This file contains all of the cases and variables that are in the original 2012 General Social Survey, but is prepared for easier use in the classroom. Changes have been made in two areas. First, to avoid confusion when constructing tables or interpreting basic analysis, all missing data codes have been set to system missing. Second, many of the continuous variables have been categorized into fewer categories, and added as additional variables to the file.

The General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2012 GSS. There are a total of 4,820 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2012 GSS featured special modules on religious scriptures, the environment, dance and theater performances, health care system, government involvement, health concerns, emotional health, financial independence and income inequality.

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. This file has a rolling panel design, with the 2008 GSS as the base year for the first panel. A sub-sample of 2,000 GSS cases from 2008 was selected for reinterview in 2010 and again in 2012 as part of the GSSs in those years. The 2010 GSS consisted of a new cross-section plus the reinterviews from 2008. The 2012 GSS consists of a new cross-section of 1,974, the first reinterview wave of the 2010 panel cases with 1,551 completed cases, and the second and final reinterview of the 2008 panel with 1,295 completed cases. Altogether, the 2012 GSS had 4,820 cases (1,974 in the new 2012 panel, 1,551 in the 2010 panel, and 1,295 in the 2008 panel).

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Lesotho in 2009 and 2016, as part of Africa Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise 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.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample in the current wave. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Lesotho ES 2009 was conducted from September 2008 to February 2009, Lesotho ES 2016 was carried out in June - August 2016. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 301 establishments was analyzed: 90 businesses were from 2009 only, 89 - from 2016 only, and 122 firms were from 2009 and 2016.

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

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Industry stratification was designed as 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).

For the Lesotho 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 for the Lesotho ES.

In 2009, it was not possible to obtain a single usable frame for Lesotho. Instead frames were obtained from two government branches: the Chamber of Commerce and the Ministry of Trade, Industry, Cooperatives and Marketing. Those frames were merged and duplicates removed to provide the frame used for the survey.

In 2016 ES, the sample frame consisted of listings of firms from two sources: for panel firms the list of 151 firms from the Lesotho 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from Lesotho Bureau of Statistics Business Register, published in August 2015, was used.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used for Lesotho ES: - Manufacturing Module Questionnaire - Services Module Questionnaire

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

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 General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2010 GSS. There are a total of 4,901 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2010 GSS featured special modules on aging, the Internet, shared capitalism, gender roles, intergroup relations, immigration, meeting spouse, knowledge about and attitudes toward science, religious identity, religious trends, genetics, veterans, crime and victimization, social networks and group membership, and sexual behavior (continuing the series started in 1988).

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. The 2006 GSS was the base year for the first panel. A sub-sample of 2,000 GSS cases from 2006 was selected for reinterview in 2008 and again in 2010 as part of the GSSs in those years. The 2008 GSS consists of a new cross-section plus the reinterviews from 2006. The 2010 GSS consists of a new cross-section of 2,044, the first reinterview wave of the 2,023 2008 panel cases with 1,581 completed cases, and the second and final reinterview of the 2006 panel with 1,276 completed cases. Altogether, the 2010 GSS had 4,901 cases (2,044 in the new 2010 panel, 1,581 in the 2008 panel, and 1,276 in the 2006 panel). The 2010 GSS is the first round to fully implement the new, rolling panel design. In 2012 and later GSSs, there will likewise be a fresh cross-section (wave one of a new panel), wave two panel cases from the immediately preceding GSS, and wave three panel cases from the next earlier GSS.

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Waves 1 and 2 are currently publicly available. Wave 3 will be publicly available in October 2023, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

The General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2012 GSS. There are a total of 4,820 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2012 GSS featured special modules on religious scriptures, the environment, dance and theater performances, health care system, government involvement, health concerns, emotional health, financial independence and income inequality.

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. This file has a rolling panel design, with the 2008 GSS as the base year for the first panel. A sub-sample of 2,000 GSS cases from 2008 was selected for reinterview in 2010 and again in 2012 as part of the GSSs in those years. The 2010 GSS consisted of a new cross-section plus the reinterviews from 2008. The 2012 GSS consists of a new cross-section of 1,974, the first reinterview wave of the 2010 panel cases with 1,551 completed cases, and the second and final reinterview of the 2008 panel with 1,295 completed cases. Altogether, the 2012 GSS had 4,820 cases (1,974 in the new 2012 panel, 1,551 in the 2010 panel, and 1,295 in the 2008 panel).

