Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample of 5,000 households across the US was purchased from Survey Sampling, Inc. located in Connecticut. This sample is based on Random Digit samples.

This sample was stratified by state to match the percentage of U.S. residents living in each of the fifty states.

The 5,000 sampled households were randomly assigned to one of three different experimental treatments (normal, personalized and personalised plus 2$ incentive)

The experiment was done for purposes of evaluating response rate effects of alternative means of contacting US residents.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

The energy statistics program has implemented many rounds of the Household Energy Survey during 1999-2011.

Because of the importance of the household sector and due to its large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special Household Energy Survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household sector and to provide data on energy consumption behavior and patterns in the society by type of energy.

The survey presents data on energy indicators pertaining to households in the Palestinian Territory. This includes statistical data on electricity and other fuel consumption by households covering type of fuel for different activities (cooking, baking, heating, lighting, and water heating).

Geographic coverage

Geographic Coverage

Analysis unit

households

Universe

The target population was all Palestinian households living in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The sample is a two-stage stratified cluster random sample.

Target Population: The target population was all Palestinian households whom are living in the Palestinian territory.

Sampling Frame: The sample of this survey is a part of the main sample of Labor Force Survey (LFS) which implemented periodically every quarter by PCBS since 1995, so this survey implement every quarter in the year (distributed over 13 weeks), the survey attached with the LFS in the first quarter of 2011, and the sample contain of 6 weeks from the eighth week to the thirteen week from the round 60 of labor force. The sample is two stage stratified cluster sample with two stages, first stage we selected a systematic random sample of 211 enumeration areas for the semi round, then in the second stage we select a random area sample of average 16 households from each enumeration area selected in the first stage.

Sampling Design: The sample of this survey is a sub-sample of the Labor Force Survey (LFS) sample, which has been conducted periodically since September 1995. The sample of LFS is distributed over 13 weeks. The sample of the survey occupies six weeks of the first quarter of 2011 within implementing LFS.

Stratification by number of households: In designing the sample of the LFS, three levels of stratification by number of households were made: Stratification by number of households: Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee camps Stratification by locality size.

Sample Unit: In the first stage, the sampling units are the enumeration areas (clusters) from the master sample. In the second stage, the sampling units are households.

Analysis Unit: The unit of analysis is the household.

Sample Size: The sample size is comprised of (3,313) Palestinian households in the West Bank and Gaza Strip, where this sample was distributed according to locality type (urban, rural and refugee camps).

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

he data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: At this stage, data was entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements such as: · To prevent the duplication of the questionnaires during data entry. · To apply integrity and consistency checks of entered data. · To handle errors in user friendly manner. · The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

The survey sample consists of about 3313 households of which 3029 households completed the interview; whereas 1950 households from the West Bank and 1079 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 95. % while in the Gaza Strip it reached 98%.

Non-response cases

No of cases non-response cases
3029 Household completed 22 Traveling households
19 Unit does not exist 56 No one at home
22 Refused to cooperate
139 Vacant Housing unit 1 No available information
25 Other
3313 Total sample size

Sampling error estimates

It includes many aspects of the survey, mainly statistical errors due to the sample, and non statistical errors referring to the workers and tools of the survey. It includes also the response rates in the survey and their effect on the assumptions. This section includes:

Sampling Errors These types of errors evolved as a result of studying a part of the population and not all of it. Because this is a sampled survey, the data will be affected by sampling errors due to using a sample and not the whole frame of the population. Differences appear compared to the actual values that could be obtained through a census. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

The results of gasoline, wood, charcoal and olive cake suffer from a high variance. This problem should be taken into consideration when dealing with the average household consumption of these types of fuel, keeping in mind that there are no problems in publishing the data at the geographical level (North of the West Bank, Middle of the West Bank, South of the West Bank and Gaza Strip). However, publishing data at the governorate level is not possible due to the high variance, especially for wood, charcoal and olive cake. The variances for the main indicators of this survey are as follows:

95% Confidence Interval C.V % Standard Error Estimate Variable
Upper Lower Value Unit
99.9 99.5 0.001 0.1 99.8 % Main Electricity Source 66.2 61.2 0.020 1.3 63.7 % Use of Solar Heaters
98.5 97.5 0.003 0.2 98.1 % Use of LPG
273 259 0.013 3.44 266 KWh Average Electricity Consumption 264 191 0.081 18.47 228 Kg Average wood Consumption
50.5 41.8 0.047 2.19 46 Liter Average Gasoline Consumption

Non Sampling Errors These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself, where some parts depend partially on estimation, (b) diversity of sources (e.g., the interviewers, respondents, editors, coders, data entry operator, etc).

The sources of these errors can be summarized as: Some of the households were not in their houses and the interviewers could not meet them. Some of the households did not give attention to the questions in questionnaire.
Some errors occurred due to the way the questions were asked by interviewers. Misunderstanding of the questions by the respondents. Answering the questions related to consumption by making estimations.

Data appraisal

The data of the survey is comparable geographically and over time by comparing the data between different geographical areas to data of previous surveys and census 2007.

Abstract

The Jerusalem Household Social Survey 2013 is one of the most important statistical activities that have been conducted by PCBS. It is the most detailed and comprehensive statistical activity that PCBS has conducted in Jerusalem. The main objective of the Jerusalem household social survey, 2013 is to provide basic information about: Demographic and social characteristics for the Palestinian society in Jerusalem governorate including age-sex structure, Illiteracy rate, enrollment and drop-out rates by background characteristics, Labor force status, unemployment rate, occupation, economic activity, employment status, place of work and wage levels, Housing and housing conditions, Living levels and impact of Israeli measures on nutrition behavior during Al-Aqsa intifada, Criminal offence, its victims, and injuries caused.

Geographic coverage

Social survey data covering the province of Jerusalem only, the type locality (urban, rural, refugee camps) and Governorate

Analysis unit

households, Individual

Universe

The target population was all Palestinian households living in Jerusalem Governorate.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for Jerusalem (J1 and J2) was based on the census implemented by PCBS in 2007 and consisting of enumeration areas. These enumeration areas were used as primary sampling units (PSUs) in the first stage of the sample selection.

The estimated sample size is 1,260 households responding in Jerusalem governorate.

Stratified cluster random sample with two-stages: First stage: Selection of a systematic random sample of 42 enumeration areas (24 EAs in J1 and 18 EAs in J2). Second stage: A sample of 30 responsive households from each enumeration area selected in the first stage.

Sample Strata The population was divided by: 1-Region (Jerusalem J1, Jerusalem J2) 2-Locality type (Jerusalem J1: urban, camp; Jerusalem J2: urban, rural, camp).

