(Version 1.1)

Users’ Manual for Handling Resampled Micro Data ofIndonesian National Social Economic Survey (SUSENAS)

SUSENAS 2011/12

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

1

History of revision of the manualVersion 1.1 in March 2018:
Revised based on the discussion.

First draft version 1.0 in November 2017
for the discussion in the workshop

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CONTENTSSUSENAS 2011/12
1. About this manual

Page 3

2. Outline of the survey

4

3. Data and metadata provided

7

4. Data import13

5. Data check27

6. Consumption

58

7. Resampling

67

Attachments

1. QuestionnaireCore questionnaire (VSEN11.K)

71

Consumption Module questionnaire (VSEN11.M)

78

2. Data dictionary

107

3. List of province and district

130

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1.
1.

About this manualThis manual was prepared for users to use the next 80% resampled micro data set of Indonesian

National Social Economic Survey (SUSENAS) 2011/12.

R data framein R workspace of“Resampled.RData”(321MB)

CSV data

Contents

Number ofcases

korrt80korrt80.csv(59MB)
korind80.csv(350MB)
food80.csv(593MB)

Core module:
household data

228,24590

Core module:
individual data

894,660

127

Consumption module:
food expenditure

6,822,367

19

kalori80.csv(724MB)
nonfood80.csv(493MB)summary80.csv(35MB)

Consumption module:
food calorie

6,822,367

16

Consumption module:
non-food expenditure

4,433,666

17

Consumption module:
household-level summary

228,245

15

korind80
food80kalori80
nonfood80
summary80

Number ofvariables

2. As for overall and survey process of SUSENAS, the manual “SUSENAS – Overall and Survey
Process” was compiled.

3. The original micro data sets composed of all the samples were provided by BPS, Indonesia basedon the Charter for Experimental Laboratory for Research Purpose Statistical Use of Micro Data.
They were reorganized and resampled at the rate of 80% by Sinfonica.

4. The above resampled data sets are available through the Institute of Statistical Mathematics (ISM)
both in R and CSV format. CSV files are readable by Excel as well as any statistical software.

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

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2. Outline of the survey
The below table describes the outline of the survey.

Objectives of the

To collect information on social and economic indicators yearly for entire

surveyprovinces.

Topics covered by

CORE questionnaire: HH characteristics and HH member characteristics,

the survey

health, education, employment, fertility, housing, poverty alleviation
program, ICT, source of household income
MODULE questionnaire: food and beverages consumption, non-food
expenditure, income, revenue, and expenditure of non-consumption

Frequency of theYearly since 1963.

surveyQuarterly since 2011 (*)

Survey period

Conducted quarterly in March, June, September and December.
Reference period: a week for food and three months for non-food.

Coverage of theGeographically, covered entire 33 provinces since 1964.

surveyAll private households.

Sample design

Three stages sampling
PSU: Enumeration area. Systematically selected 30,000 PSUs were divided
for quarter consisted of 7,500 PSUs.
SSU: A census block selected for each PSU using PPS.
FSU: 10 households for each census block. In total, 300,000 households.
Quarterly survey was designed for national and province level estimation.
Yearly pooled data was designed for district level estimation.

Data collection

Face to facemethodData entry andAt regional office: Data entry and data check.

data check

At provincial office: Recheck and reinvestigation if necessary.
At central office: Raw data was split into subject groups, and data check at
the specific part of the questionnaire was conducted. Then all clean data wascompiled.

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Publication

The results of the SUSENAS 2011 first quarter were published in the formof three volumes, i.e.,
volume 1

consumption/expenditure at national level,

volume 2

calories and protein consumption at national and the provincelevel, andvolume 3

consumption/expenditure at province level.

Note: Frequency of the survey is semester (6 months) from 2015 onward.
According to the Delegates from Indonesia, the survey design was changed responding thegovernment’s needs, especially of the President. The priority of survey design is placed on quick
release of the detailed survey results at district level for the poverty measurement.

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Map of Provinces

Sulawesi
Kalimantan
Irian Jaya

Sumatra
MalukuJava

Lesser Sunda Islands

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3. Data and metadata provided

The following data files in compressed RAR format were provided by BPS in February 2014..
RAR format

Remarks

kor11rt.rar (10MB)Core module: household data

kor11ind.rar (25.5MB)

Core module: individual data

blok41_exp.rar (99.6MB)

Consumption module: food expenditure

blok41_kalori.rar (82.0MB)

Consumption module: food calorie

blok42.rar (65.4MB)

Consumption module: non-food expenditure

blok43.rar (12.1MB)

Consumption module: household-level summary

In addition, the following documents were provided.

File name

Remarks

Kuesioner Susenas Kor Juni 2011(English).pdf

Core module questionnaire in English

Kuesioner Susenas Module Konsumsi Juni

Consumption module questionnaire in English

VSEN11_K_Final.pdf

Core module questionnaire in local language

VSEN11_M_Final.pdf
Pedoman Pencacahan Kor Final 2011.pdf
Layout Susenas Kor Module 2011.xls

Consumption module questionnaire in local
language
Enumerator’s manual for core module in local
languagein local language

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Homepage of BPS
Indonesia - National Socio-Economic Survey 2011 (Pooled)
http://microdata.bps.go.id/mikrodata/index.php/catalog/411 (Accessed on 21 August 2017)

Under the study description tab, abstract, scope and coverage are described in detail.

Overview

Abstract
Susenas (National Socio-economic Survey) was held for the first time in year 1963. In thelast two decades, up to year 2010, Susenas was conducted every year. Susenas was
designed to have 3 modules (Module of Household Consumption/Expenditure, Module of
Education and Socio-culture, and also Module of Health and Housing) and each module
should be conducted every 3 years. Household Consumption/ Expenditure Module of
Susenas shall be conducted in year 2011 .

To improve the accuracy of data result and in line with the increased frequency ofhousehold consumption/expenditure data request for quarterly GDP/GRDP and poverty
calculation, data collection of household consumption/expenditure, it is planned thatstarting in 2011 it should be held quarterly. Each year, collecting data shall be conductedin March, June, September, and December.

In accordance with the 5-year cycle, in year 2012, BPS (Central Statistical Agency) shallIndonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

9have planned Survei Biaya Hidup-SBH (Cost of Living Survey) with the aim to generate acommodity package and a weigh diagram in the calculation of Consumer Price Index(CPI). Data of food and non-food consumption expenditures as well as household
characteristics collected in SBH and Susenas has the same concept/definition, but different
implementation time. In order to be more efficient in the utilization of resources of the twosurveys and to have a better quality of results achieved, in year 2011 a trial of Susenas
and SBH integration shall be conducted in 7 cities (Medan, Sampit, Denpasar, Kudus,
Bulukumba, Tual, and South Jakarta).

Poverty data, CPI/Inflation data, GDP/GRDP are BPS strategic data that have to bereleased on time. Therefore, planning, field preparation, processing, and presentation ofdata Susenas 2011 activities and trial of integrating Susenas and SBH must be inaccordance with the set schedule.

Activities of Susenas 2011 preparation shall be conducted in year 2010, covering activities
of workshop/training of chief instructor with the aim to synchronize the perception toward
the concept/definition as well as procedure and protocol of survey implementation.
National instructor training will also be conducted in year 2010.

Units of Analysis
Household Members (Individual) and Household

Scope

Notes
The types of data collected by core questionnaires:a. General description of household members ie name, relationship to the head ofhousehold, sex, age, marital status. Description of household members who are victims ofcrime. Description of household members who travel. Description birth certificate. and a
description of household members who attend pre-school education.

b. Description of place of birth, residence 5 years ago and the presence of mother in thehousehold.

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c. Description of health include the state of people's health and infant health.

d. Description of education of household members above 5 years and access to theinternet.

e. Specification employment activities of household members 10 years and over.

f. Description fertility for women have been married and how to prevent pregnancy for awoman is married.

g. Description of housing tenure include the residence, floor area, sources of drinking
water, waste water facility is great, and so forth.

h. Other socioeconomic explanation, among others, include the use of facilities of poverty
alleviation programs (free health care, raskin, sound card, use the credit facility), as well
as household assets and guarantees.

i. Specification includes information and communications technology mastery homephone, cell phone (mobile phone), the number of mobile numbers and control computers.

The types of data collected by questionnaires consumption module include:a. A description of the quantity and value of consumption of food, beverages, and tobacco
both purchase and own production or delivery. Consumption of food, beverages andtobacco covers 215 commodities which are divided into 14 groups.

b. Information on household spending on non-food items, including housing andhousehold facilities, miscellaneous goods and services, clothing, footwear and headgear,
durable goods, taxes, fees, and insurance, as well as expenses for the purposes of the party
and ceremony.

Keywords
Birth control, Children, Child health, Consumption (Economics), Income, Education,
Employment, Family, Fertility, Health, Heatlh services, Housing, Information technology,

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11Indonesia, Literacy, Living standards, Mass media, Breast milk, Occupations, Internet,
Married, Recreation, Energy, Poverty, Assets, Social conditions, Insurance, Transport,
Victims of Crime, Water supply, Sector, Part, Chapter
Coverage

Geographic Coverage
National coverage, representative to the district level

Universe
Susenas 2011 cover 300,000 household sample spread all over Indonesia where eachquarter distribute about 75,000 household sample (including 500 households additional
sample for Survey in Maluku Province).

The result from each quarter can produce national and provincial level estimates. Meanwhilefrom the cumulative four quarter, the data can be presented until the district/municipality level.

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 IHSN
Indonesia - Survei Sosial Ekonomi Nasional 2011, Pooled
http://catalog.ihsn.org/index.php/catalog/3035 (accessed on 19 August 2017)

The survey reports for the first quarter in 2011 and guidelines for local offices are provided in
English.

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4. Data import

List of data files provided (SUSENAS 2011)

Compressed RAR-format

DBF-format

R data frame

dimensionunit of records

CORE modulekor11rt.rar

10MB

kor11rt.dbf

70.8MB

kor11rt

285,307 x 89

Householdkor11ind.var

25.5MB

kor11ind.dbf

327MB

kor11ind

1,118,239 x 125

Individual

blok41_exp.dbf

1.73GB

blok41.exp

11,656,558 x 18

Expenditure item

EXPENDITURE module
blok41_exp.rar

99.6MBof food
blok41_kalori.rar

82.0MB

blok41_kalori.dbf

1.45GB

blok41.kalori

11,656,558 x 15

Expenditure itemof food

blok42.rar

65.4MB

blok42.dbf

754MB

blok42

6,714,271 x 16

Expenditure itemof non-food

blok43.rar

12.1MB

blok43,dbf

43,7MB

blok43285,307 x 14

Household

Strategy to import data files
1. To import DBF files into R.

Be careful about a big DBF file such as 1.7 GB.

2. To use the variable of B1R7 as the household identifier.
3. The household weight is given as the variable of FWT in Core module data files, and as WERT in
Consumption module data files.

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kor11rt> library(foreign)> kor11rt<-read.dbf("kor11rt.dbf")> dim(kor11rt)[1] 285307
89> colnames(kor11rt)[1] "B1R1""B1R2"[8] "B2R2""B2R3"[15] "B4BR1C" "B4BR1D"[22] "B6R6""B6R7"[29] "B6R12A" "B6R12B"[36] "B6R15" "B7R1A"[43] "B7R3A1" "B7R3A2"[50] "B7R3A7L" "B7R3B"[57] "B7R4F" "B7R4G"[64] "B7R5BB" "B7R5BC"[71] "B7R6A" "B7R6B"[78] "B8R1""B8R2A"[85] "B9R1B" "FWT""B1R5""B1R7""B2R4""B2R5""B6R1""B6R2""B6R8""B6R9A""B6R13A" "B6R13B""B7R1B" "B7R1BL""B7R3A3" "B7R3A4""B7R4A" "B7R4B""B7R4H" "B7R4I""B7R5BD" "B7R5BE""B7R6C" "B7R6D""B8R2B" "B8R2C""EXP_CAP" "KABU""B1R8""B4BR1""B6R3""B6R9B""B6R13C""B7R2A""B7R3A5""B7R4C""B7R4J""B7R5BF""B7R6E""B8R3A""SERIES""B1R11""B4BR1A""B6R4""B6R10""B6R14A""B7R2B""B7R3A6""B7R4D""B7R5A""B7R5BG""B7R6F""B8R3B""B2R1""B4BR1B""B6R5""B6R11""B6R14B""B7R2C""B7R3A7""B7R4E""B7R5BA""B7R5BH""B7R6G""B9R1A"# B1R7 (sample code number) is a unique household identifier.
> length(unique(kor11rt$B1R7))[1] 285307# FWT is the household weight. The estimated number of household is 62,630,214.
> sum(kor11rt$FWT)[1] 62630214# SERIES is also a unique identifier.
> length(unique(kor11rt$SERIES))[1] 285307> str(kor11rt)'data.frame':
285307 obs. of 89 variables:$ B1R1: num 11 11 11 11 11 11 11 11 11 11 ...
$ B1R2: num 1 1 1 1 1 1 1 1 1 1 ...
$ B1R5: num 1 1 1 1 1 1 1 1 1 1 ...
$ B1R7: num 2e+06 2e+06 2e+06 2e+06 2e+06 ...
$ B1R8: num 1 1 1 1 1 1 2 2 2 2 ...
$ B1R11 : num 1 1 1 1 1 1 1 1 1 1 ...
$ B2R1: num 6 8 8 2 3 3 4 7 4 5 ...
$ B2R2: num 1 0 0 0 0 1 1 2 1 0 ...
$ B2R3: num 5 8 8 2 3 2 3 5 3 5 ...
$ B2R4: num 4 8 8 2 3 2 2 5 2 5 ...
$ B2R5: num 1 4 3 1 2 1 1 2 1 2 ...
$ B4BR1 : num 2 2 2 2 2 2 2 2 2 2 ...

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$ B4BR1A : num NA NA NA NA NA NA NA NA NA NA ...
$ B4BR1B : num NA NA NA NA NA NA NA NA NA NA ...
$ B4BR1C : num NA NA NA NA NA NA NA NA NA NA ...
$ B4BR1D : num NA NA NA NA NA NA NA NA NA NA ...
$ B6R1: num 1 2 1 1 1 1 1 1 1 1 ...
$ B6R2: num 1 2 1 1 1 1 1 1 1 1 ...
$ B6R3: num 1 1 1 1 1 1 1 1 5 1 ...
$ B6R4: num 1 1 1 1 1 1 1 1 NA 1 ...
$ B6R5: num 4 4 4 4 4 4 4 4 4 4 ...
$ B6R6: num 2 2 1 2 2 2 2 1 2 3 ...
$ B6R7: num 4 1 3 3 3 4 1 1 3 3 ...
$ B6R8: num 30 500 84 35 84 48 35 40 40 32 ...
$ B6R9A : num 7 2 11 2 2 2 5 2 1 5 ...
$ B6R9B : num 2 NA NA NA NA NA 1 NA NA 3 ...
$ B6R10 : num 1 NA 1 NA NA NA 1 NA NA 1 ...
$ B6R11 : num 3 1 3 1 1 1 3 1 1 1 ...
$ B6R12A : num 7 5 11 4 11 11 5 3 11 5 ...
$ B6R12B : num 3 3 3 3 3 3 3 2 3 3 ...
$ B6R13A : num 4 1 1 1 1 3 1 1 1 1 ...
$ B6R13B : num NA 1 1 1 1 3 1 1 3 1 ...
$ B6R13C : num 3 1 1 1 1 3 1 1 3 1 ...
$ B6R14A : num 1 1 1 1 1 1 1 1 1 1 ...
$ B6R14B : num 6 3 2 1 4 1 1 3 3 2 ...
$ B6R15 : num 7 4 4 4 4 4 4 4 4 4 ...
$ B7R1A : num 2 1 2 1 1 2 2 2 2 2 ...
$ B7R1B : num NA 1 NA 1 1 NA NA NA NA NA ...
$ B7R1BL : Factor w/ 1213 levels "-","ABBRI","ABCD",..: NA NA NA NA NA NA NA NA NA
NA ...
$ B7R2A : num 2 2 2 1 2 2 1 2 2 2 ...
$ B7R2B : num NA NA NA 15 NA NA 10 NA NA NA ...
$ B7R2C : num NA NA NA 1700 NA NA 2150 NA NA NA ...
$ B7R3A1 : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R3A2 : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R3A3 : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R3A4 : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R3A5 : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R3A6 : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R3A7 : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R3A7L: Factor w/ 830 levels "AANDEL","ADIRA",..: NA NA NA NA NA NA NA NA NA
NA ...
$ B7R3B : num NA NA NA NA NA NA NA NA NA NA ...
$ B7R4A : num 2 2 2 2 2 2 2 2 1 2 ...
$ B7R4B : num 2 1 1 1 2 1 1 1 1 1 ...
$ B7R4C : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R4D : num 2 2 1 1 2 2 2 2 2 1 ...
$ B7R4E : num 2 2 2 2 2 2 2 2 2 2 ...
$ B7R4F : num 2 1 1 2 1 2 2 1 1 1 ...
$ B7R4G : num 2 2 1 2 2 2 2 2 2 2 ...
$ B7R4H : num 2 1 1 2 1 1 2 1 1 1 ...
$ B7R4I : num 2 1 2 2 2 2 1 2 2 2 ...

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$ B7R4J : num$ B7R5A : num$ B7R5BA : num$ B7R5BB : num$ B7R5BC : num$ B7R5BD : num$ B7R5BE : num$ B7R5BF : num$ B7R5BG : num$ B7R5BH : num$ B7R6A : num$ B7R6B : num$ B7R6C : num$ B7R6D : num$ B7R6E : num$ B7R6F : num$ B7R6G : num$ B8R1: num$ B8R2A : num$ B8R2B : num$ B8R2C : num$ B8R3A : num$ B8R3B : num$ B9R1A : num$ B9R1B : num
$ FWT
: num
$ EXP_CAP: num$ KABU: num$ SERIES : num

2 2 2 2 2 2 2 2 2 2 ...
2 1 1 1 1 1 1 1 2 1 ...
2 NA NA NA NA NA NA NA 1 NA ...
2 NA NA NA NA NA NA NA 2 NA ...
2 NA NA NA NA NA NA NA 2 NA ...
1 NA NA NA NA NA NA NA 2 NA ...
2 NA NA NA NA NA NA NA 2 NA ...
2 NA NA NA NA NA NA NA 2 NA ...
2 NA NA NA NA NA NA NA 2 NA ...
2 NA NA NA NA NA NA NA 2 NA ...
2 2 2 2 2 1 2 2 2 2 ...
2 2 2 2 2 2 2 2 2 2 ...
2 2 2 2 2 2 2 2 1 2 ...
2 2 2 2 2 2 2 2 2 2 ...
2 2 2 1 1 2 1 2 2 2 ...
2 2 2 2 2 2 2 2 2 2 ...
2 2 2 2 2 2 2 2 2 2 ...
2 2 2 2 2 1 2 2 2 2 ...
1 1 1 1 1 2 1 1 1 1 ...
1 7 7 2 3 NA 2 5 2 3 ...
4 7 7 2 3 NA 2 5 2 3 ...
2 2 1 2 2 1 2 2 2 2 ...
2 2 1 2 2 2 2 1 2 2 ...
4 3 11 8 11 18 4 18 17 11 ...
2 1 1 1 2 1 2 1 1 2 ...
50.5 61.9 55.1 65.9 64.6 ...
366071 719905 419555 1022717 507294 ...
1101 1101 1101 1101 1101 ...
6.62e+10 8.82e+10 2.21e+10 6.62e+10 8.82e+10 ...

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kor11ind> kor11ind<-read.dbf("kor11ind.dbf")> dim(kor11ind)[1] 1118239
125> colnames(kor11ind)[1] "B1R1""B1R2""B1R5""B1R7""B1R8""NART""B1R11"[8] "HB""JK""UMUR""KWN""JAHAT1" "JAHAT2" "PERGI1"[15] "PERGI2" "PERGI3" "AKTE1" "AKTE2" "PRASKL1" "PRASKL2" "PRASKL3"[22] "PRASKL4" "B5_TL1" "B5_TL2" "B5_TT1" "B5_TT2" "B5_IBU" "B5_INFO"[29] "B5R1A""B5R1B" "B5R1C" "B5R1D""B5R1E" "B5R1F" "B5R1G"[36] "B5R1H""B5R2""B5R3""B5R4A""B5R4B1" "B5R4B2" "B5R4B3"[43] "B5R5""B5R6A" "B5R6B" "B5R6C""B5R6D" "B5R6E" "B5R6F"[50] "B5R6G""B5R6H" "B5R7""B5R8""B5R9A" "B5R9B" "B5R9C"[57] "B5R9D""B5R9E" "B5R9F" "B5R10A" "B5R10B" "B5R11A" "B5R11B"[64] "B5R12A" "B5R12B" "B5R12C" "B5R12D" "B5R12E" "B5R13A" "B5R13B1"[71] "B5R13B2" "B5R13B3" "B5R14" "B5R15""B5R16" "B5R17" "B5R18A"[78] "B5R18B" "B5R19A" "B5R19B" "B5R19C" "B5R20" "B5R21A" "B5R21B"[85] "B5R21C" "B5R21D" "B5R21E" "B5R21F" "B5R22" "B5R23A" "B5R23B"[92] "B5R24A1" "B5R24A2" "B5R24A3" "B5R24A4" "B5R24B" "B5R25" "B5R26"[99] "B5R27A" "B5R27B" "B5R28A" "B5R28B" "B5R29" "B5R30" "B5R31"[106] "B5R32""B5R33" "B5R34A1" "B5R34A2" "B5R34A3" "B5R34B1" "B5R34B2"[113] "B5R34B3" "B5R34C1" "B5R34C2" "B5R34C3" "B5R35" "B5R36" "B5R37"[120] "B5R38""B5R38L" "FWT""EXP_CAP" "KABU""SERIES"# B1R7 (sample code number) is the unique household identifier, and compatible with
B1R7 in kor11rt.
> length(unique(kor11ind$B1R7))[1] 285307> table(unique(kor11ind$B1R7)==unique(kor11rt$B1R7))
TRUE285307# The estimated number of household members is 241,133,719.
kor11ind$FWT)[1] 241133719# B2R1 (household size) in kor11rt is equal to the number of household members within
the household.
> t<-tapply(kor11ind$NART,kor11ind$B1R7,length)> length(t)[1] 285307> s<-kor11rt[order(kor11rt$B1R7),]> table(t==s$B2R1)
TRUE
285307

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# SERIES is a unique identifier of the sample household.
> length(unique(kor11ind$SERIES))[1] 285307# The combination of B1R7 and NART is a unique person identifier.
> kor11ind$PID<-kor11ind$B1R7*100+kor11ind$NART> length(unique(kor11ind$PID))[1] 1118239# Structure of the data frame# All variables of kor11ind are numeric.
> n<-0> for(j in 1:ncol(kor11ind)){+ if(mode(kor11ind[,j])=="numeric") n<-n+1+ }> n[1] 125

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blok41.exp : food expenditure> blok41.exp<-read.dbf("blok41_exp.dbf")> dim(blok41.exp)[1] 11656558
18> colnames(blok41.exp)[1] "B1R1" "B1R2" "B1R5" "B1R7" "B1R8" "KODE" "B2R1"[9] "B41K4" "B41K5" "B41K6" "B41K7" "B41K8" "B41K9" "RH"[17] "WEIND" "SERIES""KLP""WERT"> str(blok41.exp)'data.frame': 11656558 obs. of 18 variables:$ B1R1 : int 11 11 11 11 11 11 11 11 11 11 ...
$ B1R2 : int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R5 : int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R7 : num 2e+06 2e+06 2e+06 2e+06 2e+06 ...
$ B1R8 : int 1 1 1 1 1 1 1 1 1 1 ...
$ KODE : int 1 2 20 21 53 54 85 97 108 110 ...
$ B2R1 : int 6 6 6 6 6 6 6 6 6 6 ...
$ KLP: int 0 1 0 20 0 53 0 85 85 85 ...
$ B41K4 : num 7 7 0 0 1 1 7 0 2 5 ...
$ B41K5 : num 67000 67000 0 0 61000 61000 20000 0 3000 17000 ...
$ B41K6 : num 0 0 4 4 0 0 0.5 0.5 0 0 ...
$ B41K7 : num 0 0 55000 55000 0 0 3000 3000 0 0 ...
$ B41K8 : num 7 7 4 4 1 1 7.5 0.5 2 5 ...
$ B41K9 : num 67000 67000 55000 55000 61000 61000 23000 3000 3000 17000 ...
$ RH: num 0 11000 0 20000 0 70000 0 10000 2000 4000 ...
$ WERT : num 50.5 50.5 50.5 50.5 50.5 ...
$ WEIND : num 303 303 303 303 303 ...
$ SERIES: num 6.62e+10 6.62e+10 6.62e+10 6.62e+10 6.62e+10 ...
> head(blok41.exp)
B1R1 B1R2 B1R5
B1R7 B1R8 KODE B2R1 KLP B41K4 B41K5 B41K6 B41K7 B41K8 B41K9
1 11
1
1 2003746
11
6 0
7 67000
00
7 67000
2 11
1
1 2003746
12
6 1
7 67000
00
7 67000
3 11
1
1 2003746
1 20
6 0
0
0
4 55000
4 55000
4 11
1
1 2003746
1 21
6 20
0
0
4 55000
4 55000
5 11
1
1 2003746
1 53
6 0
1 61000
00
1 61000
6 11
1
1 2003746
1 54
6 53
1 61000
00
1 61000
RH WERT WEIND
SERIES1
0 50.45 302.7 66183730380
2 11000 50.45 302.7 66183730380
30 50.45 302.7 66183730380
4 20000 50.45 302.7 66183730380
50 50.45 302.7 66183730380
6 70000 50.45 302.7 66183730380> length(unique(blok41.exp$B1R7))

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20[1] 285307# KODE is the food item codes including subtotal codes. (1-229)> d<-blok41.exp> range(d$KODE)[1] 1 229
# KLP: food group codes> klp.no<-unique(d$KLP)> klp.no<-klp.no[order(klp.no)]> klp.no[1]
0 1 10 20 53 71 85 115 127 151 158 167 181 191 223# if KLP=0, the KODE is subtotal.
# if KLP>0, KLP represents the subgroup which the KODE belongs to.
# Example> d[d$B1R7==2003746,c("B1R7","KODE","KLP","B41K9")]
B1R7 KODE KLP B41K9
1 2003746
1 0 67000 # subtotal
2 2003746
2 1 67000
3 2003746
20 0 55000 # subtotal
4 2003746
21 20 55000
5 2003746 53 0 61000 # subtotal
6 2003746
54 53 61000
7 2003746
85 0 23000 # subtotal
8 2003746
97 85 3000
9 2003746 108 85 3000
10 2003746 110 85 17000
11 2003746 151 0 14000 # subtotal
12 2003746 155 151 14000
13 2003746 167 0
900 # subtotal
14 2003746 168 167
900
15 2003746 181 0 18000 # subtotal
16 2003746 182 181 18000
17 2003746 223 0 30000 # subtotal
18 2003746 224 223 30000

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Consistency check between subtotal and sum of items# Records of subtotal> x<-subset(blok41.exp,KLP==0)# Sum of subtotal records at household level> xfood<-tapply(x$B41K9,x$B1R7,sum)> dim(xfood)[1] 285307# Records of items, excluding subtotal.
> y<-subset(blok41.exp,KLP>0)# Sum of items at household level> yfood<-tapply(y$B41K9,y$B1R7,sum)# Comparison between xfood and yfood> table(xfood==yfood)TRUE
285307

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blok41.karoli : food calorie> blok41.kalori<-read.dbf("blok41_kakori.dbf")> dim(blok41.kalori)[1] 11656558
15> colnames(blok41.kalori)[1] "B1R1""B1R2""B1R5"[9] "KALORI" "PROTEIN" "LEMAK""B1R7""KARBO""B1R8""WERT""KODE""WEIND""B2R1""KLP""SERIES"> str(blok41.kalori)'data.frame':
11656558 obs. of 15 variables:$ B1R1: int 11 11 11 11 11 11 11 11 11 11 ...
$ B1R2: int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R5: int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R7: int 2003746 2003746 2003746 2003746 2003746 2003746 2003746 2003746
2003746 2003746 ...
$ B1R8: int 1 1 1 1 1 1 1 1 1 1 ...
$ KODE: int 1 2 20 21 53 54 85 97 108 110 ...
$ B2R1: int 6 6 6 6 6 6 6 6 6 6 ...
$ KLP: int 0 1 0 20 0 53 0 85 85 85 ...
$ KALORI : num 25354 25354 3488 3488 2070 ...
$ PROTEIN: num 593 593 544 544 188 ...
$ LEMAK : num 102 102 128 128 140 ...
$ KARBO : num 5428 5428 0 0 0 ...
$ WERT: num 50.5 50.5 50.5 50.5 50.5 ...
$ WEIND : num 303 303 303 303 303 ...
$ SERIES : num 6.62e+10 6.62e+10 6.62e+10 6.62e+10 6.62e+10 ...
- attr(*, "data_types")= chr "N" "N" "N" "N" ...

