I
1
0
No Yes263

1:2 Three wheelers(Blank)Motor car van
1:2
Motor cars / Vans(Blank)Bus lorry
1:2 Bus / Lorry(Blank)Tractor 2 wheel
1:2 Tractors (Two wheel)(Blank)Tractor 4 wheel
1:2 Tractors (Four wheel)(Blank)Pesticider
1:2 Sprayers(Blank)Paddy blower
1:2 Threshers(Blank)Water pumps
1:2 Water pumps(Blank)Boats
1:2 Boats(Blank)Fishing nets
1:2 Fishing nets
(Blank)MOTOR_CAR_VAN

32

1

N

I

1

0

No

Yes

BUS_LORRY

33

1

N

I

1

0

No

Yes

TRACTOR_2_WHEEL

34

1

N

I

1

0

No

Yes

TRACTOR_4_WHEEL

35

1

N

I

1

0

No

Yes

PESTICIDER

36

1

N

I

1

0

No

Yes

PADDY_BLOWER

37

1

N

I

1

0

No

Yes

WATER_PUMPS

38

1

N

I

1

0

No

Yes

BOATS

39

1

N

I

1

0

No

Yes

FISHING_NETS

40

1

N

I

1

0

No

Yes

Sri Lanka HIES 2009/10 Manual (Version 1.1)

264

R20
Record: Section - 6B - Debtness-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label------------------------------------------------------------------------------------------------------------Banks
1:2 Banks(Blank)Bank amount
100:1000000(Blank)

BANKS

18

1

N

I

1

0

No

Yes

BANK_AMOUNT

19

7

N

I

1

0

No

Yes

FINANCE

26

1

N

I

1

0

No

Yes

FINANCE_AMOUNT

27

7

N

I

1

0

No

Yes

EMPLOYER

34

1

N

I

1

0

No

Yes

EMPLOYER_AMOUNT

35

7

N

I

1

0

No

Yes

LENDER

42

1

N

I

1

0

No

Yes

LENDER_AMOUNT

43

7

N

I

1

0

No

Yes

RETAIL_SHOPS

50

1

N

I

1

0

No

Yes

RETAIL_SHOP_AMOUNT

51

7

N

I

1

0

No

Yes

Bank AmountFinance
1:2 Finance(Blank)Finance amount
100:1000000 Finance Amount(Blank)Employer
1:2 Own Place of work

(Blank)Employer amount
100:300000(Blank)Employer amount

Lender
1:2 Money Lenders(Blank)Lender amount
100:100000 Money Lenders Amount(Blank)Retail shops
1:2 Retail outlets(Blank)Retail shop amount
100:3000 Retail outlets Amount(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

265

PAWNING
1:2 Pawning(Blank)Pawning amountPAWNING_AMOUNT
100:100000 Pawning Amount(Blank)Instalment goods
INSTALMENT_GOODS
1:2 Durable goods purchased in instalments(Blank)Instalement amount
INSTALEMENT_AMOUNT
100:100000 Instalement amount(Blank)OTHER_DEBTS
Other
1:2 Other(Blank)OTHER_AMOUNTother amount
100:100000 Other mount(Blank)Pawning

Sri Lanka HIES 2009/10 Manual (Version 1.1)

58

1

N

I

1

0

No

Yes

59

7

N

I

1

0

No

Yes

66

1

N

I

1

0

No

Yes

67

7

N

I

1

0

No

Yes

74

1

N

I

1

0

No

Yes

75

7

N

I

1

0

No

Yes

266

R22
Record: Section - 7 - Basic facilities-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Bus halt
BUS_HALT
18
2
N
I
1
0
No Yes0:99 Bus Halt
(Blank)Bus halt time
BUS_HALT_TIME
20
3
N
I
1
0
No Yes1:120 BHTime(Blank)Pre school
PRE SCHOOL
23
2
N
I
1
0
No Yes0:99 Pre School(Blank)N
I
1
0
No YesPre school time
PRE_SCHOOL_TIME
25
3
1:120 Pre SchTime(Blank)N
I
1
0
No YesPrimery school
PRIMERY_SCHOOL
28
2
0:99 Primary School(Blank)Primery school time
1:120 Primary SchoolTime(Blank)Secondery school
0:99 Secondery School(Blank)Sec school time
1:120 SeconderySchoolTime(Blank)Hospital
0:99 Base Hospital(Blank)Hospital time
1:120 Base Hospital Time(Blank)PRIMERY_SCHOOL_TIME

30

3

N

I

1

0

No

Yes

SECONDERY_SCHOOL

33

2

N

I

1

0

No

Yes

SEC SCHOOL TIME

35

3

N

I

1

0

No

Yes

HOSPITAL

38

2

N

I

1

0

No

Yes

HOSPITAL_TIME

40

3

N

I

1

0

No

Yes

Sri Lanka HIES 2009/10 Manual (Version 1.1)

267

Matrenity home
0:99 Maternity Home
(Blank)Maternity home time
1:120 Maternity HomeTime(Blank)Gov dispensary
0:99 Government Dispensary(Blank)Gov dispensary time
1:120 Government DispensaryTime(Blank)Private dispensary
0:99 Private Dispensary(Blank)Private dispensary time
1:120 Private DispensaryTime(Blank)Maternity clinic
0:99 MatenityClinic(Blank)Maternity clinic time
1:120 MaternityclinicTime(Blank)DMO
0:99 DMO(Blank)DMO time
1:120 DMOTime(Blank)MCUCPC
0:99 MCUCPC(Blank)MCUCPC time
1:120 MCUCPCTime(Blank)DS office0:99 DS Office(Blank)DS office time
1:120 DSOfficeTime(Blank)

MATRENITY_HOME

43

2

N

I

1

0

No

Yes

MATERNITY_HOME_TIME

45

3

N

I

1

0

No

Yes

GOV_DISPENSARZ

48

2

N

I

1

0

No

Yes

GOV_DISPENSARY_TIME

50

3

N

I

1

0

No

Yes

PRIVATE DISPENSARY

53

2

N

I

1

0

No

Yes

PRIVATE_DISPENSARY_TIME

55

3

N

I

1

0

No

Yes

MATERNITY_CLINIC

58

2

N

I

1

0

No

Yes

MATERNITY_CLINIC_TIME

60

3

N

I

1

0

No

Yes

DMO

63

2

N

I

1

0

No

Yes

DMO_TIME

65

3

N

I

1

0

No

Yes

MCUCPC

68

2

N

I

1

0

No

Yes

MCUCPC_TIME

70

3

N

I

1

0

No

Yes

DS_OFFICE

73

2

N

I

1

0

No

Yes

DS_OFFICE_TIME

75

3

N

I

1

0

No

Yes

Sri Lanka HIES 2009/10 Manual (Version 1.1)

268

GN office0:99 GNOffice(Blank)GN office time
1:120 GNOfficeTime(Blank)Post office
0:99 Post office(Blank)Post office time
1:120 PostOfficeTime(Blank)Bank
0:99 Bank(Blank)Bank time
1:120 BankTime(Blank)Agri office
0:99 AgriOffice(Blank)Agri office time
1:120 AgriOfficeTime(Blank)Is power lines near
1:2
(Blank)Is tel lines near
1:2
(Blank)Is water service near
1:2
(Blank)GN_OFFICE

78

2

N

I

1

0

No

Yes

GN_OFFICE_TIME

80

3

N

I

1

0

No

Yes

POST OFFICE

83

2

N

I

1

0

No

Yes

POST_OFFICE_TIME

85

3

N

I

1

0

No

Yes

BANK

88

2

N

I

1

0

No

Yes

BANK_TIME

90

3

N

I

1

0

No

Yes

AGRI_OFFICE

93

2

N

I

1

0

No

Yes

AGRI_OFFICE_TIME

95

3

N

I

1

0

No

Yes

IS_POWER_LINES_NEAR

98

1

N

I

1

0

No

Yes

IS_TEL_LINES_NEAR

99

1

N

I

1

0

No

Yes

IS WATER SERVICE NEAR

100

1

N

I

1

0

No

Yes

Sri Lanka HIES 2009/10 Manual (Version 1.1)