Mode of data collection

Face-to-face [f2f]

Research instrument

A survey questionnaire the main tool for gathering information, so do not need to check the technical specifications for the phase of field work, as required to achieve the requirements of data processing and analysis, has been designed form the survey after examining the experience of other countries on the subject of social surveys, covering the form as much as possible the most important social indicators as recommended by the United Nations, taking into account the specificity of the Palestinian community in this aspect.

Cleaning operations

Phase included a set of data processing Activities and operations that have been made to the Forms to prepare her for the analysis phase, This phase included the following operations: Before the introduction of audit data: at this stage was Check all the forms using the instructions To check to make sure the field of logical data and re- Incomplete, including a second field. Data Entry: The data entry Central to the central headquarters in Al-Bireh, was organized The data entry process using the Access Program Where the form has been programmed through this program. Was marked by the program that was developed in the Device properties and features the following: The possibility of dealing with an exact copy of the form The computer screen. The ability to conduct all tests and possibilities Possible and logical sequence of data in the form. Maintain a minimum of errors Portal Digital data or errors of field work. Ease of use and deal with the software and data (User-Friendly). The possibility of converting the data to the other formula can be Use and analysis of the statistical systems Analysis such as SPSS.

Response rate

during the field work we visit 1,820 family in Jerusalem Governorate, where the final results of the interviews were as follows: The number of families who were interviewed (1,188) in Jerusalem Governorate, (715) in J1, (473) in J2.

Sampling error estimates

Accuracy of the Data

Statistical Errors Data of this survey can be affected by statistical errors due to use of a sample. Variance was calculated for the most important indicators and demonstrates the ability to disseminate results for Jerusalem governorate. However, dissemination of data by J1 and J2 area indicates values with a high variance

Non-Statistical Errors It is possible for non-statistical errors to occur at all stages of project implementation or during the collection or entry of data. These errors can be summarized as non-response errors, response errors (respondent), corresponding errors (researcher) and data entry errors. To avoid errors and reduce their impact, strenuous efforts were made in the intensive training of researchers on how to conduct interviews, the procedures that must be followed during the interview and aspects that should be avoided. Practical exercises and theory were covered during the training session. Errors gradually decreased with the accumulation of experience by the field work team, which consisted of permanent and non-permanent researchers who conduct work on every PCBS survey.

In general, non-statistical errors were related to the nature of the Social Survey of Jerusalem and can be summarized as follows: · Many households considered the specific details of the survey as interference in their private lives. · Israeli impact on Palestine (curfew and closure). · Some households thought the survey was related to social assistance or to taxes. · Hesitation by households in the Jerusalem area to supply data because they were afraid of Israeli procedures against them if they participated in a Palestinian survey or activity.

Data Processing The data processing stage consisted of the following operations: 1. Editing and coding prior to data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.
2. Data entry: At this stage, data were entered into the computer using a data entry template designed in Access. The data entry program was prepared to satisfy a number of requirements such as:
· Duplication of the questionnaires on the computer screen. · Logic and consistency check of data entered. · Possibility for internal editing of question answers. · Maintaining a minimum of digital data entry and field work errors. · User-friendly handling. · Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

Data entry began on April 17, 2013 and finished on July 14, 2013. Data cleaning and checking processes were initiated simultaneously with the data entry. Thorough data quality checks and consistency checks were carried out and SPSS for Windows version 10.0 was used to perform the final tabulation of results.

Possibility of Comparison At this stage, comparison can be made for time series periods and other sources. Where the survey results were compared with the data in 2010. The data were compared with the final results of the Population, Housing and Establishments Census of 2007 for Jerusalem and the results were very consistent.

Abstract

The objective of the survey was to obtain data on: Number of enterprises and persons engaged in the economic survey series by activity. Value of output, intermediate consumption and stocks. Value added components. Payments and transfers. Assets and capital formation. Contribution of the surveyed activities to the GDP and other national accounts variables.

Geographic coverage

West Bank and Gaza Strip

Analysis unit

Enterprise constitutes the primary sampling unit (PSU)

Universe

Industrial Enterprises (private sector)

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design: The sample of the Industrial Survey is a single-stage stratified random - systematic sample in which the enterprise constitutes the primary sampling unit (PSU). Three levels of strata were used to arrive at an efficient representative sample (i.e. economic activity, size of employment and geographical levels). The sample size for the Remaining West Bank and Gaza Strip amounted to (2167) enterprises out of the (14340) enterprises that comprise the survey frame of 2000.

Mode of data collection

Face-to-face [f2f]

Research instrument

They are tow forms of the industrial survey questionnaire 2000, the first one is related to household and branches, the second is related to non-finance companies sector

Cleaning operations

For insuring quality and consistency of data a set of measures were taken into account for strengthening accuracy of data as follows: ·Preparing data entry program before data collection for checking readiness of the program for data entry. ·A set of validation rules were applied on the program for checking consistency of data. ·Efficiency of the program was checked through pre-testing in entering few questionnaires, including incorrect information for checking its efficiency, in capturing these information. ·Well trained data keyers were selected and trained for the main data entry. ·Weekly or biweekly data files were received by project management for checking accuracy and consistency, notes of correction are provided for data entry management for correction.

Response rate

Response rate: 79.5%.

Sampling error estimates

Statistical Errors: The findings of the survey are affected by statistical errors due to using sampling in conducting the survey for the units of the target population, which increases the chances of having variances from the actual values we expect to obtain from the data had we conducted the survey using comprehensive enumeration.. The variance of the key goods in the survey was computed and dissemination was carried out on the level of Palestinian Territory for reasons related to sample design and computation of the variance of the different indicators.

Non-Statistical Errors These types of errors could appear on one or all the survey stages that include data collection and data entry: Response errors: these types of errors are related to, responders, fieldworkers, and data entry personnel's. And to avoid mistakes and reduce the impact has been a series of actions that would enhance the accuracy of the data through a process of data collection from the field and the data processing.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was provided by the National Institute of Statistics (DANE) and corresponded to the 1993 national census.

The sample used was a two-stage, non-equal cluster, probabilistic and stratified sample. Thirty-three municipalities were sampled. Some PSUs (Orinoquia, the Amazonian Triangle, and the Pacific Coast - Coast from Nariño, Cauca and Valle, and the state of Chocó- with an urban population that represents less than 2% of the urban population of the country) were eliminated as access was difficult (vast areas, bad roads and high transport cost ).

From the sample of 6,000, females accounted for 65.4% and males for 34.6%.

The major problems reported which affected the response rate were the violence, fear of kidnapping, and political instability.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

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 2003(LFS).

