May Furlough Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Compositional and furlough effects from quarterly Labour Force Survey earnings data for the period 2008 to 2021, UK. Experimental estimates.

The Foreign Service Act of 1980 mandated a comprehensive revision to the operation of the Department of State and the personnel assigned to the US Foreign Service. As the statutory authority, the Foreign Affairs Manual (FAM), details the Department of Sta

An overview of the similarities and differences between the fortnightly BICS furlough estimates and HMRC’s CJRS data, over the period 1 May to 31 July 2020.

This dataset provides information about the number of properties, residents, and average property values for Furlough Road cross streets in Conrad, MT.

US Federal Funding Gaps History

Historical US Federal Funding Gaps & Political Party Control

By Throwback Thursday [source]

About this dataset

This dataset is a comprehensive historical record of federal funding gaps in the United States, spanning from 1976 to 2018. It provides detailed information on each funding gap, including the start and end dates, total duration in days, and whether or not employees were furloughed.

The dataset also includes data on the political party control during each funding gap, specifically for both the Senate and the House of Representatives. For each chamber, it indicates which party had control - either Democrats or Republicans - as well as any representation by Independent members.

Additionally, this dataset contains valuable insights into the impact of federal funding gaps on government employees. It records the number of employees who were furloughed during each gap, allowing for analysis of workforce disruption and potential economic consequences.

By leveraging this dataset's wealth of information on federal funding gaps in the United States over more than four decades, researchers can gain a deeper understanding of these significant events in governmental operations and their broader implications for various stakeholders

How to use the dataset

Introduction:

• Understanding the Columns: a) Start Date: The date when a federal funding gap began. b) End Date: The date when a federal funding gap ended. c) Total days: The duration of the federal funding gap in days. d) Employees furloughed: A boolean value indicating whether or not employees were furloughed during that specific funding gap. (True = Employees were furloughed, False = No employee was furloughed.) e) Number of Employees Furloughed: The actual count of employees who were furloughed during that specific funding gap. f) Senate Control: The political party that had control over the Senate during each particular period specified. (Categorical - Democratic, Republican) g) Senate Democrats: The number of Democratic senators serving during that specific funding gap. h) Senate Republicans: The number of Republican senators serving during that particular period specified. i) Senate Independents: The number of Independent senators serving at that time frame. j ) House Control :He political party that had control over House Representatives throughoted specific dataried by each perticularnce k ) House Democrats -

• Analyzing Duration and Furloughs: You can compute various statistics about federal funding gaps using relevant columns such as 'Start Date,' 'End Date,' 'Total days,' 'Employees furloughed,' 'Number of Employees Furloughed. For example:

  • Calculate the average duration of funding gaps during a specific time period.
  • Determine the total number of funding gaps that resulted in employee furloughs.
  • Analyze the average number of employees furloughed during various periods.

• Understanding Party Control: The dataset includes information about political party control over Senate and House Representatives during funding gaps. • Analyzing Senate Control:

  • Determine which party controlled the Senate during each funding gap period.
  • Compare the prevalence of Democratic, Republican, or Independent control over time.
  1. Exploring

Research Ideas

• Analyzing the impact of federal funding gaps on government employees: This dataset can be used to study the number of employees who were furloughed during each funding gap and analyze the duration of their furlough. It can provide insights into the economic effects and hardships faced by government workers during such periods.
• Examining the political dynamics during funding gaps: By analyzing the control of both the House of Representatives and Senate during each funding gap, this dataset can shed light on how political party control affected negotiations and resolutions. It can help identify patterns or trends in bipartisan cooperation or conflict during these periods.
• Comparing different funding gaps over time: With information on start dates, end dates, and total days for each gap, this dataset allows for comparisons across different periods in history. Researchers can assess whether funding gaps have become more frequent or longer-lasting over time and identify any patterns that may exist in relation to economic factors or political developments

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset d...

Graph and download economic data for All Employees, Local Government Education (CES9093161101) from Jan 1955 to Sep 2025 about establishment survey, education, government, employment, and USA.

