Users’ Manual for Handling Resampled Micro Data of
Cambodia Socio-Economic Survey (CSES)
CSES 2009

Version 2.0

2016
The Institute of Statistical Mathematics (ISM)
andStatistical Information Institute for Consulting and Analysis (SINFONICA)

1

History of revision of the manualSecond version 2.0 in March 2016
- Totally revised based on the discussion during the seventh workshop in December 2015

First draft version 1.0 in December 2015

Manual for Cambodia CSES 2009 (Version 2.0)

2CONTENTSCSES 2009
Chapter 1. About this Manual

Page 4

Chapter 2. Outline of CSES 2009

6

Chapter 3. Data and metadata provided

16

Chapter 4. Data import
4.1 Data file structure24

4.2 Import STATA files into R

28

4.3 Names and types of variables

31

Chapter 5. Data check5.1 Summary of each data file79

5.2 Frequency of categorical variables

132

5.3 Relationship among data files and identifiers

155

5.4 Weight157

5.5 Sample allocation

163

5.6 Integrity check of household identifier

170

5.7 Integrity check of individual identifier

172

Chapter 6. Household expenditure

178

6.1 Food items

179

6.2 Non-food items

185

6.3 Housing

189

Chapter 7. Household income
7.0 Provided summary file of household income

202

7.1 Salary208

7.2 Agricultural income

211

7.3 Non-agricultural income

217

7.4 Income Own House

219

7.5 Property income

223

7.6 Transfers received

229

7.7 Negative transfers

237

Manual for Cambodia CSES 2009 (Version 2.0)

3Chapter 8. Demography
8.1 Demographic characteristics242

8.2 Household structure

254

Chapter 9. Migration and Transfer

265

Chapter 10. Resampled micro data to be provided

273

Supplement:
1. Village data282

S1.1 Data Import
S1.2 Names and types of variables
S1.3 Village identifier
S1.4 Village questionnaire

2. Diary sheet

311

S2.1 Diary data of expenditureS2.2 Diary data of income
S2.3 Diary sheet form

Appendix:
1. Household questionnaire with variable names

330

2. Codebook

392

3. Income and expenditure composition

403

Manual for Cambodia CSES 2009 (Version 2.0)

4Chapter 1. About this Manual
1. This manual was prepared for users to use the next 80% resampled micro data sets of Cambodia
Socio-Economic Survey 2009.
80% resampled micro data set (44 files)
In CSV format[1] "dy_expenditure_80.csv""dy_income_80.csv"[3] "hhexp_80.csv""IncomeCSES09_80.csv"[5] "s01a_hhmembers_80.csv""s01b_foodconsumption_80.csv"[7] "s01c_nonfoodexpenses_80.csv""s01d_vulnerability_80.csv"[9] "s02_education_80.csv""s03a_migration_past_80.csv"[11] "s03b_migration_current_80.csv""s04_housing_80.csv"[13] "s05a_landown_80.csv""s05b_cropsproduction_80.csv"[15] "s05c_costcrops_80.csv""s05d_cropsinventory_80.csv"[17] "s05e1_animals_80.csv""s05e2_animalsexpenses_80.csv"[19] "s05f1_fisharea_80.csv""s05f2_fishexpenses_80.csv"[21] "s05f3_fishincome_80.csv""s05g1_forestincome_80.csv"[23] "s05g2_forestexpenses_80.csv""s05h1_bussiness_80.csv"[25] "s05h2_bussinessexpenses_80.csv" "s05h3_bussinessincome_80.csv"[27] "s06_liabilities_80.csv""s07_incomeother_80.csv"[29] "s08_construction_80.csv""s09_durables_80.csv"[31] "s10_healthmother_80.csv""s11_health2years_80.csv"[33] "s12_health5years_80.csv""s13b_healthexpenses_80.csv"[35] "s14_disability_80.csv""s15_labor7days_80.csv"[37] "s16_labor12months_80.csv""s17b_theft_80.csv"[39] "s17c_accidents_80.csv""s17d_violence_80.csv"[41] "s18_presenseinhh_80.csv""s99_singlequestions_80.csv"[43] "weighthh_80.csv""weightpersons_80.csv"
In R format[1] "dy.expenditure.80""dy.income.80"[3] "hhexp.80""IncomeCSES09.80"[5] "s01a.hhmembers.80""s01b.foodconsumption.80"[7] "s01c.nonfoodexpenses.80""s01d.vulnerability.80"[9] "s02.education.80""s03a.migration.past.80"

Manual for Cambodia CSES 2009 (Version 2.0)

5[11] "s03b.migration.current.80""s04.housing.80"[13] "s05a.landown.80""s05b.cropsproduction.80"[15] "s05c.costcrops.80""s05d.cropsinventory.80"[17] "s05e1.animals.80""s05e2.animalsexpenses.80"[19] "s05f1.fisharea.80""s05f2.fishexpenses.80"[21] "s05f3.fishincome.80""s05g1.forestincome.80"[23] "s05g2.forestexpenses.80""s05h1.bussiness.80"[25] "s05h2.bussinessexpenses.80" "s05h3.bussinessincome.80"[27] "s06.liabilities.80""s07.incomeother.80"[29] "s08.construction.80""s09.durables.80"[31] "s10.healthmother.80""s11.health2years.80"[33] "s12.health5years.80""s13b.healthexpenses.80"[35] "s14.disability.80""s15.labor7days.80"[37] "s16.labor12months.80""s17b.theft.80"[39] "s17c.accidents.80""s17d.violence.80"[41] "s18.presenseinhh.80""s99.singlequestions.80"[43] "weighthh.80""weightpersons.80"

2. The original micro data sets composed of all the samples of CSES were provided by National
Institute of Statistics (NIS), Cambodia in 2014 based on the Charter for Experimental Laboratory
for Research Purpose Statistical Use of Micro Data, and resampled at the rate of 80% by Sinfonica.

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

4. This manual was first compiled in December 2015 by;
Hiroshige Furuta
Visiting Senior Research Fellow, Sinfonica

Manual for Cambodia CSES 2009 (Version 2.0)

6Chapter 2. Outline of CSES 2009

Summary
Objectives of the

The main objective of the survey is to collect statistical information about thesurveyliving conditions of the Cambodian population and the extent of poverty. The
survey results can be used for identifying problems and making decisions basedon statistical data.

Topicscoveredby the survey

The household questionnaire covered the following topics;


Demographic characteristics of household’s members

Household incomeHousehold expenditure

Durable goods



Education

Migration



Housing

Health

Disability

Economic activity / Employment



Assets, housing and facilitiesParticipation in hungry elimination and poverty reduction

Frequency of theAnnually since2007.

surveyCSES 2004 and CSES 2009 were large sample surveys (12,000 households),
whereas the years between have small samples (about 3,600 households).

Survey periodThe annual sample was divided into 12 monthly samples. Data collection
was carried out from January 2009 to December 2009.



For a household, survey period was one month. Recall data on income andexpenditure was collected in the household questionnaire. Reference
period of food was 7 days. That of non-food was from a month to 12
months depending on items. Also, the diary questionnaire on income andexpenditure was requested to record during the survey month.

Coverage of thesurveyGeographically, the survey covered the whole country. Cambodia
consisted of 24 provinces.



Private households with one or more persons, including nomadic
households. Institutional households such as long term hospitals, prisons,monasteries, military quarters were excluded.

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7

Sample designThree-stage stratified sample selection
Sampling frame: General Population Census 2008 for sampling of villages(14,073 villages)
Strata: province and urban/rural (48 strata)
PSU: 720 villages were selected using PPS.
SSU: one EA was selected in each village. In a few large villages morethan one EA were selected.
FSU: In each selected EA, 10 households were selected in urban and 20
households in rural.



Sample size
In total 12,000 households

Response rate
Almost a 100 percent response rate. The number of household responded
was 11,971.

WeightDatacollection

method

Two kind of weights: household weight and person weightFace to faceIn total 50 teams for field work. A team consisted of a supervisor andfour interviewers.

Each interviewer was in charge of 10 household

during each month.
During a survey month, different questions were asked during the first visit
and in the following four weeks.

Data entry anddata check

Supervisors from the field delivered questionnaires to NIS. Data editing
and coding was implemented in NIS.



Data entry and data processing was carried out using new CSES databasesystem developed in-house.

Publication anddissemination

Preliminary results from CSES 2009 were presented at a seminar in
October 2010. The first report in December 2010.



Reports and tables are available at NIS website.
available at NADA.

Manual for Cambodia CSES 2009 (Version 2.0)

Metadata is also

8The below mainly describes on CSES 2007 to 2012.

3.1 Background of the survey
The Cambodia Socio-Economic Survey (CSES) has been conducted by the national Institute of
Statistics (NIS) intermittently in the period 1993 to 2004. Since 2007 NIS conducts the CSES
annually. The 2004 and 2009 were large sample surveys (12,000 households), whereas the years
between have small samples (about 3,600 households).

CSES asks questions to a country wide sample of households and household members about housing
conditions, education, economic activities, household production and income, household level andstructure of composition, health, victimization, etc. There are also questions related to people in thelabour force.

The data from the CSES provide important information about living conditions in Cambodia andhave a wide range of use. Poverty reduction is a major commitment by the Government of
Cambodia. Accurate statistical information about the living standards of the population and theextent of poverty is an essential instrument to assist the Government in diagnosing the problems, indesigning effective policies for reducing poverty and in monitoring and evaluating the progress ofpoverty reduction.

Cambodia is still a predominant rural and agricultural society. The vast majority of the population
get their subsistence in households as self-employed in agriculture. The level of living is determinedby the household’s command over labour and resources for own-production in terms of land andlivestock for agricultural activities, equipments and tools for fishing, forestry and construction
activities and income-earning activities in the informal and formal sector. The CSES aims toestimate household income and consumption/expenditure as well as a number of other household
and individual characteristics.

3.2 Objectives of the survey
The main objective of the survey is to collect statistical information about living conditions of theCambodian population and the extent of poverty. The survey can be used for identifying problems
and making decisions based on statistical data.

3.3 Topics covered by the survey
The earlier CSES rounds have all made it possible to report sets of indicators on eight main areas ofsocial concern;

Manual for Cambodia CSES 2009 (Version 2.0)

9



Demographic characteristics



HousingAgriculture

Education

Labour force

Health and nutrition

Victimization

Household income and consumption

These eight areas were also covered by corresponding modules in the CSES 2009. The household
questionnaire is basically the same as before. There are some changes though, mostly minor except
for the questions on current economic activity. In CSES 2010 some changes have been introduced in
the household questionnaire.

3.4 Coverage of the survey
2.3.1 The CSES covers the whole country.

2.3.2 Target population
The target population for CSES is all “normal” households in Cambodia. The term normal is definedin the Population Census 2008 as households that are not institutional households, homelesshouseholds, boat population households or households of transient population.
(Institutional households are boarding houses, military barracks, prisons, student dormitories, etc.)

2.5 Sample design: three-stage stratified design
The sampling design of CSES is a three-stage design with villages, enumeration areas andhouseholds as sampling units in each stage.
In large sample year, that is, 2004 and 2009, the survey is aimed for estimates of smaller domains,
but during the small sample years it is aimed for estimates on national level and large domains suchas the capital Phnom Penh and other urban and other rural areas.

Stage1:
PSU (Primary sampling unit): village

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10

Sampling frame: Preliminary data from the General Population Census 2008 was used to construct
the CSES 2009 sampling frame for the first stage sampling. All villages except ‘special settlements’
were included in the frame. In all, the first stage sampling frame of villages consisted of 14,073
villages.

Stratification: province and urban/rural
In total there are 48 strata. Each stratum of villages was sorted by district, commune and village
code.

Definition of urban and rural
In 2008 Census, the following criteria was applied for every commune to treat as urban;a) Population density exceeding 200 per km2b) Percentage of male employment in agriculture below 50 percentc) Total population of the commune should exceed 2,000

Allocation of the sample over strata: For CSES 2009 survey it was decided to have a sample of
720 villages. Total sample size was divided into two; one sample size for urban villages and theother for rural villages. The calculation of the sample size for urban and rural area were done using
the proportion of consumption in the two parts of the population. Data on consumption from CSES
2007 survey was used.

Note: The sample design for CSES 2010 is basically the same as the CSES 2009 design. For the2010 survey a subsample of 360 EAs (stage 2 units) was selected from the CSES 2009 sample of
720 EAs. The selection was done by SRS(simple random sampling) within strata.

Total

Urban

Rural

CSES 2009

720 villages

240

480

CSES 2010

360

136

224

The allocation of urban and rural sample size over provinces was done so that each province got its
proportional share (approximately) of the sample.

Sample selection: A random sample of villages was selected from each stratum. The sampling
method was “without replacement systematic sampling with probabilities proportional to size”. The
size measure used was the number of households in the village according to the sampling frame. The
selection of villages was done at NIS.

Manual for Cambodia CSES 2009 (Version 2.0)

11Stage 2:
SSU (Secondary sampling unit): EA

Sample selection: One EA was selected by SRS, in each village selected in stage 1. In a few large
villages more than one EA was selected. The selection was done at NIS.

Stage 3:
FSU (Final sampling unit): Household

Sample selection: For CSES 2009, in each selected EA a sample of 10 households (urban villages)
or 20 households (rural villages) was selected. The selection of households was done in the field. All
households in selected EAs were listed by the enumerator. The sample of households was thenselected from the list by systematic sampling with a random start (the start value controlled by NIS).
The sampling resulted in a sample of 12,000 households, 2,400 urban and 9,600 rural households.

For CSES 2010 only 10 households are selected in each rural EA, as compared to 20 households in2009. In urban areas 10 households were selected, just as in 2009.
The sampling resulted in a sample of 3,600 households, 1,360 urban households and 2,240 rural
households.

CSES 2009
Total
Number of selected

CSES 2010
Urban

720

Rural

Total

240

480

10

20

2,400

9,600

Urban
360

Rural
136

224

10

10

1,360

2,240

EAs
Number ofhouseholds per EA
Number of sample

12,0003,600

households

2.6 Sampling weights
The sampling weights were calculated in two steps;

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12

Step1: Preliminary weights
The probability of being selected to the sample was calculated for each household, giving thepreliminary sampling weight as the ratio 1/probability (=inverse to the probability).

Step2: Final weights
The probability sampling weights were added over all sample households within each stratum. The
sum of the weights is an estimate of the total number of households in the stratum. This estimate wascompared to the number of households according to demographic projections based on the 2008
Population Census. The preliminary sampling weights were then ‘calibrated’ so that the sum of theweights should agree with the demographic projections.

Remarks: Response rate
According to the description of NADA, the CSES enjoyed almost a 100 percent response rate. The
high response rate together with close and systematic fieldwork supervision by the core groupmembers were a major contribution for achieving high quality survey results.

Household weight and individual weight?

2.7 Quality of the estimates from CSES
2.7.1 Standard errors or confidence intervals are presented for some important estimates in thetechnical section as well as appendix 1 in the CSES 2010 report. The standard errors have been
calculated by the Taylor linearization method.

<table>2.7.2 NIS has not published household income statistics from CSES from 1999 to 2008 due to theinsufficient quality of survey data collection on income. It was published in 2009 and onwards.
There are a lot of challenges in estimation of household income data, i.e. households with observed
negative income, extreme values, missing values, and so forth. This was discussed in the paper on“Estimation and Quality of Household Income Data From Cambodian Socio-Economic Survey(CSES)” by Nor Vanndy, NIS.

2.8 Data collection method

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13

Data collection mode: Face to face

Questionnaire:
Three different questionnaires or forms were used in the survey.
Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure,
crop production, health, education, retail prices and sales prices of agriculture, employment andwages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules
on migration, education and literacy, housing conditions, crop production, household liabilities,
durable goods, construction activities, nutrition, fertility and child care, child feeding andvaccination, health of children, mortality, current economic activity, health and illness, smoking,
HIV/AIDS awareness and victimization.

Data collection dates:
The fieldwork started in January of the survey year and ended in December of the survey year.

Field work:(For CSES 2012)
Interviewers and supervisors were initially divided into teams of five persons (one supervisor andfour interviewers), making in total 50 teams for the field work. Each month, 25 teams were working
in the field with a workload of 10 households per interviewer. In urban area, 4 PSUs were allocatedto one team while in rural areas, 2 PSUs were allocated. The fieldwork plan was designed in order togather around 60 households monthly per team.

For a given month, the team arrived in the village three days before the first day of the month to tend
to preparatory tasks like discussing with village authorities, filling out the Household Listing Form,and thereafter sample those households to be interviewed.
The Village Form was filled out by the supervisor.
The Household Questionnaire had 16 sections that were filled out by the interviewer during the first
visit to the household, and in the following four weeks according to the following scheme;

FIRST VISIT: Initial visit

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14

WEEK 1: Education and literacy, Housing
WEEK 2: Household economic activities, Household liabilities, Household income from othersources, and other expenditures (partial non-food recall)
WEEK 3: Durable goods and other expenses, Construction activities in the past 12 months,
Nutrition, Fertility and child care, Mortality
WEEK 4: Health check of children, Current economic activity, Health, HIV/AIDS, Victimization

Once the month ended, the team went back to the NIS headquarters in Phnom Penh. Questionnaires
from the same PSU was delivered to the Data management team by the supervisor in a packet
including all of the documents used and produced in the fieldwork, including maps, enumeration
lists, questionnaires, diaries, etc.

Fifty (50) supervisors and 200 enumerators were recruited by NIS and trained for the fieldwork. The
training took place in Phnom Penh and lasted three weeks for supervisors and two weeks forenumerators. Before the start of each fieldwork month, there were briefing and retraining sessions.
Each fieldwork team included one supervisor and four enumerators. In urban areas one enumerator
was responsible for one PSU and for interviewing 10 households, while in rural areas twoenumerators were responsible for one PSU and for interviewing 20 households. In all, 125
enumerators and supervisors, divided into 25 teams, were carrying out the fieldwork at the sametime. Two such team groups were formed and each team group alternated monthly.

2.9 Data processing
The NIS team commenced their work of checking and coding in beginning of February after the firstmonth of fieldwork was completed. Supervisors from the field delivered questionnaires to NIS.
SIDA project expert and NIS Survey manager helped in solving relevant matters that become
apparent when reviewing questionnaires on delivery.

In late 2006 and beginning of 2007 a new system for data processing and storage were introduced
for the Cambodia Socio-Economic Survey (CSES). It includes a relational database system forstoring CSES data in SQL format and application framework developed in-house for data-entry.
Since NIS staff already was familiar with Visual Basic and Microsoft SQL Server data base software
the transition from previous data processing was also implemented to host the new CSES system andfacilitate for concurrent data-entry.

Manual for Cambodia CSES 2009 (Version 2.0)

15

The application and storage platform developed in 2006 and supervised by Statistics Sweden
consultancy has since been used consecutively for all CSES data processing from 2007 and onwards.

Manual for Cambodia CSES 2009 (Version 2.0)

16

Chapter 3. Data and metadata provided

CSES 2009 micro data sets were provided to Sinfonica three times in 2011, 2014 and 2015 by NIS on
the occasion of JICA project team’s visiting Cambodia.

The contents of each data set differs as in the following tables.
The findings are;1) Data set provided in 2014 is a part of data set provided in 2015.
2) Micro data of Village questionnaire is only in data set provided in 2011.
3) Micro data of Diary sheet is only in data set provided in 2011. On the other, Diary data in data
sets provided in 2014 and 2015 is not a raw data but an aggregated household-level data.
4) So-called Codebook is not provided.

However, the most important is that the values of some variables, especially income variables, of data
files are slightly different. It means that the latest files reflects the latest revision of data. So, we
decided to use the latest files which were provided on the occasion of the seventh international
workshop of micro data database in December 2015.

Data set provided in 2011Household data in SPSS format

File names in SPSS format (47 files)[1] "AreaInfo.sav""DiaryExpenditure.sav"[3] "DiaryExpenditure1.sav""DiaryExpenditure2.sav"[5] "DiaryExpenditure3.sav""DiaryExpenditure4.sav"[7] "DiaryIncome.sav""HHConstruction.sav"[9] "HHCostCultivationCrops.sav" "HHDurableGoods.sav"[11] "HHFishCultivation1.sav""HHFishCultivation2.sav"[13] "HHFishCultivation3.sav""HHFoodConsumption.sav"[15] "HHForestryHunting1.sav""HHForestryHunting2.sav"[17] "HHHousing.sav""HHIncomeOtherSource.sav"[19] "HHInventoryCrops.sav""HHLandOwnership.sav"[21] "HHLiabilities.sav""HHLivestock1.sav"[23] "HHLivestock2.sav""HHMembers.sav"

Manual for Cambodia CSES 2009 (Version 2.0)17[25] "HHNonAgriculture1.sav""HHNonAgriculture2.sav"[27] "HHNonAgriculture3.sav""HHOtherInfo.sav"[29] "HHProductionCrops.sav""HHRecallNonFood.sav"[31] "HHVulnerability.sav""PersonAccidents.sav"[33] "PersonDisability.sav""PersonEcoCurrent.sav"[35] "PersonEcoUsual.sav""PersonEducation.sav"[37] "PersonHealthU2.sav""PersonHealthU5.sav"[39] "PersonIllness.sav""PersonMaternalHealth.sav"[41] "PersonMigrationCurrent.sav" "PersonMigrationPast.sav"[43] "PersonPresence.sav""PersonTheft.sav"[45] "PersonViolence.sav""WEIGHTHouseholds.sav"[47] "WEIGHTPersons.sav"

Village data in SPSS format

File names in SPSS format (14 files)[1] "DemograpInfo.sav""EcoInfrastructure.sav" "Education.sav"[4] "Employment.sav""FoodPrices.sav""Health.sav"[7] "Medical.sav""MedicinePrices.sav""NonFoodItems.sav"[10] "RainfallDisaster.sav" "Recruitment.sav"[13] "Resources.sav"

"RentalPrices.sav""SalesPrices.sav"

Metadata"CSES 2009 Diary ENG_ Final.xls"

Diary questionnaire"CSES2009 ENG Village_Final_010709.xls"

Village questionnaire"CSES2009 ENG.xls"

Household questionnaire"CSES2009 field manuals ENG_Final.DOC"

Enumerators’ manual"Tech_CSES2009.doc"

Technical manual

Data set provided in 2014


Household data in STATA format

Manual for Cambodia CSES 2009 (Version 2.0)18File names in STATA format (42 files)



[1] "areainfo.dta""S01-17_hhotherinfo.dta"[3] "S01A_hhmembers.dta""S01B_hhfoodconsumption.dta"[5] "S01C_hhrecallnonfood.dta""S02_personeducation.dta"[7] "S03A_personmigrationpast.dta""S03B_personmigrationcurrent.dta"[9] "S04_hhhousing.dta""S05A_hhlandownership.dta"[11] "S05B_hhproductioncrops.dta""S05C_hhcostcultivationcrops.dta"[13] "S05D_hhinventorycrops.dta""S05E1_hhlivestock1.dta"[15] "S05E2_hhlivestock2.dta""S05F1_hhfishcultivation1.dta"[17] "S05F2_hhfishcultivation2.dta""S05F3_hhfishcultivation3.dta"[19] "S05G_hhforestryhunting2.dta""S05G2_hhforestryhunting1.dta"[21] "S05H1_hhnonagriculture1.dta""S05H2_hhnonagriculture2.dta"[23] "S05H3_hhnonagriculture3.dta""S06_hhliabilities.dta"[25] "S07_hhincomeothersource.dta""S08_hhconstruction.dta"[27] "S09_hhdurablegoods.dta""S10_personmaternalhealth.dta"[29] "S11_personhealthu2.dta""S12_personhealthu5.dta"[31] "S13_personillness.dta""S14_persondisability.dta"[33] "S15_personecocur.dta""S16_personecousu.dta"[35] "S17B_persontheft.dta""S17C_personaccidents.dta"[37] "S18_personpresence.dta""weighthouseholds.dta"[39] "weighthouseholds_area.dta""weightpersons.dta"[41] "Yearly_hhexpenditure.dta""Yearly_hhincome.dta"

Metadata"CSES2009 Diary Questionnaire ENG.xls"

Diary questionnaire"CSES2009 HH Questionnaire ENG.xls"

Household questionnaire

Data set provided in 2015
Household data in STATA format

File names in STATA format (42 files)[1] "areainformation.dta""hhweight.dta"

Manual for Cambodia CSES 2009 (Version 2.0)

19[3] "hhweight_area.dta""persweight.dta"[5] "S01-17_hhotherinfo.dta""S01A_hhmembers.dta"[7] "S01B_hhfoodconsumption.dta""S01C_hhrecallnonfood.dta"[9] "S02_personeducation.dta""S03A_personmigrationpast.dta"[11] "S03B_personmigrationcurrent.dta" "S04_hhhousing.dta"[13] "S05A_hhlandownership.dta""S05B_hhproductioncrops.dta"[15] "S05C_hhcostcultivationcrops.dta" "S05D_hhinventorycrops.dta"

[17] "S05E1_hhlivestock1.dta""S05E2_hhlivestock2.dta"[19] "S05F1_hhfishcultivation1.dta""S05F2_hhfishcultivation2.dta"[21] "S05F3_hhfishcultivation3.dta""S05G_hhforestryhunting2.dta"[23] "S05G2_hhforestryhunting1.dta""S05H1_hhnonagriculture1.dta"[25] "S05H2_hhnonagriculture2.dta""S05H3_hhnonagriculture3.dta"[27] "S06_hhliabilities.dta""S07_hhincomeothersource.dta"[29] "S08_hhconstruction.dta""S09_hhdurablegoods.dta"[31] "S10_personmaternalhealth.dta""S11_personhealthu2.dta"[33] "S12_personhealthu5.dta""S13_personillness.dta"[35] "S14_persondisability.dta""S15_personecocur.dta"[37] "S16_personecousu.dta""S17B_persontheft.dta"[39] "S17C_personaccidents.dta""S18_personpresence.dta"[41] "Yearly_hhexpenditure.dta""Yearly_hhincome.dta"

Metadata"CSES 2009 Diary Questionnaire ENG.xls"

Diary questionnaire"CSES2009 Village Questionnaire ENG.xls"

Village questionnaire"CSES2009 HH Questionnaire ENG withHousehold questionnaire with variable names

variable and file names.xls""CSES2009 Field Manual ENG.DOC"

Enumerators’ manual“2007 Continuous household surveys in

Sampling issues in a continuous Cambodian

Cambodia.doc”

socio-economic survey

Manual for Cambodia CSES 2009 (Version 2.0)

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Data set provided on the occasion of the seventh workshop in December 2015
Household data in STATA format

File names in STATA format (44 files)[1] "09dy_expenditure.dta""09dy_income.dta"[3] "09hh_s01a_hhmembers.dta""09hh_s01b_foodconsumption.dta"[5] "09hh_s01c_nonfoodexpenses.dta""09hh_s01d_vulnerability.dta"[7] "09hh_s02_education.dta""09hh_s03a_migration_past.dta"[9] "09hh_s03b_migration_current.dta" "09hh_s04_housing.dta"

[11] "09hh_s04_housingpractice.dta""09hh_s05a_landown.dta"[13] "09hh_s05b_cropsproduction.dta""09hh_s05c_costcrops.dta"[15] "09hh_s05d_cropsinventory.dta""09hh_s05e1_animals.dta"[17] "09hh_s05e2_animalsexpenses.dta""09hh_s05f1_fisharea.dta"[19] "09hh_s05f2_fishexpenses.dta""09hh_s05f3_fishincome.dta"[21] "09hh_s05g1_forestincome.dta""09hh_s05g2_forestexpenses.dta"[23] "09hh_s05h1_bussiness.dta""09hh_s05h2_bussinessexpenses.dta"[25] "09hh_s05h3_bussinessincome.dta""09hh_s06_liabilities.dta"[27] "09hh_s07_incomeother.dta""09hh_s08_construction.dta"[29] "09hh_s09_durables.dta""09hh_s10_healthmother.dta"[31] "09hh_s11_health2years.dta""09hh_s12_health5years.dta"[33] "09hh_s13b_healthexpenses.dta""09hh_s14_disability.dta"[35] "09hh_s15_labor7days.dta""09hh_s16_labor12months.dta"[37] "09hh_s17b_theft.dta""09hh_s17c_accidents.dta"[39] "09hh_s17d_violance.dta""09hh_s17d_violence.dta"[41] "09hh_s18_presenseinhh.dta""09hh_s99_singlequestions.dta"[43] "09hz_weighthh.dta""09hz_weightpersons.dta"

Summary data related income and expenditure in STATA format"IncomeCSES09.dta"

Household-level income summary data

"ExpenditureCSES09.dta"

Expenditure data by item and household"Per capita Consumption

Household-level expenditure summary data

ExpenditureCSES09.dta"

Manual for Cambodia CSES 2009 (Version 2.0)

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Information on Income and expenditure composition"Income_Composition_CSES2009.doc""Clarification to HH Income analysis.doc""Expenditure composition CSES2009.doc"

Manual for Cambodia CSES 2009 (Version 2.0)

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

Other metadata available on the web

NIS Homepage

http://www.nis.gov.kh/index.php/en/find-statistic/publications/
reports/cses-reports.html (Accessed on 15 April 2015)
CSES survey reports in English are available.

NADA (National

http://nada.nis.gov.kh/index.php/catalog/11

Data Archives)(Accessed on 15 April 2015)
Study description, survey report and other metadata are available.

Manual for Cambodia CSES 2009 (Version 2.0)

23

IHSN

http://catalog.ihsn.org/index.php/catalog/81(Accessed on 15 April 2015)
Almost same as NADAHousehold questionnaire, diary questionnaire and village questionnaire in
English are available in pdf.

“Field operation manual for interviewer and supervisor” (in English) isavailable.

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Chapter 4. Data Import
4.1 Data file structureStructure of data files and the correspondence with sections of the questionnaire are shown in thenext table.

No
1

Data file

Corresponding section of the questionnaire

09dy_expenditure

Unit

Cases

Variables

Diary questionnaire_Household

HH / date/

262099923

expenditures and consumption of own-

item

87891223

produced209dy_income

Diary questionnaire_Household

HH / date/income and receipts

item

3

S01A_hhmembers

1A

List of household members

Person

57105

20

4

S01B_foodconsumption

1B

Food consumption

HH / item

143921

16

5

S01C_nonfoodexpenses

1C

Recall non-food expenditure

HH / item

86155

16

6

S01D_vulnerability

1D

Vulnerability

HH

11980

27

7

S02_education2

Education and literacy

Person

53647

37

8

S03A_migration_past

3A

Past migration

Person

51275

35

9

S03B_migration_current

3B

Current migration

Person

8049

28

10

S04_housing

4

Housing

HH

11970

66

11

S04_housingpractice4

Housing

HH

11971

49

12

S05A_landown

5A

Land ownership

HH / plot

16458

45

13

S05B_cropsproduction

5B

Production of crops

HH/plot/crop

17773

23

14

S05C_costcrops

5C

Cost of cultivationof crops

HH / plot

16911

29

15

S05D_cropsinventory

5D

Inventory of crops

HH / crop

7308

16

16

S05E1_animals

5E

Inputs and outputs of livestock andHH / livestock

84350

26

17

S05E2_animalsexpenses

poultry raising activities

HH / item

14486

14

18

S05F1_fisharea

Inputs and outputs from fish cultivation

HH / pond

227

17

19

S05F2_fishexpenses

and fishing/trapping of aquaticproducts

HH / item

7853

14

20

S05F3_fishincome

HH / item

12364

14

21

S05G1_forestincome

Inputs and outputs from forestry and

HH / item

5928

14

22

S05G2_forestexpenses

hunting

HH / product

19398

17

23

S05H1_bussiness

List of household non-agricultural

HH / activity

4843

23

24

S05H2_bussinessexpenses

economic activities

HH / cost

15720

18

25

S05H3_bussinessincome

HH / revinue

5360

18

5F

5G

5H

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26

S06_liabilities

6

Household liabilities

HH / loan

4732

20

27

S07_incomeother7

Householdincome from other sources

HH / source

6862

16

28

S08_construction

8

Construction activities

HH / building

11322

35

29

S09_durables

9

Durable goods

HH / item

93366

22

30

S10_healthmother10

Maternal health

Person

4447

34

31

S11_health2years

11Child health

Person

2357

40

32

S12_health5years

12

Health check of children under 5

Person

5617

29

33

S13B_healthexpenses

13

Health care seeking and expenditure

Person

57082

26

34

S14_disability

14

Disability

Person

57082

27

35

S15_labor7days

15

Current economic activity

Person

51460

49

36

S16_labor12months

16

Usual economic activity

Person

51460

23

37

S17B_theft

17

Victimization

Person

328

22

38

S17C_accidents

Person

654

20

39

S17D_violance

Person

57074

26

40

S17D_violence

Person

57074

26

41

S18_presenseinhh

Person57074

31

Questions from various sections

HH

11971

31

Household weights

HH

11971

11

Person weights

Person57105

12Household summary of income

HH

11971

103

18

Summary of presence in thehousehold42

S99_singlequestions

43

weighthh2)

44

weightpersons

45

IncomeCSES09

No

2)

Data file

Corresponding

section

of

the

Unit

Cases

questionnaire
1areainfo

Cover

PSU

720

2

S01-17_hhotherinfo

Questions from various sections

HH

11971

3

S01A_hhmembers

1A

List of household members

Person57105

4

S01B_hhfoodconsumption

1B

Food consumption

HH / item

143921

5

S01C_hhrecallnonfood

1C

Recall non-food expenditure

HH / item

86155

6

S02_personeducation2

Education and literacy

Person53647

7

S03A_personmigrationpast

3A

Past migration

Person51275

8S03B_personmigrationcurrent

3B

Current migration

Person8049

Manual for Cambodia CSES 2009 (Version 2.0)26

9S04_hhhousing

4

Housing

HH

11971

10

S05A_hhlandownership

5ALand ownership

HH / plot

16458

11

S05B_hhproductioncrops

5B

Production of crops

HH/plot/crop

17773

12

S05C_hhcostcultivationcrops

5C

Cost of cultivationof crops

HH / plot

16911

13

S05D_hhinventorycrops

5D

Inventory of crops

HH / crop

7308

14

S05E1_hhlivestock1

5E

Inputs and outputs of livestock

HH

and poultry raising activities

livestock

15

S05E2_hhlivestock2

16

S05F1_hhfishcultivation1

17

/

84350

HH / item

14486

Inputs and outputs from fish

HH / pond

227

S05F2_hhfishcultivation2

cultivation and fishing/trapping

HH / item

7853

18

S05F3_hhfishcultivation3of aquaticproducts

HH / item

12364

19

S05G2_hhforestryhunting1

Inputs and outputs from forestry

HH / product

19398

20

S05G_hhforestryhunting2

and hunting

HH / item

5928

21

S05H1_hhnonagriculture1

HH / activity

4843

22

S05H2_hhnonagriculture2

HH / cost

15716

23

S05H3_hhnonagriculture3

HH / revinue

5360

24

S06_hhliabilities

6

Household liabilities

HH / loan

4732

25

S07_hhincomeothersource

7

Householdincome

HH / source

6862

5F

5G
5H

List

of

householdnon-agricultural economic activities

from

other

sources26

S08_hhconstruction

8

Construction activities

HH / building

11322

27

S09_hhdurablegoods

9

Durable goods

HH / item

93366

28

S10_personmaternalhealth10

Maternal health

Person4447

29

S11_personhealthu2

11Child health

Person2357

30

S12_personhealthu5

12Health check of children under 5

Person5617

31

S13_personillness

13

HealthPerson

57082

care

seekingandexpenditure32

S14_persondisability

14

Disability

Person57082

33

S15_personecocurrent

15

Current economic activity

Person51460

34

S16_personecousu

16

Usual economic activity

Person51460

35

S17B_persontheft

17

Victimization

Person328

36

S17C_personaccidents

Person654

37

S18_personpresence

Person

57074

HH

11971

HH

11971

18

Summary of presence in thehousehold38

weighthouseholds

Household weights

39

weighthouseholds_area

Householdweights

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area information40

weightpersons

Person weights

41

Yearly_hhexpenditure

Summaries
computedbyfromhouseholdthePerson

57105

HH

11971

HH

11971

Diary

Questionnaire:
Expenditure (Derivedvariables)
42

Yearly_hhincomee

Summaries
computedbyfromhousehold
the

Diary

Questionnaire:
Income (Derived variables)
Remarks:
1. No. 10 and No. 11 seem to be the same, and the former (No. 10) is adopted.

2. No. 39 and No. 40 seem to be the same, and the latter (No. 40) is adopted.

3.

There are two kind of weights; household weight and person weight. However, the detailed

description on calculating weights is not found in the survey report. There will be an issue on whichweight should be applied when merging household data and person data.

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4.2 Import STATA data files into RImported the following 45 STATA files into R> (infiles<-list.files())[1] "09dy_expenditure.dta"[3] "09hh_s01a_hhmembers.dta"[5] "09hh_s01c_nonfoodexpenses.dta"[7] "09hh_s02_education.dta"[9] "09hh_s03b_migration_current.dta"[11] "09hh_s04_housingpractice.dta"[13] "09hh_s05b_cropsproduction.dta"[15] "09hh_s05d_cropsinventory.dta"[17] "09hh_s05e2_animalsexpenses.dta"[19] "09hh_s05f2_fishexpenses.dta"[21] "09hh_s05g1_forestincome.dta"[23] "09hh_s05h1_bussiness.dta"[25] "09hh_s05h3_bussinessincome.dta"[27] "09hh_s07_incomeother.dta"[29] "09hh_s09_durables.dta"[31] "09hh_s11_health2years.dta"[33] "09hh_s13b_healthexpenses.dta"[35] "09hh_s15_labor7days.dta"[37] "09hh_s17b_theft.dta"[39] "09hh_s17d_violance.dta"[41] "09hh_s18_presenseinhh.dta"[43] "09hz_weighthh.dta"[45] "IncomeCSES09.dta""09dy_income.dta""09hh_s01b_foodconsumption.dta""09hh_s01d_vulnerability.dta""09hh_s03a_migration_past.dta""09hh_s04_housing.dta""09hh_s05a_landown.dta""09hh_s05c_costcrops.dta""09hh_s05e1_animals.dta""09hh_s05f1_fisharea.dta""09hh_s05f3_fishincome.dta""09hh_s05g2_forestexpenses.dta""09hh_s05h2_bussinessexpenses.dta""09hh_s06_liabilities.dta""09hh_s08_construction.dta""09hh_s10_healthmother.dta""09hh_s12_health5years.dta""09hh_s14_disability.dta""09hh_s16_labor12months.dta""09hh_s17c_accidents.dta""09hh_s17d_violence.dta""09hh_s99_singlequestions.dta""09hz_weightpersons.dta"> library(foreign)> outfiles<-list()> for(j in 1:length(infiles)){+ df<-read.dta(infiles[j],convert.factors=F)+ outfiles<-c(outfiles,list(df))+ }> length(outfiles)[1] 45# 45 R data frames were stored in the list “outfiles”.

# Made list of data file name, number of records and variables> Rnames<-sub(".dta","",infiles)

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29

> Rnames<-gsub("_",".",Rnames)> Rnames<-sub("_",".",Rnames)> Rnames<-sub("09hh.","",Rnames)> Rnames<-sub("09hz.","",Rnames)> for(j in 1:length(infiles)){+ if(j==1) cat("data frame

nrowncol\n")+ cat(j,": ",format(Rnames[j],width=27),": ",+ format(nrow(outfiles[[j]]),width=6),",",+ format(ncol(outfiles[[j]]),width=3),"\n")+ }data frame
1 : 09dy.expenditure
2 : 09dy.income
3 : s01a.hhmembers
4 : s01b.foodconsumption
5 : s01c.nonfoodexpenses
6 : s01d.vulnerability
7 : s02.education
8 : s03a.migration.past
9 : s03b.migration.current
10 : s04.housing
11 : s04.housingpractice
12 : s05a.landown
13 : s05b.cropsproduction
14 : s05c.costcrops
15 : s05d.cropsinventory
16 : s05e1.animals
17 : s05e2.animalsexpenses
18 : s05f1.fisharea
19 : s05f2.fishexpenses
20 : s05f3.fishincome
21 : s05g1.forestincome
22 : s05g2.forestexpenses
23 : s05h1.bussiness
24 : s05h2.bussinessexpenses
25 : s05h3.bussinessincome
26 : s06.liabilities
27 : s07.incomeother
28 : s08.construction
29 : s09.durables
30 : s10.healthmother
31 : s11.health2years
32 : s12.health5years
33 : s13b.healthexpenses
34 : s14.disability
35 : s15.labor7days
36 : s16.labor12months
37 : s17b.theft
38 : s17c.accidents

nrow
ncol
: 2620999 , 23
: 878912 , 23:57105 , 32
: 143921 , 16:86155 , 16:
11980 , 27:53647 , 37:
51275 , 35:8049 , 28: 11970 , 66
: 11971 , 49
: 16458 , 45
: 17773 , 23
: 16911 , 29:7308 , 16: 84350 , 26
: 14486 , 14:
227 , 17:7853 , 14: 12364 , 14
: 19398 , 17:5928 , 14:4843 , 23: 15720 , 18:5360 , 18:4732 , 20:6862 , 16: 11322 , 35
: 93366 , 22:4447 , 34:
2357 , 40:
5617 , 29: 57082 , 26
: 57082 , 27
: 51460 , 49
: 51460 , 23:
328 , 22:654 , 20

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39 :
40 :
41 :
42 :
43 :
44 :
45 :

s17d.violances17d.violences18.presenseinhhs99.singlequestions
weighthhweightpersons
IncomeCSES09

:
:
:
:
:
:
:

57074 , 2657074 , 2657074 , 31
11971 , 42
11971 , 1157105 , 1211971 , 103

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31

4.3 Names and types of variables

Naming rule of variables

The provided micro data set does not include so-called codebook or data dictionary for all variables.
For this point, the delegates from Cambodia to the seventh workshop clarified as follows;“The data files are prepared separately by section in the questionnaire andprovince+district+commune+village+household number=household Identification andprovince+district+commune+village+household number +person number (person identification )for key variable to merge among them.

Variable: Variable was named refer to section number and question number
Example:1. Variable: Q01AC08 is represented as “01. A. LIST OF HOUSEHOLD MEMBERS” andcolumn 8 (Does the mother of ..[NAME].. live in the household?). etc.
2. Value label: the value label of Q01AC11A is referred to questionnaire:
1 = Khmer
2 = Cham
3 = Other local ethnic group
4 = Chinese
5 = Vietnamese
6 = Thai
7 = Lao
8 = Other”

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# Displayed the names and types of variables> for(j in 1:45){+ cat("##",j,"#### ",Rnames[j]," ##########\n")+ print(str(outfiles[[j]]))+ cat("\n\n")+ }## 1 #### 09dy.expenditure ##########'data.frame':
2620999 obs. of 23 variables:$ hhid: int 100101 100101 100101 100101 100101 100101 100101 100101 100101
100101 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 5 5 5 5 5 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ pkid: int 65201 65203 65206 65208 65211 65213 65216 65217 65222 65224 ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100101" ...
$ lineid: int 101 102 103 104 105 106 107 108 109 110 ...
$ diaryexp_q2: int 101 101 201 201 201 201 201 301 301 301 ...
$ diaryexp_q5: int 4 4 9 10 5 4 1 9 4 6 ...
$ diaryexp_q6: num 500 500 2 NA 2 300 2 2 500 700 ...
$ diaryexp_q7: int 7500 1200 3000 1500 5200 3500 1000 3000 3500 1200 ...
$ diaryexp_q8: int 1 1 1 1 1 1 1 1 1 1 ...
$ diaryexp_q9: int 2 2 2 4 2 2 2 1 2 3 ...
$ diaryexp_q10: int 1 1 1 1 1 1 1 1 1 1 ...
$ diaryexp_q11: int 1090 1354 6010 5440 5060 1090 1221 6010 1090 2041 ...
$ diaryexp_q2_string: chr "0101" "0101" "0201" "0201" ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)"

Manual for Cambodia CSES 2009 (Version 2.0)33"other rural (outside p.p.)"
NULL## 2 #### 09dy.income ##########'data.frame':
878912 obs. of 23 variables:$ hhid: int 100101 100101 100101 100101 100101 100101 100101 100101 100101
100101 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 5 5 5 5 5 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ pkid: int 20432 20433 20434 20437 20440 20444 20445 20447 20449 20452 ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100101" ...
$ lineid: int 101 102 103 104 106 105 107 108 109 110 ...
$ diaryinc_q2: int 601 601 1201 1201 1801 1501 2101 2601 2601 3001 ...
$ diaryinc_q5: int 10 3 3 10 4 10 4 4 9 10 ...
$ diaryinc_q6: num NA 1 1.2 NA 500 NA 300 500 1 NA ...
$ diaryinc_q7: int 69000 500 8000 374000 300 374000 500 10000 3500 8000 ...
$ diaryinc_q8: int 1 2 2 1 2 1 2 2 2 1 ...
$ diaryinc_q9: int 1 2 3 1 2 1 2 7 7 19 ...
$ diaryinc_q10: int 101 202 302 101 202 101 202 701 701 1299 ...
$ diaryinc_q2_string : chr "0601" "0601" "1201" "1201" ...
$ diaryinc_q10_string: chr "0101" "0202" "0302" "0101" ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 3 #### s01a.hhmembers ##########

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34

'data.frame': 57105 obs. of 32 variables:$ hhid: int 100101 100101 100101 100101 100101 100102 100102 100102 100102 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010105 10010201 10010202 10010203
10010204 10010301 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 4 4 4 4 6 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q01ac01: int 1 2 3 4 5 1 2 3 4 1 ...
$ q01ac03: int 1 2 2 2 1 1 2 2 1 1 ...
$ q01ac04a: int 17 5 28 15 24 18 5 24 8 2 ...
$ q01ac04b: int 12 6 12 3 7 3 5 3 8 3 ...
$ q01ac04c: int 1970 1974 1995 2000 2007 1969 1974 1995 1998 1960 ...
$ q01ac05: int 38 34 13 8 1 39 34 13 10 48 ...
$ q01ac06: int 1 2 3 3 3 1 2 3 3 1 ...
$ q01ac07: int NA NA 1 1 1 NA NA 1 1 NA ...
$ q01ac08: int 1 NA 2 2 2 NA NA 2 2 NA ...
$ q01ac09: int 1 1 4 NA NA 1 1 4 NA 1 ...
$ q01ac10: int 2 1 NA NA NA 2 1 NA NA 2 ...
$ q01ac11a: int 1 1 1 1 1 1 1 1 1 1 ...
$ q01ac11b: int 1 1 1 1 1 1 1 1 1 1 ...
$ q01ac12a: int 1 0 0 0 0 0 0 0 0 0 ...
$ q01ac12b: int 0 NA NA NA NA NA NA NA NA NA ...
$ q01ac12c: int NA NA NA NA NA NA NA NA NA NA ...
$ q01ac13: int 1 1 1 1 1 1 1 1 1 2 ...
$ q01ac14: int NA NA NA NA NA NA NA NA NA 3 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"

Manual for Cambodia CSES 2009 (Version 2.0)

35

NULL

## 4 #### s01b.foodconsumption ##########'data.frame':
143921 obs. of 16 variables:$ hhid: int 100101 100101 100101 100101 100101 100101 100101 100101 100101 100101 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 5 5 5 5 5 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100101" ...
$ q01bc01: int 1 3 4 6 7 8 10 11 12 13 ...
$ q01bc03: int 0 50400 2500 1500 1200 0 500 0 3000 7000 ...
$ q01bc04: int 16000 0 0 0 600 2000 0 11000 0 0 ...
$ q01bc05: int 16000 50400 2500 1500 1800 2000 500 11000 3000 7000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 5 #### s01c.nonfoodexpenses ##########'data.frame': 86155 obs. of 16 variables:$ hhid: int 100101 100101 100101 100101 100101 100101 100101 100101 100101 100102 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 5 5 5 5 4 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...

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36

$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100101" ...
$ q01cc01: int 1 2 3 4 5 6 8 10 13 1 ...
$ q01cc04: int 12000 50000 20000 9500 21000 40000 100000 80000 800000 1500 ...
$ q01cc05: int 0 0 0 0 0 0 0 0 220000 0 ...
$ q01cc06: int 12000 50000 20000 9500 21000 40000 100000 80000 1020000 1500 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 6 #### s01d.vulnerability ##########'data.frame': 11980 obs. of 27 variables:$ hhid: int 100101 100102 100103 100104 100105 100106 100107 100108 100109 100110 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 4 6 3 7 9 2 4 4 7 ...
$ weight3: num 1331 1065 1597 798 1863 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100102" "0100103" "0100104" ...
$ q01dq1: int 1 1 2 1 1 1 2 1 2 2 ...
$ q01dq2: int 2 1 2 1 2 1 1 1 1 2 ...
$ q01dq3: int 3 NA 4 NA 0 NA NA NA NA 0 ...
$ q01dq4_1: int 0 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_2: int 0 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_3: int 0 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_4: int 0 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_5: int 0 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_6: int 0 NA 0 NA 0 NA NA NA NA 0 ...

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37

$ q01dq4_7: int 0 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_8: int 0 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_9: int 0 NA 0 NA 0 NA NA NA NA 1 ...
$ q01dq4_10 : int 0 NA 1 NA 0 NA NA NA NA 1 ...
$ q01dq4_11 : int 1 NA 0 NA 0 NA NA NA NA 0 ...
$ q01dq4_12 : int 1 NA 0 NA 1 NA NA NA NA 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 7 #### s02.education ##########'data.frame': 53647 obs. of 37 variables:$ hhid: int 100101 100101 100101 100101 100102 100102 100102 100102 100103 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010201 10010202 10010203 10010204
10010301 10010302 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 4 4 4 4 6 6 ...
$ weight3: num 1331 1331 1331 1331 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q02c01: int 1 2 3 4 1 2 3 4 1 2 ...
$ q02c02: int 1 2 1 1 1 1 1 1 1 2 ...
$ q02c03: int 1 2 1 1 1 1 1 1 1 2 ...
$ q02c04: int 1 2 1 1 1 1 1 1 1 2 ...
$ q02c05: int 7 NA 6 2 8 8 8 5 10 NA ...
$ q02c06: int 7 NA 6 2 8 8 7 4 10 NA ...
$ q02c07: int 2 NA 1 1 2 2 1 1 2 NA ...
$ q02c08: int NA NA 7 3 NA NA 8 5 NA NA ...

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38

$ q02c09: int NA NA 1 1 NA NA 1 1 NA NA ...
$ q02c10: int NA NA 1 2 NA NA 1 1 NA NA ...
$ q02c11: int NA NA NA NA NA NA NA NA NA NA ...
$ q02c12: int 2 2 1 2 2 2 1 2 2 2 ...
$ q02c13: int NA NA 1 NA NA NA 1 NA NA NA ...
$ q02c14: int NA NA 4 NA NA NA 4 NA NA NA ...
$ q02c15: int 2 2 1 1 2 2 1 1 2 2 ...
$ q02c16a: int NA NA NA NA NA NA NA NA NA NA ...
$ q02c16b: int NA NA 140000 NA NA NA 156000 110000 NA NA ...
$ q02c16c: int NA NA 15000 10000 NA NA 19000 13000 NA NA ...
$ q02c16d: int NA NA 4000 3000 NA NA 6000 2500 NA NA ...
$ q02c16e: int NA NA 430000 220000 NA NA 440000 15600 NA NA ...
$ q02c16f: int NA NA 250000 NA NA NA 7000 0 NA NA ...
$ q02c16g: int NA NA NA NA NA NA 0 300 NA NA ...
$ q02c16h: int 0 0 839000 233000 0 0 628000 141400 0 0 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 8 #### s03a.migration.past ##########'data.frame': 51275 obs. of 35 variables:$ hhid: int 100101 100101 100101 100101 100102 100102 100102 100102 100103
100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010201 10010202 10010203 10010204
10010301 10010302 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 4 4 4 4 6 6 ...
$ weight3: num 1331 1331 1331 1331 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...

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$ persid_string : chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100101" ...
$ q03ac01: int 1 2 3 4 1 2 3 4 1 2 ...
$ q03ac02: int 1 1 1 1 2 1 1 1 1 2 ...
$ q03ac03: int NA NA NA NA 15 NA NA NA NA 23 ...
$ q03ac04b: int NA NA NA NA 88 NA NA NA NA 2 ...
$ q03ac04c: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac05: int NA NA NA NA 4 NA NA NA NA 4 ...
$ q03ac06b: int NA NA NA NA 1 NA NA NA NA 2 ...
$ q03ac06c: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac07: int 2 2 2 2 2 2 2 2 2 2 ...
$ q03ac08: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac09a: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac09b: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac10: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac11: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac12: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac13: int NA NA NA NA NA NA NA NA NA NA ...
$ q03ac04b_string: chr "" "" "" "" ...
$ q03ac04c_string: chr "" "" "" "" ...
$ q03ac06b_string: chr "" "" "" "" ...
$ q03ac06c_string: chr "" "" "" "" ...
$ q03ac12_string : chr "" "" "" "" ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 9 #### s03b.migration.current ##########'data.frame':
8049 obs. of 28 variables:$ hhid: int 100106 100106 100106 100201 100202 100202 100202 100204 100204 100204 ...
$ persid: int 10010631 10010632 10010633 10020131 10020231 10020232 10020235 10020431
10020432 10020433 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 265 265 ...

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$ hhsize: int 9 9 9 11 5 5 5 6 6 6 ...
$ weight3: num 2395 2395 2395 2912 1324 ...
$ psu: int 1001 1001 1001 1002 1002 1002 1002 1002 1002 1002 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010631" "010010632" "010010633" "010020131" ...
$ hhid_string : chr "0100106" "0100106" "0100106" "0100201" ...
$ q03bc01: int 31 32 33 31 31 32 35 31 32 33 ...
$ q03bc03: int 1 2 1 2 2 1 1 1 2 2 ...
$ q03bc04: int 33 25 22 38 50 48 41 39 33 22 ...
$ q03bc05b: int 88 88 88 88 88 88 9 88 88 12 ...
$ q03bc05c: int NA NA NA NA NA NA NA NA NA NA ...
$ q03bc06: int 1991 2000 2007 1995 1999 1999 2001 2007 2007 2004 ...
$ q03bc07: int 3 3 3 3 3 3 1 3 3 4 ...
$ q03bc08: int 631 548 631 631 631 541 532 631 631 NA ...
$ q03bc09: int 6 9 6 5 88 14 14 8 3 17 ...
$ q03bc10: int 631 548 631 631 631 541 532 631 631 NA ...
$ q03bc11: int 2 2 2 2 2 2 2 1 1 2 ...
$ q03bc12: int NA NA NA NA NA NA NA 250000 1500000 NA ...
$ q03bc13a: int NA NA NA NA NA NA NA 3 3 NA ...
$ q03bc13b: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr "unique hh identification " "" "province (2)" "urban or rural
households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 10 #### s04.housing ##########'data.frame': 11970 obs. of 66 variables:$ hhid: int 100101 100102 100103 100104 100105 100106 100107 100108 100109 100110 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 4 6 3 7 9 2 4 4 7 ...
$ weight3: num 1331 1065 1597 798 1863 ...

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41

$ psu
: int
$ stratum: int$ surveymonth: int$ region: int$ year: int$ hhid_string: chr$ q04_01: int$ q04_02: int$ q04_03: int$ q04_04: int$ q04_05: int$ q04_06: int$ q04_07: int$ q04_08: int$ q04_09: int$ q04_10m1: int$ q04_10m2: int$ q04_10m3: int$ q04_11: int$ q04_12: int$ q04_13: int$ q04_14m1 : int$ q04_14m2: int$ q04_14m3: int$ q04_15: int$ q04_16: int$ q04_17: int$ q04_18a: int$ q04_18b: int$ q04_18c: int$ q04_18d: int$ q04_18e: int$ q04_19a: int$ q04_19b: int$ q04_20: int$ q04_21: int$ q04_22a: int$ q04_22b: int$ q04_22c1: int$ q04_22c2 : int$ q04_22c3: int$ q04_22d: int$ q04_23a: int$ q04_23b: int$ q04_23c: int$ q04_23d: int$ q04_23e: int$ q04_23f: int$ q04_23g: int$ q04_24: int$ q04_25a: int$ q04_25b: int$ q04_26: int$ phnonpenh : int$ otherrural : int$ urban: int

1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
11 11 11 11 11 11 11 11 11 11 ...
1 1 1 1 1 1 1 1 1 1 ...
2 2 2 2 2 2 2 2 2 2 ...
2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
"0100101" "0100102" "0100103" "0100104" ...
1 1 1 1 1 1 1 1 3 1 ...
60 24 30 20 81 42 35 48 66 42 ...
2 1 1 1 2 2 1 1 1 1 ...
2 3 1 2 2 2 1 2 2 2 ...
4 4 4 4 4 4 4 4 4 3 ...
2 2 2 4 2 2 2 2 2 2 ...
1 1 1 1 1 1 3 1 1 1 ...
7 6 6 7 7 7 7 7 6 9 ...
NA 10 20 NA NA NA NA NA 20 NA ...
NA 1 2 NA NA NA NA NA 1 NA ...
NA 3 3 NA NA NA NA NA 2 NA ...
NA 2 NA NA NA NA NA NA NA NA ...
NA 5 5 NA NA NA NA NA 10 NA ...
7 7 6 7 7 7 7 7 6 6 ...
NA NA 20 NA NA NA NA NA 20 20 ...
NA NA 2 NA NA NA NA NA 1 1 ...
NA NA 3 NA NA NA NA NA 2 NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA 5 NA NA NA NA NA 10 25 ...
2000 5000 0 8000 15000 0 7800 3000 0 0 ...
1 1 1 3 1 1 3 3 3 1 ...
1 1 1 NA 1 1 NA NA NA 2 ...
2 2 2 NA 2 2 NA NA NA 2 ...
2 2 2 NA 2 2 NA NA NA 2 ...
2 2 2 NA 2 2 NA NA NA 2 ...
2 2 2 NA 2 2 NA NA NA 2 ...
2 2 2 8 2 2 2 8 2 7 ...
NA NA NA NA NA NA NA NA NA 2 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
1 1 1 2 1 2 1 5 1 1 ...
1 1 2 1 2 1 1 NA 2 2 ...
NA NA 2 NA 2 NA NA NA 1 1 ...
NA NA 1 NA NA NA NA NA NA NA ...
NA NA NA NA NA NA NA NA NA NA ...
NA NA 2 NA 3 NA NA NA 2 2 ...
10000 10000 15000 20000 70000 34000 0 15000 35000 15000 ...
0 0 3000 0 2800 0 0 10000 0 0 ...
0 0 1000 0 0 0 0 0 1500 4000 ...
10000 50000 80000 7500 1000 40000 12000 0 18000 60000 ...
10000 0 0 9000 0 69000 0 3000 0 0 ...
0 0 0 0 0 0 8000 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
1 1 1 1 1 1 1 1 2 1 ...
NA NA NA NA NA NA NA NA NA NA ...
80000 35000 35000 40000 100000 50000 35000 100000 80000 50000 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
0 0 0 0 0 0 0 0 0 0 ...
1 1 1 1 1 1 1 1 1 1 ...

Manual for Cambodia CSES 2009 (Version 2.0)

42

$ publiclight: int 1 1 1 1 1 1 0 1 1 1 ...
$ pre_1: num 301507 163920 163920 163920 301507 ...
$ log_25b: num 11.3 10.5 10.5 10.6 11.5 ...
$ pre_2: num 12.1 11.7 11.7 11.7 12.1 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "individual hh identification number" "province (2)" "urban orrural households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 16..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"..$ q04_04: Named int 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "bamboo, thatch/leaves, grass" "wood or logs" "plywood" "concrete,
brick, stone" ...
..$ q04_05: Named int 1 2 3 4 5 6 7 8 9 10.. ..- attr(*, "names")= chr "thatch/leaves/grass" "tiles" "fibrous cement" "galvanized ironor aluminium" ...
..$ q04_06: Named int 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr"earth, clay" "wooden planks" "bamboo strips""cement/brick/stone" ...
..$ q04_07: Named int 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "publicly-provided electricity/city power" "generator" "battery""kerosene lamp" ...
..$ LABB: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "piped in dwelling or on premises" "public tap" "tubed/piped
well or borehole" "protected dug well" ...
..$ q04_17: Named int 1 2 3.. ..- attr(*, "names")= chr "yes, always" "sometimes" "no, never (>>19)"..$ q04_19a: Named int 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "pour flush (or flush) connected to sewerage" "pour flush (orflush) to septic tank or pit" "pour flush (or flush) to elsewhere (i.e. not a sewer or pit/tank)""pit latrine with slab" ...
..$ q04_19b: Named int 1 2 3.. ..- attr(*, "names")= chr "public toilet/pit latrine or shared with others (any type)""open land" "other "..$ q04_22a: Named int 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "firewood" "charcoal" "liquefied petroleum gas lpg" "kerosene" ...
..$ q04_22b: Named int 1 2.. ..- attr(*, "names")= chr "yes (>>q23)" "no"..$ q04_24: Named int 1 2 3 4.. ..- attr(*, "names")= chr "owned by the household" "not owned but no rent is paid" "rented"

Manual for Cambodia CSES 2009 (Version 2.0)43"other"
NULL## 11 #### s04.housingpractice ##########'data.frame': 11971 obs. of 49 variables:$ hhid_string: chr "0100101" "0100102" "0100103" "0100104" ...
$ q04_01: int 1 1 1 1 1 1 1 1 3 1 ...
$ q04_02: int 60 24 30 20 81 42 35 48 66 42 ...
$ q04_03: int 2 1 1 1 2 2 1 1 1 1 ...
$ q04_04: int 2 3 1 2 2 2 1 2 2 2 ...
$ q04_05: int 4 4 4 4 4 4 4 4 4 3 ...
$ q04_06: int 2 2 2 4 2 2 2 2 2 2 ...
$ q04_07: int 1 1 1 1 1 1 3 1 1 1 ...
$ q04_08: int 7 6 6 7 7 7 7 7 6 9 ...
$ q04_09: int NA 10 20 NA NA NA NA NA 20 NA ...
$ q04_10m1: int NA 1 2 NA NA NA NA NA 1 NA ...
$ q04_10m2: int NA 3 3 NA NA NA NA NA 2 NA ...
$ q04_10m3: int NA 2 NA NA NA NA NA NA NA NA ...
$ q04_11: int NA 5 5 NA NA NA NA NA 10 NA ...
$ q04_12: int 7 7 6 7 7 7 7 7 6 6 ...
$ q04_13: int NA NA 20 NA NA NA NA NA 20 20 ...
$ q04_14m1 : int NA NA 2 NA NA NA NA NA 1 1 ...
$ q04_14m2: int NA NA 3 NA NA NA NA NA 2 NA ...
$ q04_14m3: int NA NA NA NA NA NA NA NA NA NA ...
$ q04_15: int NA NA 5 NA NA NA NA NA 10 25 ...
$ q04_16: int 2000 5000 0 8000 15000 0 7800 3000 0 0 ...
$ q04_17: int 1 1 1 3 1 1 3 3 3 1 ...
$ q04_18a: int 1 1 1 NA 1 1 NA NA NA 2 ...
$ q04_18b: int 2 2 2 NA 2 2 NA NA NA 2 ...
$ q04_18c: int 2 2 2 NA 2 2 NA NA NA 2 ...
$ q04_18d: int 2 2 2 NA 2 2 NA NA NA 2 ...
$ q04_18e: int 2 2 2 NA 2 2 NA NA NA 2 ...
$ q04_19a: int 2 2 2 8 2 2 2 8 2 7 ...
$ q04_19b: int NA NA NA NA NA NA NA NA NA 2 ...
$ q04_20: int 0 0 0 0 0 0 0 0 0 0 ...
$ q04_21: int 0 0 0 0 0 0 0 0 0 0 ...
$ q04_22a: int 1 1 1 2 1 2 1 5 1 1 ...
$ q04_22b: int 1 1 2 1 2 1 1 NA 2 2 ...
$ q04_22c1 : int NA NA 2 NA 2 NA NA NA 1 1 ...
$ q04_22c2: int NA NA 1 NA NA NA NA NA NA NA ...
$ q04_22c3: int NA NA NA NA NA NA NA NA NA NA ...
$ q04_22d: int NA NA 2 NA 3 NA NA NA 2 2 ...
$ q04_23a: int 10000 10000 15000 20000 70000 34000 0 15000 35000 15000 ...
$ q04_23b: int 0 0 3000 0 2800 0 0 10000 0 0 ...
$ q04_23c: int 0 0 1000 0 0 0 0 0 1500 4000 ...
$ q04_23d: int 10000 50000 80000 7500 1000 40000 12000 0 18000 60000 ...
$ q04_23e: int 10000 0 0 9000 0 69000 0 3000 0 0 ...
$ q04_23f: int 0 0 0 0 0 0 8000 0 0 0 ...
$ q04_23g: int 0 0 0 0 0 0 0 0 0 0 ...
$ q04_24: int 1 1 1 1 1 1 1 1 2 1 ...
$ q04_25a: int NA NA NA NA NA NA NA NA NA NA ...
$ q04_25b: int 80000 35000 35000 40000 100000 50000 35000 100000 80000 50000 ...
$ q04_26: int 0 0 0 0 0 0 0 0 0 0 ...
$ hhid: int 100101 100102 100103 100104 100105 100106 100107 100108 100109 100110 ...
- attr(*, "datalabel")= chr ""Manual for Cambodia CSES 2009 (Version 2.0)

44

- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%7s" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 7 251 252 251 251 251 251 251 251 252 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "" "how many hhs reside in same unit" "what is the fllor area ofthe housing in square meters?" "how many rooms are use by hh (other than kitchchen, toilet andtathrooms)?" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 11..$ q04_04 : Named int 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr "bamboo, thatch/leaves, grass" "wood or logs" "plywood" "concrete,
brick, stone" ...
..$ q04_05 : Named int 1 2 3 4 5 6 7 8 9 10.. ..- attr(*, "names")= chr "thatch/leaves/grass" "tiles" "fibrous cement" "galvanized ironor aluminium" ...
..$ q04_06 : Named int 1 2 3 4 5 6 7 8 9.. ..- attr(*, "names")= chr"earth, clay" "wooden planks" "bamboo strips""cement/brick/stone" ...
..$ q04_07 : Named int 1 2 3 4 5 6 7.. ..- attr(*, "names")= chr "publicly-provided electricity/city power" "generator" "battery""kerosene lamp" ...
..$ LABB : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "piped in dwelling or on premises" "public tap" "tubed/piped
well or borehole" "protected dug well" ...
..$ q04_17 : Named int 1 2 3.. ..- attr(*, "names")= chr "yes, always" "sometimes" "no, never (>>19)"..$ q04_19a: Named int 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "pour flush (or flush) connected to sewerage" "pour flush (orflush) to septic tank or pit" "pour flush (or flush) to elsewhere (i.e. not a sewer or pit/tank)""pit latrine with slab" ...
..$ q04_19b: Named int 1 2 3.. ..- attr(*, "names")= chr "public toilet/pit latrine or shared with others (any type)""open land" "other "..$ q04_22a: Named int 1 2 3 4 5 6 7 8.. ..- attr(*, "names")= chr "firewood" "charcoal" "liquefied petroleum gas lpg" "kerosene" ...
..$ q04_22b: Named int 1 2.. ..- attr(*, "names")= chr "yes (>>q23)" "no"..$ q04_24 : Named int 1 2 3 4.. ..- attr(*, "names")= chr "owned by the household" "not owned but no rent is paid" "rented""other"
NULL## 12 #### s05a.landown ##########'data.frame': 16458 obs. of 45 variables:$ hhid: int 100101 100101 100102 100102 100102 100102 100103 100105 100106 100106 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 4 4 4 4 6 7 9 9 ...
$ weight3: num 1331 1331 1065 1065 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...

Manual for Cambodia CSES 2009 (Version 2.0)

45$ hhid_string: chr "0100101" "0100101" "0100102" "0100102" ...
$ q05ac01: int 1 2 1 2 3 4 1 1 1 2 ...
$ q05ac02: num 7200 5000 9200 9200 6400 4800 5000 10000 9600 6400 ...
$ q05ac03: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05ac04a: int 300 120 500 500 300 200 300 400 400 350 ...
$ q05ac04b: int 2 2 2 2 2 2 2 2 2 2 ...
$ q05ac04c: int 2 2 3 3 3 3 2 3 2 2 ...
$ q05ac05a: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac05b: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac05c: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac06a: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac06b: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac06c: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac07: int 1 1 1 1 1 1 1 3 4 1 ...
$ q05ac08: int 2000 2005 1994 1995 1995 1999 2006 1980 2001 1980 ...
$ q05ac09: int 4 3 4 4 4 4 3 1 4 1 ...
$ q05ac10: int 9500000 5000000 1500000 1500000 150000 700000 1200000 NA 5700000 NA ...
$ q05ac11: int 4500000 10000000 70000000 7000000 500000 3500000 2500000 7000000 80000000
700000 ...
$ q05ac12: int 1 1 1 1 1 1 2 2 1 2 ...
$ q05ac13a: int 4 4 4 4 4 4 NA NA 4 NA ...
$ q05ac13b: int 4 4 4 4 4 4 NA NA 4 NA ...
$ q05ac14: int 1 1 1 4 1 1 NA NA 1 NA ...
$ q05ac15: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05ac16a: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05ac16b: int NA NA NA NA NA NA NA NA 2 NA ...
$ q05ac16c: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac17: int 4 4 4 4 4 4 4 2 3 4 ...
$ q05ac18a: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05ac18b: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac18c: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac19: int NA NA NA NA NA NA NA NA NA NA ...
$ q05ac20: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05ac21: int 2000 2005 1994 1995 1996 1999 2006 1980 2001 1980 ...
$ q05ac22: int 3 3 3 3 3 3 3 3 3 3 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)"

Manual for Cambodia CSES 2009 (Version 2.0)46"other rural (outside p.p.)"
NULL## 13 #### s05b.cropsproduction ##########'data.frame': 17773 obs. of 23 variables:$ hhid: int 100101 100101 100102 100102 100102 100102 100103 100105 100106 100106 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 4 4 4 4 6 7 9 9 ...
$ weight3: num 1331 1331 1065 1065 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100102" "0100102" ...
$ wetdry: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05bc01: int 1 2 1 2 3 4 1 1 1 2 ...
$ q05bc02: int 1 2 1 2 3 4 1 1 1 2 ...
$ q05bc03b: chr "102" "102" "102" "102" ...
$ q05bc04: num 7200 5000 9200 9200 6400 4800 5000 10000 3200 6400 ...
$ q05bc05: num 3500 2000 9200 9200 6400 4800 3200 9000 3200 6400 ...
$ q05bc06: int 900 300 2000 2000 1650 1200 800 2400 500 850 ...
$ q05bc07: num 10 5 0 0 0 0 50 0 0 0 ...
$ q05bc08: num 0 0 0 0 0 0 35 500 0 0 ...
$ q05bc09: int 650 650 600 600 600 600 700 800 800 800 ...
$ pastyear: int 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 14 #### s05c.costcrops ##########'data.frame': 16911 obs. of 29 variables:

Manual for Cambodia CSES 2009 (Version 2.0)

47$ hhid: int 100101 100101 100102 100102 100102 100102 100103 100105 100106 100106 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 4 4 4 4 6 7 9 9 ...
$ weight3: num 1331 1331 1065 1065 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100102" "0100102" ...
$ wetdry: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05cc01: int 1 2 1 2 3 4 1 1 1 2 ...
$ q05cc02: int 1 2 1 2 3 4 1 1 1 2 ...
$ q05cc03: int 32500 22700 60000 60000 45000 30000 35000 12000 40000 56000 ...
$ q05cc04: int 100000 65000 130000 130000 100000 90000 100000 271000 4500 300000 ...
$ q05cc05: int 0 0 0 0 0 0 0 0 0 0 ...
$ q05cc06: int 0 0 0 0 0 0 0 0 0 0 ...
$ q05cc07: int 0 0 40000 40000 32000 20000 0 0 0 0 ...
$ q05cc08: int 16800 4800 0 0 0 0 50000 0 0 0 ...
$ q05cc09: int 19400 13000 0 0 0 0 120000 160000 40000 110000 ...
$ q05cc10: int 0 0 0 0 0 0 72000 0 0 0 ...
$ q05cc11: int 0 0 0 0 0 0 0 0 0 0 ...
$ q05cc12: int 0 0 0 0 0 0 0 0 0 0 ...
$ q05cc13: int 13000 52000 20000 20000 17000 14000 70000 65000 30000 75000 ...
$ q05cc14: int 0 0 0 0 0 0 0 0 0 0 ...
$ q05cc15: int 0 0 0 0 0 0 0 0 0 0 ...
$ q05cc16: int 181700 157500 250000 250000 194000 154000 447000 508000 114500 541000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 15 #### s05d.cropsinventory ##########'data.frame':
7308 obs. of 16 variables:

Manual for Cambodia CSES 2009 (Version 2.0)

48

$ hhid: int 100105 100201 100203 100204 100204 100205 100207 100210 100306 101202 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 265 265 265 265 ...
$ hhsize: int 7 11 4 6 6 10 5 6 6 4 ...
$ weight3: num 1863 2912 1059 1588 1588 ...
$ psu: int 1001 1002 1002 1002 1002 1002 1002 1002 1003 1012 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 3 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100105" "0100201" "0100203" "0100204" ...
$ q05dc01: int 1 1 1 1 2 1 1 1 1 1 ...
$ q05dc02b: chr "102" "102" "102" "102" ...
$ q05dc03: num 100 1000 2500 3000 3000 2500 10800 2000 1500 20000 ...
$ q05dc04: int 1000 750 750 750 750 750 750 750 700 900 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 16 #### s05e1.animals ##########'data.frame': 84350 obs. of 26 variables:$ hhid: int 100101 100101 100101 100101 100101 100101 100101 100101 100101 100101 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 5 5 5 5 5 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100101" ...
$ q05e1c01: int 1 2 3 4 5 6 7 8 9 10 ...

Manual for Cambodia CSES 2009 (Version 2.0)

49

$ q05e1c03: int 2 2 2 2 2 2 1 2 2 2 ...
$ q05e1c04: int NA NA NA NA NA NA 4 NA NA NA ...
$ q05e1c05: int NA NA NA NA NA NA 1 NA NA NA ...
$ q05e1c06: int NA NA NA NA NA NA 72000 NA NA NA ...
$ q05e1c07: int NA NA NA NA NA NA 4 NA NA NA ...
$ q05e1c08: int NA NA NA NA NA NA 72000 NA NA NA ...
$ q05e1c09: int NA NA NA NA NA NA 0 NA NA NA ...
$ q05e1c10: int NA NA NA NA NA NA 50000 NA NA NA ...
$ q05e1c11: int NA NA NA NA NA NA 0 NA NA NA ...
$ q05e1c12: int NA NA NA NA NA NA 0 NA NA NA ...
$ q05e1c13: int NA NA NA NA NA NA 0 NA NA NA ...
$ q05e1c14: int NA NA NA NA NA NA 0 NA NA NA ...
$ q05e1c15: int NA NA NA NA NA NA 0 NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 17 #### s05e2.animalsexpenses ##########'data.frame': 14486 obs. of 14 variables:$ hhid: int 100101 100102 100103 100105 100106 100107 100108 100109 100201 100202 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 4 6 7 9 2 4 4 11 5 ...
$ weight3: num 1331 1065 1597 1863 2395 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1002 1002 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100102" "0100103" "0100105" ...
$ q05e2c01: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05e2c03: int 15000 15000 26000 50000 60000 15000 120000 90000 1950000 30000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""Manual for Cambodia CSES 2009 (Version 2.0)

50

- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 18 #### s05f1.fisharea ##########'data.frame':
227 obs. of 17 variables:$ hhid: int 100101 102204 102208 102313 102313 103414 201402 203107 300103 300201 ...
$ province: int 1 1 1 1 1 1 2 2 3 3 ...
$ urbanrural : int 1 2 2 2 2 2 1 2 1 1 ...
$ weight: num 266 236 236 224 224 ...
$ hhsize: int 5 5 2 5 5 4 3 4 5 5 ...
$ weight3: num 1331 1178 471 1120 1120 ...
$ psu: int 1001 1022 1022 1023 1023 1034 2014 2031 3001 3002 ...
$ stratum: int 11 12 12 12 12 12 21 22 31 31 ...
$ surveymonth: int 1 8 8 9 9 8 10 11 1 1 ...
$ region: int 2 3 3 3 3 3 2 3 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0102204" "0102208" "0102313" ...
$ q05f1c01: int 1 1 1 1 2 1 1 1 1 1 ...
$ q05f1c02: int 1 1 4 1 1 1 1 1 1 1 ...
$ q05f1c03: int 20 120 32 80 80 8 56 300 300 450 ...
$ q05f1c04: int 500000 1100000 130000 1000000 1000000 500000 3600000 2000000 4000000
16000000 ...
$ q05f1c05 : int 200000 0 0 15000 15000 30000 50000 100000 200000 800000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2

Manual for Cambodia CSES 2009 (Version 2.0)

51.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 19 #### s05f2.fishexpenses ##########'data.frame':
7853 obs. of 14 variables:$ hhid: int 100101 100101 100109 100201 100203 100205 100207 100301 100301 100302 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 265 265 ...
$ hhsize: int 5 5 4 11 4 10 5 5 5 2 ...
$ weight3: num 1331 1331 1065 2912 1059 ...
$ psu: int 1001 1001 1001 1002 1002 1002 1002 1003 1003 1003 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100109" "0100201" ...
$ q05f2c01: int 1 2 5 5 5 5 5 5 10 5 ...
$ q05f2c03: int 30000 80000 30000 80000 120000 80000 20000 13000 22500 14000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 20 #### s05f3.fishincome ##########'data.frame': 12364 obs. of 14 variables:$ hhid: int 100101 100101 100101 100109 100109 100109 100109 100109 100201 100203 ...

Manual for Cambodia CSES 2009 (Version 2.0)

52

$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 4 4 4 4 4 11 4 ...
$ weight3: num 1331 1331 1331 1065 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1002 1002 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100109" ...
$ q05f3c01 : int 1 2 4 1 2 3 4 5 2 2 ...
$ q05f3c03: int 130000 100000 55000 100000 300000 20000 45000 7000 5400000 280000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 21 #### s05g1.forestincome ##########'data.frame': 19398 obs. of 17 variables:$ hhid: int 100102 100103 100109 100201 100205 100301 100302 100303 100304 100305 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 265 265 ...
$ hhsize: int 4 6 4 11 10 5 2 2 8 3 ...
$ weight3: num 1065 1597 1065 2912 2647 ...
$ psu: int 1001 1001 1001 1002 1002 1003 1003 1003 1003 1003 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100102" "0100103" "0100109" "0100201" ...
$ q05g1c01: int 2 2 2 2 2 6 2 2 2 2 ...
$ q05g1c03: int 0 0 0 0 0 0 0 0 0 0 ...
$ q05g1c04: int 610000 950000 215000 35000 45000 25000 1800000 190000 180000 132000 ...
$ q05g1c05: int 0 0 0 0 0 0 0 0 0 0 ...

Manual for Cambodia CSES 2009 (Version 2.0)

53

$ q05g1c06: int 610000 950000 215000 35000 45000 25000 1800000 190000 180000 132000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 22 #### s05g2.forestexpenses ##########'data.frame':
5928 obs. of 14 variables:$ hhid: int 100102 100103 100109 100205 100301 100302 100303 100304 100305 100306 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 265 242 ...
$ hhsize: int 4 6 4 10 5 2 2 8 3 6 ...
$ weight3: num 1065 1597 1065 2647 1210 ...
$ psu: int 1001 1001 1001 1002 1003 1003 1003 1003 1003 1003 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100102" "0100103" "0100109" "0100205" ...
$ q05g2c01: int 2 5 5 5 2 5 5 5 5 3 ...
$ q05g2c03: int 120000 20000 12000 30000 15000 25000 17000 38000 20000 25000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"

Manual for Cambodia CSES 2009 (Version 2.0)

54..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 23 #### s05h1.bussiness ##########'data.frame':
4843 obs. of 23 variables:$ hhid: int 100105 100106 100107 100108 100201 100202 100210 100401 100403 100404 ...
$ province : int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 265 ...
$ hhsize: int 7 9 2 4 11 5 6 3 4 2 ...
$ weight3: num 1863 2395 532 1065 2912 ...
$ psu: int 1001 1001 1001 1001 1002 1002 1002 1004 1004 1004 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 7 7 7 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100105" "0100106" "0100107" "0100108" ...
$ q05h1c01: int 1 1 1 1 1 1 1 1 1 1 ...
$ q05h1c04: chr "7420" "5610" "4920" "4780" ...
$ q05h1c05: int 3 3 1 2 6 2 1 1 1 1 ...
$ q05h1c06a : int NA 2 NA 1 NA NA NA 2 2 2 ...
$ q05h1c06b : int NA 3 NA NA NA NA NA 3 NA NA ...
$ q05h1c06c : int NA NA NA NA NA NA NA NA NA NA ...
$ q05h1c06d : int NA NA NA NA NA NA NA NA NA NA ...
$ q05h1c06e : int NA NA NA NA NA NA NA NA NA NA ...
$ q05h1c06f : int NA NA NA NA NA NA NA NA NA NA ...
$ q05h1c06g : int NA NA NA NA NA NA NA NA NA NA ...
$ q05h1c06h : int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3

Manual for Cambodia CSES 2009 (Version 2.0)

55.. ..- attr(*, "names")= chr"other rural (outside p.p.)"
NULL"phnom penh (urban and rural)" "other urban (ouside p.p.)"## 24 #### s05h2.bussinessexpenses ##########'data.frame': 15720 obs. of 18 variables:$ hhid: int 100105 100105 100106 100106 100106 100106 100107 100107 100107 100107 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 7 7 9 9 9 9 2 2 2 2 ...
$ weight3: num 1863 1863 2395 2395 2395 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100105" "0100105" "0100106" "0100106" ...
$ q05h2c01 : int 9 14 4 5 6 9 1 3 4 11 ...
$ q05h2c03 : num 5.00e+04 4.00e+05 5.00e+04 1.98e+08 2.00e+05 ...
$ q05h2c04: int NA NA NA NA NA NA NA NA NA NA ...
$ q05h2c05 : int NA NA NA NA NA NA NA NA NA NA ...
$ q05h2c06: int NA NA NA NA NA NA NA NA NA NA ...
$ q05h2c07: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 25 #### s05h3.bussinessincome ##########'data.frame':
5360 obs. of 18 variables:$ hhid: int 100105 100106 100107 100108 100108 100201 100202 100210 100401 100401 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...

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$ hhsize: int 7 9 2 4 4 11 5 6 3 3 ...
$ weight3: num 1863 2395 532 1065 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1002 1002 1002 1004 1004 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 7 7 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100105" "0100106" "0100107" "0100108" ...
$ q05h3c01: int 3 7 8 5 9 5 5 3 5 9 ...
$ q05h3c03: num 1.40e+07 2.52e+09 5.40e+06 3.90e+07 5.00e+04 6.75e+07 2.52e+07 7.50e+07
2.40e+06 1.50e+05 ...
$ q05h3c04: int NA NA NA NA NA NA NA NA NA NA ...
$ q05h3c05: int NA NA NA NA NA NA NA NA NA NA ...
$ q05h3c06: int NA NA NA NA NA NA NA NA NA NA ...
$ q05h3c07: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 26 #### s06.liabilities ##########'data.frame':
4732 obs. of 20 variables:$ hhid: int 100101 100105 100107 100108 100110 100210 100301 100302 100307 100308 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 7 2 4 7 6 5 2 4 4 ...
$ weight3: num 1331 1863 532 1065 1863 ...
$ psu: int 1001 1001 1001 1001 1001 1002 1003 1003 1003 1003 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100105" "0100107" "0100108" ...
$ q06_c01: int 1 1 1 1 1 1 1 1 1 1 ...
$ q06_c02: int 3 24 0 26 24 5 10 2 2 12 ...

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$ q06_c03: int 5 12 12 5 5 3 5 2 2 12 ...
$ q06_c04: int 1 8 8 4 4 4 9 4 9 9 ...
$ q06_c05: int 7 7 9 8 4 4 3 3 1 3 ...
$ q06_c06: int 1000000 8000000 1500000 4000000 900000 3000000 6000000 100000 200000
1500000 ...
$ q06_c07: int 1000000 4000000 1500000 4000000 800000 3000000 3500000 100000 200000
1500000 ...
$ q06_c08: num 0 2 3 3 10 3 3 5 3 3 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 27 #### s07.incomeother ##########'data.frame':
6862 obs. of 16 variables:$ hhid: int 100103 100105 100106 100109 100110 100201 100204 100205 100301 100302 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 6 7 9 4 7 11 6 10 5 2 ...
$ weight3: num 1597 1863 2395 1065 1863 ...
$ psu: int 1001 1001 1001 1001 1001 1002 1002 1002 1003 1003 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100103" "0100105" "0100106" "0100109" ...
$ q07_c01: int 11 2 2 6 2 2 2 2 3 2 ...
$ q07_c03: int 80000 5000000 0 200000 500000 0 200000 250000 3600000 220000 ...
$ q07_c04: int 0 0 800000 0 0 4800000 4200000 4800000 0 0 ...
$ q07_c05: int 80000 5000000 800000 200000 500000 4800000 4400000 5050000 3600000 220000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...

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- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 28 #### s08.construction ##########'data.frame': 11322 obs. of 35 variables:$ hhid: int 100101 100102 100103 100104 100105 100106 100107 100108 100110 100201 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 4 6 3 7 9 2 4 7 11 ...
$ weight3: num 1331 1065 1597 798 1863 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1002 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100102" "0100103" "0100104" ...
$ q08_c01: int 1 1 1 1 1 1 1 1 1 1 ...
$ q08_c02a: int 1 1 1 1 1 1 1 1 1 1 ...
$ q08_c02b : int NA NA NA NA NA NA 3 NA NA NA ...
$ q08_c02c: int NA NA NA NA NA NA NA NA NA NA ...
$ q08_c03: num 60 24 30 20 81 42 35 48 42 70 ...
$ q08_c04: int 2003 1994 1990 2006 2008 1991 2004 1997 1993 1970 ...
$ q08_c05: num 2.3e+07 6.0e+05 5.0e+05 2.1e+06 3.6e+07 8.0e+05 2.3e+06 1.3e+06 1.2e+06
3.5e+06 ...
$ q08_c06: int 80000 35000 35000 40000 100000 50000 35000 100000 50000 70000 ...
$ q08_c07: int 2 2 2 2 2 2 2 2 2 2 ...
$ q08_c08: int NA NA NA NA NA NA NA NA NA NA ...
$ q08_c09: int 2 2 2 2 1 2 2 2 2 1 ...
$ q08_c10: int NA NA NA NA 1 NA NA NA NA NA ...
$ q08_c11a: int NA NA NA NA 7 NA NA NA NA NA ...
$ q08_c11b: int NA NA NA NA 2008 NA NA NA NA NA ...
$ q08_c12a: int NA NA NA NA 12 NA NA NA NA NA ...
$ q08_c12b: int NA NA NA NA 2008 NA NA NA NA NA ...
$ q08_c13: int NA NA NA NA 4 NA NA NA NA 3 ...
$ q08_c14: int NA NA NA NA 42000000 NA NA NA NA NA ...
$ q08_c15: int NA NA NA NA 3180000 NA NA NA NA NA ...
$ q08_c16: int NA NA NA NA 3600000 NA NA NA NA 200000 ...
$ q08_c17: int NA NA NA NA 0 NA NA NA NA NA ...

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$ q08_c18: int NA NA NA NA 0 NA NA NA NA NA ...
$ q08_c19: int NA NA NA NA 0 NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 29 #### s09.durables ##########'data.frame': 93366 obs. of 22 variables:$ hhid: int 100101 100101 100101 100101 100101 100101 100101 100101 100101 100101 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 5 5 5 5 5 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100101" "0100101" "0100101" ...
$ q09_c03: int 801 818 803 804 811 823 890 815 892 893 ...
$ q09_c04: int 1 1 3 1 1 2 1 4 1 1 ...
$ q09_c05a: int 1 1 1 1 1 1 1 1 1 1 ...
$ q09_c05b: int NA NA 1 NA NA 1 NA 1 NA NA ...
$ q09_c05c: int NA NA 1 NA NA NA NA 1 NA NA ...
$ q09_c05d : int NA NA NA NA NA NA NA 1 NA NA ...
$ q09_c06a : int 1 NA 1 NA NA NA NA NA NA NA ...
$ q09_c06b : int NA 1 2 1 1 2 1 4 1 1 ...
$ q09_c07: int 25000 NA 250000 NA NA NA NA NA NA NA ...
$ q09_c08: int NA 600000 260000 4200000 35000 100000 25000 175000 200000 16000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...

