Abstract

The World Bank has launched a fast-deploying high-frequency phone-based survey of households to generate near real time insights into the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based policy responses to the crisis. At a time when conventional modes of data collection are not feasible, this phone-based rapid data collection method offers a way to gather granular information on the transmission mechanisms of the crisis on the populations, to identify gaps in policy responses, and to generate insights to inform scaling up or redirection of resources as the crisis unfolds.

Geographic coverage

National

Analysis unit

Individual, Household-level

Sampling procedure

A mobile frame was generated via random digit dialing (RDD), based on the National Numbering Plans from the Malaysian Communications and Multimedia Commission (MCMC). All possible subscriber combinations were generated in DRUID (D Force Sampling's Reactive User Interface Database), an SQL database interface which houses the complete sampling frame. From this database, complete random telephone numbers were sampled. For Round 1, a sample of 33,894 phone numbers were drawn (without replacement within the survey wave) from a total of 102,780,000 possible mobile numbers from more than 18 mobile providers in the sampling frame, which were not stratified. Once the sample was drawn in the form of replicates (subsamples) of n = 10.000, the numbers were filtered by D-Force Sampling using an auto-dialer to determine each numbers' working status. All numbers that yield a working call disposition for at least one of the two filtering attempts were then passed to the CATI center human interviewing team. Mobile devices were assumed to be personal, and therefore the person who answered the call was the selected respondent. Screening questions were used to ensure that the respondent was at least 18 years old and within the capacity of either contributing, making or with knowledge of household finances. Respondents who had participated in Round 1 were sampled for Round 2. Fresh respondents were introduced in Round 3 in addition to panel respondents from Round 2; fresh respondents in Round 3 were selected using the same procedure for sampling respondents in Round 1.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is available in three languages, including English, Bahasa Melayu, and Mandarin Chinese. It can be downloaded from the Downloads section.

Response rate

In Round 1, the survey successfully interviewed 2,210 individuals out of 33,894 sampled phone numbers. In Round 2, the survey successfully re-interviewed 1,047 individuals, recording a 47% response rate. In Round 3, the survey successfully re-interviewed 667 respondents who had been previously interviewed in Round 2, recording a 64% response rate. The panel respondents in Round 3 were added with 446 fresh respondents.

Sampling error estimates

In Round 1, assuming a simple random sample, with p=0.5 and n=2,210 at the 95% CI level, yields a margin of sampling error (MOE) of 2.09 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 2.65% percentage points.

In Round 2, the complete weight was for the entire sample adjusted to the 2021 population estimates from DOSM’s annual intercensal population projections. Assuming a simple random sample with p=0.5 and n=1,047 at the 95% CI level, yields a margin of sampling error (MOE) of 3.803 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 3.54 percentage points.

Among both fresh and panel samples in Round 3, assuming a simple random sample, with p=0.5 and n=1,113 at the 95% CI level yields a margin of sampling error (MOE) of 2.94 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 3.34 percentage points.

Among panel samples in Round 3, with p=0.5 and n=667 at the 95% CI level yields a margin of sampling error (MOE) of 3.80 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 4.16 percentage points.

Abstract

Facebook’s Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality. This survey covers topics about gender dynamics and norms, unpaid caregiving, and life during the COVID-19 pandemic. Aggregated data is available publicly on Humanitarian Data Exchange (HDX). De-identified microdata is also available to eligible nonprofits and universities through Facebook’s Data for Good (DFG) program. For more information, please email dataforgood@fb.com.

Geographic coverage

This survey is fielded once a year in over 200 countries and 60 languages. The data can help researchers track trends in gender equality and progress on the Sustainable Development Goals.

Analysis unit

• Public Aggregate Data on HDX: country or regional levels
• De-identified Microdata through Facebook Data for Good program: Individual level

Universe

The survey was fielded to active Facebook users.

Kind of data

Sample survey data [ssd]

Sampling procedure

Respondents were sampled across seven regions: - East Asia and Pacific; Europe and Central Asia - Latin America and Caribbean - Middle East and North Africa - North America - Sub-Saharan Africa - South Asia

For the purposes of this report, responses have been aggregated up to the regional level; these regional estimates form the basis of this report and its associated products (Regional Briefs). In order to ensure respondent confidentiality, these estimates are based on responses where a sufficient number of people responded to each question and thus where confidentiality can be assured. This results in a sample of 461,748 respondents.

The sampling frame for this survey is the global database of Facebook users who were active on the platform at least once over the past 28 days, which offers a number of advantages: It allows for the design, implementation, and launch of a survey in a timely manner. Large sample sizes allow for more questions to be asked through random assignment of modules, avoiding respondent fatigue. Samples may be drawn from diverse segments of the online population. Knowledge of the overall sampling frame allowed for more rigorous probabilistic sampling techniques and non-response adjustments than is typical for online and phone surveys

Mode of data collection

Internet [int]

Research instrument

The survey includes a total of 75 questions, split across into the following sections: - Basic demographics and gender norms - Decision making and resource allocation across household members - Unpaid caregiving - Additional household demographics and COVID-19 impact - Optional questions for special groups (e.g. students, business owners, the employed, and the unemployed)

Questions were developed collaboratively by a team of economists and gender experts from the World Bank, UN Women, Equal Measures 2030, and Ladysmith. Some of the questions have been borrowed from other surveys that employ alternative modes of administration (e.g., face-to-face, telephone surveys, etc.); this allows for comparability and identification of potential gaps and biases inherent to Facebook and other online survey platforms. As such, the survey also generates methodological insights that are useful to researchers undertaking alternative modes of data collection during the COVID-19 era.

In order to avoid “survey fatigue,” wherein respondents begin to disengage from the survey content and responses become less reliable, each respondent was only asked to answer a subset of questions. Specifically, each respondent saw a maximum of 30 questions, comprising demographics (asked of all respondents) and a set of additional questions randomly and purposely allocated to them.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error and nonresponse error.

Data appraisal

Survey Limitations The survey only captures respondents who: (1) have access to the Internet (2) are Facebook users (3) opt to take this survey through the Facebook platform. Knowledge of the overall demographics of the online population in each region allows for calibration such that estimates are representative at this level. However, this means the results only tell us something about the online population in each region, not the overall population. As such, the survey cannot generate global estimates or meaningful comparisons across countries and regions, given the heterogeneity in internet connectivity across countries. Estimates have only been generated for respondents who gave their gender as male or female. The survey included an “other” option but very few respondents selected it, making it impossible to generate meaningful estimates for non-binary populations. It is important to note that the survey was not designed to paint a comprehensive picture of household dynamics but rather to shed light on respondents’ reported experiences and roles within households

Abstract

The liberalized economic system in Armenia has led to a sharp growth in individual housing construction by individuals for their own use. High rates of individual housing construction may be observed in some geographic (regional) locations. However a lack of accurate administrative registers of licences for construction, the prevalence of some constructions (built without any license), create particular difficulties in producing reliable and comprehensive statistical data collection on individual housing construction.