Chris Furlough Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

BackgroundLong-term health conditions can affect labour market outcomes. COVID-19 may have increased labour market inequalities, e.g. due to restricted opportunities for clinically vulnerable people. Evaluating COVID-19’s impact could help target support.AimTo quantify the effect of several long-term conditions on UK labour market outcomes during the COVID-19 pandemic and compare them to pre-pandemic outcomes.MethodsThe Understanding Society COVID-19 survey collected responses from around 20,000 UK residents in nine waves from April 2020-September 2021. Participants employed in January/February 2020 with a variety of long-term conditions were matched with people without the condition but with similar baseline characteristics. Models estimated probability of employment, hours worked and earnings. We compared these results with results from a two-year pre-pandemic period. We also modelled probability of furlough and home-working frequency during COVID-19.ResultsMost conditions (asthma, arthritis, emotional/nervous/psychiatric problems, vascular/pulmonary/liver conditions, epilepsy) were associated with reduced employment probability and/or hours worked during COVID-19, but not pre-pandemic. Furlough was more likely for people with pulmonary conditions. People with arthritis and cancer were slower to return to in-person working. Few effects were seen for earnings.ConclusionCOVID-19 had a disproportionate impact on people with long-term conditions’ labour market outcomes.

Federal Funding Gaps in the US

Overview of US Federal Funding Gaps (1976-2018)

By Throwback Thursday [source]

About this dataset

The dataset includes columns such as Start Date, End Date, Total days, Employees furloughed, Number of Employees Furloughed, Senate Control, Senate Democrats, Senate Republicans ,Senate Independents ,House Control ,House Democrats ,House Republicans and House Independents.

Start Date indicates the date when each federal funding gap began. On the other hand End date shows when these funding gaps came to an end. By examining this information for each gap individually along with calculations from other columns like Total days one can gain insights into how long each funding gap lasted.

Numerical values such as number of employees affected by furloughs are provided within columns like Employees furloughed and Number of Employees Furloughed. The latter column represents a total count for all affected employees throughout a particular funding gap period.

This dataset delves even deeper into political dynamics by revealing which political party was in control during each federal funding gap period through columns like Senate Control and House Control. Specifically defining whether Democrats or Republicans were leading is very crucial to understand any potential ramifications associated with those particular party affiliations being at power during a given time period.

Moreover,the numerical data found under columns named Senate Democrats,Senate Republicans,Senate Independents indicate how many members from respective parties were active participants within United States Senate for each individual government fund shutdown event.As we continue through other sections more details about representation will be present .

Similarly,namesake parties committed to House representatives then find representation factors being unearthed and can be seen through President's Government House Control metric here . Columns like House Democrats, House Republicans and House Independents serve as additional measures to provide a census of who controlled the power dynamic during these respective campaign titanic struggles.Thus , for each federal funding gap period one can ascertain which political party held sway over the decisions made within America's lower parliamentary body.

In total,this comprehensive dataset offers profound insights into how the United States government experienced financial funding gaps throughout several decades of its history. The information provided in this dataset is crucial for anyone looking to study, analyze, or understand the dynamics, duration, impacts, and control factors associated

How to use the dataset

• Understand the Columns:

  • Start Date: The date when a federal funding gap started.
  • End Date: The date when a federal funding gap ended.
  • Total days: The duration of each federal funding gap in days.
  • Employees furloughed: A brief description of the employees affected by each funding gap, providing an insight into different government sectors impacted.
  • Number of Employees Furloughed: The total number of employees who were furloughed during each funding gap.
  • Senate Control and House Control: Political party in control of both chambers during each funding gap (Democrats or Republicans).

• Gain Insight into Duration and Employee Impact: Explore which federal funding gaps had longer durations and higher numbers of furloughed employees. Sort or filter based on Total days or Number of Employees Furloughed columns, respectively, to identify significant instances.

• Analyze Political Party Control: Observe which political party was in control during different periods. Analyze if there is any correlation between party control and decision-making leading to a governmental shutdown.

• Compare Senate and House Representation: Compare Republican, Democrat, Independent representation within both chambers during each period using respective columns like Senate Republicans, House Democrats, etc., providing insights into potential political dynamics affecting these gaps.

• Highlight Interesting Findings: Communicate your data-driven discoveries by visualizing interesting trends with graphs or summarizing them through storytelling techniques.

• Respect Data Privacy Please note that while analyzing the dataset, it is essential to respect any data privacy guidelines and not draw conclusions about individual employees or reveal any sensitive information.

Best of luck with your analysis!

