Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

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

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take 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

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

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

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 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. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

Summary : Fuel demand is shown to be influenced by fuel prices, people's income and motorization rates. We explore the effects of electric vehicle's rates in gasoline demand using this panel dataset.

Files : dataset.csv - Panel dimensions are the Brazilian state ( i ) and year ( t ). The other columns are: gasoline sales per capita (ln_Sg_pc), prices of gasoline (ln_Pg) and ethanol (ln_Pe) and their lags, motorization rates of combustion vehicles (ln_Mi_c) and electric vehicles (ln_Mi_e) and GDP per capita (ln_gdp_pc). All variables are all under the natural log function, since we use this to calculate demand elasticities in a regression model.

adjacency.csv - The adjacency matrix used in interaction with electric vehicles' motorization rates to calculate spatial effects. At first, it follows a binary adjacency formula: for each pair of states i and j, the cell (i, j) is 0 if the states are not adjacent and 1 if they are. Then, each row is normalized to have sum equal to one.

regression.do - Series of Stata commands used to estimate the regression models of our study. dataset.csv must be imported to work, see comment section.

dataset_predictions.xlsx - Based on the estimations from Stata, we use this excel file to make average predictions by year and by state. Also, by including years beyond the last panel sample, we also forecast the model into the future and evaluate the effects of different policies that influence gasoline prices (taxation) and EV motorization rates (electrification). This file is primarily used to create images, but can be used to further understand how the forecasting scenarios are set up.

Sources: Fuel prices and sales: ANP (https://www.gov.br/anp/en/access-information/what-is-anp/what-is-anp) State population, GDP and vehicle fleet: IBGE (https://www.ibge.gov.br/en/home-eng.html?lang=en-GB) State EV fleet: Anfavea (https://anfavea.com.br/en/site/anuarios/)

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 "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 GSS panel dataset has three waves of interviews: originally sampled and interviewed in 2006, interviewed for the second time in 2008, and interviewed for the third wave in 2010. This file contains those 2,000 respondents who were pre-selected among the 2006 samples and those variables that were asked at least twice in three waves. Survey items on religion include the following: religious preference, religion raised in, spouse's religious preference, frequency of religious service attendance, religious experiences, and religious salience.

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 dataset contains panel data for a sample of 15 countries (Australia, Austria, Canada, China, Denmark, France, Germany, Israel, Italy, Japan, Republic of Korea, Spain, Sweden, Switzerland and United States) over the period 2006-2015. The series used are available for a small number of developed countries and for a relatively short time period. Solar PV module prices, imports of solar PV panels and public budget for R&D in PV are in real terms and were obtained by dividing them by the United States GDP deflator. The series are obtained from five main sources. Imports value of solar PV panels series are taken from Commodity Trade Statistics database (COMTRADE). PV panels (cells and modules) are a part of the category HS 854140, "Photosensitive Semiconductor Devices, Photovoltaic Cells and Light-Emitting Diodes". Solar PV module prices, cumulative installed PV capacity and public budget for R&D in PV series are constructed from the PVPS report Trends in Photovoltaic Applications of the International Energy Agency (IEA). Population density, political stability index, renewable energy consumption and per capita carbon dioxide emissions series are all obtained from the World Bank (WB). Real GDP per capita series is taken from Federal Reserve Bank of St. Louis (FRED). Technological development in PV and crude oil import price series are drawn from the Organisation for Economic Co-operation and Development (OECD) database. Since crude oil import price series are not available for China and Israel, we use the West Texas Intermediate spot crude oil price as a proxy. The dummy for presence of feed-in tariff is constructed from the OECD database.

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.

We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively weak assumptions about errors and permits avoiding the weak instruments problem associated with differencing. We also propose a simple test for selection bias that is based on the addition of a selection term to the first-difference equation and subsequent testing for significance of this term. The methods are applied to estimating dynamic earnings equations for women.

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Peru in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

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

Geographic coverage

National

Analysis unit

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

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural 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.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2006-2017 Peru Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

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

Industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews. In middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Peru ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

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 the refusal to respond (-8) as a different option from don’t know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

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; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

This archive contains the replication files for "Sample selection in linear panel data models with heterogeneous coefficients" by Alyssa Carlson and Riju Joshi, in Journal of Applied Econometrics (2023). All codes and data are provided. There are two folders corresponding to the simulation study and the empirical application of the paper. To replicate, navigate to the respective folder and follow the README instructions. All codes and datasets are in Stata.

