Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2021. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

Download Employee Vehicle Personal Use Excel SheetThis dataset lists the employee name and taxable benefit for personal use of City of Greater Sudbury Vehicle as travel expenses for the year 2020. Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Data for other years is available in separate datasets. Updated quarterly when expenses are prepared.

It is important to identify any barriers in recruitment, hiring, and employee retention practices that might discourage any segment of our population from applying for positions or continuing employment at the City of Tempe. This information will provide better awareness for outreach efforts and other strategies to attract, hire, and retain a diverse workforce.This page provides data for the Employee Vertical Diversity performance measure. The performance measure dashboard is available at 2.20 Employee Vertical Diversity. Additional InformationSource:PeopleSoft HCM, Maricopa County Labor Market Census DataContact: Lawrence LaVictoireContact E-Mail: lawrence_lavicotoire@tempe.govData Source Type: Excel, PDFPreparation Method: PeopleSoft query and PDF are moved to a pre-formatted Excel spreadsheet.Publish Frequency: Every six monthsPublish Method: ManualData Dictionary

Context: This dataset contains information about employees in a company, including their educational backgrounds, work history, demographics, and employment-related factors. It has been anonymized to protect privacy while still providing valuable insights into the workforce.

Columns:

1. Education: The educational qualifications of employees, including degree, institution, and field of study.

2. Joining Year: The year each employee joined the company, indicating their length of service.

3. City: The location or city where each employee is based or works.

4. Payment Tier: Categorization of employees into different salary tiers.

5. Age: The age of each employee, providing demographic insights.

6. Gender: Gender identity of employees, promoting diversity analysis.

7. Ever Benched: Indicates if an employee has ever been temporarily without assigned work.

8. Experience in Current Domain: The number of years of experience employees have in their current field.

9. Leave or Not: a target column

Usage: This dataset can be used for various HR and workforce-related analyses, including employee retention, salary structure assessments, diversity and inclusion studies, and leave pattern analyses. Researchers, data analysts, and HR professionals can gain valuable insights from this dataset.

Potential Research Questions: 1. What is the distribution of educational qualifications among employees? 2. How does the length of service (Joining Year) vary across different cities? 3. Is there a correlation between Payment Tier and Experience in Current Domain? 4. What is the gender distribution within the workforce? 5. Are there any patterns in leave-taking behavior among employees?

Acknowledgments: We would like to acknowledge the contributions of our HR department in providing this dataset for research and analysis purposes.

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%.

Analysis of ‘2.20 Employee Vertical Diversity (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/bf0518c8-7314-4b2f-bf86-09856a8fc5cc on 11 February 2022.

--- Dataset description provided by original source is as follows ---

It is important to identify any barriers in recruitment, hiring, and employee retention practices that might discourage any segment of our population from applying for positions or continuing employment at the City of Tempe. This information will provide better awareness for outreach efforts and other strategies to attract, hire, and retain a perse workforce.

This page provides data for the Employee Vertical Diversity performance measure.

The performance measure dashboard is available at 2.20 Employee Vertical Diversity.

Additional Information

Source:PeopleSoft HCM, Maricopa County Labor Market Census Data

Contact: Lawrence LaVictoire

Contact E-Mail: lawrence_lavicotoire@tempe.gov

Data Source Type: Excel, PDF

Preparation Method: PeopleSoft query and PDF are moved to a pre-formatted excel spreadsheet.

Publish Frequency: Manual

Publish Method: Every six months

Data Dictionary

--- Original source retains full ownership of the source dataset ---

Sample data for exercises in Further Adventures in Data Cleaning.

This dataset includes data that the Employment and Training Administration's Office of Foreign Labor Certification (OFLC) collected from Permanent Employment Certification (PERM) applications during previous fiscal years. It includes information on employers, geography, and job details for participants in the PERM program. Historical PERM public disclosure data is available on the OFLC website in the Performance Data section. Data is available as Excel files in aggregate form at https://www.dol.gov/agencies/eta/foreign-labor/performance.

Turnover data by fiscal year for the City of Tempe compared to the seven market cities which included Chandler, Gilbert, Glendale, Mesa, Phoenix, Peoria and Scottsdale. There are two totals, one with and one without retires.

Please note that the Valley Benchmark Cities’ annual average is unavailable for FY 2020/2021 due to a gap in data collection during that year.

Please note that corrections were made to the data, including historic data, due to additional review and research on the data on 10/2/2024.

This page provides data for the Employee Turnover performance measure.

The performance measure dashboard is available at 5.07 Employee Turnover.

Additional Information

Source: Department Reports
Contact: Lawrence La Victoire
Contact E-Mail: lawrence_lavictoire@tempe.gov
Data Source Type: Excel
Preparation Method: Extracted from PeopleSoft and requested data from other cities is entered manually into a spreadsheet and calculations are conducted to determine percent of turnover per fiscal year
Publish Frequency:Annually
Publish Method: Manual
Data Dictionary

The data files are related to content, construct and criterion validity of yogic personal excellence inventory (YPEI) derived from the most quoted yogic classical text Patanjali Yoga Darshan. The developed YPEI is a novel and significant psychometric tool for mapping personal excellence level of an individual before prescribing personalised bio-psycho-socio-spiritual protocol for health promotion/healing/spiritual advancement. This research adds notable value in health psychology, complementary and alternative medicine and self-management. 1. Content Validity Data Excel sheet containing ten experts’ ratings on the relevance of the items of the original pool of YPEI. 2. EFA Data Excel sheet containing responses of 721 participants on 111 items retained post content validity analysis. 3. CFA Data Excel sheet containing responses of 364 participants on 71 items retained post exploratory factor analysis. 4. Criterion Validity Excel sheet containing responses of 146 participants on the final 43 items of the Yogic Personal Excellence Inventory along with Vikruti Subdosha Questionnaire, Vedic Personality Inventory, and Personal Efficacy Scale for determining convergent and discriminant validity.