> head(blok41.kalori)
B1R1 B1R2 B1R5
B1R7 B1R8 KODE B2R1 KLP KALORI PROTEIN LEMAK KARBO
WERT
1 11
1
1 2003746
11
6 0 25354 593.25 101.5 5428.5 50.45023
2 11
1
1 2003746
12
6 1 25354 593.25 101.5 5428.5 50.45023
3 11
1
1 2003746
1 20
6 0 3488 544.00 128.0
0.0 50.45023
4 11
1
1 2003746
1 21
6 20 3488 544.00 128.0
0.0 50.45023
5 11
1
1 2003746
1 53
6 0 2070 188.00 140.0
0.0 50.45023
6 11
1
1 2003746
1 54
6 53 2070 188.00 140.0
0.0 50.45023
WEIND
SERIES
1 302.7014 66183730380
2 302.7014 66183730380
3 302.7014 66183730380
4 302.7014 66183730380
5 302.7014 66183730380
6 302.7014 66183730380> length(unique(blok41.kalori$B1R7))[1] 285307

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# The records of blok41.karli correspond to the records of blok41.exp.
> x<-with(blok41.exp,B1R1*1000+KODE)> y<-with(blok41.kalori,B1R7*1000+KODE)> table(x==y)TRUE
11656558

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blok42 : non-food expenditure> blok42<-read.dbf("blok42.dbf")> dim(blok42)[1] 6714271

16> colnames(blok42)[1] "B1R1" "B1R2" "B1R5" "B1R7" "B1R8"[10] "B42K3" "B42K4" "B42K5" "B42K6" "WERT""KODE" "B2R1" "KLP""WEIND" "SERIES""B42K2"> str(blok42)'data.frame':
6714271 obs. of 16 variables:$ B1R1 : int 11 11 11 11 11 11 11 11 11 11 ...
$ B1R2 : int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R5 : int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R7 : int 2003746 2003746 2003746 2003746 2003746 2003746 2003746 2003746
2003746 2003746 ...
$ B1R8 : int 1 1 1 1 1 1 1 1 1 1 ...
$ KODE : int 230 232 238 254 255 257 261 262 263 264 ...
$ B2R1 : int 6 6 6 6 6 6 6 6 6 6 ...
$ KLP : int 0 230 230 230 230 230 0 261 261 261 ...
$ B42K2 : num 3 3 15 0 0 0 0 0 0 0 ...
$ B42K3 : int 374000 291000 12000 44000 0 27000 299000 24000 0 15000 ...
$ B42K4 : int 383000 291000 11000 44000 10000 27000 304000 27000 10000 19000 ...
$ B42K5 : int 376000 291000 14000 44000 0 27000 1032000 11000 19000 19000 ...
$ B42K6 : int 1133000 873000 37000 132000 10000 81000 1635000 62000 29000 53000 ...
$ WERT : num 50.5 50.5 50.5 50.5 50.5 ...
$ WEIND : num 303 303 303 303 303 ...
$ SERIES: num 6.62e+10 6.62e+10 6.62e+10 6.62e+10 6.62e+10 ...
- attr(*, "data_types")= chr "N" "N" "N" "N" ...

> head(blok42)
B1R1 B1R2 B1R5
B1R7 B1R8 KODE B2R1 KLP B42K2 B42K3 B42K4 B42K5
B42K6
WERT
1
11
1
1 2003746
1 230
60
3 374000 383000 376000 1133000 50.45023
211
1
1 2003746
1 232
6 230
3 291000 291000 291000 873000 50.45023
311
1
1 2003746
1 238
6 230
15 12000 11000 14000
37000 50.45023
411
1
1 2003746
1 254
6 230
0 44000 44000 44000 132000 50.45023
511
1
1 2003746
1 255
6 230
0
0 10000
0
10000 50.45023
611
1
1 2003746
1 257
6 230
0 27000 27000 27000
81000 50.45023
WEIND
SERIES
1 302.7014 66183730380
2 302.7014 66183730380
3 302.7014 66183730380
4 302.7014 66183730380
5 302.7014 66183730380
6 302.7014 66183730380> length(unique(blok42$B1R7))[1] 285307

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 Consistency check between subtotal and sum of items> x<-subset(blok42,KLP==0)> xnfood<-tapply(x$B42K6,x$B1R7,sum)> dim(xfood)[1] 285307> y<-subset(blok42,KLP>0)> ynfood<-tapply(y$B42K6,y$B1R7,sum)> table(xfood==yfood)TRUE
285307

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blok43 : household-level summary> blok43<-read.dbf("blok43.dbf")> dim(blok43)[1] 285307
14> colnames(blok43)[1] "B1R1" "B1R2" "B1R5"[10] "EXPEND" "KAPITA" "WERT""B1R7" "B1R8" "B2R1""WEIND" "SERIES""KALORI" "FOOD""NFOOD"> str(blok43)'data.frame':
285307 obs. of 14 variables:$ B1R1 : int 11 11 11 11 11 11 11 11 11 11 ...
$ B1R2 : int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R5 : int 1 1 1 1 1 1 1 1 1 1 ...
$ B1R7 : int 2003746 2003700 2003690 2003730 2003720 2003710 2003747 2003783
2003701 2003691 ...
$ B1R8 : int 1 1 1 1 1 1 2 2 2 2 ...
$ B2R1 : int 6 8 8 2 3 3 4 7 4 5 ...
$ KALORI: num 1118 1918 1288 2276 1948 ...
$ FOOD : num 1152429 4268571 1825286 1275000 700714 ...
$ NFOOD : num 1044000 1490667 1531153 770433 821167 ...
$ EXPEND: num 2196429 5759238 3356439 2045433 1521881 ...
$ KAPITA: num 366071 719905 419555 1022717 507294 ...
$ WERT : num 50.5 61.9 55.1 65.9 64.6 ...
$ WEIND : num 303 495 441 132 194 ...
$ SERIES: num 6.62e+10 8.82e+10 2.21e+10 6.62e+10 8.82e+10 ...
- attr(*, "data_types")= chr "N" "N" "N" "N" ...

> head(blok43)
B1R1 B1R2 B1R5
B1R7 B1R8 B2R1
KALORIFOOD
NFOOD EXPENDKAPITA1 11
1
1 2003746
1
6 1118.304 1152428.6 1044000.0 2196429 366071.4
2 11
1
1 2003700
1
8 1918.204 4268571.4 1490666.7 5759238 719904.8
3 11
1
1 2003690
1
8 1288.168 1825285.7 1531153.3 3356439 419554.9
4 11
1
1 2003730
1
2 2275.696 1275000.0 770433.3 2045433 1022716.7
5 11
1
1 2003720
1
3 1948.319 700714.3 821166.7 1521881 507293.7611
1
1 2003710
1
3 3032.657 2312142.9 1720675.0 4032818 1344272.6
WERT
WEIND
SERIES
1 50.45023 302.7014 66183730380
2 61.90950 495.2760 88242948000
3 55.14486 441.1588 22060626900
4 65.87309 131.7462 66183201900
5 64.60864 193.8259 88243828800
6 59.81643 179.4493 22060847100> length(unique(blok43$B1R7))[1] 285307

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5. Data Check
SummaryComparing with the provided file layout, the variable of survey quarter, TRIWULAN might bedeleted from household file (kor11rt). The consumption pattern differs by survey quarters.
Therefore, resampling of micro data without considering survey quarter might cause the fatal
problem. That is, it might distort the yearly average consumption pattern. Also, without the dataof survey quarter, it is difficult to assess the quality of the resampled micro data.

According to the Delegates from Indonesia for 2017 WS, it is the policy of BPS that thevariable of survey quarter is missing. There are two types of SUSENAS 2011 data set to beprovided for users. One is SUSENAS 2011 Pooled without the variable of survey quarter butwith variables of province and district, in order to avoid misuse by users. Districts within aprovince are divided into four groups depending on the survey quarter. Therefore, the provincelevel results by survey quarter represent only the results of the respective district groups.
The other data set is SUSENAS 2011 first quarter or SUSENAS 2011 third quarter, with thevariables of survey month and province, but without the variable of district. However, thesecond quarter and the fourth quarter are not available.
Comparing with the questionnaire, the two area variables, B1R3 (Subregency) and B1R4
(Village) might be missing in the provided micro data, which are very important and useful
information for small area analysis.

The module questionnaire includes questions on expenditure as well as income. However,income data is neither included in the provided data set, nor published.

According to the Delegates from Indonesia, BPS are not confident about publishing quality andreliable income data. Followings are several reasons from not including household income in
SUSENAS data sets;-We know that household income information from respondents is not fully addressed forvarious reasons such as taxes or government assistance.
-Data from household income is used as one of the formation of household balance sheets in thenational balance sheet system from various other data on household income such as data frombanks, financial institutions and others. Household income data from SUSENAS still need to be
explored more deeply to represent overall household income. Estimates of household income
from self-production (fruits or vegetables from their own gardens) are one of the otherIndonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

28

problems.
- Household income is also the sum of all individual income within the household and the
respondent does not know the income of other household members (the estimate is low).
Household identifier:
The variable of B1R7 (sample code number) will be used as the unique household identifier.
The another option will be the variable of SERIES which is 13-digits number

According to the Delegates for 2017 WS, BPS later generated the unique variable URUT as ahousehold identifier.
Weight:
The household weight variable is given as FWT in core data files, and as WERT in module data
files.
The individual weight variable WEIND in module data files is the product of WERT and B2R1
(household size).

Note: In the previous SUSENAS 2006, the individual weight was defined differently.

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kor11rt : household data> summary(kor11rt)
B1R1
B1R2
Min. :11.00 Min. : 1.00
1st Qu.:18.00 1st Qu.: 4.00
Median :35.00 Median : 9.00
Mean :42.15 Mean :21.73
3rd Qu.:63.00 3rd Qu.:23.00
Max. :94.00 Max. :79.00
B1R11
Min. :1
1st Qu.:1
Median :1
Mean :1
3rd Qu.:1
Max. :1

B2R1
Min. : 1.000
1st Qu.: 3.000
Median : 4.000
Mean : 3.919
3rd Qu.: 5.000
Max. :25.000

B1R5Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.589
3rd Qu.:2.000
Max. :2.000

B2R2
Min.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean:0.3722
3rd Qu.:1.0000
Max.
:7.0000

B2R5
Min. : 0.000
1st Qu.: 1.000
Median : 2.000
Mean : 1.861
3rd Qu.: 2.000
Max. :19.000

B4BR1
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.991
3rd Qu.:2.000
Max.
:2.000

B4BR1D
Min. :0.00
1st Qu.:0.00
Median :0.00
Mean :0.33
3rd Qu.:0.00
Max. :9.00
NA's :282828
B6R5
Min. :1.000
1st Qu.:2.000
Median :4.000
Mean :3.368
3rd Qu.:4.000
Max. :7.000

B6R1
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0633rd Qu.:1.000
Max.
:2.000B6R6Min. :1.00
1st Qu.:1.00
Median :1.00
Mean :1.55
3rd Qu.:2.00
Max. :4.00

B6R9B
Min. :1.00
1st Qu.:2.00
Median :2.00
Mean :2.02
3rd Qu.:2.00
Max. :3.00

B6R10
Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.72
3rd Qu.:2.00
Max.
:4.00

B1R7
Min. :2003602
1st Qu.:2074929
Median :2146255
Mean :2146255
3rd Qu.:2217582
Max. :2288908
B2R3
Min. : 1.000
1st Qu.: 2.000
Median : 3.000
Mean : 3.547
3rd Qu.: 4.000
Max. :25.000

B1R8
Min. : 1.00
1st Qu.: 3.00
Median : 5.00
Mean : 5.49
3rd Qu.: 8.00
Max. :10.00
B2R4
Min. : 1.000
1st Qu.: 2.000
Median : 3.000
Mean : 3.127
3rd Qu.: 4.000
Max. :25.000

B4BR1A
Min. :0.00
1st Qu.:1.00
Median :1.00
Mean :0.98
3rd Qu.:1.00
Max. :9.00
NA's :282828
B6R2
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.087
3rd Qu.:1.000
Max. :2.000
B6R7
Min. :1.00
1st Qu.:1.00
Median :3.00
Mean :2.82
3rd Qu.:4.00
Max. :6.00

B4BR1B
B4BR1C
Min. :0.00
Min. :0.00
1st Qu.:0.00
1st Qu.:0.00
Median :0.00
Median :0.00
Mean :0.09
Mean :0.18
3rd Qu.:0.00
3rd Qu.:0.00
Max. :3.00
Max. :3.00
NA's :282828 NA's :282828
B6R3
B6R4
Min. :1.00 Min. :1.00
1st Qu.:1.00 1st Qu.:1.00
Median :1.00 Median :1.00
Mean :1.62 Mean :1.21
3rd Qu.:1.00 3rd Qu.:1.00
Max. :7.00 Max. :4.00
NA's :56506
B6R8
B6R9A
Min. : 3.00 Min. : 1.000
1st Qu.: 36.00 1st Qu.: 3.000
Median : 54.00 Median : 6.000
Mean : 64.99 Mean : 5.605
3rd Qu.: 80.00 3rd Qu.: 7.000
Max. :7306.00 Max. :12.000

B6R11
Min. :1.000
1st Qu.:1.000
Median :3.000
Mean :2.421
3rd Qu.:3.000
Max. :3.000

B6R12A
Min. : 1.000
1st Qu.: 5.000
Median : 6.000
Mean : 6.445
3rd Qu.: 8.000
Max. :12.000

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B6R12B
Min. :1.000
1st Qu.:3.000
Median :3.000
Mean :2.742
3rd Qu.:3.000
Max. :3.000

30NA's :112952 NA's
:58652
B6R13A
B6R13B
B6R13C
B6R14A
B6R14B
Min. :1.00
Min. :1.00
Min. :1.000 Min. :1.000 Min. :1.00
1st Qu.:1.00
1st Qu.:1.00
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00
Median :1.00
Median :1.00
Median :1.000 Median :1.000 Median :2.00
Mean :1.83
Mean :1.43
Mean :2.195 Mean :1.413 Mean :2.33
3rd Qu.:3.00
3rd Qu.:1.00
3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:3.00
Max. :4.00
Max. :4.00
Max. :6.000 Max. :5.000 Max. :6.00
NA's :59667
NA's :51943
B6R15
B7R1A
B7R1B
Min. :1.00
Min. :1.000 Min. :1.00
1st Qu.:2.00
1st Qu.:2.000 1st Qu.:1.00
Median :4.00
Median :2.000 Median :1.00
Mean :4.78
Mean :1.792 Mean :2.04
3rd Qu.:7.00
3rd Qu.:2.000 3rd Qu.:4.00
Max. :9.00
Max. :2.000 Max. :4.00
NA's :225892
B7R1BL
B7R2A
B7R2B
ASKES
: 2786 Min. :1.000 Min.
: 1.00KTP: 1601 1st Qu.:1.000 1st Qu.: 5.00
JAMKESDA: 1316 Median :2.000 Median : 9.00
KTP/KARTU KELUARGA: 1247 Mean :1.505 Mean : 11.94
JAMINAN KESEHATAN DARI PERUSAHAAN: 1088 3rd Qu.:2.000 3rd Qu.: 15.00(Other): 9160 Max. :2.000 Max. :120.00
NA's:268109
NA's :144158
B7R2C
B7R3A1
B7R3A2
B7R3A3
B7R3A4
Min. :1000
Min.
:1.000Min. :1.000 Min. :1.00 Min. :1.000
1st Qu.:1700
1st Qu.:2.000
1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000
Median :2000
Median :2.000
Median :2.000 Median :2.00 Median :2.000
Mean :2076
Mean:1.968Mean :1.993 Mean :1.99 Mean :1.975
3rd Qu.:2250
3rd Qu.:2.000
3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:2.000
Max. :7000
Max.
:2.000Max. :2.000 Max. :2.00 Max. :2.000
NA's :144158
B7R3A5
B7R3A6
B7R3A7B7R3A7L
B7R3B
Min. :1.000 Min. :1.000 Min. :1.00 BRI: 272 Min. :1.00
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00 LPD: 158 1st Qu.:1.00
Median :2.000 Median :2.000 Median :2.00 GAPOKTAN: 139 Median :4.00
Mean :1.983 Mean :1.984 Mean :1.99 BANK
: 103 Mean :3.64
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.00 CU:
87 3rd Qu.:5.00
Max. :2.000 Max. :2.000 Max. :2.00 (Other) : 2096 Max. :7.00
NA's:282452 NA's :271674
B7R4A
B7R4B
B7R4C
B7R4D
B7R4E
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :2.000 Median :1.000 Median :2.000 Median :2.000 Median :2.000
Mean :1.667 Mean :1.433 Mean :1.964 Mean :1.841 Mean :1.966
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max. :2.000 Max. :2.000 Max. :2.000
Max. :2.000 Max. :2.000
B7R4F

B7R4G

B7R4H

B7R4I

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

B7R4J

31

Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.942
3rd Qu.:2.000
Max. :2.000

Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.873
3rd Qu.:2.000
Max. :2.000

Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.695
3rd Qu.:2.000
Max. :2.000

Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.983
3rd Qu.:2.000
Max. :2.000

Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.935
3rd Qu.:2.000
Max. :2.000

B7R5A
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.282
3rd Qu.:2.000
Max. :2.000

B7R5BA
B7R5BB
B7R5BC
B7R5BD
Min. :1.00
Min. :1.00
Min. :1.00
Min. :1.00
1st Qu.:2.00
1st Qu.:2.00
1st Qu.:1.00
1st Qu.:1.00
Median :2.00
Median :2.00
Median :1.00
Median :1.00
Mean :1.81
Mean :1.86
Mean :1.33
Mean :1.48
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:2.00
Max. :2.00
Max. :2.00
Max. :2.00
Max. :2.00
NA's :204789
NA's :204789 NA's
:204789 NA's :204789
B7R5BE
B7R5BF
B7R5BG
B7R5BH
B7R6A
Min. :1.00
Min.
:1.00
Min. :1.00
Min. :1.00
Min. :1.000
1st Qu.:2.00
1st Qu.:2.00
1st Qu.:2.00
1st Qu.:2.00
1st Qu.:2.000
Median :2.00
Median :2.00
Median :2.00
Median :2.00
Median :2.000
Mean :1.96
Mean
:1.98
Mean :1.96
Mean :1.98
Mean :1.895
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:2.000
Max. :2.00
Max.
:2.00
Max. :2.00
Max. :2.00
Max. :2.000
NA's :204789 NA's
:204789 NA's :204789 NA's :204789
B7R6B
B7R6C
B7R6D
B7R6E
B7R6F
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000
Median :2.000 Median :2.000 Median :2.000 Median :2.00 Median :2.000
Mean :1.944 Mean :1.984 Mean :1.983 Mean :1.76 Mean :1.995
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.00 3rd Qu.:2.000
Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.00 Max. :2.000
B7R6G
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.963
3rd Qu.:2.000
Max. :2.000

B8R1
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.935
3rd Qu.:2.000
Max. :2.000

B8R2A
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean :1.254
3rd Qu.:2.000
Max. :2.000

B8R3A
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.944
3rd Qu.:2.000
Max. :2.000

B8R3B
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.916
3rd Qu.:2.000
Max. :2.000

B9R1A
Min. : 1.000
1st Qu.: 3.000
Median : 8.000
Mean : 8.713
3rd Qu.:13.000
Max. :20.000

EXP_CAP
Min. : 59208
1st Qu.: 301082

KABU
Min. :1101
1st Qu.:1809

B8R2B
Min. : 1.0
1st Qu.: 1.0
Median : 2.0
Mean : 1.9
3rd Qu.: 2.0
Max. :17.0
NA's :72443
B9R1B
Min. :0.000
1st Qu.:1.000
Median :2.000
Mean :1.463
3rd Qu.:2.000
Max. :2.000

SERIES
Min. :2.206e+10
1st Qu.:5.266e+11

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

B8R2C
Min. : 1.00
1st Qu.: 1.00
Median : 2.00
Mean : 2.36
3rd Qu.: 3.00
Max. :99.00
NA's :58180
FWT
Min. : 1.00
1st Qu.: 83.25
Median : 147.88
Mean : 219.52
3rd Qu.: 308.54
Max. :1963.09

32

Median : 458984
Mean : 625772
3rd Qu.: 731226
Max. :85324937

Median :3506
Mean :4237
3rd Qu.:6307
Max. :9471

Median :1.196e+12
Mean :2.070e+12
3rd Qu.:2.621e+12
Max. :1.734e+13# Data check of categorical variables of kor11rt> Categorical.variables<-c(12,17:23,25:38,40,43:49,51:79,82:85)> for(j in Categorical.variables){+ cat("\n-----",colnames(kor11rt)[j],"----------------------------------")+ print(table(kor11rt[,j],useNA="ifany"))+ }----- B4BR1 ---------------------------------1
22479 282828----- B6R1 ---------------------------------1
2267192 18115----- B6R2 ---------------------------------1
2260533 24774----- B6R3 ---------------------------------1
2
3
4
5
6
7
2288019834 10718
5912 23139
6224
679----- B6R4 ---------------------------------1
2
3
4
<NA>
2066303594 10300
8277 56506----- B6R5 ---------------------------------1
2
3
4
5
6
7
4972 111372
5459 129135 14112 12211
8046----- B6R6 ---------------------------------12
3
4162370 95719 20463
6755----- B6R7 ---------------------------------1
2
3
4
5
6
75225 19379 106319 55080 24478
4826----- B6R9A ---------------------------------1
2
3
4
5
6
7
8
9
10
11
13885 38367 29118 6398 33087 69293 24709 30525 14744 13044 11486

12651----- B6R9B ---------------------------------1
2
3<NA>36798 95577 39980 112952

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

33----- B6R10 ---------------------------------1
2
3
4
<NA>
123158 57241 33832 12424 58652----- B6R11 ---------------------------------1
2
372514 20239 192554----- B6R12A ---------------------------------1
2
3
4
5
6
7
8
9
10
11
16
161 39609 3682 49872 80266 28309 28985 14307 27919 10182

12
1999

----- B6R12B ---------------------------------1
2
324630 24348 236329----- B6R13A ---------------------------------12
34
179788 33968 11884 59667----- B6R13B ---------------------------------1
2
3
4
<NA>
172823 17330 26772
8715 59667----- B6R13C ---------------------------------1
2
3
4
5
6
1615767598 45352 44810 21758
4213----- B6R14A ---------------------------------12345233364 20495
2217 24010
5221----- B6R14B ---------------------------------1
2
3
4
5
6
<NA>
102154 70140 18566
3558
1531 37415 51943----- B6R15 ---------------------------------1
2
3
4
5
6
7
2766 96016
425 45317
1730
48 135946

8
1728

9
1331

----- B7R1A ---------------------------------1
259415 225892----- B7R1B ---------------------------------1
2
3
4
<NA>
35747
2915
3555 17198 225892----- B7R2A ---------------------------------1
2141149 144158----- B7R3A1 ---------------------------------1
2
9231 276076

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

34----- B7R3A2 ---------------------------------1
2
1944 283363----- B7R3A3 ---------------------------------1
22745 282562----- B7R3A4 ---------------------------------1
26995 278312----- B7R3A5 ---------------------------------1
2
4760 280547----- B7R3A6 ---------------------------------1
24672 280635----- B7R3A7 ---------------------------------1
22855 282452----- B7R3B ---------------------------------1
2
3
4
5
6
7
<NA>
3907
694
1106
3007
1772
1882
1265 271674----- B7R4A ---------------------------------1
2
94940 190367----- B7R4B ---------------------------------1
2161843 123464----- B7R4C ---------------------------------1
210159 275148----- B7R4D ---------------------------------1
245385 239922----- B7R4E ---------------------------------1
2
9821 275486----- B7R4F ---------------------------------1
216427 268880----- B7R4G ---------------------------------1
236172 249135

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

35----- B7R4H ---------------------------------1
286902 198405----- B7R4I ---------------------------------1
2
4827 280480----- B7R4J ---------------------------------1
2
18427 266880----- B7R5A ---------------------------------1
2
204789 80518----- B7R5BA ---------------------------------1
2<NA>
15586 64932 204789----- B7R5BB ---------------------------------1
2<NA>
11148 69370 204789----- B7R5BC ---------------------------------1
2<NA>53846 26672 204789----- B7R5BD ---------------------------------1
2<NA>41756 38762 204789----- B7R5BE ---------------------------------1
2<NA>3198 77320 204789----- B7R5BF ---------------------------------1
2<NA>1404 79114 204789----- B7R5BG ---------------------------------1
2<NA>3199 77319 204789----- B7R5BH ---------------------------------1
2<NA>1895 78623 204789----- B7R6A ---------------------------------1
229939 255368----- B7R6B ---------------------------------1
216045 269262

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

36----- B7R6C ---------------------------------1
2
4599 280708----- B7R6D ---------------------------------1
24796 280511----- B7R6E ---------------------------------1
268591 216716----- B7R6F ---------------------------------1
2
1397 283910----- B7R6G ---------------------------------1
210522 274785----- B8R1 ---------------------------------1
2
18493 266814----- B8R2A ---------------------------------1
2
212864 72443----- B8R3A ---------------------------------1
215971 269336----- B8R3B ---------------------------------1
224085 261222----- B9R1A ---------------------------------1
2
3
4
5
6
7
8
16
53382 5821 43427 9381 2642 3188 6333 20972
9788
17
18
19
20
2111 30534 3278 16061

