Abstract

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

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

Geographic coverage

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

Analysis unit

1- Household/families. 2- Individuals.

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which were enumerated in 1997; the enumeration area consists of buildings and housing units and is composed of an average of 120 households. The enumeration areas were used as Primary Sampling Units (PSUs) in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 299 enumeration areas. Second stage: selection of a systematic random sample of 12-18 households from each enumeration area selected in the first stage. A person (18 years and more) was selected from each household in the second stage.

Sample strata:

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

Sample Size:

The calculated sample size is 3,781 households.

Target cluster size:

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

Detailed information/formulas on the sampling design are available in the user manual.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

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

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

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

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

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

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

Second section: The second section of the questionnaire includes a list of 54 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-54 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year.

Cleaning operations

Raw Data

Both data entry and tabulation were performed using the ACCESS and SPSS software programs. The data entry process was organized in 6 files, 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, consistency checks and cross-validation. Complete manual inspection was made of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Harmonized Data

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

Response rate

The survey sample consists of about 3,781 households interviewed over a twelve-month period between January 2004 and January 2005. There were 3,098 households that completed the interview, of which 2,060 were in the West Bank and 1,038 households were in GazaStrip. The response rate was 82% in the Palestinian Territory.

Sampling error estimates

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

Abstract

The survey was conducted in South Sudan between July 2014 and December 2014 as part of Enterprise Surveys roll-out, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

In South Sudan, data from 738 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Two levels of stratification were used in this country: industry and region. The size was not available in the sampling frame for most contacts.

For industry stratification, the universe was divided into manufacturing sector and two service sectors (retail and other services).

Regional stratification was defined in four regions: Juba, Nimule, Torit and Yei.

The sampling frame was built using data from the National Bureau of Statistics as well are municipal commercial registries.

The sampling frame was generated with the aim of obtaining interviews at 720 establishments. Establishments with undefined size were included as part of this sample frame for South Sudan in order to ensure a representative sample. Size information collected during the survey process can then be used to categorize these firms. Establishments with undefined location were included as part of this sample frame for Sudan in order to ensure a representative sample. Location information collected during the survey process can then be used to categorize these firms.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The eligible manufacturing industries have been surveyed using the Manufacturing Module Questionnaire (includes a common set of core variables, plus manufacturing specific questions). Eligible service establishments have been covered using the Services Module Questionnaire. Each variation of the questionnaire is identified by the index variable, a0.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.42. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.07.

Abstract

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

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

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

Geographic coverage

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

Analysis unit

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

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

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

Sample and Frame:

The sampling frame consists of all enumeration areas which were enumerated in 1997; the enumeration area consists of buildings and housing units and is composed of an average of 120 households. The enumeration areas were used as Primary Sampling Units (PSUs) in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 299 enumeration areas. Second stage: selection of a systematic random sample of 12-18 households from each enumeration area selected in the first stage. A person (18 years and more) was selected from each household in the second stage.

Sample strata:

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

Sample Size:

The calculated sample size is 3,781 households.

Target cluster size:

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

Detailed information/formulas on the sampling design are available in the user manual.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

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

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

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

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

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

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

Second section: The second section of the questionnaire includes a list of 54 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-54 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year.

Cleaning operations

Raw Data

Both data entry and tabulation were performed using the ACCESS and SPSS software programs. The data entry process was organized in 6 files, 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, consistency checks and cross-validation. Complete manual inspection was made of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Harmonized Data

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

Response rate

The survey sample consists of about 3,781 households interviewed over a twelve-month period between January 2004 and January 2005. There were 3,098 households that completed the interview, of which 2,060 were in the West Bank and 1,038 households were in GazaStrip. The response rate was 82% in the Palestinian Territory.

Sampling error estimates

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

Abstract

This survey was conducted in Indonesia between April 2015 and November 2015, as part of the Enterprise Survey project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 1,320 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into seven manufacturing industries and two services industries- Food and Beverages (ISIC Rev. 3.1 code 15), Garments (ISIC code 18), Textiles (ISIC code 17), Chemicals (ISIC code 24), Rubber and Plastics (ISIC code 25), Non-metallic mineral products (ISIC code 26), Other Manufacturing (ISIC codes 16, 19-23, 27-37), Retail (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.

Regional stratification for the Indonesia ES was done across nine regions: Jawa Barat, Jawa Timur, Jawa Tengah, DKI Jakarta, Banten, Sulawesi Selatan, Sumatera Utara, Bali and Lampung.

The sample frame consisted of listings of firms from four sources: First, for panel firms the list of 1,444 firms from the Indonesia 2009 ES was used. Second, for fresh firms (i.e., firms not covered in 2009), economic census data from Statistics Indonesia known in Indonesia as Badan Pusat Statistik, henceforth BPS, was used. 2006 BPS data was used for service firms and small manufacturing firms and 2012 BPS data was used for medium and large manufacturing firms.

Data for service firms were updated by cross-checking with lists from several business associations namely Aprindo 2013 for retail, AKI 2013, AKSINDO 2012 and Gapenri 2014 for construction, PHRI 2012 for hotels and restaurants and ALFI/ILFA 2014 for transportation.