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Solomon Islands and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Solmon Islands, after five rounds of data collection from 2020-2020, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in two non-consecutive weeks of each month. Data for April 2023-December 2023 were a repeated cross section, while January 2024 established the first month of a panel, the was continued to September 2024. Each month has approximately 550 households in the sample and is representative of urban and rural areas, but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Solomon Islands. There is one date file for household level data with a unique household ID. and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Geographic coverage

Urban and rural areas of Solomon Islands.

Analysis unit

Household, individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The initial sample was drawn from information provided by a major phone service provider in Solomon Islands, covering all the provinces in the country. It had a probability-based weighted design, with a proportionate stratification to achieve geographical representation. The geographical distribution compared to the 2019 Census is listed below for the first month of the HFPS monthly survey:

Choiseul : Census: 4.3%, HFPS: 5.2% Western : Census: 14.4%, HFPS: 13.7% Isabel : Census: 4.8%, HFPS: 4.7% Central : Census: 3.6%, HFPS: 5.2% Ren Bell : Census: 0.6%, HFPS: 1.4% Guadalcanal: Census: 19.8%, HFPS: 21.1% Malaita : Census: 23.1%, HFPS: 18.7% Makira : Census: 5.6%, HFPS: 5.6% Temotu: Census: 3.0%, HFPS: 3% Honiara: Census: 20.7%, HFPS: 21.3%

Source: Census of Population and Housing 2019

Note: The values in the HFPS column represent the proportion of survey participants residing in each province, based on the raw HFPS data from April.

In April 2023, the geographic distribution of World Bank HFPS participants was generally similar to that of the census data at the province level, though within provinces, areas with less mobile phone connectivity are likely to be underrepresented. One indication of this is that urban areas constituted 38.2 percent of the survey sample, which is a slight overrepresentation, compared to 32.5 percent in the Census 2019.

A monthly panel was established in January 2024, that is ongoing as of March 2025. In each subsequent month after January 2024, the survey firm would first attempt to contact all households from the previous month and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households. Across all months of the survey a total of, 9,926 interviews were completed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire, which can be found in the External Resources of this documentation, is available in English, with Solomons Pijin translation. There were few changes to the questionnaire across the survey months, but some sections were only introduced in 2024, namely energy access questions and questions to inform the baseline data of the Solomon Islands Government Integrated Economic Development and Climate Resilience (IEDCR) project.

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 9,926 in the household dataset and 62,054 in the individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Malawi in 2009 and 2014, as part of Africa Enterprise Surveys roll-out, an initiative of the World Bank.

New Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample in the current wave. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Malawi ES 2014 was conducted between April 2014 and February 2015, Malawi ES 2009 was carried out in May - July 2009. The objective of the Enterprise 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.

Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

Data from 673 establishments was analyzed: 436 businesses were from 2014 ES only, 63 - from 2009 ES only, and 174 firms were from both 2009 and 2014 panels.

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

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

For the Malawi ES, multiple sample frames were used: a sample frame was built using data compiled from local and municipal business registries. Due to the fact that the previous round of surveys utilized different stratification criteria in the 2009 survey sample, the presence of panel firms was limited to a maximum of 50% of the achieved interviews in each stratum. That sample is referred to as the panel.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used for Malawi ES 2009 and 2014: - Manufacturing Module Questionnaire - Services Module Questionnaire

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

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.

Recent research has pointed to large gaps in labor productivity between the agricultural and non-agricultural sectors in low-income countries, as well as between workers in rural and urban areas. Most estimates are based on national accounts or repeated cross-sections of micro-survey data, and as a result typically struggle to account for individual selection between sectors. This paper uses long-run individual-level panel data from two low-income countries (Indonesia and Kenya) to explore these gaps. Accounting for individual fixed effects leads to much smaller estimated productivity gains from moving into the non-agricultural sector (or urban areas), reducing estimated gaps by roughly 67 to 92%. Furthermore, gaps do not emerge up to five years after a move between sectors. We evaluate whether these findings imply a re-assessment of the conventional wisdom regarding sectoral gaps, discuss how to reconcile them with existing cross-sectional estimates, and consider implications for the desirability of sectoral reallocation of labor.

The use of multilevel regression models is prevalent in policy analysis to estimate the effect of group level policies on individual outcomes. In order to allow for the time varying effect of group heterogeneity and the group specific impact of time effects, we propose a group interactive fixed effects approach that employs interaction terms of group factor loadings and common factors in this model. For this approach, we consider the least squares estimator and associated inference procedure. We examine their properties under the large n and fixed T asymptotics. The number of groups, G, is allowed to grow but at a slower rate. We also propose a test for the level of grouping to specify group factor loadings, and a GMM approach to address policy endogeneity with respect to idiosyncratic errors. Finally, we provide empirical illustrations of the proposed approach using two empirical examples.