9

10

11

940 18533 38920

12

13

14

15

3985 13026

885

2100

----- B9R1B ---------------------------------0
1
216072 121018 148217

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

37

kor11ind : individual data> summary(kor11ind)
B1R1
B1R2
Min. :11.0
Min. : 1.00
1st Qu.:18.0
1st Qu.: 4.00
Median :35.0
Median : 9.00
Mean :42.7
Mean :21.64
3rd Qu.:64.0
3rd Qu.:23.00
Max. :94.0
Max. :79.00

B1R5
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.591
3rd Qu.:2.000
Max. :2.000

NART
Min. : 1.000
1st Qu.: 1.000
Median : 3.000
Mean : 2.853
3rd Qu.: 4.000
Max. :25.000

B1R11
Min. :1
1st Qu.:1
Median :1
Mean:1
3rd Qu.:1
Max.
:1

HB
Min.
:1.000
1st Qu.:1.000
Median :3.000
Mean:2.596
3rd Qu.:3.000
Max.
:9.000KWN
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.635
3rd Qu.:2.000
Max. :4.000

JAHAT1
Min. :1.000
1st Qu.:7.000
Median :7.000
Mean :6.941
3rd Qu.:7.000
Max. :7.000

B1R7Min. :2003602
1st Qu.:2072228
Median :2149216
Mean :2147490
3rd Qu.:2221986
Max. :2288908
JK
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.497
3rd Qu.:2.000
Max. :2.000

B1R8
Min. : 1.00
1st Qu.: 3.00
Median : 5.00
Mean : 5.45
3rd Qu.: 8.00
Max. :10.00

UMUR
Min. : 0.00
1st Qu.:12.00
Median :27.00
Mean :28.76
3rd Qu.:43.00
Max. :98.00

JAHAT2PERGI1PERGI2
Min. :1.0
Min. : 0.0000 Min. :1.0
1st Qu.:2.0
1st Qu.: 0.0000 1st Qu.:2.0
Median :2.0
Median : 0.0000 Median :7.0
Mean :1.8
Mean : 0.2199 Mean :5.4
3rd Qu.:2.0
3rd Qu.: 0.0000 3rd Qu.:7.0
Max. :2.0
Max. :90.0000 Max. :9.0
NA's :1105772
NA's :995007
PERGI3AKTE1
AKTE2
PRASKL1
PRASKL2
Min. :11.0
Min.
:1.0
Min. :1.0
Min. :1.0
Min. :1.0
1st Qu.:19.0
1st Qu.:1.0
1st Qu.:1.0
1st Qu.:3.0
1st Qu.:1.0
Median :33.0
Median :2.0
Median :4.0
Median :3.0
Median :1.0
Mean :39.3
Mean
:1.9
Mean :3.7
Mean :2.7
Mean :1.9
3rd Qu.:61.0
3rd Qu.:3.0
3rd Qu.:6.0
3rd Qu.:3.0
3rd Qu.:4.0
Max. :94.0
Max.
:4.0
Max. :6.0
Max. :3.0
Max. :5.0
NA's :995007 NA's
:710878 NA's :964711 NA's :966065 NA's :1087385
PRASKL3
PRASKL4B5_TL1
B5_TL2
B5_TT1
Min. :1.0
Min. : 1.0
Min. : 1.00 Min. : 0.00 Min. : 0.00
1st Qu.:1.0
1st Qu.: 1.0
1st Qu.:18.00 1st Qu.: 4.00 1st Qu.:15.00
Median :1.0
Median : 1.0
Median :35.00 Median : 9.00 Median :33.00
Mean :1.2
Mean : 2.1
Mean :42.16 Mean :20.63 Mean :38.43
3rd Qu.:1.0
3rd Qu.: 3.0
3rd Qu.:63.00 3rd Qu.:22.00 3rd Qu.:62.00
Max. :2.0
Max. :10.0
Max. :94.00 Max. :79.00 Max. :94.00
NA's :1088180
NA's :1094664
B5_TT2
B5_IBU
B5_INFO
B5R1A
B5R1B
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. :1.000 Min. :1.000
1st Qu.: 3.00 1st Qu.: 0.00 1st Qu.: 1.00 1st Qu.:2.000 1st Qu.:2.000
Median : 8.00 Median : 0.00 Median : 2.00 Median :2.000 Median :2.000
Mean :19.69 Mean : 0.96 Mean : 1.86 Mean :1.893 Mean :1.864
3rd Qu.:20.00 3rd Qu.: 2.00 3rd Qu.: 2.00 3rd Qu.:2.000 3rd Qu.:2.000

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

38

Max.

:79.00

B5R1C
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.874
3rd Qu.:2.000
Max. :2.000

Max.

:20.00

B5R1D
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.985
3rd Qu.:2.000
Max. :2.000

Max.

:25.00

B5R1E
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.984
3rd Qu.:2.000
Max. :2.000

Max.

:2.000

B5R1F
Min. :1.00
1st Qu.:2.00
Median :2.00
Mean :1.95
3rd Qu.:2.00
Max. :2.00

B5R1H
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.894
3rd Qu.:2.000
Max. :2.000

Max.

:2.000

B5R1G
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :1.984
3rd Qu.:2.000
Max. :2.000

B5R2
B5R3
B5R4A
B5R4B1
Min. :1.0
Min. : 1.0
Min. :1.0
Min. :1.0
1st Qu.:1.0
1st Qu.: 2.0
1st Qu.:1.0
1st Qu.:1.0
Median :1.0
Median : 3.0
Median :1.0
Median :2.0
Mean :1.5
Mean : 5.5
Mean :1.3
Mean :1.7
3rd Qu.:2.0
3rd Qu.: 7.0
3rd Qu.:2.0
3rd Qu.:2.0
Max. :2.0
Max. :30.0
Max. :2.0
Max. :2.0
NA's :784812
NA's :940463 NA's :784812 NA's :897298
B5R4B2B5R4B3
B5R5
B5R6A
B5R6B
Min. :1.0
Min.
:1Min. :1.0
Min. : 0.0
Min. : 0.0
1st Qu.:1.0
1st Qu.:2
1st Qu.:1.0
1st Qu.: 0.0
1st Qu.: 0.0
Median :1.0
Median :2
Median :2.0
Median : 0.0
Median : 0.0
Mean :1.1
Mean:2
Mean :1.6
Mean : 0.1
Mean : 0.1
3rd Qu.:1.0
3rd Qu.:2
3rd Qu.:2.0
3rd Qu.: 0.0
3rd Qu.: 0.0
Max. :2.0Max.
:2Max. :2.0
Max. :30.0
Max. :30.0
NA's :897298 NA's
:897298 NA's :784812 NA's :970894 NA's :970894
B5R6C
B5R6D
B5R6E
B5R6F
B5R6G
Min. : 0.0
Min.
: 0.0
Min. : 0.0
Min. : 0
Min. : 0
1st Qu.: 0.0
1st Qu.: 0.0
1st Qu.: 0.0
1st Qu.: 0
1st Qu.: 0Median : 0.0
Median : 0.0
Median : 0.0
Median : 0
Median : 0
Mean : 0.3
Mean
: 0.6
Mean : 0.4
Mean : 0
Mean : 0
3rd Qu.: 0.0
3rd Qu.: 1.0
3rd Qu.: 1.0
3rd Qu.: 0
3rd Qu.: 0
Max. :30.0
Max.
:30.0
Max. :30.0
Max. :30
Max. :22
NA's :970894 NA's
:970894 NA's :970894 NA's :970894 NA's :970894
B5R6H
B5R7
B5R8
B5R9A
B5R9B
Min. : 0Min.
:1.000Min. :1.00 Min. : 0.0
Min. : 0.0
1st Qu.: 0
1st Qu.:2.000
1st Qu.:2.00 1st Qu.: 0.0
1st Qu.: 0.0
Median : 0
Median :2.000
Median :2.00 Median : 0.0
Median : 0.0
Mean : 0
Mean:1.786
Mean :1.98 Mean : 3.5
Mean : 1.6
3rd Qu.: 0
3rd Qu.:2.000
3rd Qu.:2.00 3rd Qu.: 5.0
3rd Qu.: 2.0
Max. :30
Max.
:2.000Max. :2.00 Max. :180.0
Max. :140.0
NA's :970894
NA's :1095953 NA's :1095953
B5R9C
B5R9D
B5R9E
B5R9F
Min. : 0.0
Min. : 0.0
Min. : 0.0
Min. : 0.0
1st Qu.: 0.0
1st Qu.: 0.0
1st Qu.: 0.0
1st Qu.: 0.0
Median : 0.0
Median : 0.0
Median : 0.0
Median : 0.0
Mean : 0.5
Mean : 0.2
Mean : 0.1
Mean : 0.1
3rd Qu.: 0.0
3rd Qu.: 0.0
3rd Qu.: 0.0
3rd Qu.: 0.0
Max. :121.0
Max. :150.0
Max. :90.0
Max. :90.0
NA's :1095953
NA's :1095953
NA's :1095953 NA's :1095953

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

39

B5R10A
Min. : 0.0
1st Qu.:15.0
Median :29.0
Mean :29.4
3rd Qu.:43.0
Max. :59.0
NA's :1012042
B5R12A
Min. :0.0
1st Qu.:1.0
Median :1.0
Mean :0.9
3rd Qu.:1.0
Max. :9.0
NA's :1012042
B5R12E
Min. :0
1st Qu.:1
Median :3
Mean :2
3rd Qu.:3
Max. :9
NA's :1012042
B5R13B3
Min. : 0.0
1st Qu.: 5.0
Median :11.0
Mean :11.5
3rd Qu.:18.0
Max. :58.0
NA's :1017510
B5R18A
Min. :1.0
1st Qu.:1.0
Median :2.0
Mean :1.7
3rd Qu.:2.0
Max. :2.0
NA's :202194
B5R20
Min. :1.0
1st Qu.:2.0
Median :2.0
Mean :1.9
3rd Qu.:2.0
Max. :2.0
NA's :106197
B5R21D
Min. :1.0

B5R10B
B5R11A
B5R11B
Min. : 1.0
Min. :1.0
Min. :1.0
1st Qu.: 7.0
1st Qu.:2.0
1st Qu.:2.0
Median :15.0
Median :2.0
Median :2.0
Mean :15.1
Mean :2.5
Mean :2.4
3rd Qu.:21.0
3rd Qu.:4.0
3rd Qu.:3.0
Max. :30.0
Max. :9.0
Max. :9.0
NA's :1116830
NA's :1012042 NA's :1012042
B5R12B
B5R12C
B5R12D
Min. :0.0
Min. :0.0
Min. :0.0
1st Qu.:1.0
1st Qu.:1.0
1st Qu.:1.0
Median :3.0
Median :3.0
Median :1.0
Mean :2.2
Mean :2.4
Mean :0.8
3rd Qu.:3.0
3rd Qu.:3.0
3rd Qu.:1.0
Max. :9.0
Max. :9.0
Max. :9.0
NA's :1012042
NA's :1012042 NA's :1012042
B5R13A
B5R13B1
B5R13B2
Min. :1.0
Min. : 0.0
Min. : 0.0
1st Qu.:1.0
1st Qu.:10.0
1st Qu.: 2.0
Median :1.0
Median :16.0
Median : 4.0
Mean :1.1
Mean :16.2
Mean : 4.7
3rd Qu.:1.0
3rd Qu.:24.0
3rd Qu.: 6.0
Max. :2.0
Max. :59.0
Max. :59.0
NA's :1012042
NA's :1017510 NA's :1017510
B5R14
B5R15
B5R16
B5R17
Min. :1.00
Min. : 1.00
Min. :1.00
Min. : 1.0
1st Qu.:2.00
1st Qu.: 1.00
1st Qu.:3.00
1st Qu.: 1.0
Median :3.00
Median : 3.00
Median :8.00
Median : 2.0
Mean :2.54
Mean : 3.99
Mean :5.63
Mean : 4.1
3rd Qu.:3.00
3rd Qu.: 7.00
3rd Qu.:8.00
3rd Qu.: 6.0
Max. :3.00
Max. :14.00
Max. :8.00
Max. :15.0
NA's :106197 NA's :202194 NA's :202194 NA's :202194
B5R18B
B5R19A
B5R19B
B5R19C
Min. : 1.0
Min. :1.00
Min. :1.00
Min. :1.00
1st Qu.: 1.0
1st Qu.:1.00
1st Qu.:1.00
1st Qu.:2.00
Median : 1.0
Median :1.00
Median :2.00
Median :2.00
Mean
: 1.7
Mean :1.12
Mean :1.64
Mean :1.95
3rd Qu.: 1.0
3rd Qu.:1.00
3rd Qu.:2.00
3rd Qu.:2.00
Max.
:10.0
Max. :2.00
Max. :2.00
Max. :2.00
NA's:843288 NA's :106197 NA's :106197 NA's :106197
B5R21A
B5R21B
B5R21CMin.
:1.0
Min. :1.0
Min. :1.0
1st Qu.:2.0
1st Qu.:1.0
1st Qu.:2.0
Median :2.0
Median :2.0
Median :2.0
Mean
:1.8
Mean :1.6
Mean :1.8
3rd Qu.:2.0
3rd Qu.:2.0
3rd Qu.:2.0
Max.
:2.0
Max. :2.0
Max. :2.0
NA's:1013784 NA's :1013784 NA's
:1013784
B5R21E
B5R21F
B5R22
Min. :1.0
Min. :1.0
Min. : 1

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1st Qu.:2.0
Median :2.0
Mean :1.9
3rd Qu.:2.0
Max. :2.0
NA's :1013784
B5R23A
Min. : 0
1st Qu.: 5Median : 6
Mean : 5
3rd Qu.: 6
Max. :12
NA's :1011190
B5R24A4
Min. :1.00
1st Qu.:1.00
Median :2.00
Mean :1.61
3rd Qu.:2.00
Max. :2.00
NA's :226079
B5R27B
Min. : 1.0
1st Qu.: 1.0
Median : 1.0
Mean : 2.3
3rd Qu.: 3.0
Max. :10.0
NA's :587202
B5R31
Min. :1.0
1st Qu.:2.0
Median :4.0
Mean :3.5
3rd Qu.:5.0
Max. :6.0
NA's :594512
B5R34A3
Min. : 0.0
1st Qu.: 1.0
Median : 3.0
Mean : 3.1
3rd Qu.: 4.0
Max. :30.0
NA's :802366
B5R34C2
Min. : 0.0
1st Qu.: 0.0
Median : 0.0

1st Qu.:1.0
1st Qu.:2.0
1st Qu.: 1Median :1.0
Median :2.0
Median : 3
Mean :1.5
Mean :1.9
Mean : 4
3rd Qu.:2.0
3rd Qu.:2.0
3rd Qu.: 5
Max. :2.0
Max. :2.0
Max. :11
NA's :1013784
NA's :1013784 NA's :969896
B5R23B
B5R24A1
B5R24A2
B5R24A3
Min. : 0
Min. :1.00
Min. :1.00
Min. :1.00
1st Qu.:2003
1st Qu.:1.00
1st Qu.:2.00
1st Qu.:1.00
Median :2006
Median :1.00
Median :2.00
Median :1.00
Mean :1666
Mean :1.43
Mean :1.79
Mean :1.49
3rd Qu.:2009
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:2.00
Max. :2011
Max. :2.00
Max. :2.00
Max. :2.00
NA's :1011190
NA's :226079 NA's :226079 NA's :226079
B5R24B
B5R25
B5R26
B5R27A
Min.
:1.00
Min. :1
Min. :1.00
Min. :1.0
1st Qu.:1.00
1st Qu.:2
1st Qu.:2.00
1st Qu.:1.0
Median :1.00
Median :2
Median :2.00
Median :1.0
Mean :1.82
Mean :2
Mean :1.97
Mean :1.4
3rd Qu.:3.00
3rd Qu.:2
3rd Qu.:2.00
3rd Qu.:2.0
Max.
:4.00
Max. :2
Max. :2.00
Max. :2.0
NA's :251639 NA's :734587 NA's :226079 NA's :226079
B5R28A
B5R28B
B5R29
B5R30
Min.
:0.0
Min. : 0.0
Min. :
0 Min. : 1.0
1st Qu.:5.0
1st Qu.:27.0
1st Qu.: 400000 1st Qu.: 3.0
Median :6.0
Median :40.0
Median : 800000 Median : 8.0
Mean
:5.7
Mean :38.8
Mean : 1232591 Mean : 7.9
3rd Qu.:7.0
3rd Qu.:49.0
3rd Qu.: 1500000 3rd Qu.:11.0
Max.
:7.0
Max. :98.0
Max. :99999999 Max. :19.0
NA's:601005 NA's :601005 NA's :649420
NA's :594512
B5R32
B5R33
B5R34A1
B5R34A2Min.
: 9Min. : 0.0
Min. : 0.0
Min. : 0.0
1st Qu.:17
1st Qu.: 9.0
1st Qu.: 1.0
1st Qu.: 0.0
Median :19
Median :18.0
Median : 1.0
Median : 1.0
Mean:20
Mean :20.5
Mean : 1.6
Mean : 1.5
3rd Qu.:22
3rd Qu.:30.0
3rd Qu.: 2.0
3rd Qu.: 2.0Max.
:85
Max. :86.0
Max. :14.0
Max. :20.0
NA's:802366 NA's :802366 NA's :802366 NA's :802366
B5R34B1
B5R34B2
B5R34B3
B5R34C1Min.
: 0.0
Min. : 0.0
Min. : 0.0
Min. : 0.0
1st Qu.: 0.0
1st Qu.: 0.0
1st Qu.: 1.0
1st Qu.: 0.0
Median : 1.0
Median : 1.0
Median : 2.0
Median : 0.0
Mean
: 1.4
Mean : 1.3
Mean : 2.8
Mean : 0.2
3rd Qu.: 2.0
3rd Qu.: 2.0
3rd Qu.: 4.0
3rd Qu.: 0.0
Max.
:13.0
Max. :20.0
Max. :30.0
Max. :12.0
NA's:802366 NA's :802366 NA's :802366 NA's :802366
B5R34C3
B5R35
B5R36
B5R37
Min.
: 0.0
Min. :1.0
Min. : 1.0
Min. :1.0
1st Qu.: 0.0
1st Qu.:1.0
1st Qu.: 4.0
1st Qu.:2.0
Median : 0.0
Median :2.0
Median : 4.0
Median :3.0

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Mean : 0.2
Mean
: 0.3
Mean :1.9
Mean : 4.5
Mean :2.6
3rd Qu.: 0.0
3rd Qu.: 0.0
3rd Qu.:3.0
3rd Qu.: 6.0
3rd Qu.:3.0
Max. :12.0
Max.
:16.0
Max. :3.0
Max. :10.0
Max. :3.0
NA's :802366 NA's
:802366 NA's :802366 NA's :993796 NA's :926809
B5R38
B5R38LFWT
EXP_CAP
Min. :1.0
JANDA:
7816 Min. :1.0 Min. : 59208
1st Qu.:1.0
SUDAH TUA:
5063 1st Qu.: 81.2 1st Qu.: 279840
Median :1.0
TUA:
2933 Median : 142.9 Median : 419343
Mean :2.9
CERAI:1068 Mean : 215.6 Mean : 567319
3rd Qu.:5.0
LANSIA :1003 3rd Qu.: 299.3 3rd Qu.: 664988
Max. :6.0(Other) : 19122 Max. :1963.1 Max. :85324937
NA's :961836 NA's
:1081234
KABU
SERIES
Min. :1101
Min. :2.206e+10
1st Qu.:1805
1st Qu.:5.156e+11Median :3509
Median :1.189e+12
Mean :4291
Mean :2.079e+12
3rd Qu.:6402
3rd Qu.:2.631e+12
Max. :9471
Max. :1.734e+13# Data check of categorical variables of kor11ind> Categorical.variables<-c(8:9,11:13,15,17:22,29:37,39:43,52:53,62:63,69,73:89,+ 92:100,104:105,117:120)> for(j in Categorical.variables){+ cat("\n-----",colnames(kor11ind)[j],"----------------------------------")+ print(table(kor11ind[,j],useNA="ifany"))+ }----- HB ---------------------------------1
2
3
4
5
6
7
285307 227607 475258 22424 47051 21630 33353

8
2164

9
3445

----- JK ---------------------------------1
2
562882 555357----- KWN ---------------------------------1
2
3
4527395 523551 15070 52223----- JAHAT1 ---------------------------------1
2
3
4
5
6
7
8396
2114
44
1249
32
632 1105772----- JAHAT2 ---------------------------------1
2<NA>2195 10272 1105772----- PERGI2 ---------------------------------1
2
3
4
5
6
7

8

9

<NA>

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23847

9189

1194

2439

3230

4620

73547

244

4922 995007----- AKTE1 ---------------------------------1
2
3
4
<NA>
187358 66475 148437
5091 710878----- AKTE2 ---------------------------------1
2
3
4
5
6
<NA>
42065 13661
8686 24755 17660 46701 964711----- PRASKL1 ---------------------------------1
2
3<NA>10928 19926 121320 966065----- PRASKL2 ---------------------------------1
2
3
4
5
<NA>
21911
772
406
3556
4209 1087385----- PRASKL3 ---------------------------------1
2
<NA>
23575
6484 1088180----- PRASKL4 ---------------------------------1
2
3
4
5
6
13429
1082
7172
193
296
441

7
330

8
38

----- B5R1A ---------------------------------1
2
119722 998517----- B5R1B ---------------------------------1
2151673 966566----- B5R1C ---------------------------------1
2
140990 977249----- B5R1D ---------------------------------1
2
16678 1101561----- B5R1E ---------------------------------1
217425 1100814----- B5R1F ---------------------------------1
255563 1062676----- B5R1G ---------------------------------1
2
18426 1099813----- B5R1H ---------------------------------1
2

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

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42

10
<NA>
552 1094664

43118025 1000214----- B5R2 ---------------------------------1
2<NA>
177776 155651 784812----- B5R4A ---------------------------------1
2<NA>220941 112486 784812----- B5R4B1 ---------------------------------1
2<NA>60692 160249 897298----- B5R4B2 ---------------------------------1
2<NA>197274 23667 897298----- B5R4B3 ---------------------------------1
2<NA>9665 211276 897298----- B5R5 ---------------------------------1
2<NA>147345 186082 784812----- B5R7 ---------------------------------1
2239699 878540----- B5R8 ---------------------------------1
2
22286 1095953----- B5R11A ---------------------------------1
2
3
4
5
6
14029 60440
711
24774
5873
365

9
<NA>
5 1012042----- B5R11B ---------------------------------1
2
3
4
5
6
15303 63600
1068
22012
3893
316

9
<NA>
5 1012042----- B5R13A ---------------------------------1
2<NA>1007295468 1012042----- B5R14 ---------------------------------1
2
3<NA>95997 268914 647131 106197----- B5R15 ---------------------------------1
2
3
4
5
6
7
444420 13584
1244 163888 19645
1441 136969
14<NA>
2935 202194

8
11015

9
51265

10
1950

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12
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----- B5R16 ---------------------------------1
2
3
4
5
6
7
8
<NA>
83815 105420 100929 54252 52345 35585
262 483437 202194----- B5R17 ---------------------------------1
2
3
4
5
6
7
8
279897 251026
7522
1327 139774 14434
1733 118298
14
15
<NA>
31459
2308 202194

9
8365

10
40206

11
1637

9
263

10
<NA>
2998 843288----- B5R18A ---------------------------------1
2<NA>274951 641094 202194----- B5R18B ---------------------------------1
2
3
4
5
6
7
213467 10388 27775
802 12005
6056
1015

8
182

----- B5R19A ---------------------------------1
2<NA>893769 118273 106197----- B5R19B ---------------------------------1
2<NA>363016 649026 106197----- B5R19C ---------------------------------1
2<NA>51399 960643 106197----- B5R20 ---------------------------------1
2<NA>104455 907587 106197----- B5R21A ---------------------------------1
2<NA>23129 81326 1013784----- B5R21B ---------------------------------1
2<NA>46183 58272 1013784----- B5R21C ---------------------------------1
2<NA>
16306 88149 1013784----- B5R21D ---------------------------------1
2<NA>14092 90363 1013784----- B5R21E ---------------------------------1
2<NA>54003 50452 1013784----- B5R21F ---------------------------------1
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9324

95131 1013784----- B5R22 ---------------------------------1
2
3
4
5
6
7
47034 20592 12875
9226 30286
997
4194

8
1689

9
826

10
456

11
<NA>
20168 969896

8
2854

9
2691

10
<NA>
18073 587202

8
43009

9
1342

10
26439

----- B5R24A1 ---------------------------------1
2<NA>508508 383652 226079----- B5R24A2 ---------------------------------1
2<NA>
183997 708163 226079----- B5R24A3 ---------------------------------1
2<NA>454375 437785 226079----- B5R24A4 ---------------------------------1
2<NA>345491 546669 226079----- B5R24B ---------------------------------1
2
3
4
<NA>
443351 175330 212782 35137 251639----- B5R25 ---------------------------------1
2<NA>15219 368433 734587----- B5R26 ---------------------------------1
2<NA>29583 862577 226079----- B5R27A ---------------------------------1
2<NA>531037 361123 226079----- B5R27B ---------------------------------1
2
3
4
5
6
7
300594 20272 142999
2513 19506 12526
9009----- B5R30 ---------------------------------1
2
3
4
5
6
7
108850 12918 82446 14422 11912
5888
9602
14
15
16
17
18
19
<NA>
1894
3928 20870
4999 57766
4264 594512----- B5R31 ---------------------------------1
2
3
4
5
6
<NA>
103222 92878 16710 166109 46679 98129 594512----- B5R35 ---------------------------------1
2
3<NA>124443 87309 104121 802366

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----- B5R36 ---------------------------------1
2
3
4
5
6
7
3811
736
7709 70519
7616 30175
909

8
48

9
91

10
<NA>
2829 993796----- B5R37 ---------------------------------1
2
3<NA>35027 15458 140945 926809----- B5R38 ---------------------------------1
2
3
4
5
6
<NA>
85276
4087
2914 13305 13816 37005 961836>

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blok41.exp : food expenditure for a week
> summary(blok41.exp)
B1R1
B1R2
Min. :11.00 Min. : 1.0
1st Qu.:18.00 1st Qu.: 4.0
Median :34.00 Median : 9.0
Mean :40.61 Mean :22.8
3rd Qu.:62.00 3rd Qu.:25.0
Max. :94.00 Max. :79.0
KODE
Min. : 1.0
1st Qu.: 86.0
Median :134.0
Mean :126.6
3rd Qu.:176.0
Max. :229.0

B1R5
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :1.556
3rd Qu.:2.000
Max. :2.000

B2R1
Min. : 1.000
1st Qu.: 3.000
Median : 4.000
Mean : 4.085
3rd Qu.: 5.000
Max. :25.000