The quality of the frame was enhanced by the verification process conducted by the contractor Kadence International. However, the sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 4.1% (108 out of 2,629 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a - For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b - Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.50. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 0.23.

Abstract

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

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

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

Geographic coverage

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

Analysis unit

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

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

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

Sample and Frame:

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

Sample Design:

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

Sample strata:

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

Target cluster size:

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

Sample Size:

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

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

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

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

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

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

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

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

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

Cleaning operations

Raw Data

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

Harmonized Data

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

Response rate

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

Sampling error estimates

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

Data appraisal

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

Abstract

The Jerusalem Household Social Survey 2010 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, 2010 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 Sample Frame Were estimated sample size of Jerusalem by 2,075 family, including 1,200 families in the Area J1, and 875 families in the Area of J2 has been the establishment of Sample Frame to Jerusalem (J2) of the General Census of Population and Housing, and Establishment, which was carried out by the PCBS at the end of 2007. And the frame is a list of counting areas, and these areas are used as units an initial preview (PSUs) in the first stage of the process of selecting the sample. Stratified cluster random sample of regular two phases: Phase 1 was selected a stratified random sample of enumeration areas from Jerusalem (J1) and Jerusalem (J2). The number of enumeration areas that have been chosen counting area 75 divided into two Areas : 40 the count of Jerusalem (J1), 35 the count of Jerusalem (J2). Phase 2 Is to choose a random sample (in a field) of the households of the selected enumeration areas are selected so that 30 families from each of the complete count has been selected in the first phase of Jerusalem (J1) and 25 families are selected at random from each Areas regularly count has been selected in the first phase of Jerusalem (J2) on the completion of the data that are a minimum of 20 families from each Areas counted in Jerusalem (J2).

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

Data processing:

Input processing programs: Program is designed input beam programming Access, entry screens have been designed and auditing as well as the tests have been developed through automated checking the input and then cleaning the rules of programming questions and to examine variables at the level of form.

Data entry: After the completion of design input, testing and making sure readiness, started work on data entry and after the entrances have been trained to deal with the programs of the entry. Work began on the introduction of survey data as of 26/07/2010 until 28/11/2010. It was the number of entrances who worked on the introduction of statements 5 entrances at a minimum, where the number of employees to enter data commensurate with the flow of forms, note that the flow of the forms on the entry could not be uniform due to the difficulty of transportation because of security conditions, and was checking the forms returned from the entry of by the auditors to complete and re-adjusted and then re-entered its final form after their arrival from the field.

Check and clean the data: Been cleaned data queries run tests and adjust input errors immediately. And re-forms containing errors form to the project manager to deal with them. After the completion of the data entry process began work on the audit and examine the data as follows: 1. Check transitions, and allowed values. 2. Check compatibility and consistency between questions per section and the various departments, and this to us ? E logical relations. 3. Tests based on certain relationships between the different questions, so that was extracted list Balastmarat is matched, review and identify the source of a bug where, if found there are errors in the input was adjusted immediately, and if there are errors, the field was being converted to field work to re-visit again , and correct errors in form, have included the stage of data cleaning in two stages: the stage of cleaning the survey data in terms of consistency and logic and linked to age and date of birth, educational status and other per capita, and consistency of questions of each section of the form of households, while the second stage have included the examination of consistency between the results of Questions Social Survey 2010 and Social Survey of Jerusalem in 2005. Surveys and other surveys such as the impact of expansion and annexation wall on the population, 2008, as well as the Labour Force Survey - third quarter 2010.

Response rate

Were selected (2,374) represented the family of Jerusalem Governorate, a sample size which is equal to the original 2,075 family as well as samples to 299 additional families of Jerusalem (J2) The number of families who were interviewed (1,709) in Jerusalem Governorate, complete Questionnaires 72.0% (1,026) in J1 85.5% (683) in J2 58.2%

Sampling error estimates

Data were collected in a manner that the survey sample and not Balhsr destruction, so she is exposed to two main types of errors. The first sampling errors (statistical errors), and the second non-statistical errors. It is intended that sampling errors of the errors resulting from sample design, so it is easy to measure, the contrast has been calculated and the effect of sample design.

The non-statistical errors are possible to occur in every stage of project implementation, through data collection, inserting, and mistakes can be summarized by the non-response, and response errors (surveyed), and the mistakes of the interview (the researcher) and data-entry errors. To avoid errors and reduce the impact it has made significant efforts through the training of researchers extensive training, and the presence of a group of experts in the concepts and terminology, medical / health, and training on how to conduct interviews, and the things that must be followed during the interview, and the things that should be avoided.

Have been trained on the data entry program entry, program, and were examined in order to see the picture of the situation and reduce any problems, there was constant contact between supervisors and checkers through ongoing visits and periodic meetings. In addition, has been drafting a set of circulars and instructions reminder to the team. Also been circulated answers to questions and problems faced by the researchers during the field work.

