Abstract

This survey was conducted in Timor-Leste between September 2015 and June 2016, 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 126 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

Dili

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. Two levels of stratification were used in this country: industry and establishment size.

Industry stratification was designed in the way that follows: the universe was stratified into manufacturing and services industry - Manufacturing (ISIC 3.1 codes 15 - 37), and Services (ISIC codes 45, 50, 51, 52, 55, 60-64, and 72).

For the Timor-Leste ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification did not take place as all interviews took place in and around Dili.

The sample frame consisted of listings of firms from two sources: First, for panel firms the list of 150 firms from the Timor-Leste 2009 ES was used. Second, for fresh firms (i.e., firms not covered in 2009), data from National Statistics Directorate (by way of PDT) was used.

The quality of the frame was enhanced by the verification process conducted by Mekong Economics. 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 6.8% (15 out of 220 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two 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 0.57. 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 0.15.

Public Use Microdata Areas (PUMAs) are statistical geographic areas defined by the U.S. Census Bureau for the dissemination of Public Use Microdata Sample (PUMS) data. Most NYC community districts have a roughly equivalent PUMA.

Public Use Microdata AreaThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Public Use Microdata Areas (PUMAs) in the United States. Per USCB, "nesting within states, or equivalent entities, PUMAs cover the entirety of the United States, Puerto Rico, Guam, and the U.S. Virgin Islands. PUMA delineations are subject to population, building block geography, geographic nesting, and contiguity criteria. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name.”Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Public Use Microdata Areas) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: TIGER/Line Shapefile, 2019, Series Information for the 2010 Census Public Use Microdata Area (PUMA) State-basedGeoplatform: TIGER/Line Shapefile, 2019, Series Information for the 2010 Census Public Use Microdata Area (PUMA) State-basedFor more information, please visit: Public Use Microdata Areas (PUMAs)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."To access other NGDA content that may interest you: NGDA Content

The 2020 NYC Public Use Microdata Areas (PUMAs) are statistical geographic areas defined for the dissemination of 2020 Public Use Microdata Sample (PUMS) data. PUMAs have a minimum population of 100,000, are aggregated from census tracts, and approximate Community Districts (CDs), or combinations of CDs (There are 59 CDs and only 55 NYC PUMAs because of such combinations). These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE. Current version: 25d

Abstract copyright UK Data Service and data collection copyright owner.

The 1961 Census Microdata for Great Britain: 9% Sample: Secure Access dataset was created from existing digital records from the 1961 Census. It comprises a larger population sample than the other files available from the 1961 Census (see below) and so contains sufficient information to constitute personal data, meaning that it is only available to Accredited Researchers, under restrictive Secure Access conditions. See Access section for further details.

The file was created under a project known as Enhancing and Enriching Historic Census Microdata Samples (EEHCM), which was funded by the Economic and Social Research Council with input from the Office for National Statistics and National Records of Scotland. The project ran from 2012-2014 and was led from the UK Data Archive, University of Essex, in collaboration with the Cathie Marsh Institute for Social Research (CMIST) at the University of Manchester and the Census Offices. In addition to the 1961 data, the team worked on files from the 1971 Census and 1981 Census.

The original 1961 records preceded current data archival standards and were created before microdata sets for secondary use were anticipated. A process of data recovery and quality checking was necessary to maximise their utility for current researchers, though some imperfections remain (see the User Guide for details).

Three other 1961 Census datasets have been created; users should obtain the other datasets in the series first to see whether they are sufficient for their research needs before considering making an application for this study (SN 8275), the Secure Access version:

• SN 8272 - 1961 Census Microdata Individual File for Great Britain: 5% Sample, which contains information on individuals in larger local authorities;
• SN 8273 - 1961 Census Microdata Household File for Great Britain: 0.95% Sample, which links household members together to allow individuals to be understood within their household context. SNs 8272 and 8273 are both available to registered UK Data Service users based in the United Kingdom (see Access section for non-UK access restrictions); and
• SN 8274 - 1961 Census Microdata Teaching Dataset for Great Britain: 1% Sample: Open Access, which can be used as a taster file and is freely available for anyone to download under an Open Government Licence.