269

R23
Record: Section - 8 - Housing-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label
1 Structure
1 Single house
2 Flat3 Attached house /Annex
4 Line room / Row house
5 Slum / Shanty
9 Other (specify)(Blank)2 Bed rooms
0:9 Bedroom(Blank)3 Area1 less than 100
2 100 - less than 250
3 250 - less than 500
4 500 - less than 750
5 750 or more than 750(Blank)STRUCTURE

18

1

N

I

1

0

No

Yes

BED_ROOMS

19

1

N

I

1

0

No

Yes

AREA

20

1

N

I

1

0

No

Yes

4 Walls

WALLS

21

1

N

I

1

0

No

Yes

1 Brick
2 Cabook
3 Cement block
4 Pressed soil blocks
5 Mud
6 Plank / Metal sheet
7 Cadjan / Palmyrah
9 Other (specify)(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

270

4 Floor

FLOOR

22

1

N

I

1

0

No

Yes

23

1

N

I

1

0

No

Yes

24

1

N

I

1

0

No

Yes

25

1

N

I

1

0

No

Yes

26

1

N

I

1

0

No

Yes

27

4

N

I

1

0

No

Yes

1 Cement
2 Terrazo / Tile
3 Mud
9 Other (specify)(Blank)4 Roof

ROOF
1 Tile2 Asbestos
3 Concrete
4 Metal sheet
5 Cadjan / Palmyrah / Straw
9 Other (specify)(Blank)5 Tenure
TENURE
1 Constructed / Purchased by an occupant
2 Inherited
3 Freely received / Received as a gift
4 Compensated
5 Rent free
6 Relie payment
7 Rent / Lease
8 Encroached
9 Other (specify)(Blank)6a Drinking water
DRINKING_WATER
1 Protected well within premises
2 Protected well outside premises
3 Unprotected well
4 Tube well
5 Tap within unit / Premises (main line)
6 Tap outside premises ( main line)
7 Stream water collected & distributed by pipe lines
8 River / Tank / Streams
9 Other (specify)(Blank)6b1 Own Other water
OWN_WATER
0:2 OwnOtherWater(Blank)6b2 Water distance
WATER_DISTANCE
0:9999 WaterDistance(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

271

6c1 Water sufficency
1:2 Drink(Blank)6c2 Other water sufficency
1:2 Bath/Wash(Blank)7a Availability of Tioilet
1 Exclusive for the household
2 Sharing with another household
3 Public Convenience
4 NoneWATER_SUFFICENCY

31

1

N

I

1

0

No

Yes

OTHER_WATER_SUFFICENCY

32

1

N

I

1

0

No

Yes

AVAILABILITY_TIOILET

33

1

N

I

1

0

No

Yes

34

1

N

I

1

0

No

Yes

35

1

N

I

1

0

No

Yes

36

1

N

I

1

0

No

Yes

37

1

N

I

1

0

No

Yes

7b Toilet type
TOILET_TYPE
1 Water seal
2 Pour flush
3 Pit
4 Other (specify)(Blank)8 Garbage dumping
GARBAGE_DUMPING
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)(Blank)9 Lite source
LITE_SOURCE
1 Kerosene
2 Electricity
3 Solar energy
4 Generator / Battery
9 Other (specify)(Blank)10 Cooking fuel
COOKING_FUEL
1 Fire wood
2 Gas
3 Kerosene
4 Electricity
5 Saw dust / Paddy husk
9 Other (specify)(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

272

11a Is collect firewood
1:2(Blank)11b Fire wood ownForestOther
0:3
Fire wood ownForestOther(Blank)11b FWD distance
0:9999 FWDDistance(Blank)12a Natural calamity
1:2
Natural calamity(Blank)12b1 Flooding
1:2 Floodind(Blank)12b2 Drought
1:2
Drought(Blank)12b3 Land slides
1:2 Land slides(Blank)12b4 Civil unrest
1:2
Civil unrest(Blank)12b5 Wild animal
1:2
Wild animal(Blank)12b6 Other calamity
1:2
Other calamity(Blank)

IS_COLLECT_FIREWOOD

38

1

N

I

1

0

No

Yes

FIRE_WOOD_OWN

39

1

N

I

1

0

No

Yes

OTHER_DISTANCE

40

4

N

I

1

0

No

Yes

NATURAL_CALAMITY

44

1

N

I

1

0

No

Yes

FLOODING

45

1

N

I

1

0

No

Yes

DROUGHT

46

1

N

I

1

0

No

Yes

LAND_SLIDES

47

1

N

I

1

0

No

Yes

CIVIL_UNREST

48

1

N

I

1

0

No

Yes

WILD_ANIMAL

49

1

N

I

1

0

No

Yes

OTHER_CALAMITY

50

1

N

I

1

0

No

Yes

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273

R24
Record: Section - 9 - Land and animal-------------------------------------------------------------------------------------------------------------Data Item
Dec Zero
Item Name
Start Len Type Type Occ Dec Char Fill
Item Label-------------------------------------------------------------------------------------------------------------Is agriland owner
IS_AGRILAND_OWNER
18
1
N
I
1
0
No Yes
1:2(Blank)Paddy own acr
PADDY_OWN_ACR
19
3
N
I
1
0
No Yes0:25 Paddy Own Acr(Blank)Paddy own rt
PADDY_OWN_RT
22
1
N
I
1
0
No Yes
0:3 Paddy own rt(Blank)Paddy own perch
PADDY_OWN_PERCH
23
2
N
I
1
0
No Yes0:39 Paddy own perch(Blank)Paddy other acr
PADDY_OTHER_ACR
25
3
N
I
1
0
No Yes0:25 Paddy other acr(Blank)Paddy other rt
PADDY OTHER RT
28
1
N
I
1
0
No Yes
0:3 PaddyOtherrt(Blank)Paddy other perch
PADDY_OTHER_PERCH
29
2
N
I
1
0
No Yes0:39 Paddy other perch(Blank)Land own acr
LAND_OWN_ACR
31
3
N
I
1
0
No Yes0:25 Land own acr(Blank)Land own rt
LAND OWN RT
34
1
N
I
1
0
No Yes
0:3 LandOwnrt(Blank)Land own perch
LAND_OWN_PERCH
35
2
N
I
1
0
No Yes0:39 Land own perch(Blank)Sri Lanka HIES 2009/10 Manual (Version 1.1)

274Land other acr LAND_OTHER_ACR
0:25 Land other acr(Blank)Land other rt
0:3 LandOtherrt(Blank)Land other perch
0:39 Land other perch(Blank)Home own acr
0:25 HomeOwnAcr(Blank)Home own rt
0:3 Home own rt(Blank)Home own perch HOME_OWN_PERCH
0:39 Home own perch(Blank)Home other acr HOME_OTHER_ACR
0:25 Home other acr(Blank)Home other rt HOME_OTHER_RT
Home other rt(Blank)Home other perch
0:39 Home other perch(Blank)Cows buffalows
1:2 Cows buffalows(Blank)Cows count
1:4 Cows count(Blank)Goats sheeps
1:2 Goats sheeps(Blank)Goat count
1:4 Goat count(Blank)Pigs
1:2 Pigs(Blank)

37

3

N

I

1

0

No

Yes

LAND_OTHER_RT

40

1

N

I

1

0

No

Yes

LAND_OTHER_PERCH

41

2

N

I

1

0

No

Yes

HOME_OWN_ACR

43

3

N

I

1

0

No

Yes

HOME_OWN_RT

46

1

N

I

1

0

No

Yes

47

2

N

I

1

0

No

Yes

49

3

N

I

1

0

No

Yes

52

1

N

I

1

0

No

Yes 0:3

HOME_OTHER_PERCH

53

2

N

I

1

0

No

Yes

COWS_BUFFALOWS

55

1

N

I

1

0

No

Yes

COWS_COUNT

56

1

N

I

1

0

No

Yes

GOATS_SHEEPS

57

1

N

I

1

0

No

Yes

GOAT_COUNT

58

1

N

I

1

0

No

Yes

PIGS

59

1

N

I

1

0

No

Yes

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275

Pigs count
1:4 Pigs count(Blank)Chickens
1:2 Chickens(Blank)Chicken count
1:4 Chicken count(Blank)Other
1:2 OtherAnimal(Blank)PIGS_COUNT