As for office work have been trained crew to check the special forms and field detection of errors, which greatly reduces the rates of errors that can occur during field work. In order to reduce the proportion of errors that can occur during entry form to the computer, the software is designed to entry so as not to allow any errors Tnasagah can get during the process of input and contains many of the conditions Logical, where they were loading the program the input of many tests on private answers each question in addition to the relations between the different questions and testing the other logical. This process has led to the disclosure of most of the errors that are not found in previous phases of work, where they were correct all errors that have been discovered.

Data were evaluated according to the following areas: 1. Definition of family members and how to register. 2. Demographic characteristics that have a relationship on Christmas. 3. Breakdown of the profession and activity.

Methods of assessment vary according to the data subject in this survey include the following: 1. Occurrences of missing values and Answers "other" and "Do not know" and examine inconsistencies between different sections or between the date of birth and other sections. Add to examine the internal consistency of the data as part of a logical data and completeness. 2. Compared to survey data with the results of surveys of the relationship and by the Central Bureau of Statistics Palestinian implementation.

Can be summarized as sources of some non-statistical errors that have emerged during the implementation of the survey including the following: Inability to meet the data in some cases the forms because of the lack of a home or be in the housing unit does not exist or are uninhabited and there are families not able to provide some data or refused to do so. Some families did not take the form subject very seriously affecting the quality of the data provided. Errors resulting from the method of asking the question by the researcher in the field. Category understand the question and answer based on his understanding of it. The inability of the technical team overseeing the

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden.

The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones.

The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

National

Analysis unit

Household Individual

Universe

National population, both sexes,18 and more years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 1199

The sample was not designed to be representative of the entire adult population. We excluded the non-urban population (communities that include less than 2000 people) which constitutes 9% of the Israeli population. There were different stages in the sampling procedure: - Division into strata (based on geographic location, community size and socio-economic characteristics). - With strata sampling of statistical areas (the smallest ecological unit). - Interviewing of specified number of persons within statistical units based on Kish-grid.

Stratification factors were used such as: - Socio-economic characteristics of statistical area - Geographical region of statistical area.

Remarks about sampling: - Final numbers of clusters or sapling points: 47 - Sample unit from office sampling: Address point in the selection area, and the procedures for continued movement.

Mode of data collection

Face-to-face [f2f]

Research instrument

The WVS questionnaire was translated from the English questionnaire by a member of the research team. The questionnaire was translated to Hebrew and Arabic. The translated questionnaire was back-translated into English and the translated questionnaire was also pre-tested with 10 face to face interviews. We used the ISSP questionnaire for the demographic questions. There have been some optional questions included: V120-121, V124-125, V36, V133, V139, V217, V83-85, V97-102. There have been some country-specific questions included in the questionnaire but there have not been included before the demographic questions. The questions included were: b38-b42b45b46, b48-b61,b63-b80 were country-specific. Also, it is important to mention that not all the questions were in the prescribed order. The sample was not designed to be representative of the entire adult population. We excluded the non-urban population (communities that include less than 2000 people) which constitutes 9% of the Israeli population. The lower age cut-off for the sample was 18 and there was not any upper age cut-off for the sample.

Response rate

The following table presents completion rate results: -Total number of starting names/addresses 3617 - Addresses established as empty, demolished or containing no private dwellings 241 - Selected Respondent had inadequate understanding of language of survey 278 -No contact at selected address 296 -No refusal at selected address 1367 -Other type of unproductive (please write in full details in the box below) 236 -Full productive interview 1199

Sampling error estimates

Estimated error: 2.9

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Viet Nam, the listing from the General Statistics Office of Vietnam, the 2021 Economic Census, was used. The registration agency is the Department of Planning and investment.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Viet Nam 2023 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the Viet Nam 2023 WBES included additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics.

Response rate

Overall survey response rate was 31.7%.

Abstract

The Women's Empowerment and Nutrition Survey (WEN) 2024 provides nationally representative data on women's and men's empowerment and women's dietary diversity within agricultural households in Sierra Leone. Implemented by Statistics Sierra Leone (Stats SL) in collaboration with the Ministry of Agriculture and Food Security (MAFS), and with technical support of FAO. The survey had four key objectives: 1. Measure women's and men's empowerment and women's dietary diversity at individual level in agricultural households, providing policymakers with data for gender-responsive planning 2. Refine and test the Women's Empowerment Metric for National Statistical Systems (WEMNS) developed by the International Food Policy Research Institute (IFPRI), Emory University, Oxford University, and the World Bank Group, https://weai.ifpri.info/wemns/ 3. Showcase the feasibility of integrating empowerment and nutrition indicators into existing agricultural survey programs 4. Build national capacity in the collection and analysis of gender and nutrition statistics

The survey captured information on empowerment (agency, rights, resources, collective action) and women's dietary diversity (MDD-W), allowing analysis of the intersection between gender inequality, agricultural participation, and nutritional outcomes.