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- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 30 #### s10.healthmother ##########'data.frame':
4447 obs. of 34 variables:$ hhid: int 100101 100104 100106 100108 100109 100201 100202 100203 100206 100207 ...
$ persid: int 10010102 10010401 10010603 10010802 10010902 10020105 10020202 10020302
10020602 10020702 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 3 9 4 4 11 5 4 5 5 ...
$ weight3: num 1331 798 2395 1065 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1002 1002 1002 1002 1002 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010102" "010010401" "010010603" "010010802" ...
$ hhid_string : chr "0100101" "0100104" "0100106" "0100108" ...
$ q10_c01: int 1 1 1 1 1 1 1 1 1 1 ...
$ q10_c02: int 2 1 3 2 2 5 2 2 2 2 ...
$ q10_c03: int 5 3 8 4 4 11 4 4 5 4 ...
$ q10_c04: int 2 2 2 2 2 1 2 1 2 1 ...
$ q10_c05: int 2 2 2 2 2 NA 2 NA 2 NA ...
$ q10_c06: int 2 1 1 1 1 1 1 1 1 2 ...
$ q10_c07: int 2 1 2 2 8 2 1 2 1 1 ...
$ q10_c08: int 1 1 1 1 1 1 1 1 1 2 ...
$ q10_c09: int 1 1 2 2 1 1 1 1 1 NA ...
$ q10_c10: int 2 1 1 2 2 1 1 1 1 1 ...
$ q10_c11: int NA 5 1 NA NA 3 3 3 3 3 ...
$ q10_c12: int 1 NA 2 1 1 NA NA NA NA NA ...
$ q10_c13: int 5 NA NA 5 3 NA NA NA NA NA ...
$ q10_c14: int 12 12 1 1 1 1 1 1 7 1 ...
$ q10_c15a: int 1 1 4 3 3 3 3 3 3 3 ...
$ q10_c15b: int 2 NA NA 2 5 NA 2 2 2 NA ...
$ q10_c15c: int 3 NA NA 5 NA NA NA NA NA NA ...
$ q10_c15d: int NA NA NA NA NA NA NA NA NA NA ...
$ q10_c16: int 2 2 2 1 1 2 1 2 1 2 ...

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$ q10_c17: int 2 2 2 1 1 2 2 2 2 2 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 31 #### s11.health2years ##########'data.frame':
2357 obs. of 40 variables:$ hhid: int 100101 100109 100207 100308 100410 100510 100601 100605 100605 100701 ...
$ persid: int 10010105 10010904 10020704 10030804 10041004 10051004 10060103 10060503
10060504 10070105 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 265 242 205 ...
$ hhsize: int 5 4 5 4 4 4 3 4 4 5 ...
$ weight3: num 1331 1065 1324 968 819 ...
$ psu: int 1001 1001 1002 1003 1004 1005 1006 1006 1006 1007 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 7 7 10 10 10 10 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010105" "010010904" "010020704" "010030804" ...
$ hhid_string : chr "0100101" "0100109" "0100207" "0100308" ...
$ q11_c01: int 1 1 1 1 1 1 1 1 2 1 ...
$ q11_c02: int 2 2 2 2 2 1 2 2 2 2 ...
$ q11_c03: int 5 4 4 4 4 4 3 3 4 5 ...
$ q11_c04: int 1 1 1 1 1 1 1 1 1 1 ...
$ q11_c05: int 1 1 2 1 1 1 1 1 1 1 ...
$ q11_c06a: int 5 4 NA 0 0 2 2 2 2 1 ...
$ q11_c06b: int NA NA NA NA NA NA 1 1 1 1 ...
$ q11_c07: int 1 1 NA 1 1 1 1 1 1 1 ...
$ q11_c08a: int 1 1 NA 2 2 2 2 2 2 2 ...
$ q11_c08b: int 1 1 NA 1 1 1 2 2 2 1 ...
$ q11_c09: int 1 1 1 1 1 1 1 1 1 1 ...
$ q11_c10: int 2 2 2 2 2 2 2 2 2 2 ...
$ q11_c11: int 1 1 1 1 1 1 2 1 1 1 ...

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$ q11_c12a: int 1 1 1 1 1 2 NA 1 1 1 ...
$ q11_c12b: int 7 8 6 12 10 NA NA 2 2 1 ...
$ q11_c12c: int 2007 2007 2008 2008 2008 NA NA 2008 2009 2009 ...
$ q11_c13a: int 2 2 1 1 1 1 NA 1 1 1 ...
$ q11_c13b: int NA NA 25 12 8 8 NA 28 23 24 ...
$ q11_c13c: int NA NA 4 12 12 12 NA 4 8 4 ...
$ q11_c13d: int NA NA 2008 2008 2008 2008 NA 2008 2009 2009 ...
$ q11_c14a: int 4 3 1 2 3 1 NA 1 1 3 ...
$ q11_c14b: int 11 12 8 12 12 12 NA 5 8 9 ...
$ q11_c14c: int 2007 2007 2008 2008 2008 2008 NA 2008 2009 2009 ...
$ q11_c15a: int 1 1 1 2 1 2 NA 1 1 1 ...
$ q11_c15b: int 5 5 1 NA 12 NA NA 5 6 10 ...
$ q11_c15c: int 2008 2008 2008 NA 2008 NA NA 2008 2009 2009 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 32 #### s12.health5years ##########'data.frame':
5617 obs. of 29 variables:$ hhid: int 100101 100104 100106 100108 100109 100201 100201 100202 100203 100205 ...
$ persid: int 10010105 10010403 10010608 10010804 10010904 10020110 10020111 10020204
10020304 10020506 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 3 9 4 4 11 11 5 4 10 ...
$ weight3: num 1331 798 2395 1065 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1002 1002 1002 1002 1002 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010105" "010010403" "010010608" "010010804" ...
$ hhid_string : chr "0100101" "0100104" "0100106" "0100108" ...
$ q12_c01: int 1 1 1 1 1 1 2 1 1 1 ...

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$ q12_c02: int 5 3 8 4 4 10 11 4 4 6 ...
$ q12_c03: int 1 1 2 1 3 1 1 3 1 1 ...
$ q12_c04: int 1 1 1 1 1 1 1 1 1 1 ...
$ q12_c05: int 7 98 98 2 2 98 98 2 98 98 ...
$ q12_c06: int 2 1 1 1 1 1 1 1 1 1 ...
$ q12_c07: int 1 1 1 1 1 1 1 1 1 1 ...
$ q12_c08: int 2 2 2 2 2 1 1 2 2 2 ...
$ q12_c09a: int NA NA NA NA NA 1 1 NA NA NA ...
$ q12_c09b: int NA NA NA NA NA 1 1 NA NA NA ...
$ q12_c10a: int 15 15 15 15 15 16 16 16 16 16 ...
$ q12_c10b: int 1 1 1 1 1 1 1 1 1 1 ...
$ q12_c11: num 77.4 92.1 104.2 99 76.5 ...
$ q12_c12: int 2 1 1 1 2 1 1 1 1 1 ...
$ q12_c13: num 9.1 12.8 16.4 14.2 8.1 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 33 #### s13b.healthexpenses ##########'data.frame': 57082 obs. of 26 variables:$ hhid: int 100101 100101 100101 100101 100101 100102 100102 100102 100102 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010105 10010201 10010202 10010203
10010204 10010301 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 4 4 4 4 6 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...

Manual for Cambodia CSES 2009 (Version 2.0)

64

$ q13bc01: int 1 2 3 4 5 1 2 3 4 1 ...
$ q13bc02: int 2 2 2 2 2 1 2 2 2 2 ...
$ q13bc03: int NA NA NA NA NA 2 NA NA NA NA ...
$ q13bc04: int NA NA NA NA NA 2 NA NA NA NA ...
$ q13bc05: int NA NA NA NA NA NA NA NA NA NA ...
$ q13bc06: int NA NA NA NA NA NA NA NA NA NA ...
$ q13bc07: int 0 3 0 0 0 0 0 0 0 0 ...
$ q13bc08: int 0 0 0 0 0 0 0 0 0 0 ...
$ q13bc09a: int NA NA NA NA NA NA NA NA NA NA ...
$ q13bc09b: int NA NA NA NA NA NA NA NA NA NA ...
$ q13bc10: int NA NA NA NA NA NA NA NA NA NA ...
$ q13bc11: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 34 #### s14.disability ##########'data.frame': 57082 obs. of 27 variables:$ hhid: int 100101 100101 100101 100101 100101 100102 100102 100102 100102 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010105 10010201 10010202 10010203
10010204 10010301 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 4 4 4 4 6 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q14_c01: int 1 2 3 4 5 1 2 3 4 1 ...
$ q14_c02a: int 0 0 0 0 0 0 0 0 0 0 ...

Manual for Cambodia CSES 2009 (Version 2.0)

65

$ q14_c02b: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c02c: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c03a: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c03b: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c03c: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c04a: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c04b: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c04c: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c05a: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c05b: int NA NA NA NA NA NA NA NA NA NA ...
$ q14_c05c: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 35 #### s15.labor7days ##########'data.frame': 51460 obs. of 49 variables:$ hhid: int 100101 100101 100101 100101 100102 100102 100102 100102 100103 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010201 10010202 10010203 10010204
10010301 10010302 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 4 4 4 4 6 6 ...
$ weight3: num 1331 1331 1331 1331 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q15_c01: int 1 2 3 4 1 2 3 4 1 2 ...
$ q15_c02: int 1 1 3 3 1 2 3 3 1 2 ...
$ q15_c03: int 1 1 2 2 1 1 2 2 1 1 ...

Manual for Cambodia CSES 2009 (Version 2.0)

66

$ q15_c04: int NA NA 2 2 NA NA 2 2 NA NA ...
$ q15_c05b: int 224 622 NA NA 631 631 NA NA 631 631 ...
$ q15_c06b: int 8620 320 NA NA 112 112 NA NA 112 112 ...
$ q15_c07: int 1 3 NA NA 3 3 NA NA 3 3 ...
$ q15_c08: int 1 3 NA NA 3 3 NA NA 3 3 ...
$ q15_c09: int 56 14 NA NA 56 30 NA NA 49 49 ...
$ q15_c10: int 24 30 NA NA 15 15 NA NA 20 20 ...
$ q15_c11: int 1 0 NA NA 0 0 NA NA 1 0 ...
$ q15_c12b: int 631 NA NA NA NA NA NA NA 835 NA ...
$ q15_c13b: int 112 NA NA NA NA NA NA NA 4920 NA ...
$ q15_c14: int 3 NA NA NA NA NA NA NA 3 NA ...
$ q15_c15: int 3 NA NA NA NA NA NA NA 1 NA ...
$ q15_c16: int 10 NA NA NA NA NA NA NA 40 NA ...
$ q15_c17: int 4 NA NA NA NA NA NA NA 6 NA ...
$ q15_c18: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c19: int 66 14 NA NA 56 30 NA NA 89 49 ...
$ q15_c20: int 443000 NA NA NA NA NA NA NA 500000 NA ...
$ q15_c21: int 3 3 NA NA 3 3 NA NA 3 3 ...
$ q15_c22: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c23: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c24: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c25a: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c25b: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c26: int NA NA 2 2 NA NA 2 2 NA NA ...
$ q15_c27a: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c27b: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c27c: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c28: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c29: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c30a: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c30b: int NA NA NA NA NA NA NA NA NA NA ...
$ q15_c31: int NA NA 7 7 NA NA 7 7 NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL

Manual for Cambodia CSES 2009 (Version 2.0)

67## 36 #### s16.labor12months ##########'data.frame': 51460 obs. of 23 variables:$ hhid: int 100101 100101 100101 100101 100102 100102 100102 100102 100103 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010201 10010202 10010203 10010204
10010301 10010302 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 4 4 4 4 6 6 ...
$ weight3: num 1331 1331 1331 1331 1065 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q16_c01: int 1 2 3 4 1 2 3 4 1 2 ...
$ q16_c02: int 1 4 5 5 1 4 5 5 1 1 ...
$ q16_c03: int 11 NA NA NA 9 NA NA NA 11 9 ...
$ q16_c04b: chr "224" "" "" "" ...
$ q16_c05: int 1 NA NA NA 3 NA NA NA 1 3 ...
$ q16_c06b: chr "8620" "" "" "" ...
$ q16_c07: int 1 NA NA NA 3 NA NA NA 3 3 ...
$ q16_c08: int 1 1 1 1 2 2 1 1 1 4 ...
$ q16_c09: int 2 NA 2 2 2 NA 2 2 3 2 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 37 #### s17b.theft ##########'data.frame':
328 obs. of 22 variables:$ hhid: int 100204 100303 100401 100407 100408 100805 102003 103305 103611 200309 ...
$ persid: int 10020403 10030301 10040102 10040701 10040802 10080502 10200303 10330501

Manual for Cambodia CSES 2009 (Version 2.0)6810361198 20030998 ...
$ province: int 1 1 1 1 1 1 1 1 1 2 ...
$ urbanrural: int 1 1 1 1 1 1 2 2 2 1 ...
$ weight: num 265 242 205 205 205 ...
$ hhsize: int 6 2 3 3 5 6 6 5 6 5 ...
$ weight3: num 1588 484 614 614 1023 ...
$ psu: int 1002 1003 1004 1004 1004 1008 1020 1033 1036 2003 ...
$ stratum: int 11 11 11 11 11 11 12 12 12 21 ...
$ surveymonth : int 1 1 7 7 7 11 1 8 2 8 ...
$ region: int 2 2 2 2 2 2 3 3 3 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010020403" "010030301" "010040102" "010040701" ...
$ hhid_string : chr "0100204" "0100303" "0100401" "0100407" ...
$ q17bc01: int 1 1 1 1 1 1 1 1 1 1 ...
$ q17bc02: int 3 1 2 1 2 2 3 1 98 98 ...
$ q17bc03: int 4 12 10 2 6 4 12 3 7 7 ...
$ q17bc04: int 1 2 1 1 1 1 1 1 2 1 ...
$ q17bc05: int 2 2 2 2 2 2 2 1 1 2 ...
$ q17bc06: int NA NA NA NA NA NA NA 2 1 NA ...
$ q17bc07: int 2 2 2 2 2 2 2 2 2 2 ...
$ q17bc08: int 250000 30000 370000 57000 170000 180000 400000 1500000 180000 50000 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr "unique hh identification " "" "province (2)" "urban or rural
households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 38 #### s17c.accidents ##########'data.frame':
654 obs. of 20 variables:$ hhid: int 100103 100104 100105 100106 100110 100110 100201 100206 100210 100405 ...
$ persid: int 10010301 10010401 10010503 10010609 10011003 10011003 10020108 10020604
10021006 10040503 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 6 3 7 9 7 7 11 5 6 4 ...
$ weight3: num 1597 798 1863 2395 1863 ...

Manual for Cambodia CSES 2009 (Version 2.0)

69

$ psu
: int 1001 1001 1001 1001 1001 1001 1002 1002 1002 1004 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 7 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010301" "010010401" "010010503" "010010609" ...
$ hhid_string : chr "0100103" "0100104" "0100105" "0100106" ...
$ q17cc01: int 1 1 1 1 1 2 1 1 1 1 ...
$ q17cc02: int 1 1 3 9 3 3 8 4 6 3 ...
$ q17cc03: int 2 3 3 2 3 1 3 5 5 3 ...
$ q17cc04: int 7 3 4 5 9 12 3 2 9 3 ...
$ q17cc05: int 2 2 1 1 1 2 1 1 1 2 ...
$ q17cc06: int 1 1 3 2 2 1 2 1 1 2 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 39 #### s17d.violance ##########'data.frame': 57074 obs. of 26 variables:$ hhid: int 100101 100101 100101 100101 100101 100102 100102 100102 100102 100103 ...
$ persid: int 10 10 10 10 10 10 10 10 10 10 ...
$ province: int NA NA NA NA NA NA NA NA NA NA ...
$ urbanrural: int NA NA NA NA NA NA NA NA NA NA ...
$ weight: int NA NA NA NA NA NA NA NA NA NA ...
$ hhsize: int NA NA NA NA NA NA NA NA NA NA ...
$ weight3: int NA NA NA NA NA NA NA NA NA NA ...
$ psu: int NA NA NA NA NA NA NA NA NA NA ...
$ stratum: int NA NA NA NA NA NA NA NA NA NA ...
$ surveymonth : int NA NA NA NA NA NA NA NA NA NA ...
$ region: int NA NA NA NA NA NA NA NA NA NA ...
$ year: int NA NA NA NA NA NA NA NA NA NA ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q17dc01: int 1 2 3 4 5 1 2 3 4 1 ...
$ q17dc02: int 2 2 2 2 2 2 2 2 2 2 ...

Manual for Cambodia CSES 2009 (Version 2.0)

70

$ q17dc03a: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc03b: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc03c: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc04: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc05: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc06: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc07: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc08: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc09: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc10: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 252 251 251 251 251 251 251 251 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 40 #### s17d.violence ##########'data.frame': 57074 obs. of 26 variables:$ hhid: int 100101 100101 100101 100101 100101 100102 100102 100102 100102 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010105 10010201 10010202 10010203
10010204 10010301 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 4 4 4 4 6 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q17dc01: int 1 2 3 4 5 1 2 3 4 1 ...
$ q17dc02: int 2 2 2 2 2 2 2 2 2 2 ...
$ q17dc03a: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc03b: int NA NA NA NA NA NA NA NA NA NA ...

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$ q17dc03c: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc04: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc05: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc06: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc07: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc08: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc09: int NA NA NA NA NA NA NA NA NA NA ...
$ q17dc10: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 41 #### s18.presenseinhh ##########'data.frame': 57074 obs. of 31 variables:$ hhid: int 100101 100101 100101 100101 100101 100102 100102 100102 100102 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010105 10010201 10010202 10010203
10010204 10010301 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural: int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 4 4 4 4 6 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth : int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ persid_string: chr "010010101" "010010102" "010010103" "010010104" ...
$ hhid_string : chr "0100101" "0100101" "0100101" "0100101" ...
$ q18_c01: int 1 2 3 4 5 1 2 3 4 1 ...
$ q18_c02a: int 1 1 1 1 1 1 1 1 1 2 ...
$ q18_c02b: int NA NA NA NA NA NA NA NA NA 5 ...
$ q18_c02c: int NA NA NA NA NA NA NA NA NA 2 ...
$ q18_c02d: int NA NA NA NA NA NA NA NA NA 10000 ...
$ q18_c03a: int 1 1 1 1 1 1 1 1 1 2 ...

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$ q18_c03b: int NA NA NA NA NA NA NA NA NA NA ...
$ q18_c03c: int NA NA NA NA NA NA NA NA NA 2 ...
$ q18_c03d: int NA NA NA NA NA NA NA NA NA 120000 ...
$ q18_c04a: int 1 1 1 1 1 1 1 1 1 2 ...
$ q18_c04b: int NA NA NA NA NA NA NA NA NA NA ...
$ q18_c04c: int NA NA NA NA NA NA NA NA NA 2 ...
$ q18_c04d: int NA NA NA NA NA NA NA NA NA 100000 ...
$ q18_c05a: int 1 1 1 1 1 1 1 1 1 1 ...
$ q18_c05b: int NA NA NA NA NA NA NA NA NA NA ...
$ q18_c05c: int NA NA NA NA NA NA NA NA NA NA ...
$ q18_c05d: int NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 42 #### s99.singlequestions ##########'data.frame': 11971 obs. of 42 variables:$ hhid: int 100101 100102 100103 100104 100105 100106 100107 100108 100109 100110 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 4 6 3 7 9 2 4 4 7 ...
$ weight3: num 1331 1065 1597 798 1863 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
$ hhid_string: chr "0100101" "0100102" "0100103" "0100104" ...
$ q01bq1: int 2 2 1 2 1 1 1 1 1 1 ...
$ q03bq1: int 2 2 2 2 2 1 2 2 2 2 ...
$ q05aq1a: int 2 2 2 2 2 2 2 2 2 2 ...
$ q05aq1b: int NA NA NA NA NA NA NA NA NA NA ...
$ q05aq2: int 1 1 1 2 1 1 2 1 1 1 ...
$ q05aq3: int 2 4 1 NA 1 4 NA 2 1 1 ...

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$ q05bq1: int 1 1 1 NA 1 1 NA 1 1 1 ...
$ q05dq1: int 2 2 2 NA 1 2 NA 2 2 2 ...
$ q05eq1: int 1 1 1 2 1 1 1 1 1 2 ...
$ q05fq1: int 1 2 2 2 2 2 2 2 2 2 ...
$ q05fq2: int 1 NA NA NA NA NA NA NA NA NA ...
$ q05fq3: int 1 2 2 2 2 2 2 2 1 2 ...
$ q05gq1: int 2 1 1 2 2 2 2 2 1 2 ...
$ q05gq2: int 2 2 2 2 2 2 2 2 2 2 ...
$ q05hq1: int 2 2 2 2 1 1 1 1 2 2 ...
$ q06_q1: int 1 2 2 2 1 2 1 1 2 1 ...
$ q08_q1: int 1 1 1 1 1 1 1 1 2 1 ...
$ q10_q1: int 1 2 2 1 2 1 2 1 1 2 ...
$ q11_q1: int 1 2 2 2 2 2 2 2 1 2 ...
$ q12_q1: int 1 2 2 1 2 1 2 1 1 2 ...
$ q13aq1: int 2 2 2 2 2 2 2 2 2 2 ...
$ q13aq2a: int NA NA NA NA NA NA NA NA NA NA ...
$ q13aq2b: int NA NA NA NA NA NA NA NA NA NA ...
$ q13aq2c: int NA NA NA NA NA NA NA NA NA NA ...
$ q13aq3: int NA NA NA NA NA NA NA NA NA NA ...
$ q13aq4: int 2 2 2 2 2 2 2 2 2 2 ...
$ q17aq1: int 1 1 1 1 1 1 1 1 2 2 ...
$ q17aq2: int 2 1 1 1 1 1 2 1 2 2 ...
$ q17bq1: int 2 2 2 2 2 2 2 2 2 2 ...
$ q17cq1: int 2 2 1 1 1 1 2 2 2 1 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 43 #### weighthh ##########'data.frame': 11971 obs. of 11 variables:$ hhid: int 100101 100102 100103 100104 100105 100106 100107 100108 100109 100110 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 4 6 3 7 9 2 4 4 7 ...

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$ weight3: num 1331 1065 1597 798 1863 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%8.0g" "%8.0g" "%9.0g" ...
- attr(*, "types")= int 253 251 251 254 251 254 252 252 251 251 ...
- attr(*, "val.labels")= chr "" "province" "urbanrural" "" ...
- attr(*, "var.labels")= chr "unique hh identification " "province (2)" "urban or rural
households" "weight" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 44 #### weightpersons ##########'data.frame': 57105 obs. of 12 variables:$ hhid: int 100101 100101 100101 100101 100101 100102 100102 100102 100102 100103 ...
$ persid: int 10010101 10010102 10010103 10010104 10010105 10010201 10010202 10010203
10010204 10010301 ...
$ province: int 1 1 1 1 1 1 1 1 1 1 ...
$ urbanrural : int 1 1 1 1 1 1 1 1 1 1 ...
$ weight: num 266 266 266 266 266 ...
$ hhsize: int 5 5 5 5 5 4 4 4 4 6 ...
$ weight3: num 1331 1331 1331 1331 1331 ...
$ psu: int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
$ stratum: int 11 11 11 11 11 11 11 11 11 11 ...
$ surveymonth: int 1 1 1 1 1 1 1 1 1 1 ...
$ region: int 2 2 2 2 2 2 2 2 2 2 ...
$ year: int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%12.0g" "%8.0g" "%8.0g" ...
- attr(*, "types")= int 253 253 251 251 254 251 254 252 252 251 ...
- attr(*, "val.labels")= chr "" "" "province" "urbanrural" ...
- attr(*, "var.labels")= chr"unique hh identification " "unique person identification""province (2)" "urban or rural households" ...
- attr(*, "version")= int 8- attr(*, "label.table")=List of 5..$ province : Named int 1 2 3 4 5 6 7 8 9 10 ...

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75.. ..- attr(*, "names")= chr"banteay meanchey" "battambang" "kampong cham" "kampong
chhnang" ...
..$ urbanrural : Named int 1 2.. ..- attr(*, "names")= chr "urban" "rural"..$ stratum: Named int 11 12 21 22 31 32 41 42 51 52 ...
.. ..- attr(*, "names")= chr "banteay meanchey urban" "banteay meanchey rural" "battambang
urban" "battambang rural" ...
..$ surveymonth: Named int 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "names")= chr "january" "february" "march" "april" ...
..$ region: Named int 1 2 3.. ..- attr(*, "names")= chr "phnom penh (urban and rural)" "other urban (ouside p.p.)""other rural (outside p.p.)"
NULL## 45 #### IncomeCSES09 ##########'data.frame':
11971 obs. of 103 variables:$ hhid: int 100101 100102 100103 100104 100105 100106 100107 100108
100109 100110 ...
$ weighthh: num 266 266 266 266 266 ...
$ weighthhpers: num 1401 1134 1669 834 2000 ...
$ hhsize: int 5 4 6 3 7 9 2 4 4 7 ...
$ salary: int 5316000 0 6000000 6312000 0 0 0 3000000 0 1140000 ...
$ diarysalarycash: int 9804000 0 720000 1710000 0 0 0 0 0 1020000 ...
$ diarysalaryinkind: int 0 0 0 0 0 0 0 0 0 0 ...
$ costcrop: int 339200 848000 447000 0 508000 1607500 0 2855000 175500
543000 ...
$ costlivestock: int 65000 165000 52000 0 90000 160000 25000 240000 180000
0 ...
$ costfish: int 2510000 0 0 0 0 0 0 0 30000 0 ...
$ costforestry: int 0 120000 20000 0 0 0 0 0 12000 0 ...
$ receiptcrop: num 770250 4110000 525000 0 1920000 ...
$ receiptlivestock: int 0 32000 0 0 20000 260000 0 80000 35000 0 ...
$ receiptfish: int 285000 0 0 0 0 0 0 0 472000 0 ...
$ receiptforestry: int 0 610000 950000 0 0 0 0 0 215000 0 ...
$ agriincome: num -1858950 3619000 956000 0 1342000 ...
$ costnonagri: num 0 0 0 0 450000 ...
$ receiptnonagri: num 0.0 0.0 0.0 0.0 1.4e+07 ...
$ nonagriincome: num 0 0 0 0 13550000 ...
$ incomeownhouse: int 660000 18000 15000 63000 962352 24000 69000 39000 036000 ...
$ bankinterest: int 0 0 0 0 0 0 0 0 0 0 ...
$ interestotherloans: int 0 0 0 0 0 0 0 0 0 0 ...
$ dividends: int 0 0 0 0 0 0 0 0 0 0 ...
$ rentfromland: int 0 0 0 0 0 0 0 0 0 0 ...
$ grosspropertyincome: int 0 0 0 0 0 0 0 0 0 0 ...
$ interestpaidagri: int 0 0 0 0 0 0 0 0 0 0 ...
$ interestpaidnonagri: int 0 0 0 0 0 0 0 0 0 0 ...
$ interestpaidownoccupied: int 0 0 0 0 78431 0 0 0 0 0 ...
$ interestpaidnet: int 0 0 0 0 0 0 43689 116504 0 72727 ...
$ propertyincome: int 0 0 0 0 0 0 -43689 -116504 0 -72727 ...
$ primaryincome: num 4117050 3637000 6971000 6375000 15854352 ...
$ pensiondomestic: int 0 0 0 0 0 0 0 0 0 0 ...
$ pensionabroad: int 0 0 0 0 0 0 0 0 0 0 ...
$ pension: int 0 0 0 0 0 0 0 0 0 0 ...
$ ngotransfers: int 0 0 0 0 0 0 0 0 0 0 ...

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$ remittancedomestic$ remittanceabroad$ totalprivatetransfers$ scholarshipgovernment$ scholarshipngo$ totalscholarship$ gifts$ othertransfer$ totaltransfers$ totalincome$ wageratio$ agriratio$ otherselfempratio$ propertyratio$ transfersratio$ diarytaxes$ diaryinterhhtransfers$ diarycashtransferchar$ diarytotalnegativetransfers$ disposableincome$ diarycostagri$ diaryreceiptagri$ diaryagriincome$ diarycostnonagri$ diaryreceiptnonagri$ diarynonagriincome$ diarybankinterest$ diaryinterestotherloans$ diarydividends$ diaryotherfinancialaccount$ diarygrosspropertyincome$ diarypropertyincome$ diaryprimaryincome$ diarypensiondomestic$ diarypensionabroad$ diarypension$ diaryinsurancedomestic$ diaryinsuranceabroad$ diaryngotransfers$ diaryremittancedomestic$ diaryremittanceabroad$ diarytotalprivatetransfers$ diaryscholarship$ diarygifts$ diaryothertransfers
96000 ...
$ diarytotaltransfers
96000 96000 ...
$ diarytotalincome$ diarydisposableincome$ psuchar$ psu1$ psu$ areaid$ dprovince$ urbanrural: int 0 0 0 0 5000000 0 0 0 0 500000 ...
: int 0 0 0 0 0 800000 0 0 0 0 ...
: int 0 0 0 0 5000000 800000 0 0 0 500000 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 80000 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 80000 0 5000000 800000 0 0 0 500000 ...
: num 4117050 3637000 7051000 6375000 20854352 ...
: num 1.29 0 0.85 0.99 0 ...
: num -0.45 1 0.14 0 0.06 ...
: num 0.16 0 0 0.01 0.7 ...
: num 0 0 0 0 0 ...
: num 0 0 0.01 0 0.24 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 182400 0 0 0 0 174000 8400 0 0 ...
: int 0 0 0 0 0 42000 0 0 0 0 ...
: int 0 182400 0 0 0 42000 174000 8400 0 0 ...
: num 4117050 3454600 7051000 6375000 20854352 ...
: int 150000 2028000 0 0 0 0 3600 6000000 30000 0 ...
: num 111600 818400 7566000 0 1642800 ...
: num -38400 -1209600 7566000 0 1642800 ...
: num 0 0 0 0 0 ...
: num 0 0 0 0 0 ...
: num 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: num 9765600 -1209600 8286000 1710000 1642800 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 120000 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 0 0 0 0 0 120000 0 0 0 0 ...
: int 0 0 0 0 0 0 0 0 0 0 ...
: int 162000 0 18000 0 0 488400 0 0 0 0 ...
: int 96000 96000 96000 96000 96000 96000 96000 96000 96000
: int
: num
: num
: chr
: chr
: int
: int
: int
: int

258000 96000 114000 96000 96000 704400 96000 96000

10023600 -1113600 8400000 1806000 1738800 ...
10023600 -1296000 8400000 1806000 1738800 ...
"100101" "100102" "100103" "100104" ...
"1001" "1001" "1001" "1001" ...
1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
361 361 361 361 361 361 361 361 361 361 ...
1 1 1 1 1 1 1 1 1 1 ...
1 1 1 1 1 1 1 1 1 1 ...

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$ dregion: int 2 2 2 2 2 2 2 2 2 2 ...
$ dzone: int 2 2 2 2 2 2 2 2 2 2 ...
$ blillan: int 10 10 10 10 10 10 10 10 10 10 ...
$ bstoran: num 2661 2661 2661 2661 2661 ...
$ agriadj: int 4000 3619000 956000 0 1342000 3532500 4000 4000 946100
342000 ...
$ nonagriadj: int 0 0 0 0 13550000 5125000 5309240 28710400 0 0 ...
$ propertyadj: int 0 0 0 0 0 0 4000 4000 0 4000 ...
$ ownhouseadj: int 660000 18000 15000 63000 962352 24000 69000 39000 036000 ...
$ adjdisp: int 5980000 3454600 7051000 6375000 20854352 9439500
5212240 31749000 946100 2022000 ...
$ adjdispcapita: num 1196000 863650 1175167 2125000 2979193 ...
[list output truncated]- attr(*, "datalabel")= chr ""- attr(*, "time.stamp")= chr ""- attr(*, "formats")= chr "%12.0g" "%9.0g" "%9.0g" "%8.0g" ...
- attr(*, "types")= int 253 254 254 251 253 253 253 253 253 253 ...
- attr(*, "val.labels")= chr "" "" "" "" ...
- attr(*, "var.labels")= chr "hhid" "weighthh" "weighthhpers" "hhsize" ...
- attr(*, "version")= int 8
NULL====================================================================================-# Generated list of variable names in each data frame> var.names<-list()> for(j in 1:45){+ var.names<-c(var.names,list(attributes(outfiles[[j]])$var.labels))+ }Example: S01A.hhmembers (j=3)> var.names[[3]][1] "Person ID"[2] "Household ID"[3] "ID number"[4] "Sex"[5] "Day of birth"[6] "Month of birth"[7] "Year of birth"[8] "Age"[9] "Relationship to the head"[10] "Father live in the household"[11] "Mother live in the household"[12] "marital status"[13] "Spouser live in the household"[14] "ethnicity"[15] "speak Khmer"[16] "speak (1st) other languages than Khmer"[17] "speak (2nd) other languages than Khmer"[18] "speak (3rd) other languages than Khmer"[19] " present all days last week"

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78[20] "No of weeks absent from home during the past 12 months"

Manual for Cambodia CSES 2009 (Version 2.0)

79Chapert 5. Data Check
5.1 Summary of each data file
# Displayed the summary of each data file
> for(j in 1:45){+ cat("##",j,"#### ",Rnames[j]," ##########\n")+ print(summary(outfiles[[j]]))+ cat("\n\n")+ }## 1 #### 09dy.expenditure ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.000
Min.
:1.00
1st Qu.: 308510
1st Qu.: 3.000
1st Qu.:2.00
Median : 804919
Median : 8.000
Median :2.00
Mean: 986380
Mean: 9.835Mean:1.78
3rd Qu.:1405104 3rd Qu.:14.000 3rd Qu.:2.00
Max.
:2400320Max.
:24.000
Max.
:2.00

weightMin.
: 45.06
1st Qu.:228.30
Median :245.88
Mean:245.09
3rd Qu.:264.81
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.935
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 819.76
Median :1151.64
Mean:1211.43
3rd Qu.:1510.53
Max.
:5319.70

surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.531
3rd Qu.:10.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.668
3rd Qu.:3.000
Max.
:3.000psu
Min.
: 1001
1st Qu.: 3085
Median : 8049
Mean
: 98643rd Qu.:14051
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean:100.1
3rd Qu.:142.0
Max.
:242.0

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

pkid
Min.
:
1
1st Qu.: 659317
Median :1317754
Mean:1318229
3rd Qu.:1977368
Max.
:2637002

diaryexp_q5
Min.
: 1.000
1st Qu.: 3.000
Median : 3.000
Mean: 4.391
3rd Qu.: 5.000
Max.
:11.000

diaryexp_q6
diaryexp_q7
diaryexp_q8
Min.
:
0.0
Min.
:
0
Min.
:1.0
1st Qu.:0.4
1st Qu.:600
1st Qu.:1.0
Median :1.0
Median :1300
Median :1.0
Mean
:
25.0
Mean
:
7358
Mean
:2.1
3rd Qu.:2.0
3rd Qu.:3000
3rd Qu.:5.0
Max.
:792000.0
Max.
:604497800
Max.
:5.0
NA's :126231
diaryexp_q10
diaryexp_q11 diaryexp_q2_stringMin.
: 1.000
Min.
:1010
Length:2620999
1st Qu.: 1.000 1st Qu.:1186 Class :character
Median : 1.000
Median :1356
Mode :character
Mean: 1.194Mean:2255
3rd Qu.: 1.000 3rd Qu.:3300
Max.
:12.000
Max.
:9998

diaryexp_q9
Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.849
3rd Qu.:2.000
Max.
:5.000hhid_string
Length:2620999
Class :character
Mode :character

lineidMin.
: 101.0
1st Qu.: 413.0
Median : 807.0
Mean: 828.5
3rd Qu.:1205.0
Max.
:5014.0

Manual for Cambodia CSES 2009 (Version 2.0)

diaryexp_q2
Min.
: 10
1st Qu.: 807
Median :1602
Mean
:1574
3rd Qu.:2309
Max.
:5001

80

## 2 #### 09dy.income ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 308619 1st Qu.: 3.00 1st Qu.:2.000
Median : 803903
Median : 8.00
Median :2.000
Mean:1009857
Mean:10.07Mean:1.906
3rd Qu.:1600416 3rd Qu.:16.00 3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weightMin.
: 45.06
1st Qu.:231.22
Median :246.75
Mean:248.05
3rd Qu.:264.07
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.887
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 827.31
Median :1152.92
Mean:1214.65
3rd Qu.:1521.39
Max.
:5319.70

stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean:102.6
3rd Qu.:162.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 6.000
Mean: 6.504
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.875
3rd Qu.:3.000
Max.
:3.000hhid_string
Length:878912
Class :character
Mode :character

lineidMin. : 0.0
1st Qu.: 307.0
Median : 515.0
Mean : 602.1
3rd Qu.: 813.0
Max.
:9904.0

diaryinc_q2
Min. : 101
1st Qu.: 809
Median :1605
Mean :1593
3rd Qu.:2401
Max.
:3112

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max.
:2009
diaryinc_q5
Min.
: 0.000
1st Qu.: 3.000
Median : 4.000
Mean: 5.687
3rd Qu.: 8.000
Max.
:11.000
diaryinc_q9
Min.
: 1.000
1st Qu.: 2.000
Median : 2.000
Mean: 2.912
3rd Qu.: 2.000
Max.
:19.000

psu
Min.
: 1001
1st Qu.: 3086
Median : 8039
Mean
:10098
3rd Qu.:16004
Max.
:24003
pkid
Min.
:
1
1st Qu.:221018
Median :441717
Mean :441868
3rd Qu.:662835
Max.
:883728

diaryinc_q6
diaryinc_q7
diaryinc_q8
Min.
:
0.0
Min.
:
0
Min.
:1.000
1st Qu.:1.0
1st Qu.:800
1st Qu.:2.000
Median :1.5
Median :1500
Median :2.000
Mean
:
14.3
Mean
:
22967
Mean
:1.808
3rd Qu.:2.5
3rd Qu.:4500
3rd Qu.:2.000Max.
:180000.0Max.
:307500000
Max.
:3.000NA's :146599
diaryinc_q10
diaryinc_q2_string diaryinc_q10_stringMin.
: 101.0
Length:878912
Length:878912
1st Qu.: 202.0
Class :character
Class :character
Median : 208.0
Mode :character
Mode :character
Mean: 275.1
3rd Qu.: 208.0
Max.
:1299.0## 3 #### s01a.hhmembers ##########
hhid
persidMin.
: 100101
Min.
: 10010101
1st Qu.: 308307
1st Qu.: 30830704
Median : 804402
Median : 80440204
Mean: 973384
Mean: 97338380
3rd Qu.:1404910
3rd Qu.:140491001
Max.
:2400320
Max.
:240032005
weightMin.
: 45.06
1st Qu.:228.20
Median :246.02

hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000

provinceMin.
: 1.000
1st Qu.: 3.000
Median : 8.000
Mean: 9.706
3rd Qu.:14.000
Max. :24.000

weight3Min.
: 45.06
1st Qu.: 965.00
Median :1299.19

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.796
3rd Qu.:2.000
Max. :2.000

psu
Min.
: 1001
1st Qu.: 3083
Median : 8044

Manual for Cambodia CSES 2009 (Version 2.0)

81

Mean
:245.94
3rd Qu.:265.13
Max.
:591.08Mean: 5.557
3rd Qu.: 7.000
Max.
:15.000

Mean:1369.00
3rd Qu.:1691.90
Max.
:5319.70

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.85
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.00
1st Qu.: 3.00
Median : 6.00
Mean
: 6.47
3rd Qu.: 9.00
Max.
:12.00regionMin. :1.000
1st Qu.:3.000
Median :3.000
Mean:2.6943rd Qu.:3.000
Max.
:3.000hhid_string
Length:57105
Class :character
Mode :character

q01ac04bMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.718
3rd Qu.: 8.000
Max.
:12.000
NA's:710
q01ac08Min.
: 1.000
1st Qu.: 2.000
Median : 2.000
Mean: 2.076
3rd Qu.: 2.000
Max.
:15.000
NA's :26091
q01ac12aMin.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean:0.4356
3rd Qu.:0.0000
Max.
:9.0000
NA's:38

q01ac01Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.279
3rd Qu.: 4.000
Max. :15.000

year
Min. :2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

persid_string
Length:57105
Class :character
Mode :character

q01ac03Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.515
3rd Qu.:2.000
Max. :2.000

q01ac04aMin.
: 1.00
1st Qu.: 5.00
Median :10.00
Mean:11.57
3rd Qu.:17.00
Max. :31.00
NA's
:1039
q01ac04c
q01ac05
q01ac06
q01ac07
Min.
:1913
Min.
: 0.00Min.
: 1.000Min.
: 0.000
1st Qu.:1970
1st Qu.:11.00
1st Qu.: 2.000
1st Qu.: 1.000
Median :1986
Median :22.00
Median : 3.000
Median : 1.000
Mean
:1982
Mean
:26.48Mean: 3.177
Mean: 1.324
3rd Qu.:1997
3rd Qu.:39.00
3rd Qu.: 3.000
3rd Qu.: 1.000
Max.
:2009
Max.
:96.00Max.
:15.000Max.
:14.000
NA's
:9
NA's
:30357q01ac09
q01ac10q01ac11a
q01ac11bMin.
:1.000Min.
: 1.00Min.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.: 1.00
1st Qu.:1.000
1st Qu.:1.000
Median :1.000 Median : 2.00 Median :1.000 Median :1.000
Mean:2.266Mean: 1.93Mean:1.068Mean:1.008
3rd Qu.:4.000
3rd Qu.: 2.00
3rd Qu.:1.000
3rd Qu.:1.000
Max.
:4.000 Max.
:14.00Max.
:8.000Max.
:2.000NA's :15607 NA's :34984 NA's :68
NA's :68q01ac12bq01ac12cq01ac13q01ac14
Min.
:0.00
Min.
:0.00
Min. :1.000 Min. : 0.0
1st Qu.:0.00
1st Qu.:0.00
1st Qu.:1.000 1st Qu.: 2.0
Median :0.00
Median :0.00
Median :1.000
Median :10.0
Mean
:1.23
Mean
:0.96
Mean
:1.041 Mean :16.6
3rd Qu.:2.00
3rd Qu.:0.00
3rd Qu.:1.000
3rd Qu.:31.0
Max.
:9.00
Max.
:9.00
Max.
:2.000
Max.
:52.0
NA's
:55440NA's:56934NA's :54864## 4 #### s01b.foodconsumption ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 308717
1st Qu.: 3.00
1st Qu.:2.000
Median : 805310
Median : 8.00
Median :2.000
Mean: 991809
Mean: 9.89Mean:1.777
3rd Qu.:1405209
3rd Qu.:14.00
3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weight3

Mean: 97343rd Qu.:14049
Max.
:24003psu

stratum

weightMin.
: 45.06
1st Qu.:227.62
Median :245.31
Mean:245.00
3rd Qu.:264.81
Max.
:591.08hhsizeMin.
: 1.00
1st Qu.: 4.00
Median : 5.00
Mean
: 4.85
3rd Qu.: 6.00
Max.
:15.00surveymonth

regionManual for Cambodia CSES 2009 (Version 2.0)

82

Min.
: 45.06
1st Qu.: 797.36
Median :1132.22
Mean:1190.42
3rd Qu.:1490.94
Max.
:5319.70

Min.
: 1001
1st Qu.: 3087
Median : 8053
Mean
: 9918
3rd Qu.:14052
Max.
:24003Min.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean:100.7
3rd Qu.:142.0
Max.
:242.0Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.504
3rd Qu.:10.000
Max.
:12.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max. :2009

hhid_string
Length:143921
Class :character
Mode :character

q01bc01
Min. : 1.000
1st Qu.: 4.000
Median : 8.000
Mean : 8.818
3rd Qu.:13.000
Max. :20.000

q01bc03
Min. :
0
1st Qu.: 2000
Median : 4900
Mean : 9156
3rd Qu.: 10000
Max. :620000
NA's :15

q01bc04
Min.
:
0
1st Qu.:0
Median :0
Mean: 1877
3rd Qu.:0
Max.
:300000
NA's:72

q01bc05
Min.
:100
1st Qu.: 2800
Median : 5500
Mean
: 11022
3rd Qu.: 14700
Max.
:456000

Min.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.661
3rd Qu.:3.000
Max.
:3.000## 5 #### s01c.nonfoodexpenses ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 308514
1st Qu.: 3.00
1st Qu.:2.000
Median : 805115
Median : 8.00
Median :2.000
Mean: 987830
Mean: 9.85Mean:1.779
3rd Qu.:1500209
3rd Qu.:15.00
3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weightMin.
: 45.06
1st Qu.:227.62
Median :245.31
Mean:244.99
3rd Qu.:264.81
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.902
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 811.93
Median :1143.47
Mean:1203.51
3rd Qu.:1503.44
Max.
:5319.70

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.669
3rd Qu.:3.000
Max.
:3.000year
Min. :2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max. :2009

psu
Min.
: 1001
1st Qu.: 3085
Median : 8051
Mean
: 9878
3rd Qu.:15002
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean:100.33rd Qu.:151.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.489
3rd Qu.: 9.000
Max.
:12.000

hhid_string
Length:86155
Class :character
Mode :character

q01cc01
Min. : 1.000
1st Qu.: 3.000
Median : 5.000
Mean : 6.123
3rd Qu.:10.000
Max. :13.000

q01cc04
Min. :
0
1st Qu.: 20000
Median : 60000
Mean : 268624
3rd Qu.: 200000
Max. :80000000
NA's :6q01cc05
Min.
:
0
1st Qu.:0
Median :
0
Mean
:
3173
3rd Qu.:0q01cc06
Min.
:
0
1st Qu.:20000
Median :60000
Mean: 272226
3rd Qu.: 200000

Manual for Cambodia CSES 2009 (Version 2.0)

83

Max.
NA's

:16000000
:82

Max.
NA's

:80000000:3## 6 #### s01d.vulnerability ##########
hhid
province
urbanrural
Min. : 100101 Min. : 1.000 Min. :1.000
1st Qu.: 308205 1st Qu.: 3.000 1st Qu.:2.000
Median : 804504
Median : 8.000
Median :2.000
Mean : 977173 Mean : 9.744 Mean :1.801
3rd Qu.:1405106 3rd Qu.:14.000 3rd Qu.:2.000
Max. :2400320 Max. :24.000 Max. :2.000

weightMin. : 45.06
1st Qu.:227.85
Median :245.19
Mean :245.48
3rd Qu.:265.05
Max. :591.08

hhsizeMin. : 1.000
1st Qu.: 3.000
Median : 5.000
Mean : 4.771
3rd Qu.: 6.000
Max. :15.000

weight3
Min. : 45.06
1st Qu.: 782.57
Median :1112.13
Mean :1173.41
3rd Qu.:1470.00
Max. :5319.70

stratum
Min. : 11.00
1st Qu.: 32.00
Median : 82.00
Mean : 99.24
3rd Qu.:142.00
Max. :242.00

surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.505
3rd Qu.:10.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.7033rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

q01dq2Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.1533rd Qu.:1.000
Max.
:2.000q01dq3Min.
: 0.000
1st Qu.: 2.000
Median : 4.000
Mean: 6.521
3rd Qu.: 9.000
Max.
:52.000
NA's:10147q01dq4_5
Min.
:0.000
1st Qu.:0.000
Median :0.000
Mean:0.119
3rd Qu.:0.000
Max.
:1.000NA's:10147q01dq4_10
Min.
:0.000
1st Qu.:0.000
Median :0.000
Mean:0.468
3rd Qu.:1.000
Max.
:1.000NA's:10147q01dq4_1
q01dq4_2
Min.
:0.000Min.
:0.000
1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000
Mean:0.067Mean:0.062
3rd Qu.:0.000 3rd Qu.:0.000
Max.
:1.000Max.
:1.000NA's:10147NA's:10147q01dq4_6
q01dq4_7
Min.
:0.000Min.
:0.00
1st Qu.:0.000 1st Qu.:0.00
Median :0.000
Median :0.00
Mean:0.173Mean:0.29
3rd Qu.:0.000 3rd Qu.:1.00
Max.
:1.000
Max.
:1.00
NA's
:10147NA's:10147q01dq4_11
q01dq4_12
Min.
:0.000Min.
:0.000
1st Qu.:0.000 1st Qu.:0.000
Median :0.000
Median :0.000
Mean:0.249Mean:0.127
3rd Qu.:0.000 3rd Qu.:0.000
Max.
:1.000Max.
:1.000NA's:10147NA's:10147q01dq4_4
Min.
:0.000
1st Qu.:0.000
Median :0.000
Mean:0.097
3rd Qu.:0.000
Max.
:1.000NA's:10147q01dq4_9
Min.
:0.000
1st Qu.:0.000
Median :1.000
Mean:0.547
3rd Qu.:1.000
Max.
:1.000NA's:10147## 7 #### s02.educationpsu
Min. : 1001
1st Qu.: 3082
Median : 8045
Mean : 9772
3rd Qu.:14051
Max. :24003

hhid_string
Length:11980
Class :character
Mode :character##########

Manual for Cambodia CSES 2009 (Version 2.0)

q01dq1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.332
3rd Qu.:2.000
Max.
:4.000q01dq4_3
Min.
:0.000
1st Qu.:0.000
Median :0.000
Mean:0.085
3rd Qu.:0.000
Max.
:1.000NA's:10147q01dq4_8
Min. :0.000
1st Qu.:0.000
Median :0.000
Mean:0.399
3rd Qu.:1.000
Max.
:1.000NA's:10147

84

hhid
Min.
: 100101
1st Qu.: 308316
Median : 804409
Mean: 973919
3rd Qu.:1404908
Max.
:2400320

persidMin.
: 10010101
1st Qu.: 30831604
Median : 80440907
Mean: 97391908
3rd Qu.:140490802
Max.
:240032005

weightMin.
: 45.06
1st Qu.:228.20
Median :246.02
Mean:245.79
3rd Qu.:265.10
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.565
3rd Qu.: 7.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 965.84
Median :1303.50
Mean:1370.23
3rd Qu.:1697.37
Max.
:5319.70

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.91
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.476
3rd Qu.: 9.000
Max.
:12.000

regionMin. :1.00
1st Qu.:3.00
Median :3.00
Mean
:2.69
3rd Qu.:3.00
Max.
:3.00

hhid_string
Length:53647
Class :character
Mode :character

q02c05Min.
: 0.000
1st Qu.: 3.000
Median : 5.000
Mean: 5.705
3rd Qu.: 8.000
Max.
:22.000
NA's :12523
q02c10Min.
:1.00
1st Qu.:1.00
Median :2.00
Mean
:1.72
3rd Qu.:2.00
Max.
:2.00
NA's
:39420q02c15Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean :1.737
3rd Qu.:2.000
Max. :2.000
NA's
:11

provinceMin.
: 1.000
1st Qu.: 3.000
Median : 8.000
Mean: 9.711
3rd Qu.:14.000
Max. :24.000

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.7943rd Qu.:2.000
Max. :2.000

psu
Min.
: 1001
1st Qu.: 3083
Median : 8044
Mean
: 9739
3rd Qu.:14049
Max.
:24003year
Min. :2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

q02c01
Min. : 1.000
1st Qu.: 2.000
Median : 3.000
Mean : 3.162
3rd Qu.: 4.000
Max. :15.000

persid_string
Length:53647
Class :character
Mode :character

q02c02q02c03q02c04
Min. :1.00 Min. :1.000 Min. :1.000
1st Qu.:1.00
1st Qu.:1.000
1st Qu.:1.000
Median :1.00 Median :1.000 Median :1.000
Mean :1.32 Mean :1.327 Mean :1.233
3rd Qu.:2.00
3rd Qu.:2.000
3rd Qu.:1.000
Max. :2.00 Max. :2.000 Max. :2.000
NA's :10
NA's :8
NA's :9q02c06
q02c07
q02c08q02c09Min.
: 0.000
Min.
:1.000
Min.
: 0.0
Min.
:1.00
1st Qu.: 3.000
1st Qu.:1.000
1st Qu.: 2.0
1st Qu.:1.00
Median : 5.000
Median :2.000
Median : 5.0
Median :1.00
Mean: 8.223Mean:1.654
Mean
: 5.5
Mean
:1.05
3rd Qu.: 8.000
3rd Qu.:2.000
3rd Qu.: 8.0
3rd Qu.:1.00
Max.
:98.000
Max.
:2.000
Max.
:16.0
Max.
:2.00
NA's :12527
NA's :12517 NA's
:39421 NA's :39421
q02c11
q02c12
q02c13q02c14Min.
: 1.00Min.
:1.000
Min.
:1.00
Min.
:1.00
1st Qu.: 6.00
1st Qu.:2.000
1st Qu.:1.00
1st Qu.:4.00
Median :11.00
Median :2.000
Median :1.00
Median :4.00
Mean: 8.03Mean:1.941
Mean
:1.42
Mean
:3.93
3rd Qu.:11.00
3rd Qu.:2.000
3rd Qu.:2.00
3rd Qu.:4.00
Max.
:12.00Max.
:2.000
Max.
:2.00
Max.
:6.00
NA's
:47204NA's:50500NA's:51840q02c16aq02c16bq02c16c
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0 1st Qu.:0 1st Qu.: 6000
Median :0Median :0Median : 14000
Mean : 125748 Mean : 50644 Mean : 29810
3rd Qu.:0 3rd Qu.:0 3rd Qu.: 28000
Max. :18000000 Max. :7680000 Max. :3600000
NA's:40366NA's:40310NA's:39583Manual for Cambodia CSES 2009 (Version 2.0)

85

q02c16dMin.
:0
1st Qu.: 5000
Median : 11000
Mean: 29728
3rd Qu.: 27000
Max.
:3600000NA's:39593q02c16hMin.
:0
1st Qu.:0
Median :
0
Mean
:
88794
3rd Qu.:6000
Max.
:46800000
NA's:36q02c16eMin.
:0
1st Qu.:0
Median :
0
Mean
:
70212
3rd Qu.:0Max.
:36000000
NA's:40302

q02c16fMin.
:
0
1st Qu.:0
Median :
0
Mean
:
45382
3rd Qu.:0Max.
:16838000
NA's:40318q02c16g
Min.
:0
1st Qu.:0
Median :
0
Mean
:
3012
3rd Qu.: 1000
Max.
:2500000NA's:40151## 8 #### s03a.migration.past ##########
hhid
persidprovince
Min.
: 100101
Min.
: 10010101
Min.
: 1.0
1st Qu.: 308314
1st Qu.: 30831352
1st Qu.: 3.0
Median : 804320
Median : 80432001
Median : 8.0
Mean: 972801
Mean: 97280135
Mean
: 9.7
3rd Qu.:1404817 3rd Qu.:140481702 3rd Qu.:14.0
Max. :2400320 Max. :240032005 Max. :24.0

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean :1.792
3rd Qu.:2.000
Max. :2.000

weightMin.
: 45.06
1st Qu.:227.99
Median :246.02
Mean:245.67
3rd Qu.:265.05
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.564
3rd Qu.: 7.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 965.22
Median :1304.49
Mean:1369.45
3rd Qu.:1697.37
Max.
:5319.70

psu
Min.
: 1001
1st Qu.: 3083
Median : 8043
Mean
: 9728
3rd Qu.:14048
Max.
:24003stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.79
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.491
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.687
3rd Qu.:3.000
Max.
:3.000hhid_string
Length:51275
Class :character
Mode :character

q03ac04c
Min. :9301
1st Qu.:9316
Median :9317
Mean
:9318

q03ac01Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.091
3rd Qu.: 4.000
Max.
:15.000

q03ac05Min.
: 1.00
1st Qu.: 4.00
Median : 5.00
Mean
: 5.53

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

q03ac02Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.379
3rd Qu.:2.000
Max.
:2.000q03ac06bMin.
: 1.00
1st Qu.: 3.00
Median : 8.00
Mean
: 9.99persid_string
Length:51275
Class :character
Mode :character

q03ac03
q03ac04bMin.
: 0.00Min.
: 1.00
1st Qu.: 7.00
1st Qu.:12.00
Median :16.00
Median :88.00
Mean:17.42Mean:57.27
3rd Qu.:30.00
3rd Qu.:88.00
Max.
:89.00Max.
:88.00NA's:31823NA's:32251q03ac06c
q03ac07
Min.
:9301
Min.
:1.000
1st Qu.:9316
1st Qu.:2.000
Median :9317
Median :2.000
Mean
:9318
Mean
:1.988Manual for Cambodia CSES 2009 (Version 2.0)

86

3rd Qu.:9317
3rd Qu.: 8.00
3rd Qu.:15.00
3rd Qu.:9317
3rd Qu.:2.000
Max.
:9601
Max.
:12.00Max.
:88.00
Max.
:9601
Max.
:2.000NA's:50847NA's:31823NA's :32115 NA's
:50983q03ac08
q03ac09a
q03ac09b
q03ac10q03ac11Min.
: 1.00
Min.
:2004
Min.
: 1.00Min.
: 1.00Min.
: 1.00
1st Qu.: 1.00 1st Qu.:2007
1st Qu.: 2.00
1st Qu.: 3.00
1st Qu.: 2.00
Median : 1.00
Median :2008
Median : 5.00
Median : 6.00
Median : 3.00
Mean
: 3.44
Mean
:2007
Mean
: 5.42Mean:19.26Mean: 2.95
3rd Qu.: 3.00 3rd Qu.:2008
3rd Qu.: 8.00 3rd Qu.:12.00 3rd Qu.: 3.00
Max.
:98.00
Max.
:2009
Max.
:12.00Max.
:99.00Max.
:11.00NA's:50682NA's:50682NA's:50682NA's:50682NA's:50682q03ac12q03ac13
q03ac04b_string
q03ac04c_string
Min. :221.0 Min. :1.00
Length:51275
Length:51275
1st Qu.:911.0 1st Qu.:2.00
Class :character Class :character
Median :921.0 Median :4.00
Mode :character Mode :character
Mean:855.1Mean:4.23
3rd Qu.:931.0 3rd Qu.:6.00
Max.
:999.0
Max.
:9.00
NA's
:50698NA's:50682q03ac06b_string
q03ac06c_string
q03ac12_string
Length:51275
Length:51275
Length:51275
Class :character
Class :character
Class :character
Mode :character
Mode :character
Mode :character## 9 #### s03b.migration.current ##########
hhid
persidprovince
Min.
: 100106
Min.
: 10010631
Min.
: 1.000
1st Qu.: 307707
1st Qu.: 30770731
1st Qu.: 3.000
Median : 802201
Median : 80220132
Median : 8.000
Mean: 964237
Mean: 96423700
Mean: 9.615
3rd Qu.:1405020
3rd Qu.:140502035
3rd Qu.:14.000
Max.
:2400317
Max.
:240031734
Max.
:24.000

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.878
3rd Qu.:2.000
Max.
:2.000weightMin.
: 45.06
1st Qu.:229.24
Median :244.83
Mean:245.43
3rd Qu.:263.21
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 3.000
Median : 4.000
Mean: 4.384
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 91.36
1st Qu.: 696.98
Median :1002.29
Mean:1078.76
3rd Qu.:1422.96
Max. :4358.38

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.03
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.887
3rd Qu.:10.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.831
3rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

q03bc01
Min. :31.00

q03bc03
Min. :1.000

q03bc04
Min. :15.00

hhid_string
Length:8049

psu
Min.
: 1001
1st Qu.: 3077
Median : 8022
Mean
: 9642
3rd Qu.:14050
Max. :24003

Manual for Cambodia CSES 2009 (Version 2.0)

persid_string
Length:8049
Class :character
Mode :characterq03bc05bMin. : 1.00

87Class :character
Mode :characterq03bc05c
Min.
:9301
1st Qu.:9316
Median :9316
Mean :9347
3rd Qu.:9316
Max.
:9799
NA's
:7581
q03bc10Min.
: 11
1st Qu.:621
Median :631
Mean
:645
3rd Qu.:724
Max.
:999
NA's
:378

1st Qu.:31.00
Median :32.00
Mean :32.23
3rd Qu.:33.00
Max. :41.00

q03bc06
Min.
:1960
1st Qu.:1997
Median :2003
Mean :2001
3rd Qu.:2007
Max.
:2009

1st Qu.:1.000
Median :1.000
Mean :1.486
3rd Qu.:2.000
Max. :2.000

q03bc07Min.
:1.000
1st Qu.:3.000
Median :3.000
Mean :2.733
3rd Qu.:3.000
Max.
:6.000

1st Qu.:24.00
Median :29.00
Mean :30.75
3rd Qu.:36.00
Max. :88.00

1st Qu.:12.00
Median :88.00
Mean :59.03
3rd Qu.:88.00
Max. :88.00
NA's :468
q03bc09Min.
: 0.00
1st Qu.: 4.00
Median : 7.00
Mean :15.12
3rd Qu.:10.00
Max. :98.00

q03bc08Min.
: 11.0
1st Qu.:631.0
Median :631.0
Mean :638.7
3rd Qu.:631.0
Max.
:999.0NA's:698q03bc12
q03bc13a
Min.
:
3000
Min.
:1.000
1st Qu.: 30000 1st Qu.:3.000
Median : 60000 Median :3.000
Mean: 323911
Mean:2.961
3rd Qu.: 200000 3rd Qu.:3.000
Max.
:61500000
Max.
:4.000
NA's
:2464
NA's
:2467

q03bc11Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.305
3rd Qu.:2.000
Max.
:2.000NA's:10q03bc13bMin. :1.000
1st Qu.:4.000
Median :4.000
Mean:3.6433rd Qu.:4.000
Max.
:4.000
NA's:7724## 10 #### s04.housing ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.000
Min.
:1.000
1st Qu.: 308205 1st Qu.: 3.000 1st Qu.:2.000
Median : 804503
Median : 8.000
Median :2.000
Mean: 976901
Mean: 9.741Mean:1.801
3rd Qu.:1405104 3rd Qu.:14.000 3rd Qu.:2.000
Max. :2400320 Max. :24.000 Max. :2.000

weightMin.
: 45.06
1st Qu.:227.68
Median :245.19
Mean:245.48
3rd Qu.:265.05
Max. :591.08

hhsizeMin.
: 1.00
1st Qu.: 3.00
Median : 5.00
Mean
: 4.77
3rd Qu.: 6.00
Max. :15.00

weight3
Min. : 45.06
1st Qu.: 782.32
Median :1112.13
Mean :1173.21
3rd Qu.:1469.88
Max. :5319.70

surveymonthMin. : 1.000
1st Qu.: 4.000
Median : 7.000
Mean : 6.504
3rd Qu.:10.000
Max. :12.000

regionMin. :1.000
1st Qu.:3.000
Median :3.000
Mean :2.703
3rd Qu.:3.000
Max.
:3.000psu
Min. : 1001
1st Qu.: 3082
Median : 8045
Mean : 9769
3rd Qu.:14051
Max. :24003

stratum
Min. : 11.00
1st Qu.: 32.00
Median : 82.00
Mean : 99.21
3rd Qu.:142.00
Max. :242.00

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:11970
Class :character
Mode :character

q04_01Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.062
3rd Qu.:1.000
Max.
:9.000

q04_04
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :2.165
3rd Qu.:2.000
Max. :9.000

q04_05
Min. : 1.000
1st Qu.: 2.000
Median : 3.000
Mean : 3.098
3rd Qu.: 4.000
Max. :10.000

q04_06
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :2.889
3rd Qu.:3.000
Max. :9.000

q04_02q04_03Min.
: 4.00 Min. : 1.000
1st Qu.: 24.00
1st Qu.: 1.000
Median : 38.00
Median : 1.000
Mean: 43.23 Mean : 1.431
3rd Qu.: 56.00
3rd Qu.: 2.000
Max.
:684.00Max.
:15.000
NA's:126
q04_07q04_08Min. :1.000 Min. : 1.000
1st Qu.:1.000 1st Qu.: 3.000
Median :3.000 Median : 5.000
Mean :2.831 Mean : 5.164
3rd Qu.:4.000 3rd Qu.: 9.000
Max. :7.000 Max. :13.000

Manual for Cambodia CSES 2009 (Version 2.0)88NA's :1
q04_09q04_10m1
q04_10m2
q04_10m3
Min.
:
0.00
Min.
: 1.000Min.
: 1.000Min.
: 1.000
1st Qu.: 7.00 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 3.000
Median : 15.00
Median : 1.000
Median : 2.000
Median : 3.000
Mean: 48.64
Mean: 1.493
Mean: 2.452
Mean: 3.534
3rd Qu.: 50.00 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 4.000
Max.
:4000.00
Max.
:12.000
Max.
:11.000
Max.
:11.000
NA's
:5112
NA's
:5405
NA's :6376
NA's
:9795
q04_11
q04_12
q04_13q04_14m1
Min.
: 1.00Min.
: 1.000
Min.
:
0.00
Min.
: 1.000
1st Qu.: 10.00
1st Qu.: 3.000
1st Qu.:7.00
1st Qu.: 1.000
Median : 20.00
Median : 4.000
Median : 20.00
Median : 1.000
Mean: 21.61
Mean: 4.399
Mean: 90.48Mean: 1.513
3rd Qu.: 30.00
3rd Qu.: 6.000
3rd Qu.: 65.00
3rd Qu.: 2.000Max.
:250.00Max.
:13.000
Max.
:5001.00
Max.
:12.000
NA's
:5406
NA's
:2
NA's
:2634
NA's
:3397
q04_14m2
q04_14m3
q04_15q04_16q04_17Min.
: 1.000Min.
: 1.00Min.
: 1.00
Min.
:
0
Min.
:1.000
1st Qu.: 2.000
1st Qu.: 3.00
1st Qu.: 10.00
1st Qu.:0
1st Qu.:1.000
Median : 2.000
Median : 3.00
Median : 20.00
Median :0Median :1.000
Mean: 2.512Mean: 3.58Mean: 25.46
Mean: 6203Mean:1.6443rd Qu.: 3.000
3rd Qu.: 4.00
3rd Qu.: 30.00
3rd Qu.: 6000
3rd Qu.:2.000
Max.
:11.000Max.
:13.00Max.
:240.00
Max.
:250000
Max.
:3.000
NA's
:4843
NA's
:9317
NA's
:3411
q04_18aq04_18bq04_18cq04_18dq04_18eMin.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :1.000 Median :2.000
Median :2.000
Median :2.000
Median :2.000
Mean:1.077Mean:1.888Mean:1.997Mean:1.992Mean:1.992
3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max. :2.000 Max.
:2.000 Max. :2.000
NA's
:2977
NA's
:2977
NA's
:2977
NA's
:2977
NA's
:2977
q04_19aq04_19bq04_20q04_21
Min. :1.000 Min. :1.000 Min. :0.0 Min. :0.0
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:
0.0 1st Qu.:
0.0
Median :7.000 Median :2.000 Median :0.0 Median :0.0
Mean :5.065 Mean :1.989 Mean : 195.8 Mean : 533.9
3rd Qu.:7.000 3rd Qu.:2.000 3rd Qu.:
0.0
3rd Qu.:0.0
Max. :8.000 Max. :3.000 Max. :100000.0 Max. :80000.0
NA's
:4786
q04_22aq04_22bq04_22c1
q04_22c2
q04_22c3Min.
:1.000Min.
:1.000Min.
: 1.000Min.
: 1.000Min.
: 1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 3.000
Median :1.000 Median :2.000 Median : 1.000 Median : 2.000 Median : 3.000
Mean:1.332Mean:1.783Mean: 1.452
Mean: 2.553
Mean: 3.676
3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 4.000
Max.
:8.000Max.
:2.000Max.
:12.000Max.
:12.000Max.
:14.000
NA's
:1293
NA's
:3612
NA's
:5729
NA's
:9939
q04_22dq04_23aq04_23bq04_23cq04_23dMin. : 0.000 Min. :0 Min. :0 Min. :0 Min. :0
1st Qu.: 2.000
1st Qu.:0
1st Qu.:0
1st Qu.:0
1st Qu.: 12000
Median : 4.000 Median :0 Median :0 Median : 950 Median : 25000
Mean : 5.171 Mean : 14684 Mean : 4464 Mean : 2725 Mean : 26070
3rd Qu.: 6.000
3rd Qu.: 10000
3rd Qu.:0
3rd Qu.: 4000
3rd Qu.: 30000
Max. :90.000 Max. :890000 Max. :240000 Max. :48000 Max. :450000

Manual for Cambodia CSES 2009 (Version 2.0)

89

NA's
:3676
q04_23eMin.
:0
1st Qu.:0
Median :0
Mean: 31833rd Qu.:0
Max. :98000
q04_25aMin.
:
0
1st Qu.: 60000
Median : 120000
Mean: 263210
3rd Qu.: 287000
Max. :2800000
NA's:11657otherrural
Min. :0.0000
1st Qu.:1.0000
Median :1.0000
Mean :0.7958
3rd Qu.:1.0000
Max. :1.0000
log_25bMin.
: 7.601
1st Qu.:10.597
Median :11.156
Mean :11.254
3rd Qu.:11.695
Max. :16.300
NA's
:315

q04_23fMin.
:
0
1st Qu.:0
Median :0
Mean: 48433rd Qu.: 6800
Max. :90000

q04_23g
Min.
:
0.0
1st Qu.:0.0
Median :
0.0
Mean
:
656.5
3rd Qu.:0.0
Max. :300000.0

q04_24Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.087
3rd Qu.:1.000
Max. :4.000
NA's :4
q04_25bq04_26phnonpenh
Min.
:
0
Min.
:
0
Min.
:0.00000
1st Qu.: 40000 1st Qu.:0
1st Qu.:0.00000
Median :70000
Median :0Median :0.00000
Mean: 148040
Mean
:
1828
Mean
:0.09298
3rd Qu.: 120000
3rd Qu.:0
3rd Qu.:0.00000
Max. :12000000 Max.
:1500000
Max.
:1.00000
NA's
:310
NA's
:35
urban
publiclight
pre_1
Min. :0.0000 Min. :0.0000 Min. : 35963
1st Qu.:0.0000
1st Qu.:0.0000
1st Qu.: 35963
Median :0.0000 Median :0.0000 Median : 35963
Mean :0.1112 Mean :0.2681 Mean : 154032
3rd Qu.:0.0000
3rd Qu.:1.0000 3rd Qu.: 173550
Max. :1.0000 Max. :1.0000 Max. :2399764
pre_2
Min.
:10.85
1st Qu.:10.85
Median :10.85
Mean :11.28
3rd Qu.:11.36
Max. :17.42## 11 #### s04.housingpractice ##########
hhid_string
q04_01
q04_02q04_03
Length:11971
Min.
:1.000Min.
: 4.00Min.
: 1.000
Class :character
1st Qu.:1.000
1st Qu.: 24.00
1st Qu.: 1.000
Mode :character
Median :1.000
Median : 38.00
Median : 1.000
Mean:1.062Mean: 43.23
Mean: 1.431
3rd Qu.:1.000
3rd Qu.: 56.00
3rd Qu.: 2.000
Max.
:9.000Max.
:684.00Max.
:15.000
NA's:126
q04_04q04_05q04_06
q04_07q04_08Min. :1.000 Min. : 1.000 Min. :1.000 Min. :1.000 Min. : 1.000
1st Qu.:1.000 1st Qu.: 2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 3.000
Median :2.000 Median : 3.000 Median :2.000 Median :3.000 Median : 5.000
Mean :2.165 Mean : 3.098 Mean :2.888 Mean :2.831 Mean : 5.164
3rd Qu.:2.000 3rd Qu.: 4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.: 9.000
Max. :9.000 Max. :10.000 Max. :9.000 Max. :7.000 Max. :13.000
NA's :1
q04_09q04_10m1
q04_10m2
q04_10m3
Min.
:
0.00
Min.
: 1.000Min.
: 1.000Min.
: 1.000
1st Qu.: 7.00 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 3.000
Median : 15.00
Median : 1.000
Median : 2.000
Median : 3.000

Manual for Cambodia CSES 2009 (Version 2.0)

90

Mean
: 48.64
Mean: 1.493
Mean: 2.452
Mean: 3.534
3rd Qu.: 50.00 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 4.000
Max.
:4000.00
Max.
:12.000
Max.
:11.000
Max.
:11.000
NA's
:5113
NA's
:5406
NA's
:6377
NA's
:9796
q04_11
q04_12
q04_13q04_14m1
Min.
: 1.00Min.
: 1.000
Min.
:
0.00
Min.
: 1.000
1st Qu.: 10.00
1st Qu.: 3.000
1st Qu.:7.00
1st Qu.: 1.000
Median : 20.00
Median : 4.000
Median : 20.00
Median : 1.000
Mean: 21.61
Mean: 4.399
Mean: 90.48Mean: 1.513
3rd Qu.: 30.00
3rd Qu.: 6.000
3rd Qu.: 65.00
3rd Qu.: 2.000Max.
:250.00Max.
:13.000
Max.
:5001.00
Max.
:12.000
NA's
:5407
NA's
:2
NA's
:2635
NA's
:3398
q04_14m2
q04_14m3
q04_15q04_16q04_17Min.
: 1.000Min.
: 1.00Min.
: 1.00
Min.
:
0
Min.
:1.000
1st Qu.: 2.000
1st Qu.: 3.00
1st Qu.: 10.00
1st Qu.:0
1st Qu.:1.000
Median : 2.000
Median : 3.00
Median : 20.00
Median :0Median :1.000
Mean: 2.512Mean: 3.58Mean: 25.46
Mean: 6204Mean:1.6443rd Qu.: 3.000
3rd Qu.: 4.00
3rd Qu.: 30.00
3rd Qu.: 6000
3rd Qu.:2.000
Max.
:11.000Max.
:13.00Max.
:240.00
Max.
:250000
Max.
:3.000
NA's
:4844
NA's
:9318
NA's
:3412
q04_18aq04_18bq04_18cq04_18dq04_18eMin.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :1.000
Median :2.000
Median :2.000
Median :2.000
Median :2.000
Mean:1.077Mean:1.888Mean:1.997Mean:1.992Mean:1.992
3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000Max.
:2.000
NA's
:2977
NA's
:2977
NA's
:2977
NA's
:2977
NA's
:2977
q04_19aq04_19bq04_20q04_21
Min. :1.000 Min. :1.000 Min. :0.0 Min. :0.0
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:
0.0 1st Qu.:
0.0
Median :7.000 Median :2.000 Median :0.0 Median :0.0
Mean :5.065 Mean :1.989 Mean : 195.8 Mean : 533.8
3rd Qu.:7.000 3rd Qu.:2.000
3rd Qu.:0.0
3rd Qu.:0.0
Max. :8.000 Max. :3.000 Max. :100000.0 Max. :80000.0
NA's
:4787
q04_22aq04_22bq04_22c1
q04_22c2
q04_22c3Min.
:1.000Min.
:1.000Min.
: 1.000Min.
: 1.000Min.
: 1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 3.000
Median :1.000 Median :2.000 Median : 1.000 Median : 2.000 Median : 3.000
Mean:1.332Mean:1.783Mean: 1.452
Mean: 2.553
Mean: 3.676
3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 4.000
Max.
:8.000Max.
:2.000Max.
:12.000Max.
:12.000Max.
:14.000
NA's
:1293
NA's
:3613
NA's
:5730
NA's
:9940
q04_22dq04_23aq04_23bq04_23cq04_23dMin. : 0.000 Min. :0 Min. :0 Min. :0 Min. :0
1st Qu.: 2.000
1st Qu.:0
1st Qu.:0
1st Qu.:0
1st Qu.: 12000
Median : 4.000 Median :0 Median :0 Median : 900 Median : 25000
Mean : 5.171 Mean : 14685 Mean : 4465 Mean : 2725 Mean : 26068
3rd Qu.: 6.000
3rd Qu.: 10000
3rd Qu.:0
3rd Qu.: 4000
3rd Qu.: 30000
Max. :90.000 Max. :890000 Max. :240000 Max. :48000 Max. :450000
NA's
:3677
q04_23eq04_23fq04_23gq04_24
Min.
:
0
Min.
:
0
Min.
:
0.0
Min.
:1.000
1st Qu.:0
1st Qu.:0
1st Qu.:0.0
1st Qu.:1.000
Median :0Median :0Median :0.0Median :1.000

Manual for Cambodia CSES 2009 (Version 2.0)

91

Mean
: 31833rd Qu.:0
Max. :98000
q04_25aMin.
:
0
1st Qu.: 60000
Median : 120000
Mean: 263210
3rd Qu.: 287000
Max.
:2800000NA's:11658Mean: 4842
3rd Qu.: 6800
Max. :90000

Mean
:
656.4
3rd Qu.:0.0
Max. :300000.0

q04_25bMin.
:
0
1st Qu.: 40000
Median :70000
Mean: 148053
3rd Qu.: 120000
Max.
:12000000
NA's
:310

Mean
:1.087
3rd Qu.:1.000
Max. :4.000
NA's :4
q04_26
hhid
Min.
:
0
Min.
: 100101
1st Qu.:0
1st Qu.: 308205
Median :0Median : 804502
Mean
:
1828
Mean
: 976828
3rd Qu.:0
3rd Qu.:1405104
Max.
:1500000
Max.
:2400320
NA's:35## 12 #### s05a.landown ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.000
Min. :1.000
1st Qu.: 308119 1st Qu.: 3.000 1st Qu.:2.000
Median : 702815 Median : 7.000 Median :2.000
Mean : 984537 Mean : 9.819 Mean :1.948
3rd Qu.:1600320 3rd Qu.:16.000 3rd Qu.:2.000
Max. :2400320 Max. :24.000 Max. :2.000

weightMin. : 45.68
1st Qu.:233.39
Median :246.76
Mean :249.47
3rd Qu.:263.24
Max. :591.08

hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.996
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 117.6
1st Qu.: 899.7
Median :1188.3
Mean:1250.2
3rd Qu.:1546.0
Max.
:5319.7psu
Min.
: 1001
1st Qu.: 3081
Median : 7028
Mean
: 9845
3rd Qu.:16003
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 72.0
Mean:100.1
3rd Qu.:162.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.208
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.935
3rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:16458
Class :character
Mode :character

q05ac01Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.776
3rd Qu.:2.000
Max.
:9.000q05ac02
Min.
:
15
1st Qu.: 2000
Median :
5000
Mean
:
9042
3rd Qu.: 10000
Max.
:3000000

q05ac03Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean :1.168
3rd Qu.:1.000
Max. :5.000

q05ac04aMin.
:
1
1st Qu.:400
Median :70000
Mean : 238431
3rd Qu.: 200000
Max. :80000000
NA's :1377
q05ac05a
q05ac05bMin. :
1 Min. :1.000
1st Qu.:240
1st Qu.:1.000
Median :1065 Median :2.000
Mean : 323886 Mean :1.532
3rd Qu.: 200000 3rd Qu.:2.000
Max. :20000000 Max. :3.000
NA's :15682
NA's :15682
q05ac06b
q05ac06c

q05ac04bMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean :1.382
3rd Qu.:2.000
Max. :3.000
NA's :1379
q05ac05c
Min. :1.000
1st Qu.:2.000
Median :3.000
Mean :2.521
3rd Qu.:3.000
Max. :4.000
NA's :15686
q05ac07q05ac04c
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean :2.352
3rd Qu.:3.000
Max. :4.000
NA's :1529
q05ac06a
Min. :
24
1st Qu.:500
Median : 120000
Mean : 326199
3rd Qu.: 395000
Max. :7125000
NA's :15859
q05ac08
q05ac09

Manual for Cambodia CSES 2009 (Version 2.0)

92

Min.
:1.000 Min. :1.000 Min. : 1.000 Min. :1950 Min. :1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.: 1.000 1st Qu.:1981 1st Qu.:1.000
Median :1.000 Median :3.000 Median : 1.000 Median :1989 Median :2.000
Mean :1.387 Mean
:2.503 Mean : 1.983 Mean :1991 Mean :2.297
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.: 2.000 3rd Qu.:2000 3rd Qu.:2.000
Max. :3.000 Max. :3.000 Max. :10.000 Max. :2009 Max. :8.000
NA's:15859NA's:15859
NA's
:4
NA's
:4
q05ac10q05ac11q05ac12q05ac13a
Min.
:
50000
Min.
:
50000
Min.
:1.00
Min.
:1.000
1st Qu.:700000
1st Qu.: 2000000
1st Qu.:1.00
1st Qu.:1.000
Median : 2000000
Median : 5000000
Median :1.00
Median :3.000
Mean: 7679617
Mean: 14460705
Mean
:1.56
Mean
:2.972
3rd Qu.: 5295000
3rd Qu.: 12000000
3rd Qu.:2.00
3rd Qu.:4.000
Max.
:960000000
Max.
:980000000
Max.
:4.00
Max.
:7.000NA's:13988NA's:8065q05ac13bq05ac14q05ac15q05ac16a
q05ac16b
Min. :1.000 Min. :1.000 Min. : 1.000 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:1.000 1st Qu.: 1.000 1st Qu.:1.000 1st Qu.:2.000
Median :4.000 Median :1.000 Median : 1.000 Median :1.000 Median :2.000
Mean:3.691Mean:1.719Mean: 5.527
Mean:1.491Mean:2.291
3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.: 1.000 3rd Qu.:1.000 3rd Qu.:2.000
Max.
:8.000Max.
:7.000Max.
:99.000
Max.
:6.000Max.
:6.000
NA's
:8065
NA's
:8065
NA's
:63
NA's
:15966q05ac16c
q05ac17
q05ac18a
q05ac18b
q05ac18c
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:3.000 1st Qu.:2.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.:3.250
Median :3.000
Median :4.000
Median :1.000
Median :6.000
Median :4.000
Mean:2.757Mean:3.106Mean:1.231Mean:4.663Mean:4.167
3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:1.000 3rd Qu.:7.000 3rd Qu.:6.250
Max.
:5.000Max.
:4.000Max.
:8.000Max.
:7.000Max.
:8.000NA's:16421NA's:16369NA's:16440q05ac19
q05ac20q05ac21q05ac22
Min.
:1979
Min.
:1.000
Min.
:1979
Min.
:1.000
1st Qu.:1986
1st Qu.:1.000 1st Qu.:1982 1st Qu.:3.000
Median :1998
Median :1.000
Median :1990
Median :3.000
Mean
:1995
Mean
:1.144
Mean
:1991
Mean
:2.9633rd Qu.:2005
3rd Qu.:1.000 3rd Qu.:2000 3rd Qu.:3.000
Max.
:2009
Max.
:2.000
Max.
:2009
Max.
:3.000NA's:15090
NA's:2472## 13 #### s05b.cropsproduction ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 307503
1st Qu.: 3.00
1st Qu.:2.000
Median : 702516
Median : 7.00
Median :2.000
Mean: 966644
Mean: 9.64Mean:1.955
3rd Qu.:1501601
3rd Qu.:15.00
3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weightMin.
: 45.68
1st Qu.:233.32
Median :246.74
Mean:249.96
3rd Qu.:262.96
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.052
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 117.6
1st Qu.: 909.4
Median :1203.7
Mean:1267.1
3rd Qu.:1566.5

surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.084
3rd Qu.: 9.000

regionMin.
:1.00
1st Qu.:3.00
Median :3.00
Mean
:2.95
3rd Qu.:3.00

psu
Min.
: 1001
1st Qu.: 3075
Median : 7025
Mean
: 9666
3rd Qu.:15016

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 72.00
Mean: 98.35
3rd Qu.:152.00

Manual for Cambodia CSES 2009 (Version 2.0)

93

Max.

:5319.7

year
Min. :2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max.
:2009

Max.

:24003Max.

hhid_string
Length:17773
Class :character
Mode :characterq05bc03b
Length:17773
Class :character
Mode :characterq05bc07
Min. :
0.00
1st Qu.:0.00
Median :
5.00
Mean
:
27.84
3rd Qu.:20.00
Max.
:10000.00
NA's :8:242.00

wetdryMin. :1.00
1st Qu.:1.00
Median :1.00
Mean :1.24
3rd Qu.:1.00
Max.
:2.00

q05bc04
Min.
:
10
1st Qu.:1800
Median :
4724
Mean
:
7502
3rd Qu.: 10000
Max. :3000000
NA's :1
q05bc08
Min. :
0.00
1st Qu.:0.00
Median :
0.00
Mean
:
48.97
3rd Qu.:5.00
Max.
:49900.00
NA's :11Max.

:12.000

q05bc01
Min. : 1.000
1st Qu.: 1.000
Median : 1.000
Mean : 1.742
3rd Qu.: 2.000
Max.
:10.000

Max.