In 2024, Tata Consultancy Services, the multinational information technology service company, had almost ******* employees worldwide. This was a decrease of approximately ****** when compared to the previous year. Tata Consultancy Services Headquartered in Mumbai, India, Tata Consultancy Services provides IT, business consulting, and outsourcing services in over ** countries. For the fiscal year ending March 31, 2023, they had revenue of approximately **** trillion Indian rupees. When broken down by region, more than half of this revenue came from the Americas. By industry vertical, banking, financial services, and insurances made up about ** percent of their total revenue. Business process outsourcing (BPO) market Outsourcing is a business practice when a company hires another company to do other services that could be done internally. As a result, this causes controversy. Those opposed it say that it gets rid of jobs domestically. Those in favor of it say that it helps maintain the nature of the free market because businesses can distribute resources in the most effective manner. In 2019, the global market size of outsourced services amounted to **** billion U.S. dollars. Regionally, most of this revenue came from the Americas. By service type, information technology outsourcing generated **** percent of revenue, while ** percent of the revenue came from business process outsourcing.

2021_FBMSREC 051

There is one file uploaded in the dataset: MS excel file. The MS Excel sheet contains the raw data gathered during the study.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Employment by industry and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

Abstract

The AES is conducted annually through mail questionnaires sent to all large scale profit making enterprises. The reference period is twelve months and this is normally the enterprise's financial year. The survey covers private, statutory bodies and public sector industries engaged in the production and sale of goods and services on the market at prices normally designed to cover the cost of production. Public sector industries include Government Print, Plant and Vehicle Hire Organisation (PVHO), Controller of Stores and Forestry Department.The AES provides information on the economic activity of large-scale enterprises in the Malawian economy with regard to their production and employment characteristics, profitability level, acquisition and issue of both real and financial claims in different sectors of the economy.

Analysis unit

Large Scale Enterprises

Universe

The survey aims at covering all large-scale profit oriented enterprises in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The enterprises selected for the survey were initially obtained from the Business Information Register (BIR). This register had more routinely updated information on large and medium scaled businesses in the years 2003 to 2005. The BIR is a dynamic database containing information on enterprises known by the National Statistical Office to be operational in Malawi. The register covers the population of large scale enterprises fairly well, but for the population of medium sized enterprises the coverage is not currently exaustive. The BIR gives information on both enterprise and the underlying establishments. The database names, addresses, type of activity, number of employees and turnover (i.e. total incomes). Currently, the BIRis being updated to enehance the framefor AES and othere related studies.

Sampling deviation

Although the AES mainly covers the large scale enterprises, due to the current importance of medium and small-scale enterprises, the survey has to progressively incorporate these companies in order to monitor the substantive growth of the economy.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Due to diversity in the nature of businesses in the different sectors, it was felt necessary to develop activity-specific questionnaires. The AES quationnaires have had minor adjustments over the three year period form 2003 to 2005, and in 2005 we used separate questionnaires for the following 12 sector groups: - Agriculture & Forestry - Manufacturing & Mining - Wholesale & Retail - Construction - Hotels & Restaurants - Banking & Intermediation - Insurance & Pension - Post and Telecommunications - Transport and Other service - Electricity & Water industries. The enterprises were handed questionnaires tailored for their activities. Enterprises were requested to respond to the survey questions, basically as reflected in their annual statements of accounts.

Cleaning operations

On receipt of the questionnaires, they were checked for errors and discrepancies. The questionnaires were checked for internal inconsistencies, but also if changes from answers in the previous survey were reasonable. Where company accounts were available, crosschecks were also made with the questionnaires to detect omissions and inconsistencies. Errors and discrepancies found were corrected after consultation with the respondent. For enterprises that failed to respond (unit non-response) and enterprises with incomplete answers (item non-response) the missing information were estimated. In cases where current annual company accounts were available, their questionnaires were completed at the office using these accounts as a basis for estimation. In other cases, missing data were imputed by applying the same growth rate as for enterprises responding for the two periods 2003-2004 and 2004-2005 respectively. Separate growth rates were calculated and used for total sales, total purchases, employment and remuneration per employee. There were two types of checks, manual check and computer validation.

The checked questionnaires were keyed in using Microsoft Excel Spreadsheet. The tabulation and further analysis were done in SPSS. The whole data processing took about three months to complete.

Response rate

In total 412 enterprises were selected in the initial survey sample. However, some of the enterprises have not responded any of the three years of interest and is therefore not included in the final results. The figures in the attached report give results based on 371 enterprises that responded to the survey at the minimum one of the three years.

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