In general, problems faced in collecting information about house construction may be separated in the following main groups: • incompleteness of indicators on volumes of individual housing construction by marz (region) breakdown, • introduction of the instruments being used in the international practice, taking into consideration peculiarities of the sphere, • lack of precise mechanisms for monitoring the process of individual housing construction, • expanding and improvement of the existing indicators set, • necessity of forming and updating of the individual housing construction register.

In this context, in order to improve the statistical accounting of house construction, it is important to conduct periodical surveys and by so doing to improve the instruments available, through the development and use of state statistical reporting forms, and to obtain some broad indicators of levels of activity in at least some regions of the Country.

Taking into account the above-mentioned, the main purpose of this survey was to improve statistics on individual housing construction. In particular, • ensuring the comparability of the statistical data on house construction with the methodologies and standards used in the international practice, • ensuring the comprehensiveness of the indicators by regional breakdown, • use of the sampling methods and improvements of their methodology in construction.

The survey results provide: - complete and reliable information on individual housing construction in some key regions, particularly studying structure and volumes of the buildings, - and increase in the quality of information, - to complement the database on house construction within the official statistics with new indicators, - a model for a register for newly built houses which can be used to monitor periodically the level housing construction activity.

The derived results enable NSSRA to improve and update its database, to expand its list of published indicators, to improve methodology, and to support more informed policy making by providing state and local selfgovernment bodies with key information.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

There were two main approaches - entire and sampling - used for the conduct of the survey.

Lists of the licenses for individual housing construction, which had been given since 2005 by the state government body in the urban development, served as the main information source for the survey.

However there were, in some regions, serious inaccuracies and lack of availability of lists of licensed permits for individual house construction. These weaknesses, together with restrictions of available financial and human resources and the objective of receiving representative data, led to a concentration of survey resources in those regions where the individual housing construction is more prevalent and where reasonably up-to-date lists of licences are available. Yerevan and the following 4 marzes - Aragatsotn, Ararat, Armavir and Kotayk- were selected. The results of the survey therefore only apply to Yerevan and to these 4 marzes.

The licenses given for individual housing construction in Yerevan city were surveyed in their entirety, but in the other marzes - by the random sampling, considering the differences between the numbers of the mentioned licenses (from 100 to 640, meanwhile 100 - in Armavir, 136 - in Aragatsotn, 304 - in Ararat, 640 -in Kotayk), based on which the sample "steps" had been determined.

Overall there were 1330 licences granted, permitting individuals to construct a house for their own use. These were predominantly in Yerevan.

Although the survey was aimed at 1330 houses, it was foreseen to survey also those buildings under construction in the neighbourhood of the surveyed buildings, which were out of the list of the buildings to be surveyed.

Mode of data collection

Face-to-face [f2f]

Abstract

Stats SA conducted the October Household Survey (OHS) annually from 1994 to 1999, based on a probability sample of a large number of households ranging from 16 000 to 30 000 households each year (depending on availability of funding). This survey was discontinued in 1999 due to the reprioritisation of surveys in the face of financial constraints. February 2000 saw the birth of the Labour Force Survey (LFS), which is a biannual survey conducted by Stats SA in March and September of each year. The LFS covers some areas previously covered by the OHS, but not all, since it is a specialised survey principally designed to measure the dynamics in the labour market. The September LFS each year does include a section designed to measure social indicators such as access to infrastructure, but again this section does not go into as much depth as the OHS used to. A need was therefore identified by our users for a regular survey designed specifically to measure the level of development and the performance of government programmes and projects. The General Household Survey (GHS) was developed for this purpose. While the survey replaces the October Household Survey (OHS), the indicators measured in the 13 nodal areas identified for the Integrated Rural Development Strategy (IRSD) formed the basis for the subject matter of the survey. The first round of the GHS was conducted in July 2002 and the second round in July 2003.

Geographic coverage

The scope of the General Household Survey 2003 was national coverage.

Analysis unit

The units of anaylsis for the General Household Survey 2003 are individuals and households.

Universe

The survey covered all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the GHS 2003 a multi-stage stratified sample was drawn using probability proportional to size principles.

The sample was drawn from the master sample, which Statistics South Africa uses to draw samples for its regular household surveys. The master sample is drawn from the database of enumeration areas (EAs) established during the demarcation phase of Census 1996. As part of the master sample, small EAs consisting of fewer than 100 households are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 households, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involves explicit stratification by province and within each province, by urban and non-urban areas. Within each stratum, the sample was allocated disproportionately. A PPS sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 000 PSUs were selected. In each selected PSU a systematic sample of ten dwelling units was drawn, thus, resulting in approximately 30 000 dwelling units. All households in the sampled dwelling units were enumerated. The master sample is divided into five independent clusters. In order to avoid respondent fatigue (the LFS is a rotating panel survey which is conducted twice yearly), the GHS sample uses a different cluster from the LFS clusters.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GHS 2003 questionnaire collected data on: Household characteristics: Dwelling type, home ownership, access to water and sanitation facilities, access to services, transport, household assets, land ownership, agricultural production Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, disability, access to social services, mortality. Women's characteristics: fertility

Response rate

Response codes Number of responses % Completed 26 469 84.7 Non-contact 897 2.9 Refusal 645 2.1 Partly completed 18 0.1 Unusable information 1 0.0 Vacant 1 510 4.8 Listing error 246 0.8 Other 1 447 4.6 Total 31 233 100.0

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2021 (LFS). The survey rounds covered a total sample of about 25,179 households (about 6,300 households per quarter).

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2020.

---> Sampling Frame: The sampling frame consists of a comprehensive sample selected from the Population, Housing and Establishments Census 2017: This comprehensive sample consists of geographical areas with an average of 150 households, and these are considered as enumeration areas used in the census and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 8,040 households in each quarter of 2021.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 536 enumeration areas for the whole round. Second stage: we select a systematic random sample of 15 households from each enumeration area selected in the first stage.