The importance of this survey lies in that it focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

The survey main objectives are: - To estimate the labor force and its percentage to the population. - To estimate the number of employed individuals. - To analyze labour force according to gender, employment status, educational level , occupation and economic activity. - To provide information about the main changes in the labour market structure and its socio economic characteristics. - To estimate the numbers of unemployed individuals and analyze their general characteristics. - To estimate the rate of working hours and wages for employed individuals in addition to analyze of other characteristics.

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:

All Palestinians aged 10 years or older living in the Palestinian Territory, excluding those living in institutions such as prisons or shelters.

Sampling Frame:

The sampling frame consisted of a master sample of Enumeration Areas (EAs) selected from the population housing and establishment census 1997. The master sample consists of area units of relatively equal size (number of households), these units have been used as Primary Sampling Units (PSUs).

Sample Design:

The sample is a two-stage stratified cluster random sample.

Stratification: Four levels of stratification were made:

1. Stratification by Governorates.
2. Stratification by type of locality which comprises: (a) Urban, (b) Rural, and (c) Refugee Camps
3. Stratification by classifying localities, excluding governorate centers, into three strata based on the ownership of households of durable goods within these localities.
4. Stratification by size of locality (number of households).

Sample Size:

The sample size in the first round consisted of 7,559 households, which amounts to a sample of around 29,149 persons aged 10 years and over (including 22,742 aged 15 years and over). In the second round the sample consisted of 7,563 households, which amounts to a sample of around 29,486 persons aged 10 years and over (including 22,916 aged 15 years and over), in the third round the sample consisted of 7,563 households, which amounts to a sample of around 29,268 persons aged 10 years and over (including 22,653 aged 15 years and over). In the fourth round the sample consisted of 7,563 households; which amounts to a sample of around 28,250 persons aged 10 years and over (including 21,926 aged 15 years and over).

The sample size allowed for non-response and related losses. In addition, the average number of households selected in each cell was 16.

Sample Rotation:

Each round of the Labor Force Survey covers all the 481 master sample areas. Basically, the areas remain fixed over time, but households in 50% of the EAs are replaced each round. The same household remains in the sample over 2 consecutive rounds, rests for the next two rounds and represented again in the sample for another and last two consecutive rounds before it is dropped from the sample. A 50 % overlap is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes). In earlier applications of the LFS (rounds 1 to 11); the rotation pattern used was different; requiring a household to remain in the sample for six consecutive rounds, then dropped. The objective of such a pattern was to increase the overlap between consecutive rounds. The new rotation pattern was introduced to reduce the burden on the households resulting from visiting the same household for six consecutive times.

Mode of data collection

Face-to-face [f2f]

Research instrument

One of the main survey tools is the questionnaire, 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

The data processing stage consisted of the following operations:

1. Editing Before Data Entry All questionnaires were then edited in the main office using the same instructions adopted for editing in the field.

2. Coding At this stage, the Economic Activity variable underwent coding according to West Bank and Gaza Strip Standard Commodities Classification, based on the United Nations ISIC-3. The Economic Activity for all employed and ever employed individuals was classified at the fourth-digit-level. The occupations were coded on the basis of the International Standard Occupational Classification of 1988 at the third-digit-level (ISCO-88).

3. Data Entry In this stage data were entered into the computer, using a data entry template BLAISE.

The data entry program was prepared in order to satisfy the following requirements:

-Duplication of the questionnaire on the computer screen. -Logical and consistency checks of data entered. -Possibility for internal editing of questionnaire answers. -Maintaining a minimum of errors in digital data entry and fieldwork. -User- friendly handling.

Accordingly, data editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data filesData editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data files

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 overall response rate for the survey was 84.3%

More information on the distribution of response rates by different survey rounds is available in Page 11 of the data user guide provided among the disseminated survey materials under a file named "Palestine 2003- Data User Guide (English).pdf".

Sampling error estimates

Since the data reported here are based on a sample survey and not on a complete enumeration, they are subjected to sampling errors as well as non-sampling errors. Sampling errors are random outcomes of the sample design, and are,

Abstract

The Livestock Survey, 2013 aims to provide data on the structure of the livestock sector as the basis for formulating future policies and plans for development. It will also update existing data on agricultural holdings from the Agricultural Census of 2010 and build a database that will facilitate the collection of agricultural data in the future via administrative records

Geographic coverage

Palestine

Analysis unit

Agricultural holding

Universe

All animal and mixed holdings in Palestine during 2013.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The animal and mixed agricultural holdings frame was created from the agricultural census data of 2010 and extracted based on the following criteria: any number of cattle or camels, at least five sheep or goats, at least 50 poultry birds (layers and broilers), or 50 rabbits, or other poultry like turkeys, ducks, common quail, or a mixture of them, or at least three beehives controlled by the holder.

A master sample of 7,297 holdings from the animal and mixed holdings frame was updated prior to sample selection.

Sample Size The estimated sample size is 5,000 holdings.

Sample Design
The sample is a one-stage stratified systematic random sample.

Sample Strata The animal and mixed holdings are stratified into three levels, which are: 1. Governorates. 2. The main agricultural activities were identified by the highest holding size in the category: these activities are the raising cattle, raising sheep and goats, raising camels, poultry farming, beehives, mixed animals. The size of the holdings were classified into five categories

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the Livestock Survey 2013 was designed based on the recommendations of the Food and Agriculture Organization of the United Nations (FAO) and the questionnaire used for the Agricultural Census of 2010. The special situation of Palestine was taken into account, in addition to the specific requirements of the technical phase of field work and of data processing and analysis The questionnaire consisted of the main items as follows: Identification data: Indicators about the holder, the holding and the respondent.

Data on holder: Included indicators on the sex, age, educational attainment, number in household, legal status of holder, and other indicators.

Holding data: Included indicators on the type of holding, tenure, main purpose of production, and other indicators.

Livestock data: Included indicators on the type, number, strain, age, sex, system of raising, main purpose of raising, number acquired or disposed of, quantity and value production, slaughtered in a holding, value of slaughtered, and other indicators.

Poultry data: Included indicators on the type, area of worked barns, average cycles per year, system of raising, quantity and value production, and other indicators.

Domestic poultry & equines data: Included indicators on type and number.

Beehive data: Included indicators such as the type, number, strain, quantity and value of production??.

Agricultural practices data: Included indicators on agricultural practices for livestock, poultry and bees.

Agricultural labor force data: Included indicators on the agricultural labor force in a holding such as the number, employment status, sex, age, average daily working hours, number of work days in an agricultural year and average daily wage.

Agricultural machinery and equipment: Included indicators on the number and source of machinery. Agricultural buildings data: Included indicators on the type and area of building.

Animal intermediate consumption: Included indicators on the type, quantity and value of animal intermediate consumption.