:3.00

q05bc02
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.767
3rd Qu.:2.000
Max.
:9.000q05bc05
q05bc06
Min.
:
0
Min.
:
0
1st Qu.:1500
1st Qu.: 250
Median :4000
Median : 700
Mean
:
6843
Mean
: 1399
3rd Qu.:9000
3rd Qu.: 1500
Max. :3000000
Max. :80000
NA's :2NA's:4q05bc09
pastyearMin. :
0Min. : 1
1st Qu.:600
1st Qu.:2008
Median :750
Median :2008
Mean
:
1790
Mean
:2008
3rd Qu.:920
3rd Qu.:2008
Max. :12600000
Max. :2009
NA's :11## 14 #### s05c.costcrops ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.000
Min. :1.000
1st Qu.: 307610 1st Qu.: 3.000 1st Qu.:2.000
Median : 702720 Median : 7.000 Median :2.000
Mean : 973134 Mean : 9.705 Mean :1.958
3rd Qu.:1501709 3rd Qu.:15.000 3rd Qu.:2.000
Max. :2400320 Max. :24.000 Max. :2.000

weightMin. : 45.68
1st Qu.:233.32
Median :246.75
Mean :249.84
3rd Qu.:262.82
Max. :591.08

hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.049
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 117.6
1st Qu.: 910.5
Median :1203.7
Mean:1265.3
3rd Qu.:1563.9
Max.
:5319.7psu
Min.
: 1001
1st Qu.: 3076
Median : 7027
Mean
: 9731
3rd Qu.:15017
Max.
:24003stratumMin.
: 11.00
1st Qu.: 32.00
Median : 72.00
Mean: 99.01
3rd Qu.:152.00
Max.
:242.00

surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.076
3rd Qu.: 9.000
Max. :12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.952
3rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:16911
Class :character
Mode :character

wetdryMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.226
3rd Qu.:1.000
Max.
:2.000q05cc01Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.718
3rd Qu.: 2.000
Max.
:10.000

q05cc02Min.
:1.000
1st Qu.:1.000

q05cc03Min.
:
0
1st Qu.: 11975

q05cc04
Min.
:
1st Qu.:00

q05cc05
Min.
:
1st Qu.:Manual for Cambodia CSES 2009 (Version 2.0)

0
0

94

Median :1.000
Mean:1.749
3rd Qu.:2.000
Max.
:9.000Median :30000
Median :35000
Median :
0
Mean
:
82005
Mean
: 137701
Mean: 23039
3rd Qu.: 79400 3rd Qu.: 150000 3rd Qu.: 30000
Max.
:19440000
Max.
:13000000
Max.
:2050000
NA's
:3
NA's
:7
NA's
:15
q05cc06
q05cc07q05cc08q05cc09
Min.
:
0.0
Min.
:
0
Min.
:
0
Min.
:0.0e+00
1st Qu.:0.0 1st Qu.:0 1st Qu.:0 1st Qu.:0.0e+00
Median :0.0Median :0Median :2000
Median :0.0e+00
Mean
:
381.8
Mean
: 22756
Mean: 11547
Mean :7.2e+04
3rd Qu.:0.0 3rd Qu.:0 3rd Qu.: 12000 3rd Qu.:8.0e+04
Max.
:2000000.0Max.
:6500000
Max.
:1200000Max.
:1.3e+07
NA's
:28
NA's
:27
NA's
:13
NA's
:22
q05cc10q05cc11q05cc12q05cc13
Min. :
0 Min. :0 Min. :0.0
Min. :
0
1st Qu.:0 1st Qu.:0 1st Qu.:
0.0
1st Qu.:0
Median :0Median :0Median :0.0Median :
0
Mean
:
68812
Mean
:
8632
Mean
:
174.6
Mean
:
12884
3rd Qu.: 60000 3rd Qu.:0 3rd Qu.:
0.0
3rd Qu.:0
Max. :12000000 Max. :4800000 Max. :1000000.0
Max. :19440000
NA's
:23
NA's
:44
NA's
:45
NA's
:51
q05cc14q05cc15q05cc16
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0 1st Qu.:0 1st Qu.: 77000
Median :0Median :0Median : 216800
Mean
:
3179
Mean
:
8850
Mean
: 451464
3rd Qu.:0 3rd Qu.:0 3rd Qu.: 496250
Max.
:3000000
Max.
:13000000
Max.
:57496000
NA's
:49
NA's
:55
NA's
:12

## 15 #### s05d.cropsinventory ##########
hhid
province
urbanruralMin.
: 100105Min.
: 1.00Min.
:1.000
1st Qu.: 401914
1st Qu.: 4.00
1st Qu.:2.000
Median : 802906
Median : 8.00
Median :2.000
Mean:1014947
Mean:10.12Mean:1.966
3rd Qu.:1600602
3rd Qu.:16.00
3rd Qu.:2.000
Max.
:2400316Max.
:24.00Max.
:2.000weightMin.
: 45.68
1st Qu.:234.55
Median :247.78
Mean:250.20
3rd Qu.:262.96
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.943
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 131.5
1st Qu.: 902.1
Median :1177.3
Mean:1241.2
3rd Qu.:1538.0
Max.
:5319.7psu
Min.
: 1001
1st Qu.: 4019
Median : 8029
Mean
:10149
3rd Qu.:16006
Max.
:24003surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.278
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.00
1st Qu.:3.00
Median :3.00
Mean
:2.96
3rd Qu.:3.00
Max.
:3.00

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:7308
Class :character
Mode :character

stratumMin.
: 11.0
1st Qu.: 42.0
Median : 82.0
Mean:103.2
3rd Qu.:162.0
Max.
:242.0q05dc01Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.267
3rd Qu.: 1.000
Max.
:10.000

q05dc02b
Length:7308
Class :character
Mode :character

Manual for Cambodia CSES 2009 (Version 2.0)

95

q05dc03Min.
:
0
1st Qu.: 390
Median : 800
Mean: 1162
3rd Qu.: 1440
Max.
:35000q05dc04
Min.
:
0.0
1st Qu.: 700.0
Median : 750.0
Mean: 947.3
3rd Qu.: 900.0
Max.
:96000.0
NA's:2## 16 #### s05e1.animals ##########
hhid
province
urbanrural
Min. : 100101 Min. : 1.000 Min. :1.000
1st Qu.: 307903 1st Qu.: 3.000 1st Qu.:2.000
Median : 802509 Median : 8.000 Median :2.000
Mean : 985473 Mean : 9.829 Mean :1.937
3rd Qu.:1501604 3rd Qu.:15.000 3rd Qu.:2.000
Max. :2400320 Max. :24.000 Max. :2.000

weightMin. : 45.68
1st Qu.:232.32
Median :246.52
Mean :249.03
3rd Qu.:264.07
Max. :591.08

hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.905
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 131.5
1st Qu.: 868.2
Median :1161.6
Mean:1223.2
3rd Qu.:1511.1
Max.
:5319.7psu
Min.
: 1001
1st Qu.: 3079
Median : 8025
Mean
: 9855
3rd Qu.:15016
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean:100.2
3rd Qu.:152.0
Max.
:242.0regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.929
3rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:84350
Class :character
Mode :characterq05e1c01
Min.
: 1.0
1st Qu.: 3.0
Median : 5.5
Mean
: 5.5
3rd Qu.: 8.0
Max.
:10.0

q05e1c04
Min.
:
0.00
1st Qu.:2.00
Median :
4.00
Mean
:
8.74
3rd Qu.: 10.00
Max.
:2500.00
NA's:66475q05e1c08
Min.
:0
1st Qu.:
60000
Median : 200000
Mean: 1221717
3rd Qu.: 1400000
Max.
:88000000
NA's:66475q05e1c12
Min.
:0
1st Qu.:0
Median :
0
Mean
:
3941

surveymonthMin.
: 1.00
1st Qu.: 3.00
Median : 6.00
Mean
: 6.39
3rd Qu.: 9.00
Max.
:12.00q05e1c03
Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.788
3rd Qu.:2.000
Max.
:2.000NA's :5q05e1c05
q05e1c06
q05e1c07
Min.
:
0.00
Min.
:
0
Min.
:
0
1st Qu.:1.00
1st Qu.:60000
1st Qu.:2
Median :2.00
Median : 180000
Median :4
Mean : 5.11 Mean : 1250579
Mean : 22
3rd Qu.:5.00
3rd Qu.: 1500000
3rd Qu.:10
Max.
:2000.00
Max.
:80000000
Max.
:80000NA's:66475NA's:66473NA's :66479
q05e1c09
q05e1c10q05e1c11
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
1st Qu.:0
Median :0Median :0Median :0
Mean: 259833
Mean: 103022
Mean: 33776
3rd Qu.:80000
3rd Qu.:0
3rd Qu.: 40000
Max.
:63000000
Max.
:60000000
Max.
:7000000NA's:66478NA's:66509NA's:66523q05e1c13
q05e1c14
q05e1c15
Min.
:
0
Min.
:
0
Min.
:
0.0
1st Qu.:0
1st Qu.:0
1st Qu.:0.0
Median :0Median :0Median :
0.0
Mean
:
35842
Mean
: 20528
Mean
:
544.1

Manual for Cambodia CSES 2009 (Version 2.0)

96

3rd Qu.:0Max.
:9000000NA's:66518

3rd Qu.:0Max.
:146000000
NA's:66527

3rd Qu.: 20000
Max.
:7200000NA's:66530

3rd Qu.:0.0
Max.
:500000.0
NA's:66551## 17 #### s05e2.animalsexpenses
hhidprovince
Min.
: 100101Min.
: 1.00
1st Qu.: 308803
1st Qu.: 3.00
Median : 803319
Median : 8.00
Mean
:1015889
Mean:10.133rd Qu.:1600310 3rd Qu.:16.00
Max.
:2400320Max.
:24.00##########
urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.946
3rd Qu.:2.000
Max.
:2.000weightMin.
: 45.68
1st Qu.:232.32
Median :245.67
Mean:249.10
3rd Qu.:263.21
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.921
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 131.5
1st Qu.: 883.4
Median :1165.6
Mean:1228.5
3rd Qu.:1510.5
Max.
:5319.7psu
Min.
: 1001
1st Qu.: 3088
Median : 8033
Mean
:10159
3rd Qu.:16003
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean:103.33rd Qu.:162.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.201
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.00
1st Qu.:3.00
Median :3.00
Mean
:2.94
3rd Qu.:3.00
Max.
:3.00

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:14486
Class :character
Mode :characterq05e2c01
Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:2.317
3rd Qu.:4.000
Max.
:6.000q05e2c03
Min.
:0
1st Qu.:15000
Median :
50000
Mean
:
200920
3rd Qu.:150000Max.
:117750000
NA's:1## 18 #### s05f1.fisharea ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 751261
1st Qu.: 7.50
1st Qu.:2.000
Median :1402214
Median :14.00
Median :2.000
Mean:1369834
Mean:13.68Mean:1.907
3rd Qu.:2001514
3rd Qu.:20.00
3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weight3psu
stratumMin.
: 348.9
Min.
: 1001Min.
: 11.0
1st Qu.: 946.1 1st Qu.: 7512 1st Qu.: 76.5
Median :1197.0 Median :14022 Median :142.0
Mean:1297.9
Mean:13698Mean:138.7
3rd Qu.:1605.2 3rd Qu.:20015 3rd Qu.:202.0
Max.
:4095.5
Max.
:24003Max.
:242.0year
hhid_string
q05f1c01
Min. :2009 Length:227
Min. :1.00
1st Qu.:2009
Class :character
1st Qu.:1.00
Median :2009 Mode :character Median :1.00
Mean
:2009
Mean :1.07
3rd Qu.:2009
3rd Qu.:1.00
Max.
:2009
Max.
:3.00
q05f1c04
q05f1c05
Min.
:
0
Min.
:
0

weighthhsizeMin.
:130.7Min.
: 2.000
1st Qu.:230.9
1st Qu.: 4.000
Median :240.2
Median : 5.000
Mean:253.1
Mean: 5.106
3rd Qu.:253.6
3rd Qu.: 6.000
Max.
:585.1Max.
:12.000
surveymonth
regionMin.
: 1.00Min.
:2.000
1st Qu.: 2.00 1st Qu.:3.000
Median : 6.00 Median :3.000
Mean: 5.93Mean:2.907
3rd Qu.: 9.00 3rd Qu.:3.000
Max.
:12.00Max.
:3.000
q05f1c02
q05f1c03
Min. :1.000 Min. : 6.0
1st Qu.:1.000
1st Qu.: 37.5
Median :1.000 Median : 80.0
Mean :1.145 Mean : 376.5
3rd Qu.:1.000 3rd Qu.: 225.0
Max.
:5.000Max.
:8000.0

Manual for Cambodia CSES 2009 (Version 2.0)

97

1st Qu.: 400000
Median : 900000
Mean: 4207211
3rd Qu.: 2000000
Max. :84000000

1st Qu.: 10000
Median : 30000
Mean: 103687
3rd Qu.: 100000
Max. :3000000## 19 #### s05f2.fishexpenses ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 401001
1st Qu.: 4.00
1st Qu.:2.000
Median : 804620
Median : 8.00
Median :2.000
Mean:1030718
Mean:10.28Mean:1.951
3rd Qu.:1701603 3rd Qu.:17.00 3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weight3
Min. : 137
1st Qu.: 905
Median :1175
Mean :1251
3rd Qu.:1545
Max.
:5320

psu
Min. : 1001
1st Qu.: 4010
Median : 8046
Mean :10307
3rd Qu.:17016
Max. :24003

stratum
Min. : 11.0
1st Qu.: 42.0
Median : 82.0
Mean :104.8
3rd Qu.:172.0
Max. :242.0

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max. :2009

hhid_string
Length:7853
Class :character
Mode :character

weightMin.
: 45.68
1st Qu.:230.65
Median :245.19
Mean:248.58
3rd Qu.:263.21
Max.
:591.08

surveymonthMin. : 1.000
1st Qu.: 4.000
Median : 6.000
Mean : 6.438
3rd Qu.: 9.000
Max. :12.000

q05f2c01
Min. : 1.000
1st Qu.: 5.000
Median : 5.000
Mean : 5.031
3rd Qu.: 5.000
Max. :12.000

hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.021
3rd Qu.: 6.000
Max.
:15.000

regionMin. :1.000
1st Qu.:3.000
Median :3.000
Mean :2.949
3rd Qu.:3.000
Max. :3.000

q05f2c03
Min. :
0
1st Qu.: 12000
Median : 30000
Mean : 133131
3rd Qu.: 70000
Max. :30000000
NA's :1## 20 #### s05f3.fishincome ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 400620
1st Qu.: 4.00
1st Qu.:2.000
Median : 805208
Median : 8.00
Median :2.000
Mean:1034623
Mean:10.32Mean:1.955
3rd Qu.:1702107
3rd Qu.:17.00
3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weightMin.
: 45.68
1st Qu.:230.53
Median :245.19
Mean:249.14
3rd Qu.:263.32
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.003
3rd Qu.: 6.000
Max.
:15.000

weight3
Min.
: 137.0
1st Qu.: 904.8
Median :1172.7
Mean :1249.7
3rd Qu.:1536.9
Max. :5319.7

psu
Min. : 1001
1st Qu.: 4006
Median : 8052
Mean :10346
3rd Qu.:17021
Max. :24003

stratum
Min. : 11.0
1st Qu.: 42.0
Median : 82.0
Mean :105.2
3rd Qu.:172.0
Max. :242.0

surveymonthMin. : 1.000
1st Qu.: 3.000
Median : 6.000
Mean : 6.391
3rd Qu.: 9.000
Max. :12.000

regionMin. :1.000
1st Qu.:3.000
Median :3.000
Mean :2.952
3rd Qu.:3.000
Max. :3.000

year
Min. :2009
1st Qu.:2009
Median :2009
Mean
:2009

hhid_string
Length:12364
Class :character
Mode :character

q05f3c01
Min. :1.00
1st Qu.:2.00
Median :2.00
Mean
:2.39

q05f3c03
Min. :
0
1st Qu.: 45000
Median : 100000
Mean: 344381

Manual for Cambodia CSES 2009 (Version 2.0)

98

3rd Qu.:2009
Max.
:2009

3rd Qu.:3.00
Max. :7.00

3rd Qu.: 250000
Max. :95000000
NA's:1## 21 #### s05g1.forestincome ##########
hhid
province
urbanruralMin.
: 100102
Min.
: 1.00Min.
:1.000
1st Qu.: 307314
1st Qu.: 3.00
1st Qu.:2.000
Median : 804020
Median : 8.00
Median :2.000
Mean:1006510
Mean:10.04Mean:1.957
3rd Qu.:1600614
3rd Qu.:16.00
3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weightMin.
: 45.06
1st Qu.:230.88
Median :245.49
Mean:250.53
3rd Qu.:264.81
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 4.792
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 814.91
Median :1138.11
Mean:1205.04
3rd Qu.:1500.44
Max.
:5319.70

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.955
3rd Qu.:3.000
Max.
:3.000year
Min. :2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max. :2009

psu
Min.
: 1001
1st Qu.: 3073
Median : 8040
Mean
:10065
3rd Qu.:16006
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean:102.43rd Qu.:162.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.514
3rd Qu.:10.000
Max.
:12.000

hhid_string
Length:19398
Class :character
Mode :character

q05g1c01
Min. : 1.000
1st Qu.: 2.000
Median : 4.000
Mean : 4.111
3rd Qu.: 6.000
Max. :10.000

q05g1c03
Min. :
0
1st Qu.:0
Median :0
Mean : 47703
3rd Qu.:0
Max. :18000000
NA's :9q05g1c04
Min.
:0
1st Qu.:40000
Median : 100000
Mean : 203789
3rd Qu.: 324000
Max. :12000000
NA's :5

q05g1c05
Min.
:0
1st Qu.:0
Median :0
Mean : 1376
3rd Qu.:0
Max. :600000
NA's :17

q05g1c06
Min.
:0
1st Qu.:45000
Median : 140000
Mean : 252939
3rd Qu.: 360000
Max. :19000000
NA's :18## 22 #### s05g2.forestexpenses ##########
hhid
province
urbanrural
Min. : 100102 Min. : 1.000 Min. :1.000
1st Qu.: 305709 1st Qu.: 3.000 1st Qu.:2.000
Median : 800656 Median : 8.000 Median :2.000
Mean : 971465 Mean : 9.689 Mean :1.949
3rd Qu.:1600617 3rd Qu.:16.000 3rd Qu.:2.000
Max. :2400320 Max. :24.000 Max. :2.000

weightMin. : 45.68
1st Qu.:234.01
Median :248.69
Mean :250.83
3rd Qu.:265.57
Max. :591.08

hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean : 4.832
3rd Qu.: 6.000
Max. :14.000

stratum
Min. : 11.00
1st Qu.: 32.00
Median : 81.00
Mean : 98.84
3rd Qu.:162.00
Max. :242.00

weight3Min.
: 91.36
1st Qu.: 831.37
Median :1152.92
Mean :1217.01
3rd Qu.:1521.78
Max. :5319.70

psu
Min.
: 1001
1st Qu.: 3057
Median : 8006
Mean : 9715
3rd Qu.:16006
Max. :24003

Manual for Cambodia CSES 2009 (Version 2.0)

99

surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 7.000
Mean: 6.605
3rd Qu.:10.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.948
3rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:5928
Class :character
Mode :character

q05g2c01
Min.
:1.000
1st Qu.:3.000
Median :5.000
Mean:4.195
3rd Qu.:5.000
Max.
:7.000## 23 #### s05h1.bussiness ##########
hhid
province
urbanruralMin.
: 100105Min.
: 1.00Min.
:1.00
1st Qu.: 402202 1st Qu.: 4.00 1st Qu.:1.00
Median :1000920
Median :10.00
Median :2.00
Mean:1030709
Mean:10.28Mean:1.66
3rd Qu.:1501002 3rd Qu.:15.00 3rd Qu.:2.00
Max.
:2400319Max.
:24.00Max.
:2.00

weightMin.
: 45.06
1st Qu.:225.52
Median :246.57
Mean:243.02
3rd Qu.:266.15
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.088
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 91.36
1st Qu.: 840.03
Median :1174.92
Mean:1239.53
3rd Qu.:1539.42
Max.
:4728.62

regionMin.
:1.00
1st Qu.:2.00
Median :3.00
Mean
:2.49
3rd Qu.:3.00
Max.
:3.00

q05g2c03
Min. :
0
1st Qu.: 10000
Median : 20000
Mean : 43003
3rd Qu.: 34000
Max. :5000000
NA's :1

psu
Min.
: 1001
1st Qu.: 4022
Median :10009
Mean:10307
3rd Qu.:15010
Max.
:24003stratumMin.
: 11.0
1st Qu.: 42.0
Median :102.0
Mean:104.43rd Qu.:152.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.478
3rd Qu.:10.000
Max.
:12.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:4843
Class :character
Mode :characterq05h1c01
Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.187
3rd Qu.:1.000
Max.
:5.000q05h1c04
Length:4843
Class :character
Mode :characterq05h1c05
Min.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.719
3rd Qu.: 2.000
Max.
:13.000NA's:6q05h1c06e
Min.
: 5.000
1st Qu.: 6.000
Median : 7.000

q05h1c06aMin.
: 1.00
1st Qu.: 2.00
Median : 2.00
Mean
: 2.49
3rd Qu.: 3.00
Max.
:13.00NA's:2243q05h1c06f
Min.
: 6.000
1st Qu.: 6.500
Median : 8.000

q05h1c06b
q05h1c06c
Min. : 1.000
Min.
: 1.000
1st Qu.: 3.000
1st Qu.: 4.000
Median : 3.000
Median : 4.000
Mean: 3.747
Mean: 4.488
3rd Qu.: 4.000
3rd Qu.: 5.000
Max.
:14.000Max.
:10.000
NA's
:4080
NA's
:4630
q05h1c06g
q05h1c06h
Min.
:7.0
Min.
:8
1st Qu.:7.5
1st Qu.:8Median :8.0
Median :8

Manual for Cambodia CSES 2009 (Version 2.0)

q05h1c06d
Min.
:1.000
1st Qu.:5.000
Median :5.000
Mean:5.328
3rd Qu.:6.000
Max.
:9.000
NA's
:4782

100

Mean
: 6.952
3rd Qu.: 7.000
Max.
:12.000
NA's :4822

Mean: 8.333
3rd Qu.: 8.750
Max.
:13.000
NA's :4837

Mean
:8.0
3rd Qu.:8.5
Max.
:9.0
NA's :4841

Mean:8
3rd Qu.:8Max.
:8NA's :4842## 24 #### s05h2.bussinessexpenses ##########
hhid
province
urbanruralMin.
: 100105Min.
: 1.00Min.
:1.000
1st Qu.: 502003
1st Qu.: 5.00
1st Qu.:1.000
Median :1200501
Median :12.00
Median :2.000
Mean:1038451
Mean:10.36Mean:1.6043rd Qu.:1500506
3rd Qu.:15.00
3rd Qu.:2.000
Max.
:2400319Max.
:24.00Max.
:2.000weightMin.
: 45.06
1st Qu.:222.18
Median :246.05
Mean:241.26
3rd Qu.:265.91
Max.
:591.08hhsizeMin.
: 1.00
1st Qu.: 4.00
Median : 5.00
Mean
: 5.06
3rd Qu.: 6.00
Max.
:15.00weight3Min.
: 91.36
1st Qu.: 811.93
Median :1165.62
Mean:1225.01
3rd Qu.:1531.11
Max.
:4728.62

surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.382
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:2.000
Median :3.000
Mean:2.399
3rd Qu.:3.000
Max.
:3.000year
Min. :2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max.
:2009

psu
Min.
: 1001
1st Qu.: 5020
Median :12005
Mean:103843rd Qu.:15005
Max.
:24003stratumMin.
: 11.0
1st Qu.: 52.0
Median :121.0
Mean:105.2
3rd Qu.:152.0
Max.
:242.0hhid_string
Length:15720
Class :character
Mode :characterq05h2c01
Min. : 1.000
1st Qu.: 4.000
Median : 8.000
Mean : 7.907
3rd Qu.:11.000
Max.
:18.000

q05h2c04
Min. :
0
1st Qu.:0
Median :0
Mean : 1591101
3rd Qu.:120000Max. :800000000
NA's:11013q05h2c05
Min. :
0
1st Qu.:0
Median :0
Mean : 356886
3rd Qu.:0
Max. :240000000
NA's:13770q05h2c03
Min. :0.000e+00
1st Qu.:6.000e+04
Median :2.000e+05
Mean :6.698e+06
3rd Qu.:1.200e+06
Max.
:8.200e+09
NA's :26
q05h2c06
q05h2c07
Min. :
0 Min. :0
1st Qu.:0
1st Qu.:0
Median :0 Median :0
Mean : 76611 Mean : 32373
3rd Qu.:0 3rd Qu.:
0
Max. :62000000 Max. :42000000
NA's:14139NA's:14176## 25 #### s05h3.bussinessincome
hhidprovince
Min.
: 100105Min.
: 1.00
1st Qu.: 500177
1st Qu.: 5.00
Median :1001610
Median :10.00
Mean:1034158
Mean:10.31
3rd Qu.:1500716
3rd Qu.:15.00
Max.
:2400319Max.
:24.00##########
urbanruralMin.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.6633rd Qu.:2.000
Max.
:2.000weightMin.
: 45.06
1st Qu.:225.52
Median :246.05
Mean:243.30
3rd Qu.:265.79
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.052
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 91.36
1st Qu.: 847.60
Median :1173.95
Mean:1232.95
3rd Qu.:1530.50

stratumMin.
: 11.0
1st Qu.: 51.0
Median :102.0
Mean:104.8
3rd Qu.:152.0

surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 6.000
Mean: 6.557
3rd Qu.:10.000

regionMin.
:1.000
1st Qu.:2.000
Median :3.000
Mean:2.492
3rd Qu.:3.000

psu
Min.
: 1001
1st Qu.: 5002
Median :10016
Mean:10341
3rd Qu.:15007

Manual for Cambodia CSES 2009 (Version 2.0)

101

Max.

:4728.62

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max.
:2009

Max.

:24003hhid_string
Length:5360
Class :character
Mode :characterq05h3c04
Min.
:0
1st Qu.:0
Median :0
Mean: 6424300
3rd Qu.: 3000000
Max.
:900000000
NA's
:3746

Max.

:242.0Max.

:12.000

Max.

:3.000q05h3c01
Min.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean : 5.485
3rd Qu.: 8.000
Max. :20.000

q05h3c05
Min.
:0
1st Qu.:0
Median :0
Mean: 1796845
3rd Qu.:0Max.
:306000000
NA's
:4717

q05h3c03
Min.
:0.000e+00
1st Qu.:7.000e+05
Median :4.405e+06
Mean :2.805e+07
3rd Qu.:1.509e+07
Max. :1.155e+10NA's :34
q05h3c06
q05h3c07
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
Median :0Median :0
Mean: 255966
Mean : 18472
3rd Qu.:0
3rd Qu.:0Max.
:46000000
Max.
:9125000
NA's
:4853
NA's
:4866

## 26 #### s06.liabilities ##########
hhid
province
urbanrural
Min. : 100101 Min. : 1.000 Min. :1.000
1st Qu.: 307018 1st Qu.: 3.000 1st Qu.:2.000
Median : 802214 Median : 8.000 Median :2.000
Mean : 959802 Mean : 9.571 Mean :1.898
3rd Qu.:1500619 3rd Qu.:15.000 3rd Qu.:2.000
Max. :2400317 Max. :24.000 Max. :2.000

weightMin. : 45.68
1st Qu.:230.57
Median :244.96
Mean :247.99
3rd Qu.:263.32
Max. :591.08

hhsizeMin. : 1.000
1st Qu.: 4.000
Median : 5.000
Mean : 5.036
3rd Qu.: 6.000
Max.
:15.000

weight3
Min. : 91.36
1st Qu.: 902.06
Median :1174.20
Mean :1249.04
3rd Qu.:1539.42
Max. :4358.38

stratum
Min. : 11.00
1st Qu.: 32.00
Median : 82.00
Mean : 97.61
3rd Qu.:152.00
Max. :242.00

surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.346
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.868
3rd Qu.:3.000
Max.
:3.000psu
Min. : 1001
1st Qu.: 3070
Median : 8022
Mean : 9598
3rd Qu.:15006
Max. :24003
year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:4732
Class :character
Mode :character

q06_c01
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.065
3rd Qu.:1.000
Max.
:6.000q06_c02
q06_c03
q06_c04
q06_c05
Min. : 0.000 Min. : 0.000 Min. : 1.000 Min. : 1.000
1st Qu.: 3.000 1st Qu.: 4.000 1st Qu.: 3.000 1st Qu.: 1.000
Median : 5.000 Median : 8.000 Median : 5.000 Median : 3.000
Mean : 7.068 Mean : 8.752 Mean : 5.504 Mean : 3.143
3rd Qu.: 10.000 3rd Qu.: 12.000 3rd Qu.: 9.000 3rd Qu.: 4.000
Max.
:312.000Max.
:241.000Max.
:10.000 Max. :10.000
NA's
:1
NA's
:28
NA's
:2
q06_c06
q06_c07
q06_c08
Min.
:
6500
Min.
:
5000
Min.
: 0.000
1st Qu.: 400000 1st Qu.: 282000 1st Qu.: 1.600

Manual for Cambodia CSES 2009 (Version 2.0)

102Median : 1000000
Mean: 2368598
3rd Qu.: 2000000
Max.
:164000000

Median : 663000
Mean: 1752659
3rd Qu.: 1500000
Max.
:120000000
NA's:2Median : 3.000
Mean: 2.941
3rd Qu.: 3.000
Max.
:35.000
NA's:13## 27 #### s07.incomeother ##########
hhid
province
urbanruralMin.
: 100103
Min.
: 1.00Min.
:1.00
1st Qu.: 307340 1st Qu.: 3.00 1st Qu.:2.00
Median : 900507
Median : 9.00
Median :2.00
Mean:1049609
Mean:10.47Mean:1.85
3rd Qu.:1702381 3rd Qu.:17.00 3rd Qu.:2.00
Max.
:2400320Max.
:24.00Max.
:2.00

weightMin.
: 45.06
1st Qu.:230.42
Median :243.98
Mean:246.20
3rd Qu.:261.74
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 3.000
Median : 5.000
Mean: 4.731
3rd Qu.: 6.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 751.16
Median :1091.00
Mean:1171.10
3rd Qu.:1480.53Max.
:5319.70

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.775
3rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max.
:2009

psu
Min.
: 1001
1st Qu.: 3073
Median : 9005
Mean
:10496
3rd Qu.:17024
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 92.0
Mean
:106.5
3rd Qu.:172.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.177
3rd Qu.: 9.000
Max.
:12.000

hhid_string
Length:6862
Class :character
Mode :character

q07_c01
Min. : 1.000
1st Qu.: 2.000
Median :10.000
Mean : 7.059
3rd Qu.:11.000
Max.
:12.000

q07_c03
Min. :
0
1st Qu.:20000
Median :60000
Mean : 588645
3rd Qu.:200000Max.
:176000000

q07_c04
Min.
:
0
1st Qu.:0
Median :0
Mean: 182947
3rd Qu.:0
Max. :72000000
NA's:5q07_c05
Min.
:
1000
1st Qu.:
30000
Median :
80000
Mean
:
771458
3rd Qu.:300000Max. :176000000## 28 #### s08.construction ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.000
Min.
:1.00
1st Qu.: 308411
1st Qu.: 3.000
1st Qu.:2.00
Median : 804215
Median : 8.000
Median :2.00
Mean: 980302
Mean: 9.775
Mean:1.82
3rd Qu.:1500206 3rd Qu.:15.000 3rd Qu.:2.00
Max.
:2400320Max.
:24.000
Max.
:2.00

weightMin.
: 45.06
1st Qu.:228.39
Median :246.02
Mean:246.19
3rd Qu.:265.10
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 4.828
3rd Qu.: 6.000
Max.
:15.000

weight3
Min. : 45.06
1st Qu.: 798.26
Median :1132.66
Mean :1189.99

surveymonthMin. : 1.000
1st Qu.: 3.250
Median : 6.000
Mean : 6.493

regionMin. :1.000
1st Qu.:3.000
Median :3.000
Mean :2.733

psu
Min. : 1001
1st Qu.: 3084
Median : 8042
Mean : 9803

stratum
Min. : 11.00
1st Qu.: 32.00
Median : 82.00
Mean : 99.57

Manual for Cambodia CSES 2009 (Version 2.0)

103

3rd Qu.:1486.66
Max. :5319.70
year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

3rd Qu.:15002
Max. :24003

3rd Qu.:151.00
Max. :242.00

hhid_string
Length:11322
Class :character
Mode :character

q08_c01Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean :1.009
3rd Qu.:1.000
Max.
:4.000

3rd Qu.: 9.000
Max. :12.000

3rd Qu.:3.000
Max. :3.000

q08_c02aq08_c02bMin.
:1.000Min.
:1.000
1st Qu.:1.000
1st Qu.:3.000
Median :1.000
Median :3.000
Mean:1.025 Mean :2.988
3rd Qu.:1.000
3rd Qu.:3.000
Max.
:4.000Max.
:4.000
NA's
:34
NA's
:11074q08_c02c
q08_c03
q08_c04
q08_c05Min.
:2.000Min.
: 6.00
Min.
:1940
Min.
:3.000e+04
1st Qu.:2.250 1st Qu.: 24.00 1st Qu.:1993 1st Qu.:4.000e+06
Median :3.500
Median : 40.00
Median :2000
Median :1.000e+07
Mean:3.167
Mean: 44.07
Mean
:1997
Mean
:4.155e+07
3rd Qu.:4.000 3rd Qu.: 56.00 3rd Qu.:2005 3rd Qu.:2.500e+07
Max. :4.000 Max. :684.00 Max.
:2009
Max.
:1.600e+10NA's:11316
NA's
:4
NA's
:1
q08_c06
q08_c07q08_c08q08_c09
Min.
:
2000
Min.
:1.000
Min.
:
2000
Min.
:1.000
1st Qu.:40000
1st Qu.:2.000
1st Qu.: 240000
1st Qu.:2.000
Median :70000
Median :2.000
Median : 480000
Median :2.000
Mean: 155252
Mean:1.992
Mean: 878244
Mean:1.921
3rd Qu.: 120000 3rd Qu.:2.000 3rd Qu.: 965250
3rd Qu.:2.000
Max. :20000000 Max. :2.000 Max. :8000000 Max. :2.000
NA's :1
NA's :11232
NA's :13q08_c10q08_c11aq08_c11bq08_c12aq08_c12bMin.
:1.000Min.
: 1.000
Min.
:2000
Min.
: 1.000
Min.
:2000
1st Qu.:2.000 1st Qu.: 2.500 1st Qu.:2008
1st Qu.: 3.000 1st Qu.:2008
Median :3.000 Median : 4.000 Median :2009
Median : 5.000 Median :2009
Mean :2.414
Mean: 5.308
Mean
:2008
Mean
: 5.533
Mean:2008
3rd Qu.:3.000 3rd Qu.: 8.000 3rd Qu.:2009
3rd Qu.: 8.000 3rd Qu.:2009Max.
:3.000Max.
:12.000
Max.
:2009
Max.
:12.000
Max.
:2009
NA's
:10446NA's:11127NA's:11127NA's:11142NA's:11142q08_c13q08_c14q08_c15q08_c16Min.
:1.000
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:2.000 1st Qu.:0 1st Qu.: 35000
1st Qu.:
30000
Median :3.000
Median :0Median : 200000
Median : 270000
Mean:2.839Mean: 666462
Mean: 2651592
Mean: 2980760
3rd Qu.:4.000 3rd Qu.: 245000 3rd Qu.: 1400000 3rd Qu.: 1610000
Max.
:5.000Max.
:69700000
Max.
:98000000
Max.
:96000000
NA's:11105NA's:10499NA's:10515NA's:10502q08_c17q08_c18
q08_c19
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0 1st Qu.:0 1st Qu.:
0Median :0Median :0Median :0
Mean: 39051
Mean: 23203
Mean: 777859
3rd Qu.: 30000 3rd Qu.:0 3rd Qu.: 50000
Max. :2000000 Max. :1500000 Max. :40000000
NA's:10452NA's:10458NA's:10713## 29 #### s09.durables ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.000
Min. :1.000Min.

weight: 45.06

Manual for Cambodia CSES 2009 (Version 2.0)

104

1st Qu.: 401106
Median : 806205
Mean : 991414
3rd Qu.:1403702
Max. :2400320

1st Qu.: 4.000
Median : 8.000
Mean : 9.885
3rd Qu.:14.000
Max. :24.000

1st Qu.:1.000
Median :2.000
Mean :1.715
3rd Qu.:2.000
Max. :2.000

1st Qu.:226.29
Median :245.05
Mean :243.56
3rd Qu.:264.81
Max. :591.08

hhsizeMin. : 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.013
3rd Qu.: 6.000
Max. :15.000

weight3
Min. : 45.06
1st Qu.: 834.86
Median :1162.19
Mean:1224.20
3rd Qu.:1529.96
Max. :5319.70

psu
Min. : 1001
1st Qu.: 4011
Median : 8062
Mean
: 99143rd Qu.:14037
Max. :24003

stratum
Min. : 11.0
1st Qu.: 42.0
Median : 82.0
Mean:100.6
3rd Qu.:142.0
Max. :242.0

surveymonthMin. : 1.00
1st Qu.: 4.00
Median : 7.00
Mean
: 6.57
3rd Qu.:10.00
Max. :12.00

regionMin.
:1.000
1st Qu.:2.000
Median :3.000
Mean:2.556
3rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:93366
Class :character
Mode :character

q09_c03
Min.
:801.0
1st Qu.:804.0
Median :818.0
Mean:834.1
3rd Qu.:841.0
Max.
:894.0q09_c04Min.
: 0.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.711
3rd Qu.: 2.000
Max.
:57.000

q09_c05aMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.058
3rd Qu.:1.000
Max.
:4.000NA's:165q09_c06bMin.
: 1.000
1st Qu.: 1.000
Median : 1.000
Mean: 1.672
3rd Qu.: 2.000
Max.
:57.000
NA's:10819q09_c05bq09_c05c
q09_c05d
Min.
:1.00
Min.
:1.00
Min.
:1.00
1st Qu.:1.00
1st Qu.:1.00
1st Qu.:1.00
Median :1.00
Median :1.00
Median :1.00
Mean
:1.04
Mean
:1.02
Mean
:1.02
3rd Qu.:1.00
3rd Qu.:1.00
3rd Qu.:1.00
Max.
:4.00
Max. :4.00
Max.
:4.00
NA's
:66319NA's:78246NA's:84239q09_c07
q09_c08
Min.
:
600
Min.
:
100
1st Qu.:30000
1st Qu.:25000
Median :100000Median :
60000
Mean
:
839907
Mean:564490
3rd Qu.:260000
3rd Qu.:200000Max.
:880000000
Max.
:450000000
NA's:76112NA's:10818q09_c06aMin.
: 1.00
1st Qu.: 1.00
Median : 1.00
Mean
: 1.26
3rd Qu.: 1.00
Max. :16.00
NA's:76113## 30 #### s10.healthmother ##########
hhid
persidprovince
urbanruralMin.
: 100101
Min.
: 10010102
Min.
: 1.000
Min. :1.000
1st Qu.: 307908
1st Qu.: 30790752
1st Qu.: 3.000
1st Qu.:2.000
Median : 804420
Median : 80442002
Median : 8.000
Median :2.000
Mean: 975137
Mean: 97513701
Mean: 9.724Mean:1.827
3rd Qu.:1500256 3rd Qu.:150025602
3rd Qu.:15.000
3rd Qu.:2.000
Max.
:2400318
Max.
:240031802
Max.
:24.000
Max.
:2.000
NA's
:1
NA's
:1
weighthhsizeweight3psu
stratum
Min. : 45.68 Min. : 2.000 Min. : 137.0 Min. : 1001 Min. : 11.00
1st Qu.:228.39
1st Qu.: 4.000
1st Qu.: 939.3
1st Qu.: 3079
1st Qu.: 32.00
Median :245.95 Median : 5.000 Median :1224.7 Median : 8044 Median : 82.00
Mean :247.88 Mean : 5.403 Mean :1340.4 Mean : 9753 Mean : 99.07
3rd Qu.:266.32
3rd Qu.: 6.000
3rd Qu.:1631.6
3rd Qu.:15003
3rd Qu.:151.75
Max. :591.08 Max. :15.000 Max. :5319.7 Max. :24003 Max. :242.00

Manual for Cambodia CSES 2009 (Version 2.0)

105

NA's
:1
NA's
:1
NA's
:1
NA's
:1
NA's
:1
surveymonth
regionyear
persid_stringMin.
: 1.000
Min. :1.000 Min. :2009 Length:4447
1st Qu.: 3.000
1st Qu.:3.000
1st Qu.:2009
Class :character
Median : 6.000
Median :3.000
Median :2009
Mode :character
Mean: 6.408Mean:2.744Mean:2009
3rd Qu.: 9.000
3rd Qu.:3.000
3rd Qu.:2009
Max.
:12.000
Max.
:3.000
Max.
:2009
NA's
:1
NA's
:1
NA's
:1
hhid_string
q10_c01q10_c02q10_c03
Length:4447
Min.
:1.000Min.
: 1.000
Min.
: 2.000
Class :character
1st Qu.:1.000
1st Qu.: 2.000
1st Qu.: 4.000
Mode :character
Median :1.000
Median : 2.000
Median : 5.000
Mean :1.024
Mean: 2.324Mean: 5.098
3rd Qu.:1.000
3rd Qu.: 2.000
3rd Qu.: 6.000
Max.
:4.000Max.
:14.000Max.
:15.000NA's:4q10_c04q10_c05
q10_c06
q10_c07q10_c08Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :2.000
Median :2.000
Median :1.000
Median :2.000
Median :1.000
Mean:1.821Mean:1.976Mean:1.159Mean:1.706Mean:1.1843rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:1.000
Max.
:8.000Max.
:2.000Max.
:8.000Max.
:8.000Max.
:8.000NA's:843q10_c09q10_c10q10_c11q10_c12q10_c13Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
Median :2.000
Median :1.000
Median :3.000
Median :1.000
Median :2.000
Mean:1.662Mean:1.163Mean:3.108Mean:1.477Mean:2.347
3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:3.000
Max.
:8.000Max.
:8.000Max.
:8.000Max.
:8.000Max.
:5.000
NA's
:783
NA's
:667
NA's
:2337
NA's
:3210
q10_c14q10_c15aq10_c15bq10_c15cq10_c15dMin.
: 1.000
Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.0
1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:5.0
Median : 2.000 Median :3.000 Median :5.000 Median :4.000 Median :5.0
Mean
: 3.811Mean:2.833Mean:3.999Mean:3.882Mean:4.5
3rd Qu.: 7.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0
Max.
:14.000
Max.
:7.000Max.
:7.000Max.
:6.000
Max.
:5.0
NA's
:2
NA's
:2408
NA's
:4126
NA's
:4423
q10_c16q10_c17
Min. :1.00 Min. :1.000
1st Qu.:1.00
1st Qu.:1.000
Median :1.00 Median :1.000
Mean :1.47 Mean :1.521
3rd Qu.:2.00 3rd Qu.:2.000
Max. :8.00 Max. :8.000## 31 #### s11.health2years ##########
hhid
persidprovince
Min.
: 100101
Min.
: 10010105
Min.
: 1.000
1st Qu.: 307907
1st Qu.: 30790556
1st Qu.: 3.000
Median : 804007
Median : 80392003
Median : 8.000

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000

Manual for Cambodia CSES 2009 (Version 2.0)

106

Mean
: 968604
3rd Qu.:1405214
Max.
:2400318

Mean: 96798229
Mean: 9.649
Mean:1.829
3rd Qu.:140516704
3rd Qu.:14.000
3rd Qu.:2.000
Max.
:240031803
Max.
:24.000
Max.
:2.000
NA's
:2
NA's
:3
NA's
:3
weighthhsizeweight3psu
stratum
Min. : 45.68 Min. : 2.000 Min. : 137.0 Min. : 1001 Min. : 11.00
1st Qu.:229.24 1st Qu.: 4.000
1st Qu.: 949.9
1st Qu.: 3079
1st Qu.: 32.00
Median :245.49 Median : 5.000 Median :1236.5 Median : 8039 Median : 82.00
Mean :248.47 Mean : 5.463 Mean :1357.5 Mean : 9677 Mean : 98.32
3rd Qu.:266.65
3rd Qu.: 6.000
3rd Qu.:1631.6
3rd Qu.:14051
3rd Qu.:142.00
Max. :591.08 Max. :15.000 Max. :5319.7 Max. :24003 Max. :242.00
NA's
:3
NA's
:3
NA's
:3
NA's
:3
NA's
:3
surveymonth
regionyear
persid_stringMin.
: 1.000
Min.
:1.000Min.
:2009
Length:2357
1st Qu.: 3.000
1st Qu.:3.000
1st Qu.:2009
Class :character
Median : 6.000
Median :3.000
Median :2009
Mode :character
Mean: 6.336Mean:2.745Mean:2009
3rd Qu.: 9.000
3rd Qu.:3.000
3rd Qu.:2009
Max.
:12.000
Max.
:3.000
Max.
:2009
NA's
:3
NA's
:3
NA's
:3
hhid_string
q11_c01q11_c02
q11_c03
Length:2357
Min.
:1.000Min.
: 1.000
Min.
: 2.000
Class :character 1st Qu.:1.000 1st Qu.: 2.000
1st Qu.: 4.000
Mode :character
Median :1.000
Median : 2.000
Median : 5.000
Mean:1.029Mean: 2.441
Mean: 5.148
3rd Qu.:1.000
3rd Qu.: 2.000
3rd Qu.: 6.000
Max. :4.000
Max.
:14.000Max.
:15.000
NA's
:34
NA's
:2
q11_c04
q11_c05
q11_c06aq11_c06bq11_c07
Min. :1.000 Min. :1.000 Min. : 0.000 Min. :1.000
Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 0.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median : 1.000 Median :1.000 Median :1.000
Mean:1.015Mean:1.021Mean: 2.122Mean:1.419Mean :1.169
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 3.000 3rd Qu.:2.000 3rd Qu.:1.000
Max.
:2.000Max.
:2.000Max.
:23.000
Max.
:5.000Max.
:2.000
NA's
:3
NA's
:36
NA's
:344
NA's
:1662
NA's
:84
q11_c08aq11_c08bq11_c09
q11_c10q11_c11Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :2.000
Median :1.000
Median :1.000
Median :2.000
Median :1.000
Mean:1.729Mean:1.345Mean:1.085Mean:2.057Mean:1.119
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:1.000
Max. :2.000 Max.
:8.000Max.
:8.000Max.
:5.000Max.
:2.000
NA's
:84
NA's
:85
NA's
:165
NA's
:166
q11_c12aq11_c12bq11_c12cq11_c13aq11_c13bMin.
:1.000Min.
: 1.000
Min.
:2007
Min.
:1.000Min.
: 1.00
1st Qu.:1.000 1st Qu.: 3.000 1st Qu.:2008 1st Qu.:1.000 1st Qu.: 8.00
Median :1.000
Median : 6.000
Median :2008
Median :1.000
Median :14.00
Mean:1.023Mean: 6.345
Mean
:2008
Mean
:1.272Mean:14.79
3rd Qu.:1.000 3rd Qu.:10.000 3rd Qu.:2009 3rd Qu.:2.000 3rd Qu.:22.00
Max.
:2.000Max.
:12.000
Max.
:2009
Max.
:2.000Max.
:30.00
NA's
:426
NA's
:485
NA's
:485
NA's
:426
NA's
:970
q11_c13cq11_c13dq11_c14aq11_c14bq11_c14cMin.
: 1.000
Min.
:2007
Min.
:0.000Min.
: 1.000
Min.
:2007
1st Qu.: 3.000
1st Qu.:2008
1st Qu.:1.000
1st Qu.: 3.000
1st Qu.:2008
Median : 6.000
Median :2008
Median :3.000
Median : 6.000
Median :2008

Manual for Cambodia CSES 2009 (Version 2.0)

107

Mean
: 6.237
Mean:2008
3rd Qu.: 9.000
3rd Qu.:2009
Max.
:12.000
Max.
:2009
NA's :964
NA's :964
q11_c15aq11_c15bMin. :1.00 Min. : 1.00
1st Qu.:1.00 1st Qu.: 4.00
Median :2.00
Median : 7.00
Mean
:1.52
Mean
: 6.55
3rd Qu.:2.00 3rd Qu.: 9.00
Max.
:2.00
Max.
:12.00
NA's
:427
NA's
:1453

Mean
:2.3243rd Qu.:3.000
Max.
:4.000NA's :426
q11_c15c
Min. :2007
1st Qu.:2008
Median :2008
Mean
:2008
3rd Qu.:2009
Max.
:2009
NA's
:1454

Mean
: 6.306
3rd Qu.: 9.000
Max.
:12.000
NA's
:617

Mean
:2008
3rd Qu.:2009
Max.
:2009
NA's :619## 32 #### s12.health5years ##########
hhid
persidprovince
urbanruralMin.
: 100101
Min.
: 10010105
Min.
: 1.000
Min.
:1.000
1st Qu.: 307914
1st Qu.: 30791405
1st Qu.: 3.000
1st Qu.:2.000
Median : 804420
Median : 80442004
Median : 8.000
Median :2.000
Mean: 977611
Mean: 97761062
Mean: 9.748
Mean:1.831
3rd Qu.:1500611
3rd Qu.:150061106
3rd Qu.:15.000
3rd Qu.:2.000
Max.
:2400318
Max.
:240031803
Max.
:24.000
Max.
:2.000
NA's
:2
NA's
:2
weighthhsizeweight3psu
stratum
Min. : 45.68 Min. : 2.000 Min. : 137.0 Min. : 1001 Min. : 11.00
1st Qu.:228.36
1st Qu.: 4.000
1st Qu.: 959.2
1st Qu.: 3079
1st Qu.: 32.00
Median :245.31
Median : 5.000
Median :1245.3 Median : 8044 Median : 82.00
Mean :247.88 Mean : 5.494 Mean :1362.5 Mean : 9776 Mean : 99.31
3rd Qu.:266.14
3rd Qu.: 7.000
3rd Qu.:1640.4
3rd Qu.:15006
3rd Qu.:152.00
Max.
:591.08Max.
:15.000
Max.
:5319.7 Max. :24003 Max. :242.00
NA's
:2
NA's
:2
NA's
:2
NA's
:2
NA's
:2
surveymonth
regionyear
persid_stringMin.
: 1.000
Min.
:1.000Min.
:2009
Length:5617
1st Qu.: 3.000
1st Qu.:3.000
1st Qu.:2009
Class :character
Median : 6.000
Median :3.000
Median :2009
Mode :character
Mean: 6.355
Mean:2.749Mean:2009
3rd Qu.: 9.000 3rd Qu.:3.000 3rd Qu.:2009
Max.
:12.000
Max.
:3.000
Max.
:2009
NA's
:2
NA's
:2
NA's
:2
hhid_string
q12_c01q12_c02q12_c03q12_c04
Length:5617
Min.
:1.000Min.
: 2.000
Min.
:1.00
Min.
:1.000Class :character
1st Qu.:1.000
1st Qu.: 4.000
1st Qu.:1.00
1st Qu.:1.000
Mode :character
Median :1.000
Median : 5.000
Median :1.00
Median :1.000
Mean:1.216Mean: 5.001
Mean
:1.63
Mean
:1.2433rd Qu.:1.000
3rd Qu.: 6.000
3rd Qu.:2.00
3rd Qu.:1.000
Max.
:5.000Max.
:15.000
Max.
:8.00
Max.
:8.000NA's :5q12_c05
q12_c06
q12_c07q12_c08q12_c09aMin.
: 0.00
Min.
:1.00
Min.
:1.000Min.
:1.000Min.
:1.000
1st Qu.: 2.00 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
Median : 5.00
Median :2.00
Median :1.000
Median :2.000
Median :2.000
Mean:36.17
Mean
:1.65
Mean
:1.055Mean:1.837Mean:1.691
3rd Qu.:98.00 3rd Qu.:2.00 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
Max.
:98.00
Max.
:8.00
Max.
:8.000Max.
:8.000Max.
:8.000
NA's
:973
NA's
:4678
q12_c09bq12_c10aq12_c10bq12_c11q12_c12Manual for Cambodia CSES 2009 (Version 2.0)

108Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.412
3rd Qu.:2.000
Max.
:8.000NA's:4678
q12_c13
Min.
: 2.40
1st Qu.: 8.40
Median :10.50
Mean :10.56
3rd Qu.:12.60
Max. :33.90
NA's :8

Min. : 1.00
1st Qu.:16.00
Median :18.00
Mean :17.64
3rd Qu.:19.00
Max.
:30.00NA's:17Min. : 1.000
1st Qu.: 3.000
Median : 6.000
Mean : 6.352
3rd Qu.: 9.000
Max.
:12.000
NA's:20

Min. : 36.80
1st Qu.: 69.80
Median : 80.50
Mean : 80.01
3rd Qu.: 91.20
Max. :132.20
NA's:8## 33 #### s13b.healthexpenses ##########
hhid
persidprovince
Min.
: 100101
Min.
: 10010101
Min.
: 1.000
1st Qu.: 308309
1st Qu.: 30830926
1st Qu.: 3.000
Median : 804403
Median : 80440253
Median : 8.000
Mean: 973360
Mean: 97336032
Mean: 9.706
3rd Qu.:1404908 3rd Qu.:140490803 3rd Qu.:14.000
Max.
:2400320
Max.
:240032005
Max.
:24.000
weightMin.
: 45.06
1st Qu.:228.20
Median :246.02
Mean:245.94
3rd Qu.:265.13
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.557
3rd Qu.: 7.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 965.00
Median :1299.40
Mean:1369.09
3rd Qu.:1692.57
Max.
:5319.70

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.85
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.00
1st Qu.: 3.00
Median : 6.00
Mean
: 6.47
3rd Qu.: 9.00
Max.
:12.00regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.6943rd Qu.:3.000
Max.
:3.000hhid_string
Length:57082
Class :character
Mode :character

q13bc05
Min.
: 1.0
1st Qu.: 4.0
Median :10.0
Mean
:13.3
3rd Qu.:25.0

q13bc01Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.279
3rd Qu.: 4.000
Max.
:15.000

q13bc06
Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.07
3rd Qu.:1.00

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.796
3rd Qu.:2.000
Max.
:2.000psu
Min.
: 1001
1st Qu.: 3083
Median : 8044
Mean
: 97343rd Qu.:14049
Max.
:24003

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

q13bc02Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.8543rd Qu.:2.000
Max.
:2.000

q13bc07Min.
:0.0000
1st Qu.:0.0000
Median :0.0000
Mean:0.1297
3rd Qu.:0.0000

Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.388
3rd Qu.:2.000
Max. :2.000
NA's
:19

persid_string
Length:57082
Class :character
Mode :character

q13bc03Min.
:1.0
1st Qu.:2.0
Median :2.0
Mean
:1.8
3rd Qu.:2.0
Max.
:2.0
NA's
:48738
q13bc08Min.
: 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean: 0.2908
3rd Qu.: 0.0000

Manual for Cambodia CSES 2009 (Version 2.0)

q13bc04
Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:2.05
3rd Qu.:2.00
Max.
:3.00
NA's
:48738
q13bc09aMin.
: 1.00
1st Qu.: 9.00
Median :10.00
Mean:10.08
3rd Qu.:14.00

109

Max.
:30.0
NA's
:56051
q13bc09bMin.
: 1.00
1st Qu.: 9.00
Median :10.00
Mean:11.39
3rd Qu.:14.00
Max.
:98.00NA's:52878

Max.
:2.00
Max.
:5.0000Max.
NA's:56051
q13bc10
q13bc11
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:4500
Median :0Median : 13000
Mean
:
7490
Mean
: 64905
3rd Qu.:3625
3rd Qu.: 50000
Max.
:2000000
Max.
:4800000NA's:48794NA's:48763## 34 #### s14.disability ##########
hhid
persidprovince
Min.
: 100101
Min.
: 10010101
Min.
: 1.000
1st Qu.: 308309
1st Qu.: 30830926
1st Qu.: 3.000
Median : 804403
Median : 80440253
Median : 8.000
Mean: 973360
Mean: 97336032
Mean: 9.706
3rd Qu.:1404908
3rd Qu.:140490803
3rd Qu.:14.000
Max.
:2400320
Max.
:240032005
Max.
:24.000
weightMin.
: 45.06
1st Qu.:228.20
Median :246.02
Mean:245.94
3rd Qu.:265.13
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.557
3rd Qu.: 7.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 965.00
Median :1299.40
Mean:1369.09
3rd Qu.:1692.57
Max.
:5319.70

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean : 98.85
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.00
1st Qu.: 3.00
Median : 6.00
Mean : 6.47
3rd Qu.: 9.00
Max.
:12.00regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean :2.694
3rd Qu.:3.000
Max.
:3.000hhid_string
Length:57082
Class :character
Mode :character

q14_c02c
Min.
:1.00
1st Qu.:4.00
Median :4.00
Mean
:4.66
3rd Qu.:6.00
Max.
:9.00
NA's
:56812q14_c04bMin.
: 1.00
1st Qu.: 4.00

q14_c01
Min. : 1.000
1st Qu.: 2.000
Median : 3.000
Mean : 3.279
3rd Qu.: 4.000
Max. :15.000

q14_c03aMin.
:1.00
1st Qu.:1.00
Median :2.00
Mean
:1.75
3rd Qu.:2.00
Max.
:3.00
NA's
:53505q14_c04cMin.
: 1.00
1st Qu.: 5.00

:30.0000:98.00:48751urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.796
3rd Qu.:2.000
Max.
:2.000psu
Min.
: 1001
1st Qu.: 3083
Median : 8044
Mean
: 97343rd Qu.:14049
Max.
:24003

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean :2009
3rd Qu.:2009
Max.
:2009

q14_c02a
Min. :0.0000
1st Qu.:0.0000
Median :0.0000
Mean :0.1338
3rd Qu.:0.0000
Max. :9.0000

q14_c03bMin.
:1.00
1st Qu.:1.00
Median :2.00
Mean
:1.87
3rd Qu.:2.00
Max.
:3.00
NA's
:56183q14_c05aMin.
:1.00
1st Qu.:4.00

Max.
NA's

persid_string
Length:57082
Class :character
Mode :character

q14_c02b
Min. :0.00
1st Qu.:2.00
Median :3.00
Mean :3.25
3rd Qu.:4.00
Max. :9.00
NA's :56186
q14_c03c
q14_c04a
Min.
:1.00
Min.
: 1.00
1st Qu.:1.00
1st Qu.: 4.00
Median :2.00
Median :14.00
Mean
:1.96
Mean
:12.28
3rd Qu.:3.00
3rd Qu.:18.00
Max.
:3.00
Max.
:98.00NA's:56811NA's:53505q14_c05bq14_c05c
Min.
:1.00
Min.
:1.00
1st Qu.:4.00
1st Qu.:4.00

Manual for Cambodia CSES 2009 (Version 2.0)

110Median :15.00
Mean:12.28
3rd Qu.:18.00
Max.
:98.00NA's:56180Median :18.00
Mean:14.47
3rd Qu.:18.00
Max.
:98.00NA's:56812

Median :4.00
Mean
:3.83
3rd Qu.:4.00
Max.
:6.00
NA's
:53505Median :5.00
Mean
:4.56
3rd Qu.:6.00
Max.
:6.00
NA's
:55793

Median :6.00
Mean
:4.99
3rd Qu.:6.00
Max.
:6.00
NA's
:56498## 35 #### s15.labor7days ##########
hhid
persidprovince
Min.
: 100101
Min.
: 10010101 Min. : 1.0
1st Qu.: 308317
1st Qu.: 30831706
1st Qu.: 3.0
Median : 804320
Median : 80432006
Median : 8.0
Mean: 972808
Mean: 97280786
Mean
: 9.7
3rd Qu.:1404816
3rd Qu.:140481602
3rd Qu.:14.0
Max. :2400320 Max. :240032005 Max. :24.0

urbanrural
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean:1.7933rd Qu.:2.000
Max. :2.000

weightMin.
: 45.06
1st Qu.:227.99
Median :246.02
Mean:245.72
3rd Qu.:265.05
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.565
3rd Qu.: 7.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 965.22
Median :1305.19
Mean:1369.89
3rd Qu.:1700.63
Max.
:5319.70

psu
Min.
: 1001
1st Qu.: 3083
Median : 8043
Mean
: 9728
3rd Qu.:14048
Max.
:24003stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.79
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 6.000
Mean: 6.483
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.688
3rd Qu.:3.000
Max.
:3.000hhid_string
Length:51460
Class :character
Mode :character

q15_c04
Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:1.98
3rd Qu.:2.00
Max.
:2.00
NA's
:34926q15_c09Min.
: 1.00
1st Qu.:21.00
Median :36.00
Mean:36.68
3rd Qu.:55.00
Max.
:90.00NA's :16534

q15_c01
Min. : 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.091
3rd Qu.: 4.000
Max. :15.000

q15_c05bMin.
: 11.0
1st Qu.:611.0
Median :631.0
Mean:661.43rd Qu.:751.0
Max.
:964.0NA's:16258q15_c10Min.
: 1.00
1st Qu.:15.00
Median :25.00
Mean:21.41
3rd Qu.:30.00
Max.
:30.00NA's:16534

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

q15_c02
Min. : 1.000
1st Qu.: 1.000
Median : 2.000
Mean : 2.296
3rd Qu.: 3.000
Max. :15.000

q15_c06b
Min.
: 111
1st Qu.: 112
Median : 160
Mean
:1925
3rd Qu.:4390
Max.
:9990
NA's
:16258q15_c11Min.
:0.000
1st Qu.:0.000
Median :0.000
Mean:0.487
3rd Qu.:1.000
Max.
:4.000NA's:16281

persid_string
Length:51460
Class :character
Mode :character

q15_c03
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.321
3rd Qu.:2.000
Max. :2.000

q15_c07Min.
:1.000
1st Qu.:3.000
Median :3.000
Mean:3.016
3rd Qu.:3.000
Max.
:8.000NA's:16263q15_c12bMin.
: 11.0
1st Qu.:631.0
Median :632.0
Mean:721.7
3rd Qu.:921.0
Max.
:964.0NA's:37630Manual for Cambodia CSES 2009 (Version 2.0)

q15_c08Min.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.802
3rd Qu.:4.000
Max. :5.000
NA's:16279q15_c13b
Min.
: 111.0
1st Qu.: 141.0
Median : 220.0
Mean: 953.7
3rd Qu.: 310.0
Max.
:9900.0
NA's:37630

111

q15_c14Min.
:1.00
1st Qu.:3.00
Median :3.00
Mean
:3.02
3rd Qu.:3.00
Max.
:8.00
NA's
:37630q15_c19
Min. : 1.0
1st Qu.: 28.0
Median : 47.0
Mean : 43.5
3rd Qu.: 56.0
Max. :140.0
NA's:16482q15_c24Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:2.01
3rd Qu.:2.00
Max.
:3.00
NA's
:48987q15_c27b
Min.
:1
1st Qu.:2
Median :3
Mean:3
3rd Qu.:3
Max. :5
NA's:51407q15_c30b
Min.
:2004
1st Qu.:2008
Median :2008
Mean
:2008
3rd Qu.:2008
Max.
:2009
NA's
:51384q15_c15q15_c16q15_c17q15_c18
Min.
:1.00
Min.
: 1.00Min.
: 1.00Min.
: 1.0
1st Qu.:3.00
1st Qu.: 7.00
1st Qu.:10.00
1st Qu.: 4.0
Median :3.00
Median :14.00
Median :16.00
Median : 7.0
Mean
:3.15
Mean
:15.69Mean:17.92Mean: 7.8
3rd Qu.:4.00
3rd Qu.:21.00 3rd Qu.:30.00 3rd Qu.:10.0
Max.
:5.00
Max.
:84.00Max.
:30.00
Max.
:87.0
NA's
:37630NA's:37630NA's:37630NA's:48576q15_c20q15_c21q15_c22q15_c23
Min. :
0 Min. :1.000 Min. : 1.00 Min. :1.00
1st Qu.: 120000 1st Qu.:3.000 1st Qu.:42.00 1st Qu.:1.00
Median : 240000 Median :3.000 Median :50.00 Median :1.00
Mean : 306928 Mean :2.897 Mean :50.06 Mean :1.13
3rd Qu.: 350000 3rd Qu.:3.000 3rd Qu.:60.00 3rd Qu.:1.00
Max. :33000000 Max. :3.000 Max. :90.00 Max. :2.00
NA's:41752NA's:16261NA's:48409NA's:48987q15_c25aq15_c25bq15_c26q15_c27aMin.
: 1.00
Min.
:2004
Min.
:1.00
Min.
:1.0
1st Qu.: 2.00 1st Qu.:2008
1st Qu.:2.00
1st Qu.:2.0
Median : 4.00
Median :2009
Median :2.00
Median :3.0
Mean
: 4.55
Mean
:2008
Mean
:1.99
Mean
:2.6
3rd Qu.: 7.00 3rd Qu.:2009
3rd Qu.:2.00
3rd Qu.:3.0
Max.
:12.00
Max.
:2009
Max.
:2.00
Max.
:6.0
NA's
:49702NA's:49830NA's:35173NA's:51325q15_c27cq15_c28q15_c29q15_c30a
Min.
:1.00
Min.
:1.00
Min.
: 0.00Min.
: 1.00
1st Qu.:3.00
1st Qu.:1.00
1st Qu.:46.00 1st Qu.: 3.00
Median :4.00
Median :2.00
Median :48.00 Median : 4.00
Mean
:3.47
Mean
:1.66
Mean
:46.96Mean: 5.133rd Qu.:4.00
3rd Qu.:2.00
3rd Qu.:56.00 3rd Qu.: 8.00
Max. :5.00
Max. :2.00
Max.
:90.00 Max. :12.00
NA's:51443NA's:51325NA's:51325NA's:51361q15_c31Min.
:1.00
1st Qu.:7.00
Median :7.00
Mean
:6.82
3rd Qu.:7.00
Max.
:9.00
NA's
:35307## 36 #### s16.labor12months ##########
hhid
persidprovince
Min.
: 100101 Min. : 10010101 Min. : 1.0
1st Qu.: 308317
1st Qu.: 30831706
1st Qu.: 3.0
Median : 804320
Median : 80432006
Median : 8.0
Mean: 972808
Mean: 97280786
Mean
: 9.7
3rd Qu.:1404816 3rd Qu.:140481602 3rd Qu.:14.0
Max. :2400320 Max. :240032005 Max. :24.0

urbanrural
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean:1.7933rd Qu.:2.000
Max. :2.000

weightMin.
: 45.06
1st Qu.:227.99
Median :246.02
Mean:245.72

psu
Min.
: 1001
1st Qu.: 3083
Median : 8043
Mean
: 9728hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.565

weight3Min.
: 45.06
1st Qu.: 965.22
Median :1305.19
Mean:1369.89

Manual for Cambodia CSES 2009 (Version 2.0)

112

3rd Qu.:265.05
Max.
:591.08

3rd Qu.: 7.000
Max.
:15.000

3rd Qu.:1700.63
Max.
:5319.70

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.79
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 6.000
Mean: 6.483
3rd Qu.: 9.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.688
3rd Qu.:3.000
Max.
:3.000hhid_string
Length:51460
Class :character
Mode :character

q16_c01Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.091
3rd Qu.: 4.000
Max.
:15.000

q16_c04b
Length:51460
Class :character
Mode :character

q16_c05
Min. :1.0
1st Qu.:3.0
Median :3.0
Mean :2.8
3rd Qu.:3.0
Max. :5.0
NA's :18528
q16_c09Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean :2.027
3rd Qu.:2.000
Max. :4.000
NA's
:5546

q16_c08Min.
: 1.000
1st Qu.: 1.000
Median : 2.000
Mean: 2.817
3rd Qu.: 4.000
Max. :11.000
NA's
:39

hhsizeMin. : 1.000
1st Qu.: 4.000
Median : 5.000
Mean : 5.085
3rd Qu.: 6.000
Max. :13.000

surveymonthMin.
: 1.000

Min.

region
:1.00

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

persid_string
Length:51460
Class :character
Mode :character

q16_c02Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:2.489
3rd Qu.:5.000
Max.
:9.000q16_c03Min.
: 1.000
1st Qu.: 6.000
Median : 8.000
Mean: 8.387
3rd Qu.:12.000
Max.
:12.000
NA's:18528q16_c06bq16_c07
Length:51460
Min. :1.000
Class :character 1st Qu.:3.000
Mode :character Median :3.000
Mean :3.004
3rd Qu.:3.000
Max. :8.000
NA's :18528## 37 #### s17b.theft ##########
hhid
persidMin.
: 100204Min.
: 10020403
1st Qu.: 500812
1st Qu.: 50081151
Median : 804608
Median : 80460800
Mean: 986874
Mean: 98687391
3rd Qu.:1402419
3rd Qu.:140241927
Max.
:2400317
Max.
:240031798
weightMin. : 65.69
1st Qu.:227.99
Median :244.33
Mean :244.59
3rd Qu.:259.73
Max. :585.07

3rd Qu.:14048
Max.
:24003

province
Min. : 1.000
1st Qu.: 5.000
Median : 8.000
Mean: 9.841
3rd Qu.:14.000
Max.
:24.000

weight3
Min. : 117.6
1st Qu.: 844.4
Median :1186.9
Mean :1233.9
3rd Qu.:1523.1
Max. :3302.2

Min.

year
:2009

urbanrural
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean:1.799
3rd Qu.:2.000
Max.
:2.000psu
Min. : 1002
1st Qu.: 5008
Median : 8046
Mean : 9869
3rd Qu.:14024
Max. :24003

persid_string
Length:328

Manual for Cambodia CSES 2009 (Version 2.0)

stratum
Min. : 11.0
1st Qu.: 52.0
Median : 82.0
Mean :100.2
3rd Qu.:142.0
Max. :242.0

113

1st Qu.: 3.000
Median : 6.000
Mean: 6.186
3rd Qu.: 9.000
Max.
:12.000

1st Qu.:3.00
Median :3.00
Mean
:2.72
3rd Qu.:3.00
Max.
:3.00

hhid_string
Length:328
Class :character
Mode :character

q17bc05Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.716
3rd Qu.:2.000
Max.
:2.000

q17bc01
Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.07
3rd Qu.:1.00
Max. :3.00

q17bc06Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.495
3rd Qu.:2.000
Max.
:3.000NA's:235

1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

Class :character
Mode :character

q17bc02Min.
: 1.00
1st Qu.: 1.00
Median : 1.00
Mean
:23.67
3rd Qu.: 5.25
Max. :98.00
q17bc07Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.985
3rd Qu.:2.000
Max.
:2.000

q17bc03Min.
: 1.000
1st Qu.: 3.000
Median : 6.000
Mean: 6.113
3rd Qu.: 9.000
Max.
:12.000

q17bc04
Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.39
3rd Qu.:2.00
Max. :3.00

q17bc08
Min.
:
0
1st Qu.:50000
Median : 120000
Mean: 1093824
3rd Qu.: 400000
Max.
:80000000
NA's:1## 38 #### s17c.accidents ##########
hhid
persidprovince
urbanruralMin.
: 100103
Min.
: 10010301
Min.
: 1.00Min.
:1.000
1st Qu.: 308308 1st Qu.: 30830805 1st Qu.: 3.00 1st Qu.:2.000
Median : 805963
Median : 80596302
Median : 8.00
Median :2.000
Mean:1037164
Mean:103716418
Mean:10.34Mean:1.8043rd Qu.:1650558 3rd Qu.:165055752 3rd Qu.:16.50 3rd Qu.:2.000
Max.
:2400315
Max.
:240031503
Max.
:24.00Max.
:2.000weighthhsizeweight3psu
stratumMin.
: 76.95 Min. : 1.000 Min. : 223.3 Min. : 1001 Min. : 11.0
1st Qu.:228.04
1st Qu.: 4.000
1st Qu.: 840.4
1st Qu.: 3083
1st Qu.: 32.0
Median :245.88 Median : 5.000 Median :1211.2 Median : 8060 Median : 82.0
Mean:245.36 Mean : 5.187 Mean :1274.0 Mean :10372 Mean :105.2
3rd Qu.:260.46
3rd Qu.: 6.000
3rd Qu.:1587.2
3rd Qu.:16506
3rd Qu.:166.2
Max. :591.08 Max. :14.000 Max. :5319.7 Max. :24003 Max. :242.0
surveymonth
regionyear
persid_string
Min. : 1.000 Min. :1.00 Min. :2009 Length:654
1st Qu.: 2.000
1st Qu.:3.00
1st Qu.:2009
Class :character
Median : 5.000 Median :3.00 Median :2009 Mode :character
Mean: 5.424
Mean
:2.72
Mean
:2009
3rd Qu.: 8.000
3rd Qu.:3.00
3rd Qu.:2009
Max.
:12.000
Max.
:3.00
Max.
:2009
hhid_string
q17cc01
q17cc02
q17cc03
q17cc04
Length:654
Min. :1.000 Min. :1.000 Min. :1.000 Min. : 1.000
Class :character
1st Qu.:1.000
1st Qu.:1.000
1st Qu.:2.000
1st Qu.: 3.000
Mode :character Median :1.000 Median :2.000 Median :3.000 Median : 5.000
Mean :1.116 Mean :2.633 Mean :2.639 Mean : 5.654
3rd Qu.:1.000
3rd Qu.:4.000
3rd Qu.:3.000
3rd Qu.: 8.000
Max. :4.000 Max. :9.000 Max. :6.000 Max. :12.000
q17cc05
q17cc06Min.
:1.000Min.
:1.000
1st Qu.:1.000 1st Qu.:1.000

Manual for Cambodia CSES 2009 (Version 2.0)

114Median :1.000
Mean:1.312
3rd Qu.:2.000
Max.
:2.000Median :2.000
Mean:2.0143rd Qu.:3.000
Max.
:5.000## 39 #### s17d.violance ##########
hhid
persidprovince
urbanrural
weightMin.
: 100101
Min.
: 10.00
Min.
: NA
Min.
: NA
Min. : NA
1st Qu.: 308308 1st Qu.: 30.00 1st Qu.: NA
1st Qu.: NA
1st Qu.: NAMedian : 804402
Median : 80.00
Median : NA
Median : NA
Median : NA
Mean
: 973234
Mean: 97.05
Mean
:NaN
Mean
:NaN
Mean
:NaN
3rd Qu.:1404907 3rd Qu.:140.00 3rd Qu.: NA
3rd Qu.: NA
3rd Qu.: NA
Max. :2400320 Max. :240.00 Max. : NA
Max. : NA
Max. : NA
NA's :57074 NA's :57074 NA's :57074
hhsizeweight3psu
stratum
surveymonth
Min.
: NA
Min.
: NA
Min.
: NA
Min.
: NA
Min.
: NA
1st Qu.: NA
1st Qu.: NA
1st Qu.: NA
1st Qu.: NA
1st Qu.: NAMedian : NA
Median : NA
Median : NA
Median : NA
Median : NA
Mean
:NaN
Mean
:NaN
Mean
:NaN
Mean
:NaN
Mean
:NaN
3rd Qu.: NA
3rd Qu.: NA
3rd Qu.: NA
3rd Qu.: NA
3rd Qu.: NA
Max.
: NA
Max.
: NA
Max.
: NA
Max.
: NA
Max.
: NA
NA's
:57074NA's:57074NA's:57074NA's:57074NA's:57074
regionyear
persid_string
hhid_string
Min.
: NA
Min.
: NA
Length:57074
Length:57074
1st Qu.: NA
1st Qu.: NAClass :character Class :character
Median : NA
Median : NA
Mode :character
Mode :character
Mean
:NaN
Mean
:NaN
3rd Qu.: NA
3rd Qu.: NA
Max.
: NA
Max.
: NA
NA's
:57074NA's:57074
q17dc01
q17dc02
q17dc03a
q17dc03b
q17dc03cMin.
: 1.000
Min.
:1.000Min.
: 1.00
Min.
:1.00
Min.
:1.00
1st Qu.: 2.000 1st Qu.:2.000 1st Qu.: 1.00 1st Qu.:3.75
1st Qu.:3.00
Median : 3.000
Median :2.000
Median : 4.00
Median :6.50
Median :4.00
Mean: 3.279
Mean:1.994Mean: 4.56
Mean
:5.72
Mean
:4.95
3rd Qu.: 4.000 3rd Qu.:2.000 3rd Qu.: 8.00 3rd Qu.:8.00
3rd Qu.:8.00
Max.
:15.000
Max.
:2.000Max.
:11.00Max.
:9.00
Max. :9.00
NA's:56752NA's:56970NA's:57037
q17dc04
q17dc05
q17dc06
q17dc07
q17dc08
Min.
:1.00
Min.
:1.00
Min.
:1.00
Min.
:1.00
Min. :1.00
1st Qu.:1.00
1st Qu.:2.00
1st Qu.:1.00
1st Qu.:2.00
1st Qu.:3.00
Median :2.00
Median :2.00
Median :1.00
Median :2.00
Median :3.00
Mean
:2.34
Mean
:1.75
Mean
:1.35
Mean
:1.89
Mean
:3.08
3rd Qu.:3.75
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:2.00
3rd Qu.:3.00
Max.
:5.00
Max.
:2.00
Max.
:3.00
Max.
:2.00
Max.
:5.00
NA's
:56752NA's:56752NA's:56995NA's:56995NA's:56752q17dc09
q17dc10
Min.
:1.00
Min.
:1.00
1st Qu.:2.00
1st Qu.:1.00
Median :2.00
Median :1.00
Mean
:1.75
Mean
:1.44
3rd Qu.:2.00
3rd Qu.:2.00
Max.
:2.00
Max.
:4.00
NA's
:56752NA's:56752

Manual for Cambodia CSES 2009 (Version 2.0)

115## 40 #### s17d.violence ##########
hhid
persidMin.
: 100101
Min.
: 10010101
1st Qu.: 308308
1st Qu.: 30830828
Median : 804402
Median : 80440202
Mean: 973234
Mean: 97323359
3rd Qu.:1404907
3rd Qu.:140490676
Max.
:2400320
Max.
:240032005

provinceMin.
: 1.000
1st Qu.: 3.000
Median : 8.000
Mean: 9.704
3rd Qu.:14.000
Max.
:24.000

weightMin.
: 45.06
1st Qu.:228.20
Median :246.02
Mean:245.94
3rd Qu.:265.13
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.558
3rd Qu.: 7.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 965.00
Median :1299.40
Mean:1369.16
3rd Qu.:1692.57
Max.
:5319.70

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.84
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.00
1st Qu.: 3.00
Median : 6.00
Mean
: 6.47
3rd Qu.: 9.00
Max.
:12.00regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.6943rd Qu.:3.000
Max.
:3.000hhid_string
Length:57074
Class :character
Mode :character

q17dc03cMin.
:1.00
1st Qu.:3.00
Median :4.00
Mean
:4.95
3rd Qu.:8.00
Max.
:9.00
NA's
:57037
q17dc08
Min.
:1.00
1st Qu.:3.00
Median :3.00
Mean
:3.08
3rd Qu.:3.00
Max.
:5.00
NA's
:56752q17dc01Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.279
3rd Qu.: 4.000
Max.
:15.000

q17dc04
Min.
:1.00
1st Qu.:1.00
Median :2.00
Mean
:2.34
3rd Qu.:3.75
Max.
:5.00
NA's
:56752q17dc09Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:1.75
3rd Qu.:2.00
Max.
:2.00
NA's
:56752

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.796
3rd Qu.:2.000
Max.
:2.000psu
Min.
: 1001
1st Qu.: 3083
Median : 8044
Mean
: 9732
3rd Qu.:14049
Max.
:24003
year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

persid_string
Length:57074
Class :character
Mode :character

q17dc02Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.9943rd Qu.:2.000
Max.
:2.000q17dc05
Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:1.75
3rd Qu.:2.00
Max.
:2.00
NA's
:56752q17dc10Min.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.44
3rd Qu.:2.00
Max.
:4.00
NA's
:56752q17dc03a
q17dc03bMin.
: 1.00Min.
:1.00
1st Qu.: 1.00
1st Qu.:3.75
Median : 4.00
Median :6.50
Mean: 4.56Mean:5.72
3rd Qu.: 8.00
3rd Qu.:8.00
Max.
:11.00
Max.
:9.00
NA's
:56752NA's:56970q17dc06
q17dc07
Min.
:1.00
Min.
:1.00
1st Qu.:1.00
1st Qu.:2.00
Median :1.00
Median :2.00
Mean
:1.35
Mean
:1.89
3rd Qu.:2.00
3rd Qu.:2.00
Max.
:3.00
Max.
:2.00
NA's
:56995NA's:56995## 41 #### s18.presenseinhh ##########
hhid
persidprovince
Min.
: 100101
Min.
: 10010101
Min.
: 1.000
1st Qu.: 308308 1st Qu.: 30830828
1st Qu.: 3.000

urbanruralMin.
:1.000
1st Qu.:2.000

Manual for Cambodia CSES 2009 (Version 2.0)

116Median : 804402
Mean: 973234
3rd Qu.:1404907
Max.
:2400320

Median : 80440202
Mean: 97323359
3rd Qu.:140490676
Max.
:240032005

weightMin.
: 45.06
1st Qu.:228.20
Median :246.02
Mean:245.94
3rd Qu.:265.13
Max.
:591.08hhsizeMin.
: 1.000
1st Qu.: 4.000
Median : 5.000
Mean: 5.558
3rd Qu.: 7.000
Max.
:15.000

weight3Min.
: 45.06
1st Qu.: 965.00
Median :1299.40
Mean:1369.16
3rd Qu.:1692.57
Max.
:5319.70

stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean: 98.84
3rd Qu.:142.00
Max.
:242.00

surveymonthMin.
: 1.00
1st Qu.: 3.00
Median : 6.00
Mean
: 6.47
3rd Qu.: 9.00
Max.
:12.00regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.6943rd Qu.:3.000
Max.
:3.000hhid_string
Length:57074
Class :character
Mode :character

q18_c01Min.
: 1.000
1st Qu.: 2.000
Median : 3.000
Mean: 3.279
3rd Qu.: 4.000
Max.
:15.000

q18_c02d
Min. :
0
1st Qu.:23000
Median : 35000
Mean : 53553
3rd Qu.: 50000
Max.
:10000000
NA's :54711
q18_c03d
Min. :
0
1st Qu.:20000
Median : 35000
Mean : 48053
3rd Qu.: 49000
Max. :10000000
NA's :54611
q18_c04d
Min.
:0
1st Qu.:20000
Median :
35000
Mean
:
58266
3rd Qu.:50000
Max.
:10000000
NA's :54644
q18_c05d
Min.
:
0
1st Qu.: 25000

q18_c03aMin. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.046
3rd Qu.:1.000
Max.
:2.000NA's :17q18_c04aMin. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.046
3rd Qu.:1.000
Max. :2.000
NA's :11q18_c05aMin.
:1.00
1st Qu.:1.00
Median :1.00
Mean
:1.04
3rd Qu.:1.00
Max.
:2.00
NA's :6

Median : 8.000
Mean: 9.704
3rd Qu.:14.000
Max.
:24.000

Median :2.000
Mean:1.796
3rd Qu.:2.000
Max.
:2.000psu
Min.
: 1001
1st Qu.: 3083
Median : 8044
Mean
: 9732
3rd Qu.:14049
Max.
:24003

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

persid_string
Length:57074
Class :character
Mode :character

q18_c02aq18_c02bMin.
:1.000Min.
:1.00
1st Qu.:1.000
1st Qu.:1.00
Median :1.000
Median :2.00
Mean:1.044Mean:2.91
3rd Qu.:1.000
3rd Qu.:5.00
Max.
:2.000
Max.
:6.00
NA's
:2
NA's
:55802q18_c03bq18_c03c
Min. :1
Min. :1.00
1st Qu.:1
1st Qu.:2.00
Median :3
Median :2.00
Mean :3
Mean :1.93
3rd Qu.:5
3rd Qu.:2.00
Max. :6
Max. :2.00
NA's :55628 NA's :54429
q18_c04bq18_c04c
Min. :1.0
Min. :1.00
1st Qu.:2.0
1st Qu.:2.00
Median :3.0
Median :2.00
Mean :3.1
Mean :1.93
3rd Qu.:4.0
3rd Qu.:2.00
Max. :6.0
Max. :2.00
NA's :55611 NA's :54446
q18_c05bq18_c05c
Min.
:1.00
Min.
:1.00
1st Qu.:1.00
1st Qu.:2.00
Median :2.00
Median :2.00
Mean
:2.89
Mean
:1.96
3rd Qu.:5.00
3rd Qu.:2.00
Max.
:6.00
Max.
:2.00
NA's :55994 NA's
:54818Manual for Cambodia CSES 2009 (Version 2.0)

q18_c02c
Min.
:1.00
1st Qu.:2.00
Median :2.00
Mean
:1.95
3rd Qu.:2.00
Max.
:2.00
NA's
:54582

117

Median :
36000
Mean
:
50760
3rd Qu.:50000
Max.
:10000000
NA's:54903## 42 #### s99.singlequestions ##########
hhid
province
urbanruralMin.
: 100101Min.
: 1.00Min.
:1.000
1st Qu.: 308205
1st Qu.: 3.00
1st Qu.:2.000
Median : 804502
Median : 8.00
Median :2.000
Mean: 976828
Mean: 9.74Mean:1.801
3rd Qu.:1405104
3rd Qu.:14.00
3rd Qu.:2.000
Max.
:2400320Max.
:24.00Max.
:2.000weightMin.
: 45.06
1st Qu.:227.74
Median :245.19
Mean:245.48
3rd Qu.:265.05
Max.
:591.08hhsizeMin.
: 1.00
1st Qu.: 3.00
Median : 5.00
Mean
: 4.77
3rd Qu.: 6.00
Max.
:15.00weight3Min.
: 45.06
1st Qu.: 782.44
Median :1112.13
Mean:1173.19
3rd Qu.:1469.86
Max.
:5319.70

psu
Min.
: 1001
1st Qu.: 3082
Median : 8045
Mean
: 9768
3rd Qu.:14051
Max.
:24003stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean
: 99.2
3rd Qu.:142.0
Max.
:242.0surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.504
3rd Qu.:10.000
Max.
:12.000

regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.7033rd Qu.:3.000
Max.
:3.000

year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

hhid_string
Length:11971
Class :character
Mode :character

q01bq1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.269
3rd Qu.:2.000
Max. :2.000

q03bq1Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.716
3rd Qu.:2.000
Max.
:2.000q05aq1aMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.931
3rd Qu.:2.000
Max. :2.000

q05aq1bMin.
:1.000
1st Qu.:1.000
Median :3.000
Mean:3.007
3rd Qu.:4.000
Max.
:7.000NA's:11142q05eq1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.295
3rd Qu.:2.000
Max.
:2.000q05aq2Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.289
3rd Qu.:2.000
Max.
:2.000q05gq2Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean :1.502
3rd Qu.:2.000
Max.
:2.000q05aq3Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.935
3rd Qu.:2.000
Max.
:9.000NA's:3461
q05fq1
q05fq2Min.
:1.000Min.
:1.000
1st Qu.:2.000 1st Qu.:1.000
Median :2.000
Median :1.000
Mean:1.978Mean:1.181
3rd Qu.:2.000 3rd Qu.:1.000
Max.
:2.000Max.
:2.000NA's :11712
q05hq1q06_q1
Min.
:1.00
Min.
:1.000
1st Qu.:1.00 1st Qu.:1.000
Median :2.00
Median :2.000
Mean
:1.66
Mean
:1.625
3rd Qu.:2.00 3rd Qu.:2.000
Max.
:2.00
Max.
:2.000q05bq1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.0843rd Qu.:1.000
Max.
:2.000NA's:3461
q05fq3Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.451
3rd Qu.:2.000
Max.
:2.000q05dq1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.255
3rd Qu.:2.000
Max.
:2.000NA's:4166q05gq1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.287
3rd Qu.:2.000
Max.
:2.000q08_q1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.062
3rd Qu.:1.000
Max.
:2.000q10_q1Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.6333rd Qu.:2.000
Max.
:2.000NA's :2

Manual for Cambodia CSES 2009 (Version 2.0)

118

q11_q1Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.808
3rd Qu.:2.000
Max.
:2.000NA's:3q13aq2cMin.
:1.000
1st Qu.:3.000
Median :4.000
Mean:3.827
3rd Qu.:4.000
Max.
:8.000NA's:11792
q17bq1Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.9743rd Qu.:2.000
Max.
:2.000q12_q1Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:1.6243rd Qu.:2.000
Max.
:2.000NA's:1q13aq3Min.
:1.000
1st Qu.:1.000
Median :2.000
Mean:2.379
3rd Qu.:3.000
Max.
:8.000NA's:11391q17cq1Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.951
3rd Qu.:2.000
Max.
:2.000## 43 #### weighthh ##########
hhid
province
Min.
: 100101Min.
: 1.00
1st Qu.: 308205
1st Qu.: 3.00
Median : 804502
Median : 8.00
Mean
: 976828
Mean: 9.743rd Qu.:1405104
3rd Qu.:14.00
Max.
:2400320Max.
:24.00weight3psu
Min.
: 45.06
Min.
: 1001
1st Qu.: 782.44
1st Qu.: 3082
Median :1112.13
Median : 8045
Mean
:1173.19
Mean: 9768
3rd Qu.:1469.86
3rd Qu.:14051
Max.
:5319.70
Max.
:24003
year
Min.
:2009
1st Qu.:2009
Median :2009
Mean
:2009
3rd Qu.:2009
Max.
:2009

q13aq1Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.952
3rd Qu.:2.000
Max.
:8.000q13aq4Min.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.997
3rd Qu.:2.000
Max.
:8.000NA's:577

urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:1.801
3rd Qu.:2.000
Max.
:2.000stratumMin.
: 11.0
1st Qu.: 32.0
Median : 82.0
Mean
: 99.2
3rd Qu.:142.0
Max.
:242.0## 44 #### weightpersons ##########
hhid
persidMin.
: 100101
Min.
: 10010101
1st Qu.: 308307
1st Qu.: 30830704
Median : 804402
Median : 80440204
Mean: 973384
Mean: 97338380

q13aq2aMin.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.8243rd Qu.:2.000
Max.
:8.000NA's:11392
q17aq1Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.325
3rd Qu.:2.000
Max.
:2.000q13aq2bMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean:2.791
3rd Qu.:3.000
Max.
:8.000NA's:11722
q17aq2Min.
:1.000
1st Qu.:1.000
Median :1.000
Mean:1.385
3rd Qu.:2.000
Max.
:2.000weightMin.
: 45.06
1st Qu.:227.74
Median :245.19
Mean:245.48
3rd Qu.:265.05
Max.
:591.08
surveymonthMin.
: 1.000
1st Qu.: 4.000
Median : 7.000
Mean: 6.504
3rd Qu.:10.000
Max.
:12.000

provinceMin.
: 1.000
1st Qu.: 3.000
Median : 8.000
Mean : 9.706

hhsizeMin.
: 1.00
1st Qu.: 3.00
Median : 5.00
Mean
: 4.77
3rd Qu.: 6.00
Max.
:15.00regionMin.
:1.000
1st Qu.:3.000
Median :3.000
Mean:2.7033rd Qu.:3.000
Max.
:3.000urbanruralMin.
:1.000
1st Qu.:2.000
Median :2.000
Mean :1.796

Manual for Cambodia CSES 2009 (Version 2.0)

119

3rd Qu.:1404910
Max.
:2400320
weightMin.
: 45.06
1st Qu.:228.20
Median :246.02
Mean:245.94
3rd Qu.:265.13
Max.
:591.08
stratumMin.
: 11.00
1st Qu.: 32.00
Median : 82.00
Mean : 98.85
3rd Qu.:142.00
Max. :242.00

3rd Qu.:140491001
3rd Qu.:14.000
3rd Qu.:2.000
Max.
:240032005
Max.
:24.000
Max.
:2.000hhsizeweight3psu
Min.
: 1.000
Min.
: 45.06
Min.
: 1001
1st Qu.: 4.000
1st Qu.: 965.00
1st Qu.: 3083
Median : 5.000
Median :1299.19
Median : 8044
Mean
: 5.557
Mean:1369.00
Mean: 97343rd Qu.: 7.000
3rd Qu.:1691.90
3rd Qu.:14049
Max.
:15.000
Max.
:5319.70
Max.
:24003surveymonth
regionyear
Min.
: 1.00Min.
:1.000 Min. :2009
1st Qu.: 3.00 1st Qu.:3.000 1st Qu.:2009
Median : 6.00 Median :3.000 Median :2009
Mean : 6.47 Mean :2.694 Mean :2009
3rd Qu.: 9.00 3rd Qu.:3.000 3rd Qu.:2009
Max. :12.00 Max. :3.000 Max. :2009## 45 #### IncomeCSES09 ##########
hhid
weighthhweighthhpers
hhsizeMin.
: 100101
Min.
: 45.06
Min.
: 47.88
Min.
: 1.00
1st Qu.: 308205 1st Qu.:227.74 1st Qu.: 783.63 1st Qu.: 3.00
Median : 804502 Median :245.19 Median :1104.61 Median : 5.00
Mean
: 976828
Mean:245.48
Mean:1166.71 Mean : 4.77
3rd Qu.:1405104 3rd Qu.:265.05 3rd Qu.:1464.39 3rd Qu.: 6.00
Max. :2400320 Max. :591.08 Max. :4890.26 Max. :15.00
salarydiarysalarycash
diarysalaryinkind
costcropMin.
:
0 Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:
0Median :0Median :0Median :0Median : 224000
Mean: 2984208
Mean: 2385452 Mean : 10306 Mean : 637314
3rd Qu.: 3840000
3rd Qu.: 2640000
3rd Qu.:0
3rd Qu.: 676000
Max. :398400000 Max. :357000000 Max. :72000000 Max. :62917000
costlivestock
costfishcostforestry
receiptcrop
Min. :
0 Min. :0 Min. :0Min. :-1.600e+04
1st Qu.:0 1st Qu.:0 1st Qu.:0
1st Qu.: 0.000e+00
Median :50000 Median :5000 Median :0Median : 5.910e+05
Mean : 396654 Mean : 110917 Mean : 21291
Mean : 3.795e+06
3rd Qu.: 260500 3rd Qu.: 30000 3rd Qu.: 15000
3rd Qu.: 1.689e+06
Max. :120250000 Max. :64400000 Max. :7500000
Max. : 1.500e+10
receiptlivestock
receiptfish
receiptforestry
agriincome
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:-6.039e+07
1st Qu.:0 1st Qu.:0 1st Qu.: 25000 1st Qu.: 7.000e+04
Median :60000 Median :50000
Median : 321500
Median : 8.100e+05
Mean:528815Mean: 355658
Mean: 409487
Mean: 3.917e+06
3rd Qu.: 282000 3rd Qu.: 230000 3rd Qu.: 500000 3rd Qu.: 2.084e+06
Max. :146080000 Max. :95450000 Max.
:19260000
Max.
: 1.500e+10
costnonagri
receiptnonagri
nonagriincome
incomeownhouseMin.
:0.000e+00
Min.
:0.000e+00
Min.
:-970930600 Min. : -1587900
1st Qu.:0.000e+00
1st Qu.:0.000e+00
1st Qu.:0
1st Qu.:90000
Median :0.000e+00
Median :0.000e+00
Median :0Median :300000Mean:9.482e+06
Mean:1.344e+07
Mean:3955622
Mean: 1138299
3rd Qu.:1.536e+06
3rd Qu.:3.200e+06
3rd Qu.:970000
3rd Qu.:720000Max.
:9.033e+09
Max.
:1.155e+10Max.
:8909620000
Max.
:480000000
bankinterest
interestotherloans dividends
rentfromland
Min. :
0 Min.
:
0
Min.
:
0
Min.
:
0