Geographic coverage

National coverage

Analysis unit

Agricultural households

Universe

Male and female members living in agricultural households aged between 18 and 64 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey targeted adult members aged 18-64 living in agricultural households. Because the MDD-W indicator is validated for women of reproductive age, the nutrition section was administered only to women.

To meet the survey's measurement objectives, three estimation domains were defined: women 18-49, women 18-64, and men 18-64. The two overlapping women's domains ensured adequate precision for MDD-W among women 18-49 while also allowing empowerment estimates for women 18-64. For each domain, the sample was designed to estimate individual empowerment indicators, a summary empowerment metric (WEMNS), the proportion of women consuming at least five MDD-W food groups with an expected sampling error of 5 percent.

The WEN sample was drawn from the sample of the national agricultural survey. First a subsample of households was selected within each stratum with simple random selection probability. Within each selected household, a fixed number of adults were randomly selected in the field after listing all household members. The subsampling of individuals was conducted using simple random sampling, stratified by gender and age. Hence, the sampling of the WEN survey adopted the same stratification criteria as the agricultural survey sample. Acknowledging clustering and intra-household correlation, the sampling design assumed a design effect of 3.5.

For more detailed information on the sampling methodology and calculations of sample size, see annex 1 of the survey report, attached as documentation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2024 Women's Empowerment and Nutrition (WEN) questionnaire encompasses multiple topics: - Time use and agency - Community participation and leadership - Financial services and credit - Property ownership and tenure security - Decision-making and control over income - Information and communication technology (ICT) access and use - Women's dietary diversity (MDD-W)

The CAPI instrument contained automated quality checks and skip patterns. The questionnaire collected information across the following modules: 1. Module D - Paid and Unpaid Activities. Captures time use in the last seven days: household chores, care work, market activities, paid work, farming (subsistence and commercial), leisure, religious/cultural events, learning. It records decision-making power over how much time respondents dedicate to each activity. 2. Module E - Participation and Leadership in Community. Assesses participation in and leadership of different community groups (government, service, financial, livelihood, and religious/social groups). Gender-specific perception questions: a. For women: perceptions of women's ability to participate and be heard in the community. b. For men: same but framed around men's participation. 3. Module F - Life Transitions and Awareness of Rights (Women only). Measures women's agreement with rights-based statements (education, work, income use, property ownership, marriage/divorce, childbearing decisions). Captures attitudes toward women's autonomy across life transitions. 4. Module G - Financial Services and Credit. Records use of financial services in the past 12 months (mobile money, bank account, ATM, credit card). Assesses access to loans from formal (banks, cooperatives, microfinance) and informal (savings groups, moneylenders, NGOs) institutions. 5. Module H - Property Ownership. Measures ownership and rights to agricultural land and dwellings. Asks about ability to sell, bequeath, or document property rights. Captures security of tenure by asking for the likelihood of involuntary loss. 6. Module I - Decision-Making and Control over Income. Assesses individual influence over use of household money (own and others'), major household purchases, decisions on personal healthcare. 7. Module J - Information Communication Technologies (ICT). Measures frequency of use of mobile phones, internet and social media platforms. 8. Module L - Sexual Harassment (Women only). Captures attitudes on acceptability of different forms of harassment: verbal disrespect, work restrictions, rumor-spreading unwanted romantic/sexual advances and exchange of work benefits for sexual favors. Asked in private, ensuring no one could overhear the questions. 9. Module M - Food and Drinks Consumed in Last 24 Hours (Women only). 24-hour dietary recall to measure Minimum Dietary Diversity for Women (MDD-W). Lists food groups (cereals, roots/tubers, legumes, nuts, dairy, meat/fish, eggs, leafy greens, vegetables, fruits, oils/fats, sweets, beverages, insects). Provides standardized recall for comparability across individuals. 10. Module N - Information on Respondent. Records literacy, schooling history, and highest education level.

Cleaning operations

The data went through the following consistency checks: 1. Rigorous internal and cross-module checks enforced skip logic and consistency 2. Rule-based imputation was applied where needed to maintain within-module coherence. For example, when a response contradicted a required filter or created a logical inconsistency between related items, the value was recorded to maintain consistency 3. No statistical/model-based imputation was used to infer substantive values. All edits and imputations followed predefined rules and were fully documented in Stata do-files

STATISTICAL DISCLOSURE CONTROL (SDC) Statistical Disclosure Control (SDC) methods have been applied to the microdata files to protect the confidentiality of respondents. Such methods include the recoding of certain variables (e.g. age and level of education), as well as suppression of some data points.

Users must therefore be aware that data protection with SDC methods involves some modifications to the data, which can result in certain unwanted consequences such as information loss and bias, which may affect the resulting estimates and their parameters.