60

1

N

I

1

0

No

Yes

CHICKENS

61

1

N

I

1

0

No

Yes

CHICKEN_COUNT

62

1

N

I

1

0

No

Yes

OTHER_ANIMALS

63

1

N

I

1

0

No

Yes

Sri Lanka HIES 2009/10 Manual (Version 1.1)

276



Data dictionary of R25:

Summary of household income and expenditure at household level
Number of records: 19,958
Variable

Description

Remarks

ID

Household identifier

wt

Weightttinc

Monthly total household income= monetary + inkind

monetary

Monthly total monetary income= wage + crop + livestock +nonagri + other + widfall

wage

Monthly income from paid employments

cropMonthly income from agricultural activities

livestockMonthly income from other agricultural activities

nonagriMonthly income from non-agricultural activities

other

Monthly income from other cash receipt

windfallMonthly income by chance or ad hoc gains

inkindMonthly total non-monetary income

ttexp

Monthly total household expenditure= food + nfood

food

Monthly household expenditure on food= Sum of g1 to g18

nfoodMonthly household expenditure on non-food= Sum of g19 to g35

g1 to g35

Monthly household expenditure by subgroup

COL_3 to COL_15 are already
added to the respective subgroups

COL_3 toMonthly household expenditure for boarders

COL_15

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277



Data dictionary of R26:

Summary of household income at individual level
Number of records: 80,872
Variable

Description

Remarks

ID

Household identifier

PID

Individual identifier

wt

WeightreceiverIs an income receiver?

relation

Relationship to the household headage10

Is age over 10 years old?

ttinc

Monthly total income after transferredtransferred

Is income transferred to the head?

befincMonthly total income before transferred

wage

Monthly income from paid employments

cropMonthly income from agricultural activities

livestockMonthly income from other agricultural activities

nonagriMonthly income from non-agricultural activities

other

Monthly income from other cash receipt

windfallMonthly income by chance or ad hoc gains

Sri Lanka HIES 2009/10 Manual (Version 1.1)

1. Yes 0. No

1. age >= 10 0. age < 10

1. Yes 0. No

Attachment 3

Sri Lanka Districts Map

41

278

43
53
71
72

62

51

61

22
21

52

92

12

23

11

81

91

13

31

82

33
32

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279

Attachment 4

Administrative structure, and urban, rural and estateThis attachment came from the research paper on “Determinants of the Poverty in Western Provincein Sri Lanka”, Research - based Training Program, AY 2010, UNSIAP by Ms. Dilhanie Deepawansa,
Department of Census and Statistics, Sri Lanka.

*******************************************************************************
3. Data and Methodology
3.1 Introduction
Poverty is not a separate phenomenon that can be studied without considering socio economic
factors such as education, health, available of drinking water, electricity etc. The Household Income
and Expenditure Survey conducted by Department of Census and Statistics collect data on different
dimensions. This study is based on the results of household Income and expenditure survey 2006/07
data.
Figure 3.1 provides a schematic presentation of the administrative structure of Sri Lanka.

Sri Lanka HIES 2009/10 Manual (Version 1.1)

280

Three kinds of local government authorities function at the lowest level of the administrative
structure. These are Municipal council, urban councils and Divisional councils (Pradeshiya sabha).
The constituents of the urban and rural sectors are determined by the kind of local governance thelowest-level administrative area units are subject to. The areas governed by Municipal councils arecalled urban sector and the areas governed by Divisional councils are called rural sector. In Sri
Lanka there is a special sector called Estate sector which is demarcated by the plantation areas in the
country and they are governed by Divisional Councils. The definition for three sectors is as follows.

Urban Sector : Area governed by either Municipal Council or Urban Council is considered as
Urban Sector.
Estate Sector : Plantation areas, which are more than 20 acres of extent and having not lessthan 10 residential labourers, are considered as estate sector.
Rural Sector : Residential areas, which do not belong to urban sector or estate sector, areconsidered as rural sector.

The population representation in these three sectors are as follows;******************************************************************************
Annex 03
Estate sector
In addition to the Urban and Rural sectors, Sri Lankan economy has a special sub-economic sector,
called Estate sector. . This sector is somewhat unique in its characteristics in all forms, ranging
from composition of household units to organization of political establishments. It is also foundunique when compared with other general economics around the world. However, the community
settlement and its economic behavior and characteristics can be compared with other overseas
Indian migrant communities in Fiji, Mauritius, Malaysia, Trinidad and Guyana, as Sri Lankan Estate

Sri Lanka HIES 2009/10 Manual (Version 1.1)

281

Sector community have adapted to the “alien environment and conditions provided within theplantation set up”. Many researchers have found that the estate sector communities of Sri Lanka
share many features and similarities with such overseas communities.
British colonization introduced radical economic changes in Sri Lanka which caused the transition
from an agriculture-based subsistence economy to a plantation-based market economy. The originof Estate sector can be traced specially to setting up of tea plantation on the island by the Britishcolonists. In 1867, a Scotsman named James Taylor cleared 19 acres of forest and planted the first
seeds. By the mid 1870s, the estate had grown to nearly 100 acres and several other estates were
found to have come up.
With the expansion of tea plantation from 1890s, labourers became somewhat scarce, demanding
high wages and not to be depended on. To solve the problem of labour scarcity, the British planters
decided to supply the estates with immigrant labours from South India. The bulk of the regularlabour force was drawn from the Tamil Districts of southern India, mainly Madurai, Tinnelvelly andTanjore. This process was assisted by the proximity and condition of poverty prevailing in these
districts of Southern India. Poverty, various forms of agrestic servitude which had engendered anagricultural labouring class, low wages, and near famine conditions combined to induce segments ofsouthern Indian population to accept employment on the plantation in Sri Lanka. As a result,immigration to Sri Lanka increased more than emigration towards a more permanent andsemipermanent
settlement of Tamil labours from South India. More needs to be known, however,regarding the dynamic of recruitment and the social composition of the Indian Tamil labour force in
Sri Lanka. The majority of who probably carried low caste status in their homeland. Unlike village
life in South India, caste identity does not emerge as the most dominants or imperative status among
the Estate Tamils, but certain aspects of caste are still important for their social organization.
Even today in plantations (Estate sector) not only in tea plantation but also in rubber and coconut,
Tamil workers are employed in large numbers.
Geographically and socially isolated, the plantations led the Estate Tamils to emerge as a relatively“closed” community limited to the territorial boundaries of the plantations. Work, housing, andcondition of life, including recreation, take place within the same physical and organizational
territory of the estate. As a result, the Tamil estate workers’ view of outside world is often limited tothe boundaries of estate where they spent their almost or entire life.
Typically a plantation provides rent-free housing, free medicines from its surgeries (dispensary), amaternity ward and midwife(on large estate) estate school, nursery, day care centers for kids,
temples, churches, transports etc., for the workers of the estates and their families. Most of theservices and needs related to the production and social life are provided within the estate.
In Tea estate, tea pickers are always females and male workers are engaged in manual work and

Sri Lanka HIES 2009/10 Manual (Version 1.1)

282

most of the factory work such as processing tea.
All successive governments elected since independence introduced various economic and social
developmental programs to address the issues persisting in the Estate sector. The estate workers did
not enjoy the benefits or have the same rights as other citizens in the country. For example, they did
not have the right to vote until they are properly registered.
There was also a pressure from various influential social groups to expel them to India as they viewthe Estate workers as a burden that was imposed on Sri Lankan economy by the British for theireconomic gains. As a result, hundreds of migrant workers were repatriated while the same numberof workers were offered Sri Lankan citizenship under Srima – Shastri fact signed between Sri Lanka
and India (1960-65)
The Estate Tamils franchise and citizenship was extended in 1986, which resulted in their
participation in political representation that influenced the successive governments to introduce
various developmental programs for the Estate Sector. This has had rather positive outcome in theirsocial, educational and political spheres.
The present government has pledged in its election manifesto to provide more radical developmental
programs to the Estate workers. Among them, the land ownership, proper housing facilities,
promising educational, health, employment and skill development and upward social mobility canbe highlighted as remarkable steps overcome miseries experienced by the Estate Workers in general.
*******************************************************************************

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Attachemnt 5Classification of industry (4-digit)
111

Growing of cereals (except rice), leguminous crops andoil seeds

891

Mining of chemical and fertilizer minerals

892

Extraction of peat

893

Extraction of salt

899

Other mining and quarrying n.e.c.