Cleaning operations

Preparation of Data Entry Program The data entry program was prepared using Oracle software and data entry screens were designed. Rules of data entry were established to guarantee successful entry of questionnaires and queries were used to check data after each entry. These queries examined variables on the questionnaire.

2.5.2 Data Entry Having designed the data entry program and tested it to verify readiness, and after training staff on data entry programs, data entry began on 4 November 2013 and finished on 8 January 2014 with 15 staff engaged in the data entry process.

2.5.3 Editing of Entered Data Special rules were formulated for editing the stored data to guarantee reliability and ensure accurate and clean data.

2.5.4 Results Extraction and Data Tabulation An SPSS program was used for extracting the results and empty tables were prepared in advance to facilitate the tabulation process. The report tables were formulated based on international recommendations, while taking the Palestinian situation into consideration in the data tabulation of the survey.

Response rate

Response rate was 94.3%

Sampling error estimates

Includes multiple aspects of data quality, beginning with the initial planning of the survey up to the final publication, plus how to understand and use the data. There are seven dimensions of statistical quality: relevance, accuracy, timeliness, accessibility, comparability, coherence, and completeness.

2.6.1 Data Accuracy
Includes checking the accuracy of data in multiple aspects, primarily statistical errors due to the use of a sample, as well as errors due to non-statistical staff and survey tools, in addition to response rates in the survey and the most important effects on estimates. This section includes the following:

Statistical Errors Survey data may be affected by sampling errors resulting from the use of a sample instead of a census. Variance estimation was carried out for the main estimates and the results were acceptable within the publishing domains as shown in the tables of variance estimation.

Data appraisal

Non-sampling Errors Non-statistical errors are probable in all stages of the project, during data collection and processing. These are referred to as non-response errors, interviewing errors, and data entry errors. To avoid and reduce the impact of these errors, efforts were exerted through intensive training on how to conduct interviews and factors to be followed and avoided during the interview, in addition to practical and theoretical exercises. Re-interview survey was conducted for 5% of the main survey and re-interview data proved that there is high level of consistency with the main indicators.

Abstract

Focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

Geographic coverage

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

Analysis unit

Household, individual

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

Sampling Frame In the absence of a population census since 1967, the major task, with regard to constructing a master sample, was developing a frame of suitable units covering the whole country. Such units have been used as the PSUs (Primary Sampling Units) in the first stage of selection. For the second stage of selection, all PSUs have been listed in the field at the household level. This provided a sampling frame for selecting the households.

Sample Design The target population: consist of all Palestinian individuals aged 15 years and above living in West Bank and Gaza Strip, excluding nomads and persons living in institutions such as prisons, shelters.

Stratification Four levels of stratification have been made: Stratification by District. Stratification by type of (Locality) which comprises: (a) Municipalities (b)Villages (c)Refugee Camps
Stratification by locality size. Stratification by cell identification in that order.

Mode of data collection

Face-to-face [f2f]

Research instrument

TThe lfs questionnaire consists of four main sections: 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. 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. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc. 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

Editing before data entry All questionnaires were edited again using the same instructions adopted for editing in the field.

Coding In this stage, the industry underwent coding according to WBGS Standard Commodities Classification, which is based on United Nations ISIC-3. The industry for all employed and ever employed individuals was classified at the fourth-digit-level. The occupations were coded on the basis of the International Standard Occupational Classification, 1988 at the third-digit-level (ISCO-88).

Data Entry In this stage data were entered to the computer using a data entry template written in BLAISE. The data entry program has satisfied many requirements such as: The duplication of the questionnaire on the computer screen. Logical and consistency checking of data entered. Possibility for internal editing of questions answers. Maintaining a minimum of digital data entry and field work errors. A User- Friendless Possibility of transferring data into another format to be used and analyzed using other statistical analytical systems such as SAS and SPSS.

Editing after data entry In this stage, all questionnaires were edited after data entry in order to minimize errors related data entry.

Response rate

" The overall response rate for the survey was 88.6%

Sampling error estimates

Detailed information on the sampling Error is available in the Survey Report.

Data appraisal

Detailed information on the data appraisal is available in the Survey Report

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample contained 5,000 addresses from a postal register that the company created with the information they could gather based on their annual assessment survey.

Telephone directories could not be used as these have a low penetration rate (around 40% for all population) and the electoral register is updated only a few months before the election. Hence, these two registers are not representative of the Ukrainian population.

A multi-level stratified sample was used.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

The objective of the survey was to obtain data on the following: • Olive press distribution according to operational status, level of automation and governorate. • Quantity of olives pressed and oil extracted according to level of automation and governorate. • Number of employees and their compensation. • Olive pressing costs, including material inputs, electricity, water, fuel, and fees and taxes. • Olive press output, including olive press return and other secondary activity. • Gross fixed capital formation of olive press activity. • Value added of olive presses. • Other olive press related variables.

Geographic coverage

The Palestinian Territory

Analysis unit

Presses constitutes the primary sampling unit

Universe

The survey was comprehensive and covered all olive presses operating in Palestine in 2013

Kind of data

Complete enumeration [enu]

Sampling procedure

The frame for the survey comprises 264 operating olive presses in Palestine. However, the non-operating presses have been visited to confirm their status.

Mode of data collection

Face-to-face [f2f]

Research instrument

The olive press questionnaire was designed with the aim of ensuring compatibility with other economic series surveys. Special attention was devoted to the main agricultural and economic variables to meet the needs of policy planners and decision makers in the agricultural field.

Cleaning operations

After collection of data from the field, questionnaires underwent manual editing and logical revision. Special software was used in data entry and processing. After that certain relations between variables were used in post-data entry editing.

Response rate

The response percent is 100%

Sampling error estimates

The most important observations regarding the eighth round of the Olive Press Survey for 2013 are as follows:

Statistical Errors The survey was implemented on the basis of a comprehensive census of all studied statistical units (olive presses) and therefore this survey is free of statistical (sampling) errors.

Non-Statistical Errors This type of error could appear in one or all stages of the survey that comprise data collection and data entry: · Non-response errors: there was a very good response from all visited presses and no non-response cases were reported for this season. · Response errors: these are related to respondents, field workers, and data entry personnel. To ensure data quality, a series of measures were implemented to support the accuracy of data collection and data processing, including: 1. Respondents: Data were collected on the quantities of olives pressed and olive oil extracted on a daily basis. This was to ensure reliable and accurate figures on the important indicators. Field workers visited the olive presses daily to check if data had been reported fully and correctly. 2. Field workers: A series of actions were implemented to support the accuracy of data collection via the following: · Selection of a specialized field work team trained theoretically and practically on the survey questionnaire for four days. · The main field work team was selected based on the training course. · Different levels of supervision and monitoring took place according to the following hierarchy: - Field workers: field workers for this survey were distributed throughout all governorates. - Field work supervisors: supervisors were distributed in the north, middle, and south of the West Bank. - Field work coordinator. 3. Data entry operators: To ensure the quality and consistency of data, a series of measures were implemented, including: - The setting up of a data entry program prior to data collection to check the operation of the program. - A series of validation rules were applied in the program to check the consistency of data. - The efficiency of the program was checked through pre-testing by entering a few questionnaires and including incorrect information to monitor efficiency in capturing erroneous data. - Well-trained data entry personnel were selected and trained for the main data entry phase. - Data files were sent to the project management to be checked for accuracy and consistency. Notes were provided for data entry management for correction purposes.