Manual for Cambodia CSES 2009 (Version 2.0)

120

1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:
0Median :0Median :0Median :0Median :
0
Mean
:
2559
Mean
:
26333
Mean
:
2717 Mean:
17044
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:
0
Max.
:8000000
Max.
:24000000
Max.
:24000000
Max.
:34440000
grosspropertyincome interestpaidagri interestpaidnonagri interestpaidownoccupied
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
1st Qu.:0
1st Qu.:0
Median :0Median :0Median :0Median :
0
Mean
:
48653
Mean
:
5278
Mean
:
2791
Mean
: 1060
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0Max.
:35916000
Max.
:2454545
Max.
:2352941
Max.
:955339
interestpaidnet propertyincome
primaryincome
pensiondomestic
Min.
:
0
Min.
:-3428571 Min. :-9.709e+08
Min.
:0
1st Qu.:0 1st Qu.:0 1st Qu.: 2.002e+06 1st Qu.:
0Median :0Median :0 Median : 4.568e+06
Median :
0
Mean
:
7709
Mean
:
40943
Mean
: 1.204e+07
Mean
:
20922
3rd Qu.:0 3rd Qu.:0 3rd Qu.: 9.242e+06 3rd Qu.:0Max.
:3428571
Max.
:35916000
Max.
: 1.500e+10 Max.
:22440000
pensionabroad
pensionngotransfers
remittancedomestic
Min. :
0 Min. :0 Min. :0Min. :
0
1st Qu.:0 1st Qu.:0 1st Qu.:0
1st Qu.:0
Median :0 Median :0 Median :0Median :
0
Mean
:
2638
Mean
:
23559
Mean
:
5698
Mean
:
83078
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
3rd Qu.:0Max.
:8200000
Max.
:22440000 Max. :9600000
Max. :82000000
remittanceabroad
totalprivatetransfers scholarshipgovernment scholarshipngo
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
1st Qu.:0
1st Qu.:0
Median :0Median :0Median :0Median :
0
Mean
:
90990
Mean
: 174068
Mean
:
5179
Mean
:
4539
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0Max.
:72000000
Max.
:82779000
Max.
:5000000
Max.
:4000000
totalscholarship
gifts
othertransfer
totaltransfers
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0 1st Qu.:0 1st Qu.:0
1st Qu.:0
Median :0Median :0Median :0Median :
0
Mean
:
9718
Mean
:
28343
Mean
:
167624
Mean:
409011
3rd Qu.:0 3rd Qu.:0
3rd Qu.:0
3rd Qu.:70000
Max.
:5000000
Max.
:20500000
Max.
:176000000
Max.
:176200000
totalincome
wageratio
agriratiootherselfempratio
Min.
:-9.709e+08
Min.
:-81.7800 Min. :-156.7800 Min. :-50.8300
1st Qu.: 2.114e+06
1st Qu.: 0.0000
1st Qu.:0.0100
1st Qu.: 0.0400
Median : 4.748e+06
Median : 0.0000
Median :0.2400Median : 0.1400
Mean: 1.245e+07
Mean: 0.2722
Mean:
0.3191Mean: 0.3083
3rd Qu.: 9.598e+06
3rd Qu.: 0.6100
3rd Qu.:0.7300
3rd Qu.: 0.4800
Max.
: 1.500e+10
Max.
: 16.0700
Max.
:
6.0900Max.
:157.7800
propertyratio
transfersratio
diarytaxes
diaryinterhhtransfersMin.
:-2.3900001
Min.
:-13.64000
Min.
:
0
Min.
:
0
1st Qu.: 0.0000000 1st Qu.: 0.00000 1st Qu.:0 1st Qu.:
0Median : 0.0000000
Median : 0.00000
Median :0Median :0
Mean: 0.0004728
Mean: 0.03739
Mean
:
16604
Mean
:
29044
3rd Qu.: 0.0000000 3rd Qu.: 0.02000 3rd Qu.:0 3rd Qu.:0Max.
: 2.5899999
Max.
: 4.32000
Max.
:43368000
Max.
:29520000
diarycashtransferchar diarytotalnegativetransfers disposableincome
Min.
:
0
Min.
:
0
Min.
:-9.722e+08

Manual for Cambodia CSES 2009 (Version 2.0)

121

1st Qu.:0
1st Qu.:0
1st Qu.: 2.048e+06
Median :0Median :0Median : 4.635e+06
Mean:173875
Mean:
219523Mean: 1.223e+07
3rd Qu.:36000
3rd Qu.:60000
3rd Qu.: 9.410e+06
Max.
:438240000
Max.
:438240000
Max.
: 1.500e+10
diarycostagri
diaryreceiptagri
diaryagriincome
diarycostnonagriMin.
:0.00e+00
Min.
:0.000e+00
Min.
:-478944000 Min. :0.000e+00
1st Qu.:0.00e+00
1st Qu.:3.180e+05 1st Qu.:
108000 1st Qu.:0.000e+00
Median :0.00e+00
Median :1.318e+06
Median :1074000
Median :0.000e+00
Mean:5.64e+05
Mean:3.154e+06
Mean:
2590184
Mean:1.106e+07
3rd Qu.:6.00e+04 3rd Qu.:2.653e+06 3rd Qu.: 2365800 3rd Qu.:1.302e+06
Max. :4.80e+08 Max. :2.276e+09 Max. :2276380800 Max. :7.624e+09
diaryreceiptnonagri diarynonagriincome diarybankinterest diaryinterestotherloans
Min.
:0.000e+00
Min.
:-7.550e+09 Min. :
0
Min.
:
0
1st Qu.:0.000e+00 1st Qu.: 0.000e+00 1st Qu.:0 1st Qu.:
0Median :0.000e+00
Median : 0.000e+00
Median :0Median :0
Mean:1.356e+07
Mean: 2.501e+06
Mean
:
16991
Mean
:
46227
3rd Qu.:4.065e+06 3rd Qu.: 1.031e+06 3rd Qu.:0 3rd Qu.:
0Max.
:3.946e+09
Max.
: 3.946e+09
Max.
:57600000
Max.
:96000000
diarydividends
diaryotherfinancialaccount diarygrosspropertyincome
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0
1st Qu.:0
1st Qu.:0
Median :0Median :0Median :
0
Mean
:
2944
Mean
:
178074
Mean:244236
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0
Max. :18000000 Max. :490671600
Max. :490671600
diarypropertyincome diaryprimaryincome diarypensiondomestic diarypensionabroad
Min.
:
0
Min.
:-7.544e+09
Min.
:
0
Min.
:0
1st Qu.:0 1st Qu.: 1.760e+06 1st Qu.:0
1st Qu.:0
Median :0 Median : 3.839e+06
Median :0Median :0
Mean:
244236Mean: 7.731e+06
Mean
:
5834
Mean
:0
3rd Qu.:0 3rd Qu.: 8.376e+06 3rd Qu.:0
3rd Qu.:0
Max. :490671600 Max.
: 3.951e+09 Max. :24000000
Max. :0
diarypension
diaryinsurancedomestic diaryinsuranceabroad diaryngotransfersMin. :
0 Min. :0
Min. :
0.00
Min. :
0
1st Qu.:0
1st Qu.:0
1st Qu.:0.00
1st Qu.:0
Median :0 Median :0
Median :0.00
Median :0
Mean :
5834 Mean :0
Mean :
5.01
Mean :
5271
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0.00
3rd Qu.:0
Max. :24000000 Max. :0
Max. :60000.00
Max. :15387600
diaryremittancedomestic diaryremittanceabroad diarytotalprivatetransfersMin. :
0Min. :
0Min. :
0
1st Qu.:0
1st Qu.:0
1st Qu.:0
Median :0Median :0Median :0
Mean:181312
Mean
:
39315
Mean
:
220627
3rd Qu.:0
3rd Qu.:0
3rd Qu.:0
Max. :168000000
Max. :176400000
Max. :176400000
diaryscholarshipdiarygifts
diaryothertransfers diarytotaltransfers
Min.
:
0
Min.
:
0
Min.
:
0
Min.
:
0
1st Qu.:0 1st Qu.:0 1st Qu.:
96000
1st Qu.:96000
Median :0Median :0Median :96000
Median :
96000
Mean
:
1385
Mean
:
66907
Mean
:
356188Mean:
656218
3rd Qu.:0 3rd Qu.: 24000 3rd Qu.:
96000
3rd Qu.: 156000
Max.
:6240000Max.
:24052800
Max.
:300096000
Max.
:300096000
diarytotalincome
diarydisposableincome psuchar
psu1
Min.
:-7.544e+09
Min.
:-7.544e+09
Length:11971
Length:11971

Manual for Cambodia CSES 2009 (Version 2.0)

122

1st Qu.: 2.075e+06
1st Qu.: 1.997e+06
Class :character
Class :character
Median : 4.260e+06
Median : 4.169e+06
Mode :character Mode :character
Mean: 8.387e+06
Mean: 8.168e+06
3rd Qu.: 8.965e+06
3rd Qu.: 8.835e+06
Max.
: 3.951e+09
Max.
: 3.951e+09
psu
areaiddprovince
urbanrural
dregionMin.
: 1001Min.
: 361.0
Min.
: 1.00Min.
:1.000Min.
:1.000
1st Qu.: 3082 1st Qu.: 537.0 1st Qu.: 3.00 1st Qu.:2.000 1st Qu.:3.000
Median : 8045
Median : 703.0
Median : 8.00
Median :2.000
Median :3.000
Mean: 9768Mean: 721.2
Mean: 9.74Mean:1.801Mean:2.7033rd Qu.:14051 3rd Qu.: 914.0 3rd Qu.:14.00 3rd Qu.:2.000 3rd Qu.:3.000
Max.
:24003
Max.
:1080.0
Max.
:24.00Max.
:2.000Max.
:3.000dzone
blillanbstoranagriadjMin.
:0.000Min.
:10.00Min.
: 450.6
Min.
:0
1st Qu.:1.000 1st Qu.:20.00 1st Qu.: 4238.4
1st Qu.:
70000
Median :1.000
Median :20.00
Median : 4793.2
Median :810000Mean:1.714Mean:18.01Mean: 4468.5
Mean: 1885374
3rd Qu.:2.000 3rd Qu.:20.00 3rd Qu.: 5206.6 3rd Qu.: 2084500
Max.
:4.000Max.
:20.00Max.
:11821.6
Max.
:220608000
nonagriadj
propertyadj
ownhouseadj
adjdisp
Min. :
0 Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.: 90000 1st Qu.: 2099125
Median :0 Median :0 Median : 300000 Median : 4660000
Mean : 3049650 Mean : 49274 Mean : 863786 Mean : 8937313
3rd Qu.: 970000 3rd Qu.:0 3rd Qu.: 720000 3rd Qu.: 9400000
Max. :963360000 Max. :35916000 Max. :12000000 Max. :966197500
adjdispcapita
adjtotaltransfers adjtotalincome
adjprimaryincome
Min. :
0 Min. :0 Min.
:0Min.
:
0
1st Qu.: 500261 1st Qu.:0 1st Qu.: 2171050 1st Qu.: 2087380
Median : 1054900
Median :0Median : 4778810
Median : 4645500
Mean: 1926313
Mean: 241387
Mean: 9073679
Mean: 8832293
3rd Qu.: 2049159 3rd Qu.: 50000 3rd Qu.: 9570000 3rd Qu.: 9307750
Max.
:164573500
Max.
:82779000
Max.
:966851500
Max.
:966821500
adjdiarytotalnegativetransfers
Min.
:
-4000
1st Qu.:0
Median :0
Mean: 136366
3rd Qu.:60000
Max.
:48252000

Manual for Cambodia CSES 2009 (Version 2.0)

123

List of colnames to identify categorical variables> for(j in 1:45){+ cat("##",j,"#### ",Rnames[j]," ##########\n")+ print(colnames(outfiles[[j]]))+ cat("\n\n")+ }## 1 #### 09dy.expenditure ##########[1] "hhid""province""urbanrural"[4] "weight""hhsize""weight3"[7] "psu""stratum""surveymonth"[10] "region""year""pkid"[13] "hhid_string""lineid""diaryexp_q2"[16] "diaryexp_q5""diaryexp_q6""diaryexp_q7"[19] "diaryexp_q8""diaryexp_q9""diaryexp_q10"[22] "diaryexp_q11""diaryexp_q2_string"## 2 #### 09dy.income ##########[1] "hhid""province""urbanrural"[4] "weight""hhsize""weight3"[7] "psu""stratum""surveymonth"[10] "region""year""pkid"[13] "hhid_string""lineid""diaryinc_q2"[16] "diaryinc_q5""diaryinc_q6""diaryinc_q7"[19] "diaryinc_q8""diaryinc_q9""diaryinc_q10"[22] "diaryinc_q2_string" "diaryinc_q10_string"## 3 #### s01a.hhmembers ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q01ac03""q01ac04a""q01ac04b""q01ac04c"[21] "q01ac06""q01ac07""q01ac08""q01ac09"[26] "q01ac11a""q01ac11b""q01ac12a""q01ac12b"[31] "q01ac13""q01ac14""weight""surveymonth""q01ac01""q01ac05""q01ac10""q01ac12c"## 4 #### s01b.foodconsumption ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q01bc01"[16] "q01bc05""weight""hhsize""surveymonth" "region""q01bc03""q01bc04"## 5 #### s01c.nonfoodexpenses ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q01cc01"[16] "q01cc06""weight""hhsize""surveymonth" "region""q01cc04""q01cc05"## 6 #### s01d.vulnerability[1] "hhid""province"##########"urbanrural""weight""hhsize"

Manual for Cambodia CSES 2009 (Version 2.0)

124[6] "weight3"[11] "year"[16] "q01dq4_1"[21] "q01dq4_6"[26] "q01dq4_11""psu""stratum""hhid_string" "q01dq1""q01dq4_2""q01dq4_3""q01dq4_7""q01dq4_8""q01dq4_12""surveymonth" "region""q01dq2""q01dq3""q01dq4_4""q01dq4_5""q01dq4_9""q01dq4_10"## 7 #### s02.education ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q02c02""q02c03""q02c04""q02c05"[21] "q02c07""q02c08""q02c09""q02c10"[26] "q02c12""q02c13""q02c14""q02c15"[31] "q02c16b""q02c16c""q02c16d""q02c16e"[36] "q02c16g""q02c16h""weight""surveymonth""q02c01""q02c06""q02c11""q02c16a""q02c16f"## 8 #### s03a.migration.past ##########[1] "hhid""persid""province""urbanrural"[5] "weight""hhsize""weight3""psu"[9] "stratum""surveymonth""region""year"[13] "persid_string" "hhid_string""q03ac01""q03ac02"[17] "q03ac03""q03ac04b""q03ac04c""q03ac05"[21] "q03ac06b""q03ac06c""q03ac07""q03ac08"[25] "q03ac09a""q03ac09b""q03ac10""q03ac11"[29] "q03ac12""q03ac13""q03ac04b_string" "q03ac04c_string"[33] "q03ac06b_string" "q03ac06c_string" "q03ac12_string"## 9 #### s03b.migration.current[1] "hhid""persid"[6] "hhsize""weight3"[11] "region""year"[16] "q03bc03""q03bc04"[21] "q03bc07""q03bc08"[26] "q03bc12""q03bc13a"##########"province""urbanrural""psu""stratum""persid_string" "hhid_string""q03bc05b""q03bc05c""q03bc09""q03bc10""q03bc13b""weight""surveymonth""q03bc01""q03bc06""q03bc11"## 10 #### s04.housing ##########[1] "hhid""province""urbanrural" "weight""hhsize"[6] "weight3""psu""stratum""surveymonth" "region"[11] "year""hhid_string" "q04_01""q04_02""q04_03"[16] "q04_04""q04_05""q04_06""q04_07""q04_08"[21] "q04_09""q04_10m1""q04_10m2""q04_10m3""q04_11"[26] "q04_12""q04_13""q04_14m1""q04_14m2""q04_14m3"[31] "q04_15""q04_16""q04_17""q04_18a""q04_18b"[36] "q04_18c""q04_18d""q04_18e""q04_19a""q04_19b"[41] "q04_20""q04_21""q04_22a""q04_22b""q04_22c1"[46] "q04_22c2""q04_22c3""q04_22d""q04_23a""q04_23b"[51] "q04_23c""q04_23d""q04_23e""q04_23f""q04_23g"[56] "q04_24""q04_25a""q04_25b""q04_26""phnonpenh"[61] "otherrural" "urban""publiclight" "pre_1""log_25b"[66] "pre_2"

Manual for Cambodia CSES 2009 (Version 2.0)

125## 11 #### s04.housingpractice[1] "hhid_string" "q04_01"[6] "q04_05""q04_06"[11] "q04_10m1""q04_10m2"[16] "q04_13""q04_14m1"[21] "q04_16""q04_17"[26] "q04_18d""q04_18e"[31] "q04_21""q04_22a"[36] "q04_22c3""q04_22d"[41] "q04_23d""q04_23e"[46] "q04_25a""q04_25b"##########"q04_02""q04_07""q04_10m3""q04_14m2""q04_18a""q04_19a""q04_22b""q04_23a""q04_23f""q04_26"## 12 #### s05a.landown ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05ac01"[16] "q05ac04a""q05ac04b""q05ac04c"[21] "q05ac05c""q05ac06a""q05ac06b"[26] "q05ac08""q05ac09""q05ac10"[31] "q05ac13a""q05ac13b""q05ac14"[36] "q05ac16b""q05ac16c""q05ac17"[41] "q05ac18c""q05ac19""q05ac20"## 13 #### s05b.cropsproduction ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "wetdry"[16] "q05bc03b""q05bc04""q05bc05"[21] "q05bc08""q05bc09""pastyear"## 14 #### s05c.costcrops ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "wetdry"[16] "q05cc03""q05cc04""q05cc05"[21] "q05cc08""q05cc09""q05cc10"[26] "q05cc13""q05cc14""q05cc15""q04_03""q04_08""q04_11""q04_14m3""q04_18b""q04_19b""q04_22c1""q04_23b""q04_23g""hhid""q04_04""q04_09""q04_12""q04_15""q04_18c""q04_20""q04_22c2""q04_23c""q04_24""weight""hhsize""surveymonth" "region""q05ac02""q05ac03""q05ac05a""q05ac05b""q05ac06c""q05ac07""q05ac11""q05ac12""q05ac15""q05ac16a""q05ac18a""q05ac18b""q05ac21""q05ac22""weight""hhsize""surveymonth" "region""q05bc01""q05bc02""q05bc06""q05bc07""weight""hhsize""surveymonth" "region""q05cc01""q05cc02""q05cc06""q05cc07""q05cc11""q05cc12""q05cc16"## 15 #### s05d.cropsinventory ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05dc01"[16] "q05dc04""weight""hhsize""surveymonth" "region""q05dc02b""q05dc03"## 16 #### s05e1.animals ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05e1c01"[16] "q05e1c05""q05e1c06""q05e1c07"[21] "q05e1c10""q05e1c11""q05e1c12"[26] "q05e1c15""weight""hhsize""surveymonth" "region""q05e1c03""q05e1c04""q05e1c08""q05e1c09""q05e1c13""q05e1c14"

Manual for Cambodia CSES 2009 (Version 2.0)

126## 17 #### s05e2.animalsexpenses ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05e2c01"## 18 #### s05f1.fisharea ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05f1c01"[16] "q05f1c04""q05f1c05""weight""hhsize""surveymonth" "region""q05e2c03""weight""hhsize""surveymonth" "region""q05f1c02""q05f1c03"## 19 #### s05f2.fishexpenses ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05f2c01""weight""hhsize""surveymonth" "region""q05f2c03"## 20 #### s05f3.fishincome ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05f3c01""weight""hhsize""surveymonth" "region""q05f3c03"## 21 #### s05g1.forestincome ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05g1c01"[16] "q05g1c05""q05g1c06"## 22 #### s05g2.forestexpenses ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05g2c01"## 23 #### s05h1.bussiness ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q05h1c01"[16] "q05h1c06a" "q05h1c06b" "q05h1c06c"[21] "q05h1c06f" "q05h1c06g" "q05h1c06h""weight""hhsize""surveymonth" "region""q05g1c03""q05g1c04""weight""hhsize""surveymonth" "region""q05g2c03""weight""hhsize""surveymonth" "region""q05h1c04""q05h1c05""q05h1c06d" "q05h1c06e"## 24 #### s05h2.bussinessexpenses ##########[1] "hhid""province""urbanrural" "weight""hhsize"[6] "weight3""psu""stratum""surveymonth" "region"[11] "year""hhid_string" "q05h2c01""q05h2c03""q05h2c04"[16] "q05h2c05""q05h2c06""q05h2c07"## 25 ####

s05h3.bussinessincome##########

Manual for Cambodia CSES 2009 (Version 2.0)

127[1] "hhid"[6] "weight3"[11] "year"[16] "q05h3c05""province""urbanrural""psu""stratum""hhid_string" "q05h3c01""q05h3c06""q05h3c07"## 26 #### s06.liabilities ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q06_c01"[16] "q06_c04""q06_c05""q06_c06"## 27 #### s07.incomeother ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q07_c01"[16] "q07_c05"## 28 #### s08.construction ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q08_c01"[16] "q08_c02c""q08_c03""q08_c04"[21] "q08_c07""q08_c08""q08_c09"[26] "q08_c11b""q08_c12a""q08_c12b"[31] "q08_c15""q08_c16""q08_c17"## 29 #### s09.durables ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q09_c03"[16] "q09_c05b""q09_c05c""q09_c05d"[21] "q09_c07""q09_c08""weight""hhsize""surveymonth" "region""q05h3c03""q05h3c04""weight""hhsize""surveymonth" "region""q06_c02""q06_c03""q06_c07""q06_c08""weight""hhsize""surveymonth" "region""q07_c03""q07_c04""weight""hhsize""surveymonth" "region""q08_c02a""q08_c02b""q08_c05""q08_c06""q08_c10""q08_c11a""q08_c13""q08_c14""q08_c18""q08_c19""weight""hhsize""surveymonth" "region""q09_c04""q09_c05a""q09_c06a""q09_c06b"## 30 #### s10.healthmother ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q10_c02""q10_c03""q10_c04""q10_c05"[21] "q10_c07""q10_c08""q10_c09""q10_c10"[26] "q10_c12""q10_c13""q10_c14""q10_c15a"[31] "q10_c15c""q10_c15d""q10_c16""q10_c17"## 31 #### s11.health2years ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q11_c02""q11_c03""q11_c04""q11_c05"[21] "q11_c06b""q11_c07""q11_c08a""q11_c08b"[26] "q11_c10""q11_c11""q11_c12a""q11_c12b"[31] "q11_c13a""q11_c13b""q11_c13c""q11_c13d"

Manual for Cambodia CSES 2009 (Version 2.0)

"weight""surveymonth""q10_c01""q10_c06""q10_c11""q10_c15b""weight""surveymonth""q11_c01""q11_c06a""q11_c09""q11_c12c""q11_c14a"

128[36] "q11_c14b""q11_c14c""q11_c15a""q11_c15b""q11_c15c"## 32 #### s12.health5years ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q12_c02""q12_c03""q12_c04""q12_c05"[21] "q12_c07""q12_c08""q12_c09a""q12_c09b"[26] "q12_c10b""q12_c11""q12_c12""q12_c13""weight""surveymonth""q12_c01""q12_c06""q12_c10a"## 33 #### s13b.healthexpenses[1] "hhid""persid"[6] "hhsize""weight3"[11] "region""year"[16] "q13bc02""q13bc03"[21] "q13bc07""q13bc08"[26] "q13bc11""weight""surveymonth""q13bc01""q13bc06""q13bc10"##########"province""urbanrural""psu""stratum""persid_string" "hhid_string""q13bc04""q13bc05""q13bc09a""q13bc09b"## 34 #### s14.disability ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q14_c02a""q14_c02b""q14_c02c""q14_c03a"[21] "q14_c03c""q14_c04a""q14_c04b""q14_c04c"[26] "q14_c05b""q14_c05c""weight""surveymonth""q14_c01""q14_c03b""q14_c05a"## 35 #### s15.labor7days ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q15_c02""q15_c03""q15_c04""q15_c05b"[21] "q15_c07""q15_c08""q15_c09""q15_c10"[26] "q15_c12b""q15_c13b""q15_c14""q15_c15"[31] "q15_c17""q15_c18""q15_c19""q15_c20"[36] "q15_c22""q15_c23""q15_c24""q15_c25a"[41] "q15_c26""q15_c27a""q15_c27b""q15_c27c"[46] "q15_c29""q15_c30a""q15_c30b""q15_c31""weight""surveymonth""q15_c01""q15_c06b""q15_c11""q15_c16""q15_c21""q15_c25b""q15_c28"## 36 #### s16.labor12months ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q16_c02""q16_c03""q16_c04b""q16_c05"[21] "q16_c07""q16_c08""q16_c09""weight""surveymonth""q16_c01""q16_c06b"## 37 #### s17b.theft ##########[1] "hhid""persid"[6] "hhsize""weight3"[11] "region""year"[16] "q17bc02""q17bc03""province""urbanrural""psu""stratum""persid_string" "hhid_string""q17bc04""q17bc05"

Manual for Cambodia CSES 2009 (Version 2.0)

"weight""surveymonth""q17bc01""q17bc06"

129[21] "q17bc07""q17bc08"## 38 #### s17c.accidents ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q17cc02""q17cc03""q17cc04""q17cc05""weight""surveymonth""q17cc01""q17cc06"## 39 #### s17d.violance ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q17dc02""q17dc03a""q17dc03b""q17dc03c"[21] "q17dc05""q17dc06""q17dc07""q17dc08"[26] "q17dc10""weight""surveymonth""q17dc01""q17dc04""q17dc09"## 40 #### s17d.violence ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q17dc02""q17dc03a""q17dc03b""q17dc03c"[21] "q17dc05""q17dc06""q17dc07""q17dc08"[26] "q17dc10""weight""surveymonth""q17dc01""q17dc04""q17dc09"## 41 #### s18.presenseinhh ##########[1] "hhid""persid""province""urbanrural"[6] "hhsize""weight3""psu""stratum"[11] "region""year""persid_string" "hhid_string"[16] "q18_c02a""q18_c02b""q18_c02c""q18_c02d"[21] "q18_c03b""q18_c03c""q18_c03d""q18_c04a"[26] "q18_c04c""q18_c04d""q18_c05a""q18_c05b"[31] "q18_c05d""weight""surveymonth""q18_c01""q18_c03a""q18_c04b""q18_c05c"## 42 #### s99.singlequestions ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""hhid_string" "q01bq1"[16] "q05aq1b""q05aq2""q05aq3"[21] "q05eq1""q05fq1""q05fq2"[26] "q05gq2""q05hq1""q06_q1"[31] "q11_q1""q12_q1""q13aq1"[36] "q13aq2c""q13aq3""q13aq4"[41] "q17bq1""q17cq1""weight""hhsize""surveymonth" "region""q03bq1""q05aq1a""q05bq1""q05dq1""q05fq3""q05gq1""q08_q1""q10_q1""q13aq2a""q13aq2b""q17aq1""q17aq2"## 43 #### weighthh ##########[1] "hhid""province""urbanrural"[6] "weight3""psu""stratum"[11] "year""weight""hhsize""surveymonth" "region"

Manual for Cambodia CSES 2009 (Version 2.0)

130## 44 #### weightpersons ##########[1] "hhid""persid""province"[6] "hhsize""weight3""psu"[11] "region""year"## 45 #### IncomeCSES09 ##########[1] "hhid"[3] "weighthhpers"[5] "salary"[7] "diarysalaryinkind"[9] "costlivestock"[11] "costforestry"[13] "receiptlivestock"[15] "receiptforestry"[17] "costnonagri"[19] "nonagriincome"[21] "bankinterest"[23] "dividends"[25] "grosspropertyincome"[27] "interestpaidnonagri"[29] "interestpaidnet"[31] "primaryincome"[33] "pensionabroad"[35] "ngotransfers"[37] "remittanceabroad"[39] "scholarshipgovernment"[41] "totalscholarship"[43] "othertransfer"[45] "totalincome"[47] "agriratio"[49] "propertyratio"[51] "diarytaxes"[53] "diarycashtransferchar"[55] "disposableincome"[57] "diaryreceiptagri"[59] "diarycostnonagri"[61] "diarynonagriincome"[63] "diaryinterestotherloans"[65] "diaryotherfinancialaccount"[67] "diarypropertyincome"[69] "diarypensiondomestic"[71] "diarypension"[73] "diaryinsuranceabroad"[75] "diaryremittancedomestic"[77] "diarytotalprivatetransfers"[79] "diarygifts"[81] "diarytotaltransfers"[83] "diarydisposableincome"[85] "psu1"[87] "areaid"[89] "urbanrural"[91] "dzone"[93] "bstoran"[95] "nonagriadj"[97] "ownhouseadj""urbanrural" "weight""stratum""surveymonth""weighthh""hhsize""diarysalarycash""costcrop""costfish""receiptcrop""receiptfish""agriincome""receiptnonagri""incomeownhouse""interestotherloans""rentfromland""interestpaidagri""interestpaidownoccupied""propertyincome""pensiondomestic""pension""remittancedomestic""totalprivatetransfers""scholarshipngo""gifts""totaltransfers""wageratio""otherselfempratio""transfersratio""diaryinterhhtransfers""diarytotalnegativetransfers""diarycostagri""diaryagriincome""diaryreceiptnonagri""diarybankinterest""diarydividends""diarygrosspropertyincome""diaryprimaryincome""diarypensionabroad""diaryinsurancedomestic""diaryngotransfers""diaryremittanceabroad""diaryscholarship""diaryothertransfers""diarytotalincome""psuchar""psu""dprovince""dregion""blillan""agriadj""propertyadj""adjdisp"

Manual for Cambodia CSES 2009 (Version 2.0)

131[99] "adjdispcapita""adjtotaltransfers"[101] "adjtotalincome""adjprimaryincome"[103] "adjdiarytotalnegativetransfers">

Manual for Cambodia CSES 2009 (Version 2.0)

132

5.2 Frequency of categoriacal variables# Displayed frequency table of categorical variables> # file names: Rnames[j](j=1 to 45)> # file list: outfiles[[j]]> # description of variable names: var.names[[j]]> # list of column numbers of categorical variables> ck<-list() # Numbers of categorical variables> ck[[1]]<-c(16,19:21)> ck[[2]]<-c(16,19:21)> ck[[3]]<-c(15,16,21:32)> ck[[4]]<-c(13)> ck[[5]]<-c(13)> ck[[6]]<-c()> ck[[7]]<-c(15:29)> ck[[8]]<-c(15,16,20,23,24,27:30)> ck[[9]]<-c(15:17,19,21:25,27,28)> ck[[10]]<-c(13,15:20,22:24,26,28:30,33:40,43:48,56)> ck[[11]]<-c()> ck[[12]]<-c(13,15,17,18,20,21,23:25,27,30:41,43,45)> ck[[13]]<-c(13:16)> ck[[14]]<-c(13:15)> ck[[15]]<-c(13,14)> ck[[16]]<-c(13,14)> ck[[17]]<-c(13)> ck[[18]]<-c(13,14)> ck[[19]]<-c(13)> ck[[20]]<-c(13)> ck[[21]]<-c(13)> ck[[22]]<-c(13)> ck[[23]]<-c(13:23)> ck[[24]]<-c(13)> ck[[25]]<-c(13)> ck[[26]]<-c(13,16,17)> ck[[27]]<-c(13)> ck[[28]]<-c(13:16,21,23,24,29)> ck[[29]]<-c(13,15:18)> ck[[30]]<-c(15:34)> ck[[31]]<-c(15:19,22:28,31,35,38)> ck[[32]]<-c(15:18,20:24,28)> ck[[33]]<-c(15:18,20,21)> ck[[34]]<-c(15:27)> ck[[35]]<-c(15:18,21,22,25,28,29,35,37,38,41:45,49)> ck[[36]]<-c(15,16,19,21:23)> ck[[37]]<-c(15:21)> ck[[38]]<-c(15:20)> ck[[39]]<-c()> ck[[40]]<-c()> ck[[41]]<-c(15,16,18,20:22,24:26,28:30)> ck[[42]]<-c(13:42)> ck[[43]]<-c()> ck[[44]]<-c()> ck[[45]]<-c()>> for(j in 1:length(ck)){

Manual for Cambodia CSES 2009 (Version 2.0)

133

+ if(length(ck[[j]])==0) {next}+ cat("\n\n##",j,"#### Frequency of variables in",Rnames[j]," ###################\n\n")+ for(k in ck[[j]]){+ variable.name<-colnames(outfiles[[j]])[k]+ variable.desc<-var.names[[j]][k]+ cat("---",variable.name,":",variable.desc)+ print(addmargins(table(outfiles[[j]][k],useNA="ifany")))+ }+ }## 1 #### Frequency of variables in 09dy.expenditure ###################--- diaryexp_q5 :
1
2
3
4
65788
56854 1533110 241661
Sum
2620999--- diaryexp_q8 :12
341891475
2604
987
26291
--- diaryexp_q9 :12
34705139 1649113 228212
35392--- diaryexp_q10 :12
342471099
41166
5494
102
12
Sum
389 2620999

5112407

6
54172

7
555

8
9
10
263003 167138 126231

11
80

7
826

8
11052

11
539

5
Sum
699642 2620999
5Sum
3143 2620999
5
88858

6
872

## 2 #### Frequency of variables in 09dy.income--- diaryinc_q5 :
0
1
2
3
4
5
1
5365
297 417717 47461
4915--- diaryinc_q8 :
1
2
3
Sum
169050 709597
265 878912--- diaryinc_q9 :
1
2
3
4
5
6
33996 629028 72426
13 4538 98392
14
15
16
19
Sum
122
2
32 12273 878912--- diaryinc_q10 :
101
201
202
203
204
205
33504
8523 361341
673
567
1565
401
402
501
502
503
504
3204 1053
624
97 1654
70
512
513
599
601
602
603
2 1835 2819
718
3
999 1001 1002 1003 1004 1005
61
26
14
116
43
151
1199 1299
Sum
7 12315 878912

6
435

7
25715

9
187

10
415

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

7
8
81 249139

8
1541

9
120

9
10
6877 146599

11
532

12
108

13
52

206
207
208
301
302
6264
888 249398
7505 65023
505
506
507
508
509
12674
51
18 73727
394
604
701
801
802
901
53 25078
1453
59
26
1006 1007
1008 1099 1101
120
27
120
25
2

303
208
510
1216
904
1
1102
2

304
1339
511
2917
905
28
1103
4

Manual for Cambodia CSES 2009 (Version 2.0)

10
22

11
Sum
25 878912

134## 3 #### Frequency of variables in s01a.hhmembers ###################--- q01ac01 :
1
2
3
4
5
6
7
8
9
10
11
12
11971 11688 10750 8842 6193 3768 2014 1012 481
221
87
41
Sum
57105
--- q01ac03 :1
2
Sum27708 29397 57105--- q01ac06 :
1
2
3
4
5
6
7
8
9
10
11
12
11971 9506 28131
138
90
534
434 3148
446 1514
318
526
Sum
57105
--- q01ac07 :
0
1
2
3
4
5
6
7
8
9
10
11
2 23664 1293 498 353 290 251 158
98
66
47
5
<NA>
Sum
30357 57105--- q01ac08 :
1
2
3
4
5
6
7
8
9
10
11
12
4340 24118 1071
444
302
309
189
134
44
39
9
11
<NA>
Sum
26091 57105--- q01ac09 :12
34 <NA>
Sum
22417
749 3210 15122 15607 57105--- q01ac10 :
1
2
3
4
5
6
7
8
9
10
11
12
9441 9859 1002 588 388 317 216 140
88
49
14
10
Sum
57105
--- q01ac11a :
1
2
3
4
5
6
7
8 <NA>
Sum
54716 1397
616
44 22911
1
23
68 57105--- q01ac11b :1
2 <NA>
Sum
56605
432
68 57105--- q01ac12a :
0
1
2
3
4
5
6
7
8
9 <NA>
Sum
51031 430 2322 326 521 413 154 1248 600
22
38 57105--- q01ac12b :
0
1
2
3
4
5
6
7
8
9 <NA>
Sum
1078
68
185
102
71
45
76
12
5
23 55440 57105--- q01ac12c :
0
1
2
3
4
5
6
7
8
9 <NA>
Sum
130
8
5
6
4
7
6
1
1
3 56934 57105--- q01ac13 :1
2
Sum54779 2326 57105--- q01ac14 :
0
1
2
3
4
5
6
7
8
9
10
11
313
127
140
135
197
33
32
33
86
2
83
5
15
16
17
18
19
20
21
22
23
24
25
26

Manual for Cambodia CSES 2009 (Version 2.0)

13
22

14
11

15
4

13
267

14
47

15
35

12
12

13
9

14
2

13
1

14
2

15
1

13
6

14 <NA>
3 34984

12
127
27

13
10
28

14
14
29

135

38
30
39
46
21

43
31
3
47
18

4
32
42
48
68

19
34
3
49
50

1
35
21
50
50

86
36
26
51
5

4
4
8
37
38
39
16
6
3
52 <NA> Sum
3 54864 57105

32
40
99

## 4 #### Frequency of variables in s01b.foodconsumption--- q01bc01 :1
2
311879 11880 10292
14
15
16
4044
3238
4715

4
9242
17
5662

5
6
4200 11451
18
19
5003
5632

7
8
11830 4237
20
Sum
2789 143921

7
179

8
5045

## 7 #### Frequency of variables in s02.education--- q02c01 :12
3
411971 11670 10076 7957
Sum
53647
--- q02c02 :12 <NA>
Sum
36488 17149
10 53647--- q02c03 :12 <NA>
Sum
36081 17558
8 53647--- q02c04 :12 <NA>
Sum
41123 12515
9 53647--- q02c05 :01231025 2941 3590 4657
15
16
17
18
210 309 109 118--- q02c06 :01
2
3
854 2452 3634 4664
15
16
17
18
149
118
371
361
--- q02c07 :12 <NA>
Sum
14223 26907 12517 53647--- q02c08 :01
2
3
271 1605 1797 1700
1516 <NA>
Sum

5
5456

6
3277

7
1742

8
839

4
4713
19
28

5
4700
20
27

6
4146
21
5

4
4675
19
68

5
4658
20
19

6
4129
21
12

4
1479

5
6
7
1240 1186 1035

9
292

6
42
28

4
43
7

20
44
20

3
45
71

12
5137

13
11813

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

94625## 5 #### Frequency of variables in s01c.nonfoodexpenses--- q01cc01 :
1
2
3
4
5
6
9293 8116 6800 11806 11402 10028

31
41
2

10
11
6193 10059###################

10
116778 3752

12
13
Sum
755 11909 86155###################

9
383

10
164

13
14

14
6

15
3

7
8
9
10
4115 3146 2754 1468
22 <NA>Sum
2 12523 53647

11
12
961 1578

13
240

14
282

7
3934
88
1158

8
9
10
3055 2531 1307
98 <NA>Sum
36 12527 53647

11
12
938 1170

13
376

14
451

8
855

11
520

13
53

14
33

9
793

11
57

10
577

Manual for Cambodia CSES 2009 (Version 2.0)

12
32

12
464

136

567
51 39421 53647--- q02c09 :12 <NA>
Sum
13465
761 39421 53647--- q02c10 :12 <NA>
Sum
3917 10310 39420 53647--- q02c11 :
1
2
3
4
593
528
186
68
--- q02c12 :1
2
Sum3147 50500 53647--- q02c13 :12 <NA>
Sum
1820 1327 50500 53647--- q02c14 :
1
2
3
4
46
74
17 1548--- q02c15 :12 <NA>
Sum
14131 39505
11 53647

5
4

6
596

7
398

8
607

5
71

6 <NA>
Sum
51 51840 53647

9
52## 8 #### Frequency of variables in s03a.migration.past--- q03ac01 :
1
2
3
4
5
6
7
8
9
11916 11604 9519 7347 5042 3004 1541 735 325
Sum
51275
--- q03ac02 :1
2
Sum
31823 19452 51275--- q03ac05 :
1
2
3
4
5
6
7
8
9
688 2002
186 3356 7262
178
29 1249 4141--- q03ac07 :1
2
Sum
593 50682 51275--- q03ac08 :
1
2
3
4
5
6
7
8
10
313
127
80
19
25
4
3
2
8
--- q03ac10 :
1
2
3
4
5
6
7
8
9
78
67
55
38
34
40
29
19
24
17
18
20
21
22
23
24
25
26
2
10
4
2
2
2
15
1
2
99 <NA>Sum
72 50682 51275--- q03ac11 :
1
2
3
4
5
6
7
8
9
126
101
288
11
9
12
17
2
12
--- q03ac12 :
221
235
241
263
265
314
411
422
513
1
1
1
1
1
1
1
2
6

10
11
39 3306

12 <NA>Sum
66 47204 53647###################

10
140

11
52

12
28

10
75

11
36

12 <NA>Sum
250 31823 51275

20
3

47
1

98 <NA>Sum
8 50682 51275

10
14
28
1

11
9
36
8

12
40
48
5

10
13

11 <NA>Sum
2 50682 51275

514
2

532
1

Manual for Cambodia CSES 2009 (Version 2.0)

541
9

13
13

14
3
50
1

548
8

14
6

15
3

15
6
56
1

16
3
60
6

550
1

611
17

137

621
622
631
1
8
11
818 832 837
6
1
1
<NA>
Sum
50698 51275--- q03ac13 :
1
2
3
120
115
14

632
1
911
14

634
9
912
4

711
16
914
1

712
5
921
216

722
2
931
174

733
1
932
1

751
7
933
17

4
97

5
93

6
9

7
32

8
29

9 <NA>
Sum
84 50682 51275## 9 #### Frequency of variables in s03b.migration.current

753
4
941
2

755
13
951
1

757
1
961
2

813
1
962
1

816
1
999
2

###################--- q03bc01 :31 32 33 34 35 36 37 38 39 40 41 Sum
3400 2077 1194 673 368 190 88 36 157
1 8049--- q03bc03 :1
2 Sum
4134 3915 8049--- q03bc04 :
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
50
56
82 159 188 246 263 332 320 395 477 410 447 436 404 480 235 242
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
177 207 264 195 191 212 194 206 118 156 114
85 146
83
62
78
43
70
51 52 53 54 55 56 57 58 59 60 61 62 63 65 69 70
73
74
31
37
31
17
29
14
19
10
2
13
1
4
2
2
1
1
1
1
75 76 77 81 87 88 Sum
1
1
2
1
2
3 8049--- q03bc05c :9301 9304 9305 9306 9307 9309 9314 9316 9317 9401 9402 9502 9507 9601 9602 9799 <NA> Sum
6
2
2
7
12
75
1 282
17
8
2
11
1
35
5
2 7581 8049--- q03bc07 :
1
2
3
4
5
6 Sum
1063 690 5901 193 132 70 8049--- q03bc08 :11 111 112 121 122 131 143 216 221 222 223 224 225 231 233 234 235 241
51
5
1
2
2
1
1
1
5
10
4
5
3
2
28
67
6
4
243 251 261 262 263 265 311 312 313 314 321 322 324 331 334 335 341 343
1
1
1
2
17
12
3
5
2
1
1
2
1
2
3
1
1
3
352 411 412 421 422 431 511 512 513 514 515 516 522 531 532 541 542 543
2
43
1
13
22
2
3
10
15
19
2
3
1
18
37 2369
1
544 545 546 547 548 549 550 611 612 613 621 622 631 632 634 711 712 721
6
13
5
1 119
1
2 489
3
7
4
46 4770
2
44
26
14
14
722 723 724 725 731 732 734 735 736 738 739 741 742 751 752 753 755 756
6
1
22
17
29
1
2
14
1
19
1
5
8
19
1
9 277
2
757 811 812 814 816 818 821 831 832 833 834 835 836 837 911 913 914 921
2
2
1
3
8
1
1
1 55
6 27
10
3
1
14
1
7 218
931 932 933 941 954 955 956 961 962 963 999 <NA> Sum
162
6
27
6
1
7
1
9
1
4
72 698 8049--- q03bc09 :
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
24 142 403 711 873 965 822 828 674 578 213 155 327 132 123
46
36
63
18 19 20 21 88 98 Sum
87 12
4 10 713 108 8049--- q03bc10 :11 111 112 121 122 131 134 141 143 215 216 221 222 223 224 225 233 234

Manual for Cambodia CSES 2009 (Version 2.0)

138

77
14
4
2
2
2
1
5
1
2
1
12
235 241 242 251 261 262 263 264 265 311 312 313
8
3
2
2
1
4
36
1
12
8
7
2
334 335 341 343 351 352 411 412 421 422 431 512
4
2 10
7
1
2 66
1 29
40
3
16
531 532 541 542 543 544 545 546 547 548 549 550
53
61 404
7
3
8
21
12
1 253
3
6
632 634 711 712 713 721 722 723 724 725 731 732
10
53
44
36
3
29
14
4
41
32
37
9
751 752 753 754 755 756 757 811 812 814 815 816
43
1
22
1 657
3
3
4
3
3
2
11
835 836 837 911 912 913 914 921 931 932 933 941
21
6
4
67
5
4
9 286 27614575
963 999 <NA> Sum
8 35 378 8049--- q03bc11 :12 <NA> Sum
5585 2454 10 8049--- q03bc13a :12
3
4 <NA> Sum
45 168 5329 40 2467 8049--- q03bc13b :12
3
4 <NA> Sum
4 36 32 253 7724 8049

133
321 32212
513 5143139
611 612429
10733 7347
6
818 8212
4951 9522
1

5
5
324 3251
1
515 5164
8
613 62137
738 73917
4
831 8321
99954 9557123981
331 3327
1
521 5224
3
622 631
60 3569741 7424
14
833 83425
43
961 96211
2## 10 #### Frequency of variables in s04.housing ###################--- q04_01 : how many hhs reside in same unit
1
2
3
4
5
6
7
9 Sum
11375 491
83
9
6
3
2
1 11970--- q04_03 : how many rooms are use by hh (other than kitchchen, toilet and tathrooms)?
1
2
3
4
5
6
7
8
9
10
11
12
15
Sum
8342 2694
619
198
52
29
14
8
2
5
3
2
2 11970--- q04_04 : walls primary construction material
1
2
3
4
5
6
7
8
9
Sum
3908 5686
70 1529 607
16
8634
34 11970--- q04_05 : roof primary construction material
1
2
3
4
5
6
7
8
9
10 Sum1862 3228
903 5392
22
64
13
472
10
4 11970--- q04_06 : floors primary construction materials
1
2
3
4
5
6
7
8
9
Sum
915 5930 3049 870 154
85 10309 11970--- q04_07 : what is the hh's main source of lighthing?
1
2
3
4
5
6
7
Sum
3209 204 4609 3702
39
7 200 11970--- q04_08 : what is the hh's main source of drinking water in wet season?
1
2
3
4
5
6
7
8
9
10
11
12
13 <NA> Sum
1822
52 2787766 1676 1218
255
54 2981
284
27
26
21
1 11970--- q04_10m1 : which hh members fetch water in wet season?
1
2
3
4
5
6
7
8
9
10
12 <NA>Sum
4709 1018
567
129
74
37
13
11
5
1
1 5405 11970--- q04_10m2 : which other hh members fetch water in wet season?
1
2
3
4
5
6
7
8
9
10
11 <NA> Sum
298 3598 1007
455
125
65
22
12
7
4
1 6376 11970--- q04_10m3 : which other hh members fetch water in wet season?

Manual for Cambodia CSES 2009 (Version 2.0)

139

1
2
3
4
5
6
7
8
9
10
11 <NA>Sum
104
84 1199
414
225
75
38
18
5
11
2 9795 11970--- q04_12 : what is the hh's main source of drinking water in dry season?
1
2
3
4
5
6
7
8
9
10
11
12
13 <NA> Sum
1965
61 3378 890 1911 2222 483
15 170754
5236
31
2 11970--- q04_14m1 : which hh members fetch water in dry season?
1
2
3
4
5
6
7
8
9
10
12 <NA>Sum
6079 1329
785
184
106
53
16
13
6
1
1 3397 11970--- q04_14m2 : which other hh members fetch water in dry season?
1
2
3
4
5
6
7
8
9
10
11 <NA>Sum
356 4451 1330
615
201
96
39
20
13
5
1 4843 11970--- q04_14m3 : which other hh members fetch water in dry season?
1
2
3
4
5
6
7
8
9
10
11
13 <NA>Sum
132
87 1407
547
280
106
47
25
7
12
2
1 9317 11970--- q04_17 : did hh boil or treated water last month?
1
2
3
Sum
7236 1757 2977 11970--- q04_18a : did you boil the water?
1
2 <NA>
Sum
8296
697 2977 11970--- q04_18b : did you filter the water?
1
2 <NA>
Sum
1007 7986 2977 11970--- q04_18c : did you treted chemicaly the water?
1
2 <NA>
Sum
27 8966 2977 11970--- q04_18d : did you use white alum for the water?
1
2 <NA>
Sum
70 8923 2977 11970--- q04_18e : did you use any other treatment the water?
1
2 <NA>
Sum
74 8919 2977 11970--- q04_19a : what toilet facilities your hh has on the premises (close to dwelling)?
1
2
3
4
5
6
7
8 Sum
1179 2920
99
157
218
197 7184
16 11970--- q04_19b : what toilet facility does your hh ususally use?
1
2
3 <NA>
Sum
163 6938
83 4786 11970--- q04_22a : what type of use your hh mainly use for cooking?
1
2
3
4
5
7
8
Sum
9654 1023 11971346
55 11970--- q04_22b : does the vendor brings the firewood/charcoal home?
12 <NA>
Sum
2319 8358 1293 11970--- q04_22c1 : which hh member colects firewood or charcoal?
1
2
3
4
5
6
7
8
9
10
11
12 <NA> Sum
6187 1223
607
168
91
55
10
8
5
1
1
2 3612 11970--- q04_22c2 : which other hh member colects firewood or charcoal?
1
2
3
4
5
6
7
8
9
10
11
12 <NA>Sum
309 3781 1274
513
169
107
42
26
7
9
2
2 5729 11970--- q04_22c3 : which other hh member colects firewood or charcoal?
1
2
3
4
5
6
7
8
9
10
11
12
14 <NA> Sum
75
80 1018
462
233
77
37
22
11
12
2
1
1 9939 11970--- q04_22d : how many hours per week are used in collecting firewood/charcoal
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
409 881 1396 1416 1100 748 534 338 322
84 37322 1701245Manual for Cambodia CSES 2009 (Version 2.0)

140

15
16
17
18
19
20
21
22
23
24
110
15
4
17
1
80
16
3
1
45
36
38
40
42
45
48
50
56
60
63
4
1
6
1
1
8
4
6
12
1
<NA>
Sum
3676 11970--- q04_24 : what was the legal status of the dwelling?
12
34 <NA>
Sum
11245
405
311
5
4 11970## 12 #### Frequency of variables in s05a.landown--- q05ac01 :
1
2
3
4
5
6
8504 4874 1959 704 271 103--- q05ac03 :
1
2
3
4
5
Sum
14831
772
602
227
26 16458--- q05ac04b :1
2
3 <NA>
Sum
9355 5685
39 1379 16458--- q05ac04c :12
34 <NA>
Sum
490 8697 5733
9 1529 16458--- q05ac05b :1
2
3 <NA>
Sum364
411
1 15682 16458--- q05ac05c :12
34 <NA> Sum
5
364
399
4 15686 16458--- q05ac06b :1
2
3 <NA>
Sum369
228
2 15859 16458--- q05ac06c :1
2
3 <NA>
Sum
6286
307 15859 16458--- q05ac07 :
1
2
3
4
5
6
11035 1667
567 1654609
452--- q05ac09 :
1
2
3
4
5
6
5917 6430 632 1838 890
33--- q05ac12 :
1
2
3
4
Sum
8382 7232
542
302 16458--- q05ac13a :
1
2
3
4
5
6
2326
44 2000 3803
73
72--- q05ac13b :
1
2
3
4
5
6
1968
30 1862 3136
67
68--- q05ac14 :
1
2
3
4
5
6
4536 2375 1092
181
140
39
--- q05ac15 :

7
41

8
1

7
7

8
35

25
9
65
1

26
1
70
1

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

9Sum
1 16458

9
418

7
617

8 Sum
101 16458

7
75

<NA>
Sum
8065 16458

7
20

8
1242

7
30

<NA>
Sum
8065 16458

10 <NA> Sum
10
4 16458<NA>
Sum
8065 16458

Manual for Cambodia CSES 2009 (Version 2.0)

28
14
72
1

30
63
85
1

35
15
90
2

141

1
2
3
4
5
6
7
8
9
13914 1420
119
41
46
29
16
12
3
--- q05ac16a :
1
2
3
4
5
6 <NA>
Sum
12683 1555 1053
46 104513
63 16458--- q05ac16b :
1
2
3
4
5
6 <NA> Sum
34 34066
46
5
1 15966 16458--- q05ac16c :12345 <NA>
Sum
5
424
3
1 16421 16458--- q05ac17 :
1
2
3
4
Sum
1749 4093 1280 9336 16458--- q05ac18a :
1
2
3
4
5
6
7
8 Sum
15090
312
674
42
1
98
167
74 16458--- q05ac18b :
1
2
3
4
6
7 <NA>
Sum
18
6
6
13
7
39 16369 16458--- q05ac18c :13478 <NA>
Sum
4
1
8
4
1 16440 16458--- q05ac20 :1
2
Sum14093 2365 16458--- q05ac22 :
1
2
3
Sum
123
364 15971 16458

11
4

77
234

88
581

99
Sum
39 16458## 13 #### Frequency of variables in s05b.cropsproduction ###################--- wetdry :1
2
Sum
13502 4271 17773--- q05bc01 :12
39820 4753 1957--- q05bc02 :1
2
39284 5224 2059--- q05bc03b :101
102
2 1646 10592
302
303
401
128
9
43
443
445
451
3
6
36
522
523
524
124
56
4
552
553
561
45
4
5
814
815
816
7
598
24
833
841
842

4
730

5
316

6
133

7
50

8
8

9
5

4
738

5
304

6
120

7
42

8
1

9
Sum
1 17773

103
159
411
96
452
19
525
43
562
1
817
7
843

104
529
412
110
453
13
526
41
564
5
819
125
844

105
8
413
173
501
74
527
3
601
4
821
77
911

201
154
414
1
502
21
528
12
602
1
822
7
921

202
63
416
4
503
2
529
3
801
61
823
2
922

203
483
417
2
504
2
530
1
802
3
824
5
924

10
Sum
1 17773

204
2
421
5
505
17
531
31
803
10
825
13
926

205
22
422
20
506
46
542
14
804
22
826
4
927

Manual for Cambodia CSES 2009 (Version 2.0)

207
7
424
16
508
1
543
8
805
4
827
64
928

208
5
425
5
510
10
545
1
811
556
828
1
941

209
9
429
3
511
49
547
4
812
38
829
8
942

301
39
442
1
521
39
551
2
813
10
830
1
952

142

44
955
1

470
956
2

20
1
997
Sum
14 17773

6

330

3

1

13

## 14 #### Frequency of variables in s05c.costcrops--- wetdry :1
2
Sum
13086 3825 16911--- q05cc01 :12
39493 4488 1815--- q05cc02 :12
38976 4906 1927

1

4
45

4
673

5
282

6
106

7
41

8
5

9
2

4
683

5
277

6
100

7
39

8
1

9
Sum
2 169115
14

6
2

104 10520
3
525 526
1
2

201
3
527
1

7
1

1

123

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

10 Sum
3 7308202 2031
4
541 552
1
1

6
8435

207 3011
13
564 811
1
1

302
19
812
4

401 4112
7
815 841
4
23

7
8435

8
8435

9
8435

412 41334
16
911 94151

421 442 445
11
1
952 Sum
2 730810Sum8435 84350## 17 #### Frequency of variables in s05e2.animalsexpenses--- q05e2c01 :12
343902 6595 200 3242

5378###################

6Sum
169 14486## 18 #### Frequency of variables in s05f1.fisharea--- q05f1c01 :
1
2
3 Sum
214 10
3 227--- q05f1c02 :
1
2
3
4
215
1
4
4

3

10
Sum
6 16911## 16 #### Frequency of variables in s05e1.animals ###################--- q05e1c01 :
1
2
3
4
5
8435 8435 8435 8435 8435--- q05e1c03 :12 <NA>
Sum
17872 66473
5 843504##################### 15 #### Frequency of variables in s05d.cropsinventory--- q05dc01 :12
35776 1214 253--- q05dc02b :101 102 103
605 6423 104
451 501 502
1
1
1

3

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

5 Sum
3 227

Manual for Cambodia CSES 2009 (Version 2.0)

143## 19 #### Frequency of variables in s05f2.fishexpenses--- q05f2c01 :12
3
413 21036

4565 6112

6
461

7
275

8
27

9
16

10
75

11
12 Sum
10 153 7853## 20 #### Frequency of variables in s05f3.fishincome--- q05f3c01 :12
341928 6439 2145 1390

5
221

6
43

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

7 Sum
198 12364## 21 #### Frequency of variables in s05g1.forestincome###################--- q05g1c01 :12
34
310 8476
197 214110
Sum
58 19398

5
250

6
6023

7
906

8
312

9
725

## 22 #### Frequency of variables in s05g2.forestexpenses ###################--- q05g2c01 :12
3
571 604 324

45
304 3918

6
137

7 Sum
70 5928## 23 #### Frequency of variables in s05h1.bussiness ###################--- q05h1c01 :
1
2
3
4
5 Sum
4070 662 94 14
3 4843--- q05h1c04 :0113 0130 0141 0145 0146 0160 0220 0810 0990 1010 1020 1030 1060 1070 1080 1100 1310
2
1
3
3
2
2
7
13
5
1
7
3
3 190
72
1 131
60
1390 1410 1520 1610 1620 1700 1810 1820 1920 2020 2030 2220 2310 2390 2420 2430 2510 2590
38 141
1
5 176
2
3
1
1
2
1
1
2
16
3
1
19
15
2610 2820 3010 3100 3210 3310 3510 3530 3600 3830 4100 4220 4310 4320 4390 4510 4520 4530
1
3
1
19
19
5
43
1
12
6
5
2
1
1
8
4
19
3
4540 4610 4620 4630 4640 4650 4660 4690 4710 4720 4730 4740 4750 4760 4770 4780 4790 4920
120
3 51
7
3
3 83
1 793 629 43 28 11 15 116 323 142 621
5010 5020 5220 5510 5590 5610 5620 5630 5810 5910 6010 6190 6200 6310 6410 6490 6610 6620
13
411
10 376
12
3
4
2
1
8
1
3
2
7
3
6
6810 6920 7110 7420 7500 7710 7720 7730 7740 7910 8130 8210 8290 8410 8510 8520 8540 8610
4
1
1
17
2
1
23
2
1
1
1
1
4
1
1
2
6
1
8620 8690 9000 9200 9320 9510 9520 9600 Sum
37 10 21 24
1
6 28 110 4843--- q05h1c05 :
1
2
3
4
5
6
7
8
9
10
12
13 <NA> Sum
2499 1780 292 121 69 33 22
9
4
4
3
1
6 4843--- q05h1c06a :
1
2
3
4
5
6
7
8
9
10
11
13 <NA> Sum
593 1051 510 221 94 82 24
96532 2243 4843--- q05h1c06b :Manual for Cambodia CSES 2009 (Version 2.0)

144

1
2
3
4
5
6
7
8
45 40 322 190 77
45
25
10--- q05h1c06c :
1
2
3
4
5
6
7
8
10
6 12 101 43 19 17
1--- q05h1c06d :
1
2
3
4
5
6
7
8
3
1
1
4 27 15
5
4--- q05h1c06e :567
8 10 12 <NA> Sum
2
8
6
3
1
1 4822 4843--- q05h1c06f :68
9 13 <NA> Sum2
2
1
1 4837 4843--- q05h1c06g :7
9 <NA> Sum
1
1 4841 4843--- q05h1c06h :8 <NA> Sum
1 4842 4843

9
5

12
1

13
1

14 <NA> Sum
2 4080 4843

92

10 <NA> Sum
2 4630 4843

9 <NA> Sum
1 4782 4843## 24 #### Frequency of variables in s05h2.bussinessexpenses--- q05h2c01 :
1
2
3
4
5
1352
98 1519 1555 2325
16
17
18
Sum
283
155
296 15720

6
154

7
480

8
529

9
2393

10
1084

## 25 #### Frequency of variables in s05h3.bussinessincome--- q05h3c01 :1
2
3
671 154 669
19 20 Sum
17 75 5360

45
44 2314639

7
888 556

9
519

10
4

11
78

12
13

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

11
847

12
142

13
364

4
10

5
5

4
5
907 161

13
23

14
28

15
7

4
478

5
2

6 Sum
1 4732
6
8
6
119

7
8
9
5 987 119778
302 105

9
146

15
1190

17
14

18
18

##################### 26 #### Frequency of variables in s06.liabilities ###################--- q06_c01 :12
34497 186 33--- q06_c04 :12
31030 37 319--- q06_c05 :12
31283 469 1790

14954

10 <NA> Sum79
2 473210 Sum38 4732## 27 #### Frequency of variables in s07.incomeother ###################--- q07_c01 :Manual for Cambodia CSES 2009 (Version 2.0)

16
29

145

1
2
201 23923
75

4
5
105 366

6
69

7
9

8
9

9
10
11
127 259 2302## 28 #### Frequency of variables in s08.construction

12 Sum948 6862###################--- q08_c01 :
1
2
3
4
Sum
11230
86
5
1 11322--- q08_c02a :1
2
3
4 <NA>
Sum
11142
14
127
5
34 11322--- q08_c02b :1
2
3
4 <NA>
Sum2
14217
15 11074 11322--- q08_c02c :2
34 <NA>
Sum
2
1
3 11316 11322--- q08_c07 :1
2
Sum
90 11232 11322--- q08_c09 :12 <NA>
Sum
889 10420
13 11322--- q08_c10 :123 <NA>
Sum
191
131
554 10446 11322--- q08_c13 :12345 <NA>
Sum
25
60
61
67
4 11105 11322## 29 #### Frequency of variables in s09.durables--- q09_c03 :
801
802
803
804
805
806
5082 7223 8118 5883 2448
779
816
817
818
819
820
821
371
108 5325 219
2
10
831
832
833
834
835
836
641 32935
23 3508 1273
894
Sum
253 93366--- q09_c05a :12
3
4 <NA>
Sum90514
217 2176294
165 93366--- q09_c05b :12
3
4 <NA>
Sum
26535
52
385
75 66319 93366--- q09_c05c :12
34 <NA> Sum
14963
18
106
33 78246 93366--- q09_c05d :12
34 <NA>
Sum
9031
15
64
17 84239 93366

807
808
3514 1627
822
823
31 1620837
838
78 11222###################

809
346
824
114
839
648

810
811
812
813
814
815
511 2462
213 2049
520 1786
825
826
827
828
829
830
419
40
67
127
466
118
840
841
890
891
892
893
284 1096 7002 6193 4921 4262

Manual for Cambodia CSES 2009 (Version 2.0)

146## 30 #### Frequency of variables in s10.healthmother--- q10_c01 :
1
2
3
4 Sum
4350 906
1 4447--- q10_c02 :
1
2
3
4
5
6
7
8
9
10
258 3374 453 162
69
45
36
26
8
9
--- q10_c03 :
2
3
4
5
6
7
8
9 10 1123 800 1188 942 630 384 230 121
70
34--- q10_c04 :
1
2
8 Sum
824 3618
5 4447--- q10_c05 :12 <NA> Sum
87 3517 843 4447--- q10_c06 :
1
2
8 Sum
3796 642
9 4447--- q10_c07 :
1
2
8 Sum
1956 2383 108 4447--- q10_c08 :
1
2
8 Sum
3664 777
6 4447--- q10_c09 :1
2
8 <NA> Sum
1282 2375
7 783 4447--- q10_c10 :
1
2
8 Sum
3789 647 11 4447--- q10_c11 :
1
2
3
4
5
6
7
8 <NA> Sum
536 801 1401 398 397
12 122 113 667 4447--- q10_c12 :12
8 <NA> Sum
1248 838
24 2337 4447--- q10_c13 :12345 <NA> Sum
346 426 262 96 107 3210 4447--- q10_c14 :
1
2
3
4
5
6
7
8
9
10
2184 163
24 375 246 208 861
34
1
6--- q10_c15a :
1
2
3
4
5
6
7 Sum
849 431 1843 1271
447
2 4447--- q10_c15b :
1
2
3
4
5
6
7 <NA> Sum
82 331 336 83 1182 15 10 2408 4447--- q10_c15c :
1
2
3
4
5
6 <NA> Sum
23 20 103 23 130 22 4126 4447--- q10_c15d :1
5 <NA> Sum###################

11
2

12
2

13
1

14 Sum
2 4447

12
8

13
7

14
5

15 <NA> Sum1
4 4447

11
138

12
186

13
4

14 <NA> Sum15
2 4447Manual for Cambodia CSES 2009 (Version 2.0)

147

3 21 4423 4447--- q10_c16 :
1
2
8 Sum
2667 1728 52 4447--- q10_c17 :
1
2
8 Sum
2464 1927 56 4447## 31 #### Frequency of variables in s11.health2years--- q11_c01 :
1
2
4 Sum
2291 65
1 2357--- q11_c02 :
1
2
3
4
5
6
7
107 1692 281 102 55 29 22--- q11_c03 :
2
3
4
5
6
7
8
8 440 593 511 345 186 118--- q11_c04 :12 <NA> Sum
2318 36
3 2357--- q11_c05 :12 <NA> Sum
2273 48 36 2357--- q11_c07 :12 <NA> Sum
1889 384 84 2357--- q11_c08a :1
2 <NA> Sum
615 1658 84 2357--- q11_c08b :1
2
8 <NA> Sum
1495 776
1 85 2357--- q11_c09 :
1
2
8 Sum
2192 159
6 2357--- q11_c10 :12345 <NA> Sum
551 1295 167 29 150 165 2357--- q11_c11 :12 <NA> Sum
1930 261 166 2357--- q11_c12a :12 <NA> Sum
1886 45 426 2357--- q11_c13a :1
2 <NA> Sum
1405 526 426 2357--- q11_c14a :01
2
3
4 <NA> Sum
137 425 271 872 226 426 2357--- q11_c15a :1
2 <NA> Sum
927 1003 427 2357###################

8
17

9
6

10
6

11
1

12
2

13
1

14 <NA> Sum
2 34 2357

9
71

10
45

11
19

12
7

13
6

14
5

15 <NA> Sum
1
2 2357Manual for Cambodia CSES 2009 (Version 2.0)

148## 32 #### Frequency of variables in s12.health5years--- q12_c01 :
1
2
3
4
5 Sum
4502 1029 76
8
2 5617--- q12_c02 :
2
3
4
5
6
7
36 1196 1465 1128 753 465--- q12_c03 :
1
2
3
8 Sum
3389 946 1276
6 5617--- q12_c04 :1
2
8 <NA> Sum
4641 906
65
5 5617--- q12_c06 :
1
2
8 Sum
2793 2686 138 5617--- q12_c07 :
1
2
8 Sum
5349 261
7 5617--- q12_c08 :
1
2
8 Sum
939 4674
4 5617--- q12_c09a :1
2
8 <NA> Sum
302 635
2 4678 5617--- q12_c09b :1
2
8 <NA> Sum
564 373
2 4678 5617--- q12_c12 :12 <NA> Sum
3425 2173 19 5617

89272 155

10
82

11
37

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

12
13## 33 #### Frequency of variables in s13b.healthexpenses--- q13bc01 :
1
2
3
4
5
11966 11683 10745 8837 6190
Sum
57082
--- q13bc02 :1
2
Sum8344 48738 57082--- q13bc03 :12 <NA>
Sum
1674 6670 48738 57082--- q13bc04 :1
2
3 <NA>
Sum
1031 5896 1417 48738 57082--- q13bc06 :12 <NA>
Sum959
72 56051 57082--- q13bc07 :

6
3768

7
2014

8
1012

9
481

13
9

14
5

15 Sum
1 5617###################

10
221

11
87

Manual for Cambodia CSES 2009 (Version 2.0)

12
41

13
22

14
11

15
4

149

0
55336

1
175

2
40

3
108

4
291

5
Sum
1132 57082## 34 #### Frequency of variables in s14.disability--- q14_c01 :
1
2
3
4
5
6
7
8
11966 11683 10745 8837 6190 3768 2014 1012
Sum
57082
--- q14_c02a :
0
1
2
3
4
5
6
7
53505 2218
311
110
607
136
107
24
--- q14_c02b :
0
1
2
3
4
5
6
7
15
49
371
59
221
78
65
25
--- q14_c02c :
1
2
3
4
5
6
7
8
4
21
26
99
38
49
13
16
--- q14_c03a :1
2
3 <NA>
Sum
1473 1523
581 53505 57082--- q14_c03b :1
2
3 <NA>
Sum
273
474
152 56183 57082--- q14_c03c :1
2
3 <NA>
Sum
82
118
71 56811 57082--- q14_c04a :
1
2
3
4
5
6
7
8
99
99
132
877
310
83
58
13
17
18
19
98 <NA> Sum
2 1559144
43 53505 57082--- q14_c04b :
1
2
3
4
5
6
7
8
16
17
34
210
67
46
10
4
98 <NA>Sum
8 56180 57082--- q14_c04c :
1
2
3
4
5
6
10
13
1
6
8
43
32
13
1
11
--- q14_c05a :
1
2
3
4
5
6 <NA>
Sum
359 392
56 1916 388 466 53505 57082--- q14_c05b :
1
2
3
4
5
6 <NA>
Sum
25
111
34
395
400
324 55793 57082--- q14_c05c :
1
2
3
4
5
6 <NA>
Sum6
65
13
72 119 309 56498 57082###################

9
481

10
221

11
87

12
41

8
27

9
Sum
37 5708288

9 <NA>
Sum
5 56186 57082

13
22

14
11

15
4

9 <NA> Sum
4 56812 57082

10
19

11
11

12
5

13
20

14
76

15
20

16
7

10
3

11
4

13
18

14
17

15
18

18
397

19
33

14
6

15
10

18
122

19
10

98 <NA>Sum
7 56812 57082## 35 #### Frequency of variables in s15.labor7days###################--- q15_c01 :Manual for Cambodia CSES 2009 (Version 2.0)

150

1
2
3
4
5
6
7
8
9
10
11963 11645 9548 7371 5062 3015 1548 740 326
141
Sum
51460
--- q15_c02 :
1
2
3
4
5
6
7
8
9
10
15424 22573 5408 3450 2186 1265 617 312 116
59
Sum
51460
--- q15_c03 :1
2
Sum34926 16534 51460--- q15_c04 :12 <NA>
Sum
276 16258 34926 51460--- q15_c07 :
1
2
3
4
5
6
7
8 <NA>
Sum1444
87 31214 1939
16 42460
13 16263 51460--- q15_c08 :12345 <NA>
Sum
8592
102 16205 10260
22 16279 51460--- q15_c11 :01234 <NA>
Sum
21355 10941 2481 376
26 16281 51460--- q15_c14 :
1
2
3
4
5
6
7
8 <NA>
Sum53
11 13593
40
1 116
12
4 37630 51460--- q15_c15 :12345 <NA>
Sum
1141
20 8305 4357
7 37630 51460--- q15_c21 :123 <NA>
Sum
581 2472 32146 16261 51460--- q15_c23 :12 <NA>
Sum
2147
326 48987 51460--- q15_c24 :123 <NA>
Sum
69 232282 48987 51460--- q15_c26 :12 <NA>
Sum
135 16152 35173 51460--- q15_c27a :
1
2
3
4
5
6 <NA>
Sum
30
16
78
2
7
2 51325 51460--- q15_c27b :12
3
45 <NA>
Sum
2
15
23
7
6 51407 51460--- q15_c27c :12345 <NA>
Sum
2
1
5
5
4 51443 51460--- q15_c28 :12 <NA>
Sum46
89 51325 51460--- q15_c31 :
1
2
3
4
5
6
7
8
9 <NA>

11
51

12
28

13
13

14
6

15
3

11
23

12
11

13
10

14
5

15
1

Sum

Manual for Cambodia CSES 2009 (Version 2.0)

151

110

109

173

140

373

2968

9282

2841

157 35307 51460## 36 #### Frequency of variables in s16.labor12months--- q16_c01 :1
2
311963 11645 9548
Sum
51460
--- q16_c02 :1
2
331675 1257 400--- q16_c05 :12
37298 215 17214--- q16_c07 :
1
2
3
1449
82 29004--- q16_c08 :12
325455 5966 2558--- q16_c09 :1
2
32531 39897 3201###################

4
57371 5062

6
3015

7
1548

8
740

9
326

4
5
1692 12982

6
3250

7
144

8
36

9
Sum
24 51460

48183

5 <NA>
Sum
22 18528 51460

4
2059

5
19

6
259

7
52

4
8973

5
70

6
2967

7
2283

10
141

11
51

12
28

8 <NA>
Sum
8 18528 51460
8
714

9
1233

10
250

11 <NA>Sum
952
39 51460

4 <NA>
Sum
285 5546 51460## 37 #### Frequency of variables in s17b.theft--- q17bc01 :
1
2
3 Sum
309 15
4 328--- q17bc02 :
1
2
3
4
5
6
7
168 46 22
73
51--- q17bc03 :
1
2
3
4
5
6
7
22 30 36 44 24 25 27--- q17bc04 :
1
2
3 Sum
210 108 10 328--- q17bc05 :1
2 Sum
93 235 328--- q17bc06 :1
2
3 <NA> Sum
48
44
1 235 328--- q17bc07 :1
2 Sum
5 323 328

91

98 Sum75 328

8
21

9
37

10
24

13
13

11
17

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

12 Sum21 328## 38 #### Frequency of variables in s17c.accidents###################--- q17cc01 :
1
2
3
4 Sum

Manual for Cambodia CSES 2009 (Version 2.0)

14
6

15
3

152

590 548
2 654--- q17cc02 :
1
2
3
4
5
6
7
231 114 139 89 33 28
8--- q17cc03 :
1
2
3
4
5
6 Sum
120 65 440
2 14 13 654--- q17cc04 :
1
2
3
4
5
6
7
67 67 77 94 58 45 50--- q17cc05 :1 2 Sum
450 204 654--- q17cc06 :
1
2
3
4
5 Sum
277 200 77 91
9 654

8
7

9 Sum
5 654

8
35

9
39

10
42

11
31

12 Sum49 654## 41 #### Frequency of variables in s18.presenseinhh--- q18_c01 :
1
2
3
4
5
6
7
8
11963 11681 10743 8836 6190 3768 2014 1012
Sum
57074
--- q18_c02a :1
2 <NA>
Sum
54580 2492
2 57074--- q18_c02c :1
2 <NA>
Sum
125 2367 54582 57074--- q18_c03a :1
2 <NA>
Sum
54409 2648
17 57074--- q18_c03b :
1
2
3
4
5
6 <NA>
Sum
365
344
209
163
191
174 55628 57074--- q18_c03c :12 <NA>
Sum
184 2461 54429 57074--- q18_c04a :1
2 <NA>
Sum
54436 2627
11 57074--- q18_c04b :
1
2
3
4
5
6 <NA>
Sum
318
315
236
234
219
141 55611 57074--- q18_c04c :1
2 <NA>
Sum
195 2433 54446 57074--- q18_c05a :1
2 <NA>
Sum
54813 2255
6 57074--- q18_c05b :
1
2
3
4
5
6 <NA>
Sum
299 309 107
86 130 149 55994 57074--- q18_c05c :9481###################

10
221

11
87

Manual for Cambodia CSES 2009 (Version 2.0)

12
41

13
22

14
11

15
4

153

1
2 <NA>
Sum
85 2171 54818 57074## 42 #### Frequency of variables in s99.singlequestions--- q01bq1 :1
2
Sum8752 3219 11971--- q03bq1 :1
2
Sum3402 8569 11971--- q05aq1a :1
2
Sum
829 11142 11971--- q05aq1b :
1
2
3
4
310
85
73 154--- q05aq2 :1
2Sum8510 3461 11971--- q05aq3 :12
343633 2919 1254 433--- q05bq1 :12 <NA>
Sum
7799
711 3461 11971--- q05dq1 :12 <NA>
Sum
5813 1992 4166 11971--- q05eq1 :1
2
Sum8435 3536 11971--- q05fq1 :1
2
Sum
259 11712 11971--- q05fq2 :1
2 <NA>
Sum212
47 11712 11971--- q05fq3 :1
2
Sum
6578 5393 11971--- q05gq1 :1
2
Sum8537 3434 11971--- q05gq2 :1
2
Sum5959 6012 11971--- q05hq1 :1
2
Sum4066 7905 11971--- q06_q1 :1
2
Sum4489 7482 11971--- q08_q1 :
1
2
Sum

5
121

6
29

7 <NA>
Sum
57 11142 11971

5
168

6
62

7
40

9
1

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

<NA>
Sum
3461 11971

Manual for Cambodia CSES 2009 (Version 2.0)

154

11231
740 11971--- q10_q1 :12 <NA>
Sum
4387 7582
2 11971--- q11_q1 :12 <NA>
Sum
2297 9671
3 11971--- q12_q1 :12 <NA>
Sum
4498 7472
1 11971--- q13aq1 :
1
2
8
Sum
579 11391
1 11971--- q13aq2a :
1
2
3
4
5
334 149
10
63
3--- q13aq2b :
1
2
3
4
5
13 13048
41
2--- q13aq2c :
1
2
3
4
5
12
15
54
56
12
--- q13aq3 :
1
2
3
4
5
278 104 107
26
17--- q13aq4 :1
2
8 <NA>
Sum
189 11180
25
577 11971--- q17aq1 :1
2
Sum8082 3889 11971--- q17aq2 :1
2
Sum
7366 4605 11971--- q17bq1 :1
2
Sum309 11662 11971--- q17cq1 :1
2
Sum592 11379 11971

6
14

7
5

8 <NA>
Sum
1 11392 11971

6
7

7
2

8 <NA>
Sum
6 11722 11971

6
21

7
1

8 <NA>
Sum
8 11792 11971

8 <NA>
Sum
48 11391 11971############################################################################################################################################################################

Manual for Cambodia CSES 2009 (Version 2.0)

155

5.3 Relationship among data files and identifiers

The files of “09dy_expenditure” and “09dy_income” are micro data from the questionnaire ofdiary sheet, and will be discussed in Supplement 2.



No. 10 and No. 11 seem to be the same, and the former (No. 10) will be discussed hereafter. Notethat the redord of hhid=”0100209” is missing.

No. 39 and No. 40 seem to be the same, and the latter (No. 40) will be discussed hereafter.

Each data file has identification variables. The common variables are as follows.

Example of “s01a.hhmembers”
Variable

Description

Type

Remarks

hhid

Household identifier

int100101 - 2400320

persidPerson identifier

int100101 - 240032005

provinceint

1 - 24

urbanruralint

1: urban, 2: rural

weightHousehold weightnum

45.06 – 591.08

hhsizeHousehold sizeint

1 - 15weight3= weight x hhsize

num

psu

int

1001 - 24003

stratumint

11 - 242

surveymonthint

1 - 12

region

int

1-3

year

int

2009

persid_string

persid in characterchr

9 digits

hhid_stringhhid in characterchr

7 digits



Object “dxx” sometimes denotes the file outfiles[[xx]], that is, the No. xx file in thismanual.

> d03<-outfiles[[3]]

The number of sample households is 11,971.

> length(unique(d03$hhid))[1] 11971

Manual for Cambodia CSES 2009 (Version 2.0)

156



The number of sample household members is 57,105.

> nrow(d03)[1] 57105> length(unique(d03$persid))[1] 57105



The variable of weight3 is defined as the next;
weight3 = weight x hhsize> table(abs(d03$weight*d03$hhsize-d03$weight3)<1)
TRUE
57105

Note: Person weight is defined differently.
hhid is equal to persid excluding the last 2 digits.

> table(d03$hhid==floor(d03$persid/100))
TRUE
57105



psu is equal to hhid excluding the last 2 digits.

> table(d03$psu==floor(d03$hhid/100))
TRUE
57105



province is equal to psu excluding the last 3 digits.

> table(d03$province==floor(d03$psu/1000))
TRUE
57105



The identifier of household-level data file is “hhid”, consisted of psu and household number.



The identifier of individual-level data file is “persid”, consisted of hhid and person number.

Manual for Cambodia CSES 2009 (Version 2.0)

157

5.4 Weight

There are two kind of weights: household weight and person weight. However, the detailed
description on calculating weights is not found in the survey report.

Household weightThe variable of household weight is found in the name of “weight” in d01 to d44 files, and“weighthh” in d45 file.

> d43<-outfiles[[43]]> d44<-outfiles[[44]]> d45<-outfiles[[45]]> table(d45$weighthh==d43$weight)
TRUE
11971



The sum of household weights in a household-level file is2,938,650; the estimated number ofhouseholds, which is consistent with the figures in the survey report.
> sum(d43$weight)[1] 2938650

Household weights are unique within PSU.

> table(tapply(d43$weight,d43$psu,function(x) length(unique(x))))1
720

Remarks:

The variable “weight3” in d01 to d44 is defined as weight multiplied by hhsize.

> table(abs(d03$weight*d03$hhsize-d03$weight3)<1)
TRUE
57105



The sum of weight in a individual-level file is equal to the sum of weight3 in a household-level
file.

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> sum(d44$weight)[1] 14044311> sum(d43$weight3)[1] 14044311

This figure is not equal to the estimated household members in the survey report; 13,966,718.

Person weightThe variable of person weight is found in the name of “weighthhpers” in d45. However, it isgiven at household-level.

> sum(d44$weight)[1] 14044311> sum(d43$weight3)[1] 14044311



The variable of individual-level person weight is not found in the data set used here. So, theperson weight data file “persweight.dta” provided earlier is added.

> persweight<-read.dta("persweight.dta",convert.factors=F)> dim(persweight)[1] 57105

3> head(persweight)
hhidpersid persweight

1 0100101 010010101

299.1656

2 0100101 010010102

267.6359

3 0100101 010010103

267.6359

4 0100101 010010104

267.6359

5 0100101 010010105

299.1656

6 0100102 010010201

299.1656> sum(persweight$persweight)[1] 13966718> wt<-persweight> hhwt<-tapply(wt$persweight,wt$hhid,sum)> length(hhwt)

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[1] 11971> table(abs(hhwt-d45$weighthhpers)<1)
TRUE
11971



The individual-level variable “persweight” is confirmed to be compatible with the householdlevel-variable “weighthhpers” in d45.

The individual-level variable “persweight” is appended to d44, “weightpersons”.

> table(as.integer(persweight$persid)==d44$persid)
TRUE
57105
> d44$persweight<-persweight$persweight> head(d44)hhidpersid province urbanrural

weight hhsize

weight3

psu stratum

1 100101 10010101

1

1 266.1367

5 1330.683 1001

11

2 100101 10010102

1

1 266.1367

5 1330.683 1001

11

3 100101 10010103

1

1 266.1367

5 1330.683 1001

11

4 100101 10010104

1

1 266.1367

5 1330.683 1001

11

5 100101 10010105

1

1 266.1367

5 1330.683 1001

11

6 100102 10010201

1

1 266.1367

4 1064.547 1001

11

surveymonth region year persweight1

1

2 2009299.1656

2

1

2 2009267.6359

3

1

2 2009267.6359

4

1

2 2009267.6359

5

1

2 2009299.1656

61

2 2009299.1656

Person weight has at most two values within the household.

> table(tapply(d44$persweight,d44$hhid,function(x) length(unique(x))))1

2

713 11258

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

Person weight is defined by hhid and sex.

# outfiles[[3]]: S01A.hhmembers# q01ac03: sex> table(d03$persid==d44$persid)
TRUE
57105
> t<-tapply(d44$persweight,list(d03$hhid,d03$q01ac03),function(x) length(unique(x)))> table(t)
t
1
23229



Estimated population size and number of households

Using the weights, the population size is estimated to 13,966,718, and the number of households isestimated to 2,938,650, which coincide with the survey report.

Basic statistics
Item
Un-weighted number ofValue

R scripts
11,971housheoldWeighted number of

2,938,650

57,105

13,966,718[1] 11971(NADA)> sum(d43$weight)

2,938,650> nrow(d44)> sum(d44$persweight)[1] 13966718

household members
Household size

11,971[1] 57105

household members
Weighted number of> nrow(d43)[1] 2938650

householdUn-weighted number ofSurvey report

4.75> sum(d44$persweight)/sum(d43$weight)[1] 4.752766

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13,966,718

161********************************************************
Note: Method of computing person weightAnalysed the relationship between household weight and person weight. It lead to the following
story (hypothesis).

# Individual-level data frame d44 includes household weight and person weight.
# Individual-level data frame d03 include psu and sex.
# Compared the ratio between person weight and household weight by psu and sex.

> table(tapply(d44$persweight,list(d03$psu,d03$q01ac03),function(x) length(unique(x))))1
1440> table(tapply(d44$persweight/d44$weight,list(d03$psu,d03$q01ac03),function(x) length(unique(x))))
1
1440



The above indicates that person weights are unique within a combination of psu and sex, as
well as, the ratios between person weight and household weight are constant within a
combination of psu and sex.



Story (hypothesis)
1) Household weight is calculated by using the projection of total population and the sum of
hhsize.

2) In each psu, the above weights for male and female are adjusted so that the sum of weights
for male and female coincide with the projection of male population and female population
respectively.
########################################################################

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

The delegates from Cambodia to the seventh international workshop in December 2015
clarified as follows;“CSES is used the two kinds of weight to avoid the bias for population estimation. For example
if you want to estimate the population (person level) the person weight should be usedotherwise the household weight should be used for household level for estimation.”“Sum of household weights is for the estimated number of households and Sum of person
weights is for the estimated number of persons.”

When merging household data and person data,“You need to check the data after merging the household data and person data whether that is
the household level or person level: If that is the household level the household weights should
be used but if that is the person level the person weights should be used.”

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5.5 Sample allocation
The sampling frame of villages was stratified by province and urban/rural. There are 24 provinces andeach village is classified as either urban or rural. The sample size of PSU is 720 villages (or ESs).
The sample allocation to strata is as follows.

# Urban/rural is defined by psu> table(tapply(d43$urbanrural,d43$psu,function(x) length(unique(x))))1
720# Number of psu by province and urban/rural> t<-tapply(d43$psu,list(d43$province,d43$urbanrural),function(x) length(unique(x)))> length(t)[1] 48> m<-matrix(t,nrow=24)> colnames(m)<-c("Urban","Rural")> rownames(m)<-formatC(1:24,width=2,flag="0")> addmargins(m)Urban Rural Sum
01

18

23

41

02

20

34 54

03

14

75 89

04

4

20 24

05

4

28 32

06

4

26 30

07

4

24 28

08

16

46 62

09

4

4

10

4

12 16

11

2

1

12

106

13

2

5

14

4

48 52

15

2

17 19

16

2

5

17

12

28 40

8

3

3 109
7

7

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18

6

5 11

19

2

5

20

2

25 27

21

2

37 39

22

2

7

9

23

2

1

3

24

2

1

3

Sum

240

480 720



The above table is the same as Table 11.1 at page 128 of the survey report.

7

Fig.

Cambodia Province Map

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Remarks: Village and psu
At the first stage of sampling, a sample of villages was selected using PPS.
At the second stage, one EA was selected in each village.
However, in a few large villages more than one EA were selected.

Note: This part is analysed using data frame of “areainformation” provided previously.
> d<-areainfo# Generated village identifier “vid”> d["vid"]<-paste(d$province,d$district,d$commune,d$village,sep="-")> head(d[c(1,2,4,6,8,17)])
psu province district commune villagevid

1 01001

01

02

09

03 01-02-09-03

2 01002

01

02

09

11 01-02-09-11

3 01003

01

02

09

17 01-02-09-17

4 01004

01

05

08

01 01-05-08-01

5 01005

01

05

08

01 01-05-08-01

6 01006

01

05

08

01 01-05-08-01# Number of sample villages by province and urban/rural> t<-tapply(d$vid,list(d$province,d$urbanrur),function(x) length(unique(x)))> colnames(t)<-c("Urban","Rural")> addmargins(t)Urban Rural Sum
01

14

23 37

02

20

34 54

03

14

75 89

04

4

20 24

05

4

28 32

06

4

26 30

07

4

24 28

08

16

46 62

09

4

4

10

4

12 16

8

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11

2

1

3

12

105

13

2

5

14

4

48 52

15

2

17 19

16

2

5

17

12

28 40

18

6

5 11

19

2

5

20

2

25 27

21

2

37 39

22

2

7

9

23

2

1

3

24

2

1

3

Sum

235

480 715



In urban of province 01 (Banteay Meanchey), 18 EAs were selected in 14 villages, and in urban

3 108
7

7

7

of province 12 (PhnomPenh), 106 EAs were selected in 105 villages.
# Number of villages by number of EAs within the village> table(tapply(d$psu,d$vid,length))
1

2

3

712

1

2

# List of sample villages with more than one EA> t<-tapply(d$psu,d$vid,length)> t[t>=2]
01-05-08-01 01-05-08-02 12-07-02-02
3

3

2> (vid.dup<-names(t[t>=2]))[1] "01-05-08-01" "01-05-08-02" "12-07-02-02"> d[is.element(d$vid,vid.dup),]
psu province

v3_a district

v5_a commune

v7_a village

4

01004

01 Banteay Meanchey

05

Ou Chrov

08

Paoy Paet

01

5

01005

01 Banteay Meanchey

05

Ou Chrov

08

Paoy Paet

01

6

01006

01 Banteay Meanchey

05

Ou Chrov

08

Paoy Paet

01

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7

01007

01 Banteay Meanchey

05

Ou Chrov

08

Paoy Paet

02

8

01008

01 Banteay Meanchey

05

Ou Chrov

08

Paoy Paet

02

9

01009

01 Banteay Meanchey

05

Ou Chrov

08

Paoy Paet02

472 12085

12

Phnom Penh

07 Ruessei Kaev

02 Tuol Sangkae

02

473 12086

12

Phnom Penh

07 Ruessei Kaev

02 Tuol Sangkae

02v9_a enum_are selected urbanrur househol surveymo v15_a v16_a

vid

4

Kbal Spean

67

14

1

6732

7

6173

72 01-05-08-01

5

Kbal Spean

67

60

1

6732

7

6173

83 01-05-08-01

6

Kbal Spean

67

18

1

6732

10

6178

24 01-05-08-01

7

Baliley

46

37

1

4634

10

120

93 01-05-08-02

8

Baliley

46

8

1

4634

11

4634

12 01-05-08-02

9

Baliley

46

2

1

4634

11

4634

45 01-05-08-02

472 Phum Tuol Sangkae

29

15

1

2657

3

4372

100 12-07-02-02

473 Phum Tuol Sangkae

29

8

1

2657

6

4372

91 12-07-02-02

“Kbal Spean” village and “Baliley” village in “Banteay Meanchey” province have three EAs
respectively, and “Phum Tuol Sangkae” village in “Phnom Penh” province has two EAs.