B1R7
Min. :2003602
1st Qu.:2073965
Median :2139878
Mean :2142025
3rd Qu.:2211223
Max. :2288908

KLP
Min. : 0.00
1st Qu.: 0.00
Median : 85.00
Mean : 89.02
3rd Qu.:167.00
Max. :223.00

B1R8
Min. : 1.000
1st Qu.: 3.000
Median : 5.000
Mean : 5.471
3rd Qu.: 8.000
Max. :10.000

B41K4
Min. : 0.000
1st Qu.: 0.350
Median : 1.000
Mean : 3.413
3rd Qu.: 3.000
Max. :1626.360

B41K5
Min. :
0
1st Qu.: 1700
Median : 4600
Mean : 11516
3rd Qu.: 12000
Max. :3095000

B41K6
Min. : 0.000
1st Qu.: 0.000
Median : 0.000
Mean : 0.375
3rd Qu.: 0.000
Max. :471.000

B41K7
Min. :
0
1st Qu.:0
Median :0
Mean : 1729
3rd Qu.:0
Max. :2016000

B41K8
Min. : 0.000
1st Qu.: 0.700
Median : 1.530
Mean : 4.297
3rd Qu.: 4.770
Max. :1626.360

B41K9
Min. :
2
1st Qu.: 2000
Median : 5000
Mean : 13246
3rd Qu.: 13500
Max. :3095000

RH
Min. :
0
1st Qu.: 1000
Median : 2500
Mean : 5149
3rd Qu.: 7000
Max. :1500000
NA's :779689

WERT
Min. : 1.00
1st Qu.: 88.63
Median : 159.29
Mean : 231.54
3rd Qu.: 328.23
Max. :1963.09

WEIND
Min. :
2.0
1st Qu.: 318.2
Median : 614.7
Mean : 934.8
3rd Qu.: 1237.3
Max. :20206.9

SERIES
Min. :2.206e+10
1st Qu.:4.838e+11Median :1.119e+12
Mean :1.927e+12
3rd Qu.:2.427e+12Max. :1.734e+13

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48### KODE ###> table(blok41.exp$KODE,useNA="ifany")
1
2
3
4
5
6
7
8
9
10
11
12
275475 274523
3475 8133 10466 12942 1289 46932
933 127577 64774 26367
13
14
15
16
17
18
19
20
21
22
23
24
9610 11289 44018 1264 1391
1164 1624 247374 13241 62615 5432 19983
25
26
27
28
29
30
31
32
33
34
35
36
42424 16275 28299 15039 31435 18849 20520 6255 2772 77401 24003 8436
37
38
39
40
41
42
43
44
45
46
47
48
1702 2983
1250 18518 2241 17973 65148 9074 14765 4040 3629 5409
49
50
51
52
53
54
55
56
57
58
59
60
44286 4913
1399 3606 106527 15380 1336
924 10399 75548 12394 1177
61
62
63
64
65
66
67
68
69
70
71
72
1788
334
1027
223 1135
3860 1530 2091 1481
997 218301 186116
73
74
75
76
77
78
79
80
81
82
83
84
18610 10514
2762
339 5765
995 5442 49144 22161 18029
621 1741
85
86
87
88
89
90
91
92
93
94
95
96
273119 137507 144042 50876 25412 46786 33005 118965 116060 41322 43536 104242
97
98
99
100
101
102
103
104
105
106
107
108
81234 39983 29004 11457 53704 38354 24246 22824 3948 12084 10582 252600
109
110
111
112
113
114
115
116
117
118
119
120
228145 148802 34481 178926
922 44744 200282 12871 5047 1453 8925
285
121
122
123
124
125
126
127
128
129
130
131
132
2403 158750 168596 4809 3260
513 213624 61029 33721 23324 4314 21238
133
134
135
136
137
138
139
140
141
142
143
144
2942 9749 22516 6343 25735 22076 70391 31599 6926 2745 1478 1780
145
146
147
148
149
150
151
152
153
154
155
156
18077 4486
4160 23947
103 10901 271176 62269 1264 203624 111541 5341
157
158
159
160
161
162
163
164
165
166
167
168
4287 266027 257461 41995 171797 152388 1691 1371 8864 6085 272901 269944
169
170
171
172
173
174
175
176
177
178
179
180
96345 86979 98243 96422 7214
3284 103267 120638 209083 18110 52857 90600
181
182
183
184
185
186
187
188
189
190
191
192
200060 186283
1616 2899 3799 41738 2880 3400 2141 1153 256698 28099
193
194
195
196
197
198
199
200
201
202
203
204
85969 58611 89535 130932 13952 38254 81059 28231 23675 33187 31696 21232
205
206
207
208
209
210
211
212
213
214
215
216
98532 11540 121383 20225 20252 82542 21152 56168 19986 23043 9253 9911
217
218
219
220
221
222
223
224
225
226
227
228
87554 20420 65097
794
296
4861 197320 115388 50040 16714 27130 15499
229
16755
### KLP ###> table(blok41.exp$KLP,useNA="ifany")03126461167
12529861
10358693 161501
181
191
245909 1337441

20593915223
241526531316247185322239 2077793

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

Consistency check# The variable B41K8 (total quantity) should be the sum of B41K4 (quantity purchased) and B41K6(quantity of own consumption). However, there are many inconsistencies.
> table(d$B41K8==(d$B41K4+d$B41K6))
FALSE
TRUE
764024 10892534# The majority cause of inconsistencies is missing data of B41K4 (quantity purchased).
> dim(d[abs(d$B41K8-(d$B41K4+d$B41K6))>0 & d$B41K4==0,1:14])[1] 753310
14# Example of inconsistencies> head(d[abs(d$B41K8-(d$B41K4+d$B41K6))>0,1:14])
B1R1 B1R2 B1R5
B1R7 B1R8 KODE B2R1 KLP B41K4 B41K5 B41K6 B41K7 B41K8 B41K9
19 11
1
1 2003700
11
8 0
0 150000
00 15.0 150000
21 11
1
1 2003700
1 10
8 0
0 60000
0 0.5 6000
23 11
1
1 2003700
1 20
8 0
0 200000
00 14.0 200000
30 11
1
1 2003700
1 71
8 0
0 90000
0 0.7 9000
32 11
1
1 2003700
1 85
8 0
0 41000
00 15.0 41000
40 11
1
1 2003700
1 115
8 0
0 20000
00 10.0 20000# The variable B41K9 (total value of consumption) should be the sum of B41K5 (amount purchased)
and B41K7 (value of own production). However, there are 29 inconsistencies.
> table(abs(d$B41K9-(d$B41K5+d$B41K7))<1)
FALSE
TRUE
29 11656529# Example of inconsistencies> head(d[abs(d$B41K9-(d$B41K5+d$B41K7))>0,1:14])
B1R1 B1R2 B1R5
B1R7 B1R8 KODE B2R1 KLP B41K4 B41K5 B41K6 B41K7 B41K8 B41K9
11081886 81
72
1 2270314
1 167
2
0 59.80 16994
00 59.30 13244
11081890 81
72
1 2270314
1 180
2 167 2.00
6875
0
0 1.50 3125
11082874 81
72
1 2270270
4 167
40 1.554500
00 0.85 3500
11082877 81
72
1 2270270
4 180
4 167 0.85
2000
0
0 0.15 1000
11082878 81
72
1 2270270
4 191
40 35.34 119000
00 13.34 89000
11082879 81
72
1 2270270
4 194
4 191 26.10 32000
00 6.10 12000

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blok41.kalori : food calorie> summary(blok41.kalori)
B1R1
B1R2
B1R5
B1R7
B1R8
Min. :11.00 Min. : 1.0 Min. :1.000 Min. :2003602 Min. : 1.000
1st Qu.:18.00 1st Qu.: 4.0 1st Qu.:1.000 1st Qu.:2073965 1st Qu.: 3.000
Median :34.00 Median : 9.0 Median :2.000 Median :2139878 Median : 5.000
Mean :40.61 Mean :22.8 Mean :1.556 Mean :2142025 Mean : 5.471
3rd Qu.:62.00 3rd Qu.:25.0 3rd Qu.:2.000 3rd Qu.:2211223 3rd Qu.: 8.000
Max. :94.00 Max. :79.0 Max. :2.000 Max. :2288908 Max. :10.000
KODE
B2R1
KLP
KALORI
Min. : 1.0 Min. : 1.000
Min. : 0.00 Min. :0.0
1st Qu.: 86.0 1st Qu.: 3.000
1st Qu.: 0.00 1st Qu.:
95.0
Median :134.0 Median : 4.000
Median : 85.00 Median : 520.8
Mean :126.6 Mean : 4.085
Mean : 89.02 Mean : 2570.1
3rd Qu.:176.0 3rd Qu.: 5.000
3rd Qu.:167.00 3rd Qu.: 1810.0
Max. :229.0 Max. :25.000
Max. :223.00 Max. :257162.0
PROTEIN
LEMAK
KARBO
WERT
Min. : 0.000 Min. :0.00 Min. :0.00 Min. : 1.00
1st Qu.: 2.645 1st Qu.:
0.73 1st Qu.:
2.02 1st Qu.: 88.63
Median : 13.000 Median :6.46 Median : 31.06 Median : 159.29
Mean : 72.735 Mean :57.61 Mean
: 411.57 Mean : 231.54
3rd Qu.: 55.180 3rd Qu.:
48.06 3rd Qu.: 175.00 3rd Qu.: 328.24
Max. :8515.660 Max. :17730.00 Max.
:55060.50 Max. :1963.09
WEIND
SERIES
Min. :
2.0
Min. :2.206e+10
1st Qu.: 318.2 1st Qu.:4.838e+11Median : 614.7
Median :1.119e+12
Mean : 934.8
Mean :1.927e+12
3rd Qu.: 1237.3
3rd Qu.:2.427e+12Max. :20206.9
Max. :1.734e+13# The first eight identification variables are identical between blok41.exp and blok41.kalori.
> for(j in 1:8){+ cat(colnames(blok41.kalori)[j],"\n")+ print(table(blok41.kalori[,j]==blok41.exp[,j]))
+ }
B1R1
TRUE
11656558
B1R2
TRUE

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B1R5
TRUE11656558
B1R7
TRUE11656558
B1R8
TRUE11656558
KODE
TRUE
11656558
B2R1
TRUE11656558
KLP
TRUE
11656558

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blok42 : non-food expenditure for three months> summary(blok42)
B1R1
B1R2
B1R5
B1R7
B1R8
Min. :11.00 Min. : 1.00 Min. :1.000 Min. :2003602 Min. : 1.00
1st Qu.:18.00 1st Qu.: 4.00 1st Qu.:1.000 1st Qu.:2076738 1st Qu.: 3.00
Median :35.00 Median :10.00 Median :2.000 Median :2143514 Median : 5.00
Mean :41.22 Mean :23.07 Mean :1.553 Mean :2144211 Mean : 5.47
3rd Qu.:62.00 3rd Qu.:26.00 3rd Qu.:2.000 3rd Qu.:2213331 3rd Qu.: 8.00
Max. :94.00 Max. :79.00 Max. :2.000
Max. :2288908 Max. :10.00
KODE
B2R1
KLP
B42K2
Min. :230.0 Min. : 1.000
Min. : 0.0 Min. :0.00
1st Qu.:257.0 1st Qu.: 3.000
1st Qu.:230.0 1st Qu.:
0.00
Median :273.0 Median : 4.000
Median :261.0 Median :0.00
Mean :279.2 Mean : 4.087
Mean :218.7 Mean :10.07
3rd Qu.:304.0 3rd Qu.: 5.000
3rd Qu.:261.0 3rd Qu.:
0.00
Max. :343.0 Max. :25.000
Max. :337.0 Max. :110009.00
B42K3
B42K4
B42K5
B42K6
Min. :
0 Min.
:
0 Min. :0 Min. :1
1st Qu.:1546 1st Qu.:
1640 1st Qu.:
4000 1st Qu.:
23000
Median :16000 Median :15580 Median :18000 Median :60000
Mean :
88544 Mean:
83765 Mean :95428 Mean : 267732
3rd Qu.:54000 3rd Qu.:
53000 3rd Qu.:
60000 3rd Qu.: 182000
Max. :697500000 Max.
:390000000 Max. :420000000 Max. :697500000
WERT
WEIND
SERIES
Min. : 1.00
Min. :
2.0
Min. :2.206e+10
1st Qu.: 88.03
1st Qu.: 316.0
1st Qu.:4.940e+11
Median : 157.05
Median : 606.8
Median :1.131e+12
Mean : 229.68
Mean : 923.2
Mean :1.952e+12
3rd Qu.: 326.35
3rd Qu.: 1220.7
3rd Qu.:2.474e+12Max. :1963.09
Max. :20206.9
Max. :1.734e+13### KODE ###> table(blok42$KODE,useNA="ifany")
230
232
233
234
235
236
238
240
242
277835 255242
9904 10249 9837 30860 247700 64636 109926
250
251
253
254
255
256
257
258
259
4682
917
2967 159193 244814 15230 207040 4213 2611
263
264
265
266
267
268
269
270
271
223608 180809 271518 100353 19575 37136 10754 6924 44029
275
276
277
278
279
280
281
282
283
1779 24850 160945 51877 1969
6773 4605 2061 87021
287
288
289
290
292
294
296
297
298
42170 94390 122246 16388 165391
6040 133704 89975 154707

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244
246
248
974 127787 7207
260
261
262
40813 277316 281241
272
273
274
36028 40561 8669
284
285
286
51940 13022 136552
299
300
301
28647 7150 15000

53

302
303
304
305
306
307
308
309
310
311
312
313
23963 204349 105641 109432 104002 14477 14291 128699 40783 29157 131539 9960
314
315
316
317
318
319
320
321
322
323
324
325
6192 25156 47409 61481 3886
4241 10587 3618 12992 4401 29182 11035
326
327
328
329
330
331
332
333
334
335
336
337
2165 9124
4904 6373 228327 196792 127438 86012 8925 3636 3289 51282
338
339
340
341
342
343
5565 3165
8302
754 35027
4358### KLP ###> table(blok42$KLP,useNA="ifany")
0
230
261
303
312
330
1170648 1556802 2704370 546482 252706 426092

337
57171

# The variable B42K6 (Total expenditure on non-food items for 1 to 3 months) should be the sum of
B42K3, B42K4 and B42K5. However, there are 448 inconsistencies.
> d<-blok42> d$sum<-d$B42K3+d$B42K4+d$B42K5> table(d$sum==d$B42K6)
FALSE

TRUE

448 6713823# Example of inconsistencies> head(d[abs(d$B42K6-d$sum)>0,c(4:13,17)])
B1R7 B1R8 KODE B2R1 KLP B42K2 B42K3
0

B42K4 B42K5

B42K6

sum

2900633 2126150

1 261

4

2900635 2126150

1 263

4 261

0 42000

2900826 2126055

1 261

2

0 780200 738200 997200 2512600 2515600

2900827 2126055

1 262

2 261

0 17700

2901425 2126606

1 261

3

0 265283 242383 278783 759949 786449

2901427 2126606

1 263

3 261

0

0

0 631600 3568300 599600 4766500 4799500

0 36800

9000

9000

14700 14700

10300 10300

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27000

44100

30900

60000

47100

57400

54

blok43 : monthly household-level summary> summary(blok43)
B1R1
B1R2
B1R5
B1R7
B1R8
Min. :11.00 Min. : 1.00 Min. :1.000 Min. :2003602 Min. : 1.00
1st Qu.:18.00 1st Qu.: 4.00 1st Qu.:1.000 1st Qu.:2074929 1st Qu.: 3.00
Median :35.00 Median : 9.00 Median :2.000 Median :2146255 Median : 5.00
Mean :42.15 Mean :21.73 Mean :1.589 Mean :2146255 Mean : 5.49
3rd Qu.:63.00 3rd Qu.:23.00 3rd Qu.:2.000 3rd Qu.:2217582 3rd Qu.: 8.00
Max. :94.00 Max. :79.00 Max. :2.000
Max. :2288908 Max. :10.00
B2R1
KALORIFOOD
NFOOD
Min. : 1.000 Min.
:1000 Min. : 51000 Min. :2667
1st Qu.: 3.000 1st Qu.:1566 1st Qu.: 673971 1st Qu.: 344516
Median : 4.000 Median :1909 Median : 995143 Median : 607204
Mean : 3.919 Mean
:2014 Mean : 1159630 Mean : 1063933
3rd Qu.: 5.000 3rd Qu.:2338 3rd Qu.: 1454357 3rd Qu.: 1127238
Max. :25.000 Max.
:4500 Max.
:23107714 Max. :252582667
EXPEND
KAPITAWERTWEINDMin. :
93602 Min.
: 59208 Min. : 1.00 Min. :2.0
1st Qu.: 1088594 1st Qu.: 301082 1st Qu.: 83.25 1st Qu.: 280.1
Median : 1663538 Median : 458984 Median : 147.88 Median : 543.5
Mean : 2223563 Mean
: 625772 Mean : 219.52 Mean : 845.2
3rd Qu.: 2602762 3rd Qu.: 731226 3rd Qu.: 308.54 3rd Qu.: 1099.6
Max. :255974810 Max.
:85324937 Max. :1963.09 Max. :20206.9
SERIES
Min. :2.206e+10
1st Qu.:5.266e+11Median :1.196e+12
Mean :2.070e+12
3rd Qu.:2.621e+12
Max. :1.734e+13

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# The variable WERT (household weight) in blok43 is identical with FWT in kor11rt.
> table(blok43$WERT==kor11rt$FWT)[1] 285307# The variable WEIND (individual weight) is WERT multiplied by B2R1 (household size).
> table(abs(blok43$WEIND-blok43$B2R1*blok43$WERT)<1)
TRUE
285307# The variable EXPEND (monthly household total expenditure) is the sum of FOOD and NFOOD.
> table(abs(blok43$EXPEND-(blok43$FOOD+blok43$NFOOD))<1)
TRUE
285307# The variable KAPITA (monthly per capita expenditure) is EXPEND divided by B2R1.
> table(abs(blok43$KAPITA-(blok43$EXPEND/blok43$B2R1))<1)
TRUE
285307

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

Relationship with other data frames# The variable FOOD (monthly household food expenditure) is the household-level B41K9 inblok41.exp multiplied by 30/7.
> d<-subset(blok41.exp,KLP>0)> dim(d)[1] 8530097

18> t<-tapply(d$B41K9,d$B1R7,sum)> length(t)[1] 285307> table(abs(t*30/7-blok43[order(blok43$B1R7),"FOOD"])<1)
TRUE
285307# The variable NFOOD (monthly household non-food expenditure) is the household-level B42K6 inblok42 divided by 3.
> d<-subset(blok42,KLP>0)> dim(d)[1] 5543623

16> t<-tapply(d$B42K6,d$B1R7,sum)> length(t)[1] 285307> table(abs(t/3-blok43[order(blok43$B1R7),"NFOOD"])<1)
TRUE
285307# The variable KALORI (daily average consumption of calorie per capita) is the household-level per
capita calorie KALORI/B2R1 in blok41.kalori divided by 7.
> d<-subset(blok41.kalori,KLP>0)> dim(d)[1] 8530097

15

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> t<-tapply(d$KALORI/d$B2R1,d$B1R7,sum)> length(t)[1] 285307> table(abs(t/7-blok43[order(blok43$B1R7),"KALORI"])<1)
TRUE
285307

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6. Consumption


The file blok43 includes monthly household-level variables of food, non-food and total
consumption.

# Average monthly consumption of food, non-food and total amount per household> d<-blok43> colnames(d)[1] "B1R1""B1R2""B1R5""B1R7"[9] "NFOOD" "EXPEND" "KAPITA" "WERT""B1R8""B2R1""KALORI" "FOOD""WEIND" "SERIES"> apply(d[,8:10],2,function(x) weighted.mean(x,d$WERT))FOOD

NFOOD EXPEND

1117853 1121512 2239365#Or,> apply(d[,8:10],2,function(x) sum(x*d$WERT))/sum(d$WERT)FOOD

NFOOD EXPEND

1117853 1121512 2239365# Average monthly consumption of food, non-food and total amount per capita> apply(d[,8:10],2,function(x) weighted.mean(x/d$B2R1,d$WEIND))
FOOD

NFOOD

EXPEND290342.4 291293.0 581635.4#Or,> apply(d[,8:10],2,function(x) sum(x*d$WERT))/sum(d$WERT*d$B2R1)
FOOD

NFOOD

EXPEND290342.4 291293.0 581635.4

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The disaggregation by subgroups will be appended to blok43.

FOOD


Generated household-level food expenditure by subgroup from records of subtotals.

> df<-blok43> subtotal<-c(1,10,20,53,71,85,115,127,151,158,167,181,191,223)> for(j in subtotal){+ d<-subset(blok41.exp,KLP==0 & KODE==j)+ df<-merge(df,d[,c("B1R7","B41K9")],by="B1R7",all.x=T)+ colnames(df)[ncol(df)]<-paste("SG",j,sep="-")+ }> dim(df)[1] 285307
28> head(df)B1R7 B1R1 B1R2 B1R5 B1R8 B2R1
KALORI
FOOD
NFOOD
EXPEND
1 2003602 11121
4 1647.614 1117714.3 1033116.7 2150831.0
2 2003603 11122
1 1969.664 303857.1 205176.7 509033.8
3 2003604 11
12
3
7 1015.076 679285.7 610061.7 1289347.4
4 2003605 11
12
4
5 1049.866 633857.1 535948.3 1169805.5
5 2003606 1112
5
6 1077.124 762000.0 797880.0 1559880.0
6 2003607 1112
6
4 1067.711 807857.1 474980.0 1282837.1
KAPITAWERTWEINDSERIES SG-1 SG-10 SG-20 SG-53 SG-71 SG-85
1 537707.7 38.15055 152.60222 44119316040 43000
NA 48000
NA 13900 26000
2 509033.8 49.33569 49.33569 88238676120 18000
NA 18000
NANA 7000
3 184192.5 55.52716 388.69012 132358080240 83000
NA 27600
NANA 11000
4 233961.1 47.07539 235.37693 176477528400 42000
NA 48000NANA 13000
5 259980.0 43.83154 262.98926 220597020600 55000
NA 69000NANA 17000
6 320709.3 54.55240 218.20961 264716556840 72000
NA 46000
NA 7500 21000
SG-115 SG-127 SG-151 SG-158 SG-167 SG-181 SG-191 SG-223
1
NA 13000 7000 14000 1900 11000 32000 51000
2NA
NA 4000
4000
900 2000
NA 17000
3NA 6000 13000
NA 2900 2000
NA 13000
4NA 8000 11000NA
900 3000
NA 22000
5NA 7000 13000
NA 1800 2000
NA 13000
6NA 9000 7000
NA 2000
NA
NA 24000# Converted to monthly> df[is.na(df)]<-0> df[,15:28]<-round(df[,15:28]*30/7,1)> head(df)B1R7 B1R1 B1R2 B1R5 B1R8 B2R1
KALORI
FOOD
NFOOD
EXPEND
1 2003602 11121
4 1647.614 1117714.3 1033116.7 2150831.0
2 2003603 11122
1 1969.664 303857.1 205176.7 509033.8
3 2003604 11
12
3
7 1015.076 679285.7 610061.7 1289347.4
4 2003605 11
12
4
5 1049.866 633857.1 535948.3 1169805.5

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5 2003606 1112
5
6 1077.124 762000.0 797880.0 1559880.0
6 2003607 1112
6
4 1067.711 807857.1 474980.0 1282837.1
KAPITAWERTWEINDSERIES
SG-1 SG-10
SG-20 SG-53
1 537707.7 38.15055 152.60222 44119316040 184285.7
0 205714.3
02 509033.8 49.33569 49.33569 88238676120 77142.9
0 77142.9
03 184192.5 55.52716 388.69012 132358080240 355714.3
0 118285.7
04 233961.1 47.07539 235.37693 176477528400 180000.0
0 205714.3
0
5 259980.0 43.83154 262.98926 220597020600 235714.3
0 295714.3
0
6 320709.3 54.55240 218.20961 264716556840 308571.4
0 197142.9
0SG-71
SG-85 SG-115 SG-127 SG-151 SG-158 SG-167 SG-181 SG-191
1 59571.4 111428.6
0 55714.3 30000.0 60000.0 8142.9 47142.9 137142.92
0.0 30000.00
0.0 17142.9 17142.9 3857.1 8571.4
0.03
0.0 47142.9
0 25714.3 55714.3
0.0 12428.6 8571.4
0.0
4
0.0 55714.3
0 34285.7 47142.9
0.0 3857.1 12857.1
0.0
5
0.0 72857.1
0 30000.0 55714.3
0.0 7714.3 8571.4
0.0
6 32142.9 90000.0
0 38571.4 30000.0
0.0 8571.4
0.0
0.0
SG-223
1 218571.4
2 72857.1
3 55714.3
4 94285.7
5 55714.3
6 102857.1# Consistency check with the variable of FOOD
> table(abs(df$FOOD-rowSums(df[,15:28]))<1)
TRUE
285307# Monthly mean expenditure by food subgroup> t<-apply(df[,c(8,15:28)],2,function(x) weighted.mean(x,df$WERT))> m<-data.frame(subgroup=names(t),mean=round(t),row.names=NULL)> groupnames<-c("Cereals","Tubers","Fish","Meat","Eggs and milk","Vegetables",+ "Legumes","Fruits","Oil and fats","Beverages stuffs","Spices","Miscellaneous",+ "Prepared food","Tobacco")> rownames(m)<-c("Food total",groupnames)> msubgroup
mean
Food total
FOOD 1117853
Cereals
SG-1 185182
Tubers
SG-10
11173
Fish
SG-20
94629
Meat
SG-53
45184
Eggs and milk
SG-71
66939
Vegetables
SG-85
89887
Legumes
SG-11529901
Fruits
SG-12750965
Oil and fats
SG-15141542
Beverages stuffs
SG-15843054

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SpicesMiscellaneous
Prepared foodTobaccoSG-16723486
SG-181
24758
SG-191 284498
SG-223 126653> df.old<-df

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NON FOODGenerated household-level non-food expenditure by subgroup from records of subtotals.