DATA PROCESSING NOTES Other processing also occurred at various stages. 1. Data Entry: Enumerators recorded responses directly into tablets during interviews, eliminating the need for separate data entry. Built-in skips, range checks, and mandatory fields minimized capture errors. Data were periodically synchronized to a central server managed by Stats SL, ensuring secure storage and timely availability for processing. 2. Data Cleaning: Upon synchronization, the dataset underwent automated validation to flag missing or invalid values, out-of-range responses, skip-pattern violations, duplicate IDs, and key cross-module consistency (e.g., roster-eligibility alignment; women-only routing for Modules F, L, M). Technical staff (Stats SL/MAFS, with FAO support) conducted manual reviews of flagged records, applying predefined editing rules and documenting all corrections. Where necessary, issues were reconciled using field notes or supervisor feedback. These steps ensured datasets were accurate and complete before analysis and reporting.

Response rate

Out of the 2 525 households originally selected, 19 households were not visited. Further, 172 of the sampled households could not be interviewed due to one or more of the following reasons: - Dwelling destroyed - Fieldwork window in the enumeration area (EA) had closed - No one at home despite repeated visits - Household not found or relocated - Entire EA deserted - No competent respondent available at the time of visit

In addition, 16 households refused individual interviews, 28 households had no eligible members, and 71 households could not be interviewed because eligible members were absent during visits and did not return by the end of the day. Thus, a total of 2 219 households were interviewed.

As a result, the rate of non-response was very low: 0.6 percent. The rate of ineligibility was also around 1.1 percent. In the households, one eligible respondent was randomly chosen per stratum (women 18-49, women 50-64, men 18-64). Thus, a household could contribute from one to three individual interviews, but never more than one individual for a single category.

Achieved

Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

These datasets contain counts of the total usual resident population and total dwelling count from the 2011 Census of Population and Housing for Mesh Blocks. Mesh Blocks are the smallest geographic region in the Australian Statistical Geography Standard (ASGS), and the smallest geographical unit for which Census data are available. In 2011, there were approximately 347,000 Mesh Blocks covering the whole of Australia without gaps or overlaps. They broadly identify land use such as residential, commercial, agricultural and parks etc. Mesh Blocks are identified with a unique 11 digit code. Most residential Mesh Blocks contain approximately 30 to 60 dwellings. Mesh Blocks have been designed to be small enough to aggregate accurately to a wide range of spatial units and thus enable a ready comparison of statistics between geographical areas. These are large enough to protect against accidental disclosure.

For 2011, Mesh Block counts are available by usual residence for basic person counts and dwelling counts.

*Persons Usually Resident: This is the count of people where they usually live, which may or may not be where they were on Census Night. This data is coded from the address supplied to the question "Where does the person usually live?". For more information about usual residence, see Place of Usual Residence in the Census Dictionary, 2011 (cat. no. 2901.0).

*Dwellings: A dwelling is a structure which is intended to have people live in it, and which is habitable on Census Night. Some examples of dwellings are houses, motels, flats, caravans, prisons, tents, humpies and houseboats. All occupied dwellings are counted in the Census. Unoccupied private dwellings are also counted with the exception of those in caravan parks, marinas and manufactured home estates. For more information about dwellings, see Dwelling Type in the Census Dictionary, 2011 (cat. no. 2901.0).

For the 2006 Census, experimental Mesh Blocks were developed and counts for usual residence population and total dwellings were provided for each Mesh Block. The boundaries were reviewed and revised in preparation for the 2011 Census.

Dataset History

No lineage information was provided with the data from the Australian Bureau of Statistics.

Dataset Citation

Australian Bureau of Statistics (2014) ABS Mesh Block Population Counts Aus 2011. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/ee39fa76-db4e-412a-af0a-115d965b5813.

Abstract

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

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

Geographic coverage

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

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all Palestinian households who are usually resident in the Palestinian Territory during 2010.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

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

Sample Design:

The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 192 enumeration areas. Second stage: selection of a systematic random sample of 24 households from each enumeration area selected in the first stage.

Note: in Jerusalem Governorate (J1), 13 enumeration areas were selected; then in the second phase, a group of households from each enumeration area were chosen using census-2007 method of delineation and enumeration. This method was adopted to ensure household response is to the maximum to comply with the percentage of non-response as set in the sample design.Enumeration areas were distributed to twelve months and the sample for each quarter covers sample strata (Governorate, locality type) Sample strata:

The population was divided by:

1- Governorate 2- Type of Locality (urban, rural, refugee camps)

Sample Size:

The calculated sample size for the Expenditure and Consumption Survey in 2010 is about 3,757 households, 2,574 households in West Bank and 1,183 households in Gaza Strip.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consists of two main parts:

First: Survey's questionnaire

Part of the questionnaire is to be filled in during the visit at the beginning of the month, while the other part is to be filled in at the end of the month. The questionnaire includes:

Control sheet: Includes household’s identification data, date of visit, data on the fieldwork and data processing team, and summary of household’s members by gender.

Household roster: Includes demographic, social, and economic characteristics of household’s members.

Housing characteristics: Includes data like type of housing unit, number of rooms, value of rent, and connection of housing unit to basic services like water, electricity and sewage. In addition, data in this section includes source of energy used for cooking and heating, distance of housing unit from transportation, education, and health centers, and sources of income generation like ownership of farm land or animals.

Food and Non-Food Items: includes food and non-food items, and household record her expenditure for one month.