Main Topics:

The file contains information on individuals in local authorities and counties. Topics covered include demographics, for example family and household circumstances, employment and qualifications, migration, commuting, car ownership, and housing.

The 1981 Census Microdata Teaching Dataset for Great Britain: 1% Sample: Open Access dataset was created from existing digital records from the 1981 Census. It can be used as a 'taster' file for 1981 Census data and is freely available for anyone to download under an Open Government Licence.

The file was created under a project known as Enhancing and Enriching Historic Census Microdata Samples (EEHCM), which was funded by the Economic and Social Research Council with input from the Office for National Statistics and National Records of Scotland. The project ran from 2012-2014 and was led from the UK Data Archive, University of Essex, in collaboration with the Cathie Marsh Institute for Social Research (CMIST) at the University of Manchester and the Census Offices. In addition to the 1981 data, the team worked on files from the 1961 Census and 1971 Census.

The original 1981 records preceded current data archival standards and were created before microdata sets for secondary use were anticipated. A process of data recovery and quality checking was necessary to maximise their utility for current researchers, though some imperfections remain (see the User Guide for details). Three other 1981 Census datasets have been created:

• SN 8241 - 1981 Census Microdata Individual File for Great Britain: 5% Sample, which contains information on individuals in larger local authorities;
• SN 8242 - 1981 Census Microdata Household File for Great Britain: 0.95% Sample, which links household members together to allow individuals to be understood within their household context. SNs 8241 and 8242 are both available to registered UK Data Service users based in the United Kingdom (see Access section for non-UK access restrictions); and
• SN 8248 - 1981 Census Microdata for Great Britain: 9% Sample: Secure Access, which comprises a larger population sample and so contains sufficient information to constitute personal data, meaning that it is only available to Accredited Researchers, under restrictive Secure Access conditions.

These Public Use Microdata Sample (PUMS) files contain records representing 1-percent samples of the occupied and vacant housing units in the United States and the people in the occupied units in 2000. Group quarters people also are included. The files contain individual weights for each person and housing unit, which when applied to the individual records, expand the sample to the relevant total. Some of the items included on the housing record are: acreage, agricultural sales, bedrooms, condominium fee, contract rent, cost of utilities, family income in 1999, farm residence, fire, hazard, and flood insurance, fuels used, gross rent, heating fuel, household income in 1999, household type, kitchen facilities, linguistic isolation, meals included in rent, mobile home costs, mortgage payment, mortgage status, plumbing facilities, presence and age of own children, presence of subfamilies in household, real estate taxes, rooms, selected monthly owner costs, size of building (units in structure), telephone service, tenure, vacancy status, value (of housing unit), vehicles available, year householder moved into unit, and year structure was built. Some of the items included on the person record are: ability to speak English, age, ancestry, citizenship, class of worker, disability status, earnings in 1999, educational attainment, grandparents as caregivers, Hispanic origin, hours worked, income in 1999 by type, industry, language spoken at home, marital status, means of transportation to work, migration Public Use Microdata Area (PUMA), migration state, mobility status, veteran period of service, years of military service, occupation, personal care limitation, place of birth, place of work PUMA, place of work state, poverty status in 1999, race, relationship, school enrollment and type of school, time of departure for work, travel time to work, vehicle occupancy, weeks worked in 1999, work limitation status, work status in 1999, and year of entry. The Public Use Microdata Sample (PUMS) files contain geographic units known as super-Public Use Microdata Areas (super-PUMAs) and Public Use Microdata Areas (PUMAs). To maintain the confidentiality of the PUMS data, minimum population thresholds are set for PUMAs and super-PUMAs. For the 1-percent state-level files, the super-PUMAs contain a minimum population of 400,000 and are composed of a PUMA or a group of contiguous PUMAs delineated on the 5-percent state-level PUMS files. Super-PUMAs are a new geographic entity for Census 2000. Super-PUMAs and PUMAs also are defined for place of residence on April 1, 1995, and place of work.