> subtotal<-c(230,261,303,312,330,337) # Codes of subtotals> for(j in subtotal){+ d<-subset(blok42,KLP==0 & KODE==j)+ df<-merge(df,d[,c("B1R7","B42K6")],by="B1R7",all.x=T)+ colnames(df)[ncol(df)]<-paste("SG",j,sep="-")+ }> dim(df)[1] 285307
34> head(df)B1R7 B1R1 B1R2 B1R5 B1R8 B2R1
KALORI
FOOD
NFOOD
EXPEND
1 2003602 11121
4 1647.614 1117714.3 1033116.7 2150831.0
2 2003603 11122
1 1969.664 303857.1 205176.7 509033.8
3 2003604 11
12
3
7 1015.076 679285.7 610061.7 1289347.4
4 2003605 11
12
4
5 1049.866 633857.1 535948.3 1169805.5
5 2003606 1112
5
6 1077.124 762000.0 797880.0 1559880.0
6 2003607 1112
6
4 1067.711 807857.1 474980.0 1282837.1
KAPITAWERTWEINDSERIES
SG-1 SG-10
SG-20 SG-53
1 537707.7 38.15055 152.60222 44119316040 184285.7
0 205714.3
02 509033.8 49.33569 49.33569 88238676120 77142.9
0 77142.9
03 184192.5 55.52716 388.69012 132358080240 355714.3
0 118285.7
04 233961.1 47.07539 235.37693 176477528400 180000.0
0 205714.3
0
5 259980.0 43.83154 262.98926 220597020600 235714.3
0 295714.3
0
6 320709.3 54.55240 218.20961 264716556840 308571.4
0 197142.9
0SG-71
SG-85 SG-115 SG-127 SG-151 SG-158 SG-167 SG-181 SG-191
1 59571.4 111428.6
0 55714.3 30000.0 60000.0 8142.9 47142.9 137142.92
0.0 30000.00
0.0 17142.9 17142.9 3857.1 8571.4
0.03
0.0 47142.9
0 25714.3 55714.3
0.0 12428.6 8571.4
0.0
4
0.0 55714.3
0 34285.7 47142.9
0.0 3857.1 12857.1
0.0
5
0.0 72857.1
0 30000.0 55714.3
0.0 7714.3 8571.4
0.0
6 32142.9 90000.0
0 38571.4 30000.0
0.0 8571.4
0.0
0.0
SG-223 SG-230 SG-261 SG-303 SG-312 SG-330 SG-337
1 218571.4 1320260 1313000 138000 271000 57090
NA
2 72857.1 534730 65000 14000
NA 1800NA
3 55714.3 767085 969000 92000
NA 2100
NA
4 94285.7 754865 836000
NANA 16980
NA
5 55714.3 776695 1575000 23000
NA 18945
NA
6 102857.1 442330 791800 12000 166000 12810
NA# Converted to monthly> df[is.na(df)]<-0> df[,29:34]<-round(df[,29:34]/3,1)> head(df)B1R7 B1R1 B1R2 B1R5 B1R8 B2R1
KALORI
FOOD
NFOOD
EXPEND
1 2003602 11121
4 1647.614 1117714.3 1033116.7 2150831.0
2 2003603 11122
1 1969.664 303857.1 205176.7 509033.8

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3 2003604 11
12
3
7 1015.076 679285.7 610061.7 1289347.4
4 2003605 11
12
4
5 1049.866 633857.1 535948.3 1169805.5
5 2003606 1112
5
6 1077.124 762000.0 797880.0 1559880.0
6 2003607 1112
6
4 1067.711 807857.1 474980.0 1282837.1
KAPITAWERTWEINDSERIES
SG-1 SG-10
SG-20 SG-53
1 537707.7 38.15055 152.60222 44119316040 184285.7
0 205714.3
02 509033.8 49.33569 49.33569 88238676120 77142.9
0 77142.9
03 184192.5 55.52716 388.69012 132358080240 355714.3
0 118285.7
04 233961.1 47.07539 235.37693 176477528400 180000.0
0 205714.3
0
5 259980.0 43.83154 262.98926 220597020600 235714.3
0 295714.3
0
6 320709.3 54.55240 218.20961 264716556840 308571.4
0 197142.9
0SG-71
SG-85 SG-115 SG-127 SG-151 SG-158 SG-167 SG-181 SG-191
1 59571.4 111428.6
0 55714.3 30000.0 60000.0 8142.9 47142.9 137142.92
0.0 30000.00
0.0 17142.9 17142.9 3857.1 8571.4
0.03
0.0 47142.9
0 25714.3 55714.3
0.0 12428.6 8571.4
0.0
4
0.0 55714.3
0 34285.7 47142.9
0.0 3857.1 12857.1
0.0
5
0.0 72857.1
0 30000.0 55714.3
0.0 7714.3 8571.4
0.0
6 32142.9 90000.0
0 38571.4 30000.0
0.0 8571.4
0.0
0.0
SG-223
SG-230SG-261 SG-303 SG-312 SG-330 SG-337
1 218571.4 440086.7 437666.7 46000.0 90333.3 19030
02 72857.1 178243.3 21666.7 4666.7
0.0
600
0
3 55714.3 255695.0 323000.0 30666.7
0.0
700
0
4 94285.7 251621.7 278666.7
0.0
0.0 5660
05 55714.3 258898.3 525000.0 7666.7
0.0 63150
6 102857.1 147443.3 263933.3 4000.0 55333.3 4270
0

# Consistency check between the variable of NFOOD and the variables of subtotals> table(abs(df$NFOOD-rowSums(df[,29:34]))<1)
FALSE TRUE
7595 277712
Remarks:
There are 7,595 inconsistencies.
# Example of inconsistencies> head(df[abs(df$NFOOD-rowSums(df[,29:34]))>=1,c(1,9,29:34)])
B1R7NFOOD SG-230 SG-261 SG-303 SG-312 SG-330 SG-337
5522 2009123 181000.00 98333.3 82666.7
00 1000018169 2021770 141333.33
0.0
0.0
0
0
0
0
18170 2021771 118000.00
0.0
0.0
0
0
0
0
18171 2021772 82666.67
0.0
0.0
0
0
0
0
18172 2021773 280000.00
0.0
0.0
0
0
0
0
18173 2021774 388300.00
0.0
0.0
0
0
0
0

> blok42[blok42$B1R7==2021770,]
B1R1 B1R2 B1R5
B1R7 B1R8 KODE B2R1 KLP B42K2 B42K3 B42K4 B42K5
417082 12 13
1 2021770
1 232
6 230
3.0 30000 30000 30000

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1 2021770
1 238
6 230 1571.0 52000 48000 56000
12 13
1 2021770
1 242
6 230
3.0 13000 13000 13000
12 13
1 2021770
1 250
6 230
1.0 16000 16000 16000
12 13
1 2021770
1 292
6 261
3.0 14000 13000 16000
12 13
1 2021770
1 296
6 261
0.8 16000 16000 16000
B42K6
WERT
WEIND
SERIES
417082 90000 298.3076 1789.846 24524070100
417083 156000 298.3076 1789.846 24524070100
417084 39000 298.3076 1789.846 24524070100
417085 48000 298.3076 1789.846 24524070100
417086 43000 298.3076 1789.846 24524070100
417087 48000 298.3076 1789.846 24524070100



A record of subtotal with KODE=230 and KLP=0 should exist in the data frame.

Hence, generated household-level non-food expenditure by subgroup from records ofitems excluding subtotals.

> d<-subset(blok42,KLP>0)> dim(d)[1] 5543623
16> m<-tapply(d$B42K6,list(d$B1R7,d$KLP),sum)> dim(m)[1] 285307
6> head(m)
230
261
303
312
330 337
2003602 1320260 1313000 138000 271000 57090 NA
2003603 534730
65000 14000
NA 1800 NA
2003604 767085 969000 92000
NA 2100 NA
2003605 754865 836000
NANA 16980 NA
2003606 776695 1575000 23000
NA 18945 NA
2003607 442330 791800 12000 166000 12810 NA> m[is.na(m)]<-0> table(abs(blok43[order(blok43$B1R7),"NFOOD"]-rowSums(m)/3)<1)
TRUE
285307==============================================================================================

Remarks:
Regarding the procedure of estimation of expenditure by using blok41 and blok42, the Delegatesfrom Indonesia for 2017 WS recommended to firstly exclude the records of subtotal, thenaggregate from item-level records.

Summary In blok42, the record of sub-total with KLP=0 should be dropped from the resampled dataset.
==============================================================================================

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> d<-data.frame(rownames(m),round(m/3,1),row.names=NULL)> colnames(d)<-c("B1R7","SG-230","SG-261","SG-303","SG-312","SG-330","SG-337")> head(d)B1R7 SG-230
SG-261 SG-303 SG-312 SG-330 SG-337
1 2003602 440086.7 437666.7 46000.0 90333.3 19030
02 2003603 178243.3 21666.7 4666.7
0.0
600
0
3 2003604 255695.0 323000.0 30666.7
0.0
700
0
4 2003605 251621.7 278666.7
0.0
0.0
5660
0
5 2003606 258898.3 525000.0 7666.7
0.0
6315
0
6 2003607 147443.3 263933.3 4000.0 55333.3
4270
0> df<-df.old> dim(df)[1] 285307

28> df<-merge(df,d,by="B1R7",all.x=T)> dim(df)[1] 285307
34> head(df)B1R7 B1R1 B1R2 B1R5 B1R8 B2R1
KALORI
FOOD
NFOOD
EXPEND
1 2003602 11121
4 1647.614 1117714.3 1033116.7 2150831.0
2 2003603 11122
1 1969.664 303857.1 205176.7 509033.8
3 2003604 11
12
3
7 1015.076 679285.7 610061.7 1289347.4
4 2003605 11
12
4
5 1049.866 633857.1 535948.3 1169805.5
5 2003606 1112
5
6 1077.124 762000.0 797880.0 1559880.0
6 2003607 1112
6
4 1067.711 807857.1 474980.0 1282837.1
KAPITAWERTWEINDSERIES
SG-1 SG-10
SG-20 SG-53
1 537707.7 38.15055 152.60222 44119316040 184285.7
0 205714.3
02 509033.8 49.33569 49.33569 88238676120 77142.9
0 77142.9
03 184192.5 55.52716 388.69012 132358080240 355714.3
0 118285.7
04 233961.1 47.07539 235.37693 176477528400 180000.0
0 205714.3
0
5 259980.0 43.83154 262.98926 220597020600 235714.3
0 295714.3
0
6 320709.3 54.55240 218.20961 264716556840 308571.4
0 197142.9
0SG-71
SG-85 SG-115 SG-127 SG-151 SG-158 SG-167 SG-181
SG-191
1 59571.4 111428.6
0 55714.3 30000.0 60000.0 8142.9 47142.9 137142.92
0.0 30000.00
0.0 17142.9 17142.9 3857.1 8571.4
0.03
0.0 47142.9
0 25714.3 55714.3
0.0 12428.6 8571.4
0.0
4
0.0 55714.3
0 34285.7 47142.9
0.0 3857.1 12857.1
0.0
5
0.0 72857.1
0 30000.0 55714.3
0.0 7714.3 8571.4
0.0
6 32142.9 90000.0
0 38571.4 30000.0
0.0 8571.4
0.0
0.0
SG-223
SG-230SG-261 SG-303 SG-312 SG-330 SG-337
1 218571.4 440086.7 437666.7 46000.0 90333.3 19030
02 72857.1 178243.3 21666.7 4666.7
0.0
600
0
3 55714.3 255695.0 323000.0 30666.7
0.0
700
0
4 94285.7 251621.7 278666.7
0.0
0.0
5660
0
5 55714.3 258898.3 525000.0 7666.7
0.0
6315
0
6 102857.1 147443.3 263933.3 4000.0 55333.3
4270
0# Monthly mean expenditure by non-food subgroup> t<-apply(df[,c(9,29:34)],2,function(x) weighted.mean(x,df$WERT))> m<-data.frame(subgroup=names(t),mean=round(t),row.names=NULL)> groupnames<-c("Housing and household facility","Goods and services","Clothing and footwear",+ "Durable goods","Taxes and insurance","Parties and ceremonies")> rownames(m)<-c("Non-food total",groupnames)

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> m
Non-food total
Housing and household facility
Goods and services
Clothing and footwear
Durable goods
Taxes and insurance
Parties and ceremonies

subgroupmean
NFOOD 1121512
SG-230 448756
SG-261 402774
SG-30373956
SG-312 125742
SG-33036223
SG-33734062> blok43.sg<-df> dim(blok43.sg)[1] 285307
34> save(blok43.sg,file="blok43.sg.RData")Monthly expenditure per household and per capita by subgroup> t1<-apply(df[,c(10,8,15:28,9,29:34)],2,function(x)+ weighted.mean(x,df$WERT))> t2<-apply(df[,c(10,8,15:28,9,29:34)],2,function(x)+ weighted.mean(x/df$B2R1,df$WEIND))> m<-data.frame(subgroup=names(t1),per.hh=round(t1),+ per.capita=round(t2),row.names=NULL)> fsg<-c("Cereals","Tubers","Fish","Meat","Eggs and milk","Vegetables",+ "Legumes","Fruits","Oil and fats","Beverages stuffs","Spices","Miscellaneous",+ "Prepared food","Tobacco")> nfsg<-c("Housing and household facility","Goods and services","Clothing and footwear",+ "Durable goods","Taxes and insurance","Parties and ceremonies")> rownames(m)<-c("TOTAL","FOOD",fsg,"NON-FOOD",nfsg)> msubgroup per.hh per.capita
TOTAL
EXPEND 2239365
581635FOOD
FOOD 1117853
290342
Cereals
SG-1 185182
48098
Tubers
SG-10
11173
2902
Fish
SG-20
94629
24578
Meat
SG-53
45184
11736
Eggs and milk
SG-71
66939
17386
Vegetables
SG-85
89887
23347
Legumes
SG-11529901
7766
Fruits
SG-12750965
13237
Oil and fats
SG-15141542
10790
Beverages stuffs
SG-15843054
11182
SpicesSG-167
23486
6100
Miscellaneous
SG-18124758
6430
Prepared foodSG-191 284498
73893
TobaccoSG-223 126653
32896
NON-FOODNFOOD 1121512
291293
Housing and household facility
SG-230 448756
116556
Goods and services
SG-261 402774
104613
Clothing and footwear
SG-30373956
19209
Durable goods
SG-312 125742
32659
Taxes and insurance
SG-33036223
9408
Parties and ceremonies
SG-33734062
8847

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7 Resampling
Strategy for resampling micro data

Select B1R7 at the rate of 80% and set as hh.selected.

> d<-kor11rt> d<-d[order(d$B1R7),]> d$sn<-1:nrow(d)> hh.selected<-d[d$sn%%5!=2,"B1R7"]> length(hh.selected)[1] 228245> length(hh.selected)/nrow(d)[1] 0.7999979



Select records in each data frame which belong to the selected B1R7.

> korrt80<-subset(kor11rt,is.element(B1R7,hh.selected))> dim(korrt80)[1] 228245

89> korind80<-subset(kor11ind,is.element(B1R7,hh.selected))> dim(korind80)[1] 894660

126> food80<-subset(blok41.exp,is.element(B1R7,hh.selected))> dim(food80)[1] 9323482

18> kalori80<-subset(blok41.kalori,is.element(B1R7,hh.selected))> dim(kalori80)[1] 9323482

15> nonfood80<-subset(blok42,is.element(B1R7,hh.selected))> dim(nonfood80)[1] 5369815

16> summary80<-subset(blok43,is.element(B1R7,hh.selected))Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

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> dim(summary80)[1] 228245



14

Added WT; the household weight for resampled data.

> korrt80$WT<-korrt80$FWT/0.8> korind80$WT<-korind80$FWT/0.8> food80$WT<-food80$WERT/0.8> kalori80$WT<-kalori80$WERT/0.8> nonfood80$WT<-nonfood80$WERT/0.8> summary80$WT<-summary80$WERT/0.8



Dropped the records of sub-total with KLP=0 in food80, kalori80 and nonfood80.

> food80<-subset(food80,KLP>0)> dim(food80)[1] 6822367

19> kalori80<-subset(kalori80,KLP>0)> dim(kalori80)[1] 6822367

16> nonfood80<-subset(nonfood80,KLP>0)> dim(nonfood80)[1] 4433666

17> ls()[1] "food80""kalori80" "korind80" "korrt80""nonfood80" "summary80"> save.image("G:\\SUSENAS2011\\Resampled_v1.1\\Resampled_v1.1.RData")



Exported the resampled data in CSV format> write.table(korrt80,file="korrt80.csv",sep=",",row.names=F)> write.table(korind80,file="korind80.csv",sep=",",row.names=F)> write.table(food80,file="food80.csv",sep=",",row.names=F)

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> write.table(kalori80,file="kalori80.csv",sep=",",row.names=F)> write.table(nonfood80,file="nonfood80.csv",sep=",",row.names=F)> write.table(summary80,file="summary80.csv",sep=",",row.names=F)>

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Attachments List
1. QuestionnaireCore questionnaireConsumption Module questionnaire

2. Data dictionarykor11rt

kor11ind

blok41.exp



blok41.kalori

blok42



blok43

3. List of province and districtIndonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

71

1

VSEN11.K

The Central Bureau of Statistics (BPS)

One set forDistrict/City BPS

National Social Economic Survey of 2011
Main Information on Household and Household Members

Quarter: 1
Confidential
I. LOCATION IDENTIFICATION
1Province2

District/City *)3

Sub-district

4

Village/kelurahan *)5Village/kelurahan classification

6

Census block number

7

Sample code number

8

Household sample sequential number

9

Name of the head of household

10Address (road/street name, alley, RT/RW/village)

11

Visit result

1. Urban

2. Rural

1. Success -> [Block III]
2. Refused -> [Block III]
3. Could not be found -> [Block III]II. SUMMARY(Filled after Block IV.A and Block V.D R.27.a with code 1)

1

Total household members

2

Total household members age 0 – 4 years

3

Total household members age 5 years and up

4

Total household members age 10 years and up

5

Total household members age 10 years and up who are working in the past 3 months
III. OFFICIAL INFORMATION
Description

Census Taker

Supervisor

1. Name2. Official Code
3. Position
4. Date

1. Provincial BPS Staff
2. District/City BPS Staff

3. Statistics Clerk
4. Partner

1. Provincial BPS Staff
2. District/City BPS Staff

Day ____ Month ____

5. Signature*) Cross out as necessary

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

3. Statistics Clerk
4. Partner

Day ____ Month ____

72

2

VI.A. HOUSEHOLD MEMBER INFORMATION

No

(1)
1
2
3
4
5
6
7
8
9
10

Householdmember (HM)name
(People who liveand eat in thehousehold,including adult,
children andtoddler)

Relationwith thehead ofhousehold(code)(2)

IV.B Any housemaid/ security
guard/ driver who received
meal but not live-in the house?
1. Yes
2. No -> [B.V]
If yes, total number:Housemaid: ___ persons
Security guard: ___ persons
Driver: ___ persons
Other: ___ persons(3)
1

Gender
1. M
2. F

Age
(year)

Maritalstatus(code)

(4)

(5)

(6)

Column 3 CodeRelation to the head of household
1. Head of household
2. Wife/husband
6. Parent/in-law
3. Child
7. Other family
4. In-law
8. Housemaid
5. Grandchild
9. OtherHaveyou
becomecrime
victim inthe pastone
year?
(code)

If
Column7 = 1 to6, is itreportedtopolice?
1. Yes
2. No

Travelfrequency*) in thepast 3
calendarmonths?
If notravel, use
code “00”

(7)

(8)

(9)

Column 6 CodeMarital Status
1. Not married
2. Married
3. Divorce
4. Divorce due to
death

Column 13 CodeMain reason of no birth certificate
1. Expensive/no fund
2. Far traveling distance
3. Do not know about birth certificate recording
4. Do not know the processing procedure
5. Do not see the need
6. OtherIf traveling (Column 9≠ 0)Household member age 0-17
years

Household member age 0-6
years

Household member age 3-6
years or Column 14 = 1 or 2

Main
purposeof thelast trip(code)

Main
destination
province ofthe last trip
(code)Have birthcertificate
fromRegistrar
Office? Can
be shown?
(code)

If Column 12 = 3
or 4, what is themain reason?
(Do not read out
the answer!)(code)

Ever/ on preschool education?
1. Yes, once
2. Yes, currently
on
3. No

If Column
14 = 1 or
2, type ofpre-school
education(code)

Followed preschool
education in
the past 3
months?
1. Yes 2. No

If Column 16 =1,transportation
facility used to
go to school(code)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

Column 7 CodeType of crime
1. Yes, thievery
2. Yes, robbery
3. Yes, homicide
4. Yes, fraud

5. Yes, rape
6. Yes, other
7. No

Column 10 CodeMain purpose of the last trip
1. Holiday/recreation
2. Professional/business3. Mission/meeting/congress
4. Education/training
5. Health

Column 15 CodePre-school education
1. Kindergarten/BA/RA
2. Playgroup
3. Daycare
4. PAUD/ Early Childhood Educ. (ECC)/BKB Integrated PAUD/Posyandu **)5. Other PAUD/ECC (PAUD-TAAM, PAUD-PAK, PAUD-BIA, TKQ & other)**)

6. Pilgrim/religion
7. Visiting friend/family
8. Sports/culture
9. Other

Column 11
Code
Destination
Province
See provincial
code after BlockX

Column 12 CodeBirth Certificate
1. Yes, can be shown
2. Yes, cannot be
shown
3. Do not have
4. Do not know

Column 17 CodeTransportation to school
1. Without vehicle
6. Other public motor vehicle
2. Bicycle
7. Private car
3. Private motorcycle
8. Office-owned motorcycle
4. Pedicab/horse-drawn carriage
9. Office-owned car
5. Public transportation with fixed route
10. Other*) Travelling household member: trip to commercial tourist destinations, or stay in commercial accommodation, and or trip distance ≥ 100 Km (return), excluding commuter, school and work**) PAUD: Early childhood education. Integrated PAUD with Toddler Family Care, PAUD – TAAM: Muslim Children PAUD, PAUD – SM: PAUD – Sunday School, PAUD – BIA: Catholic Children PAUD, TKQ:
Koran Kindergarten

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3

V. INDIVIDUAL INFORMATION ON HEALTH,EDUCATION, EMPLOYMENT, AND FAMILY PLANNING
AND FERTILITY
Name: ____
Birthplace,

Sequential No: ____
1)
Province/country: ____
1)
District/city: ____
Residence location five years ago:
1)
Province/country: ____
1)
District/city: ____
[Fill with “00” if household member age = 00-04 years]
Biological mother sequential number: ____
[Fill with “00” if the biological mother is not living in thishousehold]Information provider:
Name: ____

Sequential No: ____

V.A. HEALTH INFORMATION (ALL AGES)
1. Any health complaint in the past one month?
(read from a to h)[Code 1 if yes, 2 if no]a. Fevere. Diarrhea
b. Coughf. Migrainec. Coldg. Toothache2)d. Asthma/breathlessness
h. Other[If all R.1 = 2, go to R.7]
2. If there is a complaint, did it disrupt job, school or daily
activities?
1. Yes
2. No -> [R.4.a]
3. Period of disruption: ____ days
4.a. Ever performed self-medication in the past 1 month?
1. Yes
2. No -> [R.5]b. Type of medicine/treatment method used:[Code 1 for yes, 2 for no]
1. Traditional medicine
2. Modern medicine

3. Other

5. Ever become an outpatient in the past one month?
1. Yes
2. No -> [R.7]6. How many times became outpatient in the past one
month?
[Fill outpatient frequency for each facility]a. State hospitalb. Private hospitalc. Doctor/polyclinic
d. Health clinic

e. Medical worker practicef. Traditional treatment
g. Maternity healer
h. Othera. State hospital ___ d. Medical worker practice ___b. Private hospital ___ e. Traditional treatment ___c. Health clinic ___f. Other ___V.B. TODDLER HEALTH (CHILD AGE 0-59 MONTHS)
10.a. Age in month: _____ months(To R.11 if ≠ 0)
b. If R.10.a = 00, age in day: ____ days
11. Who assisted the birth process?
[Fill code to box]
1. Doctor
2. Midwife
3. Other paramedic

First
a. □

4. Maternity healer
5. Family
6. Other

Last
b. □

12. How many times the child received immunization?
[Code 0 for no immunization yet]a. BCGb. DPTc. Poliod. Measles/Morbili
e. Hepatitis B

13.a. Ever provided with breast milk?
1. Yes
2. No -> [Other household member]b. If “yes” [R.13.a = 1], period of breast milk provision [fillin days if age < 1 month and in months if age is ≥ 1month]:
1. Period of provided with breast milk: ___ 1□
2. Breast milk exclusive: ___2□
3. Breast milk and complimentary food: ___ 3□
V.C. EDUCATIONAL INFORMATION(FOR HOUSEHOLD MEMBER AGE FIVE YEARS UP)
14. Schooling participation:
1. No/never in school -> [R.19]
2. Still in school
3. No longer in school
15. The highest education type and level currently
studied/passed:
1. Elementary school 8. M. Aliyah (Islamic school)
2. M. Ibtidaiyah
9. Vocational School
3. Package A
4. Public junior high
5. M. Tsanawiyah

10. Package C
11. Diploma 1/ 2
12. Diploma 3/Bachelor Degree

6. Package B
7. Senior High13. Diploma 4/S1
14. S2/S3

16. Highest class/level studied/currently studied:1 2 3 4 5 6 7 8 (Graduated)
17. Highest diploma obtained:
1. No elementary sch. diploma 9. M. Aliyah

7. Ever become an outpatient in the past six months?
1. Yes
2. No

2. Elementary school
3. M. Ibtidaiyah

10. Vocational school
11. Package C

8. Ever become an inpatient in the past one year?
1. Yes
2. No -> [Block V.B]

4. Package A
5. Junior high
6. M. Tsanawiyah

12. Diploma 1/ 2
13. Diploma 3
14. Diploma 4/S1

7. Package B
8. Senior high

15. S2/S3

9. Inpatient period (in days):

1)

Cross out as necessary
For example: Measles, ear infection, liver disease,convulsions, palsy, senile, accident etc.