Research Ideas

• Analyzing the impact of federal funding gaps: This datas...

NOTE: This data set was updated on 5/15/15 by request of the data owner. Each annual salary listed in this report is 12 times that particular employee's monthly adjusted salary rate as of June 30, 2011 (between July 1, 2010 and June 30, 2011). "Annual Salary" includes most differential payments (such as work-out-of-classification and bilingual differential), but excludes payments for overtime, shift differential, benefits, and vacation payout. The report does not account for unpaid furlough leave that management employees began taking in fiscal year 2010-2011; neither does it reflect step decreases and unpaid furlough leave that some classified employees began taking after June 2009. This report does not include annual salaries for employees of the Oregon University System, semi-independent agencies, temporary employees, or records protected by court order. For more State of Oregon Workforce/salary information please visit the Oregon Transparency Website: http://www.oregon.gov/transparency/.

This dataset is a listing of all current City of Chicago employees, complete with full names, departments, positions, and annual salaries. For hourly employees the annual salary is estimated. The final column lists the approximate annual salary with furlough days/salary reductions. Data Owner: Human Resources. Frequency: Data is updated quarterly. Last Updated: September, 2011. For information on the positions and related salaries detailed in the budget as of January 1, 2011, visit the "Budget - Positions and Salaries in 2011 Appropriation Ordinance" dataset: http://j.mp/mC2VLO

Annual salaries paid to employees by the Cook County Comptroller as of March 5, 2013. Does not include deduction for furlough or closure days for employees subject to furloughs and closures. Does not include amounts paid by other governmental entities. For 2014 salaries see https://datacatalog.cookcountyil.gov/resource/hdna-35se

Records of grievances filed by covered entities (for instance, employees who are not members of a bargaining unit). Includes:rn- statement of grievance, supporting documentation, and evidencern- statements of witnesses, records of interviews and hearingsrn- examiner’s findings, recommendations, decisionsrnrnRecords of disciplinary and performance-based actions against employees. Includes:rn- performance appraisal, performance improvement plan, and supporting documentsrn- recommended action, employee’s replyrn- records of hearings and decisionsrn- records of appealsrnrnRecords of adverse actions (suspension, removal, reduction in grade, reduction in pay, or furlough) against employees. Includes:rn- proposed adverse action, employee's replyrn- statements of witnesses rn- records of hearings and decisionsrn- letters of reprimandrn- records of appeals

Higher Education Wages

Pennsylvania's Employee Salaries

By Education [source]

About this dataset

This dataset contains a comprehensive look at the salaries earned by employees within Pennsylvania State System of Higher Education for 2013. Understand the market trends and value of positions held within this system to help students prepare for their future career aspirations as well as empower employers with detailed data. This is an essential piece to have when looking into what fair compensation looks like in higher education and obtaining important insight regarding salary negotiation, wage structure, and workforce optimization

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to this dataset on wage disparities in Pennsylvania's higher education system! This dataset contains salary information for employees of the Pennsylvania State System of Higher Education (PASSHE) in 2013. You can use this information to uncover trends and differences in pay between different universities, departments, and job positions.

Getting Started: - Open the file “2013_Salaries_Pennsylvania_State_System_of_Higher_Education.csv” and explore its contents. This dataset is composed of 8 columns: Name, Base Pay, University or Office, Position, Gender/Race/Ethnicity, Furlough Reduction % Reduction Days Number Total Pay Contract Type Max Probationary Step Payscale Group Admission Type Total Headcount Full-Time Employees Part-time employees and position code. - To begin your analysis you can start by identifying basic statistics about the data such as averages or counts for each column of data you are interested in such as salary range by university or office and job title groups such as deans versus other staff members . - Next identify which universities pay their staff the highest salaries? What kind of differences do you see across schools? Once you have identified key trends look into potential differences within each university using gender/race/ethnicity filters to identify wage disparities across demographic groups since unfortunately wage discrepancy still exists today even when controlling for variables like education experience seniority etc - Finally create graphs tables etc that present your findings effectively so that policy makers HR departments other stakeholders have a better insight into what changes need to be implemented so all personnel are treated equally with fair wages regardless of background! Good luck!

