This dataset is was created by re-compiling available open, gender/sex-disaggregated Feed the Future datasets for Bangladesh and applying standard processing methods to enhance their accessibility and interoperability. This process entailed the standardization of variable names and labels, the creation of derived socio-economic indicators such as dietary diversity scores, household dependency ratios, and household age and gender composition. This dataset allows researchers to easily use data for Bangladesh, as well as make cross country comparisons with other standardized datasets. Moreover, the provision of household GIS coordinates (offset for confidentiality purposes) allows users to match data at different levels. This work combines multi-topic household and community socio-economic and agricultural surveys with biophysical datasets from multiple sources, including remote sensing, for a thorough comparison of different phenomena. These biophysical sources include the International Soil Reference and Information Centre (ISRIC) World Soil Information, NASA MODIS vegetation indices and land surface temperature data, and the HarvestChoice spatially-disaggregated subnational crop production statistics database (Spatial Production Allocation Model; SPAM).

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the …

+++++++++++++++ Version 3.0.0 +++++++++++++++

We carried out an harmonization of the Eurobarometer 2004-2021(spring). This dataset includes 35 single standard Eurobarometers, and morethan 140 variables about EU policies, attitudes towards Europe and the EU, identity, cognitive mobilization, political institutions, socio-political characteristics and partisanship, etc.

The harmonization was carried out using existing Eurobarometer datasets published by GESIS. To allow the user to replicate the harmonization and be able to modify some codes if needed, we publish one example of do-file used to pursue the harmonization, as well as the corresponding (harmonized) dataset. The user can find the do-file containing the codes used to modify and clean EB 953 (ZA7783, conducted in spring 2021) according to the harmonization procedure that we followed. Moreover, the user can find the cleaned dataset for EB 953 that was obtained after running the do-file. The files are named “EB 953.do” and “953_new.dta”.

We include: - a harmonized dataset ("harmonised_EB_2004-2021.dta"), - a technical report ("User Guide Harmonized Eurobarometer 2004-2021"), - a summary of the original survey questions corresponding to the variables included in the dataset ("Trends_EBs_1970-2021.xlsx"), - one of the do-files used to carry out the harmonization (“EB 953.do” ), - one of the datasets used before merging all datasets (“953_new.dta”).

Harmonized Landsat Sentinel is a NASA initiative to produce a Virtual Constellation of surface reflectance (SR) data from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard the Landsat 8-9 and Sentinel-2 remote sensing satellites, respectively. The combined measurement enables global observations of the land every 2–3 days. Input products are Landsat 8-9 Collection 2 Level 1 top-of-atmosphere reflectance and Sentinel-2 L1C top-of-atmosphere reflectance, which NASA radiometrically harmonizes to the maximum extent, resamples to common 30-meter resolution, and grids using the Sentinel-2 Military Grid Reference System (MGRS) UTM grid. Because of this, the products are different from Landsat 8-9 Collection 2 Level 2 surface reflectance and Sentinel-2 L2A surface reflectance.

Changes since the last version: in the .csv export there was a naming problem.