2)18.a. Received education in the past 3 months?
1. Yes
2. No -> [R.19]

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b. If yes (R.18.a = 1), transportation facility generally used
to go to school:
1. Without vehicle
2. Bicycle
3. Private motorcycle

6. Other public motor vehicle
7. Private car
8. Office-owned motorcycle

4. Pedicab/horse-drawn carriage
9. Office-owned car
5. Public transportation with fixed route
10. Other
19. Can read and write:[Code 1 for yes, 2 for no]a. Latin alphabets
b. Arabian alphabets

21. If yes (R.20 = 1), location/media to access the Internet:[Code 1 for yes, 2 for no]
3. Office
5. Cellphone
4. School
6. Other(for example: Portable modem)

FOR HOUSEHOLD MEMBER AGE 5 – 24 YEARS OLD
22. If R.14 = 1 or 3, reason why never go to school or nolonger in school:
1. No money
2. Working
3. Married/handle household
4. Feel sufficient education
5. Underage
6. Bashful due to economic
Condition

7. Far distance to school
8. Disabled
9. Waiting forannouncement
10. Not accepted
11. Other

23. If R.14 = 3, when did you quit school?
[Fill with ‟00‟ and „0000‟ if quit prior to 2001]
Month: ____

Year: ____

V.D. EMPLOYMENT(FOR HOUSEHOLD MEMBER AGE 10 YEARS UP)
24. a. Did you perform the following activity in the past one
week?
1. Work
1. Yes 2. No
2. School
1. Yes 2. No
3. Handling household
1. Yes3
4. Other beside personal activity
1. Yes[If R.24.a.1 to 4 = 2, go to R.25]

2. No
2. No

b. From activities 1 to 4 above which stated “Yes,” what
activity used the most time in the past one week?
1
2
3
4[If R.24.a.1 = 1, go to R.26]
25. Do you have work/business, but temporarily not working
for the past one week?
1. Yes
2. No
26. Are you looking for work or preparing for business during
the past one week?
1. Yes
2. No

3to go to work:
1. Without vehicle

6. Other public motor vehicle

2. Bicycle
7. Private car
3. Private motorcycle
8. Office-owned motorcycle
4. Pedicab/horse-drawn carriage
9. Office-owned car
5. Public transportation with fixed route
10. Other
ONLY FOR WORKING HOUSEHOLD MEMBER[R.24.a.1 = 1 or R.25 = 1]c. Other alphabets

20. Ever access the Internet in the past 3 months?
1. Yes
2. No -> [R.22]

1. House
2. Internet kiosk

27.a. Did you work in the past 3 months?
1. Yes
2. No
b. If yes (R.27.a = 1), transportation facility generally used

28. a. Total work days in the past one week: ____ daysb. Total work hour from all jobs in the past one week:____ hours
29. The amount of net wage/salary (money and goods)
generally received in a month from the main job:
Rp _____
30. Main business/job from workplace in the past one week:
1. Rice and crops agriculture
2. Horticulture
3. Plantation
4. Fisheries
5. Livestock
6. Forestry & other agriculture
7. Mining and quarrying
8. Processing industry
9. Electricity and gas
10. Construction/building
11. Trading
12. Hotel and restaurant
13. Transportation and warehousing
14. Information and communications
15. Finance and insurance
16. Educational services
17. Health services
18. Public, governmental and individual services
19. Other
31. Position/status of the main job in the past one week:
1. Self-owned business/freelance
2. Self-owned business with non-permanent/non-paid
worker
3. Self-owned business with permanent/paid
worker
4. Worker/employee/staff
5. Freelancer
6. Family or non-paid worker
V.E. FERTILITY & FAMILY PLANNING(FOR WOMEN AGE 10 YEARS AND UP WITH A STATUS
OF MARRIED, DIVORCED, DIVORCE DUE TO DEATH
OF SPOUSE(Block IV Column 4 = 2 & Column 6 = 2, 3 or 4)
32. Age when married for the first time ____ years

Other activities include: sports, course, picnic andsocial activities (organization, volunteering)

33. Total years in marriage: ____ years

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34. Total child born from marriage
a. Member of household, live birthb. Member of household, still alivec. Member of household, alreadydeceased

M

F

M+F

3. Cement

36. If currently using (R.35 = 1), the selected family planningdevice/method:
1. Women/tubectomy
2. Men/vasectomy
3. IUD/spiral
4. Injection
5. Implant/norplant

6. Birth control pill
7. Condom/rubber
8. Intravag/tissue/womencondom
9. Female condom
10. Traditional method[Continue to other household member]

37. If without family planning [R.35 = 2 or 3], still want child?
1. Yes, soon (< 2 years) -> [Other household member]
2. Yes, later (≥ 2 years)
3. No
38. Main reason of not using family planning:
1. Fertility reason (barren, menopause, fasting, tradition,
want child)
2. Against family planning
3. Do not know family planning device/method
4. Afraid of family planning side effects
5. Do not know
6. Other ( ____ )VI. HOUSING INFORMATION
1. Censused residential building is:
1. Residential building
2. Mixed-use building
2. Number of household in the censused building:
1. One household
2. More than one households
3. Residential building status:
1. Owned
6. Company house
2. Lease
7. Other
3. Rent
4. Free lease, owned by other people
5. Free lease, owned by parents/family
4. If R.3 = 1 (privately owned), land status of residentialbuilding:

5. Type of most roof material:
1. Concrete
2. Roof tile
3. Shingle
4. Iron sheet
6. Type of most wall material:
1. Concrete

4. Other

7. Type of most flooring material:
1. Marble/ceramics/granite
4. Wood
2. Terrazzo/tiles
5. Soil

35. The use/wearing of family planning device/method?
1. Using it now
2. No longer use it -> [R.37]
3. Never use it -> [R.37]

1. Freehold title (HM)
2. Building rights title (HGB)

2. Wood

3. Right to use title (HP)
4. Other
5. Asbestos
6. Fiber/palm
7. Other

3. Bamboo

6. Other

8. Floor area: ____ square meter
9.a. Source of drinking water:
1. Branded bottled water -> [R.11]
2. Recycled bottled water -> [R.11]
3. Pipe with meter -> [R.10]
4. Pipe, retail payment-> [R.11]
5. Terrestrial well/pump
6. Protected/covered well
7. Unprotected/uncovered well
8. Protected spring
9. Unprotected spring
10. River -> [R.10]
11. Rainwater -> [R.10]
12. Other -> [R.10]
b. If R.9.a = 5 to 9 (pump/well/spring), distance to theclosest feces containment:
1. < 10 m
2. ≥ 10 m

3. Do not know

10. If R.9.a = 03, 05 to 12, drinking water facility use:
1. Personal

3. Public

2. Mutual

4. None11. Method to obtain drinking water:
1. Buying
3. Not buying
2. Customer-based
12.a. Source of water for bath/washing:
1. Branded bottled water
2. Recycled bottled water
3. Pipe with meter
4. Pipe, retail payment
5. Terrestrial well/pump
6. Protected/covered well
7. Unprotected/uncovered well
8. Protected spring
9. Unprotected spring
10. River
11. Rainwater
12. Otherb. The water is obtained by:
1. Buying
3. Not buying
2. Customer-based
13.a. Defecation facility use:
1. Personal
2. Mutualb. Type of toilet:
1. Goose neck/leher angsa
2. Pit toilet/plengsenganc. Final disposal location:
1. Tank/septic tankIndonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

3. Public
4. None -> [R.13.c]
3. Squat toilet/cemplung
4. None4. Pit hole

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6
2. Pool/field
3. River/lake/sea
14.a. Source of lighting:
1. PLN electricity

5. Beach/open field/farm
6. Other
4. Oil lamp/torch

2. Non-PLN electricity
5. Other
3. Paraffin lamp/petromak
b. If PLN electricity (R.14.a = 1), the installed capacity:
1. 450 Watts
2. 900 Watts
3. 1,300 Watts

4. 2,200 Watts
5. > 2,200 Watts
6. No meter

15. Main fuel/energy for cooking:
1. Electricity
2. Gas/LPG
3. City gas
4. Kerosene

5. Charcoal
6. Briquettes
7. Firewood
8. OtherVII. OTHER SOCIO-ECONOMIC INFORMATION
VII.A. POVERTY ERADICATION PROGRAM
1. a. Any household member who received free healthservice in the past 6 months?
1. Yes
2. No -> [R.2]b. If “yes” (R.1.a = 1), the type of card used:
1. National health insurance (Jamkesmas)
2. Health card (Kartu sehat)
3. Poor statement (SKTM)
4. Other: _____
2. a. Is the household ever received/purchased cheap
rice/rice for poor people (raskin) in the past 3 months?
1. Yes
2. No -> (R.3.a)b. If “yes” (R.2.a = 1) the last amount of rice purchased:___ kgc. How much per kg paid by the household for the lastpurchase of cheap rice? Rp _____
3. a. Is the household ever received a business credit in thepast one year?
[Code 1 for yes, 2 for no]
1. Public Empowerment National Program (PNPM
Mandiri)
2. Other government program
3. Public Business Credit (KUR)
4. Bank program other than KUR
5. Cooperatives program
6. Individual
7. Other ( ____ )[If R.3.a 1 to 7 = 2, go to R.4]
b. If received more than one type of credits, which one isthe largest? ____
(Write one code of business credit, 1 to 7 from Details
3.a)
VII.B. ASSETS AND COLLATERAL

d. Cable TV
e. AC

i. Motorboat
j. Car

5. a. Is the household earned sufficient income to meet
daily expenditure?
1. Yes -> [R.6]2. No
b. If “not” (R.5.a = 2), from where the household meetthe shortage?
[Code 1 for yes, 2 for no]a. Use savings (in bank/house)b. Sell assetsc. Borrow from family
d. Borrow from friend, neighbore. Borrow from creditor
f. Borrow cash from bankg. Borrow from cooperatives
h. Pawn goods
6. Is there any payment collateral/health insurance foroutpatient/inpatient needs as follows:[Code 1 for yes, 2 for no]a. Health Insurance for Civil Servant/Veteran/Pensioner
b. Jamsostekc. Private health insurance
d. Benefit/reimbursement by company
e. Health insurance/health card/poor insurance/poor card
f. Health fundg. Other health insurance
VIII. COMMUNICATIONS AND INFORMATIONTECHNOLOGY
1. Any landline telephone in the household?
1. Yes
2. No
2.a. Any household member who have cellular phone(cellphone)?
1. Yes
2. No -> [R.3]b. If “yes”, total household members who have active
cellphone numbers: ____ peoplec. Total cellphone numbers owned by household members:____ numbers
3. Is the household has computer?
[Code 1 for yes, 2 for no]a. Desktop/PC

b. Laptop/notebook

IX. SOURCE OF INCOME FOR HOUSEHOLD
1. Largest source of household income (fromhousehold member with the largest income):a. Job field: ______ (write as complete as possible)(See Code at Block V.D Details 30)
b. Job status:
0. Income receiver
1. Worker/employee
2. Businessperson

4. Is the household owned the following assets:a. Bicycle
f. Water heaterb. Motorcycle
g. Gas container size 12 kg & up
c. Boath. Refrigerator

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X. NOTES

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7

Information for Block IV.A Column 11 Code: Main destination province of the last trip
11. Aceh
12. North Sumatra
13. West Sumatra
14. Riau
15. Jambi

32. West Java
33. Central Java
34. Yogyakarta
35. East Java
36. Banten

64. East Kalimantan
71. North Sulawesi
72. Central Sulawesi
73. South Sulawesi
74. Southeast Sulawesi

16. South Sumatra
17. Bengkulu
18. Lampung
19. Bangka Belitung
21. Kepulauan Riau
31. Greater Jakarta

51. Bali
52. West Nusa Tenggara
53. East Nusa Tenggara
61. West Kalimantan
62. Central Kalimantan
63. South Kalimantan

75. Gorontalo
76. West Sulawesi
81. Maluku
82. North Maluku
91. West Papua
94. PapuaIndonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

78

REPUBLIC OF INDONESIASTATISTICS INDONESIA

VSEN11.M
Made 1 set for regency/municipality

2011 NATIONAL SOCIO-ECONOMIC SURVEY
EXPENDITURE FOR FOOD - NON FOOD CONSUMPTION AND
HOUSEHOLD INCOME

Quarter:

CONFIDENTIAL

I. IDENTIFICATION
1Province

2

Regency/municipality*)

3

Sub Regency

4

Village / kelurahan *)

5

Area

6

Census block number

7

Sample code number

8

Serial number of household

9

The name of Household head

10

Address (street, number of neighborhood community etc)

1. Urban

2. RuralII. HOUSEHOLD INFORMATION
1

Number of household member

2

Name & serial number of respondent

III. ENUMERATING CHARACTERISTICS
Characteristic
1. Name

Enumerator

Supervisor……………………………………………

……………………………………………

2. Code3. Occupation
4. Date

1. Provincial staff
3. Sub regency staff
2. Regency/municipality staff 4.Hired worker
Date

1. Provincial staff
3. Sub regency staff
2. Regency/municipality staff 4.Hired worker

Month

5. Signature*) cross the unneeded

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Date

Month

1

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1

DONT USE THIS PAGE

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IV.1. FOOD, BEVERAGES, AND TOBACCONo.
Detailurut(1)(2)
1 A. CEREALS [R.2-R.9]

Unit of quantity
(3)

2

Rice

Kg

3

Glutinous rice

Kg

4

Fresh corn with husk

Kg

5

Dryshelled corn/corn rice

Kg

6

Rice meal

Kg

7Corn meal

Kg

8

Wheat flour

Kg

9

OthersKg

10

B. TUBERS [R.11-R.19]

11

Cassava

Kg

12

Sweet potatoes

Kg

13

Sago flour

Kg

14

Taro

Kg

15

Potatoes

Kg

16

Dried cassava

Kg

17

Flour dried cassava

Kg

18

Cassava flour

Kg

19

OthersKg

20

C. FISH/SHRIMP/SQUID/SHELL [R.21-R.52]
1) Fresh Fish

21

Yellow tail/fusiliersKg

22

Eastern tuna/skipjack tuna

Kg

23

MackerelKg

24

Trevallies

Kg

25

Indian mackerel

Kg

26

Anchovies

Kg

27

Milk fish

Kg

28

Snake head

Kg

29

Mozambique tilapia

Kg

30

Common carp

Kg

31

CatfishKg

32

Barramundi

Kg

33

Baronang

Kg

34

OthersKg2) Fresh Shrimp and other fresh seafoods
35

Shrimp36

Common squid/cuttle fish

Kg

Total (0,00)(4)

Purchase (cash/owed)
V a l u e (Rp)
(5)

□□ □□
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,
,
,
,
,
,
,
,

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,
,
,
,
,
,
,
,

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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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,
,
,
,
,
,
,
,
,
,
,
,
,
,

,

Kg
,
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81
3

CONSUMPTIONS DURING PREVIOUS WEEK
Total (0,00)(6)

Own production, given, etc
V a l u e (Rp)
(7)

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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

□□ □□
□□ □□

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□□ □□
,
,
,
,
,
,
,
,

□□ □□
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,
,
,
,
,
,
,
,
,

,
,
,
,
,
,
,
,
,
,
,
,
,
,

,
,

Total (4) + (6) (0,00)(8)

Total consumption
V a l u e (5) + (7) (Rp)
(9)

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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

82
4

IV.1. FOOD, BEVERAGES, AND TOBACCONo.
Detailurut(1)(2)
37 Mud crab/swim crab

Unit of quantity
(3)
Kg

38

Cockle/snail

Kg

39

OthersKg3) Preserved fish
40

Indian mackerel

Ounce

41

Mackerel

Ounce

42

Eastern tuna/skipjack tuna

Ounce

43

Anchovies

Ounce

44

Trevallies

Ounce

45

Snakeskin gourame

Ounce

46

Milk fishes

Ounce

47

Snake head

Ounce

48

Canned fish

Ounce

49

OthersOunce4) Shrimp and other preserved seafoods
50

ShrimpsOunce

51

Common squids

Ounce

52

OthersOunce

53

D. MEAT [R.54-R.70]
1) Fresh meat

54

Beef

Kg

55

Buffalo meat

Kg

56

Lamb meat

Kg

57

Pork

Kg

58

Broiler meat

Kg

59

Local chicken meat

Kg

60

Other poultry meat

Kg

61

Other meat

Kg2) Preserved meats
62

Dried beef

Kg

63

Shredded fried meat64

Canned meat

Kg

65

OthersKg

Ounce3) Others
66

Liver

Kg

67

Innards excluding liverKg

68

Trimming

Kg

69

Bone (untrimmed)

Kg

70

OthersTotal (0,00)(4)

Purchase (cash/owed)
V a l u e (Rp)
(5)

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Kg
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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

83
5

CONSUMPTIONS DURING PREVIOUS WEEK (CONTINUED)
Total (0,00)(6)

Own production, given, etc
V a l u e (Rp)(7)

Total (4) + (6) (0,00)
(8)

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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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Total consumption
V a l u e (5) + (7) (Rp)
(9)

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,
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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□

84
6

IV.1. FOOD, BEVERAGES, AND TOBACCO
No.
urut
(1)

Unit of quantity

Detail(2)(3)

Total (0,00)(4)71

E. EGGS AND MILK [R.72-R.84]

72

Broiler egg

Kg

73

Local chicken egg

Unit

74

Duck egg

Unit

75

Quail egg

Unit

76

Other egg

Unit

77

Salted egg

Unit

78

Fresh milk

Liter

79

Preserved milk

250ml*)

80

Sweet canned liquid milk

397gr**)

81

Canned powder milk

Kg

82

Baby powder milk

400gr

83

CheeseOunce

84

Milk product

Ounce

85

F. VEGETABLES [R.86-R.114]

86

SpinachKg

87

Swamp cabbage

Kg

88

Cabbage

Kg

89

Chinese cabbage

Kg

90

Mustard greens

Kg

91

Beans

Kg

92

String bean

Kg

93

TomatoOunce

94

CarrotKg

95

Cucumber

Kg

96

Cassava leaf

Kg

97

Aubergine

Kg

98

Bean sprout

Kg

99

SquashKg

100

Unripe corn

Ounce

101

Soup/stir-fried vegetables

Unit

102

Sour vegetable soup

Unit

103

Young jackfruit

Kg

104

Unripe papaya

Kg

105

Mushroom

Ounce

106

Petai beans

Ounce

107

Stink beans

Kg

108

Onion

Ounce

109

GarlicIndonesia SUSENAS 2011/12
Pooled Manual (Version 1.1)
Ounce,

Purchase (cash/owed)
V a l u e (Rp)
(5)

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85
7

CONSUMPTIONS DURING PREVIOUS WEEK (CONTINUED)
Total (0,00)(6)

Own production, given, etc
V a l u e (Rp)
(7)

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,

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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

Total (4) + (6) (0,00)(8)

Total consumption
V a l u e (5) + (7) (Rp)
(9)

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Indonesia SUSENAS 2011/12 Pooled Manual, (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

86
8

IV.1. FOOD, BEVERAGES, AND TOBACCO
No.
urut
(1)

Unit of quantity

Detail(2)(3)

110 Chillies

Ounce

111 Green chili

Ounce

112 Cayenne pepper

Ounce

113 Canned vegetable

Kg

114 Others

Kg

115

G. LEGUMES [R.116-R.126]

116 Peanuts without shellKg

117 Peanuts with shellKg

118 Soybean

Kg

119 Mungbean

Kg

120 Red kidney bean

Ounce

121 Other beanKg

122 Tofu, soybean curd

Kg

123 Fermented soybean cakeKg

124 Fermented soybean paste

Ounce

125 Fermented soya cakeOunce

126 Others

Ounce

127

H. FRUITS [R.128-R.150]

128 Orange

Kg

129 Mango

Kg

130 Apple

Kg

131 Avocado

Kg

132 Rambutan

Kg

133 Lanzon

Kg

134 Durian

Kg

135 Zalacca

Kg

136 Pineapple

Kg

137 "Ambon" bananaKg138 "Raja" banana

Kg

139 Other banana

Kg

140 Papaya

Kg

141 Rose-apple

Kg

142 SapodillaKg

143 Carambola

Kg

144 Spanish plum

Kg

145 Watermelon

Kg

146 Melon

Kg

147 Jack fruit

Kg

Total (0,00)(4)

Purchase (cash/owed)
V a l u e (Rp)
(5)

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□□ □□
,
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,
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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

87
9

CONSUMPTIONS DURING PREVIOUS WEEK (CONTINUED)
Total (0,00)(6)

Own production, given, etc
V a l u e (Rp)
(7)

□□ □□
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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

Total (4) + (6) (0,00)
(8)

□□ □□
□□ □□
□□ □□
□□ □□
□□ □□

Total consumption
V a l u e (5) + (7) (Rp)
(9)

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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

88
10

IV.1. FOOD, BEVERAGES, AND TOBACCO
No.
urut
(1)

Unit of quantity

Detail(2)(3)

148 Tomato

Kg

149 Canned fruit

Kg

150 Others

Kg

151

I. OILS AND FATS [R.152-R.157]

152 Coconut oil

Liter

153 Corn oil

Liter

154 Other frying oil

Liter

155 Coconut

Unit

156 Margarine

Ounce

157 Others

Liter

158

J. BEVERAGE STUFF [R.159-R.166]

159 Cane sugar

Ounce

160 Brown sugar

Ounce

161 T e aOunce

162 Powdered/bean coffee

Ounce

163 Instant cocoa

150gr

164 Powdered cocoa

Ounce

165 Syrup

620ml*)

166 Others...........

167

K. SPICES [R.168-R.180]

168 Salt

Ounce

169 Candlenut

Ounce

170 Coriander

Ounce

171 Pepper

Ounce

172 Tamarind

Ounce

173 Nutmeg

Ounce

174 CloveOunce

175 Fish paste

Ounce

176 Soya sauce

140ml*)

177 Monosodium glutamate

Gram

178 Chili sauce/tomato sauce

140ml*)

179 SpiceOunce

180 Other spice...........

181

L. MISCELLANEOUS FOOD ITEM [R.182-R.190]

Total (0,00)(4)

Purchase (cash/owed)
V a l u e (Rp)
(5)

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□□ □□

182 Instant noodle

80gr**)

183 Wheat noodle

Kg

184 Rice noodle

Ounce

185 Macaroni

Ounce,
Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1),,,□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

89
11

CONSUMPTIONS DURING PREVIOUS WEEK (CONTINUED)
Total (0,00)(6)

Own production, given, etc
V a l u e (Rp)
(7)

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,
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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

Total (4) + (6) (0,00)
(8)

□□ □□
□□ □□
□□ □□

Total consumption
V a l u e (5) + (7) (Rp)
(9)

,
,
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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

90
12

IV.1. FOOD, BEVERAGES, AND TOBACCO
No.
urut
(1)

Unit of quantity

Detail(2)(3)

186 Crisps

Ounce

187 Fried chips

Ounce

188 Seaweed

Pack (7 gr)

189 Porridge in package

150 gr*)

190 Others
191...........
M. PREPARED FOOD AND BEVERAGES [ R.192-R.222](copied from VSEN11.LPK)

Prepared food
192 Ordinary bread

Small pack

193 Other bread

Piece

194 Cookies

Ounce

195 Boil or steam cake

Piece

196 Fried food

Piece

197 Porridge of mung beanBowl

198 Kind of salad with peanuts sauce

Plate

199 A plate of rice accompanied by a mixture of dishes

Plate

200 Fried rice

Plate

201 Rice

Plate

202 Rice steamed in a banana leaf or coconut leaf

Bowl

203 Soup

Bowl

204 Roasted meat on skewer/satay

Plate/5 skewer

205 Noodle (with meatball/boiled/fried)

Bowl

206 Instant noodle

Bowl

207 Snack for children

Ounce

208 Fish (fried, roasted, etc)

Piece

209 Chicken/meat (fried, roasted, etc)

Piece

210 Other prepared food...........

211 Mineral water (bottle)

600ml*)

212 Mineral water (gallon)

Gallon213 Packed tea

250ml*)

214 Packed juice

200ml**)

215 CO2 drink

250ml*)

216 Health drink

100ml*)

217 Other drinks (coffee, milk, etc)

Glass

218 Ice cream

Small bowl

219 Other ice

Glass 200ml

Total (0,00)(4)

Purchase (cash/owed)
V a l u e (Rp)
(5)

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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

91
13

CONSUMPTIONS DURING PREVIOUS WEEK (CONTINUED)
Total (0,00)(6)

Own production, given, etc
V a l u e (Rp)
(7)

□□ □□
□□ □□
□□ □□
□□ □□
□□ □□

□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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Total (4) + (6) (0,00)
(8)

□□ □□
□□ □□
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□□ □□
□□ □□

Total consumption
V a l u e (5) + (7) (Rp)
(9)

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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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IV.1. FOOD, BEVERAGES, AND TOBACCO
No.
urut
(1)

Unit of quantity

Detail(2)(3)

Total (0,00)(4)

Purchase (cash/owed)
V a l u e (Rp)(5)

Alcohol ic Beverages
220 B e e r

620ml**)

221 Wine

620ml**)

222 Other alcoholic beverage

620ml**)

223

□□ □□
□□ □□
□□ □□
,
,
,

N. TOBACCO AND BETEL [R.224-R.229]

224 Clove filter cigarettes

Pack

225 Clove non filter cigarettes

Pack

226 Cigarettes

Pack

227 Tobacco

Ounce

228 Betel/areca nut

Pack

229 Others...........

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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

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CONSUMPTIONS DURING PREVIOUS WEEK (CONTINUED)
Total (0,00)(6)

Own production, given, etc
V a l u e (Rp)
(7)

□□ □□
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□□ □□
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□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

Total (4) + (6) (0,00)(8)

Total consumption
V a l u e (5) + (7) (Rp)
(9)

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Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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IV.2. EXPENDITURE OF NON FOOD GOODS
No.
urut

Detail

(1)

(2)

230
231

232
233
234
235
236

A. HOUSING AND HOUSEHOLD FACILITY [R.231-R.260]
Status of the house of living is:
1. Owned property
3. Monthly rent
5. Official house
2. Annaul rent
4. Free
6. Othera. If the house is own property/free of rent, estimation monthly renting:Rp ....................................

b. The length (in month) of staying in the house during previous 3 months :................. montha. If the house is annually rent, average rent value in a month:

Rp ....................................

b. The length (in month) of staying in the renting house during previous 3 months : ................. montha. If the house is monthly rent, average rent value in a month:

Rp ....................................

b. The length (in month) of staying in the renting house during previous 3 months : ................. montha. If the house is free of rent/official house/other, average estimation renting value in a month:b. The length (in month) of staying in the free house during previous 3 months:

Rp ....................................

................. month

Maintenance costs of the house (painting, plastering, painting the wall, roof, window glass, hinge, etc, including wages for the workers)
Electricity

237

Used of electricity (quantity):a month ago:................................... kwh

Note: If household doesn’t know KwH unit (for example: Non-PLN user), How to account:
Number of watt used times number of hour of monthly using electricity divided 1000

238

Value :

239

Water (“PAM” = Municipal Water Corporation/carrying pole water/purchased) Quantity: A month ago: .................. m3

240

Value:

241

LPG (Liquid Propane Gas)

242

Value:

243

City Gas

244

Value:

245

Gasoline

246

Value:

247

Generator
i. Type and number of fuel used (BBM):
1. Gasoline
2. Diesel oil
3. Kerosene
Value:

248
249
250

A month ago:

Quantity: a month ago:............................... kg□□□□

Quantity: a month ago:............................... m3□□□□

Quantity: a month ago:............................... Liter□□□□............................. Liter

Value:
ii. Lubricant oil: Value:□□□□a month ago:□□□□□............................. Liter□□□

Value:

251

iii. Maintenance and repair

252

Charcoal/coal/briket
Value:A month ago:

253

Value:

254

Firewood and other fuel255

Others (flashlight, storage battery, matches, mosquito repellent, lamp, air freshener, Liquid floor cleaner, etc)............................ Kg□□□ □Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

,

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DURING LAST 1 TO 3 MONTHS (IN RUPIAHS)
Fill each enumeration period consumption for a month
3 month ago

2 month ago

1 month ago...... - ..... until ...... - .....
(3)...... - ..... until ...... - .....
(4)...... - ..... until ...... - .....
(5)

Total= Column (3) + (4) + (5)(6)□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□
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IV.2. EXPENDITURE OF NON FOOD GOODS
No.
urut

Detail

(1)

(2)

256

Post and Telecomunication
Phone bill (home)

257

Mobile phone bill

258

Phone card/public phone/phone shop

259

Post stuff (stamp, etc.)260

Others (internet)

261

B. GOODS AND SERVICES [R.262-R.302]

262

Bathing soap, toothpaste, and shampoo

263

Cosmetic articles (perfume, hair cream, deodorant, skin powder, wire dental care/braces, nail clippers, hair spray, wig, lipstic, comb,etc.), and sanitary napkin

264

Treatment of skin, face, nails, hair (expenses of cutting, curl, cream bath, ‘lulur’/herbal cosmetics used to lighten of skin, spa, etc)

265

Laundry soap (bars, powders, creams, and liquid)

266

Clothes maintenance material (softener and fragrances, bleacing, lubricant, camphor, etc)

267

Newspapers, magazine, books, and stationeries (exluding for education) including magazine rent

268

Other stuffs (tissue, baby diaper, satai stick, etc.)
Health Care (including give birth fees and drugs that can not be specified)269

Public Hospital

270

Private Hospital271

Sub ordinary Public Health Center

272

Medical Doctor (including private medical doctor in public hospital)

273

Paramedical274

Traditional treatment

275

Traditional birth attendant
Drug costs (only drugs purchased in pharmacies, drug stores, etc)

276

Take medicine with recipe (from doctor, midwives, etc)

277

Self treatment/take medicine without recipe

278

Purchasing traditional medicine

279

Purchasing glasses, hand/leg artificial, & wheel chair
Health Preventive Cost

280

Pregnancy examination cost281

Children Under-fives immunization cost

282

Medical check-up

283

Contraception cost

284

Take care of health (vitamin, medicine herbs, etc.)