SUMMARY:

Vumonic provides its clients email receipt datasets on weekly, monthly, or quarterly subscriptions, for any online consumer vertical. We gain consent-based access to our users' email inboxes through our own proprietary apps, from which we gather and extract all the email receipts and put them into a structured format for consumption of our clients. We currently have over 1M users in our India panel.

If you are not familiar with email receipt data, it provides item and user-level transaction information (all PII-wiped), which allows for deep granular analysis of things like marketshare, growth, competitive intelligence, and more.

VERTICALS:

• Ecommerce (Amazon, Flipkart, Myntra, Nykaa)
• Taxi (Uber, Ola)
• Food Delivery (Swiggy, Zomato)
• OTT (Netflix, Amazon Prime Video, Disney+)
• Appstore (Apple App Store and Google Playstore)
• OTA (Expedia, Booking.com, GoIbibo)
• E-wallets (PhonePe, PayTM)
• Education (Byju's, Unacademy)

PRICING/QUOTE:

Our email receipt data is priced market-rate based on the requirement. To give a quote, all we need to know is:

• what vertical you are interested in
• how often do you wish to receive the data, and
• do you want any backdata (e.g. from 2019 onwards)

Send us over this info and we can answer any questions you have, provide sample, and more.

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.

We include Stata syntax (dummy_dataset_create.do) that creates a panel dataset for negative binomial time series regression analyses, as described in our paper "Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer". We also include a sample dataset for clarity (dummy_dataset.dta), and a sample of that data in a spreadsheet (Appendix 2).

The variables contained therein are defined as follows:

case: binary variable for case or control status (takes a value of 0 for controls and 1 for cases).

patid: a unique patient identifier.

time_period: A count variable denoting the time period. In this example, 0 denotes 10 months before diagnosis with cancer, and 9 denotes the month of diagnosis with cancer,

ncons: number of consultations per month.

period0 to period9: 10 unique inflection point variables (one for each month before diagnosis). These are used to test which aggregation period includes the inflection point.

burden: binary variable denoting membership of one of two multimorbidity burden groups.

We also include two Stata do-files for analysing the consultation rate, stratified by burden group, using the Maximum likelihood method (1_menbregpaper.do and 2_menbregpaper_bs.do).

Note: In this example, for demonstration purposes we create a dataset for 10 months leading up to diagnosis. In the paper, we analyse 24 months before diagnosis. Here, we study consultation rates over time, but the method could be used to study any countable event, such as number of prescriptions.

Abstract

Across the world, the number of migrants displaced by civil conflict is on the rise. Recent estimates suggest that nearly 65.6 million people have been forcibly displaced within their own countries or across borders, and that most of them (84 percent) are living in developing countries (UNHCR 2017). Despite the persistence and scale of this displacement, there exists little evidence, or even basic data, addressing the core policy problem: what type of programs should be prioritized to maintain or improve the wellbeing of natives and refugees. The Cox's Bazar Panel Survey (CBPS) endeavours to provide such data through a comprehensive, large-sample survey that tracks both host and refugee households over time in Cox's Bazar, Bangladesh, the site of one of the world's largest refugee camps.

Geographic coverage

Cox's Bazar District

Analysis unit

Household

Individual

Universe

Households residing in Cox's Bazar camps. See enclosed Basic Information Document for further details.

Kind of data

Survey Data

Anthropometric Data

Sampling procedure

The sample is representative of three strata: i) residents of the refugee camps, ii) host communities within 15km of refugee camps, iii) host communities further than 15km from refugee camps. Samples were selected via a multistage procedure that selected small geographic areas as PSUs, listed each PSU, and then drew households from that listing. See Basic Information Document for complete details.