The Monitoring the Future (MTF) project is a long-term epidemiologic and etiologic study of substance use among youth and adults in the United States. It is conducted at the University of Michigan's Institute for Social Research, and funded by a series of investigator-initiated research grants from the National Institute on Drug Abuse. MTF has two components: MTF Main and MTF Panel. From its inception in 1975, the cross-sectional MTF Main study has collected data annually from nationally representative samples of 12,000-19,000 high school seniors in 12th grade located in approximately 135 schools nationwide. Beginning in 1991, similar annual cross-sectional surveys of nationally representative samples of 8th and 10th graders have been conducted. In all, approximately 45,000 students annually respond to about 100 drug use and demographic questions, as well as to about 200 additional questions divided among multiple survey forms on other topics such as attitudes toward government, social institutions, race relations, changing gender roles, educational aspirations, occupational aims, and marital plans. The longitudinal MTF Panel study conducts follow-up surveys with representative subsamples of respondents from each 12th grade cohort participating in MTF Main. From each cohort, a sample of about 2,450 students are selected for longitudinal follow-up, with an oversampling of students who reported prior drug use during their 12th grade survey. Longitudinal follow-up currently spans modal ages 19-30 and 35-60. For surveys at modal ages 19-30, the sample is randomly split into two halves (approx. 1,225 each) to be followed every other year. One half-sample begins its first follow-up the year after high school (at modal age 19), and the other half-sample begins its first follow-up in the second year after high school (at modal age 20). Thus, six young adult follow-up (FU) surveys occur between modal ages 19-30, at modal ages 19/20 (FU1), 21/22 (FU2), 23/24 (FU3), 25/26 (FU4), 27/28 (FU5), and 29/30 (FU6). After age 30, respondents are surveyed every five years: 35, 40, 45, 50, 55, and 60 (these are referred to as FZ surveys). The FZ surveys cover many of the same topics as the 12th grade and FU surveys and include additional questions on life events and health. MTF Panel surveys for the young adults (ages 19-30) were conducted using mailed paper surveys from 1977-2017. In 2018 and 2019, a random half of all those aged 19-30 received a mailed paper survey, while the other half were surveyed using a new procedure that encouraged participation using web surveys (web-push). The FZ surveys (ages 35-60) were conducted using mailed paper surveys through the 2019 data collection. More information about the MTF project can be accessed through the Monitoring the Future website. Annual reports are published by the research team, describing the data collection and trends over time.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Dominican Republic in 2010 and 2016, as part of Latin America and the Caribbean Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise 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.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Dominican Republic ES 2010 was conducted in March - September 2011, ES 2016 was carried out in August 2016 - April 2017. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 719 establishments was analyzed: 257 businesses were from 2010 ES only, 256 - from 2016 only, and 206 firms were from 2010 and 2016.

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

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size and region.

Industry stratification was designed as 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).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

In 2016, regional stratification was done across three regions: Santo Domingo, Santiago-Puerto Plata-Espaillat and the Rest of the country.

The sample frame consisted of listings of firms from three sources: for panel firms the list of 360 firms from the Dominican Republic 2010 ES was used and for fresh firms (i.e., firms not covered in 2010) a listing of firms obtained from El Directorio de Empresas y Establecimientos (DEE) 2015 and Oficina Nacional de Estadística (ONE), were used.

In 2010, regional stratification was defined in two locations: Santo Domingo and the rest of the country (constituted by urban centers around Santiago and Higuey). For the purposes of sampling, the rest of the country was treated as one area.

The sample frame for 2010 ES was provided by the Oficina Nacional de Estadistica (ONE), dated 2009.

Mode of data collection

Face-to-face [f2f]

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.