*****************************************************************************


Survey month

The annual sample was divided into 12 monthly samples of equal sizes. The monthly samples
consisted of 20 urban and 40 rural villages.
# Number of psu by survey month and urban/rural> t<-tapply(d43$psu,list(d43$urbanrural,d43$surveymonth),function(x) length(unique(x)))> length(t)[1] 24> m<-matrix(t,nrow=2)> rownames(m)<-c("Urban","Rural")> colnames(m)<-1:12> addmargins(m)
1

2 3

4 5

6 7

8

9 10 11 12 Sum

Urban 20 20 20 20 20 20 20 20 20 20 20 20 240
Rural 40 40 40 40 40 40 40 40 40 40 40 40 480
Sum

60 60 60 60 60 60 60 60 60 60 60 60 720

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

At the third stage of sampling, a sample of households was selected in each selected EA. The
number of sample households in one EA is 10 in urban, and 20 in rural. In total 12,000
households were selected.
CSES 2009 enjoyed almost a 100 percent response rate.

> nrow(d43)/12000*100[1] 99.75833# Number of EAs by number of responded households within the EA and urban/rural> by(d43,d43$urbanrural,function(df) table(tapply(df$hhid,df$psu,length)))d43$urbanrural: 18

9 10

1 13 226------------------------------------------------d43$urbanrural: 2
18 19 20
2 10 468# Number of responded households by province and urban/rural> t<-table(d43$province,d43$urbanrural)> colnames(t)<-c("Urban","Rural")> rownames(t)<-formatC(1:24,width=2,flag="0")> addmargins(t)Urban Rural

Sum

01

178

459

637

02

197

676

873

03

140 1499 1639

04

40

400

440

05

40

559

599

06

40

520

560

07

40

479

519

08

159

917 1076

09

40

80

120

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10

40

240

280

11

19

20

39

12

1053

13

20

100

14

40

960 1000

15

20

340

360

16

20

100

120

17

120

560

680

18

59

100

159

19

20

99

119

20

20

500

520

21

20

738

758

22

20

140

160

23

20

20

40

24

20

20

40

60 1113<-Phnom Pen

120

Sum 2385 9586 11971*************************************************************************************

Remarks: Region
NIS often uses three regions; “Phnom Pen”(including urban and rural), “Other urban” and “Other
rural” in a table.
# Number of sample households by three regions> t<-table(d43$region)> names(t)<-c("Phnom Pen","Other urban","Other rural")> addmargins(t)Phnom Pen Other urban Other rural
1113

1332

9526

Sum
11971

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5.6 Integrity check of household identifier “hhid”# HHID: a set of hhid in weighthouseholds# Each value of hhid in every data file should be an element of HHID.
# If not, it will be an isolated hhid.
# Number of isolated hhid in each data frame> HHID<-d43$hhid> for(j in 1:45){+ cat(formatC(j,width=2,flag="0"),":",sum(!is.element(outfiles[[j]]$hhid,HHID)),+ ":",length(setdiff(outfiles[[j]]$hhid,HHID)),":",Rnames[j],"\n")+ }01 : 0 : 0 : 09dy.expenditure
02 : 0 : 0 : 09dy.income
03 : 0 : 0 : s01a.hhmembers
04 : 0 : 0 : s01b.foodconsumption
05 : 0 : 0 : s01c.nonfoodexpenses
06 : 0 : 0 : s01d.vulnerability
07 : 0 : 0 : s02.education
08 : 0 : 0 : s03a.migration.past
09 : 0 : 0 : s03b.migration.current
10 : 0 : 0 : s04.housing
11 : 1 : 1 : s04.housingpractice
12 : 0 : 0 : s05a.landown
13 : 0 : 0 : s05b.cropsproduction
14 : 0 : 0 : s05c.costcrops
15 : 0 : 0 : s05d.cropsinventory
16 : 0 : 0 : s05e1.animals
17 : 0 : 0 : s05e2.animalsexpenses
18 : 0 : 0 : s05f1.fisharea
19 : 0 : 0 : s05f2.fishexpenses
20 : 0 : 0 : s05f3.fishincome
21 : 0 : 0 : s05g1.forestincome
22 : 0 : 0 : s05g2.forestexpenses
23 : 0 : 0 : s05h1.bussiness
24 : 0 : 0 : s05h2.bussinessexpenses
25 : 0 : 0 : s05h3.bussinessincome
26 : 0 : 0 : s06.liabilities
27 : 0 : 0 : s07.incomeother
28 : 0 : 0 : s08.construction
29 : 0 : 0 : s09.durables
30 : 0 : 0 : s10.healthmother
31 : 0 : 0 : s11.health2years
32 : 0 : 0 : s12.health5years
33 : 0 : 0 : s13b.healthexpenses
34 : 0 : 0 : s14.disability
35 : 0 : 0 : s15.labor7days
36 : 0 : 0 : s16.labor12months
37 : 0 : 0 : s17b.theft
38 : 0 : 0 : s17c.accidents
39 : 0 : 0 : s17d.violance
40 : 0 : 0 : s17d.violence
41 : 0 : 0 : s18.presenseinhh
42 : 0 : 0 : s99.singlequestions

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43 : 0 : 0 : weighthh
44 : 0 : 0 : weightpersons
45 : 0 : 0 : IncomeCSES09

S04.housingpractice

The isolated hhid in S04.housingpractice which has no counterpart in HHID is 100209.

> setdiff(d11$hhid,HHID)[1] 100209
On the other, the missing hhid in S004.hhhousingpractice is 100309.

> setdiff(HHID,d11$hhid)[1] 100309> HHID[11:30][1] 100201 100202 100203 100204 100205 100206 100207 100208[9] 100210 100301 100302 100303 100304 100305 100306 100307[17] 100308 100309 100310 100401> d11$hhid[11:30][1] 100201 100202 100203 100204 100205 100206 100207 100208[9] 100209 100210 100301 100302 100303 100304 100305 100306[17] 100307 100308 100310 100401

Summary:
The value 0100209 of hhid in S04.housingpractice should be read as 100309.



For this point, the delegates from Cambodia to the seventh workshop clarified;“0100208”, “0100209” and “0100210” are household identification so they are different households.

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5.7 Integrity check of individual identifier “persid”# PID: a set of persid in d44# Each value of persid in every individual-level data file should be an element of PID.
# If not, it will be an isolated persid.
# fileno: Numbers of individual-level data files# Number of isolated persid in each data frame> fileno<-c(3,7:9,30:41)> PID<-d44$persid> length(PID)[1] 57105> head(PID)[1] 10010101 10010102 10010103 10010104 10010105 10010201> for(j in fileno){+ cat(formatC(j,width=2,flag="0"),":",sum(!is.element(outfiles[[j]]$persid,PID)),+ ":",length(setdiff(outfiles[[j]]$persid,PID)),":",Rnames[j],"\n")+ }03 : 0 : 0 : s01a.hhmembers
07 : 0 : 0 : s02.education
08 : 0 : 0 : s03a.migration.past
09 : 8049 : 8049 : s03b.migration.current
30 : 1 : 1 : s10.healthmother
31 : 3 : 2 : s11.health2years
32 : 2 : 2 : s12.health5years
33 : 0 : 0 : s13b.healthexpenses
34 : 0 : 0 : s14.disability
35 : 0 : 0 : s15.labor7days
36 : 0 : 0 : s16.labor12months
37 : 75 : 70 : s17b.theft
38 : 0 : 0 : s17c.accidents
39 : 57074 : 25 : s17d.violance
40 : 0 : 0 : s17d.violence
41 : 0 : 0 : s18.presenseinhh

S03B. migration.current
No problem, because persons in this data file are not household members. Person number (q03bc01)
is defined as 31 and over.
> d<-outfiles[[9]]> table(d$q03bc01)
31 32 33 34 35
3400 2077 1194 673 368

36
190

37
88

38
36

39
15

40
7

41
1

S10.healthmother> d<-outfiles[[30]]> setdiff(d$persid,PID)[1] 40131207> d[d$persid==40131207,]
hhid persid province urbanrural weight hhsize weight3
1261 401312 40131207
NA
NA
NA
NA
NA
psu stratum surveymonth region year persid_string
1261 NA
NA
NA
NA
NA
040131207

Manual for Cambodia CSES 2009 (Version 2.0)173hhid_string q10_c01 q10_c02 q10_c03 q10_c04 q10_c05 q10_c06
0401312
1
7
10
2
2
1
q10_c07 q10_c08 q10_c09 q10_c10 q10_c11 q10_c12 q10_c13
1261
1
1
2
1
8
1
3
q10_c14 q10_c15a q10_c15b q10_c15c q10_c15d q10_c16 q10_c17
1261
1
3
5
NA
NA
1
1
1261

> d03[d03$hhid==401312,]
hhid
persid province urbanrural
weight hhsize
15913 401312 40131201
42 252.7299
315914 401312 40131202
42 252.7299
315915 401312 40131203
42 252.7299
3weight3 psu stratum surveymonth region year persid_string
15913 758.1897 4013
4211
3 2009040131201
15914 758.1897 4013
4211
3 2009040131202
15915 758.1897 4013
4211
3 2009040131203
hhid_string q01ac01 q01ac03 q01ac04a q01ac04b q01ac04c
15913
0401312
1
1
6
1
1963
15914
0401312
2
2
29
4
1965
15915
0401312
3
1
6
2
1999
q01ac05 q01ac06 q01ac07 q01ac08 q01ac09 q01ac10 q01ac11a
15913
46
1
NA
NA
1
2
2
15914
44
2
NA
NA
1
1
2
15915
10
8
NA
NA
NA
NA
2
q01ac11b q01ac12a q01ac12b q01ac12c q01ac13 q01ac14
15913
1
7
NA
NA
1
NA
15914
1
7
NA
NA
1
NA
15915
1
7
NA
NA
1
NA

Summary
According to the description of the questionnaire, the record of S10.healthmother is a woman
living in the household with living children under 5 years old. However, the household roster
with hhid=401312 does not satisfy such condition.
Possible cause of isolated persid might be an error of 40131207. But there is no way to find thecorrect persid for researchers outside NIS.

S11.personhealthu2# q11_c02: ID of the mother# q11_c03: ID of the child# perseid = hhid + q11_c03> d<-outfiles[[31]]> setdiff(d$persid,PID)[1]
NA 150061106


The values of q11_c03 (child ID) are missing in two cases; hhid=1300517 and 2101009.

> table(is.na(d$q11_c03))
FALSE TRUE2355
2
> d[is.na(d$q11_c03),"hhid"]Manual for Cambodia CSES 2009 (Version 2.0)

174[1] 1300517 2101009
Household members in hhid=1300517> d03[d03$hhid==1300517,]
hhid
persid province urbanrural
weight hhsize
38445 1300517 130051701
13
2 286.1398
438446 1300517 130051702
13
2 286.1398
438447 1300517 130051703
13
2 286.1398
438448 1300517 130051704
13
2 286.1398
4weight3 psu stratum surveymonth region year
38445 1144.559 13005
132
3
3 200938446 1144.559 13005
132
3
3 200938447 1144.559 13005
132
3
3 200938448 1144.559 13005
132
3
3 2009persid_string hhid_string q01ac01 q01ac03 q01ac04a q01ac04b
38445
130051701
1300517
1
1
5
7
38446
130051702
1300517
2
2
3
6
38447
130051703
1300517
3
2
1
2
38448
130051704
130051741
34q01ac04c q01ac05 q01ac06 q01ac07 q01ac08 q01ac09 q01ac10
38445
1975
33
1
NA
NA
1
2
38446
1983
25
2
NA
NA
1
1
38447
2005
4
3
1
2
NA
NA
38448
2008
1
3
1
2
NA
NA
q01ac11a q01ac11b q01ac12a q01ac12b q01ac12c q01ac13
38445
1
1
0
NA
NA
1
38446
1
1
0
NA
NA
1
38447
1
1
0
NA
NA
1
38448
1
1
0
NA
NA
1
q01ac14
38445
NA
38446
NA
38447
NA
38448
NA

Household members in hhid=2101009> d03[d03$hhid==2101009,]
hhid
persid province urbanrural
weight hhsize
53154 2101009 210100901
21
2 250.4301
753155 2101009 210100902
21
2 250.4301
753156 2101009 210100903
21
2 250.4301
753157 2101009 210100904
21
2 250.4301
753158 2101009 210100905
21
2 250.4301
753159 2101009 210100906
21
2 250.4301
753160 2101009 210100907
21
2 250.4301
7weight3 psu stratum surveymonth region year
53154 1753.011 21010
212
11
3 200953155 1753.011 21010
212
11
3 200953156 1753.011 21010
212
11
3 200953157 1753.011 21010
212
11
3 200953158 1753.011 21010
212
11
3 200953159 1753.011 21010
212
11
3 200953160 1753.011 21010
212
11
3 2009persid_string hhid_string q01ac01 q01ac03 q01ac04a q01ac04b
53154
210100901
2101009
1
1
12
3
53155
210100902
21010092
2
8
9Manual for Cambodia CSES 2009 (Version 2.0)

175

53156
53157
53158
53159
53160

210100903
21010093
2
9
9210100904
2101009
4
2
29
2
210100905
21010095
2
8
10210100906
2101009
6
1
11
6210100907
21010097130
6q01ac04c q01ac05 q01ac06 q01ac07 q01ac08 q01ac09 q01ac10
53154
1966
43
1
NA
NA
1
2
53155
1969
40
2
NA
NA
1
1
53156
1991
18
3
1
2
4
NA
53157
1993
16
3
1
2
4
NA
53158
1996
13
3
1
2
4
NA
53159
1997
12
3
1
2
NA
NA
53160
2009
0
3
1
2
NA
NA
q01ac11a q01ac11b q01ac12a q01ac12b q01ac12c q01ac13
53154
1
1
0
NA
NA
1
53155
1
1
0
NA
NA
1
53156
1
1
0
NA
NA
2
53157
1
1
0
NA
NA
1
53158
1
1
0
NA
NA
1
53159
1
1
0
NA
NA
1
53160
1
1
0
NA
NA
1
q01ac14
53154
NA
53155
NA
53156
49
53157
NA
53158
NA
53159
NA
53160
NA

Household members in hhid=1500611> d03[d03$hhid==1500611,]
hhid
persid province urbanrural
weight hhsize
43535 1500611 150061101
15
2 261.0462
343536 1500611 150061102
15
2 261.0462
343537 1500611 150061103
15
2 261.0462
3
weight3 psu stratum surveymonth region year
43535 783.1385 15006
152
7
3 200943536 783.1385 15006
152
7
3 200943537 783.1385 15006
152
7
3 2009persid_string hhid_string q01ac01 q01ac03 q01ac04a q01ac04b
43535
150061101
1500611
1
1
14
1
43536
150061102
1500611
2
2
5
6
43537
150061103
150061131
23
4q01ac04c q01ac05 q01ac06 q01ac07 q01ac08 q01ac09 q01ac10
43535
1986
23
1
NA
NA
4
NA
43536
1988
21
7
NA
NA
4
NA
43537
1989
20
7
NA
NA
4
NA
q01ac11a q01ac11b q01ac12a q01ac12b q01ac12c q01ac13
43535
1
1
0
NA
NA
1
43536
1
1
0
NA
NA
1
43537
1
1
0
NA
NA
1
q01ac14
43535
NA
43536
NA
43537
NA

Manual for Cambodia CSES 2009 (Version 2.0)

176



The household consists of three siblings. The value 150061106 of persid might be mistaken,
but there is no way to find the correct persid for researchers outside NIS.

Summary
Revision of S11.health2years
hhid
13005172101009
1500611Error
q11_c03=NA
persid= blank
q11_c03=NA
persid= blankhhid=1500611
persid= 150061101

Revision
4130051704
7210100907
Unknown
Unknown

S12.health5years# q12_c02: ID of the child# perseid = hhid + q11_c02> d<-outfiles[[32]]> setdiff(d$persid,PID)[1] 40131210 150061106
Household members in hhid=401312> d03[d03$hhid==401312,]
hhid
persid province urbanrural
weight hhsize
15913 401312 40131201
42 252.7299
315914 401312 40131202
42 252.7299
315915 401312 40131203
42 252.7299
3weight3 psu stratum surveymonth region year persid_string
15913 758.1897 4013
4211
3 2009040131201
15914 758.1897 4013
4211
3 2009040131202
15915 758.1897 4013
4211
3 2009040131203
hhid_string q01ac01 q01ac03 q01ac04a q01ac04b q01ac04c
15913
0401312
1
1
6
1
1963
15914
0401312
2
2
29
4
1965
15915
0401312
3
1
6
2
1999
q01ac05 q01ac06 q01ac07 q01ac08 q01ac09 q01ac10 q01ac11a
15913
46
1
NA
NA
1
2
2
15914
44
2
NA
NA
1
1
2
15915
10
8
NA
NA
NA
NA
2
q01ac11b q01ac12a q01ac12b q01ac12c q01ac13 q01ac14
15913
1
7
NA
NA
1
NA
15914
1
7
NA
NA
1
NA
15915
1
7
NA
NA
1
NA



The household with hhid=401312 does not have a child under five years old.

Manual for Cambodia CSES 2009 (Version 2.0)

177

Household members in hhid= 1500611> d03[d03$hhid== 1500611,]
hhid
persid province urbanrural
weight hhsize
43535 1500611 150061101
15
2 261.0462
343536 1500611 150061102
15
2 261.0462
343537 1500611 150061103
15
2 261.0462
3
weight3 psu stratum surveymonth region year
43535 783.1385 15006
152
7
3 200943536 783.1385 15006
152
7
3 200943537 783.1385 15006
152
7
3 2009persid_string hhid_string q01ac01 q01ac03 q01ac04a q01ac04b
43535
150061101
1500611
1
1
14
1
43536
150061102
1500611
2
2
5
6
43537
150061103
150061131
23
4q01ac04c q01ac05 q01ac06 q01ac07 q01ac08 q01ac09 q01ac10
43535
1986
23
1
NA
NA
4
NA
43536
1988
21
7
NA
NA
4
NA
43537
1989
20
7
NA
NA
4
NA
q01ac11a q01ac11b q01ac12a q01ac12b q01ac12c q01ac13
43535
1
1
0
NA
NA
1
43536
1
1
0
NA
NA
1
43537
1
1
0
NA
NA
1
q01ac14
43535
NA
43536
NA
43537
NA



The household with hhid="1500611" does not have a child under five years old.

S17B.persontheft# outfiles[[37]]: S17B.persontheft# q17bc02: ID of the person exposed to theft.
#
If no specific person enter 88> d<-outfiles[[37]]> table(d$q17bc02,useNA="ifany")
1
2
3
4
5
6
7
9 98
168 46 22
7
3
5
1
1 75

Summary:
No problem because q17bc02 = 98 is allowed.

s17d.violanceThis data frame is not used. Instead, s17d.violence will be used.

Manual for Cambodia CSES 2009 (Version 2.0)

178

Chapter 6. Household Expenditure
Background
CSES data has been collected by interviewers (NIS officials) using both recall and diary methods.
There are some descriptions on how to use the data in the survey report as below.

Page

Related description in the survey report

6

Expenditure: the household expenditure in Cambodia 2009 was calculated using recall
data from the household questionnaire.

104

Consumption data in the 2009 survey was collected using recall questions in the household
questionnaire. Consumption data was also collected in a Diary where all expendituretransactions and consumption of own produced goods during the survey month werereported. The diary method was introduced in CSES 2004. For calculating poverty
estimates the recall data was used all years. However, for 2004 poverty estimates were
calculated using both methods. The most recent presentation of Poverty Estimates was
carried out by the World Bank.

104

The result presented in this chapter is compiled from recall data. The household
questionnaire had two sets of questions, one for food expenditure/consumption and thesecond set for non-food expenditure. The questionnaire was designed to collect data onpurchase in cash, consumption of own production, consumption of items received as wages
in kind. It also included gifts, free collection and barter, and in kind expenditure.
The food section comprised 20 items covering all food, including alcoholic, tobacco, andfood taken away from home, prepared meals bought outside and eaten at home. The nonfood section embraced 13 items covering all

non-food expenditure except

housing. Expenditure on housing was collected in the Housing module. The reference
period for food items was the last seven days. For non-food items the reference periodvaried from last month to last 12 month (see the Questionnaire in appendix 8).
105

In this report the monthly consumption is calculated. The consumption concept used in thisreport differ from the calculation of consumption for poverty estimates where adjustmentsfor price differences, rental values etc. is done, see section Definition and World Bank
report.

107 -The grouping of items follows the questionnaire, except for domestic salaries and108

gambling. Domestic salaries are included in Furniture, household operation etc. Gamblingis excluded to be consistent with the income concept where income from Gambling andlotteries is excluded in total income.
For “Housing” charges on water, sewage, wastewater disposal, garbage collection and fuelManual for Cambodia CSES 2009 (Version 2.0)

179

for lighting and cooking are included as well as paid rent. For owner occupied houses thehousehold was asked to estimate the value for rent of a similar house. Expenditure spent on
maintenance and minor repairs is also included. All this data are collected in the Housing
module.
Food share are calculated as the share of total consumption. Food includes all food items,
non-alcoholic and alcoholic beverages.
Table

Classification of consumption composition used in tables is as below;

6.1 FOOD ITEMSCreated household-level data frame consisted of vectors of food items.

# outfiles[[4]]: S01B.foodconsumption# q01bc01: Item number (01 to 20)# q01bc05: Total consumption in the last 7 days
> d<-outfiles[[4]]> df<-tapply(d$q01bc05,list(d$hhid,d$q01bc01),sum)> dim(df)[1] 1197120> head(df)1
100101 16000

2

3

4

5

6

NA 50400 2500 NA 1500

100102 16000 35000 11000
100103 20000 25000

7

8

9

10

1800 2000NA

500 11000

NA NA 2000 11000 2000

NA 6000 NA 2000

2600

NA

11

NA 2000

6000

NA 2000

9000

Manual for Cambodia CSES 2009 (Version 2.0)

180100104 12000

5500

NA 3000 NA 500

100105 19000 74500 49000

NA NA 5200

100106 65000 49000 23000 5000 NA 1500
12

900

NA

NA

NA 12500

1900

NA

NA

NA 12000

7800 5000 2500 1500

13 14

15

16

17

18

19

20

1001013000 7000 NA

NA

NA

NA

NA

NA

NA

100102NA 1700 NA

NA

NA

NA

NA

NA 3000

100103NA 7800 NA

NA

NA 8000

NA

NA

NA

100104NA 1400 NA 6000

NA

NA 9000

NA

1001053000 9000 NA

100106 10000 5000 NA

NA

NA 2000

NA 1000

NA

NA

NA

NA

NA

NA

NA

NA

3000

> df[is.na(df)]<-0> df<-as.data.frame(df)> hhfood<-df> head(hhfood)12100101 16000

3

4 5

6

7

8

9

0 50400 2500 0 1500 1800 2000

100102 16000 35000 11000

10110 500 11000

0 0 2000 11000 2000

0 2000 6000

100103 20000 25000

0 6000 0 2000 2600

00 2000 9000

100104 12000

0 3000 0

900

0

0

0 12500

0 0 5200 1900

000 12000

5500

100105 19000 74500 49000

500

100106 65000 49000 23000 5000 0 1500 7800 5000 2500 1500 3000
12

13 14

15

16

17

18

19

20

1001013000 7000 0

0

0

0

0

0

0

1001020 1700 0

0

0

0

0

0 30001001030 7800 000 80000001001040 1400 0 6000

00 900001001053000 9000 000 20000 1000

0

0

0

0

0

0

0

4 5

6

7

8

0 218400 10833 0

6500

7800

8667

0 21670 08667 47667

8667

0 86670 26000 08667 11267

00 8667100106 10000 5000 00# Converted to monthly> hhfood<-hhfood*52/12> head(round(hhfood))
1210010169333

10010269333 151667

100103

86667 108333

3

47667

9

10

Manual for Cambodia CSES 2009 (Version 2.0)

18110010452000 23833

0 13000 0

10010582333 322833 212333

100106 281667 212333
11

12

2167

3900

0

0

0

0 0 22533

8233

0

0

0

99667 21667 06500 33800 21667 10833 6500

13 14

15

16

17

18

19

20
0

100101 47667 13000 30333

0

0

0

0

0

0

100102 26000

0

7367

0

0

0

0

0

0 13000

100103 39000

0 33800

000 34667000100104 54167

00 2600000 39000

0

6067

0

100105 52000 13000 39000

00 8667

0 433300100106 13000 43333 21667

0

0

0

0

0

0

0

Remarks:
Here, the multiplier of 52/12 is used instead of 30/7.

# Generated data frame exp.food with variables of hhid and 20 item groups> exp.food<-data.frame(hhid=rownames(hhfood),round(hhfood),row.names=NULL)> colnames(exp.food)[2:21]<-paste("item",formatC(1:20,width=2,flag="0"),sep="")> head(exp.food)hhid item01 item02 item03 item04 item05 item06 item07 item08 item09 item10
1 10010169333

0 218400

10833

0

6500

7800

8667

0

2167

2 10010269333 151667

47667

0

0

8667 47667

8667

0

8667

3 100103

86667 108333

0

26000

0

8667 11267

0

0

8667

4 100104

52000 23833

0

13000

0

2167

3900

0

0

0

5 100105

82333 322833 212333

0

0

22533

8233

0

0

0

21667

0

6500 33800 21667

10833

6500

6 100106 281667 212333

99667

item11 item12 item13 item14 item15 item16 item17 item18 item19 item20
1

47667

13000 30333

0

0

0

0

0

0

0

2

26000

0

7367

0

0

0

0

0

0

13000

3

39000

0 33800

0

0

0

34667

0

0

0

4

54167

0

6067

0

26000

0

0

0 39000

0

5

52000

13000 39000

0

0

8667

0

4333

0

0

6

13000

43333 21667

0

0

0

0

0

0

0

Manual for Cambodia CSES 2009 (Version 2.0)

182

# Generated the variables;# food: sum of items 01:15,18:20# alcohol: sum of items 16 and 17# total: sum of items 01:20> exp.food$food<-rowSums(exp.food[,c(2:16,19:21)])> exp.food$alcohol<-rowSums(exp.food[,17:18])> exp.food$total<-rowSums(exp.food[,2:21])> head(exp.food)hhid item01 item02 item03 item04 item05 item06 item07 item08 item09 item10 item11
1 10010169333

0 218400

10833

0

6500

7800

8667

0

2167

47667

2 10010269333 151667

47667

0

0

8667 47667

8667

0

8667

26000

3 100103

86667 108333

0

26000

0

8667 11267

0

0

8667

39000

4 100104

52000 23833

0

13000

0

2167

3900

0

0

0

54167

5 100105

82333 322833 212333

0

0

22533

8233

0

0

0

52000

21667

0

6500 33800 21667

10833

6500

13000

6 100106 281667 212333

99667

item12 item13 item14 item15 item16 item17 item18 item19 item20

food alcohol total

1

13000

30333

0

0

0

0

0

0

0 414700

0 414700

2

0

7367

0

0

0

0

0

0 13000 388702

0 388702

3

0

33800

0

0

0 34667000 322401

34667 357068

4

0

6067

0

26000

0

0

0 39000

0 220134

0 220134

5

13000

39000

0

0

8667

0

4333

0

0 756598

8667 765265

6

43333

21667

0

0

0

0

0

0

0 772634

0 772634

Average monthly amount of food consumption by food items (in thousand Riels)# Estimated monthly total amount of food consumption (in million Riels)> hw<-d43$weight# household weight> tm<-sapply(exp.food[,2:24],function(x) round(sum(x*hw)))> round(tm/10^6)# in million Riels> round(tm/10^6)# in million Riels# in Riels

item01 item02 item03 item04

item05

item06

item07

item08 item09 item10 item11324372 320056 219448

43116

35027

41372

13239915913

item12 item13 item14 item15

item16

item17

item18item19 item20

25360 100667

44039

33527

23003

106958

24732

25776

total
1679780

Manual for Cambodia CSES 2009 (Version 2.0)

16118

16383

97828

food alcohol

33687 1602214

77566

183

# Average monthly amount of food consumption by food item (in thousand Riels)# Per household> (NH<-sum(hw)) # estimated number of households[1] 2938650> (th<-round(tm/NH/1000))
item01 item02 item03 item04

item05

item06

item07

15

12

14

45

item12 item13 item14 item15

item16

item17

item18

15

11

8

110

9

109

34

75

8

9

item08 item09 item10 item11
55

item19 item20
36

11

6

33

food alcohol
545

26

total
572

# Per capita> (NP<-sum(d44$persweight))# estimated number of household members[1] 13966718> (tp<-round(tm/NP/1000))
item01 item02 item03 item04

item05

item06

item07333

9

item12 item13 item14 item15

item16

item17

item18

3

2

2

23

2

23

7

16

2

2

item08 item09 item10 item1111

item19 item20
8

2

1

7

food alcohol
115

6

total
120

# Item names> t0<-c("Cereals","Fish","Meat & poultry","Eggs","Dairy products","Oil and fats",+ "Fresh vegetables","Tuber","Pulses and legumes","Prepared and preserved vegetables",+ "Fruit etc.","Dried nuts and edible seeds","Sugar, salt and spices","Tea, coffee, cocoa",+ "Non-alcoholic beverages","Alcoholic beverages","Tobacco products","Other food products",+ "Food taken away from home","Prepared meals bought outside and eaten at home")> t0<-c(t0,"Food and non-alcoholic beverages","Alcohol and tobacco","Food total")

Manual for Cambodia CSES 2009 (Version 2.0)

184

# Average monthly amount of food consumption per household and per capita# by food items (in thousand Riels)> (m<-data.frame(item=t0,perhh=th,percapita=tp,row.names=NULL))
item perhh percapita
1

Cereals

110

23

2

Fish

109

23

3

Meat & poultry

75

16

4

Eggs

15

3

5

Dairy products

12

3

6

Oil and fats

14

3

7

Fresh vegetables

45

9

8

Tuber

5

1

9

Pulses and legumes

5

1

10

Prepared and preserved vegetables

6

1

11

Fruit etc.

33

7

12

Dried nuts and edible seeds

9

2

13

Sugar, salt and spices

34

7

14

Tea, coffee, cocoa

8

2

15

Non-alcoholic beverages

9

2

16

Alcoholic beverages

15

3

17

Tobacco products

11

2

18

Other food products

8

2

19

Food taken away from home36

8

20 Prepared meals bought outside and eaten at home

11

2

21

Food and non-alcoholic beverages

545

115

22

Alcohol and tobacco

26

6

23

Food total

572

120

Manual for Cambodia CSES 2009 (Version 2.0)

185

6.2 NON-FOOD ITEMSCreated household-level data frame consisted of vectors of non-food items.

# outfiles[[5]]: S01C.nonfoodexpenses# q01cc01: Non-food item (01 to 13)# q01cc06: Total expenditure in Riels# Reference periods differ by item and are indicated on the questionnaire> d<-outfiles[[5]]> df<-tapply(d$q01cc06,list(d$hhid,d$q01cc01),sum)> dim(df)[1] 1197113> head(df)
1

4

5

9

10 11

12

13

100101 12000

50000 20000 9500

21000

40000 NA 100000 NA

80000 NA

NA

1020000

10010225000 20000 12000 200000

50000 NA 100000 NA 385000 NA

NA

60000046000

60000 NA

45000 NA

NA

3200001500

39000 40000 8500 180000

48000 NA

80000 NA 1000000 NA

NA

550000100105 20000

66000 40000 63000 150000

80000 NA 300000 NA 250000 NA 45000

3000000

1500

100103 83000
1001041001062NA

3NA 3800

NA 200000 20000 6500

6 715000 1000000 NA

8

NA NA

NA NA

50000 NA

8 9

10 11

NA 11000000> df[is.na(df)]<-0> df<-as.data.frame(df)> head(df)145100101 12000

50000 20000 9500

21000

40000 0 1e+05 0

80000

0

0 1020000

10010225000 20000 12000 200000

50000 0 1e+05 03850000060000060000 0 0e+00 0

45000

0

0

32000005500001500

100103 83000
10010420

30 3800

46000

6 7

12

13

1500

39000 40000 8500 180000

48000 0 8e+04 0 1000000

0100105 20000

66000 40000 63000 150000

80000 0 3e+05 02500000 45000 3000000

15000 1000000 0 0e+00 0

50000

0

100106

0 200000 20000 6500

# Converted to monthly# Item 01-04: a month# Item 05: 6 months# Item 06-13: 12 months

Manual for Cambodia CSES 2009 (Version 2.0)0 11000000

186> hhnonfood<-df> hhnonfood[,5]<-df[,5]/6> hhnonfood[,6:13]<-df[,6:13]/12> head(round(hhnonfood))
1

4

5

6 7

8 9

10 11

100101 12000

50000 20000 9500

3500

3333 08333 06667 00 85000

10010225000 20000 12000 33333

4167 08333 0 32083 0

0 50000

1500

2100103 83000

35000 01500

39000 40000 8500 30000

4000 0100105 20000

66000 40000 63000 25000

6667 0 25000 0 20833 0 3750 250000

1001060 38000 200000 20000 6500

2500 83333 0

0 0

13

7667

1001040123750 00 266676667 0 83333 0

0 458330 04167 00 916667> exp.nonfood<-data.frame(hhid=rownames(hhnonfood),round(hhnonfood),row.names=NULL)> colnames(exp.nonfood)[2:14]<-paste("item",formatC(1:13,width=2,flag="0"),sep="")> head(exp.nonfood)hhid item01 item02 item03 item04 item05 item06 item07 item08 item09 item10 item11
1 100101

12000 50000

20000

9500

3500

3333

0

8333

0

6667

0

2 100102

1500 25000

20000

12000 33333

4167

0

8333

0 32083

0

3 100103

83000

0

0

3800

7667

5000

0

0

0

3750

0

4 100104

1500 39000

40000

8500 30000

4000

0

6667

0 8333305 100105

20000 66000

40000

63000 25000

6667

0 25000

0 208330

6 100106

0 200000

20000

6500

83333

0

0

0

2500

0

4167

item12 item13
1

0

85000

2

0

50000

3

0

26667

4

0

45833

5

3750 250000

60 916667

Average monthly amount of non-food consumption by food items (in thousand Riels)# Total amount of non-food consumption (in million Riels)> t2<-sapply(exp.nonfood[,2:14],function(x) sum(x*hw))

Manual for Cambodia CSES 2009 (Version 2.0)

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# Total monthly amount of non-food consumption by item (in million Riels)> tm<-round(t2/10^6)> (tm<-c(tm,total=sum(tm)))
item01 item02 item03 item04 item05 item06 item07 item08 item09 item10 item11 item12244126 163554 58978
item1346856

88176 31943

4830 31686

6170

83874 30776

1850

total

195409 988228# Average monthly amount of non-food consumption by item (in thousand Riels)# Per household> th<-round(t2/NH/1000)> (th<-c(th,total=sum(th)))item01 item02 item03 item04 item05 item06 item07 item08 item09 item10 item11 item12
83

56

item13

total

66

337

20

16

30

11

2

11

2

29

10

1

# Per capita> tp<-round(t2/NP/1000)> (tp<-c(tp,total=sum(tp)))
item01 item02 item03 item04 item05 item06 item07 item08 item09 item10 item11 item12
17

12

item13

total

14

68

4

3

6

2

0

2

0

6

2

0

# Item names> t0<-c("Medical care","Transportation","Communication","Personal care","Clothing and footwear",+ "Furniture, furnishings and household equipment and operation","Domestic salaries",+ "Recreation within Cambodia","Recreation abroad","Education","Personal effects",+ "Gambling","Miscellaneous items")> t0<-c(t0,"Non-food total")

Manual for Cambodia CSES 2009 (Version 2.0)

188

# Average monthly amount of non-food consumption per household and per capita# by non-food items (in thousand Riels)> (m2<-data.frame(item=t0,perhh=th,percapita=tp,row.names=NULL))
item perhh percapita
1

Medical care

83

17

2

Transportation

56

12

3

Communication

20

4

4

Personal care

16

3

5

Clothing and footwear

30

6

6

Furniture, furnishings and household equipment and operation

11

2

7

Domestic salaries

2

0

8

Recreation within Cambodia

11

2

9

Recreation abroad

2

0

10

Education

29

6

11

Personal effects

10

2

12

Gambling

1

0

13

Miscellaneous items

66

14

14

Non-food total

337

68

Manual for Cambodia CSES 2009 (Version 2.0)

189

6.3 HOUSING# outfiles[[10]]: S04.housing> d<-outfiles[[10]]> nrow(d)[1] 11970> d$hhid[11:30][1] 100201 100202 100203 100204 100205 100206 100207 100208 100210 100301 100302 100303[13] 100304 100305 100306 100307 100308 100310 100401 100402



hhid=100209 and 100309 are not included.

# Variable descriptions> data.frame(variable=colnames(outfiles[[10]]),+ description=attributes(outfiles[[10]])$var.labels)variable

description

1

hhid

individual hh identification number

2

province

province (2)3urbanruralurban or rural households

4

weightweight5

hhsize# of members with less than 12 months absence

6

weight3

weight by household size

7

psu

primary sampling unit (clusters)

8

stratum(province code * 10) + urban/rural classification

9 surveymonth

month of the survey for each psu

10regionphnom penh, o. urban & o. rural

11

year

year cses

12 hhid_string13

q04_01

how many hhs reside in same unit

14

q04_02

what is the fllor area of the housing in square meters?

15

q04_03how many rooms are use by hh (other than kitchchen, toilet and tathrooms)?

16

q04_04walls primary construction material17

q04_05roof primary construction material18

q04_06floors primary construction materials

19

q04_07

what is the hh's main source of lighthing?

20

q04_08what is the hh's main source of drinking water in wet season?

Manual for Cambodia CSES 2009 (Version 2.0)

190

21

q04_09distance from home to water source in wet season (meters)?

22

q04_10m1

which hh members fetch water in wet season?

23

q04_10m2

which other hh members fetch water in wet season?

24

q04_10m3

which other hh members fetch water in wet season?

25

q04_11minutes per day for fetching drinking water in wet season?

26

q04_12

what is the hh's main source of drinking water in dry season?

27

q04_13

distance from home to water source in dry season (meters)?

28

q04_14m1

which hh members fetch water in dry season?

29

q04_14m2

which other hh members fetch water in dry season?

30

q04_14m3

which other hh members fetch water in dry season?

31

q04_15minutes per day for fetching drinking water in dry season?

32

q04_16

water charges pay last month (riels)?

33

q04_17

did hh boil or treated water last month?

34

q04_18a

did you boil the water?

35

q04_18b

did you filter the water?

36

q04_18c

did you treted chemicaly the water?

37

q04_18d

did you use white alum for the water?

38

q04_18e

did you use any other treatment the water?

39

q04_19awhat toilet facilities your hh has on the premises (close to dwelling)?

40

q04_19bwhat toilet facility does your hh ususally use?

41q04_20expenditure in sewage or waste water disposal last montlh?

42q04_21

how much did your hh spend for garbage collection lat month?

43q04_22awhat type of use your hh mainly use for cooking?

44q04_22bdoes the vendor brings the firewood/charcoal home?

45q04_22c1

which hh member colects firewood or charcoal?

46

q04_22c2

which other hh member colects firewood or charcoal?

47

q04_22c3

which other hh member colects firewood or charcoal?

48

q04_22d

how many hours per week are used in collecting firewood/charcoal

49

q04_23ahow much did the hh spend last month on electricity?

50

q04_23b

how much did the hh spend last month on gas (lpg)?

51

q04_23c

how much did the hh spend last month on kerosene?

52

q04_23d

how much did the hh spend last month on firewood?

53

q04_23e

how much did the hh spend last month on charcoal?

54q04_23f

how much did the hh spend last month on battery?

55

q04_23g

how much did the hh spend last month on other?

56

q04_24

what was the legal status of the dwelling?

Manual for Cambodia CSES 2009 (Version 2.0)

191

57

q04_25a58

q04_25b how much would you have to pay per month to rent a similar dwelling? (estimated59

q04_26 how much did you spend on maintenance and minor repairs of the dwelling last mon

60

if rented, how much did you pay for rent of this house last month?

phnonpenh

61 otherrural
62

urban

63 publiclight
64

pre_1

65

log_25b66

pre_2

unstandardized predicted value

unstandardized predicted value> df<-d[c(1,32,41,42,49:55,57:59)]> head(df)hhid q04_16 q04_20 q04_21 q04_23a q04_23b q04_23c q04_23d q04_23e q04_23f q04_23g
1 100101

2000

0

0

10000

0

0

10000

10000

0

0

2 100102

5000

0

0

10000

0

0

50000

0

0

0

3 100103

0

0

0

15000

3000

1000

80000

0

0

0

4 100104

8000

0

0

20000

0

0

7500

9000

0

0

5 100105

15000

0

0

70000

2800

0

1000

0

0

0

6 100106

0

0

0

34000

0

0

40000

69000

0

0

q04_25a q04_25b q04_26
1

NA

80000

0

2

NA

35000

0

3

NA

35000

0

4

NA

40000

0

5

NA 100000

0

6

NA

0

50000

> dim(df)[1] 1197014> df[is.na(df)]<-0> df["housing"]<-rowSums(df[,2:14])> head(df)hhid q04_16 q04_20 q04_21 q04_23a q04_23b q04_23c q04_23d q04_23e q04_23f q04_23g
1 100101

2000

0

0

10000

0

0

10000

10000

0

0

2 100102

5000

0

0

10000

0

0

50000

0

0

0

Manual for Cambodia CSES 2009 (Version 2.0)

192

3 100103

0

0

0

15000

3000

1000

80000

0

0

0

4 100104

8000

0

0

20000

0

0

7500

9000

0

0

5 100105

15000

0

0

70000

2800

0

1000

0

0

0

6 100106

0

0

0

34000

0

0

40000

69000

0

0

q04_25a q04_25b q04_26 housing
1

0

80000

0

112000

2

0

35000

0

100000

3

0

35000

0

134000

4

0

40000

0

84500

5

0 100000018880060019300050000> colnames(df)<-c("hhid","water","sewage","garbage","electricity","gas","kerosene","firewood",+ "charcoal","battery","other","house.rent","imputed.rent","maintenance","housing")> head(df)hhid water sewage garbage electricity

gas kerosene firewood charcoal battery other1 100101

2000

0

0

10000

0

0

10000

10000

0

0

2 100102

5000

0

0

10000

0

0

50000

0

0

0

3 10010300015000 3000

1000

80000

0

0

0

4 100104

8000

0

0

20000

0

0

7500

9000

0

0

5 100105 15000

0070000 2800

0

1000

0

0

0

6 100106

0

0

34000

0

40000

69000

0

0

0

0

house.rent imputed.rent maintenance housing
1

0

80000

0

112000

2

0

35000

0

100000

3

0

35000

0

134000

4

0

40000

0

84500

5

0

1000000188800

6

0

50000

0

193000> exp.housing<-df

Manual for Cambodia CSES 2009 (Version 2.0)

193



Grouping of items of expenditure

No

Group

Items

Data frame

Source data file1

Food and non-alcoholic

01 to 15, 18 to 20

exp.food

S01B.foodconsumption

beverages
2

Alcohol and tobacco

16, 17

exp.food

S01B.foodconsumption

3

Clothing and footwear

5, 11(personal effects)

exp.nonfood

S01C.nonfoodexpenses

4

Housing, water, electricity

exp.housing

S04.housing

5

Furnishing etc.

6

exp.nonfood

S01C.nonfoodexpenses

6

Health1

exp.nonfood

S01C.nonfoodexpenses

7

Transportation2

exp.nonfood

S01C.nonfoodexpenses

8

Communication

3

exp.nonfood

S01C.nonfoodexpenses

9

Recreation and culture

8, 9

exp.nonfood

S01C.nonfoodexpenses

10

Education

10

exp.nonfood

S01C.nonfoodexpenses

11

Miscellaneous goods

13, 4(personal care)

exp.nonfood

S01C.nonfoodexpenses

12

Domestic salaries

7

exp.nonfood

S01C.nonfoodexpenses

Note: Item 12 (gambling) in non-food was excluded.

Converted hhid of exp.food and exp.nonfood from factor to integer> exp.food$hhid<-as.integer(as.character(exp.food$hhid))> exp.nonfood$hhid<-as.integer(as.character(exp.nonfood$hhid))Generated household-level data frame “hhexp” of monthly consumption by groups# Confirmed hhid of exp.food and exp.nonfood> table(exp.food$hhid==exp.nonfood$hhid)
TRUE
11971
# One record is missing in exp.housing> dim(exp.housing)[1] 1197016> hhexp<-exp.food[,c("hhid","food","alcohol")]> hhexp$clothing<-rowSums(exp.nonfood[,c(6,12)])> head(hhexp)
hhid

food alcohol clothing

1 100101 414700

0

3500

2 100102 388702

0

33333

Manual for Cambodia CSES 2009 (Version 2.0)

194

3 100103 322401

34667

7667

4 100104 220134

0

30000

5 100105 756598

8667

25000

6 100106 772634

0

2500

> hhexp<-merge(hhexp,exp.housing[,c("hhid","housing")],by="hhid",all.x=T)> head(hhexp)
hhid

food alcohol clothing housing

1 100101 414700

03500 112000

2 100102 388702

033333 100000

3 100103 322401

34667

7667 134000

4 100104 220134

0

5 100105 756598

8667

25000 188800

6 100106 772634

02500 193000

30000

84500

> dim(hhexp)[1] 119715> hhexp[hhexp$hhid==100309,]
hhid

food alcohol clothing housing

28 100309 907833

0

6667

NA

> hhexp[is.na(hhexp)]<-0> hhexp$furnishing<-exp.nonfood[,7]> hhexp$health<-exp.nonfood[,2]> hhexp$transportation<-exp.nonfood[,3]> hhexp$communication<-exp.nonfood[,4]> hhexp$recreation<-rowSums(exp.nonfood[,c(9,10)])> hhexp$education<-exp.nonfood[,11]> hhexp$miscellaneous<- rowSums(exp.nonfood[,c(14,5)])> hhexp$domsalary<-exp.nonfood[,8]> hhexp$total<-rowSums(hhexp[,2:13])> hhexp$foodshare<-round((hhexp$food+hhexp$alcohol)/hhexp$total*100,1)> head(hhexp)
hhid

food alcohol clothing housing furnishing health transportation communication

1 100101 414700

03500 112000

3333

12000

50000

20000

2 100102 388702

033333 100000

4167

1500

25000

20000

3 100103 322401

34667

7667 134000

5000

83000

0

0

Manual for Cambodia CSES 2009 (Version 2.0)

195

4 100104 220134

0

5 100105 756598
6 100106 772634

30000

84500

4000

1500

39000

40000

8667

25000 188800

6667

20000

66000

40000

0

2500 193000

83333

0

20000020000

recreation education miscellaneous domsalary

total foodshare

1

8333

6667

94500

0 725033

57.2

2

8333

32083

62000

0 675118

57.6

3

0

3750

30467

0 620952

57.5

4

6667

83333

54333

0 563467

39.1

5

25000

20833

3130000 1470565

52.0

6

0

4167

923167

0 2198801

35.1#######################################################

Manual for Cambodia CSES 2009 (Version 2.0)

196



Estimate average monthly per household and per capita consumption by three region# hhexp: monthly household expenditure by group# outfiles[[43]]: household weight and region# Average monthly expenditure per household by group (in thousand Riels)> t1<-sapply(hhexp[,2:14],function(x) weighted.mean(x,d43$weight))/1000> round(t1)foodalcohol

clothing

housingfurnishinghealth

545

26

40

213

11

83

transportation

communication

recreation

education miscellaneous

domsalary

56

20

13

29

82

2

total
1120

# Average monthly expenditure per capita by group (in thousand Riels)> (NP<-sum(d44$persweight))[1] 13966718> t2<-sapply(hhexp[,2:14],function(x) sum(x*d43$weight))/1000/NP> round(t2)foodalcohol

clothing

housingfurnishinghealth

115

6

9

45

2

17

transportation

communication

recreation

education miscellaneous

domsalary

12

4

3

6

17

0

total
236

# Combined hhexp and d43> hhexp<-cbind(hhexp,d43[,2:10])> head(hhexp)
hhid

food alcohol clothing housing furnishing health transportation communication

1 100101 414700

03500 112000

3333

12000

50000

20000

2 100102 388702

033333 100000

4167

1500

25000

20000

3 100103 322401

34667

7667 134000

5000

83000

0

0

4 100104 220134

0

30000

4000

1500

39000

40000

84500

Manual for Cambodia CSES 2009 (Version 2.0)

197

5 100105 756598

8667

25000 188800

6667

20000

66000

40000

6 100106 772634

02500 193000

83333

0

20000020000

recreation education miscellaneous domsalary

total foodshare province urbanrural

1

8333

6667

94500

0 725033

57.2

1

1

2

8333

32083

62000

0 675118

57.6

1

1

3

0

3750

30467

0 620952

57.5

1

1

4

6667

83333

54333

0 563467

39.1

1

1

5

25000

20833

3130000 1470565

52.0

1

1

6

0

4167

923167

0 2198801

35.1

1

1

weight hhsize

weight3

psu stratum surveymonth region

1 266.1367

5 1330.6835 1001

11

1

2

2 266.1367

4 1064.5468 1001

11

1

2

3 266.1367

6 1596.8202 1001

11

1

2

4 266.1367

3 798.4101 1001

11

1

2

5 266.1367

7 1862.9569 1001

11

1

2

6 266.1367

9 2395.2302 1001

11

1

2

# Aveage monthly expenditure per household by group and region> (t<-by(hhexp,hhexp$region,function(df)+ round(sapply(df[,2:14],function(x) weighted.mean(x,df$weight)/1000))))hhexp$region: 1
food

alcohol

clothing

housingfurnishing

951

23

67

769

23

73

communication

recreation

education miscellaneous

domsalary

total

64

52

6

2472

135

140

health transportation
168------------------------------------------------------------------------------hhexp$region: 2
food

alcohol

clothing

housingfurnishing

694

37

54

349

17

87

communication

recreation

education miscellaneous

domsalary

total

37

26

5

1554

51

122

health transportation76------------------------------------------------------------------------------hhexp$region: 3
food

alcohol

clothing

housingfurnishing

483

25

36

136

9

84

communication

recreation

education miscellaneous

domsalary

total

Manual for Cambodia CSES 2009 (Version 2.0)

health transportation
41

198

13

7

14

71

1

> m<-matrix(unlist(t),nrow=13)> colnames(m)<-c("Phnom Pen","Other urban","Other rural")> rownames(m)<-colnames(hhexp)[2:14]> cbind(Cambodia=round(t1),m)
Cambodia Phnom Pen Other urban Other rural
food

545

951

694

483

alcohol

26

23

37

25

clothing

40

67

54

36

housing

213

769

349

136

furnishing

11

23

17

9

health

83

73

87

84

transportation

56

168

76

41

communication

20

64

37

13

recreation

13

52

26

7

education

29

135

51

14

miscellaneous

82

140

122

71

domsalary

2

6

5

1

1120

2472

1554

921

total

Manual for Cambodia CSES 2009 (Version 2.0)

921

199

# Average monthly expenditure per capita by group and region> (t<-by(hhexp,hhexp$region,function(df)+ round(sapply(df[,2:14],function(x) sum(x*df$weight)/1000/+ sum(df$weight3)))))> m<-matrix(unlist(t),nrow=13)> colnames(m)<-c("Phnom Pen","Other urban","Other rural")> rownames(m)<-colnames(hhexp)[2:14]> cbind(Cambodia=round(t2),m)
Cambodia Phnom Pen Other urban Other rural
food

115

189

145

102

alcohol

6

5

8

5

clothing

9

13

11

8

housing

45

153

73

29

furnishing

2

5

4

2

health

17

15

18

18

transportation

12

33

16

9

communication

4

13

8

3

recreation

3

10

5

1

education

6

27

11

3

miscellaneous

17

28

26

15

domsalary

0

1

1

0

236

492

325

194

total

Manual for Cambodia CSES 2009 (Version 2.0)

200



Comparison with the survey report

Average monthly consumption (in thousand Riels)
Per household

Per capita

My results

ReportMy results

ReportCambodia

1,120

1,119

236

254

Phnom Penh

2,472

2,466

492

538

Other urban

1,554

1,553

325

351

Other rural

921

920

194

212

> trial.exp<-hhexp.save> head(trial.exp)
hhidfood alcohol clothing housing furnishing health transportation communication

1 100101 414700

03500 112000

3333

12000

50000

20000

2 100102 388702

033333 100000

4167

1500

25000

20000

3 100103 322401

34667

7667 134000

5000

83000

0

0

4 100104 220134

0

30000

84500

4000

1500

39000

40000

5 100105 756598

8667

25000 188800

6667

20000

66000

40000

6 100106 772634

02500 193000

83333

0

20000020000

recreation education miscellaneous domsalary

total foodshare

1

8333

6667

94500

0 725033

57.2

2

8333

32083

62000

0 675118

57.6

3

0

3750

30467

0 620952

57.5

4

6667

83333

54333

0 563467

39.1

5

25000

20833

3130000 1470565

52.0

6

0

4167

923167

0 2198801

35.1

Summary:
The data file “hhexp” with 11,971 rows and 15 columns will be included in the resampled dataset, for reference.

Manual for Cambodia CSES 2009 (Version 2.0)

201



Consumption results in the survey report at page 104

Manual for Cambodia CSES 2009 (Version 2.0)

202

Chapter 7. Household Income
7.0 Provided summary file of household income
Problems faced

The provided data files did not include the summary file of household income at first, and it caused
difficulty in estimating household income from micro data. But it was provided later on request.

The STATA file of “IncomeCSES09.dta” includes 11,971 sample households and 103 variables
including disposable income and total income.

Although some variables are yet clear, the weighted mean of many variables are confirmed to becompatible with the tables in the survey report.



Variable names included in “IncomeCSES09”> d45<-outfiles[[45]]> dim(income)[1] 11971103> colnames(d45)[1] "hhid"[3] "weighthhpers"[5] "salary"[7] "diarysalaryinkind"[9] "costlivestock"[11] "costforestry"[13] "receiptlivestock"[15] "receiptforestry"[17] "costnonagri"[19] "nonagriincome"[21] "bankinterest"[23] "dividends"[25] "grosspropertyincome"[27] "interestpaidnonagri"[29] "interestpaidnet"[31] "primaryincome"[33] "pensionabroad"[35] "ngotransfers"[37] "remittanceabroad"[39] "scholarshipgovernment"[41] "totalscholarship"[43] "othertransfer"[45] "totalincome"[47] "agriratio"[49] "propertyratio"[51] "diarytaxes"[53] "diarycashtransferchar""weighthh""hhsize""diarysalarycash""costcrop""costfish""receiptcrop""receiptfish""agriincome""receiptnonagri""incomeownhouse""interestotherloans""rentfromland""interestpaidagri""interestpaidownoccupied""propertyincome""pensiondomestic""pension""remittancedomestic""totalprivatetransfers""scholarshipngo""gifts""totaltransfers""wageratio""otherselfempratio""transfersratio""diaryinterhhtransfers""diarytotalnegativetransfers"

Manual for Cambodia CSES 2009 (Version 2.0)

203[55] "disposableincome""diarycostagri"[57] "diaryreceiptagri""diaryagriincome"[59] "diarycostnonagri""diaryreceiptnonagri"[61] "diarynonagriincome""diarybankinterest"[63] "diaryinterestotherloans""diarydividends"[65] "diaryotherfinancialaccount""diarygrosspropertyincome"[67] "diarypropertyincome""diaryprimaryincome"[69] "diarypensiondomestic""diarypensionabroad"[71] "diarypension""diaryinsurancedomestic"[73] "diaryinsuranceabroad""diaryngotransfers"[75] "diaryremittancedomestic""diaryremittanceabroad"[77] "diarytotalprivatetransfers""diaryscholarship"[79] "diarygifts""diaryothertransfers"[81] "diarytotaltransfers""diarytotalincome"[83] "diarydisposableincome""psuchar"[85] "psu1""psu"[87] "areaid""dprovince"[89] "urbanrural""dregion"[91] "dzone""blillan"[93] "bstoran""agriadj"[95] "nonagriadj""propertyadj"[97] "ownhouseadj""adjdisp"[99] "adjdispcapita""adjtotaltransfers"[101] "adjtotalincome""adjprimaryincome"[103] "adjdiarytotalnegativetransfers"



Weighted mean of each numerical variable in d45# Estimated number of households by region> den<-tapply(d45$weighthh,d45$dregion,sum)> den<-c(sum(den),den)
> den
1
2
3
2938650.3 261450.8 284739.8 2392459.6# Variable numbers to be calcurated> fileno<-c(5:83,93:103)# Weighted mean of variables by region> for(j in fileno){+ if(j==fileno[1]) cat(" No : Variable: Cambodia Phnom Penh Urban
Rural","\n")+ num<-tapply(d45[,j]*d45$weighthh,d45$dregion,sum)+ num<-c(sum(num),num)+ t<-round(num/den/12)+ cat(format(j,width=3),": ",format(colnames(d45)[j],width=30),": ",+ format(t[1],width=8),format(t[2],width=8),format(t[3],width=8),format(t[4],width=8),"\n")+ }Estimated amount of monthly income per household by region(in Riels)
No : Variable
5 : salary
6 : diarysalarycash:::

Cambodia
240710192370Phnom Penh Urban
Rural
765289380964
166691704830 341557 118612

Manual for Cambodia CSES 2009 (Version 2.0)

204

7 :
8 :
9 :
10 :
11 :
12 :
13 :
14 :
15 :
16 :
17 :
18 :
19 :
20 :
21 :
22 :
23 :
24 :
25 :
26 :
27 :
28 :
29 :
30 :
31 :
32 :
33 :
34 :
35 :
36 :
37 :
38 :
39 :
40 :
41 :
42 :
43 :
44 :
45 :
46 :
47 :
48 :
49 :
50 :
51 :
52 :
53 :
54 :
55 :
56 :
57 :
58 :
59 :
60 :
61 :
62 :

diarysalaryinkind
costcrop
costlivestock
costfishcostforestry
receiptcrop
receiptlivestock
receiptfish
receiptforestry
agriincome
costnonagri
receiptnonagri
nonagriincome
incomeownhousebankinterest
interestotherloans
dividends
rentfromland
grosspropertyincome
interestpaidagriinterestpaidnonagriinterestpaidownoccupied
interestpaidnet
propertyincome
primaryincome
pensiondomestic
pensionabroad
pensionngotransfers
remittancedomestic
remittanceabroad
totalprivatetransfers
scholarshipgovernment
scholarshipngo
totalscholarship
gifts
othertransfer
totaltransfers
totalincome
wageratio
agriratiootherselfempratio
propertyratio
transfersratio
diarytaxes
diaryinterhhtransfers
diarycashtransferchar
diarytotalnegativetransfers
disposableincome
diarycostagri
diaryreceiptagri
diaryagriincome
diarycostnonagri
diaryreceiptnonagri
diarynonagriincome
diarybankinterest

:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:

865
54474
33592
9249
1812
333744
45167
30065
34896
3442827686751108430
339755
92388
173
2066
238
1535
4012
464
228
85
642
3370
1020505
1683
224
1907
504
6801
7243
14045
395
389
784
2167
14097
33503
1054008
0
0
0
0
0
1386
2264
13713
17363
103664546619
2635932169748763311081099204768
1527

5
4142
5895
1878
2
11776
12435
7535
1050
20870
30535974693663
1640066
520368
580
5166
2286
9355
17388
9
454
116
680
16708
2963300
5282
2461
7743
483
12512
20751
33262
972
113
1085
11410
24779
78763
3042064
0
0
0
0
0
161
7936
16627
24723
3017340
4863
15865
11002
24756403936547
1460907
4072

788
25678
22221
10199
654
54041
32374
15715
11957
55215
16724842313345
640861117452
834
3966
0
7247
12047
120
386
135
697
11349
1205841
3020
22
3042
1412
18197
17524
35721
1189
1079
2268
1045
10476
53964
1259805
0
0
0
0
0
1080
3538
15448
20067
1239738
34665
1729961383312320044236227242229
0Manual for Cambodia CSES 2009 (Version 2.0)

968
63402
37972
9941
2147
402218
50266
34235
41325
414028411409573229161820
42634
50
1501
42
1
1594
554
185
76
631
963
786136
1131
4
1134
399
4821
4544
9365
238
337
575
1290
13360
26122
812258
0
0
0
0
0
1556
1492
13189
16237
79602252605
301448248843529733616573
86840
1430

205

63 :
64 :
65 :
66 :
67 :
68 :
69 :
70 :
71 :
72 :
73 :
74 :
75 :
76 :
77 :
78 :
79 :
80 :
81 :
82 :
83 :
93 :
94 :
95 :
96 :
97 :
98 :
99 :
100 :
101 :
102 :
103 :

diaryinterestotherloans:
diarydividends:
diaryotherfinancialaccount:diarygrosspropertyincome:
diarypropertyincome:
diaryprimaryincome:
diarypensiondomestic:
diarypensionabroad:
diarypension:
diaryinsurancedomestic:
diaryinsuranceabroad:
diaryngotransfers:
diaryremittancedomestic:
diaryremittanceabroad:
diarytotalprivatetransfers:
diaryscholarship:diarygifts:
diaryothertransfers:
diarytotaltransfers:
diarytotalincome:
diarydisposableincome:
bstoran:agriadj:
nonagriadj:
propertyadj:
ownhouseadj:
adjdisp:adjdispcapita:
adjtotaltransfers:
adjtotalincome:
adjprimaryincome:
adjdiarytotalnegativetransfers :

3847
204
15446
21023
21023
636000
447
0
447
0
0
378
14444
3021
17465
150
5662
29317
53420
689421672058389
1618772503904064
70139
735542
158285
19407
746586727180
11044

1966
4992
406
1059
11636610120
12280916172
12280916172
2299552 539077
1558
1554
0
0
1558
1554
0
0
0
0
1012
437
32345
15190
10765
1639
43110
16828
0
0
3413
5226
14975
50753
64068
74799
23636216138762338898593809
232
193
21689
63911
87768750264517404
12088
30350797836
2015904 1088516
414395 233865
53984
43488
2039560 1100932
1985576 1057444
23656
12416

3916
81
5051
10478
10478
465741
194
0
194
0
0
302
12399
2339
14738
184
5960
28334
49712
515453499216429
1888561518161651
41340
55361412130212762
5631165503549502

SUMMARY:

In the above table, at least, the results of monthly disposable income per household andmonthly total income per household are the same as figures in Table 9.1a of the survey
report at page 98.

Manual for Cambodia CSES 2009 (Version 2.0)206

Manual for Cambodia CSES 2009 (Version 2.0)

207



Income composition by source of income will be discussed in the following sections.

Source of income

Section in this chapter

Primary income
Salary7.1

Self employment
Income agriculture

7.2

Income non-agriculture

7.3

Income own house

7.4

Property income

7.5Transfers received

7.6

Total income
Transfers paid / negative income

7.7

Disposable incomePrepared data frame of monthly household income with variables of income components.

INCOME# hhinc: Household-level data frame of income componemts to be generated.
> hhinc<-outfiles[[43]][,c("hhid","weight","hhsize")]> head(hhinc)hhidweight hhsize

1 100101 266.1367

52 100102 266.1367

43 100103 266.1367

64 100104 266.1367

35 100105 266.1367

7

6 100106 266.1367

9> dim(hhinc)[1] 11971

3

Manual for Cambodia CSES 2009 (Version 2.0)

208

7.1 SALARY> d<-outfiles[[35]][,c("persid","hhid","q15_c20")]> d[is.na(d)]<-0> t<-tapply(d$q15_c20,d$hhid,sum)> df<-data.frame(hhid=names(t),salary=t)> dim(df)[1] 119632> hhinc<-merge(hhinc,df,by="hhid",all.x=T)> hhinc[is.na(hhinc)]<-0> head(hhinc)hhidweight hhsize salary

1 100101 266.1367

5 443000

2 100102 266.1367

43 100103 266.1367

6 500000

4 100104 266.1367

3 526000

5 100105 266.1367

70

6 100106 266.1367

9

0

0

> hhinc.save<-hhincWeighted mean of monthly salary (in Riels)> weighted.mean(hhinc$salary,hhinc$weight)[1] 240925.3> weighted.mean(d45$salary,d45$weighthh)/12[1] 240709.9

Comparison with d45 at household level> table(abs(d45$salary/12-hhinc$salary)<1)
FALSE TRUE
13 11958# List of discrepancies> hhider<-d45[d45$salary/12!=hhinc$salary,"hhid"]> hhider[1]302305303407402217501620 602210 603020 801810

803920Manual for Cambodia CSES 2009 (Version 2.0)900818

209[10] 1202203 1401613 1403905 1800702> cbind(d45[is.element(d45$hhid,hhider),c("hhid","salary")],+ hhinc.salary=hhinc[is.element(hhinc$hhid,hhider),"salary"])hhid salary hhinc.salary1815

30230501800002037

303407

0

20000

3546

402217 4440000

5200003869

50162002850004578

602210 3600000

6500004748

603020 3600000

3400005456

801810 7320000

8100005884

803920 240000

2000006461

900818 480000

4400006991

1202203 3480000

5800008288

1401613

0

72000

8740

1403905014000010236 1800702 8040000

1020000# Micro data with hhid in hhider> d[is.element(d$hhid,hhider)&d$q15_c20>0,]
persidhhid q15_c20

7756

30230502 302305 180000

8697

30340702 303407

20000

15111 40221702 402217 150000
15112 40221703 402217 150000
15114 40221705 402217 220000
16477 50162002 501620 285000
19626 60221003 602210 300000
19627 60221004 602210

20000

19630 60221007 602210 300000
19633 60221011 602210

30000

20358 60302001 603020

40000

20361 60302004 603020 150000
20362 60302005 603020 150000
23424 80181002 801810 200000

Manual for Cambodia CSES 2009 (Version 2.0)

21023425 80181003 801810 200000
23426 80181004 801810 410000
25330 80392002 803920 180000
25331 80392004 803920

20000

27924 90081801 900818

40000

27925 90081802 900818 400000
30201 120220303 1202203 290000
30202 120220304 1202203 290000
35978 140161303 1401613

72000

37860 140390501 1403905 140000
44258 180070203 1800702 320000
44261 180070206 1800702 350000
44262 180070207 1800702 350000

Manual for Cambodia CSES 2009 (Version 2.0)

211

7.2 AGRIINCOME
Generated the following variables of monthly agricultural income.

No

Variable of incomeFilecomponent

No

SectionVariables to be used

Reference
periodCOST
1cost.crop

14

5C

q05cc16

12M

2

cost.livestock1

16

5E1

q05e1c10

12M

3

cost.livestock2

17

5E2

q05e2c03

12M

4

cost.fish1

18

5F1

q05f1c05

1M

5

cost.fish2

19

5F2

q05f2c03

12M

6

cost.forestry

22

5G

q05g2c03

12M

7interest.agri

26

6

Subset of q06_c05==1,

1M

q06_c08*q06_c07/(100+q06_c08)
RECEIPT8

rcpt.crop

13

5B(q05bc06 – q05bc07) x q05bc09

12M

9

rcpt.livestock

16

5E1

q05e1c09 + q05e1c11 to q05e1c15

12M

10

rcpt.fish20

5F3

q05f3c03

12M

11

rcpt.forestry

21

5G2

q05g1c06

12M

12agri.income

The above receipts - costs

1) Cost.crop> d<-outfiles[[14]][,c("hhid","q05cc16")]> d[is.na(d)]<-0> t<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(t),cost.crop=t)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)> head(hhinc)hhidweight hhsize salary cost.crop

1 100101 266.1367

5 443000

28266.67

2 100102 266.1367

4070666.67

3 100103 266.1367

6 500000

37250.00

4 100104 266.1367

3 526000NA

Manual for Cambodia CSES 2009 (Version 2.0)

212

5 100105 266.1367

7042333.33

6 100106 266.1367

90 133958.33

2) Cost.livestock1> d<-outfiles[[16]][,c("hhid","q05e1c10")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),cost.livestock1=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

3) Cost.livestock2> d<-outfiles[[17]][,c("hhid","q05e2c03")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),cost.livestock2=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

4) Cost.fish1> d<-outfiles[[18]][,c("hhid","q05f1c05")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),cost.fish1=df)> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

5) Cost.fish2> d<-outfiles[[19]][,c("hhid","q05f2c03")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),cost.fish2=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

6) Cost.forestry> d<-outfiles[[22]][,c("hhid","q05g2c03")]

Manual for Cambodia CSES 2009 (Version 2.0)

213

> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),cost.forestry=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

7) Interest.agri> hhinc.save<-hhinc> d<-subset(outfiles[[26]],q06_c05==1)> d[is.na(d)]<-0> d$int<-d$q06_c08*d$q06_c07/(100+d$q06_c08)> df<-tapply(d$int,d$hhid,sum)> df<-data.frame(hhid=names(df),interest.agri=df)> hhinc<-merge(hhinc,df,by="hhid",all.x=T)> head(hhinc)hhidweight hhsize salary cost.crop cost.livestock1 cost.livestock2 cost.fish1

1 100101 266.1367

5 443000

28266.67

4166.667

1250.000

2000002 100102 266.1367

4070666.67

12500.000

1250.000

NA

3 100103 266.1367

6 500000

37250.00

2166.667

2166.667

NA

4 100104 266.1367

3 526000

NA

NA

NA

NA

5 100105 266.1367

7042333.33

3333.333

4166.667

NA

6 100106 266.1367

90 133958.33

8333.333

5000.000NA

cost.fish2 cost.forestry interest.agri
1

9166.667

NA

NA

2

NA

10000.000

NA

3

NA

1666.667

NA

4

NA

NA

NA

5

NA

NA

NA

6

NA

NA

NA

8) Receipt.crop> d<-outfiles[[13]][,c("hhid","q05bc06","q05bc07","q05bc09")]> d[is.na(d)]<-0> d$rcpt<-(d[,2]-d[,3])*d[,4]Manual for Cambodia CSES 2009 (Version 2.0)

214

> df<-tapply(d$rcpt,d$hhid,sum)> df<-data.frame(hhid=names(df),rcpt.crop=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

9) Receipt.livestock> hhinc.save<-hhinc> d<-outfiles[[16]][,c("hhid","q05e1c09","q05e1c11","q05e1c12","q05e1c13",+ "q05e1c14","q05e1c15")]> d[is.na(d)]<-0> d$rcpt<-rowSums(d[,2:7])> df<-tapply(d$rcpt,d$hhid,sum)> df<-data.frame(hhid=names(df),rcpt.livestock=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

10) Receipt.fish> d<-outfiles[[20]][,c("hhid","q05f3c03")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),rcpt.fish=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

11) Receipt.forestry> d<-outfiles[[21]][,c("hhid","q05g1c06")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),rcpt.forestry=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)> head(hhinc)hhidweight hhsize salary cost.crop cost.livestock1 cost.livestock2 cost.fish1

1 100101 266.1367

5 443000

28266.67

4166.667

1250.000

2000002 100102 266.1367

4070666.67

12500.000

1250.000

NA

3 100103 266.1367

6 500000

37250.00

2166.667

2166.667

NA

Manual for Cambodia CSES 2009 (Version 2.0)

215

4 100104 266.1367

3 526000

NA

NA

NA

NA

5 100105 266.1367

7042333.33

3333.333

4166.667

NA

6 100106 266.1367

90 133958.33

8333.333

5000.000NA

cost.fish2 cost.forestry interest.agri rcpt.crop rcpt.livestock rcpt.fish
1

9166.667

NA

2

NA

3

NA

64187.5

0.000

23750

10000.000

NA 342500.0

2666.667

NANA

1666.667

NA

43750.0

0.000

NA

4

NA

NA

NA

NA

NA

NA

5

NA

NA

NA 160000.0

1666.667

NA

6

NA

NA

NA 420000.0

21666.667

NA

rcpt.forestry
1

NA

2

50833.33

379166.67

4

NA

5

NA

6

NA

12) Agri.income> hhinc[is.na(hhinc)]<-0> hhinc$agri.income<-rowSums(hhinc[,12:15])-rowSums(hhinc[,5:11])> head(hhinc)hhidweight hhsize salary cost.crop cost.livestock1 cost.livestock2 cost.fish1

1 100101 266.1367

5 443000

28266.67

4166.667

1250.000

2e+05

2 100102 266.1367

4070666.67

12500.000

1250.000

0e+00

3 100103 266.1367

6 500000

37250.00

2166.667

2166.667

0e+00

4 100104 266.1367

3 526000

0.00

0.000

0.000

0e+00

5 100105 266.1367

7042333.33

3333.333

4166.667

0e+00

6 100106 266.1367

90 133958.33

8333.333

5000.000

0e+00

cost.fish2 cost.forestry interest.agri rcpt.crop rcpt.livestock rcpt.fish
1

9166.667

0.000

2

0.000

3

0

64187.5

0.000

23750

10000.000

0 342500.0

2666.667

00.000

1666.667043750.0

0.000

0

4

0.000

0.000

0

0.0

0.000

0

5

0.000

0.000

0 160000.0

1666.667

0Manual for Cambodia CSES 2009 (Version 2.0)

216

6

0.000

0.000

0 420000.0

21666.667

0rcpt.forestry agri.income
1

0.00 -154912.50

250833.33

301583.33

379166.67

79666.67

4

0.00

0.00

5

0.00

111833.33

6

0.00

294375.00> hhinc.save1<-hhincWeighted mean of monthly agricultural income> round(sapply(hhinc[,5:16],function(x) weighted.mean(x,hhinc$weight)))cost.crop cost.livestock1 cost.livestock2

cost.fish1

cost.fish2

54474

12721

20871

1968

7281

cost.forestry

interest.agri

rcpt.croprcpt.livestock

rcpt.fish

1812

5745

333744

45167

30065

rcpt.forestry

agri.income

34896

339000

SummaryWeighted mean of each component variable, except “interest.agri”, in the above table is the same
as figures derived from the household-level summary file “IncomeCSES09”.

The sum of “agri.income” and “interst.agri” is equal to “agriincome” of IncomeCSES09. That is,“agri.income” in d45 is calculated from components excluding “interest.agri”.

Manual for Cambodia CSES 2009 (Version 2.0)

217

7.3 NON-AGRI INCOME
No

Variable of incomeFilecomponent

No

SectionVariables to be used

Reference
periodCOST
1cost.nonagri

24

5H2

q05h2c03 to q05h2c07

12M

2interest.nonagri

26

6

Subset of q06_c05==2,

1M

q06_c08*q06_c07/(100+q06_c08)
RECEIPT3

rcpt.nonagri

25

5H3

Subset of q05h3c01 != 13,
q05h3c03 to q05h3c07

4nonagri.income

The above receipts - costs

1) Cost.nonagri> d<-outfiles[[24]]> d[is.na(d)]<-0> d$cost<-rowSums(d[,14:18])> df<-tapply(d$cost,d$hhid,sum)> df<-data.frame(hhid=names(df),cost.nonagri=df)> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

2) Interest.nonagri> d<-subset(outfiles[[26]],q06_c05==2)> d[is.na(d)]<-0> d$int<-d$q06_c08*d$q06_c07/(100+d$q06_c08)> df<-tapply(d$int,d$hhid,sum)> df<-data.frame(hhid=names(df),interest.nonagri=df)> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

3) Receipt.nonagri> d<-subset(outfiles[[25]],q05h3c01!=13)> d[is.na(d)]<-0> d$rcpt<-rowSums(d[,14:18])> df<-tapply(d$rcpt,d$hhid,sum)> df<-data.frame(hhid=names(df),rcpt.nonagri=df)

Manual for Cambodia CSES 2009 (Version 2.0)

12M

218

> df[,2]<-df[,2]/12> hhinc<-merge(hhinc,df,by="hhid",all.x=T)

4) Nonagri.income> hhinc[is.na(hhinc)]<-0> hhinc$nonagri.income<-hhinc$rcpt.nonagri-hhinc$cost.nonagri-hhinc$interest.nonagri> head(hhinc)hhidweight hhsize salary cost.crop cost.livestock1 cost.livestock2 cost.fish1

1 100101 266.1367

5 443000

28266.67

4166.667

1250.000

2e+05

2 100102 266.1367

4070666.67

12500.000

1250.000

0e+00

3 100103 266.1367

6 500000

37250.00

2166.667

2166.667

0e+00

4 100104 266.1367

3 526000

0.00

0.000

0.000

0e+00

5 100105 266.1367

7042333.33

3333.333

4166.667

0e+00

6 100106 266.1367

90 133958.33

8333.333

5000.000

0e+00

cost.fish2 cost.forestry interest.agri rcpt.crop rcpt.livestock rcpt.fish
1

9166.667

0.000

2

0.000

3

0

64187.5

0.000

23750

10000.000

0 342500.0

2666.667

00.000

1666.667043750.0

0.000

0

4

0.000

0.000

0

0.0

0.000

0

5

0.000

0.000

0 160000.0

1666.667

0

6

0.000

0.000

0 420000.0

21666.667

0rcpt.forestry agri.income cost.nonagri interest.nonagri rcpt.nonagri nonagri.income
1

0.00 -154912.50

0

0

0

0

2

50833.33

301583.33

0

0

0

0

3

79166.67

79666.67

0

0

0

0

4

0.00

0.00

0

0

0

0

5

0.00

111833.33

37500

0

116666711291676

0.00

294375.00

165229170210000000

193477083> hhinc.save2<-hhincWeighted mean of non-agri income> round(sapply(hhinc[,17:20],function(x) weighted.mean(x,hhinc$weight)))cost.nonagri interest.nonagri
7684472872

rcpt.nonagri

nonagri.income

1108430

337112

Manual for Cambodia CSES 2009 (Version 2.0)

219

7.4 INCOME OWN HOUSE

Estimating imputed rent is a very difficult work. The survey report describes about this at page
102, as below.

“The method that is used in CSES originates from the view that income from owner occupied
house can be treated as an investment and that one can look for an alternative investment of thecapital in the owner occupied house. This alternative investment can be the long-term return from
Government bonds. Income from owner-occupied dwellings and houses is calculated bysubtract the remaining debt from the market value of the dwelling. This value is multiplied by thelong-term interest for Government bonds. A problem with this method is that it can yield
unreasonable high estimates of imputed rent in large cities with high land values. In Cambodia
this is the case in Phnom Penh. To prevent unreasonable high values of imputed rent to distort theresults, imputed rent is limited to a maximum 12 million Riels per year.”

Variables used
Item

File

SectionVariables to be usedNo

periodLong-term interest fora constant of 3%, which is considered

Government bonds

an annual rate

Market value of the

28

S08

dwelling

q08_c05: How much would you have to
pay to buy a building like this in thevillage? (in Riels)

Outstanding loan

26

S06

Subset of q06_c05==7: Primary purpose= Purchase/improvement of dwelling
Q06_c07: How much is the outstanding
loan now (this month)? Interest should
not be included. (in Riels)

Interest rate

26

Reference

S06

q06_c08: If interest is charged, whatis the monthly rate of interest? (%)

income.ownhouse1{q08_c05 – q06_c07/(1+q06_c08/100)}*0.03/12

income.ownhouse2

Min{income.ownhouse1, 12*10^6}Manual for Cambodia CSES 2009 (Version 2.0)

220

Note:


It is not clear whether the 3% of the long-term interest for Government bonds is monthly or annual.



The median of q06_c08 (monthly interest rate) is 3%.

In 7,532 cases, the results of using 3% as annual rate are the same as Yearly.Income. This supports that the 3% isannual rate.

1) Market value of the dwelling> oh<-outfiles[[43]][,c("hhid","weight","hhsize")]> d<-outfiles[[28]][,c("hhid","q08_c05")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),market.value=df)> oh<-merge(oh,df,by="hhid",all.x=T)> head(oh)hhidweight hhsize market.value

1 100101 266.1367

52.3e+07

2 100102 266.1367

46.0e+05

3 100103 266.1367

65.0e+05

4 100104 266.1367

3

2.1e+06

5 100105 266.1367

7

3.6e+07

6 100106 266.1367

98.0e+05

2) Remaining debt
> d<-outfiles[[26]][,c("hhid","q06_c05","q06_c07","q06_c08")]> dim(d)[1] 4732

4> d<-subset(d,q06_c05==7)> dim(d)[1] 3024> head(d)hhid q06_c05 q06_c07 q06_c08
1100101

7 1000000

021001057 4000000

2

32 101503

7 400000

3

40 101607

7 6000000

0Manual for Cambodia CSES 2009 (Version 2.0)

221

52 101901

7 700000

0

71 102101

7 1000000

2> d$remaining.debt<-d$q06_c07/(1+d$q06_c08/100)> head(d)hhid q06_c05 q06_c07 q06_c08 remaining.debt
1100101

7 1000000

01000000.0

21001057 4000000

23921568.6

32 101503

7 400000

3388349.5

40 101607

7 6000000

06000000.0

52 101901

7 700000

0700000.0

71 102101

7 1000000

2980392.2> df<-tapply(d$remaining.debt,d$hhid,sum)> df<-data.frame(hhid=names(df),remaining.debt=df)> oh<-merge(oh,df,by="hhid",all.x=T)> head(oh)hhidweight hhsize market.value remaining.debt

1 100101 266.1367

52.3e+07

1000000

2 100102 266.1367

46.0e+05NA

3 100103 266.1367

65.0e+05NA

4 100104 266.1367

3

2.1e+06NA

5 100105 266.1367

7

3.6e+07

3921569

6 100106 266.1367

98.0e+05

NA3) Monthly income.ownhouse> oh[is.na(oh)]<-0> oh$income.ownhouse1<-(oh$market.value-oh$remaining.debt)*0.03/12> oh$income.ownhouse2<-ifelse(oh$income.ownhouse1>10^6,10^6,oh$income.ownhouse1)> head(oh)hhidweight hhsize market.value remaining.debt income.ownhouse1

1 100101 266.1367

52.3e+07

1000000

55000.00

2 100102 266.1367

46.0e+0501500.00

3 100103 266.1367

65.0e+0501250.00

4 100104 266.1367

3

2.1e+0605250.00

5 100105 266.1367

7

3.6e+07

392156980196.08

Manual for Cambodia CSES 2009 (Version 2.0)

222

6 100106 266.1367

98.0e+0502000.00

income.ownhouse2
1

55000.00

2

1500.00

3

1250.00

45250.00

580196.08

6

2000.00

The value of income.ownhouse1 was replaced with the upper limit in 135 cases.

> table(oh$income.ownhouse1>10^6)
FALSE TRUE
11836



135

Weighted mean of income own house> round(sapply(oh[,4:7],function(x) weighted.mean(x,oh$weight)))
market.value
38409505

remaining.debt income.ownhouse1 income.ownhouse2
69648

95850

71933

Remarks:
The delegates from Cambodia to the seventh workshop explained the concept and definition of
imputed rent used in SES as follows;“The respondent was asked to impute for their own house but in case they could not impute therespondent was asked impute the same price of the closed (in the village) house has been rented withthe similar characteristic of their own housing. “Manual for Cambodia CSES 2009 (Version 2.0)

223

7.5 PROPERTY INCOME
No

Item

File

SectionVariables to be usedNo1bank.interest

27

Reference
periodS07

Subset of q07_c01==712M

q07_c05
2

interest.otherloans

27

S07

Subset of q07_c01==912M

q07_c053

dividents

27

S07

Subset of q07_c01==812M

q07_c05
4

rent.fromland

25

S05H3

Subset of q05h3c01==1312Mq05h3c03 to q05h3c07: Activities
5

gross.property.income

Sum of the above four items

12M

6interest.paidnet

26

1M

S06

Subset of q06_c05==3-6, 8-10
Q06_c08*q06_c07/(100+q06_c08)
q06_c05:What was the primary purpose forwhich your household borrowed the money?

7

property.income= gross.property.income (minus) interest.paidnet

primary.income> pi<-outfiles[[43]][,c("hhid","weight","hhsize")]

1) Bank interest> d<-subset(outfiles[[27]],q07_c01==7)[,c("hhid","q07_c05")]> dim(d)[1] 9 2> dhhid q07_c05
2897

802302 4800000

3712 1200909 1920000
3880 1204110 1200000
4049 1207210 4000000
4131 1209901

2400005587 1900105 4000000

Manual for Cambodia CSES 2009 (Version 2.0)

2245591 1900206 8000000
6207 2100109 6350400
6688 2103611

120000> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),bank.interest=df)> df[,2]<-df[,2]/12> pi<-merge(pi,df,by="hhid",all.x=T)> pi[is.na(pi)]<-0> head(pi)hhidweight hhsize bank.interest

1 100101 266.1367

50

2 100102 266.1367

40

3 100103 266.1367

60

4 100104 266.1367

30

5 100105 266.1367

70

6 100106 266.1367

902) Interest.otherloans> d<-subset(outfiles[[27]],q07_c01==9)[,c("hhid","q07_c05")]> dim(d)[1] 1272> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),interest.otherloans=df)> df[,2]<-df[,2]/12> pi<-merge(pi,df,by="hhid",all.x=T)> pi[is.na(pi)]<-0

3) Dividents> d<-subset(outfiles[[27]],q07_c01==8)[,c("hhid","q07_c05")]> dim(d)[1] 9 2> d[is.na(d)]<-0

Manual for Cambodia CSES 2009 (Version 2.0)

225

> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),dividents=df)> df[,2]<-df[,2]/12> pi<-merge(pi,df,by="hhid",all.x=T)> pi[is.na(pi)]<-0> pi.save<-pi4) Rent from land
> d<-subset(outfiles[[25]],q05h3c01==13)> dim(d)[1] 23 18> d[is.na(d)]<-0> head(d)hhid province urbanrural
917

weight hhsize

weight3

psu stratum surveymonth3061093

2 247.5491

3 742.6473

3061

32

2

2854 1202001

12

1 229.8146

3 689.4438 12020

121

12902 1202704

12

1 351.8365

3 1055.5094 12027

121

10

3024 1204008

12

1 353.0140

6 2118.0840 12040

121

23156 1205701

12

1 290.7731

4 1163.0923 12057

121

43162 1205703

12

1 290.7731

2 581.5461 12057

121

4

region year hhid_string q05h3c01 q05h3c03 q05h3c04 q05h3c05 q05h3c06 q05h3c07
917

3 20090306109

13

98000

0e+00

0

0

0

2854

1 20091202001

13

2400000

0e+00

0

0

0

2902

1 2009120270413

8000000

0e+00

0

0

0

3024

1 2009120400813

700000

0e+00

0

0

0

3156

1 20091205701

13

0

6e+06

0

0

0

3162

1 20091205703

13

0

1e+07

0

0

0

> d$rent<-rowSums(d[,14:18])> df<-tapply(d$rent,d$hhid,sum)> df<-data.frame(hhid=names(df),rent.fromland=df)> df[,2]<-df[,2]/12> pi<-merge(pi,df,by="hhid",all.x=T)> pi[is.na(pi)]<-0> pi.save<-pi

Manual for Cambodia CSES 2009 (Version 2.0)

2265) Gross property income> pi$grossproperty.income<-rowSums(pi[,4:7])> head(pi)hhidweight hhsize bank.interest interest.otherloans dividents rent.fromland

1 100101 266.1367

5

0

0

0

0

2 100102 266.1367

4

0

0

0

0

3 100103 266.1367

6

0

0

0

0

4 100104 266.1367

3

0

0

0

0

5 100105 266.1367

7

0

0

0

0

6 100106 266.1367

9

0

0

0

0

grossproperty.income
1

0

2

0

3

0

4

0

5

0

6

0

6) Interest paid net> d<-subset(outfiles[[26]],is.element(q06_c05,c(3:6,8:10)))> dim(d)[1] 267820> d[is.na(d)]<-0> d$paid<-d$q06_c08*d$q06_c07/(100+d$q06_c08)> df<-tapply(d$paid,d$hhid,sum)> df<-data.frame(hhid=names(df),interest.paidnet=df)> pi<-merge(pi,df,by="hhid",all.x=T)> pi[is.na(pi)]<-0> pi.save<-pi7) Property income> pi$property.income<-pi$grossproperty.income-pi$interest.paidnet> head(pi)hhidweight hhsize bank.interest interest.otherloans dividents rent.fromland

Manual for Cambodia CSES 2009 (Version 2.0)

227

1 100101 266.1367

5

0

0

0

0

2 100102 266.1367

4

0

0

0

0

3 100103 266.1367

6

0

0

0

0

4 100104 266.1367

3

0

0

0

0

5 100105 266.1367

7

0

0

0

0

6 100106 266.1367

9

0

0

0

0

grossproperty.income interest.paidnet property.income
1

0

0

0

2

0

0

0

3

0

0

0

4

0

0

0

5

0

0

0

6

0

0

0

> pi.save<-piWeighted mean

> round(sapply(pi[,4:10],function(x) weighted.mean(x,hhinc$weight)))bank.interest

interest.otherloans

dividents

rent.fromland

173

2066

238

1535

grossproperty.income

interest.paidnet

property.income

4012

7890

-3879

> table(pi$property.income>0)
FALSE TRUE
11810

161

> table(pi$property.income<0)
FALSE TRUE
10064 1907

Remarks;

Property income is positive in 161 cases, and negative in 1907 cases.