910

Support activities for petroleum and natural gas
extraction (Provided on Contract Basis)

990

112

Growing of rice113

Growing of vegetables and melons, roots and tubers

114

Growing of sugar cane

115

Growing of tobacco

116

Growing of fibre crops

119

Growing of other non-perennial crops

121

Growing of grapes

122

Growing of tropical and subtropical fruits

123

Growing of citrus fruits

124

Growing of pome fruits and stone fruits

125

Growing of other tree and bush fruits and nuts

126

Growing of oleaginous fruits

127

Growing of beverage crops

128

Growing of spices, aromatic, drug and pharmaceutical

Support activities for other mining and quarrying

1010 Processing and preserving of meat
1020 Processing and preserving of fish, crustaceans andmolluscs
1030 Processing and preserving of fruit and vegetable
1040 Manufacture of vegetable and animal oils and fats
1050 Manufacture of dairy products
1061 Manufacture of grain mill products
1062 Manufacture of starches and starch products
1071 Manufacture of bakery products
1072 Manufacture of sugar
1073 Manufactureofcocoa,

chocolate

and

sugar

confectionerycrops

1074 Manufacture of macaroni, noodles, couscous andsimilar farinaceous products
1075 Manufacture of prepared meals and dishes

129

Growing of other perennial crops

130

Plant propagation

141

Raising of cattle and buffaloes

142

Raising of horses and other equines

143

Raising of camels and camelids

144

Raising of sheep and goats

145

Raising of swine/pigs

146

Raising of poultry

149

Raising of other animals

150

Mixed farming (both crop and livestock)161

Support activities for crop production

162

Support activities for animal production

163

Post-harvest crop activities (Preparation for primary

1079 Manufacture of other food products n.e.c.
1080 Manufacture of prepared animal feeds
1101 Distilling, rectifying and blending of spirits
1102 Manufacture of wines
1103 Manufcture of malt liquors and malt
1104 Manufacture of soft drinks; production of mineralwaters and other bottled waters
1200 Manufacture of tobacco products
1311 Preparation and spinning of textile fibres
1312 Weaving of textiles
1313 Finishing of textile

market)1391 Manufacture of knitted and crochted fabrics
1392 Manufacture of madeup textile articles, except apparel
1393 Manufacture of carpets and rugs

164

Seed processing for propagation

170

Hunting. Trapping and related service activites

210

Silviculture and other forestry activities

220

Logging

230

Gathering of non-wood forest products

240

support services to forestry

311

Marine Fishing312

Freshwater fishing

321

Marine aquaculture

322

Freshwater aquaculture710

Mining of iron ores

729

Mining of other non ferrous metal ores

810

Quarrying of stone, sand and clay

1394 Manufacture of twine, cordage, rope and cables of coir
fibres1399 Manufacture of other textile n.e.c.
1410 Manufacture of wearing apparel except fur apparel
1420 Manufacture of articles of fur
1430 Manufacture of knitted and crocheted apparel
1511 Tanning and dressing of leather, dressing and dyeingof leather
1512 Manufacture of luggage, handbags and the likesaddlery and harness
1520 Manufacture of footwear
1610 Saw-milling and planning of wood
1621 Manufacture of veneer sheets and wood based panels

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284

1622 Manufacture of Builder's carpentry and joinery

metal

1623 Manufacture of wooden containers

2513 Manufacture of steam generators, except central

1629 Manufacture of other products of wood, manufacture ofarticles of cork straw and plaiting materials

2520 Manufacture of weapons and ammunition

1701 Manufacture of pulp, paper and paper board

2591 Forging, pressing, stamping and roll-forming of metal,

1702 Manufacture of corrugated paper and paper board andof containers of paper and paper board
1709 Manufacture of other articles

heading hot water boilers

powder metallurgy
2592 Treatment and coating of metal, machiningof paper and paperboard2593 Manufacture of cutlery, hand tools and general
hardware1811 Printing

2599 Manufacture of other fabricated metal products n.e.c.

1812 Service activities related to printing

2610 Manufacture of electronic components & boards

1820 Reproduction of recorded media

2620 Manufacture of computers and peripheral equipment

1910 Manufacture of coke oven products

2630 Manufacture of communication equipment

1920 Manufacture of refined petroleum products

2640 Manufacture of consumer electronics

2011 Manufacture of basic chemicals

2651 Manufacture of measuring, testing, navigating and2012 Manufacture of fertilizers and nitrogen compounds
2013 Manufacture of plastics and synthetic rubber in primaryforms

2652 Manufacture of watch and clocks
2660 Manufacture2021 Manufacture of pesticides and other agro chemical
products

ofirradiation,electro

medicalandelectrotherapeutic equipment
2670 Manufacture of optical instruments and photographic

2022 Manufacture of paints, varnishes and similar coatings
printing ink and mastics
preparations,equipment
2680 Manufacture of magnetic and optical media

2023 Manufacture of soap and detergents, cleaning andpolishing

control equipments

perfumes

andtoiletpreparations

2710 Manufactureofelectric

motors,generators,

transformers and electricity distribution & control
apparatus

2029 Manufacture of other chemical products n.e.c.

2720 Manufacture of batteries and accumulators

2030 Manufacture of man-made fibres

2731 Manufacture of fibre optic cables

2100 Manufacture of pharmaceuticals, medicinal, chemical

2732 Manufacture of other electronic and electric wire andand botanical products

cables2211 Manufacture of rubber tyres and tubes,

retreading

and rebuilding of rubber tyres2733 Manufacture of wiring devices
2740 Manufacture of electric lighting equipments

2219 Manufacture of other rubber products

2750 Manufacture of domestic appliance2220 Manufacture of plastic products

2790 Manufacture of other electrical equipment

2310 Manufacture of glass and glass products

2811 Manufacture of engine and turbines, except aircraft,vehicle and cycle engine

2391 Manufacture of refractory products2392 Manufacture of clay building materials

2812 Manufacture of fluid power equipments

2393 Manufacture of other porcelain and ceramic products

2813 Manufacture of pumps, compressors,taps and valves2394 Manufacture of cement, lime and plaster

2814 Manufacture of bearings, gears, gearing and driving

2395 Manufacture of articles ofconcrete, cement andelements
2815 Manufacture of ovens, farnaces& furnace burners

plaster
2396 Cutting, shaping and finishing of stones

2816 Manufacture of lifting and handling equipments

2399 Manufacture of non-metallic mineral products n.e.c.

2817 Manufacture of office machinery and equipment(except computers and peripheral equipment)

2410 Manufacture of basic iron and steel
2420 Manufacture of basic precious and other non-ferrous

2818 Manufacture of power-driven hand tools
2819 Manufacture of other general- purpose machinery

metals2431 Casting of iron and steel

2821 Manufacture of agricultural and forestry machinery

2432 Casting of non-ferrous metals

2822 Manufacture of metal-forming machinery and machinetools

2511 Manufacture of structural metal products2512 Manufacture of tanks, reservoirs

and containers of2823 Manufacture of machinery for metallurgy

Sri Lanka HIES 2009/10 Manual (Version 1.1)

285

2824 Manufacture of machinery for mining,quarrying andconstruction

services
4100 Construction of Buildings2825 Manufacture of machinery for food, beverages andtobacco processing