---> Sample strata: The population was divided by: 1- Governorate (17 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 536 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2020 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. From the beginning of March 2020, with the spread of the COVID-19 pandemic and the home quarantine imposed by the government, the personal (face to face) interview was replaced by the phone interview for households who had phone numbers from previous rounds, and for those households that did not have phone numbers, they were referred to and interviewed in person (face to face interview). Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 32,160 households of which 25,179 households completed the interview; whereas 16,355 households from the West Bank and 8,824 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 79.8% while in the Gaza Strip it reached 90.5%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total non-response rate reached 16.7% which is very low once compared to the

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The basic goal of the Household and Consumption Survey is to provide a necessary database for formulating national policies at various levels. This survey provides the contribution of the household sector to the Gross National Product (GNP). It determines the incidence of poverty, and provides weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Furthermore, this survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) and governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

All Palestinian households who are usually resident in the Palestinian Territory during 2011.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

Sample and Frame: The sampling frame consists of all enumeration areas which were enumerated in 2007, each numeration area consists of buildings and housing units with average of about 120 households in it. These enumeration areas are used as primary sampling units PSUs in the first stage of the sampling selection.

Sample Size: The calculated sample size for the Expenditure and Consumption survey 2011 is about 4,317 households, 2,834 households in West Bank and 1,483 households in Gaza Strip.

Sample Design: The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 215 enumeration areas. Second stage: selection of a systematic random sample of 24 households from each enumeration area selected in the first stage.

Note: in Jerusalem Governorate (J1), 14 enumeration areas were selected. In the second stage, a group of households from each enumeration area were chosen using the 2007 census method of delineation and enumeration to obtain 24 responsive households. This ensures household response is the maximum to comply with the percentage of non-response as set in the sample design.

Enumeration areas were distributed to twelve months and the sample for each quarter covers sample strata (Governorate, locality type)

Sample strata: The population was divided by: 1- Governorate 2- Type of Locality (urban, rural, refugee camps)

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First: Survey's Questionnaire Part of the questionnaire is to be filled in during the visit at the beginning of the month, while the other part is to be filled in at the end of the month. The questionnaire includes: Control Sheet: Includes household's identification data, date of visit, data on the fieldwork and data processing team, and summary of household's members by gender. Household Roster: Includes demographic, social, and economic characteristics of household's members. Housing Characteristics: Includes data like type of housing unit, number of rooms, value of rent, and connection of housing unit to basic services like water, electricity and sewage. In addition, data in this section includes source of energy used for cooking and heating, distance of housing unit from transportation, education, and health centers, and sources of income generation like ownership of farm land or animals. Food and Non-Food Items: includes food and non-food items, and household record her expenditure for one month. Durable Goods Schedule: Includes list of main goods like washing machine, refrigerator, TV. Assistances and Poverty: Includes data about cash and in kind assistances (assistance value, assistance source), also collecting data about household situation, and the procedures to cover expenses. Monthly and Annual Income: Data pertinent to household's income from different sources is collected at the end of the registration period.

Second: List of Goods The classification of the list of goods is based on the recommendation of the United Nations for the SNA under the name Classification of Personal Consumption by purpose. The list includes 55 groups of expenditure and consumption where each is given a sequence number based on its importance to the household starting with food goods, clothing groups, housing, medical treatment, transportation and communication, and lastly durable goods. Each group consists of important goods. The total number of goods in all groups amounted to 667 items for goods and services. Groups from 1-21 includes goods pertinent to food, drinks and cigarettes. Group 22 includes goods that are home produced and consumed by the household. The groups 23-45 include all items except food, drinks and cigarettes. The groups 50-55 include durable goods. The data is collected based on different reference periods to represent expenditure during the whole year except for cars where data is collected for the last three years.

Registration Form The registration form includes instructions and examples on how to record consumption and expenditure items. The form includes columns: * Monetary: If the good is purchased, or in kind: if the item is self produced. * Title of the service of the good * Unit of measurement (kilogram, liter, number) * Quantity * Value

The pages of the registration form are colored differently for the weeks of the month. The footer for each page includes remarks that encourage households to participate in the survey. The following are instructions that illustrate the nature of the items that should be recorded: * Monetary expenditures during purchases * Purchases based on debts * Monetary gifts once presented * Interest at pay * Self produced food and goods once consumed * Food and merchandise from commercial project once consumed * Merchandises once received as a wage or part of a wage from the employer.

Cleaning operations

Raw Data

Data editing took place through a number of stages, including: 1. Office editing and coding 2. Data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consisted of 5,272 households, weights were modified to account for the non-response rate. The response rate was 88%.

Total sample size = 5,272 Households Household completed = 4317 Households Traveling households = 66 Households Unit does not exist = 48 Households No one at home = 135 Households Refused to cooperate = 347 Households Vacant housing unit = 222 Households No available information = 6 Households Other= 30 Households

Response and non-response rates formulas:

Percentage of over-coverage errors = Total cases of over-coverage*100% Number of cases in original sample = 5% Non-response rate = Total cases of non-response*100% Net sample size = 12% Net sample = Original sample - cases of over-coverage Response rate = 100% - non-response rate= 88%

Sampling error estimates

The impact of errors on data quality was reduced to a minimum due to the high efficiency and outstanding selection, training, and performance of the fieldworkers.

Procedures adopted during the fieldwork of the survey were considered a necessity to ensure the collection of accurate data, notably: 1- Develop schedules to conduct field visits to households during survey fieldwork. The objectives of the visits and the data collected on each visit were predetermined. 2- Fieldwork editing rules were applied during the data collection to ensure corrections were implemented before the end of fieldwork activities 3- Fieldworkers were instructed to provide details in cases of extreme expenditure or consumption by the household. 4- Questions on income were postponed until the final visit at the end of the month 5- Validation rules were embedded in the data processing systems, along with procedures to verify data entry and data edit.

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

Abstract

The Household Income and Expenditure Survey is a survey collecting data on income, consumption and expenditure patterns of households, in accordance with methodological principles of statistical enquiries, which are linked to demographic and socio-economic characteristics of households. A Household Income and expenditure Survey is the sole source of information on expenditure, consumption and income patterns of households, which is used to calculate poverty and income distribution indicators. It also serves as a statistical infrastructure for the compilation of the national basket of goods used to measure changes in price levels. Furthermore, it is used for updating of the national accounts.

The main objective of the NHIES 2009/2010 is to comprehensively describe the levels of living of Namibians using actual patterns of consumption and income, as well as a range of other socio-economic indicators based on collected data. This survey was designed to inform policy making at the international, national and regional levels within the context of the Fourth National Development Plan, in support of monitoring and evaluation of Vision 2030 and the Millennium Development Goals. The NHIES was designed to provide policy decision making with reliable estimates at regional levels as well as to meet rural - urban disaggregation requirements.