Durable Goods Schedule: Includes list of main goods like washing machine, refrigerator,TV.

Assistances and Poverty: Includes data about cash and in kind assistances (assistance value,assistance source), also collecting data about household situation, and the procedures to cover expenses.

Monthly and annual income: Data pertinent to household’s income from different sources is collected at the end of the registration period.

Second: List of goods

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

Registration form

The registration form includes instructions and examples on how to record consumption and expenditure items. The form includes columns: 1.Monetary: If the good is purchased, or in kind: if the item is self produced. 2.Title of the service of the good 3.Unit of measurement (kilogram, liter, number) 4. Quantity 5. Value

The pages of the registration form are colored differently for the weeks of the month. The footer for each page includes remarks that encourage households to participate in the survey. The following are instructions that illustrate the nature of the items that should be recorded: 1. Monetary expenditures during purchases 2. Purchases based on debts 3.Monetary gifts once presented 4. Interest at pay 5. Self produced food and goods once consumed 6. Food and merchandise from commercial project once consumed 7. Merchandises once received as a wage or part of a wage from the employer.

Cleaning operations

Raw Data

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

Harmonized Data

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

Response rate

The survey sample consisted of 4,767 households, which includes 4,608 households of the original sample plus 159 households as an additional sample. A total of 3,757 households completed the interview: 2,574 households from the West Bank and 1,183 households in the Gaza Strip. Weights were modified to account for the non-response rate. The response rate in the Palestinian Territory 28.1% (82.4% in the West Bank was and 81.6% in Gaza Strip).

Sampling error estimates

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

Abstract

The Viet Nam Multiple Indicator Cluster Survey (MICS) was carried by General Statistics Office of Viet Nam (GSO) in collaboration with Viet Nam Committee for Population, Family and Children (VCPFC). Financial and technical support by the United Nations Children's Fund (UNICEF).

In the World Summit for children held in New York in 1990, the Government of Vietnam committed itself to the implementation of the World Declaration and Plan of Action for children.

In implementation of directive 34/1999/CT-TTg on 27 December 1999 on promoting the implementation of the end-decade goals for children, reviewing the National Plan of Action for children, 1991-2000 and designing the National Plan of Action for children, 2001-2010, in the framework of the “Development of Social Indicators” project, the General Statistical Office (GSO) has chaired and coordinated with the Viet Nam Committee for the Protection and Care for Children (CPCC) to conduct the survey evaluating the end- decade goals for children, 1991-2000 (MICS). MICS has covered a sample size of 7628 households in 240 communes and wards representing the whole country, the urban area, the rural area and the 8 geographical areas in 61 towns/provinces. Field activities to collect data lasted 2 months, May- June/2000. The survey was technically supported by statisticians from EAPRO, UNICEF regional offices, UNICEF Hanoi on sample and questionnaire designing, data input software, not least the software analyzing and calculating the estimates generalizing the results of survey.

Survey Objectives: The end-decade survey on children is aimed at. · Providing up-to-date and reliable data to analyse the situation of children and women in 2000. · Providing data to assess the implementation of the World summit goals for children and of the National Plan of Action for Vietnamese Children, 1991-2000. · Serving as a basis (with baseline data and information) for development of the National Plan of Action for Children, 2001-2010. · Building professional capacity in monitoring, managing and evaluating all the goals of child protection, care and education at all levels.

Geographic coverage

The 2000 MICS of Vietnam was a nationally representative sample survey.

Analysis unit

Households, Women, Child.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Viet Nam Multiple Indicator Cluster Survey (MICSII) was designed to provide reliable estimates on a large number of indicators on the situation of children and women at the national level, for urban and rural areas, and for 8 regions: Red River Delta, North West, North East, North Central Coast, South Central Coast, Central Highlands, South East, and Mekong River Delta. Regions were identified as the main sampling domains and the sample was selected in two stages: At the first stage, 240 EAs are sellected. After a household listing was carried out within the selected enumeration areas, a systematic sample of 1/3 of households in each EA was drawn. The survey managed to visit all of 240 selected EAs during the fieldwork period. The sample was stratified by region and is not self-weighting. For reporting national level results, sample weights are used.

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

The questionnaires for MICS in Vietnam are based on the New York UNICEF module questionnaires with some modifications and additions to fit in with Vietnam's context and to evaluate the goals set out in the National Plan of Action. The questionnaires have been arranged in such a way as to prevent the loss of questionnaire sheets and to facilitate the logic control between the items in the modules. Questionnaires include 3 sections. Section 1: general questions to be administered to families and family members. Section 2: questions for child bearing-age women (aged 15-49). Section 3: for children under 5.