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

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DURING LAST 1 TO 3 MONTHS (IN RUPIAHS)
Fill each enumeration period consumption for a month
3 month ago

2 month ago

1 month ago...... - ..... until ...... - .....
(3)...... - ..... until ...... - .....
(4)...... - ..... until ...... - .....
(5)

Total= Column (3) + (4) + (5)(6)□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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IV.2. EXPENDITURE OF NON FOOD GOODS
No.
urut

Detail

(1)(2)
School fee and non/formal education cost

285

Development school contribution/admission fee

286

School fee

287

Other cost of school contribution

288

Text books

289

Stationery (pen, pencil, eraser, ruler, calculator, etc.)290

Non-formal education cost

291

Motor vehicle's fuel, light service, and motor maintenance:
a. Gasoline

292
293

□□□□

a month ago: ........................ Liter□□□□a month ago: ........................ Liter□□□ □Value:b.

294
295

a month ago: ........................ Liter

Diesel oil
Value:

c.

296

Lubricant:,Value:

297

d.

298

Transport expenses (bus, train, plane, etc.)

299

Hotel, inn, cinema, theater, sports, set-top box, cable TV subscriptions / Indovision and other recreation (excluding transport andpurchase of goods for recreation)

300

Domestic servant, security, and driver (salary or wages)

301

Financial service charge (ATM services, credit card services, transfer fees, etc.)

302

Other services (ID card, driver's license, birth certificate, copy, photo, etc.)

303

Service and repair (brake fluid, battery acid, battery, brake, clutch, etc.)

C. CLOTHING, FOOTWEAR, AND HEADGEAR [R.304-R.311]

304

Ready-made clothes for men (jackets, uniforms, shirts, jackets, gloves, pants, shirts, underwear, etc..)

305

Ready-made clothes for women (uniforms, dresses, long cloth, blouse, blaze / jacket women, nightgowns, sweaters, skirts, sarongs,
scarves, “angkin”, underwear, etc..)

306

Ready-made clothes for children (uniforms, shirts, pants, sweaters, shirts, underwear, diapers, etc..)

307

Materials clothing for men, women, and children (wool, polyester, cotton, silk, etc..)

308

Wages sewing, repairing clothes, sewing thread, and other goods for the purposes of tailoring

309

Footwear (shoes, sandals, socks, etc)

310

Headgear for men, women, and children (hat, cap, scarf, etc..)

311

Others (towel, belt, shoe polish, tie, laundry, etc.)
Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

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DURING LAST 1 TO 3 MONTHS (IN RUPIAHS)
Fill each enumeration period consumption for a month
3 month ago

2 month ago

1 month ago...... - ..... until ...... - .....
(3)...... - ..... until ...... - .....
(4)...... - ..... until ...... - .....
(5)□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□

Total= Column (3) + (4) + (5)(6)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□
Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

100
22

IV.2. PENGELUARAN UNTUK BARANG-BARANG
No.
urut

Detail

(1)

(2)

312

D. DURABLE GOODS [R.313-R.329]

313

Furniture (tables, chairs, beds, wardrobes, display cabinets, display racks, glass / mirrors, cabinets, cribs, etc..)

314

Household furnishings (sewing machines, refrigerators, fans, washing machines, air conditioners, etc..)

315

Household equipments (mattresses, pillows, tablecloths, bed linen, ashtrays, pillowcases, blankets, mats, curtains, rugs, etc..)

316

Home appliances (iron, broom, scissors, knives, machetes, hoes, saws, vacuum cleaner, coat hanger, soldering equipment, etc..)

317

Kitchen utensils (rack plate, stove, pots, pans, buckets, kitchen knives, pans, spoons, flasks, plates, glasses, mixers, rice cookers,
blenders, microwaves, ovens, and other dishes made of glass / ceramic / melamine / plastic, etc..)

318

Decoration stuff (wall hangings, aquariums, decorative items made of ceramic, porcelain, onyx, marble, wood, etc..)

319

Furniture and utensils repairs

320

Hand phone and other accessories

321

Watch, clock, camera, glasses, and repairs

322

Umbrella, bag, and repairs

323

Jewelry and repairs

324

Toys and repair, imitation jewelry

325

Electronics (Television, radio, video, DVD, cassette, cassette radio, guitar, piano / organ, computer) and repair

326

Tools and sports equipment (chess, racket, ball, net, bet, sticks, including bathing suits, soccer shoes, wheels shoes , goggles) andrepairs

327

Vehicles (cars, motorcycles, bicycles, etc..) and major repairs

328

Domestic animal and plant maintenance329

Other durable goods (electrical installation / phone / tap, swing, stroller, etc..) and repairs

330

E. TAXES AND INSURANCES [R.331-R.336]

331

Buildings and land taxes

332

Motor and non-motor vehicle taxes

333

Other contributions (dues RT / RW, trash, security, cemetery, parking, etc..)

334

Health insurance

335

Live insurance and general insurance (death insurance, accident, car, house, etc..)

336

Others (ticket, Income Tax, etc..)

337
338
339

F. PARTIES AND CEREMONIES [R.338-R.343]
Wedding (equipment rental tool bride such as, chairs, tents, dishes, and bridal makeup services such as cost, the prince, and the rentalof buildings, etc..)
Circumcision and birthday (Bengkong costs, the cost of doctors / paramedics / excisors, food boxes, ribbon / paper trimmer room /balloons, chair rental, rent a building, rent entertainment)

340

Religious festival (chair rental, tent rental, etc..)

341

Pilgrimage cost (ONH)342

Religious/traditional ceremony (called Ustad, Reverend, offerings, etc..)

343

Funeral expenses (cost of washing
theSUSENAS
corpse, theshroud,the grave
service,Indonesia
2011/12PooledManualdigger(Version
1.1) casket, crematory fees, cremation fees, etc..)

101
23

DURING LAST 1 TO 3 MONTHS (IN RUPIAHS)
Fill each enumeration period consumption for a month
3 month ago

2 month ago

1 month ago...... - ..... until ...... - .....
(3)...... - ..... until ...... - .....
(4)...... - ..... until ...... - .....
(5)□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□□□□□□□□□ □□□□□□□□ □□□□□□□□

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

Total= Column (3) + (4) + (5)(6)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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24

IV.3.1. RECAPITULATION OF FOOD, BEVERAGES, AND TOBACCO EXPENDITURES (IN RUPIAHS)[Copied from Block IV.1 Column (9)]No.

Type of expenses

(1)

1

(2)

(3)

Cereals□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

a. Rice (R.2-R.3)
b. Others (R.4-R.9)
2

Tuber (R.10)

3

Fish/Shrimp/Squid/Shella. Fresh (R.21-R.39)
b. Preserved (R.40-R.52)

4

Meat (R.53)

5

Egg and Milka. chicken/duck/quail egg (R.72-R.77)
b. Whole milk, condensed milk, milk powder, etc. (R.78-R.84)

6

Vegetables (R.85)

7

Legumes (R.115)

8

Fruits (R.127)

9

Oils and Fats (R.151)

10

Beverages stuffs (R.158)

11

Spices (R.167)

12

Other Consumption
a. Instant noodles, wet noodles, vermicelli, macaroni / noodles dry (R.182-R.185)
b. Others (R.186 - R.190)

13

Prepared foods and beverages□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

a. Prepared foods (R.192-R.210)
b. Non alcoholic beverages (R.211-R.219)
c. Alcoholic beverages (R.220-R.222)
14

A week ago

Tobacco and betela. Cigarett (R.224-R.226)
b. Others (R.227-R.229)

15

SUB TOTAL (R.1 s.d. R.14)

16

AVERAGE FOOD EXPENDITURE A MONTH[(R.15) x 30/7]

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□□□□□□□□□□□□□□□□

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25

IV.3.2. RECAPITULATION OF NON-FOOD EXPENDITURES (IN RUPIAHS)[Copied from Block IV.2 Column (6)]No.

Type of expenses

(1)

17

(2)

(3)

Housing and Household facility
a. monthly rent, yearly rent, estimates of the rent (own, rent-free, official), etc.
(R.232-R.235)
b. Maintenance costs of the house (R.236)
c. Electricity bills, water, gas, kerosene, firewood, etc.
(R.238, R.240, R.242, R.244, R.246, R.248, R.250, R.251, R.253-R.255)
d. home phone bill, hand phone pulse, public telephone, telecommunication shop,
postal .256-R.260)

18

Last 3 months□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

Good and services□□□□□□□□□□
b. Health costs (hospitals, health centers, physician practices, traditional health care,□□□□□□□□□□
medicine, etc..) (R.269-R.284)
c. Education cost (R.285-R.290)□□□□□□□□□□
d. Transportasi, pengangkutan, bensin, solar, minyak pelumas (R.292, R.294,□□□□□□□□□□
R.296-R.298)
e. other services (R.299-R.302)□□□□□□□□□□
Clothing, Footwear, and Headgear (R.303)□□□□□□□□□□
Durable Goods (R.312)□□□□□□□□□□
a. Toilet /laundry soap, cosmetics, hair care / face care, tissue, etc. (R.262-R.268)

19
20
21

Taxes and Insurances□□□□□□□□□□
b. Other contributions (R.333)□□□□□□□□□□c. Health insurance (R.334)□□□□□□□□□□
d. Other (any other life insurance, general insurance, income tax, traffic tickets, etc..)□□□□□□□□□□(R.335, R.336)
Parties and Ceremonies (R.337)□□□□□□□□□□SUB TOTAL (R.17-R.22)□□□□□□□□□□AVERAGE MONTHLY NON FOOD EXPENDITURE [R.23 Column (3) divided 3]□□□□□□□□□□
a. Buildings, land, motor vehicles taxes (R.331, R.332)

22
23
24

25

AVERAGE MONTHLY HOUSEHOLD EXPENDITURE[Block IV.3.1, R.16 Column (3) + Block IV0.3.2, R.24 Column (3)]Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)□□□□□□□□□□

104

V. INCOME, REVENUE AND EXPENSES OF NON CONSUMPTION

A. Income from wages/salary either in cash or goods/services received during the last month (Rp)
Income from wages/salary in cash

serialnumber of
respondent

Name

(1)

(2)

□□
□□
□□
□□

Main work

Additional work

Income fromwages/salary ingoods/services

(3)

(4)

(5)

(6)

Value of production

Production cost(including wages/salary)

Income[Column 3 – Column 4]

(3)

(4)

(5)

Overtime, honoraria, etc

Total
Column (3) through (6)
(7)

Total

(1)

(2)

1

Agricultural crops

2

Other agricultural (non-food crops, livestock, poultry, fisheries, forestry, and hunting)3

Non agricultural enterprises (industry, commerce, transportation, services, buildings,
construction, excavation, etc.)
T o t a l□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

C. Income from ownership and not household business during the last 3 months (Rp)
(1)

(2)

1

Approximate rental homes

2

Others (interest savings, lease of land, non business income, dividends, royalties, sales of used goods, etc..)
T o t a l
Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

(3)□□□□□□□□□□□□□□□□□□□□□□□□□□□

26

B. Income from household business during the last 3 months (Rp)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

105

D.

Transfer’s revenue and expenditure, and financial transactions during the last 3 months
Revenues

V a l u e (Rp)

(1)

(2)

1. Revenue of transfer (shipment and delivery of money,
bond, scholarship, pension, claims of life & loss
insurance, received packages of food / goods, capital
goods insurance claims)
2.

□□□□□□□□□

Revenues from financial transactions (making
savings, return on receivables, claims of insurance/
pension/education, get a social gathering, borrowmoney, return on accounts receivable, pledge ofgoods)□□□□□□□□□

Total□□□□□□□□□(3)

1. Expenditure transfer (send and give money, givefood/goods, capital goods insurance premiums)

2. Expenses from financial transactions (savings,
pay off debt, premiums of life insurance/pension/education, paying social gathering, lend money,
paying accounts payable, redeem the pledge ofgoods)

V a l u e (Rp)(4)□□□□□□□□□□□□□□□□□□□□□□□□□□□

Total

Household Revenues and expenditures during the last 3 months (Rp)
RevenueV a l u e (Rp)

(1)

(2)

27

E.

Expenditures

Expenditure(3)

1. Wages and salary[Block V.A total Column (7) multiply by 3]

Average monthly household expenditure□□□□□□□□□ 1. [Block
IV.3.2 R.25 Column (3) multiply 3]

2. Income from household business[Jumlah Blok V.B Column (5)]□□□□□□□□□
2. Transfer expenditure□□□□□□□□□ [Total Block V.D R. Column (4)]□□□□□□□□□
T o t a l□□□□□□□□□

3. Income from ownership and not household business[Jumlah Blok V.C]
4. Transfer’s revenue[Total Block V.D R. Column (2)]
Total

Difference in Revenue and Expenditures [total Column (2) – total Column (4)]Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

V a l u e (Rp)(4)□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□□

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28
VI. N O T E S

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

107

Attachment 2

Data Dictionary
kor11rt: Core questionnaire at household-level
No

Variable

Description

Type

Response categories/Remarks

1

b1r1

ProvinceN

11 - 94

11. Aceh

32. West Java

64. East Kalimantan

12. North Sumatra

33. Central Java

71. North Sulawesi

13. West Sumatra

34. Yogyakarta

72. Central Sulawesi

14. Riau

35. East Java

73. South Sulawesi

15. Jambi

36. Banten

74. Southeast Sulawesi

16. South Sumatra

51. Bali

75. Gorontalo

17. Bengkulu

52. West Nusa Tenggara

76. West Sulawesi

18. Lampung

53. East Nusa Tenggara

81. Maluku

19. Bangka Belitung

61. West Kalimantan

82. North Maluku

21. Kepulauan Riau

62. Central Kalimantan

91. West Papua

31. Greater Jakarta

63. South Kalimantan

94. Papua2

b1r2

District/CityN

1 – 79,See attachment 3

3

b1r5

Area

C

1. Urban,4

b1r7

Sample code number

N

Household identifier (7-digit)

5

b1r8

Household sample sequential numberN

1 - 106

b1r11

Visit resultC= 1: Success

7

b2r1

Household size

N

8

b2r2

Number of household members age 0N

2. Rural– 4 years
9b2r3

Number of household members age 5

Nyears and over
10

b2r4

Number of household members age

N

10 years and over
11

b2r5

Number of household members age

N

10 years and over who are working in
the past 3 months
12

b4br1

Any housemaid/security guard/driverC

1. Yes,who received meal but not live-in thehouse?

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13

b4br1aNumber of housemaidN14b4br1b

Number of security guardN15

b4br1cNumber of driverN

16

b4br1dNumber of others in the household

N

17

b6r1

Censuses residential building is:C

1. Residential building
2. Mixed-use building

18

b6r2

Number of household in the censuses

Cbuilding:
19

b6r3

Residential building status:

1. One household
2. More than one households

C

1. Owned
2. Lease
3. Rent
4. Free lease, owned by otherpeople5. Free lease, owned byparents/family
6. Company house
7. Other20

b6r4

Land status of residential building:C

1. Freehold title (HM)
2. Building rights title (HGB)
3. Right to use title (HP)
4. Other21

b6r5

Type of most roof material :C

1. Concrete
2. Roof tile
3. Shingle
4. Iron sheet
5. Asbestos
6. Fiber/palm
7. Other22

b6r6

Type of most wall material :C

1. Concrete
2. Wood
3. Bamboo
4. Other23

b6r7

Type of most flooring material :C

1. Marble/ceramics/granite
2. Terrazzo/tiles
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4. Wood
5. Soil

6. Other24

b6r8

Floor area:(square meter)

N

25

b6r9a

Source of drinking water:C

1. Branded bottled water ->[R.11]
2. Recycled bottled water ->[R.11]
3. Pipe with meter -> [R.10]
4. Pipe, retail payment-> [R.11]
5. Terrestrial well/pump
6. Protected/covered well
7. Unprotected/uncovered well
8. Protected spring
9. Unprotected spring
10. River -> [R.10]
11. Rainwater -> [R.10]
12. Other -> [R.10]

26

b6r9b

Distancetotheclosestfeces

C

1. < 10 m
2. ≥ 10 m

containment:

3. Do not know
27

b6r10

The use of drinking water facilities:

C

1. Personal
2. Mutual
3. Public
4. None28

b6r11

Method to obtain drinking water:C

1. Buying
2. Customer-based
3. Not buying

29

b6r12aSource of water for bath/washing:C

1. Branded bottled water
2. Recycled bottled water
3. Pipe with meter
4. Pipe, retail payment
5. Terrestrial well/pump
6. Protected/covered well
7. Unprotected/uncovered well
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9. Unprotected spring
10. River
11. Rainwater
12. Other30

b6r12b

The water is obtained by:C

1. Buying
2. Customer-based
3. Not buying

31

b6r13aDefecation facility use:C

1. Personal
2. Mutual
3. Public
4. None -> [R.13.c]32

b6r13bType of toilet:

C

1. Goose neck/leher angsa
2. Pit toilet/plengsengan
3. Squat toilet/cemplung
4. None33

b6r13cFinal disposal location:

C

1. Tank/septic tank

2. Pool/field
3. River/lake/sea
4. Pit hole
5. Beach/open field/farm
6. Other34

b6r14aSource of lighting:C

1. PLN electricity
2. Non-PLN electricity
3. Paraffin lamp/petromak
4. Oil lamp/torch
5. Other35

b6r14b

The installed capacity:C

1. 450 Watts
2. 900 Watts
3. 1,300 Watts
4. 2,200 Watts
5. > 2,200 Watts
6. No meter

36

b6r15

Main fuel/energy for cooking:C

1. Electricity
2. Gas/LPG
3. City gas
4. Kerosene

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5. Charcoal
6. Briquettes
7. Firewood
8. Other37

b7r1a

Any household member who receivedC

1. Yes
2. No -> [R.2]

free health service in the past 6months?
38

b7r1b

The type of card used:C

1. National health insurance(Jamkesmas)
2. Health card (Kartu sehat)
3. Poor statement (SKTM)
4. Other: _____

39

b7r1bl

40

b7r2a

F
Is the household ever purchasedC

1. Yes
2. No -> (R.3.a)cheap rice/rice for poor people(raskin) in the past 3 months?
41

b7r2b

The amount of rice purchased: (kg)

N

42

b7r2c

How much per kg paid by theNhousehold for the last purchase ofcheap rice?
43

b7r3a1C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No-

C

1. Yes 2. No

Business credit accepted - Public
Empowerment

National

Program(PNPM Mandiri)44

b7r3a2Business credit accepted - othergovernment program

45

b7r3a3Business credit accepted - Public
Business Credit (KUR)46

b7r3a4

Business credit accepted - bankprogram other than KUR

47

b7r3a5Business

creditacceptedcooperatives program
48

b7r3a6

Business credit accepted - individualC

1. Yes 2. No49

b7r3a7

Business credit accepted - otherC

1. Yes 2. No

50b7r3a7l

51

b7r3b

F
If received more than one type ofC

1. Public Empowerment

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credits, which one is the largest?

National Program (PNPM
Mandiri)
2. Other government program
3. Public Business Credit(KUR)
4. Bank program other thanKUR
5. Cooperatives program
6. Individual
7. Other ( ____ )

52

b7r4a

Is the household owned the bicycle?

C

1. Yes 2. No

53

b7r4b

Is

C

1. Yes 2. No

thehousehold

owned

the

motorcycle?
54

b7r4c

Is the household owned the boat?

C

1. Yes 2. No

55

b7r4d

Is the household owned the cable TV?

C

1. Yes 2. No

56

b7r4e

Is the household owned the ACC

1. Yes 2. No

57

b7r4f

Is the household owned the waterC

1. Yes 2. No

C

1. Yes 2. No

heater?
58

b7r4g

Is the household owned the gas
container size > 12 kg?

59

b7r4h

Is

the

household

owned

the

C

1. Yes 2. No

owned

the

C

1. Yes 2. No

refrigerator?
60

b7r4i

Is

the

householdmotorboat?
61

b7r4j

Is the household owned the car?

C

1. Yes 2. No

62

b7r5a

Is the household earned sufficientC

1. Yes -> [R.6]
2. No

income to meet daily expenditure?
63

b7r5baThe household use savings (inC

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. Nobank/house) to meet the shortage64

b7r5bb

The household sell assets to meet the
shortage65

b7r5bcThe household borrow from family tomeet the shortage

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66

b7r5bdThe household borrow from friend,C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. Noneighbor to meet the shortage67

b7r5beThe household borrow from creditor
to meet the shortage68

b7r5bf

The household borrow cash frombank to meet the shortage69

b7r5bgThe

householdborrowfromcooperatives to meet the shortage70

b7r5bh

The household pawn goods to meetthe shortage

71

b7r6a

For

outpatient/inpatient

HealthInsurance

needs

for

-

Civil

Servant/Veteran/Pensioner
72

b7r6b

needs

-

C

1. Yes 2. No

needs

-

C

1. Yes 2. No

needs

-

C

1. Yes 2. No-

C

1. Yes 2. No-

C

1. Yes 2. No

For outpatient/inpatient needs - OtherC

1. Yes 2. NoForoutpatient/inpatient

Jamsostek
73

b7r6c

For

outpatient/inpatient

Private health insurance
74

b7r6d

For

outpatient/inpatient

Benefit/reimbursement by company
75

b7r6e

For

outpatient/inpatient

needs

Health insurance/health card/poor
insurance/poor card
76

b7r6f

For

outpatient/inpatient

needs

Health fund
77

b7r6g

health insurance
78

b8r1

Any telephone in the household?

C

1. Yes 2. No

79

b8r2a

Any household member who haveC

1. Yes
2. No -> [R.3]cellular phone (cellphone)?
80

b8r2b

Total household members who haveN

active cellphone numbers - people
81

b8r2c

Total cellphone numbers owned byNhousehold members - numbers
82

b8r3a

Is the household has a desktop/PC?

83

b8r3b

Is

the

household

has

a

C

1. Yes 2. No

C

1. Yes 2. No

laptop/notebook?

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84

b9r1a

Sources of household income (from aC

1. Rice and crops agriculture

household member with the greatest

2. Horticulture

income) - job field

3. Plantation
4. Fisheries
5. Livestock
6. Forestry & other agriculture
7. Mining and quarrying
8. Processing industry
9. Electricity and gas
10. Construction/building
11. Trading
12. Hotel and restaurant
13. Transportation andwarehousing
14. Information andcommunications
15. Finance and insurance
16. Educational services
17. Health services
18. Public, governmental andindividual services
19. Other

85

b9r1b

Source of household income (fromC

0. Income receiverhousehold member with the largest

1. Worker/employee

income) - job status

2. Businessperson

86

fwt

Household weightN87

exp_cap

Average monthly expenditure perNcapita88

kabu

Province and distrit89

seriesAdditional code of sample household

N=B1R1*100+B1R2

In total, 89 variables

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kor11ind: Core questionnaire at individual level
No

Variable

Description

Type

Response categories/Remarks

1

b1r1

Province2

b1r2

District/City

3

b1r5

Area

4

b1r7

Sample code number

5

b1r8

Household sample sequential number

6

nart

Serial Number of Household MemberN

1 - 257

b1r11

Visit result(= 1: Success)C(= 1: Success)8hb

Relationship with the head of household

C

1. Head of household
2. Wife/husband
3. Child
4. In-law
5. Grandchild6. Parent/in-law
7. Other family
8. Housemaid
9. Other9

jk

GenderC

1. Male,2. Female

10

umur

Age (year)N

0 - 9811kwn

Marital statusC

1. Not married
2. Married
3. Divorce
4. Divorce due to death12jahat1Have you become crime victim in the pastC

1. Yes, thievery
2. Yes, robbery

one year?

3. Yes, homicide
4. Yes, fraud
5. Yes, rape
6. Yes, other
7. No
13

jahat2

Is it reported to police?

C

14pergi1

Travel frequency in the past 3 calendarN

1.Yes 2.Nomonths?
15

pergi2Main purpose of the last tripC

1. Holiday/recreation
2. Professional/businessIndonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)

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3. Mission/meeting/congress
4. Education/training
5. Health
6. Pilgrim/religion
7. Visiting friend/family
8. Sports/culture
9. Other16

pergi3Main destination province of the last tripN

11 – 94,

17

akte1

Have birth certificate from Registrar Office?

C

1. Yes, can be shown

Can be shown?

See Provincial code

2. Yes, cannot be shown
3. Do not have
4. Do not know

18

akte2

what is the main reason?

C

1. Expensive/no fund
2. Far traveling distance
3. Do not know about birth certificate
recording
4. Do not know the processing
procedure
5. Do not see the need
6. Other19

praskl1

Ever/ on pre-school education?

C

1. Yes, once
2. Yes, currently on
3. No

20

praskl2

Type of pre-school education

C

1. Kindergarten/BA/RA
2. Playgroup
3. Daycare
4. PAUD/ Early Childhood Educ.
(ECC)/BKB Integrated
PAUD/Posyandu **)5. Other PAUD/ECC (PAUD-TAAM,
PAUD-PAK, PAUD-BIA, TKQ &other)**)

21

praskl3

Followed pre-school education in the past 3C

1. Yes 2. No

C

1. Without vehicle

months?
22

praskl4

Transportation facility used to go to school

2. Bicycle

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3. Private motorcycle
4. Pedicab/horse-drawn carriage
5. Public transportation with fixedroute
6. Other public motor vehicle
7. Private car
8. Office-owned motorcycle
9. Office-owned car
10. Other23b5_tl1Birthplace - province/country24b5_tl2Birthplace - district/city25

b5_tt1

Residencelocation

five

years

ago

-

five

years

ago

-

province/country26b5_tt2

Residencelocation

district/city27

b5_ibuBiological mother sequential number28

b5_info

Information provider - Sequential no

29

b5r1a

Any health complaints in the past one monthNC

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes- fever30

b5r1b

Any health complaints in the past one month- cough31

b5r1c

Any health complaints in the past one month- cold32

b5r1d

Any health complaints in the past one month- asthma/breathlessness

33

b5r1e

Any health complaints in the past one month- diarrhea

34

b5r1f

Any health complaints in the past one month- migraine

35

b5r1g

Any health complaints in the past one month- toothache

36

b5r1h

Any health complaints in the past one month- other37

b5r2

If there is a complaint, did it disrupt job,

2. No -> [R.4.a]school or daily activities?
38

b5r3

Period of disruption: (days)N

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39

b5r4a

Ever performed self-medication in the past 1

Cmonth?
40

b5r4b1

1. Yes
2. No -> [R.5]

Self medicine/treatment method - traditionalC

1. Yes 2. No

C

1. Yes 2. No

medicine41

b5r4b2

Self medicine/treatment method - modern
medicine42

b5r4b3Self medicine/treatment method - otherC

1. Yes 2. No

43

b5r5

Ever become an outpatient in the past one

C

1. Yes
2. No -> [R.7]month?
44b5r6a

How many times became outpatient in theNlast month - state hospital
45

b5r6b

How many times became outpatient in theNlast month - private hospital
46

b5r6c

How many times became outpatient in theNlast month - doctor/polyclinic
47

b5r6d

How many times became outpatient in theNlast month - health clinic
48

b5r6e

How many times became outpatient in theNlast month - medical worker practice
49

b5r6f

How many times became outpatient in theNlast month - traditional treatment
50

b5r6g

How many times became outpatient in theNlast month - maternity healer
51

b5r6h

How many times became outpatient in theNlast month - other
52

b5r7

Ever become an outpatient in the past six

C

1. Yes 2. No

C

1. Yesmonths?
53

b5r8Ever become an inpatient in the past oneyear?