Geographic coverage

National Coverage

Analysis unit

Individuals and Households

Universe

Every week of the four weeks period of a survey round all persons in the household were asked if they spent at least 4 nights of the week in the household. Any person who spent at least 4 nights in the household was taken as having spent the whole week in the household. To qualify as a household member a person must have stayed in the household for at least two weeks out of four weeks.

Kind of data

Sample survey data [ssd]

Sampling procedure

The targeted population of NHIES 2009/2010 was the private households of Namibia. The population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in the survey. However, private households residing within institutional settings were covered. The sample design for the survey was a stratified two-stage probability sample, where the first stage units were geographical areas designated as the Primary Sampling Units (PSUs) and the second stage units were the households. The PSUs were based on the 2001 Census EAs and the list of PSUs serves as the national sample frame. The urban part of the sample frame was updated to include the changes that take place due to rural to urban migration and the new developments in housing. The sample frame is stratified first by region followed by urban and rural areas within region. In urban areas further stratification is carried out by level of living which is based on geographic location and housing characteristics. The first stage units were selected from the sampling frame of PSUs and the second stage units were selected from a current list of households within each selected PSU, which was compiled just before the interviews.

PSUs were selected using probability proportional to size sampling coupled with the systematic sampling procedure where the size measure was the number of households within the PSU in the 2001 Population and Housing Census. The households were selected from the current list of households using systematic sampling procedure.

The sample size was designed to achieve reliable estimates at the region level and for urban and rural areas within each region. However the actual sample sizes in urban or rural areas within some of the regions may not satisfy the expected precision levels for certain characteristics. The final sample consists of 10 660 households in 533 PSUs. The selected PSUs were randomly allocated to the 13 survey rounds.

Sampling deviation

All the expected sample of 533 PSUs was covered. However a number of originally selected PSUs had to be substituted by new ones due to the following reasons.

Urban areas: Movement of people for resettlement in informal settlement areas from one place to another caused a selected PSU to be empty of households.

Rural areas: In addition to Caprivi region (where one constituency is generally flooded every year) Ohangwena and Oshana regions were badly affected from an unusual flood situation. Although this situation was generally addressed by interchanging the PSUs betweensurvey rounds still some PSUs were under water close to the end of the survey period. There were five empty PSUs in the urban areas of Hardap (1), Karas (3) and Omaheke (1) regions. Since these PSUs were found in the low strata within the urban areas of the relevant regions the substituting PSUs were selected from the same strata. The PSUs under water were also five in rural areas of Caprivi (1), Ohangwena (2) and Oshana (2) regions. Wherever possible the substituting PSUs were selected from the same constituency where the original PSU was selected. If not, the selection was carried out from the rural stratum of the particular region. One sampled PSU in urban area of Khomas region (Windhoek city) had grown so large that it had to be split into 7 PSUs. This was incorporated into the geographical information system (GIS) and one PSU out of the seven was selected for the survey. In one PSU in Erongo region only fourteen households were listed and one in Omusati region listed only eleven households. All these households were interviewed and no additional selection was done to cover for the loss in sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

The instruments for data collection were as in the previous survey the questionnaires and manuals. Form I questionnaire collected demographic and socio-economic information of household members, such as: sex, age, education, employment status among others. It also collected information on household possessions like animals, land, housing, household goods, utilities, household income and expenditure, etc.

Form II or the Daily Record Book is a diary for recording daily household transactions. A book was administered to each sample household each week for four consecutive weeks (survey round). Households were asked to record transactions, item by item, for all expenditures and receipts, including incomes and gifts received or given out. Own produce items were also recorded. Prices of items from different outlets were also collected in both rural and urban areas. The price collection was needed to supplement information from areas where price collection for consumer price indices (CPI) does not currently take place.

Cleaning operations

The questionnaires received from the regions were registered and counterchecked at the survey head office. The data processing team consisted of Systems administrator, IT technician, Programmers, Statisticians and Data typists.

Data capturing

The data capturing process was undertakenin the following ways: Form 1 was scanned, interpreted and verified using the “Scan”, “Interpret” & “Verify” modules of the Eyes & Hands software respectively. Some basic checks were carried out to ensure that each PSU was valid and every household was unique. Invalid characters were removed. The scanned and verified data was converted into text files using the “Transfer” module of the Eyes & Hands. Finally, the data was transferred to a SQL database for further processing, using the “TranScan” application. The Daily Record Books (DRB or form 2) were manually entered after the scanned data had been transferred to the SQL database. The reason was to ensure that all DRBs were linked to the correct Form 1, i.e. each household’s Form 1 was linked to the corresponding Daily Record Book. In total, 10 645 questionnaires (Form 1), comprising around 500 questions each, were scanned and close to one million transactions from the Form 2 (DRBs) were manually captured.

Response rate

Household response rate: Total number of responding households and non-responding households and the reason for non-response are shown below. Non-contacts and incomplete forms, which were rejected due to a lot of missing data in the questionnaire, at 3.4 and 4.0 percent, respectively, formed the largest part of non-response. At the regional level Erongo, Khomas, and Kunene reported the lowest response rate and Caprivi and Kavango the highest. See page 17 of the report for a detailed breakdown of response rates by region.

Data appraisal

To be able to compare with the previous survey in 2003/2004 and to follow up the development of the country, methodology and definitions were kept the same. Comparisons between the surveys can be found in the different chapters in this report. Experiences from the previous survey gave valuable input to this one and the data collection was improved to avoid earlier experienced errors. Also, some additional questions in the questionnaire helped to confirm the accuracy of reported data. During the data cleaning process it turned out, that some households had difficulty to separate their household consumption from their business consumption when recording their daily transactions in DRB. This was in particular applicable for the guest farms, the number of which has shown a big increase during the past five years. All households with extreme high consumption were examined manually and business transactions were recorded and separated from private consumption.

Abstract

The basic goal of this survey is to provide the necessary database for formulating national policies at various levels. It represents the contribution of the household sector to the Gross National Product (GNP). Household Surveys help as well in determining the incidence of poverty, and providing weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Generally, the Household Expenditure and Consumption Survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which enumerated in 1997 and the numeration area consists of buildings and housing units and has in average about 150 households in it. We use the enumeration areas as primary sampling units PSUs in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is stratified cluster systematic random sample with two stages: The calculated sample size is 1,616 households, the completed households were 1,281 (847 in the west bank and 434 in the Gaza strip). First stage: selection a systematic random sample of 120 enumeration areas. Second stage: selection a systematic random sample of 12-18 households from each enumeration area selected in the first stage.

Sample strata:

We divided the population by: 1- Region (North West Bank, Middle West Bank, South West Bank, Gaza Strip) 2- Type of Locality (urban, rural, refugee camps)

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 12 households.