Manual for Cambodia CSES 2009 (Version 2.0)

228

# Appended income.ownhouse and property.income to hhinc> hhinc.save<-hhinc> hhinc<-merge(hhinc,oh[,c("hhid","income.ownhouse2")],by="hhid",all.x=T)> hhinc.save<-hhinc> hhinc<-merge(hhinc,pi[,-2],by="hhid",all.x=T)> hhinc.save<-hhinc# Generated primary income.
> colnames(hhinc)[1] "hhid""hhsize""salary"[4] "cost.crop""cost.livestock1""cost.livestock2"[7] "cost.fish1""cost.fish2""cost.forestry"[10] "interest.agri""rcpt.crop""rcpt.livestock"[13] "rcpt.fish""rcpt.forestry""agri.income"[16] "cost.nonagri""interest.nonagri""rcpt.nonagri"[19] "nonagri.income""income.ownhouse2""bank.interest"[22] "interest.otherloans""dividents""rent.fromland"[25] "grossproperty.income" "interest.paidnet""property.income"> hhinc$primary.income<-rowSums(hhinc[,c(3,15,19,20,27)])

Manual for Cambodia CSES 2009 (Version 2.0)

229

7.6 TRANSFERS RECEIVED
No

Item

File

SectionVariables to be usedNo1

pension.domestic

27

Reference
periodS07

Subset of q07_c01==1

12M

q07_c032

pension.abroad

27

S07

Subset of q07_c01==1

12M

q07_c043

pension= pension.domestic + pension.abroad

12M

4ngo.transfer

27

12M

S07

Subset of q07_c01==5
q07_c05

5

remittance.domestic1

27

S07

Subset of q07_c01==2:

12M

Remittances from other relatives
or others(not reported incurrent migration section)
q07_c03
remittance.domestic2

9

S03B

Subset of q03bc05b>0: Total value

12Mof the transfers and cash gifts
sent to the household
q03bc12
6

remittance.abroad1

27

S07

Subset of q07_c01==212M

q07_c04
remittance.abroad2

9

S03B

Subset of q03bc05c>0

12M

q03bc12
7

total.private.transfers

8

scholarship.governmen= remittance.domestic + remittance.abroad

12M

27

12M

S07

t
9

scholarship.ngo

Subset of q07_c01==3
q07_c05

27

S07

Subset of q07_c01==412M

q07_c05
10

total.scholarship= scholarship.government + scholarship.ngo

12M

11

gifts

27

12M

S07

Subset of q07_c01==10,11
q07_c05

12

other.transfer

27

S07

Subset of q07_c01==1212M

q07_c05

13

total.transfers

Sum of the above 6 items

Manual for Cambodia CSES 2009 (Version 2.0)

12M

230

> tr<-outfiles[[43]][,c("hhid","weight","hhsize")]

1) Pension domestic> d<-subset(outfiles[[27]],q07_c01==1)[,c("hhid","q07_c03")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),pension.domestic=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-02) Pension abroad> d<-subset(outfiles[[27]],q07_c01==1)[,c("hhid","q07_c04")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),pension.abroad=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0

3) Total pension> tr$pension<-tr$pension.domestic+tr$pension.abroad> tr.save<-tr> head(tr)hhidweight hhsize pension.domestic pension.abroad pension

1 100101 266.1367

5

0

0

0

2 100102 266.1367

4

0

0

0

3 100103 266.1367

6

0

0

0

4 100104 266.1367

3

0

0

0

5 100105 266.1367

7

0

0

0

6 100106 266.1367

9

0

0

0

> table(tr$pension>0)
FALSE TRUE
11770

201

Manual for Cambodia CSES 2009 (Version 2.0)

231# 4) NGO transfers> d<-subset(outfiles[[27]],q07_c01==5)[,c("hhid","q07_c05")]> dim(d)[1] 3662> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),ngo.transfers=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0# 5) Remittance domestic> d<-subset(outfiles[[27]],q07_c01==2)[,c("hhid","q07_c03")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),remittance.domestic1=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0> d<-subset(outfiles[[9]],q03bc05b>0)[,c("hhid","q03bc12")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),remittance.domestic2=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0> head(tr)hhidweight hhsize pension.domestic pension.abroad pension ngo.transfers

1 100101 266.1367

5

0

0

0

0

2 100102 266.1367

4

0

0

0

0

3 100103 266.1367

6

0

0

0

0

4 100104 266.1367

3

0

0

0

0

5 100105 266.1367

7

0

0

0

0

Manual for Cambodia CSES 2009 (Version 2.0)

232

6 100106 266.1367

9

0

0

0

remittance.domestic1 remittance.domestic2
1

0.0

0

2

0.0

0

3

0.0

0

4

0.0

0

5

416666.7

0

6

0.0

0

> table(tr$remittance.domestic1+tr$remittance.domestic2>0)
FALSE TRUE
8324 3647# 6) Remittance abroad> d<-subset(outfiles[[27]],q07_c01==2)[,c("hhid","q07_c04")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),remittance.abroad1=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0> d<-subset(outfiles[[9]],q03bc05c>0)[,c("hhid","q03bc12")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),remittance.abroad2=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0> tr.save<-tr> table(tr$remittance.abroad1+tr$remittance.abroad2>0)
FALSE TRUE
11431

540

# 7) Total private transfers> colnames(tr)[1] "hhid""hhsize""pension.domestic"

Manual for Cambodia CSES 2009 (Version 2.0)

0

233

[4] "pension.abroad""pension""ngo.transfers"[7] "remittance.domestic1" "remittance.domestic2" "remittance.abroad1"[10] "remittance.abroad2"> tr$total.private.transfers<-rowSums(tr[,7:10])> tr.save<-tr# 8) Scholarship government> d<-subset(outfiles[[27]],q07_c01==3)[,c("hhid","q07_c05")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),scholarship.gov=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0# 9) Scholarship NGO> d<-subset(outfiles[[27]],q07_c01==4)[,c("hhid","q07_c05")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),scholarship.ngo=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0# 10) Total scholarship> tr$total.scholarship<-tr$scholarship.gov+tr$scholarship.ngo> tr.save<-tr> head(tr)hhidweight hhsize pension.domestic pension.abroad pension ngo.transfers

1 100101 266.1367

5

0

0

0

0

2 100102 266.1367

4

0

0

0

0

3 100103 266.1367

6

0

0

0

0

4 100104 266.1367

3

0

0

0

0

5 100105 266.1367

7

0

0

0

0

Manual for Cambodia CSES 2009 (Version 2.0)

234

6 100106 266.1367

9

0

0

0

0

remittance.domestic1 remittance.domestic2 remittance.abroad1 remittance.abroad2
1

0.0

0

0.00

0

2

0.0

0

0.00

0

3

0.0

0

0.00

0

4

0.0

0

0.00

0

5

416666.7

0

0.00

0

6

0.0

0

66666.67

0total.private.transfers scholarship.gov scholarship.ngo total.scholarship
1

0.00

0

0

0

2

0.00

0

0

0

3

0.00

0

0

0

4

0.00

0

0

0

5

416666.67

0

0

0

6

66666.67000> table(tr$scholarship.gov>0)
FALSE TRUE
11896

75

> table(tr$scholarship.ngo>0)
FALSE TRUE
11866

105

# 11) Gifts> d<-subset(outfiles[[27]],q07_c01==10|q07_c01==11)[,c("hhid","q07_c05")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),gifts=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0# 12) Other transfers

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235

> d<-subset(outfiles[[27]],q07_c01==12)[,c("hhid","q07_c05")]> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),other.transfers=df)> df[,2]<-df[,2]/12> tr<-merge(tr,df,by="hhid",all.x=T)> tr[is.na(tr)]<-0# 13) Total transfers> colnames(tr)[1] "hhid""weight""hhsize"[4] "pension.domestic""pension.abroad""pension"[7] "ngo.transfers""remittance.domestic1""remittance.domestic2"[10] "remittance.abroad1""remittance.abroad2""total.private.transfers"[13] "scholarship.gov""scholarship.ngo""total.scholarship"[16] "gifts""other.transfers"> tr$total.transfers<-rowSums(tr[,c(6,7,12,15:17)])> tr.save<-tr> table(tr$total.transfers>0)
FALSE TRUE
5873 6098

Weighted mean of transfers received> round(sapply(tr[,4:17],function(x) weighted.mean(x,hhinc$weight)))
pension.domestic

pension.abroad

pension

1683

224

1907

ngo.transfers

remittance.domestic1

remittance.domestic2

504

6801

7765

remittance.abroad1

remittance.abroad2 total.private.transfers

7243

4427

22313

scholarship.gov

scholarship.ngo

total.scholarship

395

389

784

gifts

other.transfers

2167

14097

Manual for Cambodia CSES 2009 (Version 2.0)

236# 14) Total income> hhinc<-merge(hhinc,tr[,-2],by="hhid",all.x=T)> hhinc$total.income<-hhinc$primary.income+hhinc$total.transfers> hhinc.save<-hhinc

Manual for Cambodia CSES 2009 (Version 2.0)

237

7.7 NEGATIVE TRANSFERS (TRANSFERS PAID)

There is a description about negative transfers in the survey report as the next.

“The CSES data has been collected both as recall data and as data from a diary. An ongoing discussionis what method is to be preferred or if there should be a combination of both. A special report handles
this issue. For the purpose to look on the two methods for measuring income, an investigation was
carried out. Checking was done to compare data in CSES with National Accounts. This investigation
ended in using recall data for income data and diary for negative transfers as taxes, transfers to otherhouseholds and for charity. For these expenditures there existed no recall data. The reasons for thisdecision were that it is more relevant to use data for a whole year for income and expenditure for
production costs and income for agriculture sector. Also with comparison with National Accounts it
looked more stable. This is also in accordance to the recommendations in the report Guidelines forConstructing Consumption Aggregates for Welfare Analysis, which says that it’s hard to collectincome data from diaries in countries where income from agriculture is important as the income tends
to fluctuate a lot over the seasons.” (at page 103)

The report also describes of “Dealing with negative incomes".

“It’s not unusual for households to have deficits or negative incomes. Almost 4 percent of allhouseholds have a disposable income less than 0. For households with income from agriculture andnon-agriculture, around 8 percent of the household have negative income. In this work negative
incomes have been replaced by 4000 Riels, around 1 USD. This method takes away the problem withnegative incomes but still give us the opportunity to measure activity in each sector, which wouldn’t
be the case if we replaced the negative numbers with 0.” (at page 103)
Negative transfers from Diary data
Item

File

Variables to be used

Reference
periodTax

DiaryExpenditure

Subset of DiaryExp_Q10==11

1M

DiaryExp_Q10
Inter HH transfers

DiaryExpenditure

Subset of DiaryExp_Q10==7 and

1M

DiaryExp_Q8==1 or 2
DiaryExp_Q10
Cash transfer charity

DiaryExpenditure

Subset of DiaryExp_Q10==8 andManual for Cambodia CSES 2009 (Version 2.0)

1M

238

DiaryExp_Q8==1 or 2
DiaryExp_Q10
Total negative

Sum of the above

1M= total.income (minus)

1M

transfers

disposable.income1

total.negative.transfers
disposable.income2

Replaced by 4,000 if disposable.income1<0

1M# DE: DiaryExpenditure> DE<-outfiles[[1]]> dim(DE)[1] 2620999

23> head(DE)hhid province urbanrural

weight hhsize weight3

psu stratum surveymonth region

1 100101

1

1 266.1367

5 1330.683 1001

11

1

2

2 100101

1

1 266.1367

5 1330.683 1001

11

1

2

3 100101

1

1 266.1367

5 1330.683 1001

11

1

2

4 100101

1

1 266.1367

5 1330.683 1001

11

1

2

5 100101

1

1 266.1367

5 1330.683 1001

11

1

2

6 100101

1

1 266.1367

5 1330.683 1001

11

1

2

year

pkid hhid_string lineid diaryexp_q2 diaryexp_q5 diaryexp_q6 diaryexp_q7

1 2009 65201

0100101

101

101

4

500

7500

2 2009 65203

0100101

102

101

4

500

1200

3 2009 65206

0100101

103

201

9

2

3000

4 2009 65208

0100101

104

201

10

NA

1500

5 2009 65211

0100101

105

201

5

2

5200

6 2009 65213

0100101

106

201

4

300

3500

diaryexp_q8 diaryexp_q9 diaryexp_q10 diaryexp_q11 diaryexp_q2_string
1

1

2

1

1090

0101

2

1

2

1

1354

0101

3

1

2

1

6010

0201

4

1

4

1

5440

0201

5

1

2

1

5060

0201

6

1

2

1

1090

0201

Manual for Cambodia CSES 2009 (Version 2.0)

239

> table(DE$diaryexp_q10)
1

2

3

4

5

6

7

8

9

10

11

2471099

41166

5494

102

88858

872

826

11052

187

415

539

12
389

# Negative transfers> nt<-outfiles[[43]][,c("hhid","weight","hhsize")]# Tax> d<-subset(DE, diaryexp_q10==11)[,c("hhid","diaryexp_q7")]> dim(d)[1] 5392> d[is.na(d)]<-0> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),tax=df)> head(df)hhid tax
100106 100106 5000
100203 100203 100
100604 100604 1000
101409 101409 2500
101910 101910 7000
102003 102003 3000
> nt<-merge(nt,df,by="hhid",all.x=T)> nt[is.na(nt)]<-0> head(nt)hhidweight hhsize

tax

1 100101 266.1367

50

2 100102 266.1367

40

3 100103 266.1367

60

4 100104 266.1367

30

5 100105 266.1367

70

6 100106 266.1367

9 5000> nt.save<-nt

Manual for Cambodia CSES 2009 (Version 2.0)

240

# Inter HH transfers> d<-subset(DE, diaryexp_q10==7&(diaryexp_q8==1|diaryexp_q8==2))> d<-d[,c("hhid","diaryexp_q7")]> d[is.na(d)]<-0> dim(d)[1] 5672> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),interhh.transfers=df)> nt<-merge(nt,df,by="hhid",all.x=T)> nt[is.na(nt)]<-0> nt.save<-nt> head(nt)hhidweight hhsize

tax interhh.transfers

1 100101 266.1367

5

0

0

2 100102 266.1367

4

0

15200

3 100103 266.1367

6

0

0

4 100104 266.1367

3

0

0

5 100105 266.1367

7

0

0

6 100106 266.1367

9 50000# Cash transfer charity> d<-subset(DE, diaryexp_q10==8&diaryexp_q8==1)[,c("hhid","diaryexp_q7")]> d[is.na(d)]<-0> dim(d)[1] 100662> df<-tapply(d[,2],d$hhid,sum)> df<-data.frame(hhid=names(df),transfer.charity=df)> nt<-merge(nt,df,by="hhid",all.x=T)> nt[is.na(nt)]<-0> nt.save<-nt> head(nt)hhidweight hhsize

tax interhh.transfers transfer.charity

1 100101 266.1367

5

0

0

0

2 100102 266.1367

4

0

15200

0

Manual for Cambodia CSES 2009 (Version 2.0)

241

3 100103 266.1367

6

0

0

0

4 100104 266.1367

3

0

0

0

5 100105 266.1367

7

0

0

0

6 100106 266.1367

9 50000

3500# Negative transfers> nt$negative.transfers<-rowSums(nt[,4:6])> nt.save<-nt> head(nt)hhidweight hhsize

tax interhh.transfers transfer.charity negative.transfers

1 100101 266.1367

5

0

0

0

0

2 100102 266.1367

4

0

15200

0

15200

3 100103 266.1367

6

0

0

0

0

4 100104 266.1367

3

0

0

0

0

5 100105 266.1367

7

0

0

0

0

6 100106 266.1367

9 5000

0

3500

8500



Weighted mean of negative transfers> round(sapply(nt[,4:7],function(x) weighted.mean(x,hhinc$weight)))
tax

interhh.transfers

285

2264

transfer.charity negative.transfers
13713

16262

# Appended negative transfers to hhinc and generated disposable.income1> hhinc<-merge(hhinc,nt[,-2],by="hhid",all.x=T)> hhinc$disposable.income1<-hhinc$total.income-hhinc$negative.transfers> hhinc.save<-hhinc# disposable.income2> hhinc$disposable.income2<-ifelse(hhinc$disposable.income1<0,+ 4000,hhinc$disposable.income1)> hhinc.save<-hhinc

Manual for Cambodia CSES 2009 (Version 2.0)

242

Chapter 8. Demography
8.1 Demographic characteristics# outfiles[[3]]: S01A.hhmembers> dim(outfiles[[3]])[1] 57105

32> d<-outfiles[[3]]# Appended person weight> library(foreign)> persweight<-read.dta("persweight.dta" )> dim(persweight)[1] 57105

3> head(persweight)
hhidpersid persweight

1 0100101 010010101

299.1656

2 0100101 010010102

267.6359

3 0100101 010010103

267.6359

4 0100101 010010104

267.6359

5 0100101 010010105

299.1656

6 0100102 010010201

299.1656> str(persweight)'data.frame':

57105 obs. of

3 variables:$ hhid: chr"0100101" "0100101" "0100101" "0100101" ...

$ persid: chr"010010101" "010010102" "010010103" "010010104" ...

$ persweight: num

299 268 268 268 299 ...

> table(d$persid==as.integer(persweight$persid))
TRUE
57105
> d<-data.frame(d,wt=persweight$persweight)

Coverage of household members

The delegates from Cambodia to the seventh workshop clarified as follows; A person is counted as a household member if he/she lives here or has been absent for

Manual for Cambodia CSES 2009 (Version 2.0)243less than 12 months.
 A person who has moved out of the household more than one year ago but still visit thehousehold but only occasionally, like only during big holidays a few times a year, is notconsidered a part of the household. However, a person has a separate residence sincemore than one year but comes home regularly, on average once a month or more often,
is still considered as a part of the household (e.g. garment workers).

# Estimated population by urban/rural (in thousand)> t<-round(tapply(d$wt,d$urbanrural,sum)/1000)> names(t)<-c("Urban","Rural")> addmargins(t)Urban RuralSum

2723 11244 13967# Estimated population by sex (in thousand)> t<-round(tapply(d$wt,d$q01ac03,sum)/1000)> names(t)<-c("Male","Female")> addmargins(t)Male Female
6811

Sum

7155 13966# Ethnicity of population aged 15 and over by geographical domains> d15<-subset(d,q01ac05>=15)> m<-round(tapply(d15$wt,list(d15$q01ac11a,d15$region),sum)/1000)> m[is.na(m)]<-0> ethnic<-c("Khmer","Cham","Other local ethnic group","Chinese",+ "Vietnamese","Thai","Lao","Other")> rownames(m)<-ethnic> colnames(m)<-c("Phonm Penh","Other urban","Other Rural")> m1<-addmargins(m,2)> round(prop.table(m1,2)*100,1)
Phonm Penh Other urban Other Rural Sum

Manual for Cambodia CSES 2009 (Version 2.0)

244

Khmer

97.3

98.5

95.4 95.9

Cham

1.5

1.0

2.8 2.5

Other local ethnic group

0.0

0.0

1.4 1.1

Chinese0.0

0.0

0.0 0.0

Vietnamese

1.1

0.4

0.3 0.4

Thai

0.0

0.1

0.0 0.0

Lao

0.0

0.0

0.0 0.0

Other

0.0

0.0

0.1 0.0> addmargins(m1,1)
Phonm Penh Other urban Other Rural Sum
Khmer

1027

996

7062 9085

Cham

16

10

209 235

Other local ethnic group00

103 103

Chinese

0

0

2

2

Vietnamese

12

4

24

40

Thai

0

1

1

2

Lao

0

0

0

0

Other

0

0

4

4

1055

1011

Sum



7405 9471

Khmer occupies 96% of the total population.

# Household size
> hhsize<-tapply(d$persid,d$hhid,length)> length(hhsize)[1] 11971



It is completely the same as hhsize of d43> table(hhsize==outfiles[[43]]$hhsize)
TRUE
11971

Manual for Cambodia CSES 2009 (Version 2.0)

245



Household size of 5.03 persons in Cambodia is relatively large.

# Household size by geographical domain> t<-by(d43,d43$region,function(df) weighted.mean(df$hhsize,df$weight))> tv<-as.vector(t)> names(tv)<-c("Phonm Penh","Other urban","Other Rural")> round(tv,2)
Phonm Penh Other urban Other Rural
5.03

4.78

4.75

# Histogram of hhsize of sample households> hist(hhsize)

1500
0

500

1000

Frequency

2000

2500

Histogram of hhsize

2

4

6

8

10

12

14

hhsize> summary(hhsize)Min. 1st Qu. Median

Mean 3rd Qu.

Max.

1.00

4.77

15.00

3.00

5.00

6.00

Household head
There are one household head in every household.

> dh<-subset(d,q01ac06==1)> dim(dh)Manual for Cambodia CSES 2009 (Version 2.0)

246

[1] 1197124> table(tapply(dh$persid,dh$hhid,length))
1
11971



SpouseThere are 5 households with two spouses. However they might be coding errors.

Regarding query on actual situation of multiple wives, the delegates from Cambodia to theseventh workshop replied “Yes that can be more than on spouse in the sample household”.

> ds<-subset(d,q01ac06==2)> t<-tapply(ds$persid,ds$hhid,length)> table(t)
t
1

2

9496

5

> t[t==2]0102509 0102805 0304720 1800815 2102220
2

2

2

2

2

> hhid.ds<-names(t[t==2])> hhid.ds# hhid with double spouses[1] "0102509" "0102805" "0304720" "1800815" "2102220"> df<-d[,c(1,2,16,20,21,24)]> colnames(df)<-c("hhid","persid","sex","age","relation","marital")> for(j in hhid.ds){+ cat(j,"\n")+ print(df[df$hhid==j,])+ }# Roster of households with double spouses
0102509hhid

persid sex age relation marital

1393 0102509 010250901

1

41

1

1

Manual for Cambodia CSES 2009 (Version 2.0)

2471394 0102509 010250902

2

37

2

1

1395 0102509 010250903

1

19

3

4

1396 0102509 010250904

2

17

2

4

1397 0102509 010250905

1

15

3

4

1398 0102509 010250906

2

11

3

NA

1399 0102509 010250907

1

6

3

NA

# Should be a daughter (3)0102805hhid

persid sex age relation marital

1686 0102805 010280501

1

44

1

1

1687 0102805 010280502

2

30

2

1

1688 0102805 010280503

2

5

3

NA

1689 0102805 010280504

1

3

3

NA

1690 0102805 010280505

2

63

2

3

# Should be a mother (6)0304720hhid

persid sex age relation marital

10924 0304720 030472001

2

35

1

1

10925 0304720 030472002

1

41

2

1

10926 0304720 030472003

1

17

3

4

10927 0304720 030472004

1

14

3

4

10928 0304720 030472005

1

10

3

NA

10929 0304720 030472006

1

7

2

NA

10930 0304720 030472007

1

4

3

NA

10931 0304720 030472008

2

0

3

NA

# Should be a son (3)1800815hhidpersid sex age relation marital

49246 1800815 180081501

2

37

1

1

49247 1800815 180081502

1

26

2

1

49248 1800815 180081503

1

17

3

4

49249 1800815 180081504

2

15

3

4

49250 1800815 180081505

2

13

3

4

49251 1800815 180081506

1

7

2

NA

# Should be a son (3)2102220hhid

persid sex age relation marital

54386 2102220 210222001

1

35

1

1

54387 2102220 210222002

2

35

2

1

54388 2102220 210222003

2

12

3

NA

Manual for Cambodia CSES 2009 (Version 2.0)

24854389 2102220 210222004

2

4

3

NA

54390 2102220 210222005

2

73

2

3



# Should be a mother (6)Revision of errors of relationship

File: outfiles[[3]]: S01A.hhmembers
persid

Error

code

in

q01ac06

Revision(Relationship to the head)
010250904

2

3010280505

2

6030472006

2

3

180081506

2

3210222005

2

6



Estimated number of households by sex of the household head

# Generated a subset of household heads in S01A.hhmembers> df<-subset(d,q01ac06==1)> dim(df)[1] 1197133> df<-df[,c(1,2,16,5,33)]> colnames(df)[3:5]<-c("sex","hhwt","perswt")> head(df)hhidpersid sex

hhwt

perswt1

100101 10010101

1 266.1367 299.1656

6100102 10010201

1 266.1367 299.1656

10 100103 10010301

1 266.1367 299.1656

16 100104 10010401

2 266.1367 267.6359

19 100105 10010501

1 266.1367 299.1656

26 100106 10010601

1 266.1367 299.1656

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# Estimated number of household by sex of the household head (using household weight)> t1<-tapply(df$hhwt,df$sex,sum)> names(t1)<-c("Male","Female")> round(addmargins(t1)/1000,0)Male Female

Sum

2305

2939

634

(in thousand)> round(prop.table(t1)*100,1)
Male Female
78.4

21.6

(%)

# Estimated number of household head by sex (using person weight)> t2<-tapply(df$perswt,df$sex,sum)> names(t2)<-c("Male","Female")> round(addmargins(t2)/1000,0)Male Female

Sum

2302

2931

629

(in thousand)> round(prop.table(t2)*100,1)
Male Female
78.5

21.5

(%)

Remarks:
There is a small gap between the results using household weight and that using person weight.



Age heaping
Age heaping is not significant.

# Number of sample household members by single year of age and sex> t<-table(d$q01ac05,d$q01ac03)> colnames(t)<-c("Male","Female")> addmargins(t,2)

Male Female

Sum

0

672

599 12711

550

534 1084

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2

553

549 11023

602

515 11174

547

498 10455

595

616 1211

6683

618 1301

7

627

589 12168607

590 1197

9662

657 131910

668

623 129111

599

589 118812

647

618 126513

709

649 135814

695

633 132815

774

639 1413

16

731

670 140117

679

657 133618

724

689 141319608

634 124220

612

627 123921

571

593 116422593

619 121223569

585 115424

539

549 108825

580

620 120026

498

614 111227

503

612 111528

514

522 103629

544

548 1092

30

451

453

904

31

277

309

586

32

241

254

495

33

243

300

543

34

274

317

591

35

302

362

664

36

351

378

729

37

343

377

720

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38

339

368

707

39

345

328

673

40

304

392

696

41

271

279

550

42

282

314

596

43

264

305

569

44

253

308

561

45

314

329

643

46

261

311

572

47

268

306

574

48

271

329

600

49

257

306

563

50

187

303

490

51

201

251

452

52

171

268

439

53

162

262

424

54

188

267

455

55

172

276

448

56

168

219

387

57

131

198

329

58

155

218

373

59

133

188

321

60

156

214

370

61

129

153

282

62

113

154

267

63

110

129

239

64

121

124

245

65

84

130

214

66

83

91

174

67

77

120

197

68

82

119

201

69

70

72

142

70

83

112

195

71

51

74

125

72

68

82

150

73

61

85

146

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74

39

74

113

75

50

82

132

76

42

58

100

77

37

57

94

78

27

57

84

79

28

39

67

80

26

46

72

81

24

22

46

82

15

25

40

83

20

25

45

84

12

24

36

85

16

27

43

86

14

16

30

87

15

13

28

88

5

12

17

89

6

9

15

90

2

5

7

91

1

3

4

92

2

3

5

93

2

2

4

94

1

4

5

95

1

3

4

96

1

2

3

> write.table(t,file="clipboard",sep="\t")Fig.

Number of sample household members by single year of age and sex(Population pyramid)

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95
90
85

Male

80

Female

75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
1,000

800

600

400

200

0

200

400

600

800

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8.2 Household structure# Maximum household size is 15.
> range(hhsize)[1] 1 15

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

Layout of records to be generated
hhid

No of persons byrelationship
T

1

2

3

…

15

Relationship of each

Marital status of each

personperson

1

2

…

3

15

1

2

3

…

15

Sex of each person

1

2

3

…

Age of each person

15

1

2

Data File: outfiles[[3]]: S01A.hhmembers
Variables:
q01ac01: ID of household member (1 to max 15)
q01ac03: sex
q01ac05: age
q01ac06: relationship to the headq01ac09: marital status> df<-outfiles[[3]][,c("hhid","persid","q01ac01","q01ac03","q01ac05","q01ac06","q01ac09")]> colnames(df)<- c("hhid","persid","no","sex","age","relation","marital")> head(df)hhidpersid no sex age relation marital

1 0100101 010010101 1

1

38

1

1

2 0100101 010010102 2

2

34

2

1

3 0100101 010010103 3

2

13

3

4

4 0100101 010010104 4

2

8

3

NA

5 0100101 010010105 5

1

1

3

NA

6 0100102 010010201 1

1

39

1

1

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

Created data frame ft: family type> ft<-data.frame(hhid=names(hhsize),hhsz=hhsize,row.names=NULL)> head(ft)hhid hhsz
1 0100101

5

2 0100102

4

3 0100103

6

4 0100104

3

5 0100105

7

6 0100106

9> dim(ft)[1] 119712> for(j in 1:15){+ d<-subset(df,relation==j)+ nj<-tapply(d$persid,d$hhid,length)+ dj<-data.frame(hhid=names(nj),nj)+ ft<-merge(ft,dj,by="hhid",all.x=T)+ colnames(ft)[ncol(ft)]<-paste("n",j,sep="")+ }> dim(ft)[1] 1197117> head(ft)hhid hhsz n1 n2 n3 n4 n5 n6 n7 n8 n9 n10 n11 n12 n13 n14 n15
1 0100101

5

1 1

3 NA NA NA NA NA NA

NA

NA

NA

NA NA NA

2 0100102

4

1 1

2 NA NA NA NA NA NA

NA

NA

NA

NA NA NA

3 0100103

6

1 1

4 NA NA NA NA NA NA

NA

NA

NA

NA NA NA

4 0100104

3

1 1

1 NA NA NA NA NA NA

NA

NA

NA

NA NA NA

5 0100105

7

1 1

5 NA NA NA NA NA NA

NA

NA

NA

NA NA NA

6 0100106

9

1 1

3 NA NA NA NA

1

NA

NA

NA NA NA

3 NA

> ft.old<-ft# Relationship, marital status, sex and age by person number> for(j in 1:15){+ d<-subset(df,no==j)[,c("hhid","relation","marital","sex","age")]+ colnames(d)<-c("hhid",paste(c("r","m","s","a"),j,sep=""))+ ft<-merge(ft,d,by="hhid",all.x=T)

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+ }
> dim(ft)[1] 1197177> colnames(ft)[1] "hhid" "hhsz" "n1"

"n2"

"n3"

[11] "n9""n10" "n11""n12"[21] "a1"

"r2"

"m2"

[31] "m4""s4"[41] "a6"

"n4"

"n5"

"n6"

"n7"

"n8"

"n13" "n14""n15" "r1"

"m1"

"s1"

"s2"

"a2"

"r3"

"m3"

"s3"

"a3"

"r4"

"a4"

"r5"

"m5"

"s5"

"a5"

"r6"

"m6"

"s6"

"r7"

"m7"

"s7"

"a7"

"r8"

"m8"

"s8"

"a8"

"r9"

[51] "m9"

"s9"

"a9"

"r10"

"m10" "s10""a10" "r11" "m11""s11"[61] "a11""r12" "m12""s12""a12" "r13""m13" "s13" "a13""r14"[71] "m14""s14" "a14""r15""m15" "s15""a15"> ft.old2<-ft# Ordered the variables as designed> ft<-ft[,c(1:17,seq(18,74,by=4),seq(19,75,by=4),seq(20,76,by=4),seq(21,77,by=4))]> colnames(ft)[1] "hhid" "hhsz" "n1"

"n2"

"n3"

[11] "n9""n10" "n11""n12"[21] "r4"

"r5"

"r6"

[31] "r14"

"n4"

"n5"

"n6"

"n7"

"n8"

"n13" "n14""n15" "r1"

"r2"

"r3"

"r7"

"r8"

"r9"

"r10" "r11" "r12""r13""r15" "m1"

"m2"

"m3"

"m4"

"m5"

[41] "m9""m10" "m11""m12"[51] "s4"

"s5"

"s6"

[61] "s14"[71] "a9"

"m6"

"m7"

"m8"

"m13" "m14""m15" "s1"

"s2"

"s3"

"s7"

"s8"

"s9"

"s10" "s11" "s12""s13""s15" "a1"

"a2"

"a3"

"a4"

"a5"

"a8"

"a10" "a11""a12""a13" "a14"

"a15"

"a6"

"a7"

> ft.old3<-ft> ft[is.na(ft)]<-0> head(ft)hhid hhsz n1 n2 n3 n4 n5 n6 n7 n8 n9 n10 n11 n12 n13 n14 n15 r1 r2 r3 r4
1 0100101

5

1 1

3 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

2 0100102

4

1 1

2 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

3 0100103

6

1 1

4 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

4 0100104

3

1 1

1 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

0

5 0100105

7

1 1

5 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

6 0100106

9

1 1

3 0

0

0 0

3 0

1

0

0

0

0

0 1

2 3

3

r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 m12

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1

3

0 0

0 0

0

0

0

0

0

0 1

1 4

0 0

0

0 0

0

0

0

0

2

0

0 0

0 0

0

0

0

0

0

0 1

1 4

0 0

0

0 0

0

0

0

0

3

3

3 0

0 0

0

0

0

0

0

0 1

1 4

4 0

0

0 0

0

0

0

0

4

0

0 0

0 0

0

0

0

0

0

0 1

1 0

0 0

0

0 0

0

0

0

0

5

3

3 3

0 0

0

0

0

0

0

0 1

1 4

4 4

4

4 0

0

0

0

0

6

3

8 8

8 10

0

0

0

0

0

0 1

1 1

4 4

0

0 0

1

0

0

0

m13 m14 m15 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 a1 a2 a3 a4 a5
1

0

0

0 1

2 2

2 1

0

0 0

0

0

0

0

0

0

0 38 34 13 8

1

2

0

0

0 1

2 2

1 0

0

0 0

0

0

0

0

0

0

0 39 34 13 10

0

3

0

0

0 1

2 1

2 2

2

0 0

0

0

0

0

0

0

0 48 46 23 13 11

4

0

0

0 2

1 2

0 0

0

0 0

0

0

0

0

0

0

0 26 36

5

0

0

0 1

2 1

1 2

1

2 0

0

0

0

0

0

0

0 53 52 23 21 19

6

0

0

0 1

2 2

2 2

2

1 2

1

0

0

0

0

0

0 56 55 29 18 16

3 0

a6 a7 a8 a9 a10 a11 a12 a13 a14 a15
1

0

0 0

0

0

0

0

0

0

0

2

0

0 0

0

0

0

0

0

0

0

3

5

0 0

0

0

0

0

0

0

0

4

0

0 0

0

0

0

0

0

0

0

5 18 14 0

0

0

0

0

0

0

0

6 4 33

0

0

0

0

0

0

6

8

> ft.old4<-ft# Number of sample households consisting of a head’s couple only
> nrow(subset(ft,hhsz==2&n2==1))[1] 475> 475/11971[1] 0.03967922# Number of sample household consisting of a head’s couple and their children# Including stepchild, adopted child and foster child> nrow(subset(ft,n2==1&n3>=1&hhsz==(n1+n2+n3+n4+n5)))[1] 6491> 6491/11971[1] 0.542227

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Remarks:# Number of stepchildren> sum(ft$n4)[1] 138# Number of adopted children> sum(ft$n5)[1] 90# Number of sample household consisting of a mother and her children> nrow(subset(ft,r1==1&s1==2&n2==0&n3>=1&hhsz==(n1+n2+n3)))[1] 849> nrow(subset(ft,r2==1&s2==2&n2==0&n3>=1&hhsz==(n1+n2+n3)))[1] 0> 849/11971[1] 0.07092139

Remarks:
Person number of household head is 1 or 2.
Number of household heads with person number=2 is 24.
> sum(ft$r1==1)+sum(ft$r2==1)[1] 11971> sum(ft$r2==1)[1] 24# Number of sample household consisting of a father and his children> nrow(subset(ft,r1==1&s1==1&n2==0&n3>=1&hhsz==(n1+n2+n3)))[1] 94> nrow(subset(ft,r2==1&s2==1&n2==0&n3>=1&hhsz==(n1+n2+n3)))[1] 0> 94/11971[1] 0.00785231

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

Summary:
The share of nuclear family in the sample household is 66.1%.

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

Generated variables g1 to g15 for household members. The values of g1 to g15 are as follows;

Value

Description

Definition

1

Grandparents’generation

relation=13 & (age-head’s age)>=35

2

Parents’generation

relation=6, 12

3

Head’s generation

relation=1, 2, 7, 11

4Children’s generation

relation=3, 4, 5, 9, 10

5Grandchildren’s generation

relation=8

6

Other relative

relation=13 & (age-head’s age)<35

7

Non-relative

relation=14, 15> for(j in 1:15){+ gen<-rep(0,nrow(ft))+ relation<-ft[,j+17]+ head.age<-ft$a1+ head.age<-ifelse(ft$r2==1,ft$a2,head.age)+ age.diff<-ft[,j+62]-ft[,63]+ gen<-ifelse(relation==13&age.diff>=35,1,gen)+ gen<-ifelse(relation==6|relation==12,2,gen)+ gen<-ifelse(relation==1|relation==2|relation==7|relation==11,3,gen)+ gen<-ifelse(relation>=3&relation<=5,4,gen)+ gen<-ifelse(relation==9|relation==10,4,gen)+ gen<-ifelse(relation==8,5,gen)+ gen<-ifelse(relation==13&age.diff<35,6,gen)+ gen<-ifelse(relation==14|relation==15,7,gen)+ ft["gen"]<-gen+ colnames(ft)[ncol(ft)]<-paste("g",j,sep="")+ }> head(ft)hhid hhsz n1 n2 n3 n4 n5 n6 n7 n8 n9 n10 n11 n12 n13 n14 n15 r1 r2 r3 r4
1 0100101

5

1 1

3 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

2 0100102

4

1 1

2 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

3 0100103

6

1 1

4 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

4 0100104

3

1 1

1 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

0

5 0100105

7

1 1

5 0

0

0 0

0 0

0

0

0

0

0

0 1

2 3

3

6 0100106

9

1 1

3 0

0

0 0

3 0

1

0

0

0

0

0 1

2 3

3

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r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 m12
1

3

0 0

0 0

0

0

0

0

0

0 1

1 4

0 0

0

0 0

0

0

0

0

2

0

0 0

0 0

0

0

0

0

0

0 1

1 4

0 0

0

0 0

0

0

0

0

3

3

3 0

0 0

0

0

0

0

0

0 1

1 4

4 0

0

0 0

0

0

0

0

4

0

0 0

0 0

0

0

0

0

0

0 1

1 0

0 0

0

0 0

0

0

0

0

5

3

3 3

0 0

0

0

0

0

0

0 1

1 4

4 4

4

4 0

0

0

0

0

6

3

8 8

8 10

0

0

0

0

0

0 1

1 1

4 4

0

0 0

1

0

0

0

m13 m14 m15 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 a1 a2 a3 a4 a5
1

0

0

0 1

2 2

2 1

0

0 0

0

0

0

0

0

0

0 38 34 13 8

1

2

0

0

0 1

2 2

1 0

0

0 0

0

0

0

0

0

0

0 39 34 13 10

0

3

0

0

0 1

2 1

2 2

2

0 0

0

0

0

0

0

0

0 48 46 23 13 11

4

0

0

0 2

1 2

0 0

0

0 0

0

0

0

0

0

0

0 26 36

5

0

0

0 1

2 1

1 2

1

2 0

0

0

0

0

0

0

0 53 52 23 21 19

6

0

0

0 1

2 2

2 2

2

1 2

1

0

0

0

0

0

0 56 55 29 18 16

3 0

a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 g11 g12
1

0

0 0

0

0

0

0

0

0

0

3 3

4 4

4 0

0

0 0

0

0

0

2

0

0 0

0

0

0

0

0

0

0

3 3

4 4

0 0

0

0 0

0

0

0

3

5

0 0

0

0

0

0

0

0

0

3 3

4 4

4 4

0

0 0

0

0

0

4

0

0 0

0

0

0

0

0

0

0

3 3

4 0

0 0

0

0 0

0

0

0

5 18 14 0

0

0

0

0

0

0

0

3 3

4 4

4 4

4

0 0

0

0

0

6 4 33

0

0

0

0

0

0

3 3

4 4

4 5

5

5 4

0

0

0

6

8

g13 g14 g15
1

0

0

0

2

0

0

0

3

0

0

0

4

0

0

0

5

0

0

0

6

0

0

0

> ft.old5<-ft

Grouping sample households based on the number of persons by generation# Defined variable pi, the number of persons in generation i> for(j in 1:7){

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262

+ ft["pi"]<-rowSums(ft[,78:92]==j)+ colnames(ft)[ncol(ft)]<-paste("p",j,sep="")+ }> head(ft[,c(1,78:99)])
hhid g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 g11 g12 g13 g14 g15 p1 p2 p3 p4 p5 p6 p7
1 0100101 3

3 4

4 4

0

0 0

0

0

0

0

0

0

0

0 0

2 3

0 0

0

2 0100102 3

3 4

4 0

0

0 0

0

0

0

0

0

0

0

0 0

2 2

0 0

0

3 0100103 3

3 4

4 4

4

0 0

0

0

0

0

0

0

0

0 0

2 4

0 0

0

4 0100104 3

3 4

0 0

0

0 0

0

0

0

0

0

0

0

0 0

2 1

0 0

0

5 0100105 3

3 4

4 4

4

4 0

0

0

0

0

0

0

0

0 0

2 5

0 0

0

6 0100106 3

3 4

4 4

5

5 5

4

0

0

0

0

0

0

0 0

2 4

3 0

0

> ft.old6<-ft# Number of persons by generation; p1 to p7> colSums(ft[,93:99])
p1

p2

p3

p4

p5

p6

p7

45 1060 22229 30319 3148

222

82

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

Codegeneration

2

3

Percentage

households

All households
1

Number of

Total

11,971

100.0

872

7.3

Single

11

283

2.4

Head and spouse only

12

472

4.0

Head, spouse and others

13

28

0.2

Other

14

89

0.7

8,431

70.5

Total
Head – Children

21

8,350

69.8

Parents - Head

22

81

0.7

2,569

21.5

Total
Head – Children - Grandchildren

31

1,521

12.7

Head – Grandchildren

32

225

1.9

Parents – Head - Children

33

813

6.8

Manual for Cambodia CSES 2009 (Version 2.0)

263

4

5

Grandparents – Parents - Head

34

3

0.0

Grandparents - Head

35

7

0.1

94

0.9

Total
Parents – Head – Children - Grandchildren

41

58

0.5

Parents – Head – Grandchildren

42

6

0.1

Grandparents – Parents – Head – Children

43

10

0.1

Grandparents – Head - Children

44

20

0.2

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

50

1

0.0

Grandchildren

Summary:
The household consisting of two generations occupies 71%, followed by 22% of three-generation household and 7% of one-generation household.

# Grouped households using p1 to p5, regardless of the value of p6 and p7.
> h<-rep(0,nrow(ft))> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4==0&ft$p5==0,14,h)> h<-ifelse(ft$hhsz==1,11,h)> h<-ifelse(ft$hhsz==2&ft$n2==1,12,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>=3&ft$p4==0&ft$p5==0&ft$n2==1,13,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4>0&ft$p5==0,21,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4==0&ft$p5==0,22,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4>0&ft$p5>0,31,h)> h<-ifelse(ft$p1==0&ft$p2==0&ft$p3>0&ft$p4==0&ft$p5>0,32,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4>0&ft$p5==0,33,h)> h<-ifelse(ft$p1>0&ft$p2>0&ft$p3>0&ft$p4==0&ft$p5==0,34,h)> h<-ifelse(ft$p1>0&ft$p2==0&ft$p3>0&ft$p4==0&ft$p5==0,35,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4>0&ft$p5>0,41,h)> h<-ifelse(ft$p1==0&ft$p2>0&ft$p3>0&ft$p4==0&ft$p5>0,42,h)> h<-ifelse(ft$p1>0&ft$p2>0&ft$p3>0&ft$p4>0&ft$p5==0,43,h)> h<-ifelse(ft$p1>0&ft$p2==0&ft$p3>0&ft$p4>0&ft$p5==0,44,h)> h<-ifelse(ft$p1>0&ft$p3>0&ft$p5>0,50,h)> t<-addmargins(table(h))

Manual for Cambodia CSES 2009 (Version 2.0)

264

> t
h
11

12

13

14

21

22

31

32

33

34

35

41

42

43

283

475

29

89 8350

81

1521

225

813

3

7

58

6

10

44

50

Sum

20

1 11971> round(t/t[length(t)]*100,1)
h
11

12

13

14

21

22

31

32

33

34

35

41

42

43

2.4

4.0

0.2

0.7 69.8

0.7

12.7

1.9

6.8

0.0

0.1

0.5

0.1

0.1

44

50

Sum

0.2

0.0 100.0> ft["code"]<-h> dim(ft)[1] 11971100> hhfamilytype<-ft

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Chapter 9. Migration and transfer# S03B_personmigrationcurrent : Current migrants data
> d<-outfiles[[9]]> dim(d)[1] 804928> head(d)hhidpersid province urbanrural

weight hhsize

weight3

psu stratum

1 100106 10010631

1

1 266.1367

9 2395.230 1001

11

2 100106 10010632

1

1 266.1367

9 2395.230 1001

11

3 100106 10010633

1

1 266.1367

9 2395.230 1001

11

4 100201 10020131

1

1 264.7066

11 2911.773 1002

11

5 100202 10020231

1

1 264.7066

5 1323.533 1002

11

6 100202 10020232

1

1 264.7066

5 1323.533 1002

11

surveymonth region year persid_string hhid_string q03bc01 q03bc03 q03bc04
1

1

2 2009010010631

0100106

31

1

33

2

1

2 2009010010632

0100106

32

2

25

3

1

2 2009010010633

0100106

33

1

22

4

1

2 2009010020131

0100201

31

2

38

5

1

2 2009010020231

0100202

31

2

50

6

1

2 2009010020232

010020232

1

48

q03bc05b q03bc05c q03bc06 q03bc07 q03bc08 q03bc09 q03bc10 q03bc11 q03bc12
1

88

NA

1991

3

631

6

631

2

NA

2

88

NA

2000

3

548

9

548

2

NA

3

88

NA

2007

3

631

6

631

2

NA

4

88

NA

1995

3

631

5

631

2

NA

5

88

NA

1999

3

631

88

631

2

NA

6

88

NA

1999

3

541

14

541

2

NA

q03bc13a q03bc13b
1

NA

NA

2

NA

NA

3

NA

NA

4

NA

NA

5

NA

NA

6

NA

NA

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> length(unique(d$hhid))[1] 3402Out of 11,971 sample households, 3,402 households have current migrants.

# Number of current migrants by sample household> migrants<-tapply(d$persid,d$hhid,length)> length(migrants)[1] 3402> head(migrants)
100106 100201 100202 100204 100205 100210
3

1

3

4

1

1

> table(migrants)
migrants
1

2

3

4

5

6

7

8

9

10

11

1324

884

521

307

177

101

52

21

8

6

1



One current migrant in 1,324 households, 2 migrants in 884 households, 3 migrants in 521
households, and so on.

# Amount of transfers received by household during the last 12 months (in Riels)> transfers<-tapply(d$q03bc12,d$hhid,sum,na.rm=T)> length(transfers)[1] 3402> head(transfers)
1001061002011002020001002041002051002101750000 48000000

500000> table(names(migrants)==names(transfers))
TRUE
3402



Confirmed that hhid, that is, names of elements of the vectors; migrants and transfers
are the same.

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# Generated data frame dd with vectors of hhid, number of current migrants and amount oftransfers.
> dd<-data.frame(hhid=names(migrants),migrants,transfers)> dim(dd)[1] 3402

3> head(dd)hhid migrants transfers
100106 100106

30100201 100201

1

0100202 100202

30100204 100204

41750000

100205 100205

1 48000000

100210 100210

1

500000# Generate data frame df by merging d43 with dd.
> df<-merge(d43,dd,by="hhid",all.x=T)> dim(df)[1] 1197113> head(df)hhid province urbanrural

weight hhsize

weight3

psu stratum

1 100101

1

1 266.1367

5 1330.6835 1001

11

2 100102

1

1 266.1367

4 1064.5468 1001

11

3 100103

1

1 266.1367

6 1596.8202 1001

11

4 100104

1

1 266.1367

3

798.4101 1001

11

5 100105

1

1 266.1367

7 1862.9569 1001

11

6 1001061

1 266.1367

9 2395.2302 1001

11

surveymonth region year migrants transfers
1

1

2 2009

NA

NA

2

1

2 2009

NA

NA

3

1

2 2009

NA

NA

4

1

2 2009

NA

NA

5

1

2 2009

NA

NA

6

1

2 200930> df[is.na(df)]<-0

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# Defined hhsize2: the expanded household size including current migrants# Note: Current migrants are not assumed as household members of another household.
> df$hhsize2<-df$hhsize+df$migrants> table(df$hhsize)
1
283

2

3

4

5

6

7

8

9

10

11

12

13

14

15

938 1908 2649 2425 1754 1002

531

260

134

46

19

11

7

4

7

8

9

10

11

12

13

14

15

17

595 1493 2320 2279 1765 1285

896

535

316

168

94

42

17

10

1

> table(df$hhsize2)
1
155

2

3

4

5

6

# Average number of household members and migrants by region (person)> m<-cbind(tapply(df$hhsize,df$region,mean),+ tapply(df$migrants,df$region,mean),+ tapply(df$hhsize2,df$region,mean))> rownames(m)<-c("Phnom Pen","Other urban","Other rural")> colnames(m)<-c("hhsize","migrants","hhsize+migrants")> round(m,2)
hhsize migrants hhsize+migrants
Phnom Pen

5.01

0.31

5.31

Other urban

4.76

0.51

5.27

Other rural

4.74

0.74

5.48



The expanded household size including current migrants by region are almost the same, but the
number of current migrants per household is larger in other rural than in Phnom Pen.

# Average amount of transfers by region (in thousand Riels)> t<-tapply(df$transfers,df$region,mean)/1000> names(t)<-c("Phnom Pen","Other urban","Other rural")> round(t,1)
Phnom Pen Other urban Other rural
159.3

249.5

136.4

Manual for Cambodia CSES 2009 (Version 2.0)

269

# Destination of migration

Next are the top five destination countries of current migrants.

Top Five Destination Countries
Code

Country

9316

Thailand

282

9309

Malaysia

75

9601

USA

35

9317

Vietnam

17

9307

South Korea

12



Count

Foreign country codes# Imported country codes> c.Country<-read.fwf("c_Country.txt",widths=c(4,25))> dim(c.Country)[1] 373> head(c.Country)
V1 V2

V3

1 9301 NA

China

2 9302 NA

Hong Kong

3 9303 NA

India

4 9304 NA

Indonesia

5 9305 NAJapan

6 9306 NA Korea North> c.Country<-c.Country[,c(1,3)]> colnames(c.Country)<-c("code","country.name")> c.Countrycode

country.name

1

9301

China

2

9302

Hong Kong

3

9303

India

4

9304

Indonesia

5

9305

Japan

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270

6

9306

Korea North

7

9307

Korea South

8

9308

Laos

9

9309

Malaysia

10 9310Myanmar11 9311Nepal

12 9312

Pakistan

13 9313

Philippines

14 9314Singapore15 9315

Sri Lanka

16 9316

Thailand

17 9317

Vietnam

18 9399

Other Asia

19 9401

Australia

20 9402

New Zealand

21 9499

Other Oceania

22 9501

Belgium

23 9502

France24 9503

Germany

25 9504

Italy

26 9505

Netherlands (Holland )27 9506

Former USSR

28 9507

Sweden29 9508

United Kingdom

30 9599

Other Europe

31 9601 United States of America
32 9602

Canada33 9699

Other America

34 9701

South Africa

35 9799

Other Africa

36 9998 Countries not classified
37 9999

Not Stated

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271

# Frequency of destination countries> t<-table(d$q03bc05c,useNA="ifany")> t
9301 9304 9305 9306 9307 9309 9314 9316 9317 9401 9402 9502 9507 9601 9602
6

2

2

7

12

75

1

282

17

8

2

11

9799 <NA>
2 7581> names(t)[17]<-"9999" # Unknown (Not stated)> df<-data.frame(code=as.integer(names(t)),cnt=as.integer(t))> head(df)code cnt
1 93016

2 93042

3 93052

4 93067

5 930712

6 930975> df<-merge(df,c.Country,by="code",all.x=T)> dim(df)[1] 173> df[order(df$cnt,decreasing=T),]
code

cnt

country.name

17 9999 7581

Not Stated

8

9316

282

Thailand

6

9309

75

Malaysia

14 960135 United States of America

9

9317

17

Vietnam

5

9307

12

Korea South12 9502

11

France10 9401

8

Australia

4

9306

7

Korea North

1

9301

6

China

15 9602

5

Canada2

2

Indonesia

9304

Manual for Cambodia CSES 2009 (Version 2.0)

1

35

5

272

3

9305

2

Japan

11 94022

New Zealand

16 9799

2

Other Africa

7

9314

1

Singapore

13 95071

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273

Chapter 10. Resampled micro data to be provided
Strategy
1. Resampling
The data files will be resampled as follows;
1.1

To order household identifier of “hhid” in d45 by province, urban/rural, psu, survey month andhhinc.

1.2

To select 80% of hhid by systematic sampling method.

1.3

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

1.4 To adjust household weight and person weight by dividing by 0.8.

2. The Village data files will not be included.

3.

The data file of “hhexp” which the author generated in Chapter 6 will be included.

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

Note: Variables of hhid_string and persid_string
Household ID and person ID are provided both in integer and character in the R data set. However,in CSV data set, leading zeros of figures in “hhid_string” and “persid_string” were removed during
conversion from R to CSV. That is, “hhid_string” is not always 7 digits, and “persid_string” is notalways 9 digits.

Original data files> d<-outfiles[[43]]> dim(d)[1] 1197111> colnames(d)[1] "hhid""province""urbanrural""weight"[6] "weight3""psu""stratum""surveymonth" "region"[11] "year"

Manual for Cambodia CSES 2009 (Version 2.0)

"hhsize"274# Appended household income from d45> d<-merge(d,outfiles[[45]][c(1,101)])> head(d)hhid province urbanrural

weight hhsize

weight3

psu stratum

1 100101

1

1 266.1367

5 1330.6835 1001

11

2 100102

1

1 266.1367

4 1064.5468 1001

11

3 100103

1

1 266.1367

6 1596.8202 1001

11

4 100104

1

1 266.1367

3

798.4101 1001

11

5 100105

1

1 266.1367

7 1862.9569 1001

11

6 1001061

1 266.1367

9 2395.2302 1001

11

surveymonth region year adjtotalincome
1

1

2 20095980000

2

1

2 20093637000

3

1

2 20097051000

4

1

2 20096375000

5

1

2 200920854352

6

1

2 20099481500# Ordered hhid by province, urbanrural, psu and total.income.
> df<-d[order(d$province,d$urbanrural,d$psu,d$surveymonth,d$adjtotalincome),]> length(df$hhid)[1] 11971# Excluded hhid=100309,
because this hhid is missing in d10.
> hhid2<-subset(df,hhid!=100309)$hhid> length(hhid2)[1] 11970# Appended persweight to d44> d44<-outfiles[[44]]> table(d44$persid==as.integer(persweight$persid))
TRUE
57105
> head(d44)Manual for Cambodia CSES 2009 (Version 2.0)

275

hhid

persid province urbanrural

weight hhsize

weight3

psu stratum

1 100101 10010101

1

1 266.1367

5 1330.683 1001

11

2 100101 10010102

1

1 266.1367

5 1330.683 1001

11

3 100101 10010103

1

1 266.1367

5 1330.683 1001

11

4 100101 10010104

1

1 266.1367

5 1330.683 1001

11

5 100101 10010105

1

1 266.1367

5 1330.683 1001

11

6 100102 10010201

1

1 266.1367

4 1064.547 1001

11

surveymonth region year persweight1

1

2 2009299.1656

2

1

2 2009267.6359

3

1

2 2009267.6359

4

1

2 2009267.6359

5

1

2 2009299.1656

61

2 2009299.1656> outfiles[[44]]<-d44# Saved data frame “hhexp” in outfiles[[46]]> outfiles[[46]]<-hhexp> length(outfiles)[1] 46# Added “hhexp” to Rnames> Rnames<-c(Rnames,"hhexp")

Resampling# Selected 80% of hhid2> Int<-5> (St<-sample(1:5,1))[1] 1> hhid.selected<-hhid2[(1:length(hhid2))%%Int!=(St-1)]> length(hhid.selected)/length(hhid2)[1] 0.8> hhid.selected<-hhid.selected[order(hhid.selected)]> head(hhid.selected)

Manual for Cambodia CSES 2009 (Version 2.0)

276[1] 100102 100103 100104 100105 100106 100107# Resampled at the rate of 80%# outfiles[[1]] to outfiles[[46]]> fileno<-(1:46)[c(-11,-39)]> Rnames.resampled<-paste(Rnames,".80",sep="")> Rnames.resampled[1] "09dy.expenditure.80""09dy.income.80"[3] "s01a.hhmembers.80""s01b.foodconsumption.80"[5] "s01c.nonfoodexpenses.80""s01d.vulnerability.80"[7] "s02.education.80""s03a.migration.past.80"[9] "s03b.migration.current.80""s04.housing.80"[11] "s04.housingpractice.80""s05a.landown.80"[13] "s05b.cropsproduction.80""s05c.costcrops.80"[15] "s05d.cropsinventory.80""s05e1.animals.80"[17] "s05e2.animalsexpenses.80""s05f1.fisharea.80"[19] "s05f2.fishexpenses.80""s05f3.fishincome.80"[21] "s05g1.forestincome.80""s05g2.forestexpenses.80"[23] "s05h1.bussiness.80""s05h2.bussinessexpenses.80"[25] "s05h3.bussinessincome.80""s06.liabilities.80"[27] "s07.incomeother.80""s08.construction.80"[29] "s09.durables.80""s10.healthmother.80"[31] "s11.health2years.80""s12.health5years.80"[33] "s13b.healthexpenses.80""s14.disability.80"[35] "s15.labor7days.80""s16.labor12months.80"[37] "s17b.theft.80""s17c.accidents.80"[39] "s17d.violance.80""s17d.violence.80"[41] "s18.presenseinhh.80""s99.singlequestions.80"[43] "weighthh.80""weightpersons.80"[45] "IncomeCSES09.80""hhexp.80"> Rnames.resampled[1]<-"dy.expenditure.80"> Rnames.resampled[2]<-"dy.income.80"

Manual for Cambodia CSES 2009 (Version 2.0)

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> outfiles.80<-list()> for(j in 1:46){+ d<-outfiles[[j]]+ outfiles.80[[j]]<-subset(d,is.element(d$hhid,hhid.selected))+ }> length(outfiles.80)[1] 46# Adjusted weight> for(j in (1:46)[c(-11,-45)]){+ outfiles.80[[j]]$HW<-outfiles.80[[j]]$weight/0.8+ }# Example> head(outfiles.80[[46]][,c("hhid","weight","HW")])hhid

weightHW

2 100102 266.1367 332.6709
3 100103 266.1367 332.6709
4 100104 266.1367 332.6709
5 100105 266.1367 332.6709
6 100106 266.1367 332.6709
7 100107 266.1367 332.6709> sum(outfiles.80[[46]]$HW)[1] 2938498> outfiles.80[[45]]$HW<-outfiles.80[[45]]$weighthh/0.8# Adjusted person weight in d44> outfiles.80[[44]]$PW<-outfiles.80[[44]]$persweight/0.8> head(outfiles.80[[44]][,c("hhid","persid","weight","HW","persweight","PW")])hhid

persidweight

HW persweightPW

6100102 10010201 266.1367 332.6709

299.1656 373.9569

7100102 10010202 266.1367 332.6709

267.6359 334.5448

8100102 10010203 266.1367 332.6709

267.6359 334.5448

9100102 10010204 266.1367 332.6709

299.1656 373.9569

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278

10 100103 10010301 266.1367 332.6709

299.1656 373.9569

11 100103 10010302 266.1367 332.6709

267.6359 334.5448> sum(outfiles.80[[44]]$PW)[1] 13990250# Number of records and variables in each resampled file
> for(j in (1:46)[c(-11,-39)]){+ cat(format(j,width=2),": ",format(Rnames.resampled[j],width=30),": ",+ format(nrow(outfiles.80[[j]]),width=7),",",+ format(ncol(outfiles.80[[j]]),width=3),"\n")+ }1 :dy.expenditure.80:

2100042 ,24

2 :dy.income.80:

704825 ,24

3 :s01a.hhmembers.80:

45758 ,

33

4 :s01b.foodconsumption.80:

115190 ,17

5 :s01c.nonfoodexpenses.80:

68976 ,

17

6 :s01d.vulnerability.80:

9583 ,

28

7 :s02.education.80:

42985 ,

38

8 :s03a.migration.past.80:

41098 ,

36

9 :s03b.migration.current.80:

6503 ,

29

10 :s04.housing.80:

9576 ,

67

12 :s05a.landown.80:

13142 ,

46

13 :s05b.cropsproduction.80:

14225 ,

24

14 :s05c.costcrops.80:

13547 ,

30

15 :s05d.cropsinventory.80:

5871 ,

17

16 :s05e1.animals.80:

67480 ,

27

17 :s05e2.animalsexpenses.80:

11581 ,

15

18 :

s05f1.fisharea.80

:

179 ,

18

19 :

s05f2.fishexpenses.80:

6350 ,

15

20 :

s05f3.fishincome.80:

9969 ,

15

21 :s05g1.forestincome.80:

15531 ,

18

22 :s05g2.forestexpenses.80:4754 ,

1523 :s05h1.bussiness.80:

3882 ,

24

24 :s05h2.bussinessexpenses.80:

12651 ,

1925 :s05h3.bussinessincome.80:

4312 ,

19

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26 :

s06.liabilities.80:3773 ,

21

27 :s07.incomeother.80:

5517 ,

1728 :s08.construction.80:

9063 ,

36

29 :s09.durables.80:

74982 ,

2330 :s10.healthmother.80:

3550 ,

35

31 :s11.health2years.80:

1902 ,

41

32 :s12.health5years.80:

4482 ,

30

33 :s13b.healthexpenses.80:

45744 ,

2734 :s14.disability.80:

45744 ,

28

35 :s15.labor7days.80:

41254 ,

50

36 :s16.labor12months.80:

41254 ,

24

37 :s17b.theft.80

:

272 ,

23

38 :

s17c.accidents.80

:

533 ,

21

40 :

s17d.violence.80:

45736 ,

2741 :s18.presenseinhh.80:

45736 ,

32

42 :s99.singlequestions.80:

9576 ,

43

43 :weighthh.80:

9576 ,

12

44 :weightpersons.80:

45758 ,

15

45 :IncomeCSES09.80:

9576 , 104

46 :hhexp.80:

9576 ,

25# Generated resampled data set in R format with Rnames.resampled> for(j in (1:46)[c(-11,-39)]){+ cmd<-paste(Rnames.resampled[j],"<-outfiles.80[[",j,"]]",sep="")+ eval(parse(text=cmd))+ }> ls()[1] "dy.expenditure.80""dy.income.80"[3] "hhexp.80""IncomeCSES09.80"[5] "s01a.hhmembers.80""s01b.foodconsumption.80"[7] "s01c.nonfoodexpenses.80""s01d.vulnerability.80"[9] "s02.education.80""s03a.migration.past.80"[11] "s03b.migration.current.80""s04.housing.80"[13] "s05a.landown.80""s05b.cropsproduction.80"

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280[15] "s05c.costcrops.80""s05d.cropsinventory.80"[17] "s05e1.animals.80""s05e2.animalsexpenses.80"[19] "s05f1.fisharea.80""s05f2.fishexpenses.80"[21] "s05f3.fishincome.80""s05g1.forestincome.80"[23] "s05g2.forestexpenses.80""s05h1.bussiness.80"[25] "s05h2.bussinessexpenses.80" "s05h3.bussinessincome.80"[27] "s06.liabilities.80""s07.incomeother.80"[29] "s08.construction.80""s09.durables.80"[31] "s10.healthmother.80""s11.health2years.80"[33] "s12.health5years.80""s13b.healthexpenses.80"[35] "s14.disability.80""s15.labor7days.80"[37] "s16.labor12months.80""s17b.theft.80"[39] "s17c.accidents.80""s17d.violence.80"[41] "s18.presenseinhh.80""s99.singlequestions.80"[43] "weighthh.80""weightpersons.80"> save.image("CSES2009.resampled80%.RData")# Converted to CSV> CSVnames<-gsub("\\.","_",Rnames.resampled)> CSVnames<-paste(CSVnames,".csv",sep="")> for(j in (1:46)[c(-11,-39)]){+ cmd<-paste("write.csv(outfiles.80[[",j,"]],'",CSVnames[j],"',row.names=F)",sep="")+ eval(parse(text=cmd))+ }# Resampled data set in csv format> list.files()[1] "dy_expenditure_80.csv""dy_income_80.csv"[3] "hhexp_80.csv""IncomeCSES09_80.csv"[5] "s01a_hhmembers_80.csv""s01b_foodconsumption_80.csv"[7] "s01c_nonfoodexpenses_80.csv""s01d_vulnerability_80.csv"[9] "s02_education_80.csv""s03a_migration_past_80.csv"[11] "s03b_migration_current_80.csv""s04_housing_80.csv"

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281[13] "s05a_landown_80.csv""s05b_cropsproduction_80.csv"[15] "s05c_costcrops_80.csv""s05d_cropsinventory_80.csv"[17] "s05e1_animals_80.csv""s05e2_animalsexpenses_80.csv"[19] "s05f1_fisharea_80.csv""s05f2_fishexpenses_80.csv"[21] "s05f3_fishincome_80.csv""s05g1_forestincome_80.csv"[23] "s05g2_forestexpenses_80.csv""s05h1_bussiness_80.csv"[25] "s05h2_bussinessexpenses_80.csv" "s05h3_bussinessincome_80.csv"[27] "s06_liabilities_80.csv""s07_incomeother_80.csv"[29] "s08_construction_80.csv""s09_durables_80.csv"[31] "s10_healthmother_80.csv""s11_health2years_80.csv"[33] "s12_health5years_80.csv""s13b_healthexpenses_80.csv"[35] "s14_disability_80.csv""s15_labor7days_80.csv"[37] "s16_labor12months_80.csv""s17b_theft_80.csv"[39] "s17c_accidents_80.csv""s17d_violence_80.csv"[41] "s18_presenseinhh_80.csv""s99_singlequestions_80.csv"[43] "weighthh_80.csv""weightpersons_80.csv"

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Supplement 1

Village Data

Remarks:
This supplement 1 describes the outline of “Village Data”, which was included in the previously
provided data set, for reference. However this data is not included in the resampled data set.

S1.1 Data import
Imported SPSS data files into R> infiles<-list.files()> infiles[1] "DemograpInfo.sav""EcoInfrastructure.sav" "Education.sav"[4] "Employment.sav""FoodPrices.sav""Health.sav"[7] "Medical.sav""MedicinePrices.sav""NonFoodItems.sav"[10] "RainfallDisaster.sav""Recruitment.sav""RentalPrices.sav"[13] "Resources.sav""SalesPrices.sav"> library(foreign)> v.files<-list()> for(j in 1:length(infiles)){+ df<-read.spss(infiles[j],to.data.frame=T,use.value.label=F)+ v.files<-c(v.files,list(df))+ }> length(v.files)[1] 14> Rnames<-sub(".sav","",infiles)> Rnames[1] "DemograpInfo""EcoInfrastructure" "Education"[4] "Employment""FoodPrices""Health"[7] "Medical""MedicinePrices""NonFoodItems"[10] "RainfallDisaster" "Recruitment"[13] "Resources""RentalPrices""SalesPrices"

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# List of data file name, number of records and variables> for(j in 1:length(infiles)){+ if(j==1) cat("data frame

nrowncol\n")+ cat(format(Rnames[j],width=27),": ",+ format(nrow(v.files[[j]]),width=6),",",+ format(ncol(v.files[[j]]),width=3),"\n")+ }data frame

nrow

ncol

DemograpInfo

:

720 ,

17

EcoInfrastructure

:

713 ,

82

Education

:

719 ,

31

Employment:

2938 ,

6

FoodPrices:

21050 ,

6

Health

:

718 ,

17

Medical:10004 ,

6

MedicinePrices:

1684 ,

6

NonFoodItems:

4058 ,

6

RainfallDisaster

:

716 ,

37

Recruitment

:

689 ,

9

RentalPrices:

7170 ,

7

Resources:

5730 ,

6

SalesPrices:

7175 ,

5

S1.2 Names and types of variables# List of the names and types of variables> for(j in 1:14){+ cat("#### ",Rnames[j]," ##########\n")+ print(str(v.files[[j]]))+ cat("\n\n")+ }Manual for Cambodia CSES 2009 (Version 2.0)

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####

DemograpInfo'data.frame':##########

720 obs. of$ pkid

: num

$ VID

17 variables:
1 2 3 4 5 6 7 8 9 10 ...

: Factor w/ 720 levels "01001","01002",..: 696 405 671 516 2 103 509

390 42 389 ...
$ S1Q1_HHsInVillage: num

135 104 273 551 197 215 234 257 537 710 ...

$ S1Q1_AsOnDay: num

6 6 3 6 31 13 10 29 15 18 ...

$ S1Q1_AsOnMonth: num

1 1 1 12 12 12 1 12 1 12 ...

$ S1Q1_AsOnYear: num2009 NA 2009 2008 2008 ...

$ S1Q2_PersonsInVillage: num

651 561 1037 2368 1049 ...

$ S1Q3_Below18Total: num

213 139 385 952 362 ...

$ S1Q3_Below18Boys: num

113 72 198 501 188 150 180 295 468 472 ...

$ S1Q3_Below18Girls: num

100 67 187 451 174 165 165 222 607 325 ...

$ S1Q4_Over18Total: num

438 422 652 1416 687 ...

$ S1Q4_Over18M: num

222 200 330 650 336 ...

$ S1Q4_Over18W: num

216 222 322 766 351 ...

$ S1Q5_TotLandArea: num

0.974 0.005 1.64 18 1.57 ...

$ S1Q6_PeopleMove: num

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

$ S1Q7_HHs5YearAgo: num

128 103 265 551 197 217 218 220 NA 705 ...

$ S1Q8_People5YearAgo: num

623 605 980 2368 1036 ...

- attr(*, "codepage")= int 1252
NULL

####

EcoInfrastructure ##########'data.frame':

713 obs. of

82 variables:$ pkid: num

5 8 9 10 11 12 13 14 15 16 ...

$ VID: Factor w/ 713 levels "01001","01002",..: 403 690 665 511 2 504 388 42

387 1 ...
$ S2Q1_TotAgriLand: num

0 73.7 124.2 721 118 ...

$ S2Q2_TotIrriAgriLand: num

0 9 0 721 99 0 0 300 0 0 ...

$ S2Q3_TotCultivation : num

0 73.7 122.2 721 118 ...

$ S2Q4_TotLand: num

0 9 0 721 99 0 0 250 0 0 ...

$ S2Q5_Crop1: Factor w/ 35 levels "","101","102",..: 1 3 3 2 25 3 1 1 1 25 ...

$ S2Q5_Crop2: Factor w/ 68 levels "","101","102",..: 1 20 52 1 45 36 1 1 1 63 ...

$ S2Q5_Crop3: Factor w/ 61 levels "","101","102",..: 1 14 47 1 59 36 1 1 1 41 ...

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$ S2Q5_Crop4: Factor w/ 56 levels "","101","102",..: 1 40 22 1 43 24 1 3 1 44 ...

$ S2Q6_ISIC1: Factor w/ 49 levels "","0620",..: 1 7 1 1 6 1 1 7 1 36 ...

$ S2Q6_ISIC2: Factor w/ 46 levels "","1010",..: 1 24 1 1 32 1 1 1 1 30 ...

$ S2Q6_ISIC3: Factor w/ 51 levels "","0130",..: 1 40 1 1 1 1 1 1 1 1 ...

$ S2Q6_ISIC4: Factor w/ 38 levels "","0145",..: 1 1 1 1 1 1 1 1 1 1 ...

$ S2Q7_BusStop: num

0 10 3 0 1 30 0 1 0 4 ...

$ S2Q8_TaxiStop: num

0 4 3 0 1 0 0 1 0 4 ...

$ S2Q9a_MotoRoad: num

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

$ S2Q9b_WheelEnter: num

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

$ S2Q10a_HasRoad: num

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

$ S2Q10b_ToRoad: num

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

$ S2Q11_Electricity: num

100 0 0 0 90 0 100 30 100 99 ...

$ S2Q12_PipedWater: num

100 0 0 0 10 0 100 10 70 0 ...

$ S2Q13_WaterWet: num

1 9 9 3 7 3 1 9 1 6 ...

$ S2Q14_WaterDry: num

1 6 4 3 7 3 1 3 1 6 ...

$ Q15_R13: num

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

$ Q15_R14: num

NA 4 3 23 0.8 2 NA NA NA 1.5 ...

$ Q15_R23: num

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

$ Q15_R24: num

0.5 4 3 NA 0.8 2 1 1 1.5 1.5 ...

$ Q15_R33: num

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

$ Q15_R34: num

10 6 4 70 0.8 2 4 1 3.5 1.5 ...

$ Q15_R43: num

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

$ Q15_R44: num

NA 4 3 23 0.8 2 1 1 1 1.5 ...

$ Q15_R53: num

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

$ Q15_R54: num

0.5 4 3 2 NA 2 1 1 1 1.5 ...

$ S2Q16_ToDistrict: num

1 10 2.5 29 0.1 2 1.5 1 1.5 5 ...

$ S2Q17_ToProvince: num

1 25 36 70 10 30 3.5 28 3 4 ...

$ S2Q18a_GovProjects : num

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

$ S2Q18b_NoProject: num

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

$ S2Q19_Project1: num

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

$ S2Q19_Project2: num

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

$ S2Q19_Project3: num

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

$ S2Q19_Project4: num

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

$ S2Q19_Project5: num

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

$ S2Q19_Project6: num

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

$ S2Q19_Project7: num

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

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$ S2Q20a_NGOProjects : num

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

$ S2Q20b_NoProject: num

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

$ S2Q21_Project1: num

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

$ S2Q21_Project2: num

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

$ S2Q21_Project3: num

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

$ S2Q21_Project4: num

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

$ S2Q21_Project5: num

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

$ S2Q21_Project6: num

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

$ S2Q21_Project7: num

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

$ Q22_R13: num

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

$ Q22_R14a: num

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

$ Q22_R14b: num

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

$ Q22_R14c: num

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

$ Q22_R15a: num

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

$ Q22_R15b: num

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

$ Q22_R15c: num

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

$ Q22_R23: num

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

$ Q22_R24a: num

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

$ Q22_R24b: num

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

$ Q22_R24c: num

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

$ Q22_R25a: num

NA 1 NA 6 NA 1 NA NA NA NA ...

$ Q22_R25b: num

NA NA NA NA NA 6 NA NA NA NA ...

$ Q22_R25c: num

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

$ Q22_R33: num

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

$ Q22_R34a: num

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

$ Q22_R34b: num

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

$ Q22_R34c: num

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

$ Q22_R35a: num

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

$ Q22_R35b: num

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

$ Q22_R35c: num

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

$ S2Q23_Industrial: num

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

$ S2Q24_PublicPhone: num

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

$ S2Q25_PrivatePhone : num

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

$ S2Q26_Internet: num

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

$ S2Q27a_CityPower: num

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

$ S2Q27b_Gas: num

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

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$ S2Q27c_Gasoline: num

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

- attr(*, "codepage")= int 1252
NULL

####

Education ##########'data.frame':

719 obs. of$ VID

31 variables:: Factor w/ 719 levels "01001","01002",..: 499 405 671 516 2 103 509 390 42

389 ...
$ Q1_PriSchool: num 1 2 2 1 2 2 2 2 1 2 ...

$ Q2a_1stProblem: num 3 NA NA 3 NA NA NA NA 3 NA ...

$ Q2b_2ndProblem: num 7 NA NA 8 NA NA NA NA 9 NA ...

$ Q2c_3rdProblem: num 10 NA NA 10 NA NA NA NA 10 NA ...

$ Q3_PriSchoolImp: num 1 NA NA 1 NA NA NA NA 1 NA ...

$ Q4_PriSchNearest : num NA 0.5 1.5 NA 0.7 0.2 1 0.5 NA 1.5 ...
$ Q5_GoPriSchool: num NA 1 3 NA 3 1 1 1 NA 1 ...

$ Q6_Hours: num NA 0 0 NA 0 0 NA 0 NA 0 ...

$ Q6_Minutes: num NA 10 30 NA 2 10 20 5 NA 15 ...

$ Q7_2ndSchool: num 2 2 2 2 2 2 2 2 2 2 ...

$ Q8a_1stProblem: num NA NA NA NA NA NA NA NA NA NA ...

$ Q8b_2ndProblem: num NA NA NA NA NA NA NA NA NA NA ...

$ Q8c_3rdProblem: num NA NA NA NA NA NA NA NA NA NA ...

$ Q9_2ndSchoolImp: num NA NA NA NA NA NA NA NA NA NA ...

$ Q10_2ndSchoolNear: num 18 0.5 2.5 2 0.8 4.2 2 0.5 1 1 ...
$ Q11_Attend: num 18 0.5 2.5 2 0.8 4.2 2.5 0.5 1 1 ...

$ Q12_Go2ndSchool: num 3 1 3 3 3 3 3 4 3 4 ...

$ Q13_Hours: num 3 0 0 0 0 0 NA 0 0 0 ...

$ Q13_Minutes: num 0 10 40 5 3 30 30 5 5 7 ...

$ Q14_UpSchool: num 2 2 2 2 2 2 2 2 1 2 ...

$ Q15_1stProblem: num NA NA NA NA NA NA NA NA 3 NA ...

$ Q15_2ndProblem: num NA NA NA NA NA NA NA NA 9 NA ...

$ Q15_3rdProblem: num NA NA NA NA NA NA NA NA 10 NA ...

$ Q16_UpSchoolImp: num NA NA NA NA NA NA NA NA 1 NA ...

$ Q17_UpNearest: num 80 0.5 2.5 8 0.98 6 2 0.5 NA 1 ...

$ Q18_GoUpSchool: num 4 1 3 3 3 3 3 4 NA 4 ...

$ Q19_Hours: num 4 0 0 0 0 0 NA 0 NA 0 ...

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$ Q19_Minutes: num NA 10 40 15 3 50 30 4 NA 7 ...

$ Q20_Literacy: num 2 2 2 2 2 2 2 2 2 2 ...

$ Q21_Program: num 2 2 2 1 2 2 1 1 1 1 ...

- attr(*, "codepage")= int 1252
NULL

####

Employment'data.frame':##########
2938 obs. of

6 variables:$ PKID: num 1 2 3 4 5 6 7 8 9 10 ...

$ VID: Factor w/ 691 levels "01001","01002",..: 476 476 476 476 399 643 643 643 643 643 ...

$ S7_Line: num 1 2 4 10 8 1 2 3 4 5 ...
$ S7_Col3: num 10000 5000 8000 5000 35000 20000 12000 10000 12000 10000 ...
$ S7_Col4: num 10000 5000 8000 5000 35000 0 12000 10000 12000 10000 ...
$ S7_Col5: num 8000 5000 6000 3000 40000 0 0 0 0 9 ...
- attr(*, "codepage")= int 1252
NULL

####

FoodPrices'data.frame':##########
21050 obs. of

6 variables:$ PKID: num

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

$ VID: Factor w/ 701 levels "01001","01002",..: 490 490 490 490 490 490 490 490 490 490 ...

$ S6A_Col1: num

3 4 5 6 7 11 12 13 14 19 ...

$ S6A_Col5: num

8000 10000 9000 12000 10000 12000 10000 1000 300 4500 ...

$ S6A_Col6: num

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

$ S6A_Col7: num

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

- attr(*, "codepage")= int 1252
NULL

####

Health'data.frame':$ VID##########
718 obs. of

17 variables:: Factor w/ 718 levels "01001","01002",..: 499 405 670 516 2 103 509 390 42

389 ...
$ Q2a_HServices: num 2 2 1 2 2 1 2 2 2 2 ...

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$ Q3a_1Major: num 1 11 3 8 10 12 7 7 1 7 ...

$ Q3b_2Major: num 3 NA 8 10 11 11 10 11 12 11 ...

$ Q3c_3Major: num 11 NA 11 11 7 8 11 12 8 12 ...

$ Q4a_1Major: num 3 10 2 7 11 8 2 2 2 7 ...

$ Q4b_2Major: num 5 7 7 8 3 7 6 10 10 10 ...

$ Q4c_3Major: num 8 11 8 11 10 2 7 7 7 11 ...

$ Q5a_ImmProg: num 2 1 2 2 2 2 2 2 2 2 ...

$ Q5b_MCH: num 1 1 2 1 1 2 1 1 2 2 ...

$ Q5c_HIV: num 2 2 2 2 1 1 1 2 1 2 ...

$ Q5d_Iodine: num 1 2 2 1 1 2 1 1 2 2 ...

$ Q6_HImprove: num 3 2 1 1 1 2 1 1 2 1 ...

$ Q7a_HIV: num 2 1 1 2 1 1 1 1 1 1 ...

$ Q7b_Total: num NA 3 5 NA 3 2 4 4 4 3 ...

$ Q7b_Males: num NA 2 2 NA 2 1 2 1 1 0 ...

$ Q7b_Females: num NA 1 3 NA 1 1 2 3 3 3 ...

- attr(*, "codepage")= int 1252
NULL

####

Medical ##########'data.frame':$ PKID

10004 obs. of

6 variables:: num 1 2 3 4 5 6 7 8 9 12 ...

$ VID: Factor w/ 716 levels "01001","01002",..: 496 496 496 496 496 496 496 496 496

496 ...
$ Q1_LineNo: num 1 2 3 4 5 6 7 8 9 10 ...
$ Q1_Col3: num 2 2 2 2 2 2 2 2 2 2 ...

$ Q1_Col4: num NA NA NA NA NA NA NA NA NA NA ...

$ Q1_Col5: num 18 80 18 18 18 80 316 80 80 18 ...

- attr(*, "codepage")= int 1252
NULL

####

MedicinePrices'data.frame':##########

1684 obs. of

6 variables:$ PKID: num

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

$ VID: Factor w/ 340 levels "01002","01004",..: 180 180 180 180 180 180 180 180 180

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180 ...
$ S6C_ItemNo: num

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

$ S6C_Col5 : num

200 200 700 300 2000 500 100 200 400 300 ...

$ S6C_Col6 : num

200 200 700 300 2000 500 100 200 400 300 ...

$ S6C_Col7 : num

200 200 700 300 2000 500 100 200 400 300 ...

- attr(*, "codepage")= int 1252
NULL

####

NonFoodItems'data.frame':##########

4058 obs. of

6 variables:$ PKID: num

1 2 3 5 6 7 8 9 10 11 ...

$ VID: Factor w/ 647 levels "01001","01002",..: 455 455 455 361 361 361 361 361 361

361 ...
$ S6B_ItemNo: num

5 20 23 1 2 3 5 6 7 8 ...

$ S6B_Col5 : num

3000 3000 1000 1100 600000 287000 3000 750 50000 500000 ...

$ S6B_Col6 : num

NA NA NA 710 615000 287000 3000 750 50000 500000 ...

$ S6B_Col7 : num

NA NA NA 1000 600000 287000 3000 1000 50000 500000 ...

- attr(*, "codepage")= int 1252
NULL

####

RainfallDisaster'data.frame':$ VID##########

716 obs. of

37 variables:: Factor w/ 716 levels "01001","01002",..: 497 403 669 514 2 507 388 42 387

1 ...
$ Q1a_RainfallDry : num

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

$ Q1b_RainfallWet : num

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

$ Q2a_DistrRainDry: num

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

$ Q2b_DistrRainWet: num

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

$ Q3a_RainTimeDry : num

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

$ Q3b_RainTimeWet : num

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

$ Q4a_CropDamDry : num

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

$ Q4b_CropDamWet : num

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

$ Q5a_HarvestDry : num

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

$ Q5b_HarvestWet : num

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

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$ Q6_Disaster5year: num

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

$ Q7_Drought3: num

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

$ Q7_Drought4: num

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

$ Q7_Drought5: num

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

$ Q7_Drought6: num

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

$ Q7_Drought7: num

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

$ Q7_Flood3: num

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

$ Q7_Flood4: num

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

$ Q7_Flood5: num

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

$ Q7_Flood6: num

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

$ Q7_Flood7: num

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

$ Q7_CropFail3: num

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

$ Q7_CropFail4: num

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

$ Q7_CropFail5: num

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

$ Q7_CropFail6: num

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

$ Q7_CropFail7: num

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

$ Q7_Fire3: num

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

$ Q7_Fire4: num

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

$ Q7_Fire5: num

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

$ Q7_Fire6: num

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

$ Q7_Fire7: num

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

$ Q7_Other3: num

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

$ Q7_Other4: num

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

$ Q7_Other5: num

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

$ Q7_Other6: num

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

$ Q7_Other7: num

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

- attr(*, "codepage")= int 1252
NULL

####

Recruitment ##########'data.frame':$ VID

689 obs. of

9 variables:: Factor w/ 688 levels "01001","01002",..: 483 389 643 500 2 91 493 374 35

373 ...
$ S10Q1_Work: num 2 2 2 2 2 2 2 2 1 2 ...

$ S10Q2_Who: num NA NA NA NA NA NA NA NA 2 NA ...

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292

$ S10Q3_Recruited: num NA NA NA NA NA NA NA NA 1 NA ...
$ S10Q4_TypeWork : num NA NA NA NA NA NA NA NA 5 NA ...
$ S10Q5_WhereSent: num NA NA NA NA NA NA NA NA 2 NA ...
$ S10Q6a_District: Factor w/ 29 levels "","0105",..: 1 1 1 1 1 1 1 1 1 1 ...

$ S10Q6b_Province: Factor w/ 13 levels "","01","02",..: 1 1 1 1 1 1 1 1 1 1 ...

$ S10Q6c_Country : Factor w/ 1 level "": 1 1 1 1 1 1 1 1 1 1 ...

- attr(*, "codepage")= int 1252
NULL

####

RentalPrices'data.frame':##########

7170 obs. of

7 variables:$ PKID: num

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

$ VID: Factor w/ 719 levels "01001","01002",..: 499 499 499 499 499 499 499 499 499 499 ...

$ S9a_Line: num

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

$ S9a_Col3: num

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

$ S9a_Col4: num

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

$ S9a_Col5: num

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

$ S9a_Col6: num

9e+05 NA NA NA NA 5e+04 NA NA NA NA ...

- attr(*, "codepage")= int 1252
NULL

####

Resources ##########'data.frame':

5730 obs. of

6 variables:$ PKID: num 1 2 3 4 5 6 7 8 9 10 ...

$ VID: Factor w/ 717 levels "01001","01002",..: 499 499 499 499 499 499 499 499 405 405 ...

$ S8_Line: num 1 2 3 4 5 6 7 8 1 2 ...
$ S8_Col3: num 1 1 1 2 1 1 1 1 3 3 ...
$ S8_Col4: num 2 2 2 4 4 2 4 3 4 4 ...
$ S8_Col5: num 2 2 2 NA NA 2 NA 3 NA NA ...
- attr(*, "codepage")= int 1252
NULL

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293

####

SalesPrices ##########'data.frame':

7175 obs. of

5 variables:$ PKID: num

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

$ VID: Factor w/ 719 levels "01001","01002",..: 499 499 499 499 499 499 499 499 499 499 ...

$ S9b_Line: num

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

$ S9b_Col3: num

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

$ S9b_Col4: num

4500000 NA NA NA NA NA NA 1200000 NA NA ...

- attr(*, "codepage")= int 1252
NULL

S1.3 Village identifier

VID is the village identifier, and it corresponds to the variable psu in weighthouseholds.area.

> dv<-v.files[[1]]# DemograpInfo> dv<-dv[order(dv$VID),]> wa<-outfiles[[1]]#weighthouseholds.area> table(dv$VID==wa$psu)
TRUE
720

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CONFIDENTIAL
All information collected in this survey isstrictly confidential and will be used forstatistical purposes only

Royal Government of Cambodia
Ministry of Planning
National Institute of Statistics

CAMBODIA SOCIO-ECONOMIC SURVEYVILLAGE QUESTIONNAIRE 2009
A. To be completed by supervisor before interview
Province /City
District /Khan
Commune/Sankat
Sample Village/Mondol
Zone
Sector (Urban=1, Rural=2)
B. To be completed by supervisor
No.