• visit_concert: This is a standard CAP variables about visiting frequencies, in numeric form.
• fct_visit_concert: This is a standard CAP variables about visiting frequencies, in categorical form.
• is_visit_concert: binary variable, 0 if the person had not visited concerts in the previous 12 months.
• artistic_activity_played_music: A variable of the frequency of playing music as an amateur or professional practice, in some surveys we have only a binary variable (played in the last 12 months or not) in other we have frequencies. We will convert this into a binary variable.
• fct_artistic_activity_played_music: The artistic_activity_played_music in categorical representation.
• artistic_activity_sung: A variable of the frequency of singing as an amateur or professional practice, like played_muisc. Because of the liturgical use of singing, and the differences of religious practices among countries and gender, this is a significantly different variable from played_music.
• fct_artistic_activity_sung: The artistic_activity_sung variable in categorical representation.
• age_exact: The respondent’s age as an integer number.
• country_code: an ISO country code
• geo: an ISO code that separates Germany to the former East and West Germany, and the United Kingdom to Great Britain and Northern Ireland, and Cyprus to Cyprus and the Turiksh Cypriot community.[we may leave Turkish Cyprus out for practical reasons.]
• age_education: This is a harmonized education proxy. Because we work with the data of more than 30 countries, education levels are difficult to harmonize, and we use the Eurobarometer standard proxy, age of leaving education. It is a specially coded variable, and we will re-code them into two variables, age_education and is_student.
• is_student: is a dummy variable for the special coding in age_education for “still studying”, i.e. the person does not have yet a school leaving age. It would be tempting to impute age in this case to age_education, but we will show why this is not a good strategy.
• w, w1: Post-stratification weights for the 15+ years old population of each country. Use w1 for averages of geo entities treating Northern Ireland, Great Britain, the United Kingdom, the former GDR, the former West Germany, and Germany as geographical areas. Use w when treating the United Kingdom and Germany as one territory.
• wex: Projected weight variable. For weighted average values, use w, w1, for projections on the population size, i.e., use with sums, use wex.
• id: The identifier of the original survey.
• rowid`: A new unique identifier that is unique in all harmonized surveys, i.e., remains unique in the harmonized dataset.

Abstract

This multi-country harmonized dataset concerning forcibly displaced populations (FDPs) and their host communities was produced by the World Bank’s Poverty and Equity Global Practice. It incorporates representative surveys conducted in 10 countries across five regions that hosted FDPs in the period 2015 to 2020. The goal of this harmonization exercise is to provide researchers and policymakers with a valuable input for comparative analyses of forced displacement across key developing country settings.

Geographic coverage

The datasets included in the harmonization effort cover key recent displacement contexts: the Venezuelan influx in Latin America’s Andean states; the Syrian crisis in the Mashreq; the Rohingya displacement in Bangladesh; and forcible displacement in Sub-Saharan Africa (Sahel and East Africa). The harmonization exercise encompasses 10 different surveys. These include nationally representative surveys with a separate representative stratum for displaced populations; sub-national representative surveys covering displaced populations and their host communities; and surveys designed specifically to provide insights on displacement contexts. Most of the surveys were collected between 2015 and 2020.

Analysis unit

Household

Universe

Forcibly displaced populations and their hosts communities.

Kind of data

Sample survey data [ssd]

Mode of data collection

Computer Assisted Personal Interview [capi]

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ALERT product.The OPERA_L3_DIST-ALERT-HLS product contains modified Copernicus Sentinel data (2020-2025).Known Issues* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).

This data set describes select global soil parameters from the Harmonized World Soil Database (HWSD) v1.2, including additional calculated parameters such as area weighted soil organic carbon (kg C per m2), as high resolution NetCDF files. These data were regridded and upscaled from the Harmonized World Soil Database v1.2

The HWSD provides information for addressing emerging problems of land competition for food production, bio-energy demand and threats to biodiversity and can be used as input to model global carbon cycles.

The data are presented as a series of 27 NetCDF v3/v4 (*.nc4) files at 0.05-degree spatial resolution, and one NetCDF file regridded to the Community Land Model (CLM) grid cell resolution (0.9 degree x 1.25 degree) for the nominal year of 2000.

Meta-analysis sample size of harmonized variables for each study.

Description and harmonization strategy for the predictor variables.

Abstract

The cleaned and harmonized version of the survey data produced and published by the Economic Research Forum represents 100% of the original survey data collected by the Central Agency for Public Mobilization and Statistics (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

----> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. this is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

----> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: gender, age, educational status, unemployment type "ever employed/never employed", occupation, economic activity, and sector for people who have ever worked.

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

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

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

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

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 Central Agency for Public Mobilization and Statistics (CAPMAS)

Sample Design and Selection

The sample of the LFS 2006 survey is a simple systematic random sample.

Sample Size

The sample size varied in each quarter (it is Q1=19429, Q2=19419, Q3=19119 and Q4=18835) households with a total number of 76802 households annually. These households are distributed on the governorate level (urban/rural).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 3 tables in addition to the identification and geographic data of household on the cover page.