Sample Size:

The calculated sample size is 1,616 households, the completed households were 1,281 (847 in the west bank and 434 in the Gaza strip).

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month, and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e., Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of house, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Assistance and poverty: includes questions about household conditions and assistances that got through the the past month.

Second section: The second section of the questionnaire includes a list of 55 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-55 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year, except the cars group the data of which was collected for three previous years. These data was abotained from the recording book which is covered a period of month for each household.

Cleaning operations

Raw Data

Data editing took place though a number of stages, including: 1. Office editing and coding 2. Data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Office.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is run on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consists of about 1,616 households interviewed over a twelve months period between (January 2006-January 2007), 1,281 households completed interview, of which 847 in the West Bank and 434 household in Gaza Strip, the response rate was 79.3% in the Palestinian Territory.

Sampling error estimates

Generally, surveys samples are exposed to two types of errors. The statistical errors, being the first type, result from studying a part of a certain society and not including all its sections. And since the Household Expenditure and Consumption Surveys are conducted using a sample method, statistical errors are then unavoidable. Therefore, a potential sample using a suitable design has been employed whereby each unit of the society has a high chance of selection. Upon calculating the rate of bias in this survey, it appeared that the data is of high quality. The second type of errors is the non-statistical errors that relate to the design of the survey, mechanisms of data collection, and management and analysis of data. Members of the work commission were trained on all possible mechanisms to tackle such potential problems, as well as on how to address cases in which there were no responses (representing 9.6%).

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2014 (LFS). The survey rounds covered a total sample of about 25,736 households, and the number of completed questionaire is 16,891.

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2014.

---> Sampling Frame: The sampling frame consists of the master sample, which was updated in 2011: each enumeration area consists of buildings and housing units with an average of about 124 households. The master sample consists of 596 enumeration areas; we used 498 enumeration areas as a framework for the labor force survey sample in 2014 and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 7,616 households in each quarter of 2014, but in the second quarter 2014 only 7,541 households were collected, where 75 households couldn't be collected in Gaza Strip because of the Israeli aggression.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 494 enumeration areas for the whole round ,and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 494 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2013 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 30,464 households of which 25,736 households completed the interview; whereas 16,891 households from the West Bank and 8,845 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 88.8% while in the Gaza Strip it reached 93.3%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Bulgaria.

Analysis unit

• Household
• Individual

Universe

The WVS for Bulgaria covers population aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

Step One: Distribution of sampling points by region and urban/rural strata The sample was designed to represent the national population of voting age by: region, type of residence, gender, age and ethnicity. When conducting representative polls on a national level, BBSS employs the method of double-clustered random probability sampling, stratified by regions (28 in total). The database of ESGRAON was used as basis for the sample. This register system contains every inhabitant in terms of the following characteristics: full three names of each person; address (settlement, street and number of home of the person). Upon this basic information various other aggregates are later superimposed, such as: election precincts settlements municipalities regions. Universe was stratified by regions (28 in total); lists of election precincts in each of the regions was prepared containing the number of persons aged 18+ in each precinct; given the targeted size of the final sample (1000), the number of individuals to be interviewed in each region was determined. This size is in direct proportion to the relative share of the respective region within the universe; next stage relates to random selection of sampling points to be used in the survey. Random selection of sampling points to be used in the survey. The sampling points were chosen to represent regions and type of settlement.

Step Two: Selection of sampling points and replacement of sampling points The random selection of the sampling points was done by means of the following algorithm: Calculation of the number of clusters to be achieved in each region (number is proportional to the size of the region); Sampling points in each region arranged in a descending order based on the criterion number of persons aged 18+ in the cluster. (NB: sampling points, that is, election precincts are comparatively uniform in terms of scale. Each sampling point contains between 400 1 000 persons aged 18+.); A cumulative column was formed by the number of individuals contained in each cluster; Based on this cumulative column, the systematic selection was achieved of the necessary number of sampling points starting by using a random start-up figure, and then a step was applied for moving down the cumulative column which step is the quotient of the size of the regional sub-universe and the number of respondents in each sampling point.

Step Three: Selection of starting points within each sampling point. Starting points were chosen by BBSS Gallup International Headquarters

Step Four: Respondent Selection Selection of a respondent is carried out using last birthday method. Every interviewer is provided with a starting address. The interviewer has to follow the rules, depending on the type of settlement an interview will be carried out.

  1. Urban areas - the selected household is each third address on the left-hand side ofthe street in urban areas, applying left turn at junctions and going back to the last crossing, if one has reached a dead-end, and    further proceeding at random but not along the branching one had been through. In a block-of-flats of up to four floors, the selected household is every fifth apartment, counting from the first on the left on the ground floor. In cases of unsuitable household (e.g. an eligible respondent is not present, the person is less then 18 years old) procedure instructs to approach the next-door apartment and to contact each further till reaching the required one, from which point to resume the standard step of every fifth apartment. In a blockof-flats of 5 floors and more, the selection is every tenth apartment counting them the same manner. 
  2. In rural areas, the selected household is every fourth inhabitable house/dwelling on both sides of the interviewers route/track and where the houses are aligned or scattered over larger territory, the instruction requires applying wave-wise approach selecting the fourth, counting from the first house on the left. In compounds of several houses behind a common fence, the procedure instructs to select the fourth one from the left (counting from the gate), or if there are less than four houses behind a common fence, then the interviewer to get out of the common yard, counting the houses as if they were along the street. In compact and wellstructured villages the selection procedure follows the instructions for urban areas. Next birthday was determined when respondents asked the person they had contacted, who is the person with the last birthday in the household. They were not listed in writing as this may be considered as personal information which we are not authorized to collect. Step Five: Respondent Substitution Substitutions were made by contacting a person with the last birthday in the household next-door. The step of every fifth apartment is resumed after finding an eligible respondent. Step Six: Callbacks (rate, method, and results) Call-backs were made usually during the same or during the next day, after the eligible respondent was not found.

The sample size for Bulgaria is N=1001 and includes population aged 18 and over for both sexes.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire from 1997-1998 has been used for Wave 5. The question which was optional in the previous wave (V43) has been included in Wave 5.