Abstract

The objective of the survey was to obtain data on: 1. Number of enterprises and persons engaged in construction contractors survey by activity. 2. Value of output, intermediate consumption and stocks. 3. Value added components. 4. Payments and transfers. 5. Assets and capital formation. 6. Contribution of the surveyed activities to the GDP and other national accounts variables.

Geographic coverage

West Bank and Gaza Strip

Analysis unit

Enterprise constitutes the primary sampling unit (PSU)

Universe

construction contractors Enterprises (private sector)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the economic surveys series is a single-stage stratified random-systematic sample in which the enterprise constitutes the primary sampling unit (PSU). Three levels of strata were used to draw up an efficient representative sample (i.e., economic activity, size of workforce and geographical location).

This methodology was used to draw a sample of middle enterprises and whole frame for large enterprises based on the framework of large and middle enterprises alone, for small enterprises data were estimated based on a time series of the results of economic surveys.

Mode of data collection

Face-to-face [f2f]

Research instrument

All of the economic surveys series used the same questionnaire, with a few different characteristics for each survey. The design of the 2011 questionnaire takes into account the major economic variables pertaining to the sector examined and the needs to be met to compile the National Accounts for Palestine.

Cleaning operations

For insuring quality and consistency of data a set of measures were taken into account for strengthening accuracy of data as follows: ·Preparing data entry program before data collection for checking readiness of the program for data entry. ·A set of validation rules were applied on the program for checking consistency of data. ·Efficiency of the program was checked through pre-testing in entering few questionnaires, including incorrect information for checking its efficiency, in capturing these information. ·Well trained data keyers were selected and trained for the main data entry. ·Weekly or biweekly data files were received by project management for checking accuracy and consistency, notes of correction are provided for data entry management for correction.

Response rate

·Response rate: 82.5%

Sampling error estimates

Statistical Errors: The findings of the survey are affected by statistical errors due to using sampling in conducting the survey for the units of the target population, which increases the chances of having variances from the actual values we expect to obtain from the data had we conducted the survey using comprehensive enumeration.. The variance of the key goods in the survey was computed and dissemination was carried out on the level of Palestinian Territory for reasons related to sample design and computation of the variance of the different indicators.

Non-Statistical Errors These types of errors could appear on one or all the survey stages that include data collection and data entry: Response errors: these types of errors are related to, responders, fieldworkers, and data entry personnel's. And to avoid mistakes and reduce the impact has been a series of actions that would enhance the accuracy of the data through a process of data collection from the field and the data processing.

Abstract

A comprehensive and detailed statistical database of any economic activity is a prerequisite for planning and policy making and this applies to economic activities that play a major role in most modern world economies.

The Palestinian Central Bureau of Statistics is pleased to issue the sixteenth volume of economic surveys for the Palestinian Territory, including statistical tables of findings. This edition presents the findings of the surveys conducted for 2010 as the reference year and covers most of the economic activities operating in the Palestinian Territory since 1994.

Economic surveys of various fields constitute the basic foundations for the compilation of National Accounts for Palestine. It is hoped that they will also fulfill the various needs and expectations of users in both the public and private sectors.

Geographic coverage

West Bank and Gaza Strip

Analysis unit

Enterprises

Universe

Communication and Information survey covers all establishments of the following main activities: 1. Publishing activities (58). 2. Motion picture video and television programme production sound recording and music publishing activities (59).
3. Programming and broadcasting activities (60). 4. Telecommunications (61). 5. Computer programming consultancy and related activities (62). 6. Information service activities (63).

Kind of data

Sample survey data [ssd]

Sampling procedure

The number of Enterprise in Communication and Information survey for the base year 2010, amounted to (467), which form the whole frame in the West bank and Gaza Strip.

Sampling deviation

non

Mode of data collection

Face-to-face [f2f]

Research instrument

They are one forms of the Communication and Information survey questionnaire 2010 of the Palestinian Territory, it's related to household and branches, and the non-financial companies sector. The questionnaire contains of the following main variables: .The persons engaged in enterprise and compensation of these employees.1 2. Value of output from the main activity and secondary activity. 3. Production inputs of goods and services. 4. Payments and transfers. 5. Taxes on production. 6. Assets and capital formation

Cleaning operations

To ensure the quality and consistency of data, a set of measures was introduced as follows: · Creation of a data entry program prior to the collection of data to ensure this would be ready. · A set of validation rules were applied to the program to check the consistency of data. · The efficiency of the program was pre-tested by entering a few questionnaires, including incorrect information, and checking its efficiency in capturing the incorrect information. · Well-trained data entry personnel were selected and trained for main data entry. · Weekly data files were received by project management to be checked for accuracy and consistency: correction notes were provided to data entry management for implementation.

Response rate

Response rate 80.1%

Sampling error estimates

Statistical Errors: The findings of the survey are not affected by statistical errors due to using comprehensive counting.

Non-Statistical Errors These types of errors could appear on one or all the survey stages that include data collection and data entry: Response errors: these types of errors are related to, responders, fieldworkers, and data entry personnel's. And to avoid mistakes and reduce the impact has been a series of actions that would enhance the accuracy of the data through a process of data collection from the field and the data processing.

Geographic coverage

West Bank and Gaza

Analysis unit

Household

Universe

It consists of all Palestinian households and individuals who are staying normally in the state of Palestine during 2013

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is two stage stratified cluster (pps) sample: First stage: selection a stratified sample of 300 EA with (pps) method. Second stage: selection a random area sample of 25 responded households from each enumeration area selected in the first stage, the selection starts from a random point in the enumeration area ( building number).