Section 1: Household questionnaire Part A: Household information panel Part B: Household listing form Part C: Education Part D: Child labour Part E: Maternal mortality Part F: Water and sanitation Part G: Salt iodization

Section 2: Questionnaire for child bearing-age women Part A: Child mortality Part B: Tetanus toxoid (TT) Part C: Maternal and newborn health Part D: Contraceptive use Part E: HIV/AIDS

Section 3: Questionnaire for children under five Part A:Birth registration and early learning Part B: Vitamin A Part C: Breastfeeding Part D: Care of illness Part E: Malaria Part F: Immunization Part G: Anthropometry

Apart from the questionnaires to collect information at family level, questionnaires are also designed to gather information at community level supplementary to some indicators that can not have data collected at family level. The information garnered includes local population, socio-economic and physical conditions, education, health and progress of projects/plans of actions for children.

Cleaning operations

To minimize the errors made by data entry staff members, all the records were double- entered by two different members. Any error detected between the two entries was re-checked to find out which one is wrong. Data cleaning started in to early September. This process was closely observed to ensure the accuracy, quality and practicality of all the data collected.

To minimize the errors due to wrong statements of respondents or wrong registration by interviewers, a cleaning programme was used to check the consistency and logic in the items of questionnaires and between the questionnaires. The cleaning programme printed out all the errors, then questionnaires were checked by qualified officials.

Response rate

8356 households were selected for the sample. Of these all were found to be occupied households and 8355 were successfully interviewed for a response rate of 100%. Within these households, 10063 eligible women aged 15-49 were identified for interview, of which 9473 were successfully interviewed (response rate 94.1%), and 2707 children aged 0-4 were identified for whom the mother or caretaker was successfully interviewed for 2680 children (response rate 99%).

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the MICS - 3 to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors can be evaluated statistically. The sample of respondents to the MICS - 3 is only one of many possible samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that different somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability in the results of the survey between all possible samples, and, although, the degree of variability is not known exactly, it can be estimated from the survey results. The sampling errors are measured in terms of the standard error for a particular statistic (mean or percentage), which is the square root of the variance. Confidence intervals are calculated for each statistic within which the true value for the population can be assumed to fall. Plus or minus two standard errors of the statistic is used for key statistics presented in MICS, equivalent to a 95 percent confidence interval.

If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the MICS - 3 sample is the result of a two-stage stratified design, and consequently needs to use more complex formulae. The SPSS complex samples module has been used to calculate sampling errors for the MICS - 3. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. This method is documented in the SPSS file CSDescriptives.pdf found under the Help, Algorithms options in SPSS.

Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and include the following:

Age distribution of the household population Age distribution of eligible women and interviewed women Age distribution of eligible children and children for whom the mother or caretaker was interviewed Age distribution of children under age 5 by 3 month groups Age and period ratios at boundaries of eligibility Percent of observations with missing information on selected variables Presence of mother in

Abstract

Objectives of the LFS; The broad objectives of the Labour Force Survey is to obtain comprehensive data on the status of the Labour Market prevailing in Tanzania. More detailed objectives are: • To provide measures of both current and usual economic activity, • To obtain a measure of the size of employment in the informal sector, • To provide measure of unemployment and underemployment, • To provide measure of cash income from non-agricultural employment of all types.

Objectives of CLS in Tanzania: The child labour survey will provide base line on the activities of the child population in Tanzania for planning purposes, policy implementation and monitoring and the evaluation of government programmes aimed at improving the status of children. The survey will: • Collect information on the character, nature, size and reasons for child labour in Tanzania, and to determine the conditions of work and their effects on the health, education and normal development of the working child, • Learn about the characteristics of the sectors where children are working, • Collect information about the migration status of the children, whether involvement in work has determined the residence for the child/family, • Create a special data base on activities of children in the country which will be updated as fresh statistical information becomes available through surveys and other administrative records, • Enhance the capacity of statistical personnel to conduct national surveys on such activities more regularly in the future, • Produce, present and disseminate to the Government employees and workers organisations, NGOs and the general public, a comprehensive report on child labour in Tanzania giving highlights of the statistical findings and results of the in - depth analysis, • To intergrate the Tanzania data into ILO's child labour data base so that, Tanzania may be included in a globle trend on child labour.

Geographic coverage

National (except for Zanzibar)

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2000/01 Integrated Labour Force Survey was carried out on a sample basis using the National Master Sample (NMS) covering only Mainland Tanzania. The rural component of the sample consists of 100 villages, while for the urban component, 122 EAs (Enumeration Areas) were used. The enumeration areas were selected from those demarcated during the 1988 Population Census.

The objective was to collect data from the usual residents of 3,660 households in urban areas and 8,000 households in rural areas, thus making a total of 11,660 households. The data collected from these households were used to estimate the labour force characteristics for the whole of Mainland Tanzania. In urban areas, 3,660 households or 30 households per enumeration area were selected to represent the urban population. A sample of 80 households in each village was selected to represent the rural population. In order to capture seasonal variations, 20 households out of 80 households were interviewed in each village each quarter.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design is, of course, a key activity in any survey. The 2000/01 ILFS questionnaire design was relatively simple given that it had all features of the 1990/91 labour force survey questionnaire, except that one copy of the new questionnaire can accommodate up to five members of a household instead of one. Two additional questionnaires CLS1 and CLS2 were included in the 2000/01 survey in order to collect information relating to child labour. Major innovations made to the questionnaire by the technical committee before its final version was printed are as follows:

The statement “list of all members of the household” on LFS1 questionnaire in column 2 was added.