These Public Use Microdata Sample (PUMS) files contain records representing a 5-percent sample of the occupied and vacant housing units in the United States and the people in the occupied units. People living in group quarters also are included. The files provide individual weights for persons and housing units, which when applied to the individual records, expand the sample to the relevant totals. Some of the items on the housing record are acreage, agricultural sales, allocation flags for housing items, bedrooms, condominium fee, contract rent, cost of utilities, family income in 1999, family, subfamily, and relationship recodes, farm residence, fire, hazard, and flood insurance, fuels used, gross rent, heating fuel, household income in 1999, household type, housing unit weight, kitchen facilities, linguistic isolation, meals included in rent, mobile home costs, mortgage payment, mortgage status, plumbing facilities, presence and age of own children, presence of subfamilies in household, real estate taxes, number of rooms, selected monthly owner costs, size of building (units in structure), state code, telephone service, tenure, vacancy status, value (of housing unit), vehicles available, year householder moved into unit, and year structure built. Some of the items on the person record are ability to speak English, age, allocation flags for population items, ancestry, citizenship, class of worker, disability status, earnings in 1999, educational attainment, grandparents as caregivers, Hispanic origin, hours worked, income in 1999 by type, industry, language spoken at home, marital status, means of transportation to work, migration Public Use Microdata Area (PUMA), migration state, mobility status, veteran period of service, years of military service, occupation, persons weight, personal care limitation, place of birth, place of work PUMA, place of work state, poverty status in 1999, race, relationship, school enrollment and type of school, time of departure for work, travel time to work, vehicle occupancy, weeks worked in 1999, work limitation status, work status in 1999, and year of entry. The Public Use Microdata Sample (PUMS) files contain geographic units known as Public Use Microdata Areas (PUMAs) and super-Public Use Microdata Areas (super-PUMAs). To maintain the confidentiality of the PUMS data, minimum population thresholds are set for PUMAs and super-PUMAs. For the 1-percent state-level files, the super-PUMAs contain a minimum population of 400,000 and are composed of a PUMA or a group of contiguous PUMAs delineated on the 5-percent state-level PUMS files. Super-PUMAs are a new geographic entity for Census 2000. The 5-percent state-level files contain PUMAs, each having a minimum population of 100,000, and corresponding super-PUMA codes. Each state is separately identified and may be comprised of one or more super-PUMAs or PUMAs. Large metropolitan areas may be subdivided into super-PUMAs and PUMAs. PUMAs and super-PUMAs do not cross state lines. Super-PUMAs and PUMAs also are defined for place of residence on April 1, 1995, and place of work.

The 1971 Census Microdata for Great Britain: 9% Sample: Secure Access dataset was created from existing digital records from the 1971 Census. It comprises a larger population sample than the other files available from the 1971 Census (see below) and so contains sufficient information to constitute personal data, meaning that it is only available to Accredited Researchers, under restrictive Secure Access conditions. See Access section for further details.

The file was created under a project known as Enhancing and Enriching Historic Census Microdata Samples (EEHCM), which was funded by the Economic and Social Research Council with input from the Office for National Statistics and National Records of Scotland. The project ran from 2012-2014 and was led from the UK Data Archive, University of Essex, in collaboration with the Cathie Marsh Institute for Social Research (CMIST) at the University of Manchester and the Census Offices. In addition to the 1971 data, the team worked on files from the 1961 Census and 1981 Census.

The original 1971 records preceded current data archival standards and were created before microdata sets for secondary use were anticipated. A process of data recovery and quality checking was necessary to maximise their utility for current researchers, though some imperfections remain (see the User Guide for details).