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire Represents the main tool for the data collection, and so it must achieve the technical specifications for all phases of the survey, and the questionnaire consists of several sections: · Cover Page: Contains the identification data for the family, the date of the visit, data on the team work of the field, office and data entry. · The Roaster: Which contains demographic, social and economic data for the family members selected. · Housing Characteristics: It includes data on the type of dwelling, tenure, number of rooms, housing unit connection to public networks (water, electricity), the method of waste disposal, the main source of energy used in the housing unit, durable goods available to the family as well as data on the confiscation / Isolation Lands of the family by the Israeli occupation and land area. · Agriculture: The family ownership of agricultural land and land area, and sources of irrigation of agricultural crops, livestock and their numbers and data on the number of workers in agriculture from family members. · Assistances and Coping Strategy: Contains data about the family receiving of all kinds of assistances (food, cash, employment, school feeding), and source of assistance, and satisfaction for assistance and the reason for the dissatisfaction for assistance. And It contains data on the length of time in which the family can survive financially in the future, and the difficulties faced by the family and the actions carried out by the family to cope with difficulties. · Consumption/Expenditures: This section contains data on household expenditure in terms of increase or decrease, as well as the average household expenditure during the past six months, the rate of household expenditure on food and water during the past six months ... etc.. · Dietary Diversity and Facing Food Shortages: Includes data about how many days the family consume some food during the past week and the origin and source of such food. · Income: This section contains data on the sources of family income and the value of the family's monthly income over the past month and the value of annual income, and the percentage of annual income from agriculture. Freedom of Movement: The data includes all restrictions on the movement of the family during the past six months, and the problems prevent any family member from access to work, land, school or university and health facilities

Cleaning operations

Both data entry and tabulation were performed using the Access and SPSS software programs. Data entry was organized corresponding to the main parts of the questionnaire.
A data entry template was designed to reflect an exact image of the questionnaire, and included various electronic checks: logical check, range checks, consisting checks and cross-validation. Complete manual inspection of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Response rate

Response rate was 83.6%

Sampling error estimates

Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, the variance table is attached with the final report. There is no problem to disseminate results at the national level and regional level (west bank , gaza strip).

Data appraisal

Non-sampling 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 in how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey and practical and theoretical training during the training course.

Also data entry staff was trained on the entry program that was examined before starting the data entry process. Continuous contacts with the fieldwork team were maintained through regular visits to the field and regular meetings during the different field visits. Problems faced by fieldworkers were discussed to clarify issues and provide relevant instructions.

Abstract

There is increasing concern in national statistical offices about coverage of informal economic activities. PCBS has given priority to transport activities, due to their importance to the Palestinian economy. The informal transport survey complements the 2012 formal transport sector survey. The PCBS began by exerting tremendous effort to establish a sampling frame. All land transport stops in major Palestinian cities were defined and data about the number and characteristics of operating vehicles were collected in order to stratify the population into homogenous stratum.

The survey covers activities of the informal sector according to (ISIC-4) for both: Non-scheduled passenger land transport (4922) Freight transport by road (4923)

Objectives: Objectives of this survey are the following. 1. Number of transport vehicles and persons engaged by activity. 2. Value of output and intermediate consumption 3. Value added components. 4. Fixed assets. 5. Other selected variables.

Geographic coverage

Palestine

Analysis unit

vehicles

Universe

The survey covers activities of the informal sector according to (ISIC-4) for both: Non-scheduled passenger land transport (4922) Freight transport by road (4923)

Vehicles:divided according to its activity to: ·Taxi passengers. ·Private passengers. ·Freight transport by road.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame: It is a list of parks that were collected in the frame survey and included Taxi park, Freight Transport by Road park, for the vehicles model (2003 and below, 2004and above). The frame amounted to (11,561) vehicles.

Sample Design: It is a list of parks (Lines) that were collected in the frame survey and included Taxi park, Freight Transport by Road park, for the vehicles model (2003 and below, 2004 and above). The frame amounted to (11,561) vehicles.

Sample Clusters: Parks were divided to clusters on the following levels: 1. Transport kind: Vehicles divided according to its activity to: - Taxi passengers. - Private passengers. - Freight transport by road.

1. Vehicles model: Vehicles divided according to its model to:
2. Model 2003 and below.
3. Model 2004 and above.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of the transport survey- outside sector was designed to take into account major economic variables pertaining to the examined phenomenon and it meets the needs of the Palestinian National Accounts. Which contains the following questions: · questions about vehicle. · Persons engaged and their compensations. · Value of output from main activity. · Intermediate consumption. · Taxes on production. · Fixed assets.

Cleaning operations

Data Entry Training: The data entry training begins before the data entry process, the training is of two parts theoretically and practically.

Data Entry Administrative: The Information System Directorate administrates the whole process with all its requirements. The data entry team is of data entry employees and a supervisor.

Editing of Data Entry: There are tow steps: First: Throughout the data entry itself since the program itself is available to correct mistakes in data entry. Second: Listing of questionnaires, which are, still have mistakes in data entry.

Data Tabulation: Primary tables are exerted after the process of data entry and editing. A process of editing data is being taken to have at the end a final correct data tables.

Response rate

The Response ratio is (98.5%)

Sampling error estimates

Statistical Errors Data of this survey affected by statistical errors due to use the sample, Therefore, the emergence of certain differences from the real values expect obtained through censuses. It had been calculated variation of the most important indicators exists and the facility with the report. And the dissemination levels of the data were particularized at the regional level in the Palestinian Territories.

Non-Statistical 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, interview in errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained in how to carry out the interview, what to discuss and what to avoid

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was a stratified random multi-stage sample representative of all inhabitants of Argentina aged 18 +. At the first stage of sampling, using the stratification as per geographical criteria, the country was divided into six regions. The sampling selection criteria adopted for Argentina was as follows:

a) Buenos Aires The official cartography provided by the National Census was used for the sample frame. The procedure consisted in stratifying the census ratio according to two criteria: • Geographical location; • And social class, defined by the educational level of the head of the household. Using this stratification, the census ratio was selected and within each one, a block was randomly selected.

b) Rest of the Country Within each locality selected (representing the first stage sampling unit), the census ratio (second stage unit) was ordered by social class and a sample within them was chosen using a random start. In each census ratio that was selected, the same criteria used for Buenos Aires area was applied to get to the final unit sample. (the respondent).

c) General Sampling Aspects: Over 250 different sampling points were selected on a mathematically random basis from within localities. In each sampling point, four interviews were conducted. Only one person per household was interviewed. If the person who opened the door matched the quota requirements (sex and age), this person was interviewed. If not, the correct target was looked for in the household.

Final Sample Size=1,555

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Response rate

36%

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

Survey Objectives The 2006 Palestinian Refugee Camps, Lebanon Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Generic - To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Generic and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Survey Content

MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation

The surveys are typically carried out by government organizations, with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Survey results

Results from the surveys, including national reports, standard sets of tabulations and micro level datasets will all be made widely available after completion of the surveys. Results from the surveys will also be made available in DevInfo format. DevInfo v5.0 is a powerful database system which has been adapted from UNICEF's ChildInfo technology to specifically monitor progress towards the Millennium Development Goals. MICS Results will also be available through UNICEF's web site dedicated to monitoring the situation of children and women at www.childinfo.org. Results of the prior round of MICS can already be found at this site.