Response rate

Total number of starting names/addresses 167 Addresses established as empty, demolished or containing no private dwellings 138 Selected respondent too sick/incapacitated to participate 005 Selected respondent away during survey period 077 Selected respondent had inadequate understanding of language of survey 004 No contact at selected address 125 No contact with selected person 004 Refusal at selected address 100 Personal refusal by selected respondent 286 Full productive interview 1001 Partial productive interview 013

Sampling error estimates

+/- 3,2%

Abstract

The basic goal of this survey is to provide the necessary database for formulating national policies at various levels. It represents the contribution of the household sector to the Gross National Product (GNP). Household Surveys help as well in determining the incidence of poverty, and providing weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Generally, the Household Expenditure and Consumption Survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which enumerated in 1997 and the numeration area consists of buildings and housing units and has in average about 150 households in it. We use the enumeration areas as primary sampling units PSUs in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is stratified cluster systematic random sample with two stages: First stage: selection a systematic random sample of 120 enumeration areas. Second stage: selection a systematic random sample of 12-18 households from each enumeration area selected in the first stage.

Sample strata:

The population is divided by: 1-Region (North West Bank, Middle West Bank, South West Bank, Gaza Strip) 2-Type of Locality (urban, rural, refugee camps)

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 12 households.

Sample Size:

The calculated sample size is 1,714 households, the completed households were 1,231 (812 in the west bank and 419 in the Gaza strip).

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month, and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e., Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of shelter, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Assistance and poverty: includes questions about household conditions and assistances that got through the the past month.

Second section: The second section of the questionnaire includes a list of 55 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-55 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year, except the cars group the data of which was collected for three previous years. These data was abotained from the recording book which is covered a period of month for each household.

Cleaning operations

Raw Data

Data editing took place through a number of stages, including: 1. Office editing and coding 2. Data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Office.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is run on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consists of about 1,714 households interviewed over a twelve months period between (January 2007-January 2008).1,231 households completed the interview, of which 812 were from the West Bank and 419 households in Gaza Strip; the response rate was 71.8% in the Palestinian Territory.

Sampling error estimates

The calculations of standard errors for the main survey estimates enable the user to identify the accuracy of estimates and the survey reliability. Total errors of the survey can be divided into two kinds: statistical errors, and non-statistical errors. Non-statistical errors are related to the procedures of statistical work at different stages, such as the failure to explain questions in the questionnaire, unwillingness or inability to provide correct responses, bad statistical coverage, etc. These errors depend on the nature of the work, training, supervision, and conducting of all the various related activities. The work team spared no effort at the different stages to minimize non-statistical errors; however, it is difficult to estimate numerically such errors due to absence of technical computation methods based on theoretical principles to tackle them. On the other hand, statistical errors can be measured. Frequently they are measured by the standard error, which is the positive square root of the variance. The variance of this survey has been computed by using the "programming package" CENVAR

Data appraisal

The impact of errors on the data quality was reduced to the minimal due to the high efficiency and outstanding selection, training, and performance of the fieldworkers. Procedures adopted during the fieldwork of the survey were considered a necessity to ensure the collection of accurate data, notably: 1) Develop schedules to conduct field visits to households during survey fieldwork. The objectives of the visits and the data that is collected on each visit were predetermined. 2) Fieldwork editing rules were applied during the data collection to ensure corrections were implemented before the end of fieldwork activities 3) Fieldworker were instructed to provide details in case of extreme expenditure or consumption of the household. 4) Postpone the questions on income to the last visit at the end of the month 5) Validation rules were embedded in the data processing systems along with procedures to verify data entry and data editing.

Sweden Business Survey: COVID-19 Effect: SO: Services: Real Estate (RE): Response Rate data was reported at 44.000 % in 11 Aug 2021. This records an increase from the previous number of 39.000 % for 15 Jul 2021. Sweden Business Survey: COVID-19 Effect: SO: Services: Real Estate (RE): Response Rate data is updated daily, averaging 44.000 % from May 2020 (Median) to 11 Aug 2021, with 19 observations. The data reached an all-time high of 67.000 % in 10 Feb 2021 and a record low of 13.000 % in 29 Jul 2020. Sweden Business Survey: COVID-19 Effect: SO: Services: Real Estate (RE): Response Rate data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.S009: Business Survey: COVID-19 Effect: Seizing Operations (Discontinued).

Sweden Business Survey: COVID-19 Effect: SO: Construction: BC: Houses: Response Rate data was reported at 23.000 % in 11 Aug 2021. This records a decrease from the previous number of 37.000 % for 15 Jul 2021. Sweden Business Survey: COVID-19 Effect: SO: Construction: BC: Houses: Response Rate data is updated daily, averaging 36.000 % from May 2020 (Median) to 11 Aug 2021, with 19 observations. The data reached an all-time high of 55.000 % in 26 Aug 2020 and a record low of 13.000 % in 29 Jul 2020. Sweden Business Survey: COVID-19 Effect: SO: Construction: BC: Houses: Response Rate data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.S009: Business Survey: COVID-19 Effect: Seizing Operations (Discontinued).

Lending Standard Index: Percentage of Respondents Who Made Changes to Their Credit Policy: Housing/Property: Unchanged data was reported at 87.900 % in Mar 2025. This records a decrease from the previous number of 90.900 % for Dec 2024. Lending Standard Index: Percentage of Respondents Who Made Changes to Their Credit Policy: Housing/Property: Unchanged data is updated quarterly, averaging 76.670 % from Mar 2016 (Median) to Mar 2025, with 29 observations. The data reached an all-time high of 97.000 % in Jun 2023 and a record low of 46.900 % in Mar 2016. Lending Standard Index: Percentage of Respondents Who Made Changes to Their Credit Policy: Housing/Property: Unchanged data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SE007: Banking Survey: Percentage of Respondents Who Made Changes to Their Credit Policy.

Japan PS: Capital Spending: Actual: Non-Mfg: Real Estate data was reported at 13,641.000 JPY bn in 2017. This records a decrease from the previous number of 14,219.000 JPY bn for 2016. Japan PS: Capital Spending: Actual: Non-Mfg: Real Estate data is updated yearly, averaging 10,755.000 JPY bn from Mar 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 17,051.000 JPY bn in 2008 and a record low of 7,643.000 JPY bn in 2004. Japan PS: Capital Spending: Actual: Non-Mfg: Real Estate data remains active status in CEIC and is reported by Development Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.S078: Capital Spending: From Enterprises: Answer Actual and Planned Survey.

Abstract

The 2008 Ghana Demographic and Health Survey (GDHS) is a national survey covering all ten regions of the country. The survey was designed to collect, analyse, and disseminate information on housing and household characteristics, education, maternal health and child health, nutrition, family planning, gender, and knowledge and behaviour related to HIV/AIDS. It included, for the first time, a module on domestic violence as one of the topics of investigation.