Three other 1971 Census datasets have been created; users should obtain the other datasets in the series first to see whether they are sufficient for their research needs before considering making an application for this study (SN 8271), the Secure Access version:

• SN 8268 - 1971 Census Microdata Individual File for Great Britain: 5% Sample, which contains information on individuals in larger local authorities;
• SN 8269 - 1971 Census Microdata Household File for Great Britain: 0.95% Sample, which links household members together to allow individuals to be understood within their household context. SNs 8268 and 8269 are both available to registered UK Data Service users based in the United Kingdom (see Access section for non-UK access restrictions); and
• SN 8270 - 1971 Census Microdata Teaching Dataset for Great Britain: 1% Sample: Open Access, which can be used as a taster file and is freely available for anyone to download under an Open Government Licence.

Abstract

The document dataset covers the Enterprise Survey (ES) panel data collected in North Macedonia in 2009, 2013 and 2019.

Macedonia ES 2009 was conducted in 2008 and 2009, while Macedonia ES 2013 was conducted between November 2012 and May 2013, and North Macedonia ES 2019 was conducted between December 2018 and October 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate.

Geographic coverage

National

Geographic coverage notes

Regions covered are selected based on the number of establishments, contribution to employment, and value added. In most cases these regions are metropolitan areas and reflect the largest centers of economic activity in a country.

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 for Macedonia 2009 ES, Macedonia 2013 ES and of 2019 North Macedonia ES were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Macedonia 2009 ES and for Macedonia 2013 ES, and in the Sampling Note for 2019 North Macedonia ES. Stratified random sampling was preferred over simple random sampling for several reasons:

a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision. b. To obtain unbiased estimates for the whole population. 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. c. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. d. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in Appendix C of the North Macedonia 2019 ES Implementation Report and in Appendix E of the Macedonia 2013 Implementation Report.

Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 3.1 codes 15-37), Retail (ISIC 52), and Other Services (ISIC 45, 50, 51, 55, 60-64, 72).

As it is standard for the ES, the North Macedonia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for North Macedonia ES 2019 was done across three regions: Skopje; Eastern Macedonia comprising Northeastern, Eastern, Southeastern, and Vardar regions; and Western Macedonia comprising Polog, Southwestern and Pelagonia regions. For Macedonia 2013 ES, regional stratification was defined in 4 regions (city and the surrounding business area) throughout Macedonia. And for Macedonia ES 2009, regional stratification was defined in 4 regions which are Eastern, North- West & West, Skopje, and South.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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.

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 the 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. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

Abstract

The 2017-18 Albania Demographic and Health Survey (2017-18 ADHS) is a nationwide survey with a nationally representative sample of approximately 17,160 households. All women age 15-49 who are usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey. Women 50-59 years old were interviewed with an abbreviated questionnaire that only covered background characteristics and questions related to noncommunicable diseases.

The primary objective of the 2017-2018 ADHS was to provide estimates of basic sociodemographic and health indicators for the country as a whole and the twelve prefectures. Specifically, the survey collected information on basic characteristics of the respondents, fertility, family planning, nutrition, maternal and child health, knowledge of HIV behaviors, health-related lifestyle, and noncommunicable diseases (NCDs). The information collected in the ADHS will assist policymakers and program managers in evaluating and designing programs and in developing strategies for improving the health of the country’s population.