Research Ideas

• Analyze salary gaps between genders and positions to identify possible pay inequality, and to create true diversity in the workplace.
• Track changes in base salaries over time for individual jobs or trends throughout a university/office over years.
• Compare average annual salaries across different universities/offices within the Pennsylvania State System of Higher Education; this information could be used to help inform decisions about where to work/study for prospective faculty and students alike!

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: 2013_Salaries_Pennsylvania_State_System_of_Higher_Education.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------| | Name | The name of the employee. (String) | | Base Pay | The base salary of the employee. (Numeric) | | University or Office | The university or office the employee works for. (String) | | Position | The position of the employee. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Education.

Office for National Statistics' national and subnational Census 2021. This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by the distance they travelled to work. The estimates are as at Census Day, 21 March 2021.Census 2021 took place during a period of rapid change. We gave extra guidance to help people on furlough answer the census questions about work. However, we are unable to determine how furloughed people followed the guidance. Take care when using this data for planning purposes. Read more about specific quality considerations in our Labour market quality information for Census 2021 methodology Distance travelled to work definition: The distance, in kilometres, between a person's residential postcode and their workplace postcode measured in a straight line. A distance travelled of 0.1km indicates that the workplace postcode is the same as the residential postcode. Distances over 1200km are treated as invalid, and an imputed or estimated value is added.Work mainly at or from home: is made up of those that ticked either the 'Mainly work at or from home' box for the address of workplace question, or the Work mainly at or from home box for the method of travel to work question.Other: includes no fixed place of work, working on an offshore installation and working outside of the UK.Distance is calculated as the straight line distance between the enumeration postcode and the workplace postcode.Quality information: As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes.Comparability with 2011: Not comparable. It is difficult to compare this variable with the 2011 Census because Census 2021 took place during a national lockdown. The government advice at the time was for people to work from home (if they can) and avoid public transport.Only those who work at a workplace or depot gave their workplace address. This means that the number of people who answered this question is a significantly smaller proportion of the population than normal.People who were on furlough (about 5.6 million), could have given details based on their patterns before or during the pandemic, or what they did during the census taking place, including Census Day. This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

Introduction

This note summarises trends in pay in London and the UK since 2010 and compares them to inflation trends. The focus is on median gross weekly earnings for all employees (full- and part-time) working in London. The counterfactual analysis is based on annual pay estimates.

Notes on the data

• The employee pay estimates in this note do not cover self-employed jobs and come from a survey of UK businesses. There are, moreover, several discontinuities in the ONS ASHE series (e.g. in 2004, 2006, 2011 and 2021). The growth rates calculated over these periods are illustrative, not precise figures.
• During the pandemic earnings estimates were affected by compositional changes and the furlough scheme, making interpretation more difficult. Data collection disruption and lower response rates also mean that estimates for 2020 and 2021 are subject to greater uncertainty.
• Real earnings (earnings adjusted for inflation) have been calculated by adjusting nominal (unadjusted) earnings using the Consumer Prices Index including owner occupiers’ housing costs (CPIH). The CPIH is the most comprehensive measure of inflation in the UK.

Office for National Statistics' national and subnational Census 2021.This dataset provides Census 2021 estimates that classify usual residents in England and Wales by their method used to travel to work (2001 specification). The estimates are as at Census Day, 21 March 2021.Census 2021 took place during a period of rapid change. We gave extra guidance to help people on furlough answer the census questions about work. However, we are unable to determine how furloughed people followed the guidance. Take care when using this data for planning purposes. Read more about specific quality considerations in our Labour market quality information for Census 2021 methodology Method of travel to workplace definition: A person's place of work and their method of travel to work. This is the 2001 method of producing travel to work variables.'Work mainly from home' applies to someone who indicated their place of work as their home address and travelled to work by driving a car or van, for example visiting clients.Quality information: As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes.Comparability with 2011: Not comparable. It is difficult to compare this variable with the 2011 Census because Census 2021 took place during a national lockdown. The government advice at the time was for people to work from home (if they can) and avoid public transport.People who were furloughed (about 5.6 million) were advised to answer the transport to work question based on their previous travel patterns before or during the pandemic. This means that the data does not accurately represent what they were doing on Census Day. This variable cannot be directly compared with the 2011 Census Travel to Work data as it does not include people who were travelling to work on that day. It may however, be partially compared with bespoke tables from 2011. This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.