Mode of data collection

Face-to-face [f2f]

The 1975-1981 TIME USE LONGITUDINAL PANEL STUDY dataset combines a round of data collected in 1981 with the principal investigators' earlier TIME USE IN ECONOMIC AND SOCIAL ACCOUNTS, 1975-1976 (ICPSR 7580), collected by F. Thomas Juster, Paul Courant, et al. This combined data collection consists of data from 620 respondents, their spouses if they were married at the time of first contact, and up to three children between the ages of three and seventeen living in the household. The key features which characterized the 1975 time use study were repeated in 1981. In both of the data collection years, adult individuals provided four time diaries as well as extensive information related to their time use in the four waves of data collection. Information pertaining to the household was collected, as well as identical measures from respondents and spouses for all person-specific information. Selected children provided two time diary reports (one for a school day and one non-school day), an academic achievement measure, and survey measures pertaining to school and family life. In addition, teacher ratings were obtained. For each adult individual who remained in the sample through the 1981 study, a time budget was constructed from his or her time diaries containing the number of minutes per week spent in each of some 223 mutually exclusive and exhaustive activities. These measures provide a description of how the sample individuals were currently allocating their time and are comparable to the 87 activity measures created from their 1975 diaries. In addition, respondent and spouse time aggregates were converted to parent time aggregates for mothers and fathers of children in the sample. To facilitate analyses on spouses, a merged data file was created for 868 couples in which both husband and wife had complete Wave I data in either 1975-1976 or 1981.

Abstract

The Ghana Socioeconomic Panel Survey is a joint effort between the Economic Growth Centre at Yale University and the Institute of Statistical, Social and Economic Research (ISSER), at the University of Ghana (Legon, Ghana). The survey is meant to remedy a major constraint on the understanding of development in low-income countries - the absence of detailed, multi-level and long-term scientific data that follows individuals over time and describes both the natural and man-made environment in which the individuals reside. Most data collection efforts are short-term - carried out at one point in time; and limited in scope – collecting information on only a few aspects of the lives of the persons in the study; and when there are multiple rounds of data collection, individuals who leave the study area are dropped. This means that the most mobile people are not included in existing surveys and studies, perhaps substantially biasing inferences about who benefits from and who bears the cost of the development process. The goal of this project is to follow all individuals, or a random subset, over time using a comprehensive set of survey instruments to shed new light on long-run processes of economic development.

The 2009 survey is the first in a series that is intended to include 5 surveys over the next 15-21 years. Surveys will be implemented approximately every 3 years. In subsequent waves of the panel, a sample of moved households and individuals who have moved out of original households to form new households or joined other households originally not in the panel sample, will be interviewed in addition to the original sample. The number of households in the Panel Study thus has the potential of increasing due to the nature of the design; tracking wholly moved and split households.

The principal objective of the panel survey is to provide a comprehensive data base for carrying out a wide range of studies of the medium- and long-term changes, or lack of changes, that take place during the process of development. The information gathered from the survey is expected to inform decision makers in the formulation of economic and social policies to: - Identify target groups for government assistance; - Construct models to stimulate the impact on individual groups of the various policy options and to analyze the impact of decisions that have already been implemented; - Access the economic situation on living conditions of households; and - Provide benchmark data for district assemblies.

Geographic coverage

The survey provides regionally representative data for the 10 regions of Ghana. In all, 5010 households from 334 Enumeration Areas (EAs) were sampled. Fifteen households were selected from each of the EAs. The number of EAs for each region was proportionately allocated based on estimated 2009 population share for each region. EAs for Upper East and Upper West regions, which have relatively smaller population sizes, were over sampled to allow for a reasonable number of households to be interviewed in these regions.

Analysis unit

Households, individuals, agricultural plots, household enterprises

Universe

Nationally representative, regionally representative for all 10 regions.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey provides regionally representative data for the 10 regions of Ghana. In all, 5010 households from 334 Enumeration Areas (EAs) were sampled. Fifteen households were selected from each of the EAs. The distribution of the enumeration areas across the regions in Ghana is presented in Table 1. The number of EAs for each region was proportionately allocated based on estimated 2009 population share for each region. EAs for Upper East and Upper West regions, which have relatively smaller population sizes, were over sampled to allow for a reasonable number of households to be interviewed in these regions.

A two-stage stratified sample design was used for the survey. Stratification was based on the regions of Ghana. The first stage involved selecting geographical precincts or clusters from an updated master sampling frame constructed from the 2000 Ghana Population and Housing Census. A total of 334 clusters (census enumeration areas) were selected from the master sampling frame. The clusters were randomly selected from the list of EAs in each region. The selection was based on a simple random sampling technique. A complete household listing was conducted in 2009 in all the selected clusters to provide a sampling frame for the second stage selection of households.