The sample for the 2017-18 ADHS was designed to produce representative results for the country as a whole, for urban and rural areas separately, and for each of the twelve prefectures known as Berat, Diber, Durres, Elbasan, Fier, Gjirokaster, Korce, Kukes, Lezhe, Shkoder, Tirana, and Vlore.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-4 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The ADHS surveys were done on a nationally representative sample that was representative at the prefecture level as well by rural and urban areas. A total of 715 enumeration areas (EAs) were selected as sample clusters, with probability proportional to each prefecture's population size. The sample design called for 24 households to be randomly selected in every sampling cluster, regardless of its size, but some of the EAs contained fewer than 24 households. In these EAs, all households were included in the survey. The EAs are considered the sample's primary sampling unit (PSU). The team of interviewers updated and listed the households in the selected EAs. Upon arriving in the selected clusters, interviewers spent the first day of fieldwork carrying out an exhaustive enumeration of households, recording the name of each head of household and the location of the dwelling. The listing was done with tablet PCs, using a digital listing application. When interviewers completed their respective sections of the EA, they transferred their files into the supervisor's tablet PC, where the information was automatically compiled into a single file in which all households in the EA were entered. The software and field procedures were designed to ensure there were no duplications or omissions during the household listing process. The supervisor used the software in his tablet to randomly select 24 households for the survey from the complete list of households.

All women age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for individual interviews with the full Woman's Questionnaire. Women age 50-59 were also interviewed, but with an abbreviated questionnaire that left out all questions related to reproductive health and mother and child health. A 50% subsample was selected for the survey of men. Every man age 15-59 who was a usual resident of or had slept in the household the night before the survey was eligible for an individual interview in these households.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the ADHS, one for the household and others for women age 15-49, for women age 50-59, and for men age 15-59. In addition to these four questionnaires, a form was used to record the vaccination information for children born in the 5 years preceding the survey whose mothers had been successfully interviewed.

Cleaning operations

Supervisors sent the accumulated fieldwork data to INSTAT’s central office via internet every day, unless for some reason the teams did not have access to the internet at the time. The data received from the various teams were combined into a single file, which was used to produce quality control tables, known as field check tables. These tables reveal systematic errors in the data such as omission of potential respondents, age displacement, inaccurate recording of date of birth and age at death, inaccurate measurement of height and weight, and other key indicators of data quality. These tables were reviewed and evaluated by ADHS senior staff, which in turn provided feedback and advice to the teams in the field.

Response rate

A total of 16,955 households were selected for the sample, of which 16,634 were occupied. Of the occupied households, 15,823 were successfully interviewed, which represents a response rate of 95%. In the interviewed households, 11,680 women age 15-49 were identified for individual interviews. Interviews were completed for 10,860 of these women, yielding a response rate of 93%. In the same households, 4,289 women age 50-59 were identified, of which 4,140 were successfully interviewed, yielding a 97% response rate. In the 50% subsample of households selected for the male survey, 7,103 eligible men age 15-59 were identified, of which 6,142 were successfully interviewed, yielding a response rate of 87%.

Response rates were higher in rural than in urban areas, which is a pattern commonly found in household surveys because in urban areas more people work and carry out activities outside the home.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017-18 Albania Demographic and Health Survey (ADHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017-18 ADHS is only one of many 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 differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017-18 ADHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix C of the survey final report.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Chad in 2009 and 2018. The Enterprise Survey is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

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 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 samples for 2009 and 2018 Chad Enterprise Surveys were selected using stratified random sampling, following the methodology explained in the Sampling Note.

Two levels of stratification were used in the Chad 2009 ES sample: firm sector and firm size. The Industry stratification was designed as follows: the universe was stratified into manufacturing and services industries. The initial sample design had a target of 75 interviews in manufacturing and 75 interviews in services.

In 2018 Chad ES, three levels of stratification were used: industry, establishment size, and region. The industry stratification was designed in the way that follows: the universe was stratified as into manufacturing and services industries- Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). Regional stratification did not take place for the Chad ES.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires - Manufacturing amd Services were used to collect the survey data.

The 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).