4210 Construction of Roads and Railways
4220 Construction of Utility Projects

2826 Manufacture of machinery for textile, apparel andleather production

4290 Construction of other Civil Engineering Projects
4311 Demolition

2829 Manufacture of other special-purpose machinery

4312 Site Preparation

2910 Manufacture of motor vehicles

4321 Electrical Installation

2920 Manufacture of bodies for motor vehicles; Manufacture

4322 Plumbing, Heat and Air-conditioning Installationof trailers & semi-trailers

4329 Other Construction installation

2930 Manufacture of parts and accessories

for motor

vehicles

4330 Building Completion and Finishing
4390 Other Specialized Construction Activities

3011 Building of ships and floating structures

4510 Sale of motor vehicles

3012 Building of pleasure & sporting boats

4520 Maintenance and repair of motor Vehicles

3020 Manufacture of railway locomotive & rolling stock

4530 Wholesale and retail sale of motor vehicle part and3030 Manufacture of air & spacecraft & related machinery
3040 Manufacture of military fighting vehicles

accessories
4540 Sale, maintenance and repair of motor cycles, and3091 Manufacture of motorcycles

related parts and accessories

3092 Manufacture of bicycles & invalid carriages

4610 Wholesale on a fee or contract basis

3099 Manufacture of other transport equipment n.e.c.

4620 Wholesale of agricultural raw materials and live

3100 Manufacture of furniture

animals

3211 Manufacture of jewellery & related articles

4630 Wholesale of food, beverages and tobacco

3212 Manufacture of imitation jewellery and related articles

4641 Wholesale of textile clothing and Foot wear

3220 Manufacture of musical instruments

4649 Wholesale of other household goods

3230 Manufacture of sports goods

4651 Wholesaleofcomputers,

computers

peripheral

equipment and software

3240 Manufacture of games & toys
3250 Manufacture of medical & dental

instruments &4652 Wholesaleofelectronicandtelecommunicationequipment and parts

supplies

4653 Wholesale of agricultural machinery, equipment and3290 Other manufacturing n.e.c.

supplies

3311 Repair of fabricated metal products
3312 Repair of machinery

4659 Wholesale of other machinery and equipment

3313 Repair of electronic & optical equipment

4661 Wholesale of solid, liquid and gaseous fuels andrelated products

3314 Repair of electrical equipment
3315 Repair of transport equipment ; except motor vehicles

4662 Wholesale of metals and metal ores

3319 Repair of other equipment

4663 Wholesale

power

generation,transmission

construction

materials,

hardware,plumbing and heating equipment and supplies

3320 Installation of industrial machinery & equipment
3510 Electric

of

and

4669 wholesale of waste and scrap and other products n.e.c4690 Non - specialized wholesale trade

distribution
3520 Manufacture of gas, distribution of gaseous fuels

4711 Retail sale in non-specialized stores with food,beverages or tobacco predominating

through mains
3530 Steam and air conditioning supply

4719 Other retail sale in non-specialized stores

3600 Water collection,treatment and supply

4721 Retail sale of food in specialized stores

3700 Sewerage4722 Retail sale of beverages in specialized stores

3811 Collection of non-hazardous waste

4723 Retail sale of tobacco products in specialized stores

3812 Collection of hazardous waste

4730 Retail sale of automotive fuel in specialized stores

3821 Treatment and disposal of non-hazardous waste

4741 Retail sale of computers, peripheral units, software and3822 Treatment and disposal of hazardous waste

telecommunication equipment in specialized stores
4742 Retail sale of audio and video equipment in specialized

3830 Materials recovery
3900 Remediation activities and other waste management

storesSri Lanka HIES 2009/10 Manual (Version 1.1)

286

4751 Retail sale of textile in specialized stores

5590 Other accommodation

4752 Retail sale of hardware, paints and glass in specialized

5610 Restaurants and mobile food service activities

stores5621 Event catering

4753 Retail sale of carpets, rugs, wall and floor coverings in
specialized stores

5629 Other food service activities
5630 Beverage Serving activities

4759 Retail sale of electrical household appliances, furniture,

5811 Book publishinglighting equipment and other household articles in

5812 Publishing of Directories and mailing lists

specialized stores

5813 Publishing of Newspapers Journals and Periodicals

4761 Retail sale of books, newspapers and stationary in
specialized stores

5819 Other publishing Activities
5820 Software publishing4762 Retail sale of music and video recording in specialized
stores5911 Motion picture, video and television programmed
production activities

4763 Retail sale of sporting equipment in specialized stores
4764 Retail sale of games and toys in specialized Stores
4771 Retail sale of clothing, footwear and leather articles in
specialized stores

5912 Motion picture, video and television programmed
post-production activities
5913 Motion picture, video and television programmed
distribution activities

4772 Retail sale of pharmaceutical and medical goods,
cosmetic and toilet articles in specialized stores

5914 Motion picture projection activities
5920 Sound recording and music publishing activities

4773 Other retail sale of new goods in specialized stores

6010 Radio broadcasting activities

4774 Retail sale of second-hand goods

6020 Television programming and broadcasting activities

4781 Retail sale via stalls and markets of food, beverages

6110 Wired telecommunications activities

and tobacco products

6120 Wireless tele communications activities

4782 Retail sale via stalls and markets of textiles, clothing
and footwear.

6130 Satellite telecommunications activities
6190 Other telecommunications activities

4789 Retail sale via stalls and markets of other goods

6201 Computer programming activities

4791 Retail sale via mail order house or via internet

6202 Computer

4799 Other retail sale not in stores, stalls or markets.
4911 passenger transport by inter-urban railways

consultancy

andcomputer

facilities

management activities
6209 Other information technology and computer service
activities

4912 Freight rail transport
4921 Urban and suburban passenger land transport

6311 Data processing, hosting and related activities

4922 Other passenger land transport

6312 Web portals

4923 Freight transport by road

6391 News agency activities

5011 Sea and costal passenger water transport

6399 Other information service activities n.e.c5012 Sea and coastal freight water transport

6411 Central Banking

5021 Inland passenger water transport

6419 Other monetary Intermediation (with or without current
account)

5022 Inland freight water transport
5110 Passenger air transport

6420 Activities of holding companies

5120 Freight air transport

6430 Trusts, funds and similar financial entities

5210 Warehousing and storage

6491 Financial leasing

5221 Service activities incidental to land transportation

6492 Other Credit Granting

5222 Service activities incidental to water transportation

6499 Other financial service activities, except insurance and5223 Service activities incidental to air transportation

pension funding activities n.e.c5224 Cargo handling

6511 Life Insurance

5229 Other transportation support activities

6512 Non-life insurance

5310 Postal activities

6520 Reinsurance

5320 Courier activities

6530 Pension funding

5510 Short term accommodation activities

6611 Administration of finanical markets

5520 Camping grounds, recreational vehicle, parks and6612 Security and commodity contracts brokerage (security

trailer parks

dealing on behalf of others)
Sri Lanka HIES 2009/10 Manual (Version 1.1)