Name

Names of persons Interviewed:

Position

12
3
4
Date of interview 1

Day:

Month:Year:

Date of interview 2

Day:

Month:Year:

Date of interview 3

Day:

Month:Year:

Date of interview 4Day:

Month:

Year:
Id:

Supervisor’s name:Supervisor’ s signature:
Supervisor’ s phone no:
C. To be completed after checking completed questionnaires

Id:

Name:
Date checkedDay:

Month:Year:

Date of re-interview(if necessary)Day:

Month:

Year:

Signature:

Manual for Cambodia CSES 2009 (Version 2.0)

FORM 2295

1. DEMOGRAPHIC INFORMATION
1 How many households live in this village?
as on day

month

year

2 How many people live in this village as of the same date?

Persons:

3 Of these, how many are below 18 years as of the same date?
Total:

Boys:

Girls:

4 Of these, how many are 18 years or more as of the same date?
Total:

Men:

Women:
Km2:

5 What is the total land area of this village?
6 During the last 5 years, have more new people moved to your village, or havethere been more people that moved out of your village?
1 = More arrivals
2 = More departures
3 = About the same of both
4 = No arrivals or departures

7 How many households were there 5 years ago?
8 How many people were there 5 years ago?
2. ECONOMY AND INFRASTRUCTURE
1 What is the total area of agricultural land available in this village?

Hectares:

2 Of which the total irrigated agricultural land is?

Hectares:

3 What is the total area prepared for paddy cultivation?

Hectares:

4 Of which the area of irrigated paddy land is?

Hectares:

5 What are the major crops that are grown in this village? (List up to 4 crops in order of importance)
Crop Nº(1)

Name of crop
(2)

1
2
3
4

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(Leave blank if none)
CODE
(3)

296

6

What are the major non-agricultural enterprises that are operating in this village? (List up to 4 most important enterprises in descendingorder of output or employment. Their can be two enterprises in the same industry)(Leave blank if none)
Description of enterpriseEnterprise Nº
(1)

CODE

(2)

(3)

1
2
3
4
(Write '0' if within the village)

7 How far from this village is the nearest bus stop?

(Write '0' if within the village)

8 How far from this village is the nearest public or private taxi stop?
9 a. Does the village have access to a motorable road?

Km.:

1 = Yes (=>> 11)Km.:

2 = Nob. Can four wheelers enter the village?
10 a. Does the village have all weather roads?
1 = Yes (=>> 11)

2 = Nob. How many kilometers away from an all-weather road is this village?
11 Approximately what percentage of households in this village have public or private electricity,
including generators but not including batteries?

Km:

%:

If no household has electricity, write "0"
12 Approximately what percentage of households in this village have piped water in dwelling or on premises?

%:

If no household has piped water, write "0"
13 What is the major source of drinking water for most people in this village during the wet season?
01 = Piped in dwelling
02 = Public tap
03 = Tubed/piped well or borehole
04 = Protected dug well (including all of the following:
Lining, headwall, platform, cover)
05 = Unprotected dug well
06 = Pond, river or stream (fetch water from pond, river,stream)07 = Pond, river or stream (pump to the house)
08 = Improved rainwater collection (catchment tank needs
to have all the following: completely closed, tap to
withdraw water and at least 3000 litres capacity
09 = Unimproved rainwater collection
10 = Water bought from tanker truck or vendor
11 = Bottled water
12 = Other (Specify)

14 What is the major source of drinking water for most people in this village during the dry season?
01 = Piped in dwelling
02 = Public tap
03 = Tubed/piped well or borehole
04 = Protected dug well (including all of the following:
Lining, headwall, platform, cover)
05 = Unprotected dug well
06 = Pond, river or stream (fetch water from pond, river,stream)07 = Pond, river or stream (pump to the house)
08 = Improved rainwater collection (catchment tank needs
to have all the following: completely closed, tap to
withdraw water and at least 3000 litres capacity
09 = Unimproved rainwater collection
10 = Water bought from tanker truck or vendor
11 = Bottled water
12 = Other (Specify)

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15 Are there the following animities/services in this village?
Is there a ..[AMENITY/ SERVICE]... How far is the nearest
in the village?
..[AMENITY/ SERVICE]...
from this village?

Line NºAmenity/service

1 = Yes (=>> Next line)
2 = NoKILOMETERS

(1)

(2)

(3)

(4)

1 Food shop or restaurant
2 Bank or loan credit unit
3 Agricultural extension worker
4 Permanent market
5 Shop selling manure and agro-chemicals
16 What is the distance to the district head quarter?

(Write '0' if within the village)Km.:

17 What is the distance to the provincial head quarter?

(Write '0' if within the village)Km.:

18 a. Are there any kind of government development projects presently functioning in this Village?
1 = Yes
2 = No (=>> 20)b. How many projects are now functioning in this village?

Line Nº19 For each kind of project indicate:Is there presently any [PROJECT].. functioning in this village?

Code:
1 = Yes2= No(Government development project)

Project(1)(2)

1 Agricultural Development (e.g., land development, seed distribution, fishery, animal health, irrigation.)
2 Infrastructure Development (e.g., road development)
3 Education/adult literacy programme
4 Health
5 Water project (bore wells, tanks, dams)
6 Village Development Committee
7 Other (specify)
20 a. Are there any kinds of NGO development projects presently functioning in this Village?
1 = Yes
2 = No (=>> 22)
b. Number of projects:

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(3)

298

Line Nº21 For each kind indicate:Code:
1 = Yes
2= NoIs there presently any [PROJECT].. functioning in this village?
(NGO development project)
Project

(1)

(2)

(3)

1 Agricultural Development (e.g., land development, seed distribution, fishery, animal health irrigation.)
2 Infrastructure Development (e.g., road development)
3 Education/adult literacy programme
4 Health
5 Water project (bore wells, tanks, dams)
6 Village Development Committee
7 Other (specify)
22 (Ask the following questions about the past 12 months)

SERIAL NUMBER

Did households in the Was the technical support forvillage receive
crops or livestock or fisheries?
technical support
regarding agriculture
from this agency ?
1 = Crops
2 = Livestock
1=Yes
3 = Fisheries
2=No(=>> Next line)

What was the main formof the technical support ?
1=Home visit
2=Radio broadcast
3=Printed bulletin
4=Newspaper article
5=Television program
6=Training
7=Other(specify)

AGENCY
(1)

(2)

(3)

(4a)

(4b)

(4c)

(5a)

(5b)

(5c)

1 Government agency
2 Community organisation, NGO
3 Private company
23 Is there any large industrial or commercial enterprise (e.g., factory, hotel, restaurant or company
employing more than 10 persons) in this village or within 10 kms of this village?
1 = Yes
2 = No24 Does the village have a public telephone?
1 = Yes
2 = No25 Are there any households with private phones?
1 = Yes

2 = No26 Does the village have an Internet café or any shop where people can get access to Internet in the village?
1 = Yes

2 = No27 Does the village have access to the following energy sources...?
1 = Yes
2 = Noa. Publicly-provided electricity/City power?

b. Gas?

c. Gasoline?

Note: Publicly provided electricity/City power should be electricity supplied by electricity department or any other agency

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299

3. RAINFALL AND NATURAL DISASTERS(Ask the following questions about the past wet and dry season, keeping in mind the main crops of the village.)
Note:

Past wet-season should refer to the wet-season last calendar year.
If interview takes place in January - June: past dry-season should refer to the dry-season last calendar year.
If interview takes place in July - December: past dry-season should refer to the dry-season this calendar year.

1 Would you say the total amount of rainfall was normal,a. ...in the past dry-season?

1 = Normal
2 = Better than Normal
3 = Worse than Normal

b. …in the past wet-season?

better than normal, or worse than normal?

2 Would you say that the distribution of rainfall across different months wasa. ...in the past dry-season?

normal, better than normal, or worse than normal?
1 = Normal
2 = Better than Normal
3 = Worse than Normal

b. …in the past wet-season?

3 Would you say that the onset of rainfall was on time, delayed or earlya. ...in the past dry-season?

(compared to normal)?

1 = On time
2 = Delayed
3 = Early

b. …in the past wet-season?

4 Would you say that the crops were damaged by floods/too much rain?

a. ...in the past dry-season?

1 = Yes
2 = Nob. …in the past wet-season?

5 Was the harvest good, normal, or bad:
1 = Good
2 = Normal
3 = Bada. ...in the past dry-season?

b. …in the past wet-season?

6 Was there any disaster in the village during the past 5 years?
1 = Yes
2 = No (=>> next section)

LINE Nº7 Mark ( x ) the years in which the following types of disaster affected the village.

(1)

TYPE
(2)

2008
(3)

2007
(4)

2006
(5)

2005

1 Drought
2 Flood
3 Crop failure due to pests
4 Major fire (forest, field)
5 Other (specify)

Manual for Cambodia CSES 2009 (Version 2.0)

(6)

2004
(7)

300

4. EDUCATION
1 Is there a primary school in this village?
1 = Yes
2 = No (=>> 4)
2 What are the major problems with primary schooling in this village?
(List the three most important problems, in order of their importance.)(Read out a few possible answers only if respondent does not respond.)
01 = School too far
02 = Poor school building
03 = Living standard of teachers is too low
04 = School budget constraint
05 = Not enough places/desks
06 = Not enough supplies
07 = Poor quality of teachers
08 = Not enough teachers
09 = Classes not held regularly
10 = Poor living standard in the village
11 = Other (specify)

a. Most important problem:b. Second most important problem:c.

Third most important problem:

3 Has the primary school for the children in this village improved, stayed the same or deteriorated over the past 5 years?
1 = Improved
2 = Stayed the same
3 = Deteriorated=>> 7

Note: Think of the building, toilet facilities/sanitation, quality of teachers, school equipment etc.
4 How far away is the primary school nearest to this village?
5 How do most of the children go to the primary school?
1 = On foot2 = Motor-drawn cart or horse-drawn cart
3 = Bicycle
4 = Motorcycle
5 = Truck
6 = Boat/Canoe
7 = Bus
8 = Others (specify)

6 How long does it typically take for children of this village to reach there? (Time one way)
HOURS:MINUTES:

7 Is there a lower secondary school in this village?
1 = Yes
2 = No (=>> 10)

Manual for Cambodia CSES 2009 (Version 2.0)

Km.:

301

8 What are the major problems with lower secondary schooling in this village?
(List the three most important problems, in order of their importance.)(Read out a few possible answers only if respondent does not respond.)
01 = School too far
02 = Poor school building
03 = Living standard of teachers is too low
04 = School budget constraint
05 = Not enough places/desks
06 = Not enough supplies
07 = Poor quality of teachers
08 = Not enough teachers
09 = Classes not held regularly
10 = Poor livingstandard in the village
11 = Other (specify)

a. Most important problem:b. Second most important problem:c.

Third most important problem:

9 Has the lower secondary school for the children of this village improved, stayed the same or deteriorated over the past 5 years?
1 = Improved
2 = Stayed the same
3 = Deteriorated=>> 14

Note: Think of the building, toilet facilities/sanitation, quality of teachers, school equipment etc.
10 How far away is the lower secondary school nearest to this village?

Km.:

11 How far away is the lower secondary school which most of the children of this village attend?

Km.:

12 How do most of the children go to the secondary school?
1 = On foot5 = Truck
2 = Motor-drawn cart or horse-drawn cart
6 = Boat/Canoe
3 = Bicycle
7 = Bus
4 = Motorcycle
8 = Others (specify)
13 How long does it typically take for children of this village to reach there? (Time one way)
HOURS:MINUTES:

14 Is there an upper secondary school in this village?
1 = Yes
2 = No (=>> 17)

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302

15 What are the major problems with upper secondary schooling in this village?
(List the three most important problems, in order of their importance.)(Read out a few possible answers only if respondent does not respond.)
01 = School too far
02 = Poor school building
03 = Living standard of teachers is too low
04 = School budget constraint
05 = Not enough places/desks
06 = Not enough supplies
07 = Poor quality of teachers
08 = Poor job prospects
09 = Financial problems for family
10 = Poor livingstandard in the village
11 = Other (specify)

a. Most important problem:b. Second most important problem:c.

Third most important problem:

16 Has the upper secondary school for the children of this village improved, stayed the same or deteriorated over the past 5 years?
1 = Improved
2 = Stayed the same
3 = Deteriorated=>> 20Note: Think of the building, toilet facilities/sanitation, quality of teachers, school equipment etc.
17 How far away is the upper secondary school nearest to this village?
18 How do most of the children go to the upper secondary school?
1 = On foot2 = Motor-drawn cart or horse-drawn cart
3 = Bicycle
4 = Motorcycle
5 = Truck
6 = Boat/Canoe
7 = Bus
8 = Others (specify)
19 How long does it typically take for children of this village to reach there? (Time one way)
HOURS:MINUTES:

20 Is there presently an adult literacy program in this village?
1 = Yes (=>> Next Part)
2 = No21 Has there been such a program before?

1 = Yes
2 = NoManual for Cambodia CSES 2009 (Version 2.0)

Km.:

303

5. HEALTH
1 Please fill-out the table below on availability of medical services:LINE NºIs there a ...[TYPE] ... Indicate number of centres,in this village?
doctors, nurses etc.

1 = Yes
2 = No (=>> col.5)

TYPE
(1)

(2)

How far away is thenearest ..[TYPE]..

=>> Next line

(3)

NUMBERKILOMETERS

(4)

(5)

01 Private clinic?
02 Dedicated drug shop?
03 Other shop selling drugs?
04 Communal Health Centre?
05 Referral (or District) Hospital?
06 Provincial Hospital?
07 National Hospital?
08 Private hospital?
09 Doctor?
10 Nurse?
11 Trained midwife?
12 Traditional Birth Attendant (TBA)?
13 Kru Khmer?
14 Other traditional healer?
2 a. Are there any other health services or personnel in this village that were not cited above?
1 = Yes
2 = No (=>> 3)
b. Please describe it:
3 What are the major health problems in this village?
(List the three most important problems, in order of their importance.)(Read out a few possible answers only if respondent does not respond.)
01 = Malaria
02 = Complications from childbirth
03 = Child malnutrition
04 = Adult malnutrition
05 = Injuries/accidents
06 = Childhood diseases (measles,chicken pox etc.)
07 = Diarrhoea
08 = Respiratory diseases (e.g., chronic cough, tuberculosis)
09 = Leprosy
10 = Dengue
11 = Fever
12 = Other (specify)

a. Most important problem:b. Second most important problem:c.

Manual for Cambodia CSES 2009 (Version 2.0)

Third most important problem:

304

4 What are the major problems with the health services (public and private) for the people in this village?
(List the three most important problems, in order of their importance.)(Read out a few possible answers only if respondent does not respond.)
01 = Lack of beds in hospital, equipment etc.
02 = Not enough medicines, drugs
03 = Poor quality of services
04 = No physician or qualified medical assistant available
05 = No secondary nurse / midwife available
06 = Health facility is not open 24 hours
07 = Health services are too expensive
08 = Long distance to better quality care
09 = Unsanitary health facilities
10 = Staff are unhelpful
11 = Staff are not friendly
12 = Other (specify)

a. Most important problem:b. Second most important problem:c.

Third most important problem:

5 Are there any of the following health programmes in the village?

1 = Yes
2 = Noa. Immunisation Programme?

b. MCH/FP Programme?
(Maternal and childhealth/Family planning)c.

d. Programme for iodine

Testing for HIV/AIDS?

deficiency/Goiter?
6 Have the health services for the people of this village improved, stayed the same or deteriorated in the past 5 years?
1 = Improved
2 = Stayed the same
3 = Deteriorated

7 a. Are there any reported cases of HIV/AIDS in the village? (Discretely enquire and fill out this information)
1 = Yes
2 = No (=>> Section 6)b. How many cases?
TOTAL:

MALES:

Manual for Cambodia CSES 2009 (Version 2.0)

FEMALES:305

6. RETAIL PRICESFOOD PRICESNew Items 2009

PRICE IN RIELSITEM NUMBER

AFirst observation Second observation

ITEM DESCRIPTIONDESCRIPTION (MAKE, QUALITY, NAME, ETC.)

UNIT

(2)

(3)

(4)

(1)

1 Kg

01 RiceRice, quality no.2 White, Neang-Menh

02 RiceRice, quality no.1 White, Phaka kanhey, Battambang 1 Kg

03 Pork rib

Pork rib, Khmer

1 Kg

04 Pork with fat

Pork, with fat, Khmer

1 Kg

05 Mud fish

Mud fish, (small )1 Kg

06 Mud fish

Mud fish, ( large )1 Kg

07 Cat fish, Andeng

Cat fish, Andeng fish

1 Kg

08 Other fresh fish (Pra)

Other fresh fish (Pra)

1 Kg

09 Sea fish

Sea fish, small, Plathou fish

1 Kg

10 Tilapia

Tilapia, medium

1 Kg

11 Pork without fat

Pork, without fat, Pure meat

1 Kg

12 Fresh Chicken

Fresh Chicken, No1, plucked

1 Kg

13 Cigarettes

Local, Rubsek, Cambodian

1 pack

14 Duck egg

Duck’s eggs

1 piece

15 Fresh beef

Fresh Beef, No.1

1 kg

16 Meals at work

Meals at work, ( cooked rice with meat )1 serving

17 Prepared meals

Prepared Meals ( fried fish )(Chi Keng)

1 fish18 Snacks

Snacks, ( noodles ) cooked

1 bowl19 Glutamate/spoon

Monosodium glutamate, seasoning Spoon,Thai

0.5kg/pack

20 Banana (Namwa)

Banana, ripe (Namwa)

1 hand21 Fat, pork (Liquid)

Pork fat (Liquid)

1 Kg

22 Sugar

Granulated sugar, ( refined ) white, No.1, Thai

1 Kg

23 Glotinous rice

Glutinous rice, good quality Khmer

1 Kg

24 Dried fish

Dried fish, Chhdor fish,big

1 Kg

25 Smoked fish

Smoked fish, Riel fish, small

50 gr

26 Coconut

Coconut, ( Matured ) Medium, Khmer

1 Piece

27 Vegatable oil

Vegetable oil/soybean oil

2 kg container

28 Cucember pickles

Cucumber pickles

1 kg

Manual for Cambodia CSES 2009 (Version 2.0)

Third observation

RIELS

RIELS

RIELS

(5)

(6)

(7)

306

FOOD PRICES (CONT.)

PRICE IN RIELSITEM NUMBER

AFirst observation Second observation

ITEM DESCRIPTIONDESCRIPTION (MAKE, QUALITY, NAME, ETC.)

UNIT

(2)

(3)

(4)

(1)

29 Cheese Fish

Cheese fish, Riel fish, small

1 kg

30 Sugar

Brown Sugar

1 kg

31 Canned fish

Canned fish, (sardines) Three ladies, 250g

1 kg

32 Powdered milk

Powdered ( baby)

1 kg-can33 Cucumber

Cucumbers, medium size

1 kg

34 Fresh duckFresh duck, Plucked

1kg

35 Sweetened milk

Condensed ( sweetened ) Alaska, 379g

1 can

36 Trakun

Trakun ( Watercress, marshcabbage) Good

1 kg

37 White gourd

White gourd

1 kg

38 Soft drink

Canned soft drinks ( Coke )330 ml can

39 Garlic

Garlic

1 kg

40 Cabbage leaves

Cabbage leaves

1 kg

41 Peanuts

Peanuts, medium, no shell

1 kg

42 Instant coffee

200 g botlle

43 Alcohol

Instant coffee, Thai
Mekong wishsky, bottle, 750ml, (SKD), domestic
Wine

44 Ginger

Ginger, medium

1 kg

45 Fish sauce

Fish sauce No.1, Kampot

750 ml/bottle

46 Lettuce, spinach

Lettuce, spinach

1kg

47 Others

Others ( mustard leaves coriander leaves, etc),
Khmer

bunch

48 Water melon

Water melon Medium,

1Piece49 Banana blossoms

Others ( banana blossoms )1kg

50 Ice tube

Ice tube

1kg

51 Mangoe

Mangoes, ripe, Local, Keochen

1dozen52 Onion leaves

Onion leaves søwkKUqay

1kg

53 Black pepper

Ground black pepper

1kg

1 bottle

Manual for Cambodia CSES 2009 (Version 2.0)

Third observation

RIELS

RIELS

RIELS

(5)

(6)

(7)

307

NON-FOOD ITEMSNew Items 2009

PRICE IN RIELSITEM NUMBER

B

First observation Second observation

ITEM DESCRIPTIONDESCRIPTION (MAKE, QUALITY, NAME, ETC.)

UNIT

(2)

(3)

(4)

(1)

01 Electricity

Electricity

Kwh

02 Motor bicycle

Motorcycle, Honda, Thai, C100

unit

03 Motor cycle

Motorcycle, Wave brand 100 cc (made in China)

1 piece04 CarSecond hand cars, Toyota Corolla 1.8G (1994 model) 1 piece

05 Kerosene

Liquid fuels ( Kerosene )1 liter

06 Water Charges

Water Charges

m3

07 GasGas, (for Cooking),15kg

1 Fug

08 Gold ringGold ring, (4 grams quality 99% of gold)

1 chi

09 Bicycles

Bicycles, MIKI (made in China)

1 piece

10 Charcoal

CharcoalKg

11 G.I Sheet

G.I Sheet, Size 8li

Kg

12 Photocopy service

Photocopy Services, (10copies)

10 copies

13 Charge passport

Charge for Passport, (Normal),1 piece

14 TV

Television JVC 14", colored, Japanunit

15 SandSand, rough1 m3

16 Cement

Cement, Elephant, P600, Thai

50 kg/sack

17 Gravel

Gravel, size 1x2, Cambodia

1 m3

18 Money transfer

Money transfer, (amount 2,000,000 Riel), domestic

1 service

19 Mobile phone

Mobile phone equipment, Nokia (6020 Series), Import 1 piece

20 Diesel

Diesel1 liter

21 Wristwatch

Wristwatch ( Men's ), Polostar, round, Use Battory,Japan

1 unit22 Lumber Teal

Lumber Teal, wood, contruction wood, 2m (l), 5cm(w), 2cm (h)1 m323 Motorcycle repair

Inner tube repair, Motorcycle, (repair of one (1)patch/hole only)

1 patch24 Shirt

Mens shirt, longsleeved, size ( M-L), Khmerpiece

25 RadioRadio, Ultra, 2 band ( AM, FM ), China, 2 batteries ofsize AAunit

26 Blouse

Blouses/ tops, womens, all color, longsleeved, Khmer piece

27 Video

Video DVD player,Sony, Malaysia

1 pieceManual for Cambodia CSES 2009 (Version 2.0)

Third observation

RIELS

RIELS

RIELS

(5)

(6)

(7)

308

MEDICINE PRICESPRICE IN RIELSITEM NUMBERCFirst observation Second observation

ITEM DESCRIPTIONDESCRIPTION (MAKE, QUALITY, NAME, ETC.)

UNIT

(2)

(3)

(4)

(1)

01 Tetracycline

250 mg, Malaysia

capsule02 Ampicillin

250 mg, France

capsule03 Penicillin

1,000,000 UI, Thaivial

04 Aspirin

500 mg, France

pill

05 Vitamin C

Vitamin C, 500 mg, Francepill

06 Rifampicin

500 mg., Australia

pill

07 Co-trimoxazol

300 mg, Korean

pill

08 Paracetamol

500 mg, France

pill

09 ORS

500 mg, Korean

packet10 Multi-Vitamin

Thai

pill

RIELS

RIELS

RIELS

(5)

(6)

(7)

7. EMPLOYMENT AND WAGESLINE NUMBER

Daily Wage Rates ( in Riels)

MALES

FEMALESCHILDREN

TYPE OF WORKRIELS / DAY

RIELS / DAY

RIELS / DAY

(2)

(3)

(4)

(5)

(1)

01 Ploughing (animal traction)*
02 Transplanting of paddy
03 Caring for crops
04 Harvesting
05 Unskilled construction work
06 Weaving
07 Rattan furniture
08 Grill worker
09 Tailoring
10 Sewing

Manual for Cambodia CSES 2009 (Version 2.0)

Third observation

309

LINE NUMBER

8. ACCESS TO COMMON PROPERTY RESOURCES DURING THE LAST 5 YEARS
Has availability:
Is there depletion throughAre there any of the following common natural
overuse of this resource at preresources available to the people of this village?
1= increased,sent?
2= stayed the same or
3=diminished
4=Never had
1 = Yes(=>> Next line)
1 = Yes, a lot
2 = Yes, but not legallyin the last 5 years?
2 = Yes, some
3 = No3 = No

(1)

(2)

Code

Availability

Depletion

(3)

(4)

(5)

1 Land for cultivation
2 Firewood/charcoal to be collected
3 Timber to be taken for house construction
4 Fish to be catched from lake or river
5 Bamboo to be taken
6 Open land for animal grazing
7 Fruits to be picked
8 Wild animals for hunting

9. SALES PRICES OF AGRICULTURAL LAND IN THE VILLAGE
9a. Rental prices

LINE NUMBER

Is there any (...) land What is the current rental price of ..[LAND TYPE]..peravailable for rent in hectare in this village?
this village?
1 = Yes
2 = No(=>> Next line)
LAND TYPE

(1)

(2)

(3)

Per month

Per season

Per year

RIELS/HECTARERIELS/HECTARERIELS/HECTARE

(4)

(5)

(6)

01 Wet-season land
02 Dry-season land
03 Wet and dry season land
04 Chamkar land
05 Kitchengarden (backyard)
06 Land with permanent crops
07 Land for raising livestock
08 Private forestry land
09 Idle agricultural land
10 Other agricultural land (Specify)

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9b. Sales prices
LINE NUMBER

Is there any [...] land What is the current sales priceavailable for sale in of ..[LAND TYPE].. per hectare
this village?
in this village?
1 = Yes
2 = No(=>> Next line)

LAND TYPE
(1)

(2)

(3)

RIELS
(4)

01 Wet-season land
02 Dry-season land
03 Wet and dry season land
04 Chamkar land
05 Kitchengarden (backyard)
06 Land with permanent crops
07 Land for raising livestock
08 Private forestry land
09 Idle agricultural land
10 Other agricultural land (Specify)
10. RECRUITMENT OF CHILDREN FOR WORK OUTSIDE THE VILLAGE.
1 Are you aware of instances in your village where children of age 5 to 17 years are being
recruited for work outside the village?
1 = Yes 2 = No (End of questionnaire)2 Who did the recruiting?
1 = Licensed recruiter or establishment
2 = Relatives (making arrangements)
3 = Friends (making arrangements)

4 = Unknown
5 = Other (Specify)

3 Do you know what type of work they are usually recruited for?
1 = Yes
2 = No (=>> 5)
4 For what type of work are they usually recruited?
1 = Factory
5 = Domestic work
2 = Construction
6 = Entertainment
3 = Mining
7 = Other (Specify)
4 = Crop/Livestock farming
5 Do you know where they are usually sent?
1 = Yes
2 = No (End of questionnaire)
6 To which district and province or other country are they usually sent?
a. District name:Code:b. Province name:

Code:

c.

Code:

Other Country:
Note: District name and Province name, use for Cambodia only

Manual for Cambodia CSES 2009 (Version 2.0)

311

Supplement 2

Diary Data

Remarks:
This supplement 2 describes the outline of “Diary Data”, which was included in the previously
provided data set, for reference. However this data is not included in the resampled data set.

S2.1 Diary data of expenditure
Two kind of data files were provided. One is aggregated data, and the other is micro data.


According to NADA, the data file “Yearly.hhexpenditure” is an aggregated data from Diary sheet,as the next.

Data file

Yearly_hhexpenditure

Content

Data contained in this file are summaries by household computed from the2009 CSES Diary Questionnaire: Expenditure(Derived variables)

Variables
Name

Label

hhid

Household ID

value

Yearly household expenditureComparison with the result of recall data

Histogram> d.con<-outfiles[[41]] # Diary data of expenditure> d.con$mv<-d.con$value/12 # Converted to monthly> hist(log(d.con$mv))

Fig. Distribution of expenditure data in Yearly.hhexpenditure

Manual for Cambodia CSES 2009 (Version 2.0)

312

2000
0

1000

Frequency

3000

Histogram of log(d$mv)

11

12

13

14

15

16

17

log(d$mv)> hist(log(hhexp$total))# Recall data

Fig. Distribution of recall data of expenditure

2000
0

1000

Frequency

3000

Histogram of log(hhexp$total)

11

12

13

14

15

16

17

log(hhexp$total)Scatter plot

> plot(hhexp$total,d.con$mv)

Manual for Cambodia CSES 2009 (Version 2.0)

1.0e+07
0.0e+00

5.0e+06

d.con$mv

1.5e+07

313

0e+00

1e+07

2e+07

3e+07

hhexp$totalGap> gap<-d.con$mv/hhexp$total-1> table(abs(gap)<0.02)
FALSE

TRUE

243 11728
Summary:
As for monthly household expenditure, the gaps between diary data and recall data are less than
2% in 98% of sample households.



Average monthly consumption per household> d.con<-outfiles[[41]] # Diary data of expenditure> d.con$mv<-d.con$value/12 # Converted to monthly# outfiles[[38]]: weighthouseholds> df<-merge(d.con,outfiles[[38]])> weighted.mean(df$mv,df$hw09a)/1000[1] 1119.239

Item
Monthly total consumption
per household

Result from YearlyExpenditure

Result of recall data in the report

1,119

1,119 (Table 9.6)

(in thousand Riels)

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314



Micro data of household expenditure in diary sheet# Data file: DiaryExpenditure.sav> library(foreign)> DE<-read.spss("DiaryExpenditure.sav",to.data.frame=T,use.value.label=F)> dim(DE)[1] 2620999

11> head(DE)pkid

HHID Lineid DiaryExp_Q2 DiaryExp_Q5 DiaryExp_Q6 DiaryExp_Q7

1

1 1201810

101

0101

3

0.5

2000

2

2 1201810

102

0101

5

0.2

1000

3

3 1201810

103

0101

3

0.7

7000

4

4 1201810

104

0101

3

0.3

2500

5

5 1201810

105

0101

3

0.3

3000

6

6 1201810

106

0101

5

2.0

6000

DiaryExp_Q8 DiaryExp_Q9 DiaryExp_Q10 DiaryExp_Q11
1

1

2

1

6010

2

1

2

1

6010

3

1

2

1

1180

4

1

2

1

1354

5

1

2

1

1354

6

1

3

1

5060

> length(unique(DE$HHID))[1] 11970One hhid=0100309 is missing.

> length(HHID)[1] 11971> table(is.element(DE$HHID,HHID))
TRUE
2620999> setdiff(HHID,DE$HHID)[1] "0100309"> summary(DE)
pkid
Min.

:

HHID
1

0307420:

Lineid619

Min.

: 101.0

DiaryExp_Q2
1304

Manual for Cambodia CSES 2009 (Version 2.0)

: 10845

315

1st Qu.: 659317

1200105:

610

1st Qu.: 413.0

Median :1317754

1800407:

577

Median : 807.0

Mean

0806215:

570

Mean

:1318229

1809

1900512:554

3rd Qu.:1205.0

Max.

1203101:

526

Max.

:2637002

Min.

: 1.000:

1st Qu.: 3.000

1st Qu.:1709:5014.0

Median : 3.000

Median :

Mean

Mean

: 4.391

3rd Qu.: 5.000
Max.

:11.000

DiaryExp_Q9
Min.

:1.000

0.0

:

0
600

1.0

Median :

25.0

Mean

1300
:

7358

Max.

:604497800

NA's:126231

DiaryExp_Q10

Mean

3rd Qu.: 1.000

Max.

Max.

Mean

:2.1

3rd Qu.:5.0
Max.

:5.0

DiaryExp_Q11
Min.

:1010

1st Qu.:1186
Median :1356: 1.194

3rd Qu.:2.000:1.0Median :1.0:792000.0

Mean

7775

1st Qu.:1.0

Max.

Median : 1.000

:

Min.

3000

Median :2.000:5.000:

3rd Qu.:1st Qu.: 1.000

7826

DiaryExp_Q8

2.0

1st Qu.:1.000:1.849Min.
1st Qu.:: 1.000

7827

:

1204

3rd Qu.:Min.

:

DiaryExp_Q7

0.4

8301

(Other):2570094

DiaryExp_Q6
Min.

:

0101

(Other):2617543
DiaryExp_Q5

8331

1909

: 828.5

3rd Qu.:1977368

:

Mean

:2255

3rd Qu.:3300:12.000

Max.

:9998



Diary sheet was recorded every day for the whole month.

Codebook of expenditure in diary sheet is as the next;

Variable

Description

Remarks

pkid

Unique id in the data file

1 to 2637002

HHID

Household id

HHID=0100309 is missing.

Lineidthe page number plus the line number

101 to 5014

DiaryExp_Q2

DATE (DD/MM)

DiaryExp_Q5

CODE OF UNIT

DiaryExp_Q6

QUANTITY

DiaryExp_Q7

VALUE IN RIELS

DiaryExp_Q8

FORM OF ACQUISITION

1=Paid in cash
2=Paid in kind

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316

3=Purchased on credit
4=Gift received
5=Stock of own-produced
DiaryExp_Q9

ORIGIN

1 = Household production
2 = Produced in Cambodia
3 = Imported from abroad
4 = Don't know the origin
5 = No product

DiaryExp_Q10

PURPOSE

01 = Own household consumption
02 = For agricultural production
03 = For manufacturing production
04 = For mining production
05 = For services production
06 = For other household production
07 = Gifts & remittances to otherhouseholds
08 = Offerings, donations, charities,etc.
09 = Interests
10 = Payback of loans
11 = Taxes
12 = Other (specify)

DiaryExp_Q11

ITEM CODE

4 digits

Remarks:
1) PURPOSE=02 to 06 should be excluded because they are not for household consumption.
2) PURPOSE=09 to 11 should be excluded because they are out of range of household consumption.

Generated a subset of consumption data with purpose=01, 07 and 08.

> DC<-subset(DE,DiaryExp_Q10==1| DiaryExp_Q10==7| DiaryExp_Q10==8)> dim(DC)[1] 2482977

11

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317



Generated household-level consumption data.

> hhDC<-tapply(DC$DiaryExp_Q7,DC$HHID,sum)> dim(hhDC)[1] 11970> head(hhDC)
0100101 0100102 0100103 0100104 0100105 0100106
589900707300289500 355600 1313000 1015100> hhDC<-data.frame(hhid=names(hhDC),value=hhDC,row.names=NULL)> dim(hhDC)[1] 119702> head(hhDC)
hhid

value

1 0100101 589900
2 0100102 707300
3 0100103 289500
4 0100104 355600
5 0100105 1313000
6 0100106 1015100


Histogram of the household expenditure generated from micro data of Diary sheet.

> hist(log(hhDC$value))

0

1000

Frequency

2000

3000

4000

Histogram of log(hhDC$value)

11

12

13

14

15

16

17

log(hhDC$value)

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318

> summary(hhDC$value)Min. 1st Qu.

MedianMean 3rd Qu.

55800

444300633600317500Max.

683800 37120000

Compared the histogram of household consumption aggregated from diary micro data and thatof recall data. The level of the former is far below and the shapes of histogram differ.

The document “New Rules for Expenditure Composition CSES – Yearly EXP”
(ItemCodesCSES09.doc) written by Ms. Agneta Sandqvist, Statistics Sweden, suggests that whencalculating Yearly Expenditure, almost all variables were calculated by using Recall data fromthe CSES household questionnaire.

It might be the reason why the results of recall data and diary data are quite similar.

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319

Supplement 2 Diary Data

S2.2 Diary data of income
Two kind of data files were provided. One is aggregated data, and the other is micro data.


According to NADA, the data file “Yearly.hhincome” is an aggregated data from Diary sheet, as
the next.

Data file

Yearly_hhincome

Content

Data contained in this file are summaries by household computed from the2009 CSES Diary Questionnaire: Income(Derived variables)

Variables
Name

Label

hhid

Household ID

hhsize44 values

Yearly household income by component (See below)> d.inc<-outfiles[[42]] # Yearly.hhincome> dim(d.inc)[1] 1197146> colnames(d.inc)[1] "hhid""hhsize"[3] "salary""costcrop"[5] "costlivestock""costfish"[7] "costforestry""receiptcrop"[9] "receiptlivestock""receiptfish"[11] "receiptforestry""agriincome"[13] "costnonagri""receiptnonagri"[15] "nonagriincome""incomeownhouse"[17] "bankinterest""interestotherloans"[19] "dividends""rentfromland"[21] "grosspropertyincome""interestpaidagri"[23] "interestpaidnonagri""interestpaidownoccupied"

Manual for Cambodia CSES 2009 (Version 2.0)

320[25] "interestpaidnet""propertyincome"[27] "primaryincome""pensiondomestic"[29] "pensionabroad""pension"[31] "ngotransfers""remittancedomestic"[33] "remittanceabroad""totalprivatetransfers"[35] "scholarshipgovernment""scholarshipngo"[37] "totalscholarship""gifts"[39] "othertransfer""totaltransfers"[41] "totalincome""diarytaxes"[43] "diaryinterhhtransfers""diarycashtransferchar"[45] "diarytotalnegativetransfers" "disposableincome"

Variables in Yearly.income are the same as the table in the document “Income Composition
CSES 2009”, which describes the method of calculation.

> df<-merge(d.inc,outfiles[[38]],by="hhid")# Appended weight> weighted.mean(df$disposableincome,df$hw09a)/12/1000[1] 1036.645# Monthly income per household by items> t<-sapply(df[,3:46],function(x) weighted.mean(x,df$hw09a)/12/1000)> data.frame(Item=names(t),Value=round(t),row.names=NULL)
Item Value1salary2412

costcrop54

3

costlivestock

34

4

costfish9

5

costforestry

2

6

receiptcrop

334

7

receiptlivestock

45

8

receiptfish

30

9receiptforestry

35

10

agriincome

344

Manual for Cambodia CSES 2009 (Version 2.0)

321

11

costnonagri

769

12

receiptnonagri 1108

13

nonagriincome

340

14

incomeownhouse

92

15

bankinterest

0

16interestotherloans

217

dividends

0

18

rentfromland

2

19

grosspropertyincome

420interestpaidagri

021interestpaidnonagri

022interestpaidownoccupied

0

23

interestpaidnet

1

24

propertyincome

3

25

primaryincome

1021

26

pensiondomestic

227

pensionabroad

028

pension2

29

ngotransfers

130

remittancedomestic

7

31

remittanceabroad

732

totalprivatetransfers

1433

scholarshipgovernment

034

scholarshipngo

035

totalscholarship

1

36

gifts

2

37

othertransfer

1438

totaltransfers

34

39

totalincome

1054

40

diarytaxes

141

diaryinterhhtransfers

242diarycashtransferchar

14

43 diarytotalnegativetransfers

1744

disposableincome

1037

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322



Distribution of disposable income> hist(log(df$disposableincome))

2000
0

1000

Frequency

3000

4000

Histogram of log(df$disposable

10

15

20
log(df$disposableincome)

440 sample households have zero or negative disposable income.

> table(df$disposableincome<=0)
FALSE TRUE
11531

440

Manual for Cambodia CSES 2009 (Version 2.0)

323



Micro data of household income in diary sheet> library(foreign)> DI<-read.spss("DiaryIncome.sav",to.data.frame=T,use.value.label=F)> dim(DI)[1] 878912

10> head(DI)pkid

HHID Lineid DiaryInc_Q2 DiaryInc_Q5 DiaryInc_Q6 DiaryInc_Q7 DiaryInc_Q8

1

1 1201810

101

0101

10

NA

60000

1

2

2 1201810

102

0201

10

NA

60000

1

3

3 1201810

103

0301

10

NA

80000

1

4

4 1201810

104

0401

10

NA

50000

1

5

5 1201810

105

0501

10

NA

75000

1

6

6 1201810

106

0601

10

NA

30000

1

DiaryInc_Q9 DiaryInc_Q10
1

6

0508

2

6

0508

3

6

0508

4

6

0508

5

6

0508

6

6

0508

> length(unique(DI$HHID))[1] 11970> setdiff(HHID,unique(DI$HHID))[1] "0100309"> summary(DI)
pkid
Min.

:

HHID

Lineid1

0101310:

383

Min.

0.0

3006

:

3136

1st Qu.:221018

1703003:309

1st Qu.: 307.0

3004

:

3055

Median :441717

0102817:

294

Median : 515.0

3108

:

3014

Mean

:441868

2102915:

276

Mean

: 602.1

2802

:

2966

3rd Qu.:662835

2103109:268

3rd Qu.: 813.0

3110

:

2883

Max.

0305210:

247

Max.

3009

:

2882

:883728(Other):877135:

DiaryInc_Q2:9904.0(Other):860976

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324

DiaryInc_Q5

DiaryInc_Q6

Min.

Min.

: 0.000

DiaryInc_Q7

DiaryInc_Q8

:

0.0

Min.

:

0

1st Qu.: 3.000

1st Qu.:1.0

1st Qu.:800

1st Qu.:2.000

Median : 4.000

Median :1.5

Median :1500

Median :2.000

Mean

Mean

:

14.3

Mean

:

22967

Mean

3rd Qu.: 8.000

3rd Qu.:2.5

3rd Qu.:4500

3rd Qu.:2.000

Max.

Max.

:180000.0

NA's:146599: 5.687:11.000

DiaryInc_Q9

DiaryInc_Q10

Min.

: 1.000

0202:361341

1st Qu.: 2.000

0208:249398

Median : 2.000

0508: 73727

Mean: 2.9120302: 65023

3rd Qu.: 2.000

0101: 33504

Max.

0701

: 25078:19.000

Max.

:307500000

Min.

Max.

:1.000:1.808:3.000(Other): 70841

Codebook of income in diary sheet

Variable

Description

Remarks

pkid

Unique id in the data file

1 to 883728

HHID

Household id

HHID=0100309 is missing.

Lineid0 to 9904

DiaryInc_Q2

DATE (DD/MM)

DiaryInc_Q5

CODE OF QUANTITY

DiaryInc_Q6

QUANTITY

DiaryInc_Q7

VALUE IN RIELS

DiaryInc_Q8

TYPE OF INCOME

1 = In cash
2 = In kind
3 = Bartered

DiaryInc_Q9

KIND OF INCOME

01 = Wage or salary (in cash or kind)
02 = Agricultural or forestry production or sales
03 = Fishing or hunting production or sales
04 = From mining production
05 = Sales of manufactured products

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325

06 = Receipts from services rendered
07 = Received as gift
08 = Remittances received
09 = Pensions or other social assistances
10 = Study support in cash or kind (Scholarships,
stipends or other)
11 = Dividends, interests, commissions, rents etc.
12 = Receipts from sale of possessions/own
property
13 = Withdrawals from savings/loans obtained
14 = Payback of loans
15 = Windfall gains/inheritance
16 = Tax refunds
17 = Maturity payment on insurance policies
18 = Lump-sum compensation for injury, legal
damages received
19 = Other (specify)
DiaryInc_Q10

ITEM CODE

4 digits# Number of records by kind of income> table(DI$DiaryInc_Q9,useNA="ifany")
1

2

3

4

33996 629028 72426

13

5

6

7

8

9

10

11

4538 98392

25715

1541

120

22

532

12

13

14

15

16

19

108

52

122

2

32 12273

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326



Method of calculating Yearly income

The manual “Income Composition CSES 2009” (IncomeGroupCSES09.doc) written by Ms. Agneta
Sandqvist, Statistics Sweden, suggest that almost all variables are collected through recall method, but
only taxes, inter household transfers, cash transfers and charities are calculated from Diary data on
Expenditure.

Manual for Cambodia CSES 2009 (Version 2.0)

CONFIDENTIAL

Royal Government
327of Cambodia
Ministry of Planning
National Institute of Statistics

All information collected in this survey isstrictly confidential and will be used forstatistical purposes onlyPSU

HH SERIAL No

HOUSEHOLD SOCIO-ECONOMIC SURVEY 2009
DAIRY SHEETS OF HOUSEHOLD EXPENDITURES & CONSUMPTION OF OWN-PRODUCED FOOD AND HOUSEHOLD INCOME & RECEIPTS - Form 4
A. To be completed by Supervisors before interview

B. To be completed by Interviewer

Province /City

Name of household Head
Address (House No., Street….) ofother identification)

District /Khan
Commune/Sankat
Sample Village/MondolFirst visit date

Zone

Last visit date

Sector (Urban=1, Rural=2)Day:

Month:

Year:

Day:

Month:Year:

DIARY SHEET MUST BE RECORDED EVERY DAY FOR THE WHOLE MONTH

Serial Number of Sample Village

Interviewer’s Name:

Sample Reference Number of Household

Interviewer’s signature:
Team Number:

Id:

No. Of the month (from 01 to 15):

C. To be completed by Supervisors after checking completed questionnaire thoroughly

D: To be completed after Re-interview (when required)Supervisor’s Name:

Name of Re-interviewer:

Id:

Date checked bySupervisor (Week 1)

Day:

Month:Year:

Date of Re-interview

Date checked bySupervisor (Week 2)

Day:Month:

Year:

Interviewer’s signature:

Day:

Month:

Year:

Day:

Month:Year:

Date checked bySupervisor (Week 3)
Date checked bySupervisor (Week 4)
Supervisor’ s Signature:

Remarks of Reinterviewer:

Manual for Cambodia CSES 2009 (Version 2.0)

Id:
Day:

Month:

Year:

328

PAGE Nº 01

Expenditures and consumption of own-produced
FOR THE HOUSEHOLDFOR THE HOUSEHOLD

FOR
NIS

FORM OFACQUISITION

1=Paid in cash
2=Paid in kind
3=Purchased on
credit4=Gift received
5=Stock of ownproduced

LINE NUMBER(1)

DATE (DD/MM)

ITEM DESCRIPTIONUNIT OFQUANTI-TY

(2)

(3)

(4)

CODE
OF UNIT(5)

QUANTITYVALUE IN RIELS

(6)

(7)

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
90

FOR ENUMERATORSManual for Cambodia CSES 2009 (Version 2.0)

TOTAL:(8)

ORIGIN
1 = Householdproduction
2 = Produced in
Cambodia
3 = Imported fromabroad
4 = Don't know theorigin5 = No product(9)

FOR NISPURPOSE
01 = Own household consumption
02 = For agricultural production
03 = For manufacturing production
04 = For mining production
05 = For services production
06 = For other household production
07 = Gifts & remittances to other households
08 = Offerings,donations,charities, etc.
09 = Interests
10 = Payback of loans
11 = Taxes
12 = Other (specify)(10)

ITEM CODE
(11)

329

PAGE Nº 01

Household Income and Receipts
FOR THE HOUSEHOLDFOR THE HOUSEHOLDFOR NISTYPE OFINCOME

LINE NUMBER
(1)

DATE
(DD/MM)

ITEM DESCRIPTIONUNIT OFQUAN-TITY

(2)

(3)

(4)

CODE
OF
QUANTITYQUAN-TITYVALUE IN RIELS

(5)

(6)

(7)

01
02
03
04
04
06
07
08
09
10
11
12
13
14
15
90

Manual for Cambodia CSES 2009 (Version 2.0)

TOTAL:FOR ENUMERATORS

01 = Wage or salary (in cash or kind)
02 = Agricultural or forestry production or sales
03 = Fishing or hunting production or sales
04 = From mining production
05 = Sales of manufactured products
06 = Receipts from services rendered
07 = Received as gift
08 = Remittances received
09 = Pensions or other social assistances
1 = In cash 10 = Study support in cash or kind (Scholarships, stipends or other)2 = In kind 11 = Dividends, interests, commissions, rents etc.
3 = Bartered 12 = Receipts from sale of possessions/own property
13 = Withdrawals from savings/loans obtained
14 = Payback of loans
15 = Windfall gains/inheritance
16 = Tax refunds
17 = Maturity payment on insurance policies
18 = Lump-sum compensation for injury, legal damages received
19 = Other (specify)(8)

FOR NIS

KIND OF INCOME(9)ITEM CODE
(10)

330

CONFIDENTIAL
All information collected in this survey
is strictly confidential and will be used
for statistical purposes only

Royal Government of Cambodia
Ministry of Planning
National Institute of Statistics

Household ID

hhid
CAMBODIA SOCIO-ECONOMIC SURVEY 2009
HOUSEHOLD QUESTIONNAIRE

A. To be completed by interviewer before interview

Province

Province /City
District /Khan
Commune/Sankat
Sample Village/Mondol
Zone

UrbanRural

Sector (Urban=1, Rural=2)

PSU?

Sample reference number of household
B. To be completed by interviewer
Name of household head

Phone:

Address (house No., street….) of otheridentification)
Date of first visit to Household

Day:

Month:Year:

Date of last visit

Day:

Month:

Year:

Team Number

Interviewer’s Id:

Interviewer’s name:Interviewer’s signature:

Interviewer’s phone no:Month and Year of Survey

Month

To be completed after filling-out the list of household members

Male:

Year:

Total members:

Female:C. To be completed by supervisor after checking completed questionnaire thoroughlySupervisor’s name:

Id:

Date checked by supervisor

Day

Month

Date checked week 1
Date checked week 2
Date checked week 3
Date checked week 4
Supervisor’ s signature:

Supervisor’ s phone no:
Reception

Id:

Date:

Preparation
Id:

Date:

Data Entry
Id:

Manual for Cambodia CSES 2009 (Version 2.0)

Date

Year

331

01. INITIAL VISIT
Respondent: head of household, spouse of the head of household, or another adult household member

INITIAL VISIT

A. LIST OF HOUSEHOLD MEMBERS
The questions should be asked of the head of household, spouse of the head of household or other adult household member if both head and spouse are absent.

A person is counted as a household member if he/she
lives here or has been absent for less than 12
months.
1 = Male
2 = Female

Write ' - ' if don´tknow, for day ormonth or year

Q01AC03

DAY

MONTH

YEAR

YEARS

(4a)

(4b)

(4c)

(5)

Q01AC04C

02(3)

Q01AC04B

01(2)

Q01AC04A(1)

What is..
Relationship to the head
[NAME] ...’sage incompleted 01 = Head
10 = Son/Daughter-inyears?
02 = Spouselaw

03 = Son/Daughter
04 = Stepchild
Write '0' if
05 = Adopted child/less than one Foster childyear of age, 06 = Parent
and07 = Sibling" - " if don´t 08 = Grand child
09 = Nephew/Nieceknow

Q01AC01

ID NUMBER

Please provide the following information on all members usually residing in this household.
Please give me the names of all household members, Sex
What is..[NAME]..’s datestarting with head of the household.
of birth?

Q01AC05

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

Manual for Cambodia CSES 2009 (Version 2.0)

11 = Brother/Sister-inlaw
12 = Parent-in-law
13 = Other relatives
14 = Servant
15 = Other non-relative
including boarder(6)

Q01AC06

Does thefather of..[NAME]..
live in thehousehold?

If YES, write
the ID CODE,
if NO write
" -"

(7)

Q01AC07332

Respondent: head of household, spouse of the head of household or another adult household member

INITIAL VISIT

Q01AC01 ID NUMBER

01. A. LIST OF HOUSEHOLD MEMBERS (CONTINUED)

Only for members aged 13 andDoes theabove:
mother of..[NAME]..
live in the
What is ..[NAME].. ´sDoes thehousehold? marital status?
spouse of..[NAME]..
live in this
1 = Married/Living
household?
togetherIf YES, write
2 = Divorced/Separated
the ID CODE,(>> 11)
if NO write

3 = Widowed (>> 11)
4 = Never married/Never If YES, write
the ID CODE,
lived with a partner
if NO write(>> 11)" -"Is ..[NAME]..khmer or Does
other ethnic group? ..[NAME]..
speak
Khmer?
1 = Khmer (>> 12)
2 = Cham
3 = Other local ethnicgroup
4 = Chinese
5 = Vietnamese
6 = Thai
7 = Lao
8 = Other (Specify)

1=Yes

" -"

(1)

(8)

01

Q01AC08(9)

Q01AC09

Can ..[NAME].speak other languages
than Khmer?

0=No
1= French
2= English
3= Chinese
4= Vietnamese
5= Thai6= Lao7= Chaam
8= Other local language
9= Other (Specify)

Has ..[NAME].. How many
been present all weeks has
days last week? ..[NAME]..
been absent
from homeduring thepast 12months?
1=Yes>> NEXTPERSON
2=No

WRITE '0' IF
LESS THAN
ONE WEEK

2=No>> NEXTPERSON

(10)

(11a)

Q01AC10

Q01AC11A

(11b)

1

2

3

(12a)

(12b)

(12c)

Q01AC11B Q01AC12A Q01AC12B Q01AC12C

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

Manual for Cambodia CSES 2009 (Version 2.0)

WEEKS
(13)

(14)

Q01AC13 Q01AC14

333

01. B. FOOD, BEVERAGES AND TOBACCO CONSUMPTION DURING THE LAST 7 DAYS
Respondent: The household member who knows most about food, beverage, tobacco consumption in the last 7 days

Q01BC01 ITEM #Q1 Did your household run any business, e.g. agriculture production (farming), producing goods, service etc.during the last 7days?

INITIAL VISIT
1 = Yes

2 = NoQ01BQ1

Note that any household´s expenditure on business purposes will not be included in this section, because it will be asked in the economic activities
For each item group try to estimate quantity of items consumed and then how
Value of consumption in Riels
much of the consumed quantity had been purchased in cash and how much was
Write '0' if nothingfrom own production or received as payment in kind for work, or as gift, or free
Purchased in cash
Own production, wages in
Total consumption
collection.
( Col 3 + Col 4)
kind, gifts, free collections(imputed value)
FOOD/BVERAGE/TOBACCO ITEMS

RIELS

RIELS

RIELS

(2)

(3)

(4)

(5)

01

Cereals (rice, bread, corn, wheat flour, rice flour, corn meal, rice cakes, noodles,
biscuits, etc.)

02

Fish (fresh fish, salted and dried fish, canned fish, shrimp, prawn, crab, etc.)

03

Meat & poultry (beef, buffalo, mutton, lamb, pork, chicken, duck, innards, incl liver,spleen, dried beef)04

Eggs (chicken egg, duck egg, quail egg, fermented/salted egg, etc.)

05

Dairy products (fresh milk, condensed or powdered milk, ice cream, cheese, otherdairy products, etc.)

06

Oil and fats (rice bran oil, vegetable oil, pork fat, butter, margarine, coconut/frying oil,
etc.)

07

Fresh vegetables (trakun, onion, shallot, cabbage, spinach, carrot, beans, chilli,
tomato, etc.)

08

Tuber (cassava, sweet potato, potato, traov, sugar beet, etc.)

09

Pulses and legumes (green gram, dhall, cowpea, bean sprout, other seeds, etc.)10Prepared and preserved vegetables (cucumber pickles, other pickles, tomato paste,
etc.)

11

Fruit (banana, orange, mango, pineapple, lemon, papaya, durian, water melon,
grape, apple, canned and dried fruits, etc.)

12

Dried nuts and edible seeds (coconut, cashew nut, lotus nut, peanut, gourd seed,other nuts)

13

Sugar, salt and spices (sugar, jaggery, salt, chocolate, candy, coriander, red pepper
spice, garlic, ginger, soy sauce, fish sauce, monosodium glutamate, etc.)

14

Tea, coffee, cocoa

15

Non-alcoholic beverages (canned or bottled soft drinks, mineral water, fruit juice,fruit syrup, etc.)16

Alcoholic beverages (beer, wine, whisky, scotch, other distilled spirits)

17

Tobacco products (cigarettes, mild tobacco, strong tobacco, etc.)

18

Other food products (fried insects, peanut preparation, flavoured ice, ice, other foodproducts)

19

Food taken away from home (meals at work, school, restaurants, snacks, coffee,
softdrinks purchased outside home)

20

Prepared meals bought outside and eaten at home

Q01BC03

Manual for Cambodia CSES 2009 (Version 2.0)

Q01BC04

Q01BC05

334

01. C. RECALL NON-FOOD EXPENDITURES
Respondent: The household member who knows most about the non-food expenditure in the household

INITIAL VISIT

Q01CC01

Only expenditure for household consumption
What was your household's expenditure on the following items during theindicated time periods?

NON-FOOD ITEMS

(1)

(2)

Value (in Riels)
Write '0' if nothing
In-cash expenditure
Time period
(3)

(4)

In-kind expenditure or gifts
given away

Total expenditure

(5)

(6)

(Col 4 + Col 5)

Last 1 month01

Medical care (doctors' fees, other medical services, drugs, hospital charges,
other medical supplies, etc.)

Q01CC04
Last 1 month

02

Transportation (personal transport equipment, operation of transport
equipment, maintenance and repair of equipment, gasoline and diesel for own
transportation, fees for public transport, moving fee, driving lessons, etc.)

03

Communication (postage stamps, fax, telephone and internet phone charges,
cell phones, phone cards, internet charges etc.)
Personal care (soap, toothpaste, razor, sanitary napkins, haircut, manicure,etc.)

Last 1 month04

Clothing and footwear (tailored clothes, ready-made clothes, rain clothes,
underwear, baby clothes, diapers, hats, shoes, boots, etc.)

Last 6 months

05

06

Furniture, furnishings and household equipment and operation (curtain,
household appliances, cooking utensils, light bulbs, soap and detergents etc.)

07
08
09

Last 1 month

Last 12 months

Domestic salaries (servant's salary, hired labour for cleaning, laundry, cooking Last 12 months
etc.)
Last 12 months
Recreation within Cambodia (entertainment services, recreational goods andsupplies, tourist travel, hotel accommodation)
Last 12 months
Recreation abroad (entertainment services, recreational goods and supplies,
tourist travel, hotel accommodation)
Last 12 months

10

Education (school fees, textbooks, private tutoring charges, etc.)

11

Personal effects (costume/gold jewelery, handbags, wallets, wristwatch,clocks, umbrella)

12Gambling (lottery, sports and animal betting: casino gambling, card games,
football, boxing, cockfighting etc.)

13

Miscellaneous items (special occasions as funeral rituals, weddings, parties, , Last 12 months
cash gifts, charity, etc.)

14

Last 12 months
Last 12 months

Total 1- 13:

Manual for Cambodia CSES 2009 (Version 2.0)

Q01CC05

Q01CC06

335

01. D. VULNERABILITY
Respondent: Head of household, spouse of the head of household or another adult household member

INITIAL VISIT

Q1 Did your family use iodized salt, yesterday?
Ask the respondent for a teaspoon full of cooking salt and test for iodine.

1 = Iodine present
2 = No iodine
3 = No salt in the household
4 = Salt not tested

Q2 In the last 12 months, has this household had enough food all days or were there

1 = Enough food all the last 12 months (>>NEXT SECTION)
2 = Not enough fooddays and weeks with very little or no food so that the household had to starve ("washungry")?

Q3 How many of the last 52 weeks did the household have so little food that it was starving ("was hungry")?

Q01DQ1Q01DQ2

Number of WEEKS:

Q01DQ3

Write '0' if less than 1 week

Q4 Which months of the last 12 months did the household starve ("was hungry")?
Q01DQ4_X(1 = January, 2 = February, 3 = March…)
Code "1" if starving and "0" otherwise

x=

Month
1 2

3

4

END OF INITIAL VISIT

Manual for Cambodia CSES 2009 (Version 2.0)

5

6

7

8

9 10 11 12

336

02. EDUCATION AND LITERACYWEEK 1

Respondent: Head of household, spouse of head of household, or another adult household member

Q02C01ID NUMBER

Please provide information on all members aged 3 years and older who usually reside in this household.
Can ..[NAME]... Can ..[NAME]... Has
How many What is the highest level ..[NAME].. has Is ..[NAME]..
What’s ..[NAME]'s..
Is the school
read a simple
write a simple..[NAME]... years has successfully completed?
currently in the current grade?
public or private?
message in any message in any ever...[NAME]…
school system?
language?
language?
attendedattended
98 = Don´t know
school?
school?
88 = No class completed
00 = Pre-school/
00 = Pre-school/Kindergarten
Kindergarten
1 = Public
01 = Class one completed
01 = Class one
2 = Private
02 = Class two completed...,02 = Class two...,
1 = Yes...................
................
2 = No (>>11)
11 = Class eleven completed
11 = Class eleven
1 = Yes
1 = Yes
1 = Yes
12 = Class twelve completed
12 = Class twelve
2 = No2 = No2 = No (>>13 = Lower Secondary school certificate If the child is on 13 = Technical/vocational
Enter
11)
completed 14 = Upper secondary school certificate holidays, he/she pre-secondary diploma/
number of 15 = Technical/vocational preis considered in certificate
years
the school
secondary diploma/certificate
14 = Technical/vocational
system16 = Technical/vocational postpost-secondary
secondary diploma/certificate
diploma/certificate
17 = College/university undergraduate
15 = College/university
18 = Bachelor degree (B.A., BSc, etc.)
undergraduate studies
19 = Masters degree (M.A., MSc, etc)
16 = Postgraduate studies
20 = Doctorate degree (PhD)
21 = Other (Specify)
YEARS
(1)

01

(2)

Q02C02

(3)

(4)

(5)

Q02C03 Q02C04 Q02C05(6)

Q02C06(7)

Q02C07

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

Manual for Cambodia CSES 2009 (Version 2.0)

(8)

(9)

Q02C08

Q02C09337

02. EDUCATION AND LITERACY (CONTINUED)WEEK 1

Respondent: Head of household, spouse of head of household, or another adult household member

1=Yes
2=No

Q02C01ID NUMBER

Please provide information on all members aged 3 years and older who usually reside in this household.
If Col. 4 = 2 or Col.7 = 2 and below 18 Has ..[NAME].. ever Is ..[NAME].. currently What kind of non-formal class is
Is ..[NAME]..
years of age
currently taking
attended non-formal attending non-formal ..[NAME].. currently attending?
private lessonsclass?
classes?
after school?
Why is ..[NAME].. not attending (has never
1 = Literacy programmes(languages,
attended) school?
(6 months)
math, science,
2 = Vocational training (Tailoring,music, sports)?
motor repairing, Khmer classical

>>12

(1)

(10)

01

Q02C1001 = Don’t want to
02 = Did not do well in school
03 = No suitable school available/school is too far
04 = No teacher/Supplies
05 = High cost of schooling
06 = Must contribute to household income
07 = Must help with household chores
08 = Too poor
09 = Due to disability
10 = Due to long term illness (over 3 months)
11 = Too young
12 = Other (specify)(11)

Q02C11

1 = Yes
2 = No (>> 15)

1 = Yes
2 = No (>> 15)

(12)

(13)

Q02C12

Q02C13

Did ..[NAME]..
attend school
past schoolyear(including nonformal class)?

music training, hairdressing,pottery...etc.
3 = Post literacy programmes
1 = Yes (>> 16a)
(Agricultural training includes such
2 = No >> NEXTas planting vegetable, mushrooms, PERSON)
raising fish, animal..
4 = Foreign Languages
5 = Computer literacy
6 = Others (Specify)

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

Manual for Cambodia CSES 2009 (Version 2.0)

(14)

Q02C14(15)

Q02C15

338

02. EDUCATION AND LITERACY (CONTINUED)WEEK 1

Respondent: Head of household, spouse of head of household, or another adult household member
Please provide information on all members aged 3 years and older who usually reside in this household.

If code 1 in col. 15, please fill up columns 16a-16h, otherwise,
leave it blank and continue with next person.

Q02C01ID NUMBER

What were the educational expenses for ..[NAME]..during the past school year including the expense on non-formal education and private lesson?

(1)

01

Write 0 if no expenses

A. School fees B. Tuition

C. Text books

D. Other school E. Allowances
supplies
for children
studying awayfrom home

F. Transport
cost

G. Gifts toteachers,
schoolbuilding
fund etc.

H. TOTAL(Col 16a - 16g)

RIELS

RIELS

RIELS

RIELS

RIELS

RIELS

RIELS

RIELS

(16a)

(16b)

(16c)

(16d)

(16e)

(16f)

(16g)

(16h)

Q02C16aQ02C16b

Q02C16c

Q02C16d

Q02C16e

Q02C16f

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

Manual for Cambodia CSES 2009 (Version 2.0)

Q02C16g

Q02C16h339

03. INFORMATION ON MIGRATION

Respondents: Household members 5 and older

WEEK 1

A. PAST MIGRATION
Please provide information on migration for all members of the household. (Don´t ask children less than 5 years)

1 = Yes(>> Col 7)
2 = NoIf less than 1
enter '0'

Q03AC01

ID NUMBER

Has ..[NAME].. How many years Which province did [NAME] move from?
always, sincehas..[NAME]..
birth, lived in this lived in thisvillage?
village?

NO OF YEARS
(1)

01

(2)

Q03AC02(3)

Q03AC03

Why did ..[NAME].. move to
In which province were ..[NAME].. born?
this village (the main reason)?

01 = Transfer of work place
02 = In search of employment
03 = Education
04 = Marriage
05 = Family moved
06 = Lost land/lost home
If moved from abroad, please write the name 07 = National calamitiesof the country
08 = Insecurity
09 = Repetriation or return
If moved within the same province write "Same". after displacement
10 = Orphaned
11 = Visiting only
12 = Other (Specify)

PROVINCE / COUNTRY PROVINCE COUNTRY
NAME
CODE
CODE
(4a)

(4b)

(4c)

Q03AC04B Q03AC04C

If born abroad, please write the nameof the country

PROVINCE / COUNTRY PROVINCE COUNTRY
NAME
CODE
CODE
(5)

Q03AC05

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

Manual for Cambodia CSES 2009 (Version 2.0)

(6a)

(6b)

(6c)

Q03AC06B Q03AC06C

340

03. INFORMATION ON MIGRATION (CONTINUED)

Respondents: Household members 5 and older

WEEK 1Q03AC01

ID NUMBER

A. PAST MIGRATION (CONTINUED)
In the past 5 How many In which year and monthyears, has times in the was ..[NAME].. 's most..[NAME].. past 5 years recent migration to
migrated to has
another country?
another..[NAME] ..
country for migrated toat least 1
anothermonth (atcountry?
the sametime) forwork?
Refer toCol. 7

1 = Yes
2 = No(>> NEXTPERSON)If 'Don't
know' enter
'98

NO OF
TIMES

YEAR

How many
Where did ..[NAME]..
months did
migrate in this last..[NAME].. stay in migration episode?
the country inthis last
01 = Bangkok
migration
02 = Other town/city inepisode?
ThailandIf still abroad 03 = Rural area in
Thailandenter '99'
04 = Seoul
05 = Other places inSouth Korea
06 = Kuala Lumpur
07 = Other places in
Malaysia
08 = Taiwan
09 = Vietnam
10 = Other (specify)
11 = Don't KnowNO OF
MONTHSMONTHS

(1)

(7)

(8)

(9a)

(9b)

01

Q03AC07

Q03AC08

Q03AC09A

Q03AC09b(10)

Q03AC10What was ..[NAME]..'s main occupation while in..[COUNTRY]..?

Why did..[NAME]..
return?
1 = Job ended
2 = Family reasons
3 = To get married
4 = Better
employment at
home
5 = Homesick
6 = Visa expired
7 = Was expelled
8 = Mistreatment orother dissatisfaction
9 = Other (specify)

For 'COUNTRY' see Col 11

If no work leave blank

Note: beggar and sex worker are occupations

OCCUPATION DISCRIPTIONNIS OCC.
CODE

(12a)

(12b)

(11)

Q03AC11

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

Manual for Cambodia CSES 2009 (Version 2.0)(13)

Q03AC12 Q03AC13

341

03. INFORMATION ON MIGRATION (CONTINUED)WEEK 1B. CURRENT MIGRANTS
Respondent: Head of household, spouse of head of household or another adult if both head and spouse are absent
Q1. In addition to the persons living in your household are there any other persons (spouse or son/daughter), 15 years and older,
who previously has been a member of your household but no longer are living in this household?
Please give me the names of spouse, if he or she is
no longer living in the household and all the children(son/daughter) 15 years and over who are no longer
living in this household (include all children of head
and/or spouse)

Sex

1 = Yes

Q03BQ1
2 = No (>>SECTION 4)

What is..
Where is ..[NAME].. currently living?
[NAME] ...’sage incompleted
years?

What year did..[NAME].. move
to [CURRENTLOCATION]..?

ID NUMBER

1 = Male
2 = Female
If in Cambodia, write the province.
If abroad, write the country

Q03BC01

If moved within the same province write "Same".

NAME
(1)

31

(2)

(3)

YEARS

PROVINCE / COUNTRY NAME

(4)

(5a)

Q03BC03 Q03BC04

32
33
33
35
36
37
38
39
40

Manual for Cambodia CSES 2009 (Version 2.0)

PROVINCE COUNTRY
CODE
CODE
(5b)

(5c)

Q03BC05B Q03BC05C

YEAR
(6)

Q03BC06

342

03. INFORMATION ON MIGRATION (CONTINUED)WEEK 1B. CURRENT MIGRANTS (CONTINUED)
Respondent: Head of household, spouse of head of household or another adult if both head and spouse are absent

ID NUMBER

Why did ..[NAME].. Whart was ..[NAME]..'s main occupation at the time of (before) moving tomove to ..[CURRENT .. [CURRENT LOCATION].?
LOCATION]..

What is the highest level ..[NAME].. has
successfully completed?
98 = Don´t know
88 = No class completed
00 = Pre-school/Kindergarten
01 = Class one completed
02 = Class two completed...,...................
11 = Class eleven completed
12 = Class twelve completed
13 = Lower Secondary school certificate
14 = Upper secondary school certificate
15 = Technical/vocational pre-secondary
diploma/certificate
16 = Technical/vocational post-secondary
diploma/certificate
17 = College/university undergraduate
18 = Bachelor degree (B.A., BSc, etc.)
19 = Masters degree (M.A., MSc, etc)
20 = Doctorate degree (PhD)
21 = Other (Specify)

1 = To take a job
Enter '0' in Col 8a if unemloyed
2 = To look for a job
3 = To go to live witha spouse
If no work leave blank
4 = To study
5 = To go to live witha relative
Note: beggar and sex worker are occupations
6 = Other (specify)

(1)

(7)

31

Q03BC07

OCCUPATION DISCRIPTIONNIS OCC.
CODE

(8a)

(8b)

(9)

Q03BC08

Q03BC09

32
33
33
35
36
37
38
39
40

Manual for Cambodia CSES 2009 (Version 2.0)

343

03. INFORMATION ON MIGRATION (CONTINUED)WEEK 1B. CURRENT MIGRANTS (CONTINUED)
Respondent: Head of household, spouse of head of household or another adult if both head and spouse are absent

Whart is ..[NAME]..'s main occupation now?

Have any
What is the total value ofmembers of this the transfers and cash
householdgifts that ..[NAME].. hasreceived
sent to the household thetransfers or gifts last 12 months?
in cash from ..
[NAME}.. the last
12 months?

ID NUMBEREnter '0' in Col. 10a if unemloyed

If no work leave blank
Note: beggar and sex worker are occupations

Through whatmeans/channels do you /does your household
receive the money?

1 = Western Union
2 = Bank transfer
3 = From the person or byother person
4 = Other (specify)

1 = Yes
2 = No(>> NEXTPERSON)Enter the two main
means

(1)

31

OCCUPATION DISCRIPTIONNIS OCC.
CODE

(10a)

(10b)

Q03BC10

RIELS
(11)

Q03BC11(12)

Q03BC12

32
33
33
35
36
37
38
39
40

Manual for Cambodia CSES 2009 (Version 2.0)

(13a)

(13b)

Q03BC13A Q03BC13B

344

04. HOUSING
Respondent: Head of household, spouse of the head of household, or another adult household member

WEEK 1

The following questions should be asked of the head of household, spouse of the head of household, or of another adult household member, if both head and spouse are absent.

Q1 How many households reside in the same housing unit as your household?

NUMBER OF HOUSEHOLDS:

Q04_01Q2 What is the floor area of the housing/dwelling unit occupied by your household?

NUMBER OF SQUARE METERS:

Q04_02

NUMBER OF ROOMS:

Q04_03Q3 How many rooms in the dwelling unit are usedby the household (other than kitchen, toilet and bathrooms)?

Q4 What is the primary construction material of the wall of the housing/dwelling unit occupied by your household?

Q04_04

WALL CODES
1 = Bamboo, Thatch/leaves, Grass
2 = Wood or logs
3 = Plywood

CODE:

4 = Concrete, brick, stone
5 = Galvanized iron or aluminium orother metal sheets
6 = Fibrous cement/Asbestos

7 = Makeshift, mixed materials
8 = Clay/dung with straw
9 = Other, specify

Q5 What are the primary construction material of the roof of the housing / dwelling unit occupied by your household?

Q04_05

ROOF CODES
1 = Thatch/leaves/grass
2 = Tiles
3 = Fibrous cement
4 = Galvanized iron or aluminium

CODE:

5 = Salvaged materials
6 = Mixed but predominantly made of galvanizediron/aluminium, tiles or fibrous cement
7 = Mixed but predominantly made of thatch/leave/grass or salvaged materials

8 = Concrete
9 = Plastic sheet
10 = Other (Specify)

Q6 What are the primary construction material of the floor of the housing / dwelling unit occupied by your household?

Q04_06

FLOOR CODES
1 = Earth, clay
2 = Wooden planks
3 = Bamboo strips

CODE:

4 = Cement/Brick/Stone
5 = Parquet, polished wood
6 = Polished stone, marble

7 = Vinyl
8 = Ceramic tiles
9 = Other (Specify)

Q7 What is your household’s main source of lighting?

Q04_07

LIGHTING SOURCE CODES
1 = Publicly-provided electricity/City power
2 = Generator

CODE:

3 = Battery
4 = Kerosene lamp

5 = Candle
6 = None

7 = Other (specify)

Q8 What is your household's main source of drinking water in wet season?

Q04_08DRINKING WATER SOURCE CODES IN WET SEASON
01 = Piped in dwelling or on premises (>> Q12)
02 = Public tap
03 = Tubed/piped well or borehole
04 = Protected dug well (including all of the following: lining,
headwall, platform, cover)
05 = Unprotected dug well
06 = Pond, river or stream (fetch water from pond, river, stream)
07 = Pond, river or stream (pump to the house)(>> Q12)CODE:

08 = Improved rainwater collection (catchment tank needs to have all thefollowing: completely closed, tap to withdraw water and at least 3000 litres
capacity (>> Q12)
09 = Unimproved rainwater collection (>> Q12)
10 = Water bought from tanker truck or vendor (Vendor brought water home,write "0" in distance and >> Q12)
11 = Water bought from tanker truck or vendor (Any household member
goes to collect, write distance in Q9 then ask Q10 and Q11.)
12 = Bottled water
13 = Other (Specify)

Q9What is the distance from home to the drinking water source in wet season (source reported in Q8)?

Q10

Which members of your household are fetching drinking water in the wet season? Q04_10Mx

Q11

How many minutes per day do they spend in total on fetching drinking water in wet season?

METERS:

IDcode (1)(2)

MINUTES PER DAY:Manual for Cambodia CSES 2009 (Version 2.0)

Q04_09(3)
Q04_11

345

04. HOUSING (CONTINUED)Q12WEEK 1

What is your household's main source of drinking water in dry season?

CODE:

DRINKING WATER SOURCE CODES IN DRY SEASON

Q04_1201 = Piped in dwelling or on premises (>> Q16)
02 = Public tap
03 = Tubed/piped well or borehole
04 = Protected dug well (including all of the following: Lining,
headwall, platform, cover)
05 = Unprotected dug well
06 = Pond, river or stream (fetch water from pond, river, stream)
07 = Pond, river or stream (pump to the house)(>> Q16)

08 = Improved rainwater collection (catchment tank needs to have all thefollowing: completely closed, tap to withdraw water and at least 3000 litres
capacity (>> Q16)
09 = Unimproved rainwater collection (>> Q16)
10 = Water bought from tanker truck or vendor (Vendor brought water home,write "0" in distance and >> Q16)
11 = Water bought from tanker truck or vendor (Any household member
goes to collect, write distance in Q13 then ask Q14 and Q15.)
12 = Bottled water
13 = Other (Specify)

Q13

What is the distance from home to the drinking water source in dry season (source reported in Q12)?

Q14

Which members of your household are fetching drinking water in the dry season? Q04_14Mx

Q15

How many minutes per day do they spend in total on fetching drinking water in dry season?

MINUTES PER DAY:

Q16

How much water charges did your household pay last month? (Put "0" for not buying water source)

RIELS:Q17

Did your household boil or otherwise treat the drinking water last month?
1 = Yes, always

3 = No, never (>>Q19)

Q18

How did you treat your drinking water last month?
a. Boil water?
b. Filter water?

IDcode (1)Q04_13(2)(3)
Q04_15
Q04_16

Q04_17
2 = Sometimesc. Chemical?
d. White alum?

1 = Yes
2 = No

Q19a

METERS:e. Other method (Specify)?

What toilet facility does your household have within the premises? (in the area close to the dwelling)
1 = Pour flush (or flush) connected to sewerage (>> Q20)
2 = Pour flush (or flush) to septic tank or pit (>> Q20)
3 = Pour flush (or flush) to elsewhere (i.e. not a sewer or pit/tank) (>> Q20)
4 = Pit latrine with slab(>> Q20)
5 = Pit latrine without slab or open pit (>> Q20)
6 = Latrine overhanging field or water (drop in the field, pond, lake, river, sea) (>> Q20)
7 = None (>> Q19b)
8 = Other, specify (>> Q20)

Q04_19A
CODE:

Q04_19B
Q19b

What toilet facility does your household usually use?
1 = Public toilet/pit latrine or shared with others (any type)
2 = Open land
3 = Other (Specify)

Q20

How much did your household spend for sewage or waste water disposal last month? (Write 0 if nothing)

RIELS:

Q04_20Q21

How much did your household spend for garbage collection last month?

RIELS:

Q04_21

CODE:(Write 0 if nothing)

Manual for Cambodia CSES 2009 (Version 2.0)

346

04. HOUSING (CONTINUED)Q22WEEK 1(a) What type of fuel does your household mainly use for cooking?
FUEL CODES
1 = Firewood
6 = Household generator (>> Q23)
2 = Charcoal
7 = None/don’t cook (>> Q23)
3 = Liquefied petroleum gas LPG (>> Q23)
8 = Other (Specify) (>> Q23)
4 = Kerosene (>> Q23)
5 = Publicly-provided electricity/City Power (>> Q23)

Q04_22A
CODE:

Q04_22B
1 = Yes (>> Q23)(b) Does the vendor bring the firewood/charcoal home?

2 = No(c) Which household members are collecting or fetching firewood or charcoal?
IDcode OF HH MEMBER(d) How many hours per week in total do they spend on collecting or

Q04_22C1(1)

Q04_22C3(3)

HOURS PER WEEK:

Q04_22D

fetching firewood/charcoal?

Q23

Q04_22C2(2)

If less than one hour write '0'

How much did the household spend on the following items last month (including lights and cooking)?
INCLUDE THE VALUE OF OWN PRODUCTION OR RECEIVED AS PAYMENT IN
KIND FOR WORK OR AS GIFT OR FREE COLLECTION

RIELS
Q04_23A

a. Electricity
b. Gas (LPG)

Q04_23B

c. Kerosene

Q04_23C

d. Firewood

Q04_23De. Charcoal

Q04_23E

f. Battery

Q04_23F

g. Other (Specify)

Q04_23G( ENTER " 0 " IF DID NOT SPEND ANYTHING )Q24

What's the legal status of the dwelling?
LEGAL STATUS CODE
1 = Owned by the household (>> Q25b)
2 = Not owned but no rent is paid (>> Q25b)
3 = Rented
4 = Other (Specify) (>> Q25b)

Q04_24CODE:

If rented:
How much did you pay for rent of this house last month? (=>> Q26)

RIELS:

Q04_25A

Q25b

How much would you have to pay per month to rent a similar dwelling? (Estimated value)

RIELS:

Q04_25B

Q26

Whether owned or rented:
How much did you spend on maintenance and minor repairs of the dwelling last month?

RIELS:

Q04_26

Q25a

END OF WEEK 1

Manual for Cambodia CSES 2009 (Version 2.0)

347

WEEK 205. HOUSEHOLD ECONOMIC ACTIVITIESRespondent: head of household, spouse of the head of household or another adult household member
The following questions should be asked of the head of household, spouse of the head of household, or of another adult household member,
if both head and spouse are absent.
A.

LAND OWNERSHIP
I would now like to ask you about all land owned or operated by your household. That means all land that is used or could be used for vegetable gardening, agriculturalor farming activities - crop cultivation, livestock raising and private forestry. (Do not include residential land not used to any of these activities)

Q1a Has the household sold any open land in the last 12 months?

1 = YES

2 = NO (>> Q2)
Q05AQ1A

1 = To adress family health issues
2 = Invest in business
3 = To weed or buy farm equipment or for otheragricultural activities
4 = To pay debt
5 = To buy motor bike or cell phoneor for other household consumption needs
6 = Rituals (marriage ceremony, funeral etc.)
7 = Other (specify)

Q1b What was the primary reason/purpose for which you sold the land?

Q05AQ1B

Q05AQ2Q2 Does anyone in your household own or operate any land that is used / could be used for vegetablegardening, agricultural or farming activities (crop cultivation, livestock raising or private forestry)?

1 = YES

2 = NO (>> NEXT SECTION E)
Q05AQ3Q3 How many plots does your household own or operate?

NUMBER OF PLOTS:

09HH_S99_SingleQuestions.sav
Note: Use aditional questionnaires
if there are more than 7 plots

Please list each plot that your household owns, or rent in from others, or used for free(including owned land that is rented out)
Do you own this land,
rent it or have it insome other way?

NUMBER

What is the area of the plot insquare meters (m2)?

PLOT

1 = Own (>> 4a)
2 = Own, but rent out(>> 5a)3 = Rented in(>> 6a)4 = Free use of land(>> 4a)5 = Other (specify)

(1)

01

(2)

Q05AC02

2

m

If owned or free use of land
Col 3 = 1 or 4

If owned but rented out
Col 3 = 2

How much would it cost to rent a plot like this in thisvillage?
In cash or in kind(>> Col 7)

Amount (if in cash)
Quantity (if in kind)

(3)

(4a)

Q05AC03

Q05AC04A

Unit
1 = Riel
2 = Kg
3 = Other(specify)(4b)
Q05AC04A

For what time
period?
1 = Month2 = Season
3 = Year
4 = Other(specify)

How much rent do you receive for this plot?
In cash or in kind(>> Col 7)

Amount (if in cash)
Quantity (if in kind)(4c)

Q05AC04A

2

02

m

03

m2

04

m2

05

m2

06

m2

07

m2

Manual for Cambodia CSES 2009 (Version 2.0)(5a)Q05AC05A

Unit
1 = Riel
2 = Kg
3 = Other(specify)(5b)
Q05AC05B

For what time
period?
1 = Month2 = Season
3 = Year
4 = Other(specify)(5c)

Q05AC05C348

WEEK 2

Please fill out the detailed information for each of the plots your household owns or rent in from others or used for free (including owned land that is rented out)
If rented in
If bought
What type of land is it?
In what year did How did you acquire it?
Col 3 = 3
Col 9 = 3 or 4
you first have/start using thisplot ?
01 = Wet-season land
1 = Given by the government
02 = Dry-season landor local authority (>> 11)
How much rent do you pay for this plot?
03 = Wet and dry season
2 = By inheritance or gift from How much did you pay to buy
In cash or in kind
this plot?
landrelatives (>> 11)

PLOT

NUMBER

A. LAND OWNERSHIP (CONTINUED)

Amount (if in cash)
Quantity (if in kind)
(1)

(6a)

01

Q05AC06A

Unit
1 = Riel
2 = Kg
3 = Other(specify)(6b)
Q05AC06B

For what time
period?
1 = Month2 = Season
3 = Year
4 = Other(specify)

04 = Chamkar land
05 = Kitchengarden(backyard)
06 = Land with permanentcrops
07 = Land for raising
livestock08 = Private forestry land
09 = Idle land
10 = Other land (specify)(6c)3 = Bought it from a relative
4 = Bought it from a nonrelative
5 = Cleared land/occupied forfree (>> 11)
6 = Donated by friend(>> 11)7 = Rented in (>> 11)
8 = Other (specify) (>>11)

Q05AC06C

Riels

YEAR
(7)

(8)

Q05AC07

Q05AC08(9)(10)

Q05AC09

Q05AC10

02
03
04
05
06
07
Please fill out the detailed information for each of the plots your household owns or rent in from others or used for free (including owned land that is rented out)
All plots

NUMBER

How much would it cost to buy aplot like this in this village today?

Do you have a paper tocertify your ownership or
rental agreement?

PLOT

1 = Yes
2 = Never had (>> 15)
3 = Lost it (>> 15)
4 = Don´t know (>> 15)

If YES in Col 12
What kind of paper do you have?

Can you show me the doucmentthat you have for this plot?

Enter answer given by respondent

Enter 8 if do not see certificate

1 = Application receipt
2 = Land investigation paper
3 = Certificate (title) from thegovernment
4 = Paper from local authority
5 = Rental contract
6 = Other (specify)
7 = Don't know / not sure

1 = Application receipt
2 = Land investigation paper
3 = Certificate (title) from thegovernment
4 = Paper from local authority
5 = Rental contract
6 = Other (specify)
7 = Don't know / not sure
8 = No paper shown

Riels
(1)

(11)

(12)

(13a)

01

Q05AC11

Q05AC12Q05AC13A(13b)

Q05AC13B

02
03
04
05
06
07

Manual for Cambodia CSES 2009 (Version 2.0)

Whose name is on theownership document or
rental contract?

1 = Head of household
2 = Spouse
3 = Both head ofhousehold and spouse
4 = Father
5 = Mother
6 = Other relative
7 = Other (specify)(14)

Q05AC14

349

05. A. LAND OWNERSHIP (CONTINUED)WEEK 2

Please fill out the detailed information for each of the plots your household owns or rent in from others (including owned land that is rented out)

PLOT

NUMBER

Who is the plot manager? Which crop did you grow on this plot in Can you add water to this plot with Have you made any investments on thisthe last seasons?
irrigation and / or water pumped
plot since you acquired it? (record up to
from the well?
3 most important investments)
Write ID Code if a person 1 = Rice
2 = Other crops (water melon, pumpkin,in your household
77 = Other, female not in
the household
88 = Other, male not in
the household
99 = Unknown

vegetables, maize, bean, potato, etc.)
3 = Fruit and nut trees (mango, coconut,
cashew etc.)
4 = Rubber
5 = Don't know which crop
6 = None

1 = Yes, dry season
2 = Yes, wet season
3 = Yes, both seasons
4 = No, cannot irrigate or pumpwater at all for this plot

Enter all crops (up to the 3 mostimportant) if you grow more than one cropEnter the 3 most important
(1)

01

(15)

Q05AC15

(16a)

(16b)

(16c)

Q05AC16A

Q05AC16B

Q05AC16C(17)

Q05AC17

02
03
04
05
06
07
When did you start to Have you ever had
have the rights to use any conflict about thisit as a collateral?
plot?

If more than one
investment, ask about 1 = Yes
the most important 2 = No (>> 22)

1 = Yes, now
2 = Yes, previously
3 = NoPLOTNUMBER

In what year did you make Can you use this plot asthese investments?
collateral for loan?

If don't know, leave
blank
YEAR
(1)

01

(19)

Q05AC19

YEAR
(20)

(21)

(22)

Q05AC20

Q05AC21

Q05AC22

02
03
04
05
06
07
Manual for Cambodia CSES 2009 (Version 2.0)

(18a)

(18b)

(18c)

Q05AC18A

Q05AC18B

Q05AC18C

350

05. B. PRODUCTION OF CROPS (INCLUDING FRUITS AND VEGETABLES ETC.)WEEK 2

Please provide the following information on crops, including fruits and vegetables, grown by your hh during the past 2 seasons. Please provide plot-wise details.
Note: Past wet-season should refer to the wet-season last calendar year.
If interview takes place in January - June: past dry-season should refer to the dry-season last calendar year.
If interview takes place in July - December: past dry-season should refer to the dry-season this calendar year.
Q05BQ1COPY
What crop(s) have yourTHE
household grown (on what plots)?
PLOT
NUMBER
FROM
PART A(1)

How big area wascultivated?

1 = Yes

How big area washarvested?

(2)

2 = No (>> Part E)

How much was How much has
produced /been the post–
harvested?
harvest loss until
the day ofinterview?

Write '0' if
nothing

Q05BC01SERIAL NUMBER

Q1 Did your household produce any crops including fruits and vegetables during the past wet-season or the past dry-season?

How much(quantity) was
given as crop
rent?

What was the saleprice of the crop
produced per kg?

Losses mean rotted,
lost, eaten by birds,
rodents, etc.
Write '0' if nothing
Write '0' if nothing

Name of crop or by-product

NIS
code

m2

m2

KG

KG

KG

RIELS / Kg

(3a)

(3b)

(4)

(5)

(6)

(7)

(8)

(9)

01

Q05BC02

Q05BC03B

PAST WET SEASON, What year?.........................................
Q05BC04m2

Q05BC05

m2 Q05BC06

m

2

m

03

m

2

m2

04

m2

m2

05

m2

m2

06

m2

m2

07

m2

m2

08

m2

m2

09

m2

m2

10

m2

m2

02

2

11
PAST DRY SEASON, What year?.........................................
12

m2

m2

13

m

2

m2

14

m2

m2

15

m2

m2

16

m2

m2

17

m2

m2

18

m2

m2

19

m2

m2

20

m2

m2

21

m2

m2

22

Manual for Cambodia CSES 2009 (Version 2.0)

Q05BC07

Q05BC08

Q05BC09351

05. C. COST OF CULTIVATION OF CROPS (INCLUDING FRUITS AND VEGETABLES ETC.)WEEK 2

Please provide the following information on cost of cultivation of crops, including fruits and vegetables, grown by your household during the past two seasons.

COPY THE
PLOT
NUMBERFROM PART B

Q05CC01SERIAL NUMBER

Please provide plot-wise details.