The Public Use Microdata Samples (PUMS) from the 1980 Census contain person- and household-level information from the "long-form" questionnaires distributed to a sample of the population enumerated in the 1980 Census. The B Sample contains information for each state, and for households and persons residing in metropolitan areas that are too small to be separately identified and/or that cross state boundaries. Standard Metropolitan Statistical Areas (SMSAs) and county groups are defined differently here than in the A Sample [CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: PUBLIC USE MICRODATA SAMPLE (A SAMPLE): 5-PERCENT SAMPLE (ICPSR 8101)]. Most states cannot be identified in their entirety. As a percentage of the l-Percent Public Use Microdata Sample (B Sample) [CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: PUBLIC USE MICRODATA SAMPLE (B SAMPLE): 1-PERCENT SAMPLE (ICPSR 8170)], this file constitutes a 1-in-1000 sample, and contains all household- and person-level variables from the original B Sample. Household-level variables include housing tenure, year structure was built, number and types of rooms in dwelling, plumbing facilities, heating equipment, taxes and mortgage costs, number of children, and household and family income. Person-level variables include sex, age, marital status, race, Spanish origin, income, occupation, transportation to work, and education.

Abstract copyright UK Data Service and data collection copyright owner.

The 1981 Census Microdata Individual File for Great Britain: 5% Sample dataset was created from existing digital records from the 1981 Census under a project known as Enhancing and Enriching Historic Census Microdata Samples (EEHCM), which was funded by the Economic and Social Research Council with input from the Office for National Statistics and National Records of Scotland. The project ran from 2012-2014 and was led from the UK Data Archive, University of Essex, in collaboration with the Cathie Marsh Institute for Social Research (CMIST) at the University of Manchester and the Census Offices. In addition to the 1981 data, the team worked on files from the 1961 Census and 1971 Census.

The original 1981 records preceded current data archival standards and were created before microdata sets for secondary use were anticipated. A process of data recovery and quality checking was necessary to maximise their utility for current researchers, though some imperfections remain (see the User Guide for details). Three other 1981 Census datasets have been created:

• SN 8242 - 1981 Census Microdata Household File for Great Britain: 0.95% Sample, which links household members together to allow individuals to be understood within their household context, and is available to registered UK Data Service users based in the United Kingdom (see Access section for non-UK access restrictions);
• SN 8243 - 1981 Census Microdata Teaching Dataset for Great Britain: 1% Sample: Open Access, which can be used as a taster file and is freely available for anyone to download under an Open Government Licence; and
• SN 8248 - 1981 Census Microdata for Great Britain: 9% Sample: Secure Access, which comprises a larger population sample and so contains sufficient information to constitute personal data, meaning that it is only available to Accredited Researchers, under restrictive Secure Access conditions.

Main Topics:

The file contains information on individuals in larger local authorities. Topics covered include demographics, for example family and household circumstances, employment and qualifications, migration, commuting, car ownership, and housing.

Abstract copyright UK Data Service and data collection copyright owner.

The 1961 Census Microdata Household File for Great Britain: 0.95% Sample dataset was created from existing digital records from the 1961 Census under a project known as Enhancing and Enriching Historic Census Microdata Samples (EEHCM), which was funded by the Economic and Social Research Council with input from the Office for National Statistics and National Records of Scotland. The project ran from 2012-2014 and was led from the UK Data Archive, University of Essex, in collaboration with the Cathie Marsh Institute for Social Research (CMIST) at the University of Manchester and the Census Offices. In addition to the 1961 data, the team worked on files from the 1971 Census and 1981 Census.

The original 1961 records preceded current data archival standards and were created before microdata sets for secondary use were anticipated. A process of data recovery and quality checking was necessary to maximise their utility for current researchers, though some imperfections remain (see the User Guide for details). Three other 1961 Census datasets have been created:

• SN 8272 - 1961 Census Microdata Individual File for Great Britain: 5% Sample, which contains information on individuals in larger local authorities, and is available to registered UK Data Service users based in the United Kingdom (see Access section for non-UK access restrictions);
• SN 8274 - 1961 Census Microdata Teaching Dataset for Great Britain: 1% Sample: Open Access, which can be used as a taster file and is freely available for anyone to download under an Open Government Licence; and
• SN 8275 - 1961 Census Microdata for Great Britain: 9% Sample: Secure Access, which comprises a larger population sample and so contains sufficient information to constitute personal data, meaning that it is only available to Accredited Researchers, under restrictive Secure Access conditions.