287

6619 Other activities auxiliary to financial service activities

8211 Combined office administrative service activities

6621 Risk and damage evaluation

8219 Photocopying,6622 Acitivities of insurance agents and brokers

doucment

preparation

and

other

specialized office support activities

6629 Other activities auxiliary for insurance and pension
funding

8220 Activities of call centres
8230 Organization of conventions and trade shows

6630 Fund management activities

8291 Activities of collection agencies and credit bureaus

6810 Real estate activities with own or leased property

8292 Packaging activities

6820 Real estate activities on a fee or contract basis

8299 Other business support service activities n.e.c6910 Legal activites

8411 General public administration activities

6920 Accounting, book keeping and auditing activities, tax

8412 Regulation of the activities of providing health care,consultancy

education, cultural services and other social services,

7010 Activities of head offices

excluding social security

7020 Management consultancy activities

8413 Regulation of and contribution to more efficient

7110 Architectural and engineering activites and related

operation of businesses

technical consultancy

8421 Foreign affairs

7120 Technical testing and analysis

8422 Defence activities

7210 Research and experimental development on natural

8423 Public order and safety activities

science and engineering

8430 Compulsory social security activities

7220 Research and experimental development on social
science and humanities

8510 Pre-primary and primary education
8521 General secondary education

7310 Advertising8522 Technical and vocational secondary education

7320 Market research and public opinion polling8530 Higher education

7410 Specialized design activities

8541 Sports and recreation education

7420 Photographic activities

8542 Cultural education

7490 Other professional,scientific and technical activities

8549 Other education n.e.c.

n.e.c

8550 Educational support activities

7500 Veterinary activities

8610 Hospital activities

7710 Renting and leasing of motor vechicles

8620 Medical and dental practice activities

7721 Renting and leasing of recreational and sports goods

8690 Other human health activities

7722 Renting of video tapes and disks

8710 Residential nursing care facilities

7729 Renting and leasing of other personal and household

8720 Residential care activities for mental retardation,
mental health and substance abuse

goods
7730 Renting and leasing of other machinery, equipment

8730 Residential care activities for the elderly and disabled
8790 Other residential care activities

and tangible goods
7740 Leasing of intellectual property and similar products,

8810 Social work activities without accommodation for theelderly and disabled

except coypyrighted works
7810 Activities of employment placement agencies

8890 Other social work activities without accommodation

7820 Temporary employment agency activities

9000 Creative, arts and entertainment activities

7830 Other human resources provision

9101 Library and archives activities

7911 Travel agency activities

9102 Museums activities and operation historical sites andbuildings

7912 Tour operator activities
7990 Other reservation service and related activities

9103 Botanical and zoological gardens and natural reserves
activities

8010 Private security activities
8020 Security systems service activities

9200 Gambling and betting activities

8030 Investigation activities

9311 Operation of sports facilities

8110 Combined facilities support activities

9312 Activities of sports clubs

8121 General cleaning of building

9319 Other sports activities

8129 Other building and industrial cleaning activities

9321 Activities of amusement parks and theme parks

8130 Landscape care and maintenance service activities

9329 Other amusement and recreation activities

Sri Lanka HIES 2009/10 Manual (Version 1.1)

288

9411 Activities of business, employers and professional
membership organizations
9412 Other

activitiesofbusinessandemployers

membership organizations
9420 Activities of trade unions
9491 Activities of religious organizations
9492 Activities of political organizations
9499 Activities of Membership Organizations
9511 Repair of computers and communication equipment
9512 Repair of communication equipment

Major industry code
Code
A
B
C
D

4-digit111-322710-9901010-3320
3510-3530

E

3600-3900

FG

4100-4390
4510-4799

HI

4910-5320
5510-5630

J
K
L
M

5811-6399
6411-6630
6810-6820
6910-7500

N

7710-8299

O

8411-8430

PQ

8510-8550
8610-8890

RS
T

9000-9329
9411-9609
9700-9820

U

9900

9521 Repair of personal and household goods
9522 Repair of household appliences and garden equipment
9523 Repair of footwear and leather goods
9524 Repair of furniture and home furnishings9529 Repair of other personal and household goods
9601 Washing and (dry-) cleaning of textile and fur products
9602 Hair dressing and other beauty treatment
9603 Funeral and related activities
9609 Other personal service activities n.e.c.
9700 Activities of households as employers of domestic
Personnel
9810 Undifferentiated goods producing activities of privatehouseholds for own use
9820 Undifferentiated service-producing activities of privatehouseholds for own use
9900 Activities of extraterritorial Organizations and bodies

Sri Lanka HIES 2009/10 Manual (Version 1.1)

Description
Agriculture, Forestry and Fishing
Mining and Quarrying
Manufacturing
Electricity, gas, steam and air
conditioning supply
Water Supply; sewerage, water
management and remediation
activities
Construction
Wholesale and retail trade; repairof motor vehicles and motor cycles
Transportation and storage
Accommodation and food service
activities
Information and Communication
Financial and Insurance Activities
Real estate activities
Professional,
Scientificandtechnical activities
Administrative and support service
activities
Public administration and defense;
compulsory social security
Education
Human health and social work
activities
Arts, entertainment and recreation
Other service activities
Activities of households as
employers, undifferentiated goods
and services producing activities ofhouseholds for own use
Activitiesofextraterritorialorganizations and bodies

289

2131 Biologists,

Attachment 6

Classification of occupation (4-digit)

Botanists,

Zoologists

andRelatedProfessionals
2132 Farming, Forestry and Fisheries Advisors
2133 Environmental Protection Professionals
2141 Industrial and Production Engineers

110

Commissioned Armed Forces Officers

120

Non-commissioned Armed Forces Officers

130

Armed Forces Occupations - Other Ranks

210

Workers Reporting Occupations Unclassifiable

220

Workers Reporting Occupations Unidentifiable or2142 Civil Engineers

Inadequately Described2143 Environmental Engineers
2144 Engineer, Industrial Machinery and Tools
2145 Chemical Engineers
2146 Mining

Engineers,

Metallurgists

andRelatedProfessionals

1111 Legislators

2149 Engineering Professionals NEC

1112 Assistant Registrar of Companies

2151 Electrical Engineers

1113 Traditional Chiefs and Heads of Village
1114 Senior Officials of Special-interest Organizations
1120 Managing Directors and Chief Executives
1211 Cooperate Managers

2152 Electronic Engineers
2153 Telecommunication Engineers
2161 Building Architects
2162 Landscape Architects

1212 Finance Managers

2163 Product and Garment Designers

1213 Human Resource Managers

2164 Town and Traffic Planners

1214 Policy and Planning Managers
1219 Business Services and Administration Managers NEC
1221 Sales and Marketing Managers
1222 Advertising and Public Relations Managers
1223 Research and Development Managers
1311 Agricultural and Forestry Production Managers
1312 Aquaculture and Fisheries Production Managers
1321 Manufacturing Managers

2165 Cartographers and Surveyors
2166 Graphic and Multimedia Designers
2211 Generalist Medical Practitioners
2212 Specialist Medical Practitioners
2221 Nursing Professionals
2222 Midwifery Professionals
2230 Traditional

andComplementary

Medicine

Professionals

1322 Mining Managers

2240 Paramedical Practitioners

1323 Construction Managers
1324 Supply, Distribution and Related Managers
1330 Information and Communications Technology Service
Managers

2250 Veterinarians
2261 Dentists
2262 Pharmacists
2263 Environmental and Occupational Health and Hygeine

1341 Child Care Services Managers

Professionals

1342 Health Services Managers

2264 Physiotherapists

1343 Aged Care Services Managers

2265 Dieticians and Nutritionists

1344 Social Welfare Managers

2266 Audiologists and Speech Therapists

1345 Education Managers
1346 Financial and Insurance Services Branch Managers
1349 Other Professional Services Managers

2267 Optometrists and Ophthalmic Opticians
2269 Health Professionals NEC
2310 University and Higher Education Teachers

1411 Hotel Managers

2320 Vocational Education Teachers

1412 Restaurant Managers

2330 Secondary Education Teachers

1420 Shop Managers
1431 Sports, Recreation and Cultural Centre Managers
1439 Services Managers NEC

2341 Primary School Teachers
2342 Early Childhood Educators
2351 Education Methods Specialists

2111 Physicists and Astronomers

2352 Special Needs Teachers

2112 Meteorologists

2353 Other Language Teachers

2113 Chemists

2354 Other Music Teachers2114 Geologists and Geophysicists
2120 Mathematicians, Actuaries and Statisticians

2355 Other Arts Teachers
2356 Information Technology Trainers

Sri Lanka HIES 2009/10 Manual (Version 1.1)

2902359 Teaching Professionals NEC

3114 Electronics Engineering Technician

2411 Accountants

3115 Mechanical Engineering Technicians2412 Financial and Investment Advisors3116 Chemical Engineering Technician (General)

2413 Financial Analysts

3117 Mining and Metallurgical Technicians2414 Assessors

3118 Draughtspersons

2421 Management and Organization Analysts

3119 Physical and Engineering Science Technicians NEC

2422 Policy Administration Professionals

3121 Mining Supervisors2423 Personnel and Careers Professionals

3122 Manufacturing Supervisors2424 Training and Staff Development Professionals

3123 Construction Supervisors

2431 Advertising and Marketing Professionals

3131 Power Production Plant Operators

2432 Public Relations Professionals

3132 Incinerator and Water Treatment Plant Operators

2433 Technical and Medical Sales Professionals (Excluding

3133 Chemical Processing Plant Controllers

Information Technology)