The 2008 GDHS is designed to provide data to monitor the population and health situation in Ghana. This is the fifth round in a series of national level population and health surveys conducted in Ghana under the worldwide Demographic and Health Surveys programme. Specifically, the 2008 GDHS has the primary objective of providing current and reliable information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, domestic violence, and awareness and behaviour regarding AIDS and other sexually transmitted infections (STIs). The information collected in the 2008 GDHS will provide updated estimates of basic demographic and health indicators covered in the earlier rounds of 1988, 1993, 1998, and 2003 surveys.

The long-term objective of the survey includes strengthening the technical capacity of major government institutions, including the Ghana Statistical Service (GSS). The 2008 GDHS also provides comparable data for long-term trend analysis in Ghana, since the surveys were implemented by the same organisation, using similar data collection procedures. It also adds to the international database on demographic and health–related information for research purposes.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The 2008 GDHS was a household-based survey, implemented in a representative probability sample of more than 12,000 households selected nationwide. This sample was selected in such a manner as to allow for separate estimates of key indicators for each of the 10 regions in Ghana, as well as for urban and rural areas separately.

The 2008 GDHS utilised a two-stage sample design. The first stage involved selecting sample points or clusters from an updated master sampling frame constructed from the 2000 Ghana Population and Housing Census. A total of 412 clusters were selected from the master sampling frame. The clusters were selected using systematic sampling with probability proportional to size. A complete household listing operation was conducted from June to July 2008 in all the selected clusters to provide a sampling frame for the second stage selection of households.

The second stage of selection involved the systematic sampling of 30 of the households listed in each cluster. The primary objectives of the second stage of selection were to ensure adequate numbers of completed individual interviews to provide estimates for key indicators with acceptable precision and to provide a sample large enough to identify adequate numbers of under-five deaths to provide data on causes of death.

Data were not collected in one of the selected clusters due to security reasons, resulting in a final sample of 12,323 selected households. Weights were calculated taking into consideration cluster, household, and individual non-responses, so the representations were not distorted.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2008 GDHS: the Household Questionnaire, the Women’s Questionnaire and the Men’s Questionnaire. The content of these questionnaires was based on model questionnaires developed by the MEASURE DHS programme and the 2003 GDHS Questionnaires.

A questionnaire design workshop organised by GSS was held in Accra to obtain input from the Ministry of Health and other stakeholders on the design of the 2008 GDHS Questionnaires. Based on the questionnaires used for the 2003 GDHS, the workshop and several other informal meetings with various local and international organisations, the DHS model questionnaires were modified to reflect relevant issues in population, family planning, domestic violence, HIV/AIDS, malaria and other health issues in Ghana. These questionnaires were translated from English into three major local languages, namely Akan, Ga, and Ewe. The questionnaires were pre-tested in July 2008. The lessons learnt from the pre-test were used to finalise the survey instruments and logistical arrangements.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. The Household Questionnaire was also used to record height and weight measurements, consent for, and the results of, haemoglobin measurements for women age 15-49 and children under five years. The haemoglobin testing procedure is described in detail in the next section.

The Household Questionnaire was also used to record all deaths of household members that occurred since January 2003. Based on this information, in each household that reported the death of a child under age five years since January 2005,3 field editors administered a Verbal Autopsy Questionnaire. Data on child mortality based on the verbal autopsy will be presented in a separate publication.

The Women’s Questionnaire was used to collect information from all women age 15-49 in half of selected households. These women were asked questions about themselves and their children born in the five years since 2003 on the following topics: education, residential history, media exposure, reproductive history, knowledge and use of family planning methods, fertility preferences, antenatal and delivery care, breastfeeding and infant and young child feeding practices, vaccinations and childhood illnesses, marriage and sexual activity, woman’s work and husband’s background characteristics, childhood mortality, awareness and behaviour about AIDS and other sexually transmitted infections (STIs), awareness of TB and other health issues, and domestic violence.

The Women’s Questionnaire included a series of questions to obtain information on women’s exposure to malaria during their most recent pregnancy in the five years preceding the survey and the treatment for malaria. In addition, women were asked if any of their children born in the five years preceding the survey had fever, whether these children were treated for malaria and the type of treatment they received.

The Men’s Questionnaire was administered to all men age 15-59 living in half of the selected households in the GDHS sample. The Men’s Questionnaire collected much of the same information found in the Women’s Questionnaire, but was shorter because it did not contain a reproductive history or questions on maternal and child health or nutrition.

Cleaning operations

The processing of the GDHS results began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the GSS office in Accra, where they were entered and edited by data processing personnel who were specially trained for this task. Data were entered using CSPro, a programme specially developed for use in DHS surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, because GSS had the opportunity to advise field teams of problems detected during data entry. The data entry and editing phase of the survey was completed in February 2009.

Response rate

A total of 12,323 households were selected in the sample, of which 11,913 were occupied at the time of the fieldwork. This difference between selected and occupied households occurred mainly because some of the selected structures were found to be vacant or destroyed. The number of occupied households successfully interviewed was 11,778, yielding a household response rate of 99 percent.

In the households selected for individual interview in the survey (50 percent of the total 2008 GDHS sample), a total of 5,096 eligible women were identified; interviews were completed with 4,916 of these women, yielding a response rate of 97 percent. In the same households, a total of 4,769 eligible men were identified and interviews were completed with 4,568 of these men, yielding a response rate of 96 percent. The response rates are slightly lower among men than women.

The principal reason for non-response among both eligible women and men was the failure to find individuals at home despite repeated visits to the household. The lower response rate for men reflects the more frequent and longer absences of men from the household

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error

Excess Income Allocation Plan in the Next 12 Months: Property (Land, House, Apartment) data was reported at 12.245 % in Jan 2025. This records a decrease from the previous number of 12.481 % for Dec 2024. Excess Income Allocation Plan in the Next 12 Months: Property (Land, House, Apartment) data is updated monthly, averaging 20.301 % from Jan 2017 (Median) to Jan 2025, with 75 observations. The data reached an all-time high of 24.012 % in May 2019 and a record low of 12.095 % in Dec 2022. Excess Income Allocation Plan in the Next 12 Months: Property (Land, House, Apartment) data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HA007: Consumer Confidence Index: Respondent's First Choice Type of Investments in the Next 12 Months. [COVID-19-IMPACT]

Abstract

The Cambodia Inter-Censal Population Survey, 2004 was designed not only to obtain the much-needed demographic data following the census, but also to serve as a means to train the staff of the NIS and Provincial Planning Offices in demographic data collection.

There are plans to produce in-depth studies on fertility, mortality, migration, literacy and education, labour force, housing and household amenities, and population projections based on the results of the survey.