Main Topics:

The file links household members together to allow individuals to be understood within their household context. Topics covered include demographics, for example family and household circumstances, employment and qualifications, migration, commuting, car ownership, and housing.

Abstract

This survey was conducted in Uzbekistan between January 2013 and October 2013 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V), a joint initiative of the World Bank Group and the European Bank for Reconstruction and Development. 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.

Data from 390 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The survey topics include firm characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement collaboration, security, government policies, laws and regulations, financing, overall business environment, bribery, capacity utilization, performance and investment activities, and workforce composition.

In 2011, the innovation module was added to the standard set of Enterprise Surveys questionnaires to examine in detail how introduction of new products and practices influence firms' performance and management.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an 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 one manufacturing industry, and two service industries (retail, and other services).

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 was defined in 3 regions (city and the surrounding business area) throughout Uzbekistan.

The database from the Statistical Committee of Uzbekistan was used as the frame for the selection of a sample with the aim of obtaining interviews at 390 establishments with five or more employees.

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 7.9 % (85 out of 1,075 establishments).

In the dataset, the variables a2 (sampling region), a6a (sampling establishment's size), and a4a (sampling sector) contain the establishment's classification into the strata chosen for each country using information from the sample frame. Variable a4a is coded using ISIC Rev 3.1 codes for the chosen industries for stratification. These codes include most manufacturing industries (15 to 37), retail (52), and (45, 50, 51, 55, 60-64, 72) for other services.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different versions of the questionnaire were used. The basic questionnaire, the Core Module, includes all common questions asked to all establishments from all sectors. The second expanded variation, the Manufacturing Questionnaire, is built upon the Core Module and adds some specific questions relevant to manufacturing sectors. The third expanded variation, the Retail Questionnaire, is also built upon the Core Module and adds to the core specific questions.

The innovation module was added to the standard set of Enterprise Surveys questionnaires to examine how introduction of new products and practices influence firms' performance and management.

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, while 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 the refusal to respond as a different option from don’t know. 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 contacted establishments per realized interview was 0.33. 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 0.11.

The 2001 Census Public Use Microdata File (PUMF) on Households and Housing contains data based on a 2.7% sample of the population enumerated in the census. It provides information on the demographic, social and economic characteristics of the households. This microdata file allows users to group and manipulate the data to suit their own requirements. The documentation (metadata) provided in the PUMF on households and housing is divided into four chapters: Chapter I contains the record layout, an essential tool for using the file; Chapter II describes the variables included in the file; Chapters III and IV deal with the sampling method and data quality. Changes in the the 2001 Census: The question on religion, which is asked every 10 years, was re-introduced in 2001. In 2001, two new questions were added: one on the birthplace of parents and the other on the language of work. A second part was also added to the question on home language. In view of the legal recognition of same-sex couples, the census definition of a common-law couple was changed to "two people of the opposite sex or of the same sex who live together as a couple but who are not legally married to each other". The question on fertility was not asked in the 2001 census.

Name: Microdata used for the development of the study on intergenerational perspectives on climate change.

Summary: This dataset contains answers from different age groups about their initiatives, preferences and visions with regard to climate change adaptation.

License: cc-BY-SA

Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.

Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the Ec are not responsible for any use that may be made of the information contained therein.

Collection Date: 08/02/2023

Publication Date: 05/02/2025

DOI: 10.5281/zenodo.14809675

Other repositories:

Author: University of Deusto

Objective of collection: This data was originally collected to analyse the different climate change prospects between generations

Description:

File 1 - answers taggedThe following document lists the answers received by each participant, showing the label(s) assigned to each participant per question. The answers have been collected qualitatively and semi-quantified by means of a human process with the participation of two researchers from the group. Each user was able to give more than one answer to the same question, which is why some participants came up more than once.

