While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis. In addition, we show through novel mathematical decomposition and simulation that only one-way FE models cleanly capture either the over-time or cross-sectional dimensions in panel data, while the two-way FE model unhelpfully combines within-unit and cross-sectional variation in a way that produces un-interpretable answers. In fact, as we show in this paper, if we begin with the interpretation that many researchers wrongly assign to the two-way FE model—that it represents a single estimate of X on Y while accounting for unit-level heterogeneity and time shocks—the two-way FE specification is statistically unidentified, a fact that statistical software packages like R and Stata obscure through internal matrix processing.

This file contains all of the cases and variables that are in the original 2014 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 2014 GSS. There are a total of 3,842 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.

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.

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.

The power of standard panel cointegration statistics may be affected by misspecification errors if structural breaks in the parameters generating the process are not considered. In addition, the presence of cross-section dependence among the panel units can distort the empirical size of the statistics. We therefore design a testing procedure that allows for both structural breaks and cross-section dependence when testing the null hypothesis of no cointegration. The paper proposes test statistics that can be used when one or both features are present. We illustrate our proposal by analysing the pass-through of import prices on a sample of European countries.

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

This Basic Information Document (BID) provides a brief overview of the Tanzania National Panel Survey (NPS), but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the NPS. As this Basic Information Document (BID) for the UPD does not describe in detail the background, development, or use of the NPS itself, the round-specific NPS BIDs should supplement the information provided here.

The NPS Uniform Panel Dataset (UPD) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPD provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPD combines the four completed rounds of the NPS – NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, other urban, rural, Zanzibar.

Analysis unit

• Households
• Individuals

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time; i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPD survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPD survey instrument, there are five distinct sections, arranged vertically: (1) the UPD - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPD and not each question will have reports for each of the UPD codes listed, the NPS-UPD survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPD-equivalent question, visually presenting their contribution to compatibility with the UPD. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPD code listing)4.

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.

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. The 2008 GSS featured special modules on attitudes toward science and technology, self-employment, terrorism preparation, global economics, sports and leisure, social inequality, sexual behaviors and religion. Items on religion covered denominational affiliation, church attendance, religious upbringing, personal beliefs, and religious experiences.

The GSS is in transition from a replicating cross-sectional design to a design that uses rotating panels. In 2008 there were two components: a new 2008 cross-section with 2,023 cases and the first re-interviews (panel) with 1,536 respondents from the 2006 GSS. The 2,023 cases in the cross-section have been previously released as a part of the 1972-2008 cumulative data. This new release includes those 1,536 re-interviewed panel cases along with the 2,023 cases. Please note that this is not a cumulative file - those cases and variables not surveyed in 2008 are excluded. Also note that, although those 1,536 cases were from the 2006 sample, this release does not include their responses in 2006. We plan to release a data file with the previous responses in the future. This release introduces new variables that were asked only of the panel cases of the 2008 GSS. The majority of variables introduced are related to the 2007 International Social Survey Program (ISSP) module on leisure time and sports.

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.

Network of 42 papers and 66 citation links related to "Financial development and governance: A panel data analysis incorporating cross-sectional dependence".

This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross-sectional dependence. A new estimator is defined that uses cross-sectional dependence for efficiency while being robust to the misspecification of the form of the cross-sectional dependence. We show that using cross-sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses nuisance parameters parsimoniously so that it exhibits good small- and large-sample properties even when the number of time periods is large. As an empirical application, we estimate the effect of attending private school on student achievement using a value-added model.

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.

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. The 2016-2020 GSS consisted of re-interviews of respondents from the 2016 and 2018 Cross-Sectional GSS rounds. All respondents from 2018 were fielded, but a random subsample of the respondents from 2016 were released for the 2020 panel. Cross-sectional responses from 2016 and 2018 are labelled Waves 1A and 1B, respectively, while responses from the 2020 re-interviews are labelled Wave 2.

The 2016-2020 GSS Wave 2 Panel also includes a collaboration between the General Social Survey (GSS) and the "https://electionstudies.org/" Target="_blank">American National Election Studies (ANES). The 2016-2020 GSS Panel Wave 2 contained a module of items proposed by the ANES team, including attitudinal questions, feelings thermometers for presidential candidates, and plans for voting in the 2020 presidential election. These respondents appear in both the ANES post-election study and the 2016-2020 GSS panel, with their 2020 GSS responses serving as their equivalent pre-election data. Researchers can link the relevant GSS Panel Wave 2 data with ANES post-election data using either ANESID (in the GSS Panel Wave 2 datafile) or V200001 in the ANES 2020 post-election datafile.

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.

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.

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 Workplace Employment Relations Survey, 2004 (also known as the Workplace Employment Relations Survey, WERS 2004, or WERS5) was a national survey of people at work. The survey was jointly sponsored by the then Department of Trade and Industry, ACAS, the ESRC and the PSI. (In June 2007, DTI became the Department for Business, Enterprise and Regulatory Reform (BERR) and then in June 2009, merged with the Department for Innovation, Universities and Skills to become the Department for Business, Innovation and Skills (BIS).)