Geographic coverage

The survey is representative and covers the whole of Palestinian refugee camps and gatherings in Lebanon.

Analysis unit

Households (defined as a group of persons who usually live and eat together)

De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Multiple Indicator Cluster Survey (MICS) in Palestinian Refugee Camps and Gatherings in Lebanon was designed to provide estimates on a large number of indicators on the situation of children and women at the geographical area and camp/gathering level, for urban and rural areas, and for 12 camps and 12 gatherings in 5 geographical areas. With this design we could monitor a large number of women and children indicators at the geographical area and camp level for urban and rural areas.

The sample population (based on the Palestinian Refugee Camps and Gatherings in Lebanon Census of 1999) was divided into equal clusters each containing 20 households (totaling 1300 clusters). Sample clusters (310 clusters, i.e. 6200 households) were drawn with uniformity, random start and a sampling fraction of 0.25.

Sampling deviation

No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three sets of questionnaires were used in the survey: 1) a household questionnaire was used to collect information on all household members, the household, and the dwelling; 2) a women’s questionnaire administered in each household to all women aged 15-49 years; 3) an under-5 questionnaire, administered to mothers or caretakers of all children under 5 living in the household.

The questionnaires included the following modules: Household Questionnaire, Household Listing, Education, Water and Sanitation Facilities, Household Background Characteristics, Child Labour, and Salt Iodization.

Questionnaire for Individual Women: Child Mortality, Tetanus Toxoid, Maternal and Newborn Health, Contraception, and - HIV/AIDS.

Questionnaire for Children Under Five: Birth Registration and Early Learning, Vitamin A, Breastfeeding, Care of Illness, Immunization, and Anthropometry.

The questionnaires are based on the MICS3 model questionnaire. Changes in format were made to the UNICEF MICS3 model Arabic version questionnaires that were pre-tested during March 2006.

Cleaning operations

Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps: 1) Questionnaire reception 2) Office editing and coding 3) Data entry 4) Structure and completeness checking 5) Verification entry 6) Comparison of verification data 7) Back up of raw data 8) Secondary editing 9) Edited data back up After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files: 10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5) 11) Recoding of variables needed for analysis 12) Adding of sample weights 13) Calculation of wealth quintiles and merging into data 14) Structural checking of SPSS files 15) Data quality tabulations 16) Production of analysis tabulations

Details of each of these steps can be found in the data processing documentation, data editing guidelines, data processing programs in CSPro and SPSS, and tabulation guidelines.

Data entry was conducted by 12 data entry operators in tow shifts, supervised by 2 data entry supervisors, using a total of 7 computers (6 data entry computers plus one supervisors computer). All data entry was conducted at the GenCenStat head office using manual data entry. For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach, that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included inthe data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses ("Other" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire.

Structure and completeness checking ensured that all questionnaires for the cluster had been entered, were structurally sound, and that women's and children's questionnaires existed for each eligible woman and child.

100% verification of all variables was performed using independent verification, i.e. double entry of data, with separate comparison of data followed by modification of one or both datasets to correct keying errors by original operators who first keyed the files.

After completion of all processing in CSPro, all individual cluster files were backed up before concatenating data together using the CSPro file concatenate utility.

Data editing took place at a number of stages throughout the processing (see Other processing), including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files

Detailed documentation of the editing of data can be found in the data processing guidelines in the MICS Manual (http://www.childinfo.org/mics/mics3/manual.php)

Response rate

The response rate of households, mothers and children was remarkably high. Of the 6200 households selected for the sample, only 33 households could not be interviewed thus making the household response rate 99.5 percent.

In the interviewed households, 4001 ever married women (age 15-49) were identified. Of these, 3955 were successfully interviewed, yielding a response rate of 98.9 percent. In addition, 2431 children under age five were listed in the household questionnaire. Questionnaires were completed for 2381 of these children, which corresponds to a response rate of 97.9 percent.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling

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 2000 (LFS).

The importance of this survey lies in that it focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

The survey main objectives are: - To estimate the labor force and its percentage to the population. - To estimate the number of employed individuals. - To analyze labour force according to gender, employment status, educational level , occupation and economic activity. - To provide information about the main changes in the labour market structure and its socio economic characteristics. - To estimate the numbers of unemployed individuals and analyze their general characteristics. - To estimate the rate of working hours and wages for employed individuals in addition to analyze of other characteristics.

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:

All Palestinians aged 10 years or older living in the Palestinian Territory, excluding those living in institutions such as prisons or shelters.

Sampling Frame:

The sampling frame consisted of a master sample of Enumeration Areas (EAs) selected from the population housing and establishment census 1997. The master sample consists of area units of relatively equal size (number of households), these units have been used as Primary Sampling Units (PSUs).

Sample Design:

The sample is a two-stage stratified cluster random sample.

Stratification: Four levels of stratification were made:

1. Stratification by Governorates.
2. Stratification by type of locality which comprises: (a) Urban, (b) Rural, and (c) Refugee Camps
3. Stratification by classifying localities, excluding governorate centers, into three strata based on the ownership of households of durable goods within these localities.
4. Stratification by size of locality (number of households).

Sample Size:

The sample size in the first quarter consisted of 7,559 households, which amounts to a sample of around 29,650 persons aged 15 years and over (including 23,677 aged 15 years and over). In the second round the sample consisted of 7,559 households, which amounts to a sample of around 29,894 persons aged 10 years and over (including 23,890 aged 15 years and over), in the third round the sample consisted of 7,559 households, which amounts to a sample of around 29,709 persons aged 10 years and over (including 23,670 aged 15 years and over). In the fourth round the sample consisted of 7,559 households; of these only 7349 households have been interviewed due to the Israeli comprehensive closure and aggression against the Palestinian people, which amounts to 28380 persons aged 10 years and over (including 22495 aged 15 years and over).

The sample size allowed for non-response and related losses. In addition, the average number of households selected in each cell was 16.

Sample Rotation:

Each round of the Labor Force Survey covers all the 481 master sample areas. Basically, the areas remain fixed over time, but households in 50% of the EAs are replaced each round. The same household remains in the sample over 2 consecutive rounds, rests for the next two rounds and represented again in the sample for another and last two consecutive rounds before it is dropped from the sample. A 50 % overlap is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes). In earlier applications of the LFS (rounds 1 to 11); the rotation pattern used was different; requiring a household to remain in the sample for six consecutive rounds, then dropped. The objective of such a pattern was to increase the overlap between consecutive rounds. The new rotation pattern was introduced to reduce the burden on the households resulting from visiting the same household for six consecutive times.