File 2 - summary based on gender and generationThe following document summarises the response data obtained, based on labels, generations and gender.

5 star: ⭐⭐

Preprocessing steps: Data transcription from written documents and oral discussions.

Reuse: NA

Update policy: No more updates are planned.

Ethics and legal aspects: Names of the persons involved have been removed.

Technical aspects:

Other:

The 1981 Census Microdata Individual File for Great Britain: 5% Sample dataset was created from existing digital records from the 1981 Census under a project known as Enhancing and Enriching Historic Census Microdata Samples (EEHCM), which was funded by the Economic and Social Research Council with input from the Office for National Statistics and National Records of Scotland. The project ran from 2012-2014 and was led from the UK Data Archive, University of Essex, in collaboration with the Cathie Marsh Institute for Social Research (CMIST) at the University of Manchester and the Census Offices. In addition to the 1981 data, the team worked on files from the 1961 Census and 1971 Census.

The original 1981 records preceded current data archival standards and were created before microdata sets for secondary use were anticipated. A process of data recovery and quality checking was necessary to maximise their utility for current researchers, though some imperfections remain (see the User Guide for details). Three other 1981 Census datasets have been created:

• SN 8242 - 1981 Census Microdata Household File for Great Britain: 0.95% Sample, which links household members together to allow individuals to be understood within their household context, and is available to registered UK Data Service users based in the United Kingdom (see Access section for non-UK access restrictions);
• SN 8243 - 1981 Census Microdata Teaching Dataset for Great Britain: 1% Sample: Open Access, which can be used as a taster file and is freely available for anyone to download under an Open Government Licence; and
• SN 8248 - 1981 Census Microdata for Great Britain: 9% Sample: Secure Access, which comprises a larger population sample and so contains sufficient information to constitute personal data, meaning that it is only available to Accredited Researchers, under restrictive Secure Access conditions.

This is a special extract of the 2000 Census 5-Percent Public Use Microdata Samples (PUMS) created by the National Archive of Computerized Data on Aging (NACDA). The file combines the individual 5-percent state files for all 50 states, the District of Columbia, and Puerto Rico as released by the United States Census Bureau into a single analysis file. The file contains information on all households that contain at least one person aged 65 years or more in residence as of the 2000 Census enumeration. The file contains individual records on all persons aged 65 and older living in households as well as individual records for all other members residing in each of these households. Consequently, this file can be used to examine both the characteristics of the elderly in the United States as well as the characteristics of individuals who co-reside with persons aged 65 and older as of the year 2000. All household variables from the household-specific "Household record" of the 2000 PUMS are appended to the end of each individual level record. This file is not a special product of the Census Bureau and is not a resample of the PUMS data specific to the elderly population. While it is comparable to the 1990 release CENSUS OF POPULATION AND HOUSING, 1990: [UNITED STATES]: PUBLIC USE MICRODATA SAMPLE: 3-PERCENT ELDERLY SAMPLE (ICPSR 6219), the sampling procedures and weights for the 2000 file reflect the methodology that applies to the 5-percent PUMS release CENSUS OF POPULATION AND HOUSING, 2000 [UNITED STATES]: PUBLIC USE MICRODATA SAMPLE: 5-PERCENT SAMPLE (ICPSR 13568). Person variables cover age, sex, relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, military service, mobility and personal care limitation, work limitation status, employment status, occupation, industry, class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absence from work, place of work, time of departure for work, travel time to work, means of transportation to work, total earnings, total income, wages and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividends, and net rental income. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage facilities, type of water source, type of heating fuel used, property value, tenure, year moved into house/apartment, type of household/family, type of group quarters, household language, number of persons in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage and property tax, condominium fees, mobile home costs, and cost of electricity, water, heating fuel, and flood/fire/hazard insurance.