The second stage of selection involved a simple random sampling of 15 of the listed households from each selected cluster. The primary objective of the second stage of selection was to ensure adequate numbers of completed individual interviews to provide estimates for key indicators with acceptable precision at the regional level. Other sampling objectives were to facilitate manageable interviewer workload within each sample area and to reduce the effects of intra-class correlation within a sample area on the variance of the survey estimates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The information gathered from the survey will assist decision makers in the formulation of economic and social policies to: - Identify target groups for government assistance - Construct models to stimulate the impact on individual groups of the various policy options and to analyze the impact of decisions that have already been implemented - Access the economic situation on living conditions of households - Provide benchmark data for district assemblies

To achieve these objectives, detailed data has been collected in the following subject areas: 1. Demographic characteristics: employment, education, migration

1. Information about non-resident spouses and relatives

2. Assets:

3. Household assets: (i) Livestock (ii) Tools (iii) Durable Goods Financial assets: (i) Borrowing (ii) Lending (iii) In-transfers (iv) Out-transfers (v) Savings

4. Agricultural Production

5. Land information: (i) Plot background (ii) Size (iii) Fallowing information, soil type, irrigation (iv) Investment, ownership, rental status (v) Crops (vi) Chemical inputs (vii) Tractor use (viii) Seeds (ix) Labour inputs

6. Sales and storage: (ii) Revenues from crop production (ii) Crop stores

7. Non-farm Household Enterprise

8. Basic Information and Assets (i) Basic information (ii) Enterprise assets

9. Information about employees (i) Information about all employees (ii) Information about four important employees (iii) Enterprises operating in the past 1 month (iv) Enterprise in a typical month

10. Accounting: General enterprise

11. Accounting: Trade/wholesale enterprise

12. Accounting: Food enterprise

13. Accounting: Services

14. Household Health

15. Insurance

16. Anthropometry

17. Immunization

18. Activities of daily living

19. Miscellaneous health

20. Health in the past 2 weeks

21. Health in the past 12 month

22. Womens' Health

23. Fertility

24. Power

25. Mens' Health

26. Reproductive Health

27. Power

28. Children's Module

29. Young child health - children younger than 5 years old

30. Digit span test- children aged 5-15

31. Raven's Pattern Cognitive Assessment- children aged 5-15

32. Math questions- children aged 9-26

33. English questions- children aged 9-26

34. Psychology/Social Networking

35. Psychology (i) Depression (ii) Subjective social welfare (iii) Regretted consumption (iv) Townsend questions (v) Trust and solidarity (vi) Time use

36. Big 5 personality questions

37. Social networking

38. Information seeking (i) Interaction with organizations (ii) Extension services (iii) Volunteerism

39. Consumption Module

40. Food items consumed

41. Clothing and footwear

42. Expenditure on other items in last 12 months

43. Fuel and other lubricants

44. Housing Characteristics

45. Part A - Rent, water, light, cooking, waste disposal, building materials

46. Part B - Dwelling type, ownership, living conditions, power supply, surroundings

The community inventory documents a broad range of natural and institutional features of the community, including political organizations, financial institutions, the presence of various development programs, and community infrastructure. There was also a questionnaire for Districts and Municipal Assemblies. As of December 2015, Seperate documentation for the Community survey and the data will be made available later.

Cleaning operations

The processing of the survey data began shortly after the fieldwork commenced. The first stage of data processing involved office editing and post-coding. Questionnaires were edited to double-check for completeness and consistency in the questionnaires returned, while the post-coding served to define new response categories to pre-coded question or define a response set for open ended questions. Once the editing and post-coding were done, the questionnaires were passed on for data entry.

The data entry program was designed in CSPro version 4.0. The entry program was designed with the necessary skip patterns and consistency checks to ensure adequate data quality and validity. All questionnaires were entered twice (100 percent verification) and the two files were compared for entry errors which were subsequently verified and corrected with the questionnaires. The data entry was completed in August of 2010. The consolidated data files in CSPro format were then converted to STATA format for further consistency checks and cleaning.