Mode of data collection

Face-to-face [f2f]

Research instrument

One of the main survey tools is the questionnaire, 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

The data processing stage consisted of the following operations: 1. Editing before data entry All questionnaires were then edited in the main office using the same instructions adopted for editing in the field.

1. Coding At this stage, the Economic Activity variable underwent coding according to West Bank and Gaza Strip Standard commodities Classification, based on the United Nations ISIC-3. The Economic Activity for all employed and ever employed individuals was classified at the fourth-digit-level. The occupations were coded on the basis of the International Standard Occupational Classification of 1988 at the third-digit-level (ISCO-88).

2. Data Entry In this stage data were entered into the computer, using a data entry template BLAISE. The data entry program was prepared in order to satisfy the following requirements:

• Duplication of the questionnaire on the computer screen.
• Logical and consistency checks of data entered.
• Possibility for internal editing of questionnaire answers.
• Maintaining a minimum of errors in digital data entry and fieldwork.
• User-friendly handling

Accordingly, data editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data files

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 overall response rate for the survey was 89.5%

More information on the distribution of response rates by different survey rounds is available in Page 11 of the data user guide provided among the disseminated survey materials under a file named "Palestine 2000- Data User Guide (English).pdf".

Sampling error estimates

Since the data reported here are based on a sample survey and not on a complete enumeration, they are subjected to sampling errors as well as non-sampling errors. Sampling errors are random outcomes of the sample design, and are, therefore, in principle measurable by the statistical concept of standard error.

A

Abstract

A. Objectives

To generate statistics for wage and salary administration and for wage determination in collective bargaining negotiations.

B. Uses of Data

Inputs to wage, income, productivity and price policies, wage fixing and collective bargaining; occupational wage rates can be used to measure wage differentials, wage inequality in typical low wage and high wage occupations and for international comparability; industry data on basic pay and allowance can be used to measure wage differentials across industries, for investment decisions and as reference in periodic adjustments of minimum wages.

C. Main Topics Covered

Occupational wage rates Median basic pay and median allowances of time-rate workers on full-time basis

Geographic coverage

National coverage, 17 administrative regions

Analysis unit

Establishment

Universe

The survey covered non-agricultural establishments employing 20 or more workers except national postal activities, central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, extra territorial organizations and bodies.

Kind of data

Sample survey data [ssd]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The 2006 BLES Survey Sampling Frame (SSF 2006) is an integrated list of establishments culled from the 2004 List of Establishments of the National Statistics Office, updated 2004 BLES Sampling Frame based on the status of establishments reported in the 2003/2004 BLES Integrated Survey (BITS). Reports on closures and retrenchments of establishments submitted to the Regional Offices of the Department of Labor and Employment were also considered in preparing the 2006 frame.

Sampling design: The OWS is a sample survey of non-agricultural establishments employing 20 persons or more where the survey domain is the industry. Those establishments employing at least 200 persons are covered with certainty and the rest are sampled (stratified random sampling). The design does not consider the region as a domain to allow for more industry coverage.

Sample size: For 2006 OWS, number of establishments covered was 7,630 of which, 6,432 were eligible units.

Note: Refer to Field Operations Manual Chapter 2 Section 2.5.

Sampling deviation

Not all of the fielded questionnaires are accomplished. During data collection, there are reports of permanent closures, non-location, duplicate listing and shifts in industry and employment outside the survey coverage. Establishments that fall in these categories are not eligible elements (three consecutive survey rounds for "can not be located" establishments) of the frame and their count is not considered in the estimation. Non-respondents are made up of refusals, strikes or temporary closures, can not be located (less than three consecutive survey rounds) and those establishments whose questionnaires contain inconsistent item responses and have not replied to the verification queries by the time output table generation commences.

Respondents are post-stratified as to geographic, industry and employment size classifications. Non-respondents are retained in their classifications. Sample values of basic pay and allowances for the monitored occupations whose basis of payment is an hour or a day are converted into a standard monthly equivalent, assuming 313 working days and 8 hours per day. Daily rate x 26.08333; Hourly rate x 208.66667.

Mode of data collection

Other [oth] mixed method: self-accomplished, mailed, face-to-face

Research instrument

The questionnaire contains the following sections:

Cover Page (Page 1) This contains the address box, contact particulars for assistance, spaces for changes in the name and location of sample establishment and head office information in case the questionnaire is endorsed to it and status codes of the establishment to be accomplished by BLES and its field personnel.

Survey Information (Page 2) This contains the survey objective and uses of the data, scope of the survey, confidentiality clause, collection authority, authorized field personnel, coverage, periodicity and reference period, due date for accomplishment and expected date when the results of the 2006 OWS would be available.

Part A: General Information (Page 3) This portion inquires on main economic activity, major products/goods or services and total employment.

Part B: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis (Pages 4-5) This section requires data on the number of time-rate workers on full-time basis by time unit and by basic pay and allowance intervals.

Part C: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis in Selected Occupations (Pages 6-9) This part inquires on the basic pay and allowance per time unit and corresponding number of workers in the two benchmark occupations and in the pre-determined occupations listed in the occupational sheet to be provided to the establishment where applicable.

Part D: Certification (Page 10) This portion is provided for the respondent's name/signature, position, telephone no., fax no. and e-mail address and time spent in answering the questionnaire.

Appropriate spaces are also provided to elicit comments on data provided for the 2006 OWS; results of the 2004 OWS; and presentation/packaging, particularly on the definition of terms, layout, font and color.

Part E: Survey Personnel (Page 10) This portion is for the particulars of the enumerators and area/regional supervisors and reviewers at the BLES and DOLE Regional Offices involved in the data collection and review of questionnaire entries.

Part F: Industries With Selected Occupations (Page 11) The list of industries for occupational wage monitoring has been provided to guide the enumerators in determining the correct occupational sheet that should be furnished to the respondent.

Results of the 2004 OWS (Page 12) The results of the 2004 OWS are found on page 12 of the questionnaire. These results can serve as a guide to the survey personnel in editing/review of the entries in the questionnaire.

Note: Refer to questionnaire and List of Monitored Occupations.

Cleaning operations

Data were manually and electronically processed. Upon collection of accomplished questionnaires, enumerators performed field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the Field Operations Manual. The forms were again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertaook the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries were returned to the establishments for verification, personally or through mail.

Note: Refer to Field Operations Manual Chapter 1 Section 1.10.

Response rate

The response rate in terms of eligible units was 87.56%.

Sampling error estimates

Estimates of the sampling errors computed.

Note: Refer to Coefficients of Variation.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Average wage rates of unskilled workers by region is compared for proximity with the corresponding minimum wage rates during the survey reference period.