2. No -> [Block V.B]

54

b5r9a

Inpatient period (in days) - state hospital

N

55

b5r9b

Inpatient period - private hospital

N

56

b5r9c

Inpatient period - health clinic

N

57

b5r9d

Inpatient period - medical worker practice

N

58

b5r9e

Inpatient period - traditional treatment

N

59

b5r9f

Inpatient period - otherN

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60

b5r10aAge of child (months)

N61

b5r10bAge of child (days)N62

b5r11aWho assisted the first birth process?

C

1. Doctor
2. Midwife
3. Other paramedic
4. Maternity healer
5. Family
6. Other63

b5r11b

Who assisted the last birth process?

C

1. Doctor
2. Midwife
3. Other paramedic
4. Maternity healer
5. Family
6. Other64

b5r12aHow many times the child received

N

immunization - BCG
65

b5r12b

How many times the child received

N

immunization - DPT
66

b5r12cHow many times the child received

N

immunization - Polio67

b5r12dHow many times the child received

N

immunization - Measles
68

b5r12eHow many times the child received

N

immunization - Hepatitis B
69

b5r13aEver provided with breast milk?

C

1. Yes
2. No -> [Other household member]70

b5r13b1

Period of provided with breast milk:Nin days if age < 1 month and in months
if age is ≥ 1

71

b5r13b2

Period - Breast milk exclusive:Nin days if age < 1 month and inmonths if age is ≥ 1

72

b5r13b3

Period - Breast milk and complementaryNmonths if age is ≥ 1

food:
73

b5r14

in days if age < 1 month and in

Schooling participation:

C

1. No/never in school -> [R.19]
2. Still in school
3. No longer in school

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74

b5r15

The highest education type and levelC

1. Elementary school
2. M. Ibtidaiyah

currently studied/passed:

3. Package A
4. Public junior high
5. M. Tsanawiyah
6. Package B
7. Senior High8. M. Aliyah (Islamic school)
9. Vocational School
10. Package C
11. Diploma 1/ 2
12. Diploma 3/Bachelor Degree
13. Diploma 4/S1
14. S2/S3
75

b5r16

Highest class/level studied/currently studied:C1 2 3 4 5 6 7 8 (Graduated)

76

b5r17

Highest diploma obtained:C

1. No elementary sch. diploma
2. Elementary school
3. M. Ibtidaiyah
4. Package A
5. Junior high
6. M. Tsanawiyah
7. Package B
8. Senior high
9. M. Aliyah
10. Vocational school
11. Package C
12. Diploma 1/ 2
13. Diploma 3
14. Diploma 4/S1
15. S2/S377

b5r18aReceived education in the past 3 months?

C

1. Yes
2. No -> [R.19]

78

b5r18bTransportation facility generally used to go
to school:

C

1. Without vehicle
2. Bicycle
3. Private motorcycle
4. Pedicab/horse-drawn carriage

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5. Public transportation with fixedroute
6. Other public motor vehicle
7. Private car
8. Office-owned motorcycle
9. Office-owned car
10. Other79

b5r19aCan read and write latin alphabetsC

1. Yes 2. No80

b5r19bCan read and write arabic alphabetsC

1. Yes 2. No

81

b5r19cCan read and write other alphabetsC

1. Yes 2. No

82

b5r20

Ever access the Internet in the past 3 months?

C

1. Yes
2. No -> [R.22]

83

b5r21aLocation/media to access the Internet - houseC

1. Yes 2. No84

b5r21b

Location/media to access the Internet -C

1. Yes 2. Nointernet kiosk
85

b5r21cLocation/media to access the Internet - officeC

1. Yes 2. No

86b5r21dLocation/media to access the Internet -C

1. Yes 2. No

C

1. Yes 2. No

C

1. Yes 2. No

C

1. No money

school87

b5r21eLocation/media to access the Internet cellphone88

b5r21fLocation/media to access the Internet other(for example: Portable modem)

89

b5r22

Reason why never go to school or no longer
in school:

2. Working
3. Married/handle household
4. Feel sufficient education
announcement
5. Underage
6. Bashful due to economic Condition
7. Far distance to school
8. Disabled
9. Waiting for
10. Not accepted
11. Other90

b5r23aMonth quit schoolN91

b5r23bYear quit school

N

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92

b5r24a1

Activities in past one week - workC

1. Yes 2. No

93b5r24a2

Activities in past one week - attending schoolC

1. Yes 2. No

94b5r24a3

Activities in past one week - handlingC

1. Yes 2. No

C

1. Yes 2. No

C

1. Work

household95

b5r24a4

Activities in past one week - other beside
personal activity

96

b5r24bWhat activity used the most time in the past
one week?

2. School
3. Handling household
4. Other beside personal activity

97

b5r25

Do you have work/business, but temporarilyC

1. Yes 2. No

C

1. Yes 2. No

not working for the past one week?
98

b5r26

Are you looking for work or preparing forbusiness during the past one week?

99

b5r27aDid you work in the past 3 months?

C

1. Yes 2. No

100

b5r27bTransportation facility generally used to go

C

1. Without vehicle
2. Bicycle

to work:

3. Private motorcycle
4. Pedicab/horse-drawn carriage
5. Public transportation with fixedroute
6. Other public motor vehicle
7. Private car
8. Office-owned motorcycle
9. Office-owned car
10. Other101

b5r28aTotal work day: (days)N102

b5r28b

Total work hour from all jobs in the past one

Nweek: (hours)
103

b5r29

The amount of net wage/salary (money andNgoods) generally received in a month fromthe main job - rupiah
104

b5r30

Main business/job from workplace in thepast one week:C

1. Rice and crops agriculture
2. Horticulture
3. Plantation

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4. Fisheries
5. Livestock
6. Forestry & other agriculture
7. Mining and quarrying
8. Processing industry
9. Electricity and gas
10. Construction/building
11. Trading
12. Hotel and restaurant
13. Transportation and warehousing
14. Information and communications
15. Finance and insurance
16. Educational services
17. Health services
18. Public, governmental andindividual services
19. Other
105

b5r31

Position/status of the main job in the past one

C

week:

1. Self-owned business/freelance
2. Self-owned business with nonpermanent/non-paid
worker
3. Self-owned business withpermanent/paid
worker
4. Worker/employee/staff
5. Freelancer
6. Family or non-paid worker

106

b5r32

First marriage at age: (Years)

N

107

b5r33

Number of years in marriage: (years)N108

b5r34a1

Children born alive - maleN109

b5r34a2

Children born alive - femaleN110

b5r34a3Children born alive - male + female

N111

b5r34b1

Children are still alive - maleN112

b5r34b2Children are still alive - female

N

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113

b5r34b3Children are still alive - male + femaleN114

b5r34c1

Children already deceased - maleN115

b5r34c2

Children already deceased - femaleN116

b5r34c3Children already deceased - male + female

N

117

b5r35

The

C

use/wearingoffamilyplanning

device/method?

1. Using it now
2. No longer use it -> [R.37]
3. Never use it -> [R.37]

118

b5r36

The selected family planning device/method:C

1. Women/tubectomy
2. Men/vasectomy
3. IUD/spiral
4. Injection
5. Implant/norplant
6. Birth control pill
7. Condom/rubber
8. Intravag/tissue/women condom
9. Female condom
10. Traditional method

119

b5r37

Still want child?

C

1. Yes, soon (< 2 years) -> [Other
household member]
2. Yes, later (≥ 2 years)
3. No

120

b5r38

Main reason of not using family planning:C

1. Fertility reason (barren, menopause,
fasting, tradition, want child)
2. Against family planning
3. Do not know family planningdevice/method
4. Afraid of family planning side
effects
5. Do not know
6. Other ( ____ )121

b5r38lThe main reason of not using family others
planning

122

fwt

Household weight123

exp_cap

Average monthly expenditure per capita

124

kabu

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125

seriesIn total, 125 variables.

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blok41.exp; Consumption module of food expenditure(unit: expenditure item, during the previous week)
No

Variable

Description

Type

Remarks1

b1r1

Province

N

2

b1r2

Regency/municipality

N

3

b1r5

Area

C

4

b1r7

Sample code number

N

5

b1r8

Serial number of household

N

6

kode

Item ID (food expenditure)

N

7

b2r1

Household size

N

8

klp

Item ID (food groups);

N

9

b41k4

Quantity of consumption purchased(cash/owed)
duringpreviousweek(kilograms)

N

10

b41k5

Value
of
consumption
purchased(cash/owed) during previous week (rupiah)

N

11

b41k6

Quantity of consumption of own production,
given, etc. during previous week (kilograms)

N

12

b41k7

Value of consumption of own production,
given, etc. during previous week (rupiah)

N

13

b41k8

Total quantity of consumption during
previous week (kilograms)N(= b41k4 + b41k6 )

14

b41k9

Total value of consumption during previous
week (rupiah)N(= b41k5 + b41k7 )15rhRange of priceN

Note: No explanation about thisvariable16

wert

Household weight

N

17

weind

Individual weight ?

N

18

Series1-229
Note: Items
included. (*)

ofsub-totalare0 if KODE is an item of sub-total,
sub-group Item ID if KODE is notitem of sub-total. (*)

N

In total, 18 variables.

Note: The records with KLP=0 (sub-total) are dropped from the resampled data.

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blok41.karoli; Consumption module of food calorie(unit: expenditure item, during the previous week)
No

Variable

Description

Type

1

b1r1

Province

N

2

b1r2

Regency/municipality

N

3

b1r5

Area

N

4

b1r7

Sample code number

N

5

b1r8

Serial number of household

N

6

kode

Item ID (food expenditure)

7

b2r1

Household size

N

8

klp

Item ID (food groups) 0-223

C

9kalori (calorie)

Calorie consumption over the past

N

1-229

C

(*)

(*)

week
10

proteinProtein consumption over the past

N

week
11

lemak (fat)Fat consumption over the past weekN

12

karbo (carbo)

Carbohydrate consumption over theNpast week
13

wert

Household weight

N

14

weind

Individual weightN

15

SeriesN

In total, 15 variables.

Note: The records with KLP=0 (sub-total) are dropped from the resampled data.

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blok42: Consumption module of non-food expenditure(unit: expenditure item, during the previous three months)
No

Variable

Label

Type

1

b1r1

Province

N

2

b1r2

Regency/municipality

N

3

b1r5

Area

N

4

b1r7

Sample code number

N

5

b1r8

Serial number of household

N

6

kode

Item ID (non-food expenditure)C

230-343
Note:

Item

of

sub-totalareincluded. (*)
7

b2r1

Household size

N

8

klp

Item ID (non-food groups )N

0 if KODE is an item of sub-total,
sub-group Item ID if KODE is notitem of sub-total. (*)

9

b42k2

Details on type of expenditures forNcommodities other than food
10

b42k3

Non-food expenditures on 3 months ago

N

11

b42k4

Non-food expenditures on 2 months ago

N

12

b42k5

Non-food expenditures on 1 months ago

N

13

b42k6

Total expenditure on non-food items for 1 toN(sum of the above three variables)

3 months
Household weight

N

14

wert

15

weind

N

16

SeriesN(= b2r1 * wert )In total, 16 variables.

Note: The records with KLP=0 (sub-total) are dropped from the resampled data.

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blok43: Core module of monthly summary at household level
No

Variable

Description

Type

1

b1r1

Province

N

2

b1r2

Regency/municipality

N

3

b1r5

Area

N

4

b1r7

Sample code number

N

5

b1r8

Serial number of household

N

6

b2r1

Household sizeN

7kalori

Daily average consumption of calorie

N(from blok41.kalori, divided by 7)(calorie)

per capita

foodMonthly household food expenditureN(from blok41.exp, multiplied by

8

30/7)
9

nfood

N

(from blok42, divided by 3)

Monthly household total expenditureN(= food + nfood )Monthly per capita expenditureN(= expend / b2r1)

Household weightN(= kor11rt$fwt )(= b2r1 * wert )Monthly

household

non-

food

expenditure10

expend(hhexp)11

kapita(pcexp)

12

wert

13

weind

N

14

SeriesN

In total, 14 variables.

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Attachment 3

List of Province and District
b1r1: Province (11-94)
b1r2: District (01-79)
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1171
1172
1173
1174
1175
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222

01. Simeulue
02. Aceh Singkil
03. Aceh Selatan
04. Aceh Tenggara
05. Aceh Timur
06. Aceh Tengah
07. Aceh Barat
08. Aceh Besar
09. Pidie
10. Bireuen
11. Aceh Utara
12. Aceh Barat Daya
13. Gayo Lues
14. Aceh Tamiang
15. Nagan Raya
16. Aceh Jaya
17. Bener Meriah
18. Pidie Jaya
71. Banda Aceh
72. Sabang
73. Langsa
74. Lhokseumawe
75. Subulussalam
01. Nias
02. Mandailing Natal
03. Tapanuli Selatan
04. Tapanuli Tengah
05. Tapanuli Utara
06. Toba Samosir
07. Labuhan Batu
08. Asahan
09. Simalungun
10. Dairi
11. Karo
12. Deli Serdang
13. Langkat
14. Nias Selatan
15. Humbang Hasundutan
16. Pakpak Bharat
17. Samosir
18. Serdang Bedagai
19. Batu Bara
20. Padang Lawas Utara
21. Padang Lawas
22. Labuhan Batu Selatan

1223
1224
1225
1271
1272
1273
1274
1275
1276
1277
1278
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1371
1372
1373
1374
1375
1376
1377
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1471
1473
1501
1502
1503
1504
1505
1506
1507
1508
1509
1571
1572
1601

23. Labuhan Batu Utara
24. Nias Utara
25. Nias Barat
71. Sibolga
72. Tanjung Balai
73. Pematang Siantar
74. Tebing Tinggi
75. Medan
76. Binjai
77. Padangsidimpuan
78. Gunungsitoli
01. Kepulauan Mentawai
02. Pesisir Selatan
03. Solok
04. Sijunjung
05. Tanah Datar
06. Padang Pariaman
07. Agam
08. Lima Puluh Kota
09. Pasaman
10. Solok Selatan
11. Dharmasraya
12. Pasaman Barat
71. Padang
72. Solok
73. Sawah Lunto
74. Padang Panjang
75. Bukittinggi
76. Payakumbuh
77. Pariaman
01. Kuantan Singingi
02. Indragiri Hulu
03. Indragiri Hilir
04. Pelalawan
05. S I A K
06. Kampar
07. Rokan Hulu
08. Bengkalis
09. Rokan Hilir
10. Kepulauan Meranti
71. Pekanbaru
73. D U M A I
01. Kerinci
02. Merangin
03. Sarolangun
04. Batang Hari
05. Muaro Jambi
06. Tanjung Jabung Timur
07. Tanjung Jabung Barat
08. Tebo
09. Bungo
71. Jambi
72. Sungai Penuh
01. Ogan Komering Ulu

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1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1671
1672
1673
1674
1701
1702
1703
1704
1705
1706
1707
1708
1709
1771
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1871
1872
1901
1902
1903
1904
1905
1906
1971
2101
2102
2103
2104
2105
2171
2172
3101
3171

02. Ogan Komering Ilir
03. Muara Enim
04. Lahat
05. Musi Rawas
06. Musi Banyuasin
07. Banyu Asin
08. Ogan Komering Ulu Selatan
09. Ogan Komering Ulu Timur
10. Ogan Ilir
11. Empat Lawang
71. Palembang
72. Prabumulih
73. Pagar Alam
74. Lubuklinggau
01. Bengkulu Selatan
02. Rejang Lebong
03. Bengkulu Utara
04. Kaur
05. Seluma
06. Mukomuko
07. Lebong
08. Kepahiang
09. Bengkulu Tengah
71. Bengkulu
01. Lampung Barat
02. Tanggamus
03. Lampung Selatan
04. Lampung Timur
05. Lampung Tengah
06. Lampung Utara
07. Way Kanan
08. Tulangbawang
09. Pesawaran
10. Pringsewu
11. Mesuji
12. Tulang Bawang Barat
71. Bandar Lampung
72. Metro
01. Bangka
02. Belitung
03. Bangka Barat
04. Bangka Tengah
05. Bangka Selatan
06. Belitung Timur
71. Pangkal Pinang
01. Karimun
02. Bintan
03. Natuna
04. Lingga
05. Kepulauan Anambas
71. B A T A M
72. Tanjung Pinang
01. Kepulauan Seribu
71. Jakarta Selatan

3172
3173
3174
3175
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3271
3272
3273
3274
3275
3276
3277
3278
3279
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324

72. Jakarta Timur
73. Jakarta Pusat
74. Jakarta Barat
75. Jakarta Utara
01. Bogor
02. Sukabumi
03. Cianjur
04. Bandung
05. Garut
06. Tasikmalaya
07. Ciamis
08. Kuningan
09. Cirebon
10. Majalengka
11. Sumedang
12. Indramayu
13. Subang
14. Purwakarta
15. Karawang
16. Bekasi
17. Bandung Barat
71. Bogor
72. Sukabumi
73. Bandung
74. Cirebon
75. Bekasi
76. Depok
77. Kota Cimahi
78. Kota Tasikmalaya
79. Banjar
01. Cilacap
02. Banyumas
03. Purbalingga
04. Banjarnegara
05. Kebumen
06. Purworejo
07. Wonosobo
08. Magelang
09. Boyolali
10. Klaten
11. Sukoharjo
12. Wonogiri
13. Karanganyar
14. Sragen
15. Grobogan
16. Blora
17. Rembang
18. Pati
19. Kudus
20. Jepara
21. Demak
22. Semarang
23. Temanggung
24. Kendal

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3325
3326
3327
3328
3329
3371
3372
3373
3374
3375
3376
3401
3402
3403
3404
3471
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3571
3572
3573
3574
3575
3576
3577
3578
3579

25. Batang
26. Pekalongan
27. Pemalang
28. Tegal
29. Brebes
71. Magelang
72. Surakarta
73. Salatiga
74. Semarang
75. Pekalongan
76. Tegal
01. Kulon Progo
02. Bantul
03. Gunung Kidul
04. Sleman
71. Yogyakarta
01. Pacitan
02. Ponorogo
03. Trenggalek
04. Tulungagung
05. Blitar
06. Kediri
07. Malang
08. Lumajang
09. Jember
10. Banyuwangi
11. Bondowoso
12. Situbondo
13. Probolinggo
14. Pasuruan
15. Sidoarjo
16. Mojokerto
17. Jombang
18. Nganjuk
19. Madiun
20. Magetan
21. Ngawi
22. Bojonegoro
23. Tuban
24. Lamongan
25. Gresik
26. Bangkalan
27. Sampang
28. Pamekasan
29. Sumenep
71. Kediri
72. Blitar
73. Malang
74. Probolinggo
75. Pasuruan
76. Kota Mojokerto
77. Kota Madiun
78. Surabaya
79. Batu

3601
3602
3603
3604
3671
3672
3673
3674
5101
5102
5103
5104
5105
5106
5107
5108
5171
5201
5202
5203
5204
5205
5206
5207
5208
5271
5272
5301
5302
5303
5304
5305
5306
5307
5308
5309
5310
5311
5312
5313
5314
5315
5316
5317
5318
5319
5320
5371
6101
6102
6103
6104
6105
6106

01. Pandeglang
02. Lebak
03. Tangerang04. Serang
71. Tangerang
72. Cilegon
73. Serang
74. Tangerang Selatan
01. Jembrana
02. Tabanan
03. Badung
04. Gianyar
05. Klungkung
06. Bangli
07. Karang Asem
08. Buleleng
71. Denpasar
01. Lombok Barat
02. Lombok Tengah
03. Lombok Timur
04. Sumbawa
05. Dompu
06. Bima
07. Sumbawa Barat
08. Lombok Utara
71. Mataram
72. Bima
01. Sumba Barat
02. Sumba Timur
03. Kupang
04. Timor Tengah Selatan
05. Timor Tengah Utara
06. Belu
07. Alor
08. Lembata
09. Flores Timur
10. Sikka
11. Ende
12. Ngada
13. Manggarai
14. Rote Ndao
15. Manggarai Barat
16. Sumba Tengah
17. Sumba Barat Daya
18. Nagekeo
19. Manggarai Timur
20. Sabu Raijua
71. Kupang
01. Sambas
02. Bengkayang
03. Landak
04. Pontianak
05. Sanggau
06. Ketapang

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6107
6108
6109
6110
6111
6112
6171
6172
6201
6202
6203
6204
6205
6206
6207
6208
6209
6210
6211
6212
6213
6271
6301
6302
6303
6304
6305
6306
6307
6308
6309
6310
6311
6371
6372
6401
6402
6403
6404
6405
6406
6407
6408
6409
6410
6471
6472
6473
6474
7101
7102
7103
7104
7105

07. Sintang
08. Kapuas Hulu
09. Sekadau
10. Melawi
11. Kayong Utara
12. Kubu Raya
71. Pontianak
72. Singkawang
01. Kotawaringin Barat
02. Kotawaringin Timur
03. Kapuas
04. Barito Selatan
05. Barito Utara
06. Sukamara
07. Lamandau
08. Seruyan
09. Katingan
10. Pulang Pisau
11. Gunung Mas
12. Barito Timur
13. Murung Raya
71. Palangka Raya
01. Tanah Laut
02. Kota Baru
03. Banjar
04. Barito Kuala
05. Tapin
06. Hulu Sungai Selatan
07. Hulu Sungai Tengah
08. Hulu Sungai Utara
09. Tabalong
10. Tanah Bumbu
11. Balangan
71. Banjarmasin
72. Banjar Baru
01. Paser
02. Kutai Barat
03. Kutai Kartanegara
04. Kutai Timur
05. Berau
06. Malinau
07. Bulungan
08. Nunukan
09. Penajam Paser Utara
10. Tana Tidung
71. Balikpapan
72. Samarinda
73. Tarakan
74. Bontang
01. Bolaang Mongondow
02. Minahasa
03. Kepulauan Sangihe
04. Kepulauan Talaud
05. Minahasa Selatan

7106
7107
7108
7109
7110
7111
7171
7172
7173
7174
7201
7202
7203
7204
7205
7206
7207
7208
7209
7210
7271
7301
7302
7303
7304
7305
7306
7307
7308
7309
7310
7311
7312
7313
7314
7315
7316
7317
7318
7322
7325
7326
7371
7372
7373
7401
7402
7403
7404
7405
7406
7407
7408
7409

06. Minahasa Utara
07. Bolaang Mongondow Utara
08. Siau Tagulandang Biaro
09. Minahasa Tenggara
10. Bolaang Mongondow Selatan
11. Bolaang Mongondow Timur
71. Manado
72. Bitung
73. Tomohon
74. Kotamobagu
01. Banggai Kepulauan
02. Banggai
03. Morowali
04. Poso
05. Donggala
06. Toli-Toli
07. Buol
08. Parigi Moutong
09. Tojo Una-Una
10. Sigi
71. Palu
01. Selayar
02. Bulukumba
03. Bantaeng
04. Jeneponto
05. Takalar
06. Gowa
07. Sinjai
08. Maros
09. Pangkajene Dan Kepulauan
10. Barru
11. Bone
12. Soppeng
13. Wajo
14. Sidenreng Rappang
15. Pinrang
16. Enrekang
17. Luwu
18. Tana Toraja
22. Luwu Utara
25. Luwu Timur
26. Toraja Utara
71. Makassar
72. Pare-Pare73. Palopo
01. Buton
02. Muna
03. Konawe
04. Kolaka
05. Konawe Selatan
06. Bombana
07. Wakatobi
08. Kolaka Utara
09. Buton Utara

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7410
7471
7472
7501
7502
7503
7504
7505
7571
7601
7602
7603
7604
7605
8101
8102
8103
8104
8105
8106
8107
8108
8109
8171
8172
8201
8202
8203
8204
8205
8206
8207
8271
8272
9101
9102
9103
9104
9105
9106
9107
9108
9109
9110
9171
9401
9402
9403
9404
9408
9409
9410
9411
9412

10. Konawe Utara
71. Kendari
72. Bau-Bau
01. Boalemo
02. Gorontalo
03. Pohuwato
04. Bone Bolango
05. Gorontalo Utara
71. Gorontalo
01. Majene
02. Polewali Mandar
03. Mamasa
04. Mamuju
05. Mamuju Utara
01. Maluku Tenggara Barat
02. Maluku Tenggara
03. Maluku Tengah
04. Buru
05. Kepulauan Aru
06. Seram Bagian Barat
07. Seram Bagian Timur
08. Maluku Barat Daya
09. Buru Selatan
71. Ambon
72. Tual
01. Halmahera Barat
02. Halmahera Tengah
03. Kepulauan Sula
04. Halmahera Selatan
05. Halmahera Utara
06. Halmahera Timur
07. Pulau Morotai
71. Ternate
72. Tidore Kepulauan
01. Fakfak
02. Kaimana
03. Teluk Wondama
04. Teluk Bintuni
05. Manokwari
06. Sorong Selatan
07. Sorong
08. Raja Ampat
09. Tambrauw
10. Maybrat
71. Sorong
01. Merauke
02. Jayawijaya
03. Jayapura
04. Nabire
08. Kepulauan Yapen
09. Biak Numfor
10. Paniai
11. Puncak Jaya
12. Mimika

9413
9414
9415
9416
9417
9418
9419
9420
9426
9427
9428
9429
9430
9431
9432
9433
9434
9435
9436
9471

13. Boven Digoel
14. Mappi
15. Asmat
16. Yahukimo
17. Pegunungan Bintang
18. Tolikara
19. Sarmi
20. Keerom
26. Waropen
27. Supiori
28. Mamberamo Raya
29. Nduga
30. Lanny Jaya
31. Mamberamo Tengah
32. Yalimo
33. Puncak
34. Dogiyai
35. Intan Jaya
36. Deiyai
71. Jayapura

Indonesia SUSENAS 2011/12 Pooled Manual (Version 1.1)