This paper re-examines health-growth relationship using an unbalanced panel of 17 advanced economies for the period 1870–2013 and employs panel generalised method of moments estimator that takes care of endogeneity issues, which arise due to reverse causality. We utilise macroeconomic data corresponding to inflation, government expenditure, trade and schooling in sample countries that takes care of omitted variable bias in growth regression. With alternate model specifications, we show that population health proxied by life expectancy exert a positive and significant effect on both real income per capita as well as growth. Our results are in conformity with the existing empirical evidence on the relationship between health and economic growth, they, however, are more robust due to the presence of long-term data, appropriate econometric procedure and alternate model specifications. We also show a strong role of endogeneity in driving standard results in growth empirics. In addition to life expectancy, other constituent of human capital, education proxied by schooling is also positively associated with real per capita income. Policy implication that follows from this paper is that per capita income can be boosted through focussed policy attention on population health. The results, however, posit differing policy implications for advanced and developing economies.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS). The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further two years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK. The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and 2003. The LSMS constitutes Wave 1 of the panel survey so there are three years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel - Wave 2 Second interview of 50% of LSMS respondents in Autumn/ Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/ Winter 2003

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change. KIND OF DATA

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 3 sample consisted of 2878 households who had been interviewed at Wave 2 and a further 73 households who were interviewed at Wave 1 but were non-contact at Wave 2 were issued. A total of 2951 households (1301 in the RS and 1650 in FBiH) were issued for Wave 3. As at Wave 2, the sample could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as a Wave 2. While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at Wave 2 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design means that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefit of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are as follows:

i. Movers out of the country altogether i.e. outside FBiH and RS. This category of mover is clear. Sample members moving to another country outside FBiH and RS will be out-of-scope for that year of the survey and not eligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. The personal details of the respondent are passed between the statistical institutes and a new interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 3 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko are followed for interview. When coding entity Brcko is treated as the entity from which the household who moved into Brcko originated.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry

As at Wave 2 CSPro was the chosen data entry software. The CSPro program consists of two main features to reduce to number of keying errors and to reduce the editing required following data entry: - Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes). The Wave 3 data entry program had more checks than at Wave 2 and DE staff were instructed to get all anomalies cleared by SIG fieldwork. The program was extensively tested prior to DE. Ten computer staff were employed in each Field Office and as all had worked on Wave 2 training was not undertaken.

Editing

Editing Instructions were compiled (Annex G) and sent to Supervisors. For Wave 3 Supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The FBTSA examined the work twelve of the twenty-two Supervisors. All Supervisors made occasional errors with the Control Form so a further 100% check of Control Forms and Module 1 was undertaken by the FBTSA and SIG members.

Response rate

The panel survey has enjoyed high response rates throughout the three years of data collection with the wave 3 response rates being slightly higher than those achieved at wave 2. At wave 3, 1650 households in the FBiH and 1300 households in the RS were issued for interview. Since there may be new households created from split-off movers it is possible for the number of households to increase during fieldwork. A similar number of new households were formed in each entity; 62 in the FBiH and 63 in the RS. This means that 3073 households were identified during fieldwork. Of these, 3003 were eligible for interview, 70 households having either moved out of BiH, institutionalised or deceased (34 in the RS and 36 in the FBiH).

Interviews were achieved in 96% of eligible households, an extremely high response rate by international standards for a survey of this type.

In total, 8712 individuals (including children) were enumerated within the sample households (4796 in the FBiH and 3916 in the RS). Within in the 3003 eligible households, 7781 individuals aged 15 or over were eligible for interview with 7346 (94.4%) being successfully interviewed. Within cooperating households (where there was at least one interview) the interview rate was higher (98.8%).

A very important measure in longitudinal surveys is the annual individual re-interview rate. This is because a high attrition rate, where large numbers of respondents drop out of the survey over time, can call into question the quality of the data collected. In BiH the individual re-interview rates have been high for the survey. The individual re-interview rate is the proportion of people who gave an interview at time t-1 who also give an interview at t. Of those who gave a full interview at wave 2, 6653 also gave a full interview at wave 3. This represents a re-interview rate of 97.9% - which is extremely high by international standards. When we look at those respondents who have been interviewed at all three years of the survey there are 6409 cases which are available for longitudinal analysis, 2881 in the RS and 3528 in the FBiH. This represents 82.8% of the responding wave 1 sample, a

Abstract

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

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

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

Geographic coverage

National

Geographic coverage notes

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

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision. b. To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. c. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. d. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

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

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

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

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

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.