3134 Petroleum and Natural Gas Refining Plant Operators

2434 Information and Communications Technology Sales
Professionals

3135 Metal Production Process Controllers
3139 Process Control Technicians NEC

2511 Systems Analysts

3141 Life Science Technicians (Excluding Medical)

2512 Software Developers

3142 Agricultural Technicians2513 Web and Multimedia Developers

3143 Forestry Technicians2514 Applications Programmers

3151 Ships' Engineers

2519 Software and Applications Developers and Analysts

3152 Ships' Deck Officers and Pilots
3153 Pilots and Related Associated Professionals

NEC2521 Database Designers and Administrators

3154 Air Traffic Controllers

2522 System Administrators

3155 Air Traffic Safety Electronic Technicians

2523 Computer Network Professionals

3211 Medical

Imaging

andTherapeutic

Equipment

Technicians

2529 Database and Network Professionals NEC
2611 Lawyers

3212 Medical and Pathology Laboratory Technicians

2612 Judges

3213 Pharmaceutical Technicians and Assistants

2619 Legal Professionals NEC

3214 Medical and Dental Prosthetic Technicians

2621 Archivists and Curators

3221 Nursing Associate Professionals

2622 Librarians and Related Information Professionals

3222 Midwifery Associate Professionals

2631 Economists

3230 Traditional and Complementary Medicine Associate2632 Sociologist, Anthropologist and Related Professionals

Professionals

2633 Philosophers, Historians and Political Scientists

3240 Veterinary Technicians and Assistants

2634 Psychologists

3251 Dental Assistants and Therapists

2635 Social Work and Counselling Professionals

3252 Medical Records and Health Information Technicians

2636 Religious Professionals

3253 Community Health Workers

2641 Authors and Related Writers

3254 Dispensing Opticians

2642 Journalists

3255 Physiotherapy Technicians and Assistants

2643 Translators, Interpreters and Other Linguists

3256 Medical Assistants

2651 Visual Artists

3257 Environmental and Occupational Health Inspectors
and Associates

2652 Musicians, Singers and Composers
2653 Dancers and Choreographers3258 Ambulance Workers

2654 Film, Stage and Related Directors and Producers

3259 Health Associate Professionals NEC

2655 Actors

3311 Securities and Finance Dealers and Brokers

2656 Announcers on Radio, Television and Other Media

3312 Credit and Loans Officers

2659 Creative and Performing Artists NEC

3313 Accounting Associate Professionals

3111 Chemical and Physical Science Technicians3314 Statistical, Mathematical and Actuarial Associate

3112 Civil Engineering Technicians
3113 Electrical Engineering TechniciansProfessionals
3315 Valuers and Loss Assessors
Sri Lanka HIES 2009/10 Manual (Version 1.1)

291

3321 Insurance Representatives

4221 Travel Consultants and Clerks

3322 Commercial and Sales Representatives

4222 Contact Centre Information Clerks

3323 Buyers

4223 Telephone Switchboard Operators

3324 Trade Brokers

4224 Hotel Receptionists

3331 Clearing and Forwarding Agents

4225 Enquiry Clerks

3332 Conference and Event Planners

4226 Receptionists (General)3333 Employment Agents and Contractors

4227 Survey and Market Research Interviewers

3334 Real Estate Agents and Property Managers

4229 Client Information Workers NEC

3339 Business Services Agents NEC

4311 Accounting and Book Keeping Clerks

3341 Office Supervisors

4312 Statistical, Finance and Insurance Clerks

3342 Legal Secretaries

4313 Payroll Clerks

3343 Administrative and Executive Secretaries

4321 Stock Clerks

3344 Medical Secretaries

4322 Production Clerks

3349 Administrative and Specialized Secretaries NEC

4323 Transport Clerks

3351 Cargo Officer

4411 Library Clerks

3352 Government Tax and Excise Officials

4412 Mail Carriers and Sorting Clerks

3353 Government Social Benefits Officials

4413 Coding, Proof-reading and Related Clerks

3354 Government Licensing Officers

4414 Scribes and Related Workers

3359 Customs, Tax and Related Government Associate

4415 Filing and Copying Clerks
4416 Personnel Clerks

Professionals NEC
3360 Other Government Associate Professionals

4419 Clerical Services Related Workers NEC

3411 Police Inspectors and Detectives

5111 Flight Attendants and Travel Stewards

3412 Legal and Related Associate Professionals

5112 Railway Sleeping Car Attendant

3421 Social Work Associate Professionals

5113 Travel Guides

3422 Religious Associate Professionals

5121 Chefs

3431 Athletes and Sports Players

5122 Cooks

3432 Sports Coaches, Instructors and Officials

5131 Waiters

3433 Fitness and Recreation Instructors and Program

5132 Bartenders
5141 Hairdressers

Leaders
3441 Photographers5142 Beauticians and Related Workers

3442 Interior Designers and Decorators

5151 Cleaning and Housekeeping Supervisors in Offices,
Hotels and Other Establishments

3443 Gallery, Museum and Library Technicians3449 Other Artistic and Cultural Associate Professionals

5152 Domestic House Keepers

3511 Information

5153 Building Caretakers

andCommunication

Technology

5161 Astrologers, Fortune-tellers and Related Workers

Operations Technicians
3512 Information and Communications Technology User

5162 Companions and Valets
5163 Undertakers and Embalmers

Support Technicians
3513 Computer Network and System Technicians

5164 Pet Groomers and Animal Care Workers

3514 Web Technicians

5165 Driving Instructors

3521 Broadcasting and Audio-visual Technicians

5169 Personal Service Workers NEC

3522 Telecommunication Engineering Technicians

5211 Stall and Market Salespersons

4110 General Office Clerks

5212 Street Food Salespersons

4120 Secretaries (General)

5221 Shop Keepers

4131 Typists and Word Processing Operators

5222 Shop Supervisors

4132 Data Entry Clerks

5223 Shop Sales Assistants

4211 Bank Tellers and Related Clerks

5230 Cashiers and Ticket Clerks

4212 Bookmakers, Croupiers and Related Gaming Workers

5241 Fashion and Other Models

4213 Pawnbrokers and Money-lenders

5242 Sales Demonstrators

4214 Debt Collectors and Related Workers

5243 Door to Door Salespersons
Sri Lanka HIES 2009/10 Manual (Version 1.1)

2925244 Contact Centre Salespersons

7111 House Builders

5245 Service Station Attendants

7112 Brick Layers and Related Workers

5246 Food Service Counter Attendants

7113 Stone Masons, Stone Cutters, Stone Splitters and5249 Sales Workers NEC

Stone Carvers

5311 Child Care Workers

7114 Concrete Placers, Concrete Finishers and Related

5312 Teachers' Aides

Workers

5321 Health Care Assistants

7115 Carpenters and Joiners

5322 Home-based Personal Care Workers

7116 Masons (General)5329 Personal Care Workers in Health Services NEC

7119 Building Frame and Related Trade Workers NEC

5411 Fire Fighters

7121 Roofers

5412 Police Officers

7122 Floor Layers and Tile Setters

5413 Prison Guards

7123 Plasterers

5414 Security Guards

7124 Insulation Workers

5415 Home Guards

7125 Glaziers

5419 Protective Service Workers NEC

7126 Plumbers and Pipe Fitters

6111 Field Crops and Vegetable Growers(Excluding

7127 Air Conditioning and Refrigeration Mechanics

Cereal Crops, Estate Crops and Minor Export Crops) -7131 Painters and Related Workers