WERS5 followed in the footsteps of earlier surveys conducted in 1980, 1984, 1990 and 1998, when the series was originally known as the Workplace Industrial Relations Survey, or WIRS - the name was changed in 1998 to better reflect the contemporary content of the series. The WIRS/WERS series from 1980 onwards is held at the UK Data Archive under GN 33176.

The purpose of each survey in the WERS series has been to provide large-scale, statistically reliable evidence about a broad range of industrial relations and employment practices across almost every sector of the economy in Great Britain. This evidence is collected with several objectives in mind. It aims to provide a mapping of employment relations practices in workplaces across Great Britain, monitor changes in those practices over time, inform policy development and permit an informed assessment of the effects of public policy, and bring about a greater understanding of employment relations as well as the labour market. To that end, the cross-section element of WERS 2004 collected information from managers with responsibility for employment relations or personnel matters; trade union or employee representatives; and employees themselves. Therefore, it included the Cross-Section Survey of Managers (MQ), Cross-Section Survey of Employee Representatives (ERQ), and Cross-Section Survey of Employees (SEQ). The cross-section survey also included a Financial Performance Questionnaire (FPQ), that detailed financial performance of the establishment over the 12 months previous to the survey (access to the FPQ data, alongside region identifiers and industry codes for the Survey of Managers and panel data, was initially restricted until April 2007, when they were deposited as part of the second edition of the study). The panel element of WERS 2004 includes the Screening Questionnaire and the Survey of Managers (comprising the Basic Workforce Data Sheet and the Management Interview).

Structure of the WERS 2004 study:
Unlike WERS 98, SN 5294 includes both the cross-section and panel surveys conducted for WERS 2004. The panel element for 2004 forms Wave 2 of the 1998-2004 panel survey. Wave 1 comprised the cross-sectional managers' survey conducted for WERS 98, and is held separately under SN 3955. Therefore, users who need Wave 1 should also order SN 3955.

Further information about the survey is available from the GOV.UK 2004 Workplace Employment Relations Survey webpages.

Secure Access version of WERS:
Users should note there is a Secure Access version of WERS which has more restrictive access conditions than this study made available under the standard End User Licence (EUL). SN 6712 includes both the cross-section and panel surveys conducted for WERS 98 and WERS 2004, and includes 1) Inter-Departmental Business Register reference numbers for businesses who have consented to the linking of WERS data to other data sources, and 2) anonymised postcodes. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Edition history:
For the fifth edition (January 2014), three additional data files, including revised weights with non-response adjustment and trade union recognition data, were deposited. The Introductory Note document has been updated accordingly. A full edition history is given in the READ file.

This study uses Monte Carlo experiments to produce new evidence on the performance of a wide range of panel data estimators. It focuses on estimators that are readily available in statistical software packages such as Stata and Eviews, and for which the number of cross- sectional units (N) and time periods (T) are small to moderate in size. The goal is to develop practical guidelines that will enable researchers to select the best estimator for a given type of data. It extends a previous study on the subject (Reed and Ye, 2011), and modifies their recommendations. The new recommendations provide a (virtually) complete decision tree: When it comes to choosing an estimator for efficiency, it uses the size of the panel dataset (N and T) to guide the researcher to the best estimator. When it comes to choosing an estimator for hypothesis testing, it identifies one estimator as superior across all the data scenarios included in the study. An unusual finding is that researchers should use different estimators for estimating coefficients and testing hypotheses. The authors present evidence that bootstrapping allows one to use the same estimator for both.

This is the appended data set of the two waves of ACCESS survey from 2014-15 and 2018 for panel data analysis.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

This survey assessed campaign influences on public opinion and voting behavior in Mexico's July 2, 2000, presidential election. The study consists of five separate surveys conducted over the course of the campaign and following the election, using a hybrid panel/ cross-sectional design. The Pre- and Post-Election Panel Data (Part 1) includes data collected from a national cross-section of 2,400 adults. All respondents were interviewed following the official start of the campaign, February 19-27, and following the election, July 7-16, while subsets of them were also interviewed April 28-May 7 and/or June 2-18. The Post-Electoral Cross-Section Data (Part 2) includes only data collected from a new and separate cross-section of 1,199 respondents, gathered to supplement the panel sample. Additional information regarding the design of the study may be found within the codebook. Respondents were queried on a wide range of issues relating to voting behavior, including exposure to media, political knowledge and engagement, opinions about salient political issues including privatization, employment, crime, the death penalty, and government services, attitudes toward the main political parties and candidates, impressions of the electoral process, voting intentions, faith in the electoral process, credibility of the media, exposure to the campaign, and opinions of current president Ernesto Zedillo and presidential candidates Cuauhtemoc Cardenas (Alliance for Mexico), Vicente Fox (Alliance for Change), and Francisco Labastida (PRI). Respondents queried following the election were asked for whom they voted and why, and whether they felt the election was clean. In addition, they were asked to assess their interest level in politics, their involvement and familiarity with the campaign media and activities, how frequently they discuss the issues, and the ability of the candidates to address important social issues. Background information on respondents includes age, gender, political party affiliation, voting history, religion, education, marital status, children, employment status, labor union membership, languages spoken, travel to the United States, socio-economic status, and household income.