Background: While there is discussion of increasing rates of mental disorders, epidemiological research finds little evidence of change over time. This research generally compares cross-sectional surveys conducted at different times. Declining response rates to representative surveys may mask increases in mental disorders and psychological distress.Methods: Analysis of data from two large nationally representative surveys: repeated cross-sectional data from the Australian National Health Survey (NHS) series (2001–2017), and longitudinal data (2007–2017) from the Household, Income and Labor Dynamics in Australia (HILDA) Survey. Data from each source was used to generate weighted national estimates of the prevalence of very high psychological distress using the Kessler Psychological Distress scale (K10).Results: Estimates of the prevalence of very high psychological distress from the NHS were stable between 2001 and 2014, with a modest increase in 2017. In contrast, the HILDA Survey data demonstrated an increasing trend over time, with the prevalence of very high distress rising from 4.8% in 2007 to 7.4% in 2017. This increase was present for both men and women, and was evident for younger and middle aged adults but not those aged 65 years or older. Sensitivity analyses showed that this increase was notable in the upper end of the K10 distribution.Conclusions: Using household panel data breaks the nexus between declining survey participation rates and time, and suggests the prevalence of very high psychological distress is increasing. The study identifies potential challenges in estimating trends in population mental health using repeated cross-sectional survey data.

This file differs from the General Social Survey 2010 in that all inapplicable values are set to system missing. The General Social Surveys (GSS) have been conducted by the "https://www.norc.org/Pages/default.aspx" Target="_blank">National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2010 GSS. There are a total of 4,901 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2010 GSS featured special modules on aging, the Internet, shared capitalism, gender roles, intergroup relations, immigration, meeting spouse, knowledge about and attitudes toward science, religious identity, religious trends, genetics, veterans, crime and victimization, social networks and group membership, and sexual behavior (continuing the series started in 1988).

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. The 2006 GSS was the base year for the first panel. A sub-sample of 2,000 GSS cases from 2006 was selected for reinterview in 2008 and again in 2010 as part of the GSSs in those years. The 2008 GSS consists of a new cross-section plus the reinterviews from 2006. The 2010 GSS consists of a new cross-section of 2,044, the first reinterview wave of the 2,023 2008 panel cases with 1,581 completed cases, and the second and final reinterview of the 2006 panel with 1,276 completed cases. Altogether, the 2010 GSS had 4,901 cases (2,044 in the new 2010 panel, 1,581 in the 2008 panel, and 1,276 in the 2006 panel). The 2010 GSS is the first round to fully implement the new, rolling panel design. In 2012 and later GSSs, there will likewise be a fresh cross-section (wave one of a new panel), wave two panel cases from the immediately preceding GSS, and wave three panel cases from the next earlier GSS.

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Benin in 2004, 2009 and 2016, as part of Africa Enterprise Surveys rollout, an initiative of the World Bank. The objective of the Enterprise 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.

Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample in the current wave. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Benin ES 2009 was conducted from May 18 to Sept. 30, 2009, Benin ES 2016 was carried out in July - October 2016. Stratified random sampling was used to select the surveyed businesses. Data was collected using face-to-face interviews.

Data from 497 establishments was analyzed: 128 businesses were from 2004 only, 53 - from 2009 only, 88 - from 2016 only, 70 - from 2004 and 2009 only, 56 - from 2009 and 2016 only and 102 firms were from 2004, 2009 and 2016.

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

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed as 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).

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

In 2016 ES, regional stratification was done across five regions: Atlantique, Borgou, Mono, Ouémé and Littoral. In 2009 ES, Cotonou and Other were the two areas selected.

In 2016 ES, the sample frame consisted of listings of firms from three sources: for panel firms, the list of 150 firms from the Benin 2009 ES was used, and for fresh firms (i.e., firms not covered in 2009) lists obtained from National Statistical Institute and Tax Directorate (2013) and the Chamber of Commerce (2016) were used.

In 2009 ES, two sample frames were used. The first one included the official list "Repertoire of Companies in Benin" (2009) from the Chambre de Commerce et d' Industrie du Benin. The second frame (the panel sample) consisted of enterprises interviewed for the Enterprise Survey in 2004, which were to be re-interviewed where they were in the selected geographical regions and met eligibility criteria.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used for Benin ES 2009 and 2016: - Manufacturing Module Questionnaire - Services Module Questionnaire

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

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.

Abstract

The Survey on Income and Living Conditions, introduced as part of the European Union harmonisation efforts, aims to produce data on income distribution, relative poverty by income, living conditions and social exclusion comparable with European Union member states. The study which uses a panel survey method is repeated every year and monitors sample of household members for four years. Every year, the study attempts to obtain two datasets: cross-sectional and panel.