Market -oriented

7132 Spray Painters and Varnishes

6112 Cereal Crop Cultivators (Excluding Paddy) (Market

7133 Building Structure Cleaners
7211 Metal Molders and Core makers-oriented)
6113 Paddy Cultivators (Market -oriented)

7212 Welders and Flame Cutters

6114 Tea Cultivators (Market -oriented)

7213 Sheet-metal Workers

6115 Rubber Cultivators (Market -oriented)

7214 Structural-metal Preparers and Erectors

6116 Coconut Cultivators (Market -oriented)

7215 Riggers and Cable Splicers

6117 Minor Export Crops Cultivators (Market -oriented)

7221 Blacksmiths,

6118 Gardeners, Horticultural and Nursery Growers (Market

Hammersmiths andForging

Press

Workers
7222 Toolmakers and Related Workers-oriented)
6119 Mixed Crops Growers (Market -oriented)

7223 Metal Working Machine Tool Setters and Operators

6121 Livestock and Dairy Producers (Market -oriented)

7224 Metal Polishers, Wheel Grinders and Tool Sharpeners

6122 Poultry Producers (Market -oriented)

7231 Motor Vehicle Mechanics and Repairers

6123 Apiarists and Seri Culturists (Market -oriented)

7232 Aircraft Engine Mechanics and Repairers

6129 Animal Producers NEC (Market -oriented)

7233 Agricultural and Industrial Machinery Mechanics and6130 Mixed Crops and Animal Producers (Market -oriented)

Repairers

6210 Forestry and Related Workers (Market -oriented)

7234 Bicycle and Related Repairers

6221 Aquaculture Workers (Market -oriented)

7310 Precision-instrument Makers & Repairers

6222 Ornamental Fish Workers (Market -oriented)

7311 Musical Instrument Makers and Tuners6223 Inland Water Fishery Workers (Market -oriented)

7312 Jewellery and Precious-metal Workers

6224 Coastal Water Fishery Workers (Market -oriented)

7313 Potters and Related Workers

6225 Deep-sea Fishery Workers (Market -oriented)

7314 Glass Makers, Cutters, Grinders and Finishers

6226 Hunters and Trappers (Market -oriented)

7315 Sign Writers, Decorative Painters, Engravers andEtchers

6311 Agricultural Farm Supervisors7316 Handicraft Workers in Wood, Cane and Related

6312 Forestry Supervisors

Materials

6320 Fishery Supervisors

7317 Handicraft Workers in Textile, Leather and Related

6330 Livestock and Dairy Farm Supervisors

Materials

6411 Subsistence Crop Farmers
6412 Subsistence Crop Farmers NEC

7318 Coir Industry Workers

6420 Subsistence Livestock Farmers

7319 Other Handicraft Workers

6430 Subsistence Mixed Crop and Livestock Farmers

7321 Pre-Press Technicians

6440 Subsistence Fishers, Hunters, Trappers and Gatherers

7322 Printers

Sri Lanka HIES 2009/10 Manual (Version 1.1)

2937323 Print Finishing and Binding Workers

8155 Fur and Leather Preparing Machine Operators

7411 Building and Related Electricians8156 Shoemakers and Related Machine Operators

7412 Electrical Mechanics and Fitters

8157 Laundry Machine Operators

7413 Electrical Instruments Repairer

8159 Textile, Fur and Leather Products Machine Operators

7421 Electronics Mechanics and Servicers
7422 Information

and

NEC

Communications

Technology

Servicers and Installers

8160 Food and Related Products Machine Operators
8171 Pulp and Paper Making Plant Operators

7511 Butchers, Fishmongers and Related Food Preparers

8172 Wood Processing Plant Operators

7512 Bakers, Pastry-cooks and Confectionery Makers

8181 Glass and Ceramics Plant Operators

7513 Dairy-products Makers

8182 Steam Engine and Boiler Operators

7514 Fruit, Vegetable and Related Preservers

8183 Packing, Bottling and Labelling Machine Operators

7515 Food and Beverage Tasters and Graders8189 Stationary Plant and Machine Operators NEC

7516 Tobacco Preparers and Tobacco Products Makers

8211 Mechanical Machinery Assemblers

7517 Other Products Makers

8212 Electrical Equipment Assemblers

7521 Wood Treaters

8213 Electronic Equipment Assemblers

7522 Cabinet-makers and Related Workers

8214 Metal, Rubber and Plastic Products Assemblers

7523 Woodworking-machine Tool Setters and Operators

8215 Wood and Related Products Assemblers

7531 Tailors, Dressmakers, Furriers and Hatters8216 Paperboard, Textile and Related Products Assemblers

7532 Textile, Leather and Related Pattern-Makers and8219 Assemblers NEC

Cutters

8311 Locomotive Engine Drivers

7533 Sewers, Embroiders and Related Workers

8312 Railway Brakers, Signallers, Shunters and Switch

7534 Upholsterers and Related Workers

Operators

7535 Pelt Dressers, Tanners and Fell mongers

8321 Motor Cycle Drivers

7536 Shoemakers and Related Workers

8322 Car, Taxi and Van Drivers

7541 Underwater Divers

8323 Three Wheeler Drivers

7542 Shot firers and Blasters

8331 Bus Drivers

7543 Product Graders and Testers (Except Foods and8332 Heavy Truck and Lorry Drivers
8341 Mobile Farm and Forestry Plant Operators

Beverages)
7544 Fumigators and Other Pest and Weed Controllers

8342 Earth-moving and Related Plant Operators

7549 Craft and Related Workers NEC

8343 Crane, Hoist and Related Plant Operators

8111 Mining and Quarries

8344 Lifting Truck Operators

8112 Mineral Ore and Stone Processing Plant Operators

8350 Ships' Deck, Crews and Related Workers

8113 Well Drillers and Borers and Related Workers

9111 Domestic Cleaners and Helpers

8114 Cement, Stone and Other Mineral Products Machine

9112 Cleaners and Helpers in Offices, Hotels and Other
Establishments

Operators

9121 Hand Launderers and Pressers

8121 Metal Processing Plant Operators
8122 Metal

Finishing,

PlatingandCoating

Machine9122 Vehicle Cleaners
9123 Window Cleaners

Operators
8131 Chemical Products Plant and Machine Operators

9129 Other Cleaning Workers

8132 Photographic Products Machine Operators

9211 Crop Farm Labourers

8141 Rubber Products Machine Operators

9212 Tea Pluckers

8142 Plastic Products Machine Operators

9213 Rubber Tappers

8143 Paper Products Machine Operators

9214 Cinnamon Tappers

8151 Fibre Preparing, Spinning and Winding Machine

9215 Livestock Farm Labourers
9216 Mixed Crops and Livestock Farm Labourers

Operators
8152 Weaving and Knitting Machine Operators

9217 Garden and Horticultural Labourers

8153 Sewing Machine Operators

9218 Forestry Labourers

8154 Bleaching, Dying and Fabric Cleaning Machine

9219 Fishery and Aquaculture Labourers

Operators

9311 Mining and Quarrying Labourers
Sri Lanka HIES 2009/10 Manual (Version 1.1)

2949312 Civil Engineering Labourers
9313 Building Construction Labourers
9321 Hand Packers
9322 Manufacturing Laborers NEC
9331 Hand and Pedal Vehicle Drivers
9332 Drivers of Animal-drawn Vehicles and Machinery
9333 Freight Handlers
9334 Shelf Fillers
9335 Transport Labourers
9411 Fast Food Preparers
9412 Kitchen Helpers
9510 Street and Related Service Workers
9520 Street Vendors
9611 Garbage Collectors
9612 Refuse Sorters
9613 Sweepers and Related Labourers
9621 Messengers, Package Deliverers and Luggage Porters
9622 Odd Job Persons
9623 Meter Readers and Vending-machine Collectors
9624 Water and Firewood Collectors
9629 Elementary Service Workers NEC

Major occupation codeCode1

4-digit1111-1439

2
32111-2659
3111-3522

45
64110-4419
5111-5419
6111-6440

787111-7549
8111-8350

909111-9629
0110-0220

Description
Manager, Senior Officials andLegislator
Professionals
Technicians
andAssociate
Professionals
Clerks and Clerical Support Works
Services and Sales Workers
Skilled Agricultural, Forestry and
Fishery Workers
Craft and Related Trade Workers
Plant and machine Operators andAssemblers
Elementary occupations
Other

Sri Lanka HIES 2009/10 Manual (Version 1.1)