----> Table 1- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

----> Table 2- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Work place - Average monthly wage

----> Table 3- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

----> Raw Data

Office editing is one of the main stages of the survey. It started once the questionnaires were received from the field and accomplished by the selected work groups. It includes: a-Editing of coverage and completeness b-Editing of consistency

----> Harmonized Data

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

These data represent fractional land use and land cover patterns annually for the years 1500 - 2100 for the globe at 0.5-degree (~50-km) spatial resolution. Land use categories of cropland, pasture, primary land, secondary (recovering) land, and urban land, and underlying annual land-use transitions, are included. Annual data on age and biomass density of secondary land, as well as annual wood harvest, are included for each grid cell. Historical land cover data for the years 1500 - 2005 are based on HYDE 3.1 and future land cover projections for the period 2006 - 2100 came from four Integrated Assessment Model (IAM) scenarios which reach different levels of radiative forcing by year 2100: MESSAGE (8.5 W/m2), AIM (6 W/m2), GCAM (4.5 W/m2), and IMAGE (2.6 W/m2). A key feature of these data is that historical reconstructions of land use were harmonized (computationally adjusted to minimize differences at the transition period) with modeled future scenarios, allowing for a seamless examination of historical and future land use. The output data present a single consistent, spatially gridded set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system. For additional information about the algorithms, inputs, and options used in creating the land use transitions data, please refer to Hurtt et al. (2006) and Hurtt et al. (2011).Data are presented as a series of twenty (20) different data products representing different past and future model scenarios. There are a total of 560 NetCDF v4 files (*.nc4), one for each combination of data product and land use variable.

This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.

The digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal responsibility or liability, whether express or implied, arising from the use of this DEM.

The OPERA DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025).

To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

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

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Graph and download economic data for Harmonized Index of Consumer Prices: All-Items HICP for United States (CP0000USM086NEST) from Dec 2001 to Dec 2024 about harmonized, all items, CPI, price index, indexes, price, and USA.

This data release contains a 17-year record (2005-2022) of discrete chlorophyll data from inland waters, collected from across the nation and territories. These data are from discrete samples (collected in the field and analyzed in the laboratory) from plankton (suspended algae) and periphyton (benthic algae) from lakes, streams, rivers, reservoirs, canals, and other sites. These data are gathered to support process and remote sensing modeling and prediction of Harmful Algal Blooms (HABs). The chlorophyll data were compiled from the Water Quality Portal (WQP) and USGS National Water Quality Lab (NWQL).

Data for uncorrected chlorophyll a, corrected chlorophyll a, and pheophytin from EPA Methods 445 and 446 are included and reported, following the conventions of EPA section 445:

• 12.1 uncorrected chlorophyll a – based on the initial optical measurement of the sample, which includes both chlorophyll a ...

ST_LUCAS is a harmonized dataset derived from the LUCAS (Land Use and Coverage Area frame Survey) dataset. LUCAS is an Eurostat activity that has performed repeated in situ surveys over Europe every three years since 2006. Original LUCAS data (https://ec.europa.eu/eurostat/web/lucas/data) starting with the 2006 survey were harmonized into common nomenclature based on the 2018 survey. ST_LUCAS dataset is provided in two versions:

• lucas_points: each LUCAS survey is represented by single record

• lucas_st_points: each LUCAS point is represented by a single location calculated from multiple surveys and by a set of harmonized attributes for each survey year

Harmonization and space-aggregation of LUCAS data were performed by ST_LUCAS system available from https://geoforall.fsv.cvut.cz/st_lucas. The methodology is described in Landa, M.; Brodský, L.; Halounová, L.; Bouček, T.; Pešek, O. Open Geospatial System for LUCAS In Situ Data Harmonization and Distribution. ISPRS Int. J. Geo-Inf. 2022, 11, 361. https://doi.org/10.3390/ijgi11070361.