The Income and Living Conditions Survey 2009 has been conducted to provide annual and regular cross-sectional data to answer questions such as:

• How equally is the income in the country distributed and how has it changed as compared to the previous years?
• How many poor people are there in the country and how do they distribute across regions? How has this situation changed as compared to the previous years?
• Who is poor? Has there been a change over time?
• How has this gap between the poor and the rich evolved over time?
• What kind of a change or transition occurs in the incomes of individuals and households? How does the direction of this change depends on characteristics and circumstances, does it decline or grow?
• How is the income distributed across sectors, types of income and household characteristics?
• How do people's living conditions change or improve over time?
• The study also aims to provide panel data to calculate indicators such as persistent income poverty and to measure net changes over time.

The cross-sectional database 2009 is documented here.

Geographic coverage

All settlements within the borders of the Republic of Turkey have been included.

Universe

All household members living in households within the borders of the Republic of Turkey. However, the study excludes the population defined as institutional population living in hospices, elderly homes, prisons, military barracks, private hospitals and in childcare centres. Migrant population has also been excluded due to practical challenges.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method: Stratified, multi-stage, clustered sampling.

Sampling unit: Household.

Sampling framework: Sampling framework has been derived from two sources:

1. For the settlements with municipal status; General Building Census conducted in 2000 by TurkStat and Numbering Study (conducted in 2000) Form Population 1 data have been used.
2. For the settlements without municipal status (Villages); data of General Population Census conducted in 2000 have been used to select the blocks which constituted the sampling unit of the first stage.

Selection of sample households: for the purposes of the study which used a two-staged sampling design; entire Turkey has been divided into blocks which covered 100 households each.

• At the first stage, blocks were selected as the first stage sampling unit
• At the second stage, households were selected from among the previously selected blocks as the final sampling unit. Prior to the selection of sample households, addresses at the blocks were updated through an "address screening study"

Sample size: Annual sampling size is 13,414 households in respect of the estimation, objectives and targeted variables of the study and in consideration of the attritions in the sample.

Substitution principle: Substitution has not been used as the sample size had been calculated by taking account of non-response.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Household registry form The form filled at the beginning of the survey provides brief information on access to the address of the household, condition of the household and of the survey. Moreover, following the first field application, modalities are identified for filling in the monitoring forms if the households included in the panel survey move home.

• Personal registry form These forms aim to identify basic demographic characteristics of the household members, changes that occur in the status of household membership of the individuals included in the panel survey, reasons for their leaving the household, the date of their departure etc. as well as individuals who join the household.

• Household and personal follow-up form There is need for following up the households which have moved home and the sample individuals who have left the household to join or found another one. Household and personal follow-up forms are used to identify their new addresses and access their contact information.

• Household questionnaire These forms attempt to collect information on the type of the occupied dwelling, status of ownership, information relating to the dwelling (number of rooms, the space actually used, heating system, dwelling facilities, goods owned etc), problems of the dwelling of the neighbourhood, status of indebtedness, rent payments, expenditures for the dwelling, the extent to which households are able to meet their general economic and basic needs and incomes earned at household level.

• Personal questionnaire These forms attempt to collect information on education, health, employment and marital status of the household members aged 15 and over, as well as the dates of employment and incomes earned during the reference year.

Recently, considerable emphasis has been placed on the problems arising out of cross-sectional dependence in panel unit root tests. This paper adopts the factor-based cross-sectional dependence paradigm of Bai and Ng (2005) but suggests alternative factor extraction methods. Some theoretical results for these methods are provided. Further, a detailed Monte Carlo study of these methods for multiple and persistent factors is undertaken. It is found that results are radically different from the serially uncorrelated single-factor case. Tests perform much worse and in some cases it is preferable not to correct at all for cross-sectional dependence.

Abstract

The Survey on Income and Living Conditions, introduced as part of the European Union harmonisation efforts, aims to produce data on income distribution, relative poverty by income, living conditions and social exclusion comparable with European Union member states. The study which uses a panel survey method is repeated every year and monitors sample of household members for four years. Every year, the study attempts to obtain two datasets: cross-sectional and panel.

The Income and Living Conditions Survey 2015 has been conducted to provide annual and regular cross-sectional data to answer questions such as:

• How equally is the income in the country distributed and how has it changed as compared to the previous years?
• How many poor people are there in the country and how do they distribute across regions? How has this situation changed as compared to the previous years?
• Who is poor? Has there been a change over time?
• How has this gap between the poor and the rich evolved over time?
• What kind of a change or transition occurs in the incomes of individuals and households? How does the direction of this change depends on characteristics and circumstances, does it decline or grow?
• How is the income distributed across sectors, types of income and household characteristics?
• How do people's living conditions change or improve over time?
• The study also aims to provide panel data to calculate indicators such as persistent income poverty and to measure net changes over time.