The Cambodia Inter-Censal Population Survey 2004 (CIPS) is a nationally representative sample survey taken between two censuses, the 1998 census and the proposed 2008 census, in order to update information on population size and growth and other population characteristics as well as household facilities and amenities. Due to the national elections and administrative issues, the CIPS was undertaken in March 2004 instead of 2003, which would have been the five-year midpoint between the 1998 and 2008 censuses.

The conduct of the CIPS 2004 is an important step in the creation of a continuous flow of data that will allow Cambodia to prepare plans and programmes supported by a strong database.

The Cambodia Inter-Censal Population Survey 2004 was conducted with the objective of providing information on the following indicators: - Sex, age and marital status - Births and Deaths - Migration status - Literacy/Educational level - Economic characteristics - Housing and household amenities - Other population and household information

These fresh data will allow for calculations and reliable projections of: - Population size and growth - Fertility - Mortality - Migration

The survey was also intended to train the national staff in sampling, data collection, data processing, analysis and dissemination.

Geographic coverage

National

Analysis unit

Individual, Household

Universe

All Population and housing for all regular households in Cambodia excluding special settlements and institutional households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design for the CIPS 2004 is a three-stage stratified cluster sampling design, it is a probability sample selection of 100 percent of the Cambodian villages coverage areas, the survey covered only regular households and excludes special settlements and institutional households.

The CIPS 2004 was conducted in a nationwide representative sample of 21,000 households within selected 700 villages (primary sampling units) out of 13,886 villages in Cambodia. The 700 villages were selected from updated frame (list of villages for Cambodia).

The General Population Census 1998 databases of the National Institute of Statistics together with the new updated list of villages that were excluded in the general population census of 1998 was used as the sampling frame for the sampling design of the CIPS 2004.

The frame has the following identification particulars: 1- Province code 2- Province name 3- District code 4- District name 5- Commune code 6- Commune name 7- Village Code 8- Village name 9- Size of village (number of households) 10- Area code (1 = Urban, 2 = Rural)

A three-stage sample design has been used for the CIPS. In the first stage a sample of villages was selected. The villages were implicitly stratified into 45 strata (21 provinces each with rural/urban strata i.e. 42 strata plus 3 provinces each totally urban, i.e. 3 urban strata). The villages were selected using linear systematic sampling with probabilities proportionate to size (PPS). The size measure used for the selection was number of households in the village according to the 1998 Census with estimation for a few additional villages not in the 1998 census frame.

In the second stage one Census Enumeration Area was selected randomly (in the head office) in each selected PSU. At the beginning of the fieldwork all households in the EA were listed. A systematic sample of 30 non-vacant households was selected as the third stage of selection.

The listing of households in the EA would become cumbersome if there are many households in the EA. This might be the case when the enumeration area had grown substantially since the census. When the EA was large (population wise) the interviewer was instructed to split the EA into two or more approximately equal-sized segments and to select one segment randomly. All households in the selected segment were listed. Out of the 700 Sample PSUs, 598 were from the rural super stratum and the remaining 102 were from the urban super stratum. For more information on sampling for the survey the general report at national level may be referred to.

Note: All provincial headquarters were treated as urban. In the case of Sihanoukville, Kep and Pailin, the entire province was treated as urban. In Phnom Penh province, the four districts of Doun Penh, Chamkar Mon, 7 Makara and Tuol Kouk were classified as urban. All the remaining areas of the country were rural. Further, urban and rural areas are being reclassified in Cambodia. While these reclassifications have already been drafted, they have not yet been approved by the Royal Government of Cambodia. Upon endorsement and adoption, the new classifications will be used in future census/surveys.

Mode of data collection

Face-to-face [f2f]

Research instrument

The draft questionnaires for the CIPS 2004 were more or less on the 1998 General Census pattern. Some modifications, however, were made by adding new questions on

(i) Whether children aged 0-14 living with own mother (ii) Whether a person's mother is alive and (iii) Details of deaths in households in the last one year with focus on maternal mortality.

Questions mentioned at (i) and (ii) were intended respectively to estimate fertility (by application of own child method) and mortality (by application of orphan hood method). The questions to be included were carefully considered by a Working Group of Cambodia Inter-Censal Population Survey 2004, whose members were mostly from Ministries, NGOs and International Agencies. The Questionnaires were tested twice in the field (both urban and rural) by NIS staff in November 2003. The purpose of the pre-test was to have a full-dressed rehearsal of the whole process and particularly to test the questions in the field so as to make corrections in wording or definitions and to estimate the time taken for enumeration area mapping, house listing, sampling and enumeration of selected household. Based on the pre-test experience the questionnaires were modified and finalized.

Two types of questionnaires were used in the CIPS 2004: Form A House-list and Form B Household Questionnaire.

The Form A was used to collect information on buildings containing one or more households during the preliminary round preceding survey night (March 3, 2004). The information collected related to: construction material of wall, roof and floor, whether it is a wholly or partly residential building, number of households within the building, name and sex of head of household and number of persons usually living in the household.

The Form B, which has five parts, was used for survey enumeration in the period closely following the reference time.

In Part I, information on usual members of the selected household present on survey night, visitors present as well as usual members absent on survey night, was collected.

Part II was used to collect information on each usual member of the household and each visitor present on survey night. The information collected included: full name, relationship to household head, sex, age, natural mother, child aged 0-14 living with own mother, marital status, age at first marriage, mother tongue, religion, place of birth, previous residence, duration of stay, reason for migration, literacy, full time education and economic characteristics.

Part III was used to collect information on females of reproductive age (15-49) as well as children born to these women.

The information collected in part IV related to household conditions and facilities: main source of light, main cooking fuel used, whether toilet facility is available, main source of drinking water and number of living rooms occupied by household.

Part V was used to record the following information in respect of deaths in the household within the last one year:- name of deceased, sex, relationship to head of household, age at death, whether the death has been registered with the civil authorities or not, the cause of death and maternal mortality information.

Cleaning operations

The completed records (Forms A, Form B, Form I, Form II, Map, and other Forms) were systematically collected from the provinces by NIS Survey Coordinators on the due date and submitted to the team receptionist at NIS. NIS Survey Coordinators formed into three teams of two persons were trained during March 7-10 to receive and arrange the completed forms and maps for processing after due checking form the field. Control forms were prescribed by DUC to record every form without any omission. These records were carefully checked, registered and stored in the record room. Editing and coding of the questionnaires were done manually, after which the questionnaires were submitted to the computer section for further processing. The instruction for editing and coding were revised and expanded. Training on editing and coding was conducted for senior staff, who in turn had to train other editors and coders.

The purpose of the editing process was to remove matters of obvious inconsistency, incorrectness and incompleteness, and to improve the quality of data collected. Coding had to be done very carefully in