List of harmonized LUCAS attributes: https://geoforall.fsv.cvut.cz/st_lucas/tables/list_of_attributes.html

ST_LUCAS dataset is provided under the same conditions (“free of charge”) as the original LUCAS data (https://ec.europa.eu/eurostat/web/lucas/data).

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

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

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Abstract

To facilitate comparisons with the Latin America and the Caribbean (LAC) High-Frequency Surveys collected in 2021, harmonized versions of the COVID-19 High Frequency Phone Surveys 2022 Brazil databases have been produced. The databases follow the same structure as those for the countries in the region (for example, see: COVID-19 LAC High Frequency Phone Surveys 2021 (Wave 1)).

The Brazil 2021 COVID-19 Phone Survey was conducted to provide information on how the pandemic had been affecting Brazilian households in 2021, collecting information along multiple dimensions relevant to the welfare of the population (e.g. changes in employment and income, coping mechanisms, access to health and education services, gender inequalities, and food insecurity). A total of 2,166 phone interviews were conducted across all Brazilian states between July 26 and October 1, 2021. The survey followed an Random Digit Dialing (RDD) sampling methodology using a dual sampling frame of cellphone and landline numbers. The sampling frame was stratified by type of phone and state. Results are nationally representative for households with a landline or at least one cell phone and of individuals of ages 18 years and above who have an active cell phone number or a landline at home.

Geographic coverage

National level.

Analysis unit

Households and individuals of 18 years of age and older.

Sampling procedure

The sample is based on a dual frame of cell phone and landline numbers that was generated through a Random Digit Dialing (RDD) process and consisted of all possible phone numbers under the national phone numbering plan. Numbers were screened through an automated process to identify active numbers and cross-checked with business registries to identify business numbers not eligible for the survey. This method ensures coverage of all landline and cellphone numbers active at the time of the survey. The sampling frame was stratified by type of phone and state. See Sampling Design and Weighting document for more detail.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Available in Portuguese. The questionnaire followed closely the LAC HFPS Questionnaire of Phase II Wave I but had some critical variations.

- `visit_concert`: This is a standard CAP variables about visiting frequencies.
- `is_visit_concert`: binary variable, 0 if the person had not visited concerts in the previous 12 months.
- `artistic_activity_played_music`: A variable of the frequency of playing music as an amateur or professional practice, in some surveys we have only a binary variable (played in the last 12 months or not) in other we have frequencies. We will convert this into a binary variable.
- `artistic_activity_sung`: A variable of the frequency of singing as an amateur or professional practice, like played_muisc. Because of the liturgical use of singing, and the differences of religious practices among countries and gender, this is a significantly different variable from played_music.
- `age_exact`: The respondent’s age as an integer number.
- `country_code`: an ISO country code
- `geo`: an ISO code that separates Germany to the former East and West Germany, and the United Kingdom to Great Britain and Northern Ireland, and Cyprus to Cyprus and the Turiksh Cypriot community.[we may leave Turkish Cyprus out for practical reasons.]
- `age_education`: This is a harmonized education proxy. Because we work with the data of more than 30 countries, education levels are difficult to harmonize, and we use the Eurobarometer standard proxy, age of leaving education. It is a specially coded variable, and we will re-code them into two variables, `age_education` and `is_student`.
- `is_student`: is a dummy variable for the special coding in age_education for “still studying”, i.e. the person does not have yet a school leaving age. It would be tempting to impute `age` in this case to `age_education`, but we will show why this is not a good strategy.
- `w`, `w1`: Post-stratification weights for the 15+ years old population of each country. Use `w1` for averages of `geo` entities treating Northern Ireland, Great Britain, the United Kingdom, the former GDR, the former West Germany, and Germany as geographical areas. Use `w` when treating the United Kingdom and Germany as one territory.
- `wex`: Projected weight variable. For weighted average values, use `w`, `w1`, for projections on the population size, i.e., use with sums, use `wex`.
- `id`: The identifier of the original survey.
- `rowid``: A new unique identifier that is unique in all harmonized surveys, i.e., remains unique in the harmonized dataset.