The cross-sectional database 2016 is documented here.

Geographic coverage

The survey is implemented on the territory of the Republic of Serbia and the processing provides data for the Republic of Serbia (total) and for the regions: Belgrade region, Vojvodina region, Sumadija and Western Serbia, and Southern and Eastern Serbia.

Universe

Observation units are households selected according to the sample plan, whether these were one-person or several-member households, as well as all household members aged 15 and over. Collective households were not included in this survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the survey is a two-stage stratified sample with enumeration areas as the primary and households as the secondary units of selection. From an original sample design that included 8,008 of the households, 6,501 households were surveyed (81.2%), which included 17,187 persons aged 15 and over.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Household questionnaire
• Personal questionnaire

Description This dataset contains responses to a yearly panel survey among entrepreneurs in Baden-Württemberg. Based on the DEEM. research project's collected data (see the DEEM. project's website for more information), we survey founders to track the development of startups in our region and to assess the quality and performance of the local Entrepreneurial Ecosystem (see Empirical entrepreneurial ecosystem research: A guide to creating multilevel datasets for more information on this multilevel dataset). Surveys are sent out to all founders of currently active startups. Surveys were made available in German and English, with respondents being able to choose their preferred language at the start of the survey. For any questions about this survey or the underlying research project, please contact us. Aims Research Integrating data on founders, firms, regional contexts and socioeconomic indicators, this data enables deeper insights into patterns and dynamics across different levels of Entrepreneurial Ecosystems (EEs) - insights often missed in traditional single-source and cross-sectional data studies. As such, this data contributes to the understanding of EEs as multilevel phenomena crucial for understanding and promoting productive entrepreneurship and economic development. Respondents We aim for a full population survey every year, instead of drawing samples. This means that all startups with an identifiable means of contact are contacted, with every potential respondent receiving a personalized survey link. Response rates typically vary between 10-15%. To increase response rates, the following approach is used: The survey is left open for a period of two months for founders to answer at their own pace, with periodic reminders sent. While the survey is designed as a panel to track founders' perceptions over time, we cannot guarantee that founders participate in more than one wave. As such, this dataset can be more accurately viewed as a "macro-panel" on the Entrepreneurial Ecosystem of BW. Usage This repository is structured as follows: The global codebook contains information on the broad concepts addressed in each survey wave, as well as the question batteries asked to address these concepts. As such it serves as a broad overview for researchers, to understand whether the data suits their research interests, and whether the relevant questions were asked in multiple years (i.e. panel analyses are possible), or whether they were included as one-off batteries. It is only available in English. The folders include the responses obtained for each survey year, as well as a wave-specific codebook with more detailed information. In contrast to the global codebook, these codebooks contain the questions and response options in both English and German, as well as meta-information about question filters and sub-groups if applicable. Additionally, for each item, basic summary statistics (number of responses per category, number of non-responses) are reported. Data for each survey wave is made available in .csv format (Comma-Separated Values) with a header row. The columns are separated via semicolons (";"). This has been done to avoid conflicts, as some text responses and system variables included commas. Please consider this when loading and using the data with the analysis software of your choice. Should any issues arise in downloading, opening or using this data, please contact us for help.

This file differs from the General Social Survey 2012 in that all inapplicable values are set to system missing. The General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2012 GSS. There are a total of 4,820 cases in the data set but their initial sampling years vary because the GSS now contains panel cases. Sampling years can be identified with the variable SAMPTYPE.

The 2012 GSS featured special modules on religious scriptures, the environment, dance and theater performances, health care system, government involvement, health concerns, emotional health, financial independence and income inequality.

The GSS has switched from a repeating, cross-section design to a combined repeating cross-section and panel-component design. This file has a rolling panel design, with the 2008 GSS as the base year for the first panel. A sub-sample of 2,000 GSS cases from 2008 was selected for reinterview in 2010 and again in 2012 as part of the GSSs in those years. The 2010 GSS consisted of a new cross-section plus the reinterviews from 2008. The 2012 GSS consists of a new cross-section of 1,974, the first reinterview wave of the 2010 panel cases with 1,551 completed cases, and the second and final reinterview of the 2008 panel with 1,295 completed cases. Altogether, the 2012 GSS had 4,820 cases (1,974 in the new 2012 panel, 1,551 in the 2010 panel, and 1,295 in the 2008 panel).

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.