(1)

(2)

Planting materials
Chemical fertilizers,(seeds, seedlings,
pesticide, weedicide
young plants):
and fungicidepurchased/supplied
from home production

Animal and plant
Electricity for theOil, gas or gasoline Storage items (eg.,manure: purchased/ farming (not including and diesel for theburlap bags, plastic
supplied from home household use!)
farming (not including sheeting etc.)
producehousehold use!)

Payment to hired
draft power (tractors/
animals) including
human labour, if any,for ploughing/harrowing

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

RIELS

RIELS

RIELS

RIELS

RIELS

RIELS

RIELS

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Q05CC06

Q05CC07

Q05CC08

Q05CC09

PAST WET SEASON
01

Q05CC02

Q05CC03

Q05CC04

Q05CC05

02
03
04
05
06
07
08
09
10
11
PAST DRY SEASON
12
13
14
15
16
17
18
19
20
21
22

Manual for Cambodia CSES 2009 (Version 2.0)

352

WEEK 205. C. COST OF CULTIVATION OF CROPS (INCLUDING FRUITS AND VEGETABLES ETC.) (CONTINUED)

Please provide the following information on cost of cultivation of crops, including fruits and vegetables, grown by your household during the past two seasons.
Please provide plot-wise details.

SERIAL NUMBER

Other hired labour Irrigation
charges (cash plus charges
kind)

(1)

Services/
Transportation
technical support of input
from government materials,
and otherequipment andagencies
products

Repair andRental paid to owner for
maintenance of farm land, farm house,farm house, farm equipment etc. rented inequipment,from others
animal shed etc.

Total
Col. 3-15

Both in cash and in kind
Write '0' if nothing

Write '0' if
nothing

Write '0' if
nothing

Write '0' if
nothing

Write '0' if
nothing

Write '0' if nothing

Write '0' if nothing

RIELS

RIELS

RIELS

RIELS

RIELS

RIELS

RIELS

(10)

(11)

(12)

(13)

(14)

(15)

(16)

PAST WET SEASON
01

Q05CC10Q05CC11Q05CC12Q05CC13

Q05CC14Q05CC15

02
03
04
05
06
07
08
09
10
11

TOTAL 01-10:
PAST DRY SEASON

12
13
14
15
16
17
18
19
20
21
22

TOTAL 12-21:

Manual for Cambodia CSES 2009 (Version 2.0)

Q05CC16

353

WEEK 205. D. INVENTORY OF CROPS (INCLUDING FRUITS AND VEGETABLES ETC.)
Please provide the following information on crops, including fruits and vegetables, grown by your household
and in your storage December 31 last year.

Q05DQ1(1)

01
02

Crop(s) that your household had in storage
December 31 last year

1 = Yes 2 = No (>> Part E )How much of …[CROP]… did What was the sales price foryour household have in…[CROP]… per kg at
storage at December 31 last December 31 last year ?
year?

Crop Item

NIS
code

KG

RIELS/KG

(2a)

(2b)

(3)

(4)

Q05DC03

Q05DC04

Q05DC02B

Q05DC01 SERIAL NUMBER

Did your household have any crops in storage December 31 last year?

03
04
05
06
07
08
09
10

Note: In this module all finished crops (including fruits and vegetables etc.) shall be reported

Manual for Cambodia CSES 2009 (Version 2.0)354

05. E. INPUTS AND OUTPUTS OF LIVESTOCK AND POULTRY RAISING ACTIVITIESWEEK 2

Q05E1C01 SERIAL NUMBER

Q1 Has your household or anyone in your household had any livestock in the past 12 months, that is from ..[MONTH].. last year?
Type of animalor birdHas anyone inyour household
raised any..[LIVESTOCK]..
in the past 12
months?
1=Yes
2=no (>> Next
animal / bird)

(2)

(1)

01 Cattle(3)

Number of..[LIVE
STOCK]..
currently
owned ?

If none, write If none, write '0''0'

Write '0' if nothing

Number of..[LIVESTOCK]..
owned
December
31 lastyear?

Total sales value of..[LIVESTOCK]..
owned December 31
last year at the prevailing prices?

Value of..[LIVESTOCK]..
sold during the past
12 months?

Q05EQ1
Total paid for..[LIVESTOCK]..
bought during thepast 12months?

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

If none write'0'

NUMBERNUMBERRIELS

NUMBER

RIELS

RIELS

RIELS

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Q05E1C04

Q05E1C03

Of the total ..[LIVE What would be the
STOCK].. currently total sales value of
owned how many..[LIVESTOCK]..
are female animals
currently owned?
/ bird?

1= Yes 2=No (>> Part F )Q05E1C05

Q05E1C06

Q05E1C07

Q05E1C08

Q05E1C09

Q05E1C10

02 Buffaloes
03 Horses, Ponies
04 Pigs
05 Sheep
06 Goats
07 Chickens
08 Ducks
09 Quail
10 Other (specify)

SERIAL NUMBER

11 TOTAL 01 - 10:

Type of animalor bird(1)

Imputed value of meat products fromlivestock/poultry in riels
Consumed in the household
during the past 12 months

Used for barter, gifts, charity,
etc. during the past 12 months

Sold

Consumed in household

Gifts, charity, barter etc.

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

Write '0' if nothing

RIELS

RIELS

RIELS

RIELS

RIELS

(11)

(12)

(13)

(14)

(15)

Q05E1C13

Q05E1C14

Q05E1C15(2)

01 Cattle

Value of other products than meat (milk, butter, eggs, hide and skin, manure etc.)
sold, consumed in household, used as gifts etc. during the past 12 months

Q05E1C11

Q05E1C12

02 Buffaloes
03 Horses, Ponies
04 Pigs
05 Sheep
06 Goats
07 Chickens
08 Ducks
09 Quail
10 Other (specify)
11 TOTAL 01 - 10:

09HH_S05e1_Animals.sav

Manual for Cambodia CSES 2009 (Version 2.0)355

05. E. INPUTS AND OUTPUTS OF LIVESTOCK AND AND PULTRY RAISING ACTIVITIES (CONTINUED)

How much did your household spend on the following
items during the past 12 months?

ITEM
NUMBER
Q05E2C01

Write '0' if nothing
AMOUNT IN RIELS

ITEMS

(1)

(2)

(3)

1

Feed and feed supplements (e.g. rice straw) for livestock/poultry - purchased

2

Feed and feed supplements (e.g. rice straw) for livestock/poultry- supplied from home farm/public land

3

Hired labour to care for the livestock/poultry (cash plus kind)

4

Veterinary services and medicine

5Service /technical support from government/other agencies

6

Transporting livestock/poultry, livestock/poultry products, manure, feed and feed
suppliments to/from market

7

WEEK 2

Q05E2C03

TOTAL 1 - 6:

09HH_S05e1_Animals.sav

Manual for Cambodia CSES 2009 (Version 2.0)

356

05. F. INPUTS AND OUTPUTS FROM FISH CULTIVATION AND FISHING/TRAPPING OF AQUATIC PRODUCTSWEEK 2

1 = Yes

2 = No (>> Q3)

Q05FQ1Q2 Does your household or anyone in your household own or operate a pond for fish or shrimp culture?

1 = Yes

2 = No (>> Q3)

Q05FQ2

Q05F1C01 POND #Q1 Did your household or anyone in your household raise fish (or any other aquatic product like frogs or
crocodiles) during the past 12 months?

Do you own this pond, rent it or have it some otherway?
1 = Own
2 = Own, but rent out
3 = Rented in from others
4 = Free use of pond
5 = Other (specify)

(1)

AREA

How many square meters is
the pond?

MONTHLY RENTMARKET VALUE
How much would you have to How much would you have to
pay to buy a pond like this in pay monthly to rent a pond likethis village?
this in this village?

SQUARE METERS

RIELS

RIELS

(3)

(4)

(5)

Q05F1C03

Q05F1C04

Q05F1C05(2)

1 Q05F1C02
2
3

09HH_S05f1_FishArea.sav
Q3 Did your household or anyone in your household catch fish, shrimp, crabs,
oysters, etc. during the past 12 months?

1 = Yes

2 = NoQ05FQ309HH_S99_SingleQuestions.sav

Q05F2C01 ITEM #If Yes on Q1 or Q2 or Q3, please ask the following questions. If No on all 3 questions (Q1-Q3) >> G
EXPENSES
How much did your household spend on the following items
during the past 12 months?

Amount spent
Write '0' if nothing

ITEM

RIELS

(2)

(3)

(1)

01 Breeding stock for raising fish/shrimp etc.

Q05F2C03

02 Feed for raising fish/shrimp etc.
03 Hired labour (cash plus Kind)
04 Ice05 Repair and maintenance of nets and traps etc.
06 Repair and maintenance of boat
07 Boat fuel08 Boat rent (cash)
09 Cash rent for tank, if leased in
10 Transportation of fish/shrimp/crab etc. to market
11 Services (technical assistance) received
12 Other (specify)
13

Total 01 - 12:

Manual for Cambodia CSES 2009 (Version 2.0)

09HH_S05f2_FishExpenses.sav

357

Q05F3C01 ITEM #05. F. INPUT AND OUTPUTS FROM FISH CULTIVATION AND FISHING/TRAPPING OF AQUATIC PRODUCTS (CONTINUED)INCOMEHow much did your household receive under the following item during the past 12
months?

Amount received
Write '0' if nothing

ITEM

RIELS

(2)

(3)

(1)

01 Proceeds from sale of fish, shrimp, crab etc. raised or captured (*)

WEEK 2(*) Do not include
fish, shrimp, crabetc (paid in-kind) for
renting boat or
tank..

Q05F3C03

02 Value of fish, shrimp, crab etc. consumed in household
03 Value of fish, shrimp, crab etc. given away as gift, charity, barter, etc.
04 Value of fish, shrimp used for drying (dried fish/shrimp, smoked fish etc.)
05 Value of fish, shrimp used for preparation of fish/shrimp sauce
06 Value of fish, shrimp used for animal feed
07 Value of fish, shrimp used for other (specify)
Total 1 - 7:

08

09HH_S05f3_FishIncome.sav
05. G. INPUTS AND OUTPUTS FROM FORESTRY AND HUNTING
Q1 Did anyone in your household collect firewood, charcoal, timber or other forest products during the past 12 months?

1 = Yes

2 = NoQ05GQ1Q2 Did anyone in your household collect palm juice, root crops, herbs, honey or hunt wild animals or birds during the past 12 months?

1 = Yes

2 = NoQ05GQ2

Q05G1C01 PRODUCT #If YES on Q1 or Q2 ask the following questions, if NO on both of them >>Part H(1)

09HH_S99_SingleQuestions.sav

What were the value of products that your household collected in this way
during the past 12 months?
Write '0' if nothing
Receipts from sale of Imputed value of
Imputed value of
products gathered or such products
such products given
hunted?
consumed in theaway for gifts, charity,
household?
barter, etc.?

INCOME

Total amount(Col. 3 - 5)

ITEM

RIELS

RIELS

RIELS

RIELS

(2)

(3)

(4)

(5)

(6)

01 Sawing logs

Q05G1C03

Q05G1C04

Q05G1C05

Q05G1C06

02 Firewood
03 Wood for charcoal
04 Rattan, bamboo, palm leaves, other fibrous material
05 Palm juice
06 Root crops, fruits and vegetables
07 Herbs
08 Honey
09 Wild animals and birds
10 Other products (specify)
11

Total 01 - 10:

09HH_S05g1_ForestIncome.sav

Manual for Cambodia CSES 2009 (Version 2.0)

358

Q05G2C01 Item #05. G. INPUTS AND OUTPUTS FROM FORESTRY AND HUNTING (CONTINUED)
EXPENSES
How much did your household spend on thefollowing items during the past 12 months?

(1)

WEEK 2

Amount spent

Write '0' if nothing

ITEMS

RIELS

(2)

(3)

01 Transport costs including transport to market

Q05G2C03

02 Fuel03 Draft animal feed
04 Hired labour charges
05 Tools, equipment, including maintenance
06 Commissions, tips, rents, etc.
07 Other (specify)
Total 1 - 7:

08

09HH_S05g2_ForestExpenses.sav

05. H. LIST OF HOUSEHOLD NON-AGRICULTURAL ECONOMIC ACTIVITIES DURING THE PAST 12 MONTHS

Q05H1C01 Activity #Q1 Did anyone in your household run an enterprise or bussiness during the past 12 months?

(1)

01

DESCRIPTION OF THE ACTIVITY

MAIN PRODUCT

(2)

(3)

1 = Yes

2 = No (>> NEXT SECTION)

Q05HQ1ID CODE OF
Main
Other household members participating in the activity
personrunning theQ05H1C06X X =
NIS
1
2
3
4
5
6
INDUSTRY enterprise/business
CODE
1º
2º
3º
4º
5º
6º
(4)

(5)

(6a)

(6b)

(6c)

(6d)

(6e)

(6f)

7

8

7º

8º

(6g)

(6h)

Q05H1C04 Q05H1C04

02
03
04
05
Note: Use aditional questionnaires if there are more than 5 acitivies running by the household

Manual for Cambodia CSES 2009 (Version 2.0)

09HH_S05h1_Bussiness.sav

359

05. H. LIST OF HOUSEHOLD NON-AGRICULTURAL ECONOMIC ACTIVITIES DURING THE PAST 12 MONTHS (CONTINUED)
Q05H2C01 COST #How much did you spend on the different items listed for activity 1, during the past 12
months, that is since ..[MONTH].. last year? (Use the same question for activity 2-5)
Write '0' if nothing

COST ITEM

Activity 1
RIELS

Activity 2
RIELS

Activity 3
RIELS

Activity 4
RIELS

Activity 5
RIELS

(2)

(3)

(4)

(5)

(6)

(7)

(1)

01

Raw material used for processing

02

Materials used for construction

03

Fuels used for production or generation of electricity, service etc.

04

Lubricants

05

Purchase of goods for resale

06

Food, drink and tobacco products served to customers

07

Electricity purchased

08

Water and sanitation charges

09

Containers, packing materials

10

Freight and transport expenses

11

Insurance, bank charges, telephone, postage and other communication

12

Office suplies, stationary and other items

13

Rents paid for land, buildings, storage, warehousing, equipment &machines

14

Repair/maintenance of buildings, equipment &machinery/material/services

15

Registration and other govt. fees, taxes and donations

16

Wages/salaries of hired labour (cash plus kind)17

Services rendered by others (commissions, etc.)

18

All other expenses not included in the list from 1 to 17

19

WEEK 2

Q05H2C03

Q05H2C04

Q05H2C05

Q05H2C06

Q05H2C07

Total 01 -18:

09HH_S05h2_BussinessExpenses.sav

Manual for Cambodia CSES 2009 (Version 2.0)

360

05. H. LIST OF HOUSEHOLD NON-AGRICULTURAL ECONOMIC ACTIVITIES DURING THE PAST 12 MONTHS (CONTINUED)
Q05H2C01 Revenue #How much did your household receive under the different items listed for activity 1, during
the past 12 months, that is since ..[MONTH].. last year?
(Use the same question for activity 2-5)
Write '0' if nothing

REVENUE ITEM

Activity 1
RIELS

Activity 2
RIELS

Activity 3
RIELS

Activity 4
RIELS

Activity 5
RIELS

(2)

(3)

(4)

(5)

(6)

(7)

(1)

01

Receipts from sale of products and by-products

02

Charges for repair services

03

Other professional and service charges and commissions, etc.

04

Charges for construction work done

05

Proceeds from sale of goods sold

06

Charges for board and lodging

07

Receipts from sales/services at hotels/restaurants

08

Charges for transport services provided (taxi, moto etc)

09

Imputed value of products/goods for resale, etc. consumed in thehousehold10

Imputed value of products/by-products used as intermediate goods

11

Imputed value of products/by-products used as gifts, charity, etc.

12

Supply of electricity, gas and water

13

Rental income from land & buildings & storage & warehousing

14

Rental income from equipment and machinery

15

Charges for financial / insurance / real estate services

16

Charges for medical services

17

Charges for educational services

18

Charges for recreational and cultural services

19

Charges for other community, social and personal services

20

All other income receipts and charges from the activity not included in(01-19)21

WEEK 2

Q05H3C03

Q05H3C04

Q05H3C05

Q05H3C06

Q05H3C07

Total 01 - 20:

09HH_S05h3_BussinessIncome.sav

Manual for Cambodia CSES 2009 (Version 2.0)

361

06. HOUSEHOLD LIABILITIESWEEK 2

Respondent: Head of household, spouse of the head of household, or another adult household member
Q1 Does your household have outstanding debts to other households or institutions?

1 = Yes

2 = No (>> NEXT SECTION)

Q06_C01

LOAN NUMBER

How old is thedebt?
(In completedmonths)In how many
What was the primary purpose for which What was the total
From whom did yourmonths will the household obtain the loan?
your household borrowed the money? amount borrowed?
debt be fully paidback?
01 = Relatives in Cambodia
01 = Agricultural activities
02 = Relatives who live abroad 02 = Non-agricultural activities
03 = Friends/neighbours
03 = Household consumption needs
04 = Moneylender
04 = Illness, injury, accident
05 = Other emergencies (fire, flood,
Put '0' if less Put '0' if less 05 = Trader
theft)
than one month than one month 06 = Landlord
07 = Employer
06 = Rituals (marriage ceremony,
08 = Bank
funeral etc)
09 = NGO
07 = Purchase/improvement of dwelling
10 = Other (specify)
08 = Purchase of consumer durables
09 = Servicing and existing debts
10 = Other (specify)

Q06_Q1

How much is the
If interest isoutstanding loan now charged, what is(this month)?
the monthly rateof interest?
Interest should not
be included

Refer to theoutstanding
loan in Col 7

If no interest,
write '0'

If more than one enter themost important(1)

MONTHSMONTHS(2)(3)

01 Q06_C02

Q06_C03

If don´t know,
leave it blank(4)

Q06_C04(5)

Q06_C05

RIELS

RIELS

PERCENTAGE

(6)

(7)

(8)

Q06_C06

Q06_C07

Q06_C08

02
03
04
05
06
09HH_S06_Liabilities.sav

Manual for Cambodia CSES 2009 (Version 2.0)

362

07. HOUSEHOLD INCOME FROM OTHER SOURCESWEEK 2

Respondent: Head of household, spouse of the head of household, or another adult household member

Q07_C01 SOURCE #How much did your household receive from ..[SOURCE].. during the last 12 months?

REVENUE ITEMS(1)

From Cambodia

From abroad

Total(Col 3 + Col 4)

Write '0' if nothing
IN RIELS

Write '0' if nothing
IN RIELS

Write '0' if nothing
IN RIELS

(3)

(4)

(5)

(2)

01 Pensions
Q07_C03
Remittances from other relatives or others (not reported in current
02 migration section)
Governmental scholarships, stipends for any student member of the
03 household
Other scholarships, stipends for any student member of the
04 household (NGO, private institutions etc.)
Transfers (assistance/support) from NGO or other institutions (not05 credit)

Q07_C04

Q07_C05

06 Income from lottery and gamblings
07 Bank interests
08 Dividends
09 Interests on loans to others
Imputed value of goods received through barter (not recorded
10 elsewhere)
11 Imputed value of gifts received (not recorded elsewhere)
12 Other (not included in 1 to 11)
13

Total received: 01 - 12:

Note: Income from economic activity will be reported in module 05 (agricultural and non-agricultural activity) and in module
13B (salary if paid employee)

END OF WEEK 2

09HH_S07_IncomeOther.sav

Manual for Cambodia CSES 2009 (Version 2.0)

363

8. CONSTRUCTION ACTIVITIES IN THE PAST 12 MONTHS
Respondent: Head of household, spouse of the head of household, or another adult household member

WEEK 3Q1 Does the household own its own dwelling or any other building(s) used for residential, agricultural, commercial or industrial purposes?
2 = No (>> NEXT SECTION)

Q08_C01 BUILDING NUMBER

1 = Yes
What is the building used What is the What year wasfor?
total area for the building
living orconstructed?
1 = Residential
other use of
2 = Agricultural
the building?
3 = Commercial(purchase/sale ofEnter the year
goods and services)
when the4 = Industrial
construction(manufacturing)
was finished

(1)

(2a)

(2b)

(2c)

1

Q08_C02A

Q08_C02B

Q08_C02C

Enter the three mostimportant

2

How much would you How much would you Is any part of this How much does your Was this building
have to pay to buy a have to pay perbuilding rented household receive in constructed,
building like this in the month to rent aout?
monthly rent for this extended or repaired
village?
building like this inbuilding?
in the last 12 months,
village?
that is, since..[MONTH].. lastyear?
The village where 1 = Yes
the buildning is 2 = No (>> 9)
1 = Yes
located
2 = No (>> NEXT
BUILDING)

SQUAREMETERS

YEAR

RIELS

RIELS

(3)

(4)

(5)

(6)

Q08_C05

Q08_C06

Q08_C03 Q08_C04

Q08_Q1RIELS(7)Q08_C07(8)(9)

Q08_C08

Q08_C09

3
4

BUILDING NUMBER

What kind of work
was it?

In what year and month In what year and month Who built this building?
did the construction start? did people start to use thisbuilding?
1 = Household members only

1 = Constructed
2 = Extension (>>14)
3 = Repair (>> 14)(if not yet used leave it
blank for month andyear)

Enter the mostimportant

MONTH

YEAR

MONTH

YEAR

(1)

(10)

(11a)

(11b)

(12a)

(12b)

1

Q08_C10

2 = Household members and other relatives
3 = Household members and hired help
4 = Contracted builder
5 = Other (specify)

How much did your household pay those
who helped constructing, extending or
repairing this building (hired or contracted)?

Write '0' if nothing and leave it blank if dont´know
For building still under work ask forthe cost up till now

RIELS
(13)

Q08_C11A Q08_C11B Q08_C12A Q08_C12B Q08_C13

2
3
4Manual for Cambodia CSES 2009 (Version 2.0)

(14)

Q08_C14

364

WEEK 3

8. CONSTRUCTION ACTIVITIES IN THE PAST 12 MONTHS (Continued)

For buildings not yet completed:
If not possible to separate If anyone in the household has If anyone else not belonging tolabour and materials:
put in own labour forthe household has put in own
constructing, extending or
labour try to estimate the value
repairing this building try toof it as if you had engaged
How much were the total
What is the estimated remaining cost of
estimate the value of it as if
someone to do it?
the building's construction, extension orWrite '0' if nothing and leave it costs?
you
hadengagedsomeonetoblank if dont´know
repair to be completed?
do it?

BUILDING NUMBER

How much did your householdspend for materials?

For building still under work
ask for the cost up till now(1)1

Q08_C15

Write '0' if nothing

Write '0' if nothing

RIELS

RIELS

RIELS

RIELS

RIELS

(15)

(16)

(17)

(18)

(19)

Q08_C16

Q08_C17

Q08_C18

2
3
4

Manual for Cambodia CSES 2009 (Version 2.0)

Q08_C19

365

09. DURABLE GOODSWEEK 3

Respondent: Head of household, spouse of the head of household, or another adult household member
For items bought or
Did you buy it, receive it as a gift, How many of this (these) For items bought or
as pay for work or in other way? ..[ITEM].. were acquired received within the last received before the
12 months:
last 12 months:
or received...:If more than one item ask foreach item and put a code ineach column. If more than 4
What was the purchase According to current
ask for the 4 most recent
value (or the imputed
prices, what do you thinkvalue) of all these
you could get if you sold
1 = Purchased..[ITEM]s..?
..[ITEM]s..?
a. Within the b. Before the
2 = Payment for services
last 12
last 12
3 = Received as a gift
TOTAL
months?
months?
RIELS
RIELS
NUMBER 4 = Other (specify)

How many of the following items does your household own?

ITEM NUMBER(Write '0' if none and =>> Next item)

ITEM

Prod- uctcode
Q09_C03

(2)

(3)

(4)

01 Radio801

Q09_C04

02 Television

802

03 Telephone817

04 Cell phone818

05 Video/VCD/DVD player/recorder807

06 Stereo

808

07 Camera (picture/video)809

08 Satellite dish

824

(1)

(5a)

(5b)

(5c)

(5d)

(6a)

(6b)

Q09_C06A

Q09_C06B(7)(8)

Q09_C05D

Q09_C05C

Q09_C05B

Q09_C05A

Home Electronics

Q09_C07

Personal transport
09 Bicycle

803

10 Motorcycle

804

11 Car829

12 Jeep/Van

830

Household equipment
13 Sewing machine

806

14 Refrigerator

810

15 Electric Kitchen/Gas Stove813

16 Washing machine819

17 Dishwasher

820

18 Freezer

821

19 Vacuum cleaner

822

20 Electric iron823

21 Electric fan

811

22 Air conditioner

812

23 Suitcases/box for store/ travelling

890

Manual for Cambodia CSES 2009 (Version 2.0)

Q09_C08

366

WEEK 3For items bought or
How many of the following items does your household own? Did you buy it, receive it as a gift, How many of this (these) For items bought or
as pay for work or in other way? ..[ITEM].. were acquired received within the last received before the
12 months:
last 12 months:(Write '0' if none and >> Next item)or received...:If more than one item ask foreach item and put a code ineach column. If more than 4
What was the purchase According to current
ask for the 4 most recent
value (or the imputed
prices, what do you thinkvalue) of all these
you could get if you sold
1 = Purchased..[ITEM]s..?
..[ITEM]s..?
2 = Payment for servicesa.
Withintheb.
Beforethe
3 = Received as a gift
PROlast 12
last 12
4 = Other (specify)
TOTAL
DUCT
(2)

(3)

(4)

24 Generator

816

Q09_C04

25 Batteries

891

(1)

(5a)

(5b)

(5c)

(5d)

Q09_C05D

NUMBER

Q09_C05C

CODE

Q09_C05B

ITEM

Q09_C05A

ITEM NUMBER

09. DURABLE GOODS

months?

months?

(6a)

(6b)

Q09_C06A

Q09_C06B

814

27 Dining set (dinning table + chairs)

815

28 Bed sets (Bed, Mattress…)

892

29 Wardrobe, cabinets

893

Computers and printers
30 Computer (desktop or laptop)

825

31 Printer826

Recreation
32 Musical instruments

827

33 Sport equipment

828

Water transport
34 Rowing boat

831

35 Motor Boat

832

Agriculture and other production
36 Cart (pulled by animal)

805

37 Tractor

833

38 Bulldozer/roller

834

39 Plough835

40 Threshing machine837

41 Harrow/rake/hoe/spade/axe…

838

42 Hand Tractor (Kou Yon)

839

43 Rice mill840

44 Water pump

836

Other items
45 Other (specify)

841

46 Other (specify)

894

RIELS

(7)

(8)

Q09_C07

Furniture
26 Sofa set

RIELS

Manual for Cambodia CSES 2009 (Version 2.0)

Q09_C08367

10. MATERNAL HEALTH

Respondent: All women with living children under 5 years old

LAST PREGNANCY & DELIVERY

Q10_Q1
1= Yes 2=No (>> SECTION 11 )Q1 Are there any women living in the household with living children under 5 years old?
(the child can be living in another household)
Please provide the following information on the last pregnancy

Q10_C01 SERIAL NUMBER

COPY ID CODE ID No. of child if living During this pregnancy did
OF THE
in the household
you have difficulty with your
MOTHERvision during daylight?
FROM ROSTER

(1)

01

(3)

Q10_C02

1 = Yes
2 = No
8 = Don't know

(4)

Q10_C03

During this pregnancy
During this pregnancy were you
did you suffer from night given or did you buy any iron
blindness?
tablets?

Show tablets

Leave blank if the childis not living in the
1 = Yes (>> 6)household2 = No
8 = Don't know

(2)

1 = Yes
2 = No
8 = Don't know

(5)

Q10_C04

Q10_C05(6)

Q10_C06

02
03

During this pregnancy did Did you see anyone foryou take any drug forantenatal care for thisintestinal parasites?
pregnancy?
Q10_C01 SERIAL NUMBER

04

I don't want to know the During this pregnancy were you given
result but were you
an injection in the arm to prevent thetested for the AIDS virus baby from getting tetanus, that is,
as part of your antenatal convulsions after birth?
care?

1 = Yes
2 = No
8 = Don't know

1 = Yes
2 = No
8 = Don't know

1 = Yes
2 = No (>>12)
8 = Don't know (>>12)

(1)

(7)

(9)

(10)

01

Q10_C07

1 = Yes
2 = No (>>10)
8 = Don't know (>>10)(8)

Q10_C08WEEK 3

Q10_C09

Q10_C1002
03
04

Manual for Cambodia CSES 2009 (Version 2.0)

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10. MATERNAL HEALTH (CONT.)

Respondent: All women with living children under 5 years old

WEEK 3

LAST PREGNANCY & DELIVERY (CONT.)

Q10_C01 SERIAL NUMBER

Please provide the following information on your last pregnancy
During thispregnancy how
many times did you
get this tetanus
injection?

If more than 7,
write 7
If don't know
enter '8'

At any time
Before this pregnancy
Where did you
before thishow many times did you give birth?
pregnancy did receive a tetanus
you receive any injection?
tetanusEnter Codeinjection?

Who assisted you in the delivery In the first six weeks In the first twoof the child?
after delivery did you months after
receive a Vitamin A delivery did you
dose like this?
receive iron tablets?

1 = Doctor/Medical assistant
2 = Nurse
If don't know 3 = Midwife
4 = Traditional birth attendant
enter '98'
5 = Relative/friend
6 = Other (Specify)
7 = None

1 = Yes
2 = No(>> 14)
8 = Don't know(>> 14)

Leave it blank
if don't know

If > 2(>> Col 14)

1 = Yes
2 = No
8 = Don't know

Show capsule
1 = Yes
2 = No
8 = Don't know

Q10_C11

Q10_C12

Q10_C13(14)

Q10_C14

(15a)

(15b)

(15c)

(15d)
Q10_C15D

01(13)

Q10_C15C(12)

Q10_C15B(11)

Q10_C15A(Enter up to 4
most important persons)

(1)

02

Show capsule03
04

Codes for col.14
Home:
01 = Your home
02 = Midwife/TBA home
03 = Other home (specify)
Public sector:
04 = National Hospital (PP)05 = Province Hospital (RH)
06 = District Hospital (RH)
07 = Health Center
08 = Health Post
09 = Military Hospital
10 = Other Public (Specify)
Private Medical Sector
11 = Private Hospital
12 = Private Clinic
13 = Other Private (Specify)
Other:
14 = Other (Specify)

Manual for Cambodia CSES 2009 (Version 2.0)

(16)

(17)

Q10_C16

Q10_C17

369

Respondents: Mothers or caretakers of children under 2 years old

11. CHILD HEALTHWEEK 3

YOUNGEST CHILD & AND ALL CHILDREN UNDER 2
1= Yes 2=No (>> SECTION 12 )Q1 Check in the household list if there are any children 00-23 months old living in the household?

COPY ID CODE COPY ID
OF THE
CODE OFMOTHER
THE
FROM
CHILD
ROSTERFROMROSTERIs this your youngest Did you ever breastfeed How long after birth did you first put thechild?
your child?
child to the breast?

1 = Yes
2 = No (>> 9)

Are you stillbreastfeeding?

If less than one hour record '00' hours 1 = Yes
If less than 24 hours record hours 2 = No
Otherwise record days

1 = Yes
2 = No(>> 9)

Q11_C01SERIAL NUMBER

Please provide the following information on the youngest child of each mother and all children 00-23 month old

(1)

01

(2)

(3)

Q11_C02(4)(5)

Q11_C03 Q11_C04

Q11_C05

HOURS

DAYS

(6a)

(6b)

Q11_C06A

Q11_C06B

02
03
04
05
06

Q11_C01SERIAL NUMBER

07

(1)

01

Now I would like to ask you about liquids your child drankyesterday during day or night.
Did your child drink …..
A. Infant formula or any other milk such as tinned,
condensed or fresh animal milk

Have this child everWhere did the child receive
received a vaccination? most of the vaccinations?

1 = Yes

2 = No (>> NEXT
B. Plain water, fruit juice such as coconut juice, tea or
coffee ot any other liquids such as sugar water, carbonated CHILD)
8 = Don't know
drinks or soup broth(>> NEXT CHILD)
1 = Yes
2 = No
8 = Don't know
A

B

(8a)

(8b)

Q11_C08A

Q11_C08B

1 = Outreach activities
2 = Health center
3 = National, provincial,
district hospital
4 = Private clinic
5 = Other (specify)(9).

Q11_C09(10)

Q11_C10

02
03
04
05
06
07

Manual for Cambodia CSES 2009 (Version 2.0)

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Q11_C07

Q11_Q1

370

11. CHILD HEALTH (CONTINUED)

Respondents: Mothers or caretakers of children under 2 years old

WEEK 3

YOUNGEST CHILD & AND ALL CHILDREN UNDER 2 (CONTINUED)

Q11_C01SERIAL NUMBER

Please provide the following information on the youngest child of each mother and all children 00-23 month old
Do you have a yellow card
where [NAME]'s vaccinations 10
are written down?
BCG
If 'Yes': Can I see the yellow a) Has [NAME] ever received BCG vaccination?
card?

b and c) What month and year did [NAME] receive b, c, and d) What day, month and year did [NAME] receive the latest
the vaccination?
vaccination?
1 = Yes
2 = No(>> NEXT CHILD)

(1)

01

Hep0
a) Did name receive a Hep0 vaccination at birth?

1 = Yes
2 = No(11)

Q11_C11

If not recorded leave blank
MONTH
YEAR

1= Yes
2= No
DAY

(12a)

(12b)

(12c)

(13a)

(13b)

(13c)

(13d)

Q11_C12A

Q11_C12B

Q11_C12C

Q11_C13A

Q11_C13B

Q11_C13C

Q11_C13D

02
03
04
05
06
07

Q11_C01SERIAL NUMBER

If child has yellow card, record the dates of the following vaccinations
from the yellow card. The interviewer must see the card.

DPT-HepB
MEASLESa) How many doses of DPT-HepB vaccine has [NAME] a) Has [NAME] ever received measles
received?
vaccination?
b and c) What month and year did [NAME] receive the b and c) What month and year did [NAME] receive
latest vaccination?
the vaccination?

0 = No dose
1 = 1 dose
2 = 2 doses
3 = 3 doses
4 = 4 doses

(1)

(14a)

01

Q11_C14A

If not recorded leave blank
MONTH
YEAR

If not recorded leave blank

1 = Yes
2 = NoIf not recorded leave blank

MONTH

YEAR

MONTH

YEAR

(14b)

(14c)

(15a)

(15b)

(15c)

Q11_C14B

Q11_C14C

Q11_C15A

Q11_C15B

Q11_C15C

02
03
04
05
06
07

Manual for Cambodia CSES 2009 (Version 2.0)

371

12. HEALTH CHECK OF CHILDREN UNDER 5

Respondents: Mothers or caretakers of children under 5 years old

Q1 Check in the household list if there are any children 00-59 months old living in the household?

WEEK 3

1= Yes 2=No (>> SECTION 13 )Q12_Q1COPY ID
CODE OFCHILD
FROMROSTER

Q12_C01SERIAL NUMBER

Please provide information on children 00-59 month old who are household members(1)

1

Does s/he have abirth certificate?
If No, PROBE
Has this child's birth
ever been registeredwith the civil authority

1 = Certificate
2 = Registered
3 = Neither
8 = Don't know
(2)

Has this child everHow manyreceived a Vitamin A months ago diddose like this?
this child takethe last dose?

Has this child taken Did this child sleep
Has this child had
any drug for intestinal under a mosquito net diarrhea in the last 2
parasitis in the last six last night?
weeks?
months?

Show capsule

1 = Yes
2 = No (>>6)
8 = Don't know (>>6)

(3)

(4)

Q12_C02 Q12_C03

Q12_C04

If less than 1month enter'00'
1 = Yes
2 = NoIf don't know 8 = Don't know
enter '98'

(5)

Q12_C05

1 = Yes
2 = No
8 = Don't know

1 = Yes
2 = No (>>10a)
8 = Don't know(>>10a)

(6)

(7)

(8)

Q12_C06

Q12_C07

Q12_C08

2
3
4
5
6
7
8

Manual for Cambodia CSES 2009 (Version 2.0)

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12. HEALTH CHECK OF CHILDREN UNDER 5 (CONTINUED)

Respondents: Mothers or caretakers of children under 5 years old

Q12_C01SERIAL NUMBER

Please provide information on children 00-59 month old who are household members
Was the child given any of the following at
any time since he/she started having
diarrhea?

Date of measurement

If not measured, leave it
blank

A fluid made from
A home fluid of
special packet called porridge water ororatyle?
cooked rice withsalt/sugar?
1 = Yes
2 = No
8 = Don't know

Height measured

(9a)

(9b)

1

Q12_C09A

Q12_C09B

Weight measured

If not measured, leave it
blank

1=Standing up
2=Lying down(Less than 24 months
old)

1 = Yes
2 = No
8 = Don't know

(1)

If the child wasmeasured:Was this heightmeasured standing
up or lying down?

DAY

MONTH

CENTIMETERS

(10a)

(10b)

(11)

Q12_C10A Q12_C10B Q12_C11

KILOGRAMS(12).

Q12_C12(13)

Q12_C13

.

2

.

.

3

.

.

4

.

.

5

.

.

6

.

.

7

.

.

8

.

.

Manual for Cambodia CSES 2009 (Version 2.0)

WEEK 3

373

13. HEALTH CARE SEEKING & EXPENDITUREWEEK 3

Respondent: Head of household or the spouse of the head of household
The following questions should be asked of the head of household, spouse of the head of household, or another adulthousehold member, if both head and spouse are absent.
A. SUBSIDIZED HOUSEHOLD HEALTHCARE
Q1 In the last 12 months, has any member of the household received free orsubsidized health care that other people would normally have to pay for?
(including private health insurance)
Q2 How did they obtain this free / subsidized treatment?

Record up to 3 ways in which theyobtained free / subsidized treatment

1 = Yes

2 = No (>> Q4)

8 = Don't know
Q13AQ1

1 = Household Priority Access Card, Equity Card, or other document that allows free orsubsidized health care
2 = Name(s) are on a List of Poor Households held by the local authorities
3 = Health facility staff asked them questions from a list / filled out a form before treatment
4 = Health facility staff provided free treatment (without asking questions or filling out aform)
5 = Have private health insurance
6 = Belong to community health insurance scheme
7 = Other (specify)
8 = Don’t know

Q13AQ2Aa.
Q13AQ2Bb.
Q13AQ2Cc.

Q13AQ3Q3 When they received free / subsidized treatment, were they treated thesame as other people who were paying for their healthcare?

1 = Yes, always treated the same
2 = Yes, most times treated the same

4 = No, most times not treated the same
5 = No, never treated the same

3 = Sometimes yes, sometimes no

8 = Don’t know

Q4 If Q1 = 2, Otherwise (>> Col.2 next page)
Do you or any member of the household have a Priority Access Card, Equity Card, or any otherdocument that allows free or subsidized health care?

Q13AQ4
1 = Yes

2 = No

8 = Don't know

09HH_S99_SingleQuestions.sav

Manual for Cambodia CSES 2009 (Version 2.0)

374

13. HEALTH CARE SEEKING & EXPENDITURE (CONTINUED)WEEK 3

Respondent: Head of household or the spouse of the head of household
Please provide information on all members who usually reside in this household.
B

ILLNESS AND HEALTHCARE EXPENDITURE DURING THE LAST 30DAYS

Q13BC01

ID NUMBER

If an illness
Please tell me if any
Was ..[NAME].. so ill thatmember of yourDid …[Name} …have this s/he could not do his/herhousehold is sick, has an illness for more than one usual activities?
illness or injury now or at year already?
any time in the last 30
days.

How many days was..[NAME].. so ill that s/he
stopped doing usual
activities?

It should be the same
illness that comes andgoes (chronic)
Enter number of days
1 = Yes
2 = No (>> 7)

1= Yes2= No (>> 7)
3= No usual activities(>> 7)(e.g. small children, old
person, etc.)

1 = Yes
2 = NoWas consultation or Has there been any other reason totreatment sought for go to a health facility or seek healththis illness/injury?
care?

If no, PROBE
Refer to the last 30
days
Has this person received care in
relation to a pregnancy,
immunization or supplementation?
1 = Yes

Refer to the last 30 days 2 = No

1 = Antenatal care
2 = Delivery
3 = Postnatal care
4 = Vitamin A or deworming
5 = Other
Number of days

(1)
01

(2)

Q13BC02(3)

Q13BC03(4)

Q13BC04

If none enter '0'(5)

Q13BC05(6)

Q13BC06

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

09HH_S13b_HealthExpenses.sav

Manual for Cambodia CSES 2009 (Version 2.0)

(7)

Q13BC07375

13. HEALTH CARE SEEKING & EXPENDITURE (CONTINUED)WEEK 3

Respondent: Head of household or the spouse of the head of household
Please provide information on all members who usually reside in this household.
B

ILLNESS AND HEALTHCARE EXPENDITURE (CONTINUED)
In the last thirty days, how In the past 30 days, which Ask if answer in Col. 8 is How much in total was spent How much in total was spent
more than 1many times did [NAME] was the first provider thaton transport to go to andon treatment at any healthseek health care forwas consulted for
return from any health provider provider in the past 30 days?
illness, injury, or any other [NAME]'s health?
In the past 30 days, which in the past 30 days?
reason?
was the last / most recent
provider that was
consulted for [NAME]'s
health?

ID NUMBER

If 0, PROBE.
Has this person bought
medicine or consulted withkru khmer, a traditional
birth attendant, or a monk

Enter Code(See below)

Enter Code(See below)

If don't know enter '98'

If don't know enter '98'

Enter number of times
sought health care

Write '0' if nothing

Write '0' if nothing

RIELS

RIELS

(10)

(11)

Q13BC10

Q13BC11

If '0' >> NEXT PERSON
(1)

01

(8)

Q13BC08

(9a)

(9b)

Q13BC09A

Q13BC09B

02
03
04
05
06
07
08
09
10
11
12
13
14
15
Codes for col. 9a and 9b
Private medical sector:
Public sector:
08 = Private hospital
01 = National hospital (PP)09 = Private clinic
02 = Provincial hospital (RH)
10 = Private pharmacy
03 = District hospital (RH)
11 = Home/Office of trained health04 = Health center
worker/nurse
05 = Health post
06 = Provincial rehabilitation centre (PRC) 12 = Visit of trained healthworker/nurseor Community based rehabilitation (CBR)
13 = Other private medical (Specify)
07 = Other public (Specify)

Not medical sector:
14 = Shop selling drugs/market
15 = Kru khmer/ Magician
16 = Monk/religious leader
17 = Traditional birth attendant
18 = Other (Specify

09HH_S13b_HealthExpenses.sav

Manual for Cambodia CSES 2009 (Version 2.0)

376

14. DISABILITY

Respondent: Head of household or the spouse of the head of household

WEEK 3

Please provide information on all members who usually reside in this household.
Does ..[NAME].. have any of thefollowing?

Q14_C01

ID NUMBEREnter the 3 most important
01 = Difficulty seeing
02 = Difficulty hearing
03 = Difficulty speaking
04 = Difficulty moving
05 = Difficulties in feeling or sensing
06 = Psychological or behaviourialdifficulties
07 = Learning difficulties
08 = Fits
09 = Other (specify)
98 = Don´t knoW

Is the difficulty …

What was the cause?

1 = Mild
2 = Moderate
3 = Severe

01 = Mine/UXO
02 = Traffic Accident
03 = Work Accident
04 = Disease(s)05 = Congenital
06 = Fever
07 = Difficulty Delivery
08 = Chemical Accident
09 = Rape
10 = Violent Attack
11 = Domestic Violent
12 = Suicide AttemptEnter one code for each of the
difficulties reported in Col 2a-2cEnter '0' if none,(>> NEXT PERSON)

13 = Mental Trauma due to
war and other traumatic events
14 = War Injuries
15 = Malnutrition
16 = Burns
17 = Torture
18 = Old Age
19 = Other (specify)
98 = Don't know

Enter one code (the most important) for each of the
difficulties reported in Col 2a-2c

The 3 most important
(1)

01

(2a)

(2b)

(2c)

(3a)

(3b)

(3c)

(4a)

(4b)

(4c)

Q14_C02A Q14_C02B Q14_C02C Q14_C03A Q14_C03B Q14_C03C Q14_C04A

Q14_C04B

Q14_C04C

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

Manual for Cambodia CSES 2009 (Version 2.0)

377

14. DISABILITY (CONTINUED)

Respondent: Head of household or the spouse of the head of household

Please provide information on all members who usually reside in this household.

ID NUMBER

Does the difficulty/difficulties prevent..[NAME].. from participation or accessto any of the following?

1 = Education (ask if aged 3 or over)2 = Housing
3 = Land ownership (ask if
aged over 18)
4 = Employment and income generation
5 = Health services
6 = Transport

Enter the three most important

(1)

01

(5a)

(5b)

(5c)

Q14_C05A Q14_C05B Q14_C05C

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

END OF WEEK 3

Manual for Cambodia CSES 2009 (Version 2.0)

WEEK 3

378

15. CURRENT ECONOMIC ACTIVITY

Respondents: All household members aged 5 years and older

WEEK 4ACTIVITY STATUS DURING THE PAST 7 DAYS
Please provide information on all members aged 5 years and older who usually reside in this household. Try to interview the household members individually

Did ..[NAME].. do any work at all, even Although ..[NAME].. did What was ..[NAME].. 's main occupation/economic activity (primary
one hour, during the past 7 days, i.e.
not work even for one occupation) during the past 7 days?
- worked or helped on a farm, grinding hour during the past 7
grain, making palm sugar, caring fordays, did ..[NAME]..
animals, weaving etc.
have a job/acitivity from- worked in a business or workplace
which he/she was(private or public sector, own account or temporarily absent?
in a business belonging to someone(e.g.: absent due toelse in your household
holiday or illness)

Q15_C01

ID NUMBERID
NUMBEROFRESPONDENT

(1)

01

Note: beggar and sex worker are occupations

1 = Yes (>> 5)

1 = Yes

2 = No2 = No (>>26)(2)

Q15_C02 Q15_C03

(3)

(4)

Occupation description

NIS OCC:
CODE

(5a)

(5b)

Q15_C04

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

Manual for Cambodia CSES 2009 (Version 2.0)

Q15_C05B

379

15. CURRENT ECONOMIC ACTIVITY (CONT.)

Respondents: All household members aged 5 years and older

WEEK 4ACTIVITY STATUS DURING THE PAST 7 DAYS (CONT.)ID NUMBER

In what kind of industry/business (economic activity) did ..[NAME].. work in his/her main
occupation/activity (e.g. agriculture, manufacturing, construction, hotel/restaurant, trade)?

(1)

01

Industry description

NIS ISIC
CODE

(6a)

(6b)

Under what type of employer did What was ..[NAME]..'s..[NAME].. work in his/her main employment status in
occupation/economic activity?
his/her main
occupation/economic
activity?
1 = Government
2 = State owned enterpirse
3 = Cambodian enterprise
4 = Foreign enterprise (private) 1 = Employee
5 = Non profit institution
2 = Employer
6 = Household sector
3 = Own account worker
7 = Embassies, International
4 = Unpaid family worker
institutions and foreign aid and(contributing family
development agencies
worker)
8 = Other, specify …..
5 = Other(specify)(7)

Q15_C06B Q15_C07

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

Manual for Cambodia CSES 2009 (Version 2.0)(8)

Q15_C08380

15. CURRENT ECONOMIC ACTIVITY (CONT.)

Respondents: All household members aged 5 years and older

WEEK 4ACTIVITY STATUS DURING THE PAST 7 DAYS (CONT.)If Col. 3 = 1 (Workduring the the past 7
days)

ID NUMBER

Otherwise (>> 11)

How many days did
Besides ..[NAME]..'s main What was ..[NAME].. 's secondary occupation/economic activity during the past 7 days?
..[NAME].. work in his/her job/activity, how many
main
additional jobs/economic
occupation/economic
activities did he/she haveactivity during the pastduring the past 7 days?
month?

How many hours did..[NAME]..work in his/her
mainoccupation/economic
activity during the past 7
days?
Note: beggar and sex worker are occupations
Enter '0' if no more jobs(>> 20)
HOURS

DAYS

NO OF JOBS

Occupation description

NIS OCC:
CODE

(9)

(10)

(11)

(12a)

(12b)

(1)

01

Q15_C09

Q15_C10

Q15_C11

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

Manual for Cambodia CSES 2009 (Version 2.0)

Q15_C12B

381

15. CURRENT ECONOMIC ACTIVITY (CONT.)

Respondents: All household members aged 5 years and older

WEEK 4ACTIVITY STATUS DURING THE PAST 7 DAYS (CONT.)In what kind of industry/business (economic activity) did ..[NAME].. work in his/hersecondary occupation/activity (e.g. agriculture, manufacturing, construction,
hotel/restaurant, trade)?

01

What was ..[NAME].. 's
employment status in
his/her secondary
occupation/economic
activity?

1 = Government
2 = State owned enterpirse
1 = Employee
3 = Cambodian enterprise
2 = Employer
4 = Foreign enterprise (private)
3 = Own account worker
5 = Non profit institution
4 = Unpaid family worker
6 = Household sector
7 = Embassies, International institutions (contributing family
worker)and foreign aid and development
5 = Other(specify)
agencies
8 = Other, specify …..

ID NUMBER(1)

Under what type of employer did..[NAME].. work in his/her secondary
occupation/economic activity?

Industry description

NIS ISIC
CODE

(13a)

(13b)

(14)

Q15_C13B Q15_C14

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

Manual for Cambodia CSES 2009 (Version 2.0)

(15)

Q15_C15382

15. CURRENT ECONOMIC ACTIVITY (CONT.)

Respondents: All household members aged 5 years and older

ID NUMBER

ACTIVITY STATUS DURING THE PAST 7 DAYS (CONT.)

(1)

01

How many hours How many days
If Col 11 more than 1
did ..[NAME]..
did ..[NAME]..
(3 or morework in his/her work in his/her
jobs/economicactivities)
secondarysecondaryoccupation/
occupation/
If Col 11 = 1 (>> Col 19)
economic activity economic
during the past 7 activity duringdays?
the past month? Besides the hours worked
during the past 7 days in..[NAME]..'s main andsecondary occupation/
economic activity, how many
hours did [NAME] work inother jobs/activities during thepast 7 days?

How many hours intotal did ..[NAME]..
work (main +secondary +addtional
occupations/
economic activities)
during the past 7
days?

Ask only if Employee(Code 1 in Col 8 - main
occupation/economic activity or
Col 15 - secondary occupation)

How much did ..[NAME].. earn insalary/wages during the last monthfrom all jobs?
(In cash or in kind)

Write "0" if nothing

HOURS

DAYS

HOURS

HOURS

RIELS

(16)

(17)

(18)

(19)

(20)

Q15_C16

Q15_C17

Q15_C18

Q15_C19

Q15_C20

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

Manual for Cambodia CSES 2009 (Version 2.0)

WEEK 4

383

15. CURRENT ECONOMIC ACTIVITY (CONT.)

Respondents: All household members aged 5 years and older

WEEK 4

ID NUMBER

ACTIVITY STATUS DURING THE PAST 7 DAYS (CONT.)
Given the total number of How many hours
If Col 21 = 2
hours worked during the would ..[NAME].. like(more hours)
past 7 days, would
to work ...more / …
If Col 21 = 1 (>> NEXT..[NAME].. like to work
less?
PERSON)less, more or unchanged
hours, given that theWas ..[NAME].. able toincome would change in a(available to) work more
Col 21 = 2 (more hours (stated in Col 19)
corresponding way?
hours)
during the past 7 days or
Col 21 = 1
start working more hours(less hours)
within 2 weeks from now(the interview)?

1 = Less hours
2 = More hours
3 = Unchanged hours (>>NEXT PERSON)

Why did.. [NAME].. work
If Col 21 = 2 (more hours) andless hours the past 7 days Col 23 = 1 (available to workthan the hours he/she
more hours)
liked to work?
If Col 23 = 2 (>> NEXT PERSON)

1 = Temporary illness
2 = Not enough work
available
3 = Other reasons

1 = Yes
2 = NoLeave it blank if don't know
months and years

HOURS
(1)

(21)

(22)

01

Q15_C21

Q15_C22How long has ..[NAME].. beenworking less hours than he/shewanted (hours stated in Col 19)
and also been available to work
more hours?

(23)

Q15_C23(24)

Q15_C24

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

Manual for Cambodia CSES 2009 (Version 2.0)

MONTHS

YEARS

(25a)

(25b)

Q15_C25A

Q15_C25B

384

15. CURRENT ECONOMIC ACTIVITY (CONT.)

Respondents: All household members aged 5 years and older

WEEK 4ACTIVITY STATUS DURING THE PAST 7 DAYS (CONT.)If No work during In what ways did
the past 7 days (Col ..[NAME]..try to find a
3 = 2 and Col 4 = 2) work?
If work (Col 3 = 1 or
Col 4 = 1 (>> NEXT 1 = Applied to
PERSON)
advertisement
ID NUMBER

2 = Contacted (potential)

Was ..[NAME]..
How many How long has ..[NAME]..
If Col 26 = 2
available for work
hours does been out of work and(Not actively seeking work)
during the past 7..[NAME].. activily been looking fordays or available to want to work work?
If Col 26 = 1 >> NEXT PERSON
start working within 2 per week?
weeks from now
Why did [NAME] not actively seek work
(interview)?
during the past 4 weeks?

Has ..[NAME].. been employers
actively seeking work 3 = Enquired with friends
relatives etc
1 = Yesduring the past 4
4 = Employment agency 2 = No
weeks?
5 = Tried to start own
business but failed
6 = Other (specify)

Write '0' ifnone

1 = Yes
2 = No(>> 31)Enter up to 3 codes

Q15_C26

02

(27b)

(27c)

Q15_C25C

01

(27a)

Q15_C25B(26)

Q15_C25A

(1)

(28)

Q15_C28

1 = Believes no work is available
2 = Awaiting result of application
3 = Waiting to start new job
Leave it blank if don't
know months and years 4 = Permanent disabled
5 = Illness/desease/injured
6 = Too young, too old, retired
7 = Student
8 = Housekeeping, caring for children,
elderly or disabled
9 = Other reasons (specify)

HOURS

MONTHS

YEARS

(29)

(30a)

(30b)

Q15_C29 Q15_C30A Q15_C30B Q15_C31

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

Manual for Cambodia CSES 2009 (Version 2.0)

(31)

385

16. USUAL ECONOMIC ACTIVITY

Respondents: All household members aged 5 years and older

WEEK 4ACTIVITIES IN THE LAST 12 MONTHS

Q16_C01

ID NUMBER

Please provide information on all members aged 5 years and older who usually reside in this household. Try to interview each member personally
What were .. [NAME].. main activity during the past 12
How many
What was the main occupations/economic activity ..[NAME].. had duringmonths?
months were .. the past 12 months?
(More than 6 months or 183 days)[NAME]..
employed during
the last 12
1 = Employed (>>3)
months?
2 = Unemployed but employed any time before (>> 3)
3 = Unemployed and never employed any time before

4 = Home maker
5 = Student
6 = Dependent (infants and children not attending school,
work cannot do any work because of permanent disability
or illness or old age)
7 = Retired (from service and for most of the time wasdoing no other work such as cultivation, business, tradeetc.)
8 = Rent-receiver or other income recepient
9 = Other (specify)

(1)

01

Q16_C02

Round off tonearest monthNote: beggar and sex worker are occupations(If 1 and 2 then ask Cols 3 to 7
If 3 - 9 >> Col. 8)

NUMBER OF
MONTHS

Occupation/Activity description

NIS
OCC.
CODE

(2)

(3)

(4a)

(4b)

Q16_C03

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

Manual for Cambodia CSES 2009 (Version 2.0)

Q16_C04B

386

16. USUAL ECONOMIC ACTIVITY (CONTINUED)

Respondents: All household members aged 5 years and older

WEEK 4ACTIVITIES IN THE LAST 12 MONTHS (CONT.)ID NUMBER

What was the employment In what kind of economic activity like agriculture, manufacturing, trade etc. did ..[NAME].. work Under what type of employer did
status in ..[NAME].. inin his/her primary occupation/economic activity during the past 12 months?
..[NAME].. work in his/her main
his/her main occupation/
occupation/economic activity?
economic activity?

(1)

01

1 = Government
2 = State owned enterpirse
3 = Cambodian enterprise
4 = Foreign enterprise (private)
5 = Non profit institution
6 = Household sector
7 = Embassies, International institutions
and foreign aid and development
agencies
8 = Other, specify …..

1 = Employee
2 = Employer
3 = Own account worker
4 = Unpaid family worker(contributing family
worker)
5 = Other (specify)(5)

Q16_C05

Industry description

NIS
ISIC
CODE

(6a)

(6b)
Q16_C06B Q16_C07

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

Manual for Cambodia CSES 2009 (Version 2.0)

(7)

387

16. USUAL ECONOMIC ACTIVITY (CONTINUED)

Respondents: All household members aged 5 years and older

ACTIVITIES IN THE LAST 12 MONTHS (CONT.)ID NUMBERAll
In terms of contribution to income or subsistence ,what was the second most important activity..[NAME].. had during the past 12 months?
01 = None
Farming (growing crops)
02 = Unpaid employment (Own account worker or
employed in family enterprise)
03 = Paid employment (wage labourer)
Livestock faming
04= Unpaid employment (Own account worker or
employed in family enterprise)
05 = Paid employment
Other acitivities
06= Fishing
07= Other household-based production or services
08 = Construction
09 = Wholesale or retail trade
10 = Transport
11 = Other paid employment (services lik teaching,cooking, child care, medical etc.)

If Col 2 = 3, 4, 6-9 (>> NEXT PERSON)In what place/Where did ..[NAME]..
work or study?
1 = Working at home
2 = Working or schooling in the same district
3 = Working or schooling in another district
4 = Working or schooling across the borderof the country

(8)

(1)

01

For employed persons, unemplyed
but employed any time during thelast 12 months and students only
(main activity)(Col 2 = 1 or 2 or 5 )Q16_C08(9)

Q16_C09

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

Manual for Cambodia CSES 2009 (Version 2.0)

WEEK 4

388

17. VICTIMIZATION
Respondent: head of household, spouse of the head of household, or of another adult household member
A

WEEK 4

HOUSEHOLD SECURITY

Q1 Do you feel safe from crime and violence in this neighborhood?

1 = Yes

2 = NoQ17AQ1Q2 Do you feel you can rely on local police to protect your family and your property?

1 = Yes

2 = NoQ17AQ2B

VICTIM OF THEFT

09HH_S99_SingleQuestions.sav

Q1 Has this household or any of its members been exposed to theft, burglary or robbery in the last 12 months, that is, since ..[MONTH].. last year?
2 = No (>> C)

Q17BC01

EVENT NUMBER

1 = Yes
Who was thevictim of theevent?

In whatWas it…
month did it
happen?

COPY ID CODE
OF PERSONFROM ROSTER

Was the event
Which authority didreported to some ..[VICTIM].. report theauthorities?
event to?

1 = .. Theft?
2 = .. Burglary?
3 = .. Robbery?

1 = Yes
2 = No (>> 7)

Q17BQ1

Did theHow much was lost
event go to by this event?
court
procedure?

1 = Village leader
2 = Police
3 = Other (specify)

1 = Yes
2 = NoIf no specificperson enter "88'

MONTH
(1)

1

(2)

Q17BC02

RIELS

(3)

(4)

Q17BC03 Q17BC04(5)

Q17BC05(6)(7)

Q17BC06(8)

Q17BC07 Q17BC0823
409HH_S17b_Theft.sav
C

VICTIM OF ACCIDENTS

Q17CC01

EVENT #Q1 Has anyone in the household had an accident that caused injury in the last 12 months?
Who was thevictim of theevent?

Where did the accident
happen?

1 = At home
2 = At work
COPY ID CODE 3 = In traffic
OF PERSON
4 = In sports
FROM ROSTER 5 = At school
6 = Other (specify)

(1)

(2)

1

Q17CC02(3)

Q17CC03

In which month
did it happen?

1 = YesWas the injury so serious How long did it take for the injury tothat medical care washeal?
needed?
1 = Less than one week
2 = 1- 2 weeks
3 = 3- 4 weeks
4 = One month or more
5 = Not yet healed

1 = Yes
2 = NoMONTH(4)

Q17CC04

2 = No (>> D)(5)

Q17CC0523
4

09HH_S17c_Accidents.sav

Manual for Cambodia CSES 2009 (Version 2.0)

(6)

Q17CC06

Q17CQ1

389

17. VICTIMIZATION (CONTINUED)

Ask each household member individually, for children ask their parents

Q17DC01

ID NUMBER

D. VICTIM OF VIOLENCE(1)

To which kind of violence?
Have youbeen exposed
to any act ofviolence that 01 = Push you, shake you, or throwcaused injury something at you.
in the last 12 02 = Twist your arm.
03 = Punch you with a fist or withmonths?

1 = Yes
2 = No(>> NEXTPERSON)(2)

01 Q17DC02

How often
Was any event WhichDid any event
have you been reported toauthority did go to court
exposed in the some
you report the procedure?
last 12authorites?
event(s) to?
months?

1 = Once
something that could hurt you.
04 = Kick you or drag you.
2 = Twice05 = Try to strangle you or burn you.
3 = Three
06 = Attack you with a knife, gun or othertimes
1 = Yestype of weapon
4 = 4-9 times 2 = No
07 = Rape, forced to have sexual intercourse
5 = 10 or more (>> 8)
when you did not want totimes
08 = Slap/Strike/Beat you with hand
09 = Slap/Strike/Beat you with object
10 = Rob you
11 = Other (specify

(3a)

(3b)

(3c)

(4)

Q17DC03A Q17DC03B Q17DC03C Q17DC04

1 = Village
leader2 = Police
3 = Other(specify)(5)

Q17DC05

Q17DC06(7)

Q17DC07

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

END OF WEEK 4

Manual for Cambodia CSES 2009 (Version 2.0)

Was this act ofWas theviolence committed injury so
by some unknown serious
or by someone
thatknown to you?
medicalRefer to the most care wasserious event
needed?
1 = Unknown
person(s) -male
1 = Yes
2 = Unknown
person(s) - female 2 = No
3 = Known person(s) - male
4 = Known person(s) - female
5 = Other (specify)

1 = Yes
2 = No(6)

WEEK 4(8)

Q17DC08(9)

How long did ittake for the
injury to heal?
1 = Less than
one week
2 = 1- 2 weeks
3 = 3- 4 weeks
4 = One monthor more(10)

Q17DC09 Q17DC10

390

Q18_C01

ID NUMBER

18. SUMMARY OF PRESENCE IN THE HOUSEHOLD

Was .[NAME]..
present all thefirst 7 days?

DAYS 1 - 7
How many of the Were .[NAME].. ´ How much were
Was .[NAME]..
first 7 days has s consumption ..[NAME].. consumption present all the..[NAME].. been expenditures
expenditures while he/she second 7 days?
present in therecorded in the was absent from home
household?
diary?
during the first 7 days?

1 = Yes (>>Next person)
2 = No

1 = Yes (>>Next person)
2 = NoNo of days
(1)

01

DURING MONTH

1 = Yes (>>Next person)
2 = No

RIELS

(2a)

(2b)

(2c)

Q18_C02A

Q18_C02B

Q18_C02CDAYS 8 - 14
How many of the Were .[NAME].. ´ How much weresecond 7 days s consum-ption ..[NAME].. consumption
has ..[NAME].. expendi-tures
expenditures while he/shebeen present in recorded in the was absent from home
the household? diary?
during the second 7
days?

(2d)
Q18_C02D

1 = Yes (>>Next person)
2 = NoNo of days

RIELS

(3a)

(3b)

(3c)

Q18_C03A

Q18_C03B

Q18_C03C

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

Manual for Cambodia CSES 2009 (Version 2.0)

(3d)
Q18_C03D

391

18. SUMMARY OF PRESENCE IN THE HOUSEHOLD (CONTINUED)ID NUMBER

Was .[NAME]..
present all thethird 7 days?

DAYS 15 - 21
DAYS 22 >>How many of the Were .[NAME].. ´ How much were
Was .[NAME].. How many of the Were .[NAME].. ´ How much were
third 7 days has s consumption ..[NAME].. consumption present the rest rest of the month s consumption ..[NAME].. consumption..[NAME].. been expenditures
expenditures while he/she of the month?
has ..[NAME].. expenditures
expenditures while he/she
present in therecorded in the was absent from homebeen present in recorded in the was absent from home
household?
diary?
during the third 7 days?
the household? diary?
during the rest of themonth?

1 = Yes (>>Next person)
2 = No

1 = Yes (>>Next person)
2 = NoNo of days
(1)

01

DURING MONTH

1 = Yes (>>Next person)
2 = No

RIELS

(4a)

(4b)

(4c)

Q18_C04A

Q18_C04B

Q18_C04C(4d)
Q18_C04D

1 = Yes (>>Next person)
2 = NoNo of days

RIELS

(5a)

(5b)

(5c)

Q18_C05A

Q18_C05B

Q18_C05C

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

Manual for Cambodia CSES 2009 (Version 2.0)(5d)
Q18_C05D

392

Appendix 2

Codebook of CSES 2009
Regarding variable codes of file number 03 to 41 (S01A.hhmembers to S18.presenseinhh, pleaserefer to Appendix 1 Household questionnaire with variable names.

Codebook was prepared for the next data files.

No

Data file

Description

1

dy_expenditure

Diary micro data2

dy_income

Diary micro data

42

S99_singlequestions

43

weighthh44

weightpersons

45

IncomeCSES09

Household summary of income

46

hhexp

Household summary of expenditure compiled by the author

Manual for Cambodia CSES 2009 (Version 2.0)

393

Identification part

Variable

Description

Remarks

hhid

Household identifier

100101-2400320

persidPerson identifier

10010101-240032005

province

Province code01− Banteay Meanchey
02− Battambang
03− Kampong Cham
04− Kampong Chhnang
05− Kampong Speu
06− Kampong Thom
07− Kampot
08− Kandal
09− Koh Kong
10− Kratie
11− Mondul Kiri
12− Phnom Penh13− Preah Vihear
14− Prey Veng
15− Pursat
16− Ratanak Kiri
17− Siem Reap
18− Preah Sihanouk
19− Stung Treng
20− Svay Rieng
21− Takeo
22− Otdar Meanchey
23− Kep
24− Pailin

urbanrural

Urban / rural

1=Urban, 2=Rural

weightHousehold weight (original)

45.06-591.08

hhsizeHousehold size

1-15

PSU

1001-24003

weight3psu
stratum
surveymonth11-242
Survey month

1-12Manual for Cambodia CSES 2009 (Version 2.0)

394

regionRegion code

1=Phnom Pen, 2=Other urban,
3=Other rural

year

Survey year

= 2009persid_string

persid in 9-digit string (original)

leading zeros removed in resampled data

hhid_stringhhid in 7-digit string (original)

leading zeros removed in resampled data

Manual for Cambodia CSES 2009 (Version 2.0)

395

Data file: dy_expenditure

Variable

Description

Remarks

pkid

Unique id in the data file

1 to 2637002

hhidHousehold id

hhid=0100309 is missing.

Lineidthe page number plus the line number

101 to 5014

DiaryExp_Q2

DATE (DD/MM)

DiaryExp_Q5

CODE OF UNIT

DiaryExp_Q6

QUANTITY

DiaryExp_Q7

VALUE IN RIELS

DiaryExp_Q8

FORM OF ACQUISITION

1=Paid in cash
2=Paid in kind
3=Purchased on credit
4=Gift received
5=Stock of own-produced

DiaryExp_Q9

ORIGIN

1 = Household production
2 = Produced in Cambodia
3 = Imported from abroad
4 = Don't know the origin
5 = No product

DiaryExp_Q10

PURPOSE

01 = Own household consumption
02 = For agricultural production
03 = For manufacturing production
04 = For mining production
05 = For services production
06 = For other household production
07 = Gifts & remittances to otherhouseholds
08 = Offerings, donations, charities,etc.
09 = Interests
10 = Payback of loans
11 = Taxes
12 = Other (specify)

DiaryExp_Q11

ITEM CODE

4 digits

Manual for Cambodia CSES 2009 (Version 2.0)

396

Data file:dy_income

Variable

Description

Remarks

pkid

Unique id in the data file

1 to 883728

hhidHousehold id

hhid=0100309 is missing.

Lineidthe page number plus

0-9904the line number
DiaryInc_Q2

DATE (DD/MM)

DiaryInc_Q5

CODE OF QUANTITY

DiaryInc_Q6

QUANTITY

DiaryInc_Q7

VALUE IN RIELS

DiaryInc_Q8

TYPE OF INCOME

1=In cash
2=In kind
3=Bartered

DiaryInc_Q9

KIND OF INCOME

01 = Wage or salary (in cash or kind)
02 = Agricultural or forestry production or sales
03 = Fishing or hunting production or sales
04 = From mining production
05 = Sales of manufactured products
06 = Receipts from services rendered
07 = Received as gift
08 = Remittances received
09 = Pensions or other social assistances
10 = Study support in cash or kind (Scholarships,
stipends or other)
11 = Dividends, interests, commissions, rents etc.
12 = Receipts from sale of possessions/own property
13 = Withdrawals from savings/loans obtained
14 = Payback of loans
15 = Windfall gains/inheritance
16 = Tax refunds
17 = Maturity payment on insurance policies
18 = Lump-sum compensation for injury, legal
damages received
19 = Other (specify)

DiaryInc_Q10

ITEM CODE

Manual for Cambodia CSES 2009 (Version 2.0)

397

Data file: IncomeCSES2009

Variable

Description

Remarks

hhid

Household identifieradjdisp

Disposable income per household

Yearly income in Riels

adjdispcapita

Disposable income per capita

Yearly income in Riels

adjtotalincome

Total income per household

Yearly income in Riels(*) The weighted mean of the above variables are compatible with the figures in the survey report.

Manual for Cambodia CSES 2009 (Version 2.0)

398

Data file: hhexp(Note: This file was generated by the author.)
Variable

Description

hhidHousehold identifier

Remarksfood

Items 01 to 15, 18 to 20

alcohol16(Alcoholic beverages), 17(Tobacco products)

clothing

5(Clothing & footwear), 11(personal effects)

housingfurnishing6

health1(Medical care)transportation2

communication

3

recreation

8(Recreation within Cambodia),9(Recreation abroad)education

10

miscellaneous

13(Miscellaneous items), 4(Personal care)domsalary7(Domestic salaries)

total

Monthly comsumption= sum of the aboves

per household (*)
in Riels
foodshare= (food+alcohol) / total(*) The weighted mean of “total” is compatible with the figures in the survey report.

Manual for Cambodia CSES 2009 (Version 2.0)

399

Data file: S99_singlequestions

Variable

Description

Remarks

hhid

Household ID

q01bq1

Did your household run any business, e.g.

1 = Yes

agriculture production (farming), producing goods,

2 = Noservice etc.?
q03bq1

In addition to the persons living in your household

1 = Yesare

2 = No

there

any

other

persons(spousesorson/daughter), 15 years and older, who previously
has been a member of your household but no longer
are living in this household?
q05aq1aq05aq1b

Has the household sold any open land in the last 12

1 = Yesmonths?

2 = NoWhat was the primary reason/purpose for which

1 = To address family health issues

you sold the land?

2 = Invest in business
3 = To weed or buy farm equipment or for otheragricultural activities
4 = To pay debt
5 = To buy motor bike or cell phone or for otherhousehold consumption needs
6 = Rituals (marriage ceremony, funeral etc.)
7 = Other (specify)

q05aq2

Does anyone in your household own or operate any

1 = Yesland that is used / could be used for vegetable

2 = Nogardening, agricultural or farming activities (cropcultivation, livestock raising or private forestry)?
q05aq3How many plots does your household own or
operate?

q05bq1

Did your household produce any crops including

1 = Yes

fruits and vegetables during the past wet-season or

2 = Nothe past dry-season?
q05dq1q05eq1

Did your household have any crops in storage

1 = Yes

December 31 last year?

2 = NoHas your household or anyone in your household

1 = Yes

had any livestock in the past 12 months?

2 = NoManual for Cambodia CSES 2009 (Version 2.0)

400

q05fq1

Did your household or anyone in your household

1 = Yesraise fish (or any other aquatic product like frogs or

2 = Nocrocodiles) during the past 12 months?
q05fq2

q05fq3

Does your household or anyone in your household

1 = Yesown or operate a pond for fish or shrimp culture?

2 = NoDid your household or anyone in your household

1 = Yes

catch fish, shrimp, crabs,oysters, etc. during the

2 = Nopast 12 months?
q05gq1

Did anyone in your household collect firewood,

1 = Yescharcoal, timber or other forest products during the

2 = Nopast 12 months?
q05gq2

Did anyone in your household collect palm juice,

1 = Yesroot crops, herbs, honey or hunt wild animals or

2 = Nobirds during the past 12 months?
q05hq1q06_q1q08_q1

Did anyone in your household run an enterprise or

1 = Yesbusiness during the past 12 months?

2 = NoQ1 Does your household have outstanding debts to

1 = Yesother households or institutions?

2 = NoDoes the household own its own dwelling or any

1 = Yesother building(s) used for residential, agricultural,

2 = Nocommercial or industrial purposes?
q10_q1q11_q1q12_q1

q13aq1

Are there any women living in the household with

1 = Yes

living children under 5 years old?

2 = NoCheck in the household list if there are any children

1 = Yes00-23 months old living in the household?

2 = NoCheck in the household list if there are any children

1 = Yes

00-59 months old living in the household?

2 = NoIn the last 12 months, has any member of the

1 = Yeshousehold received free or subsidized health care

2 = Nothat other people would normally have to pay

8 = Don't knowfor?(including private health insurance)
q13aq2aHow (1st) did they obtain this free / subsidized

1 = Household Priority Access Card, Equity

treatment?

Card, or other document that allows free orsubsidized health care
2 = Name(s) are on a List of Poor Households
held by the local authorities

Manual for Cambodia CSES 2009 (Version 2.0)

401

3 = Health facility staff asked them questions
from a list / filled out a form before treatment
4 = Health facility staff provided free treatment(without asking questions or filling out a form)
5 = Have private health insurance
6 = Belong to community health insurance
scheme7 = Other (specify)
8 = Don’t know
q13aq2b

How (2nd) did they obtain this free / subsidized

Same as q13aq2atreatment?
q13aq2c

How (3rd) did they obtain this free / subsidized

Same as q13aq2atreatment?
q13aq3

When they received free / subsidized treatment,

1 = Yes, always treated the same

were they treated the same as other people who

2 = Yes, most times treated the same

were paying for their healthcare?

3 = Sometimes yes, sometimes no
4 = No, most times not treated the same
5 = No, never treated the same
8 = Don’t know

q13aq4

Do you or any member of the household have a

1 = YesPriority Access Card, Equity Card, or any other

2 = Nodocument that allows free or subsidized health

8 = Don't knowcare?
q17aq1

q17aq2

q17bq1

Do you feel safe from crime and violence in this

1 = Yesneighborhood?

2 = NoDo you feel you can rely on local police to protect

1 = Yes

your family and your property?

2 = NoHas this household or any of its members been

1 = Yes

exposed to theft, burglary or robbery in the last 12

2 = Nomonthsq17cq1

Has anyone in the household had an accident that

1 = Yescaused injury in the last 12 months?

2 = NoManual for Cambodia CSES 2009 (Version 2.0)

402

Data file: weighthh

Variable

Description

Remarks

hhid

Household identifier

weightHousehold weight (original)hhsizeHousehold size

weight3
HW=weight*hhsize
Adjusted household weight= weight / 0.8

Data file: weightpersons

Variable

Description

Remarks

hhid

Household identifier

persidIndividual identifier

weightHousehold weight (original)hhsizeHousehold size

weight3=weight*hhsize

persweight

Person weight (original)

HW

Adjusted household weight= weight / 0.8

PW

Adjusted person weight= persweight / 0.8

Manual for Cambodia CSES 2009 (Version 2.0)

403

Income Composition CSES 2009
When Diary in the beginning of the Variable name data is collected in Diary, otherwise it is collected
through Recall method.
Negative transfers are calculated from Diary data on Expenditure.
Variable

SQL-table

Rule

SalaryvPersonEcoCurrent

SalaryWages *12
Sum HHID

CostCrop

vHHCostCultivation- Total3_15
Crops
vHHLivestock1
TotalPaidBought12Months +vHHLivestock2
AmountInRiels

Q05CC16MonthlyRentPond*12 +AmountSpent

Q05F1C05
Q05F2C03

ExpActivity12M

Q05G2C03

ReceiptCrop

vHHFishCultivation1
vHHFishCultivation2
vHHForestryHunting2
vHHProductionCrops(ProducedOrHarvested –PostHarvestLoss) *SalesPriceCropsProduced

Q05BC06
Q05BC07
Q05BC09

ReceiptLivestock

vHHLivestock1

Q05E1C09
Q05E1C11-C15

ReceiptFish

vHHFishCultivation3
vHHForestryHunting1

ValueSoldPast12Months +(ConsumedInHHPast12Months +UseForBarterGiftCharity +ValueLivestockProductsSold +ValueLivestockProductsConsumed+ ValueLivestockUsedAsGifts)
AmountReceived
TotalAmountRiels

Q05G1C06

CostLivestock

CostFish

CostForestry

ReceiptForestry

AgriIncome

CostNonAgri

vHHNonAgriculture2

Note:
Question
number in
Questionnaireand Diary
House-hold
Q15C20

Q05E1C10
Q05E2C03

Q05F3C03(ReceiptCrop + ReceiptLivestock+ ReceiptFish +ReceiptForestry) minus(CostCrop + CostLivestock +CostFish + CostForestry +(see below)
InterestPaidAgri)(Activity1 + Activity2 +Q05H2C03-C07
Activity3 + Activity4 +Activity5) +(see below)
InterestPaidNonAgri

Manual for Cambodia CSES 2009 (Version 2.0)

404

ReceiptNonAgri vHHNonAgriculture3(Activity1 + Activity2 + Activity3 +Activity4 + Activity5)
If RevenueNumber is not 13

Q05H2C03C07
Q05H3C01
13 is
separate

ReceiptNonAgri minus CostNonAgri

NonAgriIncome

3% of(HowMuchToBuyBuilding –(RemainingLoan/1+
MonthlyRateInterest))
If PrimaryPurposeBorrMoney = 7

Q08_C05
Q06_C07
Q06_C08

vHHIncomeOtherSource

AmountRielsTotal
If SourceNumber = 7

Q07_C05
Q07_C01

InterestOther- vHHIncomeOtherLoans
SourceAmountRielsTotal
If SourceNumber = 9

Q07_C05
Q07_C01

Dividends

vHHIncomeOtherSource

AmountRielsTotal
If SourceNumber = 8

Q07_C05
Q07_C01

RentFromLand

vHHNonAgriculture3 (Activity1 + Activity2 + Activity3 +Activity4 + Activity5)
If RevenueNumber = 13

Q05H2C03-C07

IncomeOwn
House

BankInterest

vHHConstructionvHHLiabilities

Q06_C05

Q05H3C01
GrossPropertyIncome
InterestPaidvHHLiabilities
Agri

BankInterest + InterestOtherLoans +Dividends + RentFromLand
MonthlyRateInterest *RemainingLoan/(100+MonthlyRateInterest)
If PrimaryPurposeBorrMoney = 1

Q06_C08
Q06_C07
Q06_C08
Q06_C052004:
3.5% *
AmountMoney
Borrowed
Q06_C08
Q06_C07
Q06_C08
Q06_C05

InterestPaidNonAgri

vHHLiabilities

MonthlyRateInterest *RemainingLoan/(100+MonthlyRateInterest)
If PrimaryPurposeBorrMoney = 2

InterestPaidOwnOccupied

vHHLiabilities

MonthlyRateInterest *Q06_C08
RemainingLoan/(100+MonthlyRateInterest) Q06_C07
If PrimaryPurposeBorrMoney = 7
Q06_C08
Q06_C05

InterestPaidNet

vHHLiabilities

MonthlyRateInterest *RemainingLoan/(100+MonthlyRateInterest)
If PrimaryPurposeBorrMoney = 3-6, 8-10

Manual for Cambodia CSES 2009 (Version 2.0)

Q06_C08
Q06_C07
Q06_C08
Q06_C05

405

PropertyIncome

GrossPropertyIncome InterestPaidNet

PrimaryIncome

Salary + AgriIncome +NonAgriIncome +IncomeOwnHouse +PropertyIncome
vHHIncomeOther- WithinCambodia
SourceIf SourceNumber = 1

Q07_C03
Q07_C01

PensionAbroadvHHIncomeOther- FromAbroad
SourceIf SourceNumber = 1

Q07_C04
Q07_C01

PensionPensionDomestic +PensionAbroadvHHIncomeOther- AmountRielsTotal
SourceIf SourceNumber = 5

Q07_C05
Q07_C01

PensionDomestic

NGOtransfers

RemittanceDomesticvHHIncomeOther- WithinCambodia
SourceIf SourceNumber = 2 +Q03BC12 if Q03BC05B > 0

RemittanceAbroadvHHIncomeOther- FromAbroad
SourceIf SourceNumber = 2 +Q03BC12 if Q03BC05C > 0

TotalPrivateTransfers

RemittanceDomestic +RemittanceAbroadvHHIncomeOther- AmountRielsTotal
SourceIf SourceNumber = 3

Q07_C05
Q07_C01vHHIncomeOther- AmountRielsTotal
SourceIf SourceNumber = 4

Q07_C05
Q07_C01

ScholarshipGovernment

ScholarshipNGO

TotalScholarship

Q07_C03
Q07_C01
Q03BC12Q03BC05B
Q07_C04
Q07_C01
Q03BC12Q03BC05C

ScholarshipGovernment +ScholarshipNGO

Gifts

vHHIncomeOther- AmountRielsTotal
SourceIf SourceNumber = 10, 11

Q07_C05
Q07_C01

OtherTransfer

vHHIncomeOther- AmountRielsTotal
SourceIf SourceNumber = 12

Q07_C05
Q07_C01

TotalTransfers

TotalIncome

TotalPrivateTransfers +TotalScholarship + Gifts +OtherTransfer + NGOtransfers+ PensionPrimaryIncome +TotalTransfers

Manual for Cambodia CSES 2009 (Version 2.0)

406

DiaryTaxes

vDiaryExpenditure

Value * 12
If Purpose = 9

DiaryInterHHtransfers

vDiaryExpenditure

Value * 12
If Purpose = 7 andAcquisition = 1,2

DiaryCashTransferChar

vDiaryExpenditure

Value * 12
If Purpose = 8 andAcquisition = 1

DiaryTotalNegative
Transfers

DiaryTaxes +DiaryInterHHtransfers +DiaryCashTransferChar

DisposableIncome

TotalIncome minus
DiaryTotalNegativeTransfers

Manual for Cambodia CSES 2009 (Version 2.0)

DiaryExp_Q7,
DiaryExp_Q10
Cannot becalc. from
RecallDiaryExp_Q7,
DiaryExp_Q10
Cannot becalc. from
RecallDiaryExp_Q7,
DiaryExp_Q10
Cannot becalc. from
Recall407

Adjustment and correction in Household Income
This text is quoted from the draft mission report from a short term mission on Household
Income carried out 11-22 October.
The adjustment and corrections described below have only been done in the tabulation
file, not in the file sent to you.

Dealing with negative income
There are no rules for depreciations in Cambodia, i.e. how to make expenditures forinvestments divided into several years. It’s not unusual for households to have deficits ornegative incomes. Some 4 percent of the households have negative incomes. This
problem has been dealt with in a rough way by replacing the negative numbers with 4000
riels. This method was recommended by Swedish income expert who has been on several
short-terms missions to Cambodia in the past few years.
The replacement of negative incomes has a great influence of the results. Before any
adjustments the Gini-coefficient for Cambodia is 0,812. After adjusting for negativevalues the Gini-coefficient drops to 0,638. Mean values, deciles and quintiles are also
affected by this adjustment.

Outliers, top incomes
The results are very sensitive not only to negative incomes but the incomes in the very
top of the distribution. During the mission we corrected three households with extreme
high incomes. We found that there were miscalculations done that resulted in extreme
high incomes. The correction of these three households had a great impact on the results.

Manual for Cambodia CSES 2009 (Version 2.0)

408

New Rules for Expenditure Composition CSES 2009 - Yearly Exp
These variables are calculated by using Recall data from the CSES household questionnaire.
Variable

SQL-table/Modulein questionnaire

Food and nonalcoholic beverages
01

Rule

Note

Sum
Bread and Cereals +Meat and poultry +Fish and seafood +Eggs and diary products +Oil and fats +Fruits, nuts and edible seeds +Fresh vegetables +Tubers, pulses and prepared/preserved
vegetables +Sugar, salt, other seasonings and otherfood products +Non-alcoholic beverages +Food out of home/Take-home food

Bread and Cereals
0111

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 01

Last 7days

Meat and poultry
0112

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 03

Last 7days

Fish and seafood
0113

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 02

Last 7days

Eggs and diary products
0114

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 04, 05

Last 7days

Oil and fats
0115

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 06

Last 7days

Fruits, nuts and edible
seeds
0116

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 11,12

Last 7days

Fresh vegetables
0117a

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 07

Last 7days

Tubers, pulses andprepared/preserved
vegetables
0117b

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 08, 09, 10

Last 7
days

Manual for Cambodia CSES 2009 (Version 2.0)

409

Sugar, salt, otherseasonings and other foodproducts
0118

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 13, 18

Last 7days

Non-alcoholic beverages
0120

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 14,15

Last 7days

Food out of home/Take-home food0130

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 19, 20

Last 7days

Alcoholic Beverages
and Tobacco02

Sum
Alcoholic beverages +TobaccoAlcoholic beverages
0210

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 16

Last 7days

Tobacco0220

vHHFoodConsumption

FoodTotalConsumption * 52
If FoodItemCode = 17

Last 7
days

Clothing andfootwearvHHRecallNonFood

TotalExpenditure * 2
If RowNo = 05

Last 6month

0300

Housing, water,electricity, gas andother fuels (utilities)
04

Sum
Actual rent +Rental value of owner-occupied housing +Maintenance and repair +Water supply and miscellaneous services
related to the dwelling +Electricity, gas and other fuels

Actual rent0410

vHHHousing

PayForRentThisHouse * 12

Per
month

Rental value of owneroccupied housing
0420
Maintenance and repair
0430

vHHHousing

PayPerMonthToRentDwelling * 12

Per
month

vHHHousing

RepairDwelling * 12

Last
month

Water supply andmiscellaneous services
related to the dwelling0440

vHHHousing(ChargeOfWaterLastMonth +SpendForSewage + SpendForGarbage) * 12

Last
month

Electricity, gas and other
fuels
0450

vHHHousing(SpendElectricity + SpendGasLPG +SpendKerosene + SpendFirewood +SpendCharcoal + SpendBattery +SpendOther) * 12

Last
month

Manual for Cambodia CSES 2009 (Version 2.0)

410

Furnishings
equipment andmaintenance05
Furnishings, household
equipment and operations
0500

Sum
Furnishings, household equipment andoperations+ Domestic salaries
vHHRecallNonFood

TotalExpenditure
If RowNo = 06

Last 12monthvHHRecallNonFood

TotalExpenditure
If RowNo = 07

Last 12monthvHHRecallNonFood

TotalExpenditure * 12
If RowNo = 01

Last
month

vHHRecallNonFood

TotalExpenditure * 12
If RowNo = 02

Last
month

vHHRecallNonFood

TotalExpenditure * 12
If RowNo = 03

Last
month

vHHRecallNonFood

TotalExpenditure
If RowNo = 08, 09

Last 12monthvHHRecallNonFood

TotalExpenditure
If RowNo = 10

Last 12month

Domestic salaries
0560

Health0600

Transportation
0700

Communication
0800

Recreation andculture0900

Education
1000

Miscellaneous goods
and services
12

Sum
Personal care +Personal effects +Insurance, financial services +Other miscellaneous services

Personal care
1210

vHHRecallNonFood

TotalExpenditure * 12
If RowNo = 04

Last
month

Personal effects
1230

vHHRecallNonFood

TotalExpenditure
If RowNo = 11

Last 12month

Other miscellaneous
services (items)
1270

vHHRecallNonFood

TotalExpenditure
If RowNo = 13

Last 12
month

Sum

Not
calculated in
the file

Total expenditure

Food and non-alcoholic beverages +Alcoholic Beverages and Tobacco +Clothing and footwear +Housing, water, electricity, gas and otherfuels +
Furnishings equipment and maintenance +Health +Transportation +Communication +Recreation and culture +Education +Miscellaneous goods and services

Manual for Cambodia CSES 2009 (Version 2.0)

411

In SuperCross:
Build in Summation of Values /Households /Household expenditures
Combined
expenditure in Riels

Food expendituresin Riels

Non food
expenditures in RielsSum
01 (Food and non-alcoholic beverages) +02 (Alcoholic Beverages and Tobacco) +03 (Clothing and footwear) +04 (Housing, water, electricity, gas andother fuels) +05 (Furnishings equipment andmaintenance) +06 (Health) +07 (Transportation) +08 (Communication) +09 (Recreation and culture) +10 (Education) +12 (Miscellaneous goods and services)
01 (Food and non-alcoholic beverages)+
0210 (Alcoholic Beverages)
0220 (Tobacco) +03 (Clothing and footwear) +04 (Housing, water, electricity, gas andother fuels +05 (Furnishings equipment andmaintenance) +06 (Health) +07 (Transportation) +08 (Communication) +09 (Recreation and culture) +10 (Education) +12 (Miscellaneous goods and services)

Manual for Cambodia CSES 2009 (Version 2.0)