The wording of question no 56 was changed to: “Are the benefits/earnings from this work appropriate in terms of hours under normal circumstances”.

Coding of Question 3.1 in CLS1 under the less than 3 hours was split into two separate codes as follows:- Less than 1 hour each day and 1 - 2 hours, each day. The reason being that the period was too long to capture information of working children.

More questions on informal sector were recommended and it was agreed to include them in the final questionnaire

Pilot test of the Questionnaire Pilot test of the questionnaire was conducted in Bagamoyo from 15/02/2000 to 25/02/2000. However, preparation for the fieldwork started two weeks before the pilot test. Special permission was obtained from the Pwani Regional Administrative Office. Sensitization in Pilot areas started immediately after District and village officials were consulted.

Training of the LFS/CLS pilot test field personnel took place at the Bagamoyo MANTEP Institute from 15th February 2000 to 24th February 2000. The training included thorough classroom mock interviews and field practice at Kilomo village and Dunda enumeration area. The pilot survey management staff (all from NBS) included five subject matter specialists as trainers and 12 potential supervisors as trainees. Trainees were provided with instruction manuals in Kiswahili and were exposed to interview techniques, consistency checks and adherence to skip patterns.

Printing of Questionnaires During the first week of April 2000, about 4,500 questionnaires were printed to cover the requirement for training of interviewers and first quarter enumeration exercise. Additional 13,200 questionnaires were printed to cover the requirement of the second, third and fourth quarters of the survey. All printing activities were done at the census printing unit and this helped speed up delivery of the first batch of questionnaires and instruction manuals

Response rate

96% response rate

Abstract

This research was conducted in Pakistan between January 2006 and December 2007. Data from 935 manufacturing and service sector registered establishments was analyzed.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. The questionnaire also assesses the survey respondents' opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Kind of data

Sample survey data [ssd]

Sampling procedure

Establishments were selected using stratified random sampling design. The survey covered manufacturing and services sectors and generated a large enough sample size for selected industries to conduct statistically robust analyses. With level of precision at a minimum 7.5 percent for 90 percent confidence intervals about estimates of population proportions and mean of log sales at the national, provincial and industry level.

The sampling frame was drawn from the 2005 Economic Census of Pakistan, conducted by Pakistan's Federal Bureau of Statistics (FBS). As the target population was formal, urban manufacturing and services establishments with more than 5 full-time employees, the census identified 583,329 manufacturing firms and 1,566,722 establishments in Wholesale/Retail trade & Restaurants.

In accordance with the size and make up of the economy, the manufacturing sector was stratified into five 2-digit Pakistan Standard Industrial Classification (PSIC) sectors: (i) food processing, (ii) textiles, apparel & leather, (iii) chemicals and products, (iv) metal and electric machinery, and (v) sports goods and handicrafts with a residual stratum based on the 14 largest cities from the four provinces of the country. Services establishments engaged in wholesale & retail trade, hotels & restaurants were grouped to constitute an independent stratum for each provincial capital.

Within each industry, the total sample size was distributed to the provincial/city sub-strata based on proportional allocation in order to be representative of the nation, the industry groups and the urban areas of each of the four provinces. Given the domination of smaller firms in sample frame, a sampling approach which oversampled larger firms was employed to ensure a sufficient number of large enterprise which otherwise might be underrepresented.

The specific steps involved: (i) extracting from the frame and dividing into activity/industry groups with selection made in proportion to each group's contribution to total industrial employment, (ii) allocating the establishments selected in to each industry group across the provinces/cities selected using a proportional allocation, and (iii) selecting the establishments for each province/city sub-stratum with a probability of selection which is inversely proportional to size (i.e. larger firms will be selected with a higher probability). Due to the oversampling of larger firms, weights were computed so that inferences about the population could be extrapolated from the sample.

The Pakistan Enterprise Survey 2007 sample was also designed to include up to 600 firms from the original sample of Pakistan ICS 2002. Out of a total of 846 establishments surveyed in 2002 (panel firms with location and other identifiers). The remaining firms were kept as potential replacements in case of non-response by an establishment of similar characteristics in the original panel sample. In the end, 402 firms were interviewed out of 795 firms contacted.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Pakistan 2007 Manufacturing Sector Questionnaire; - Pakistan 2007 Services Sector Questionnaire.

The survey is fielded via two instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm.

The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Response rate

The field work involved a sample of almost 2700 firms with more than 2300 firms contacted in order to complete the survey of 1337 firms - 57 percent success rate. Of the 1000 non-successful contacts, about 45 percent were not located due to poor contact information and 25 percent refused to participate. Of the rest, 20 percent were closed and 10 percent were either non-responsive or produced non-usable data. For the non-panel sample, the response rate was slightly higher at 60 percent, but of the 612 nonresponding firms, 55 percent were not found due to insufficient contact information, 21 percent refused participation, 11 percent were non-usable and 13 percent were confirmed as closed.