Maintain the state employee turnover rate at or below the annual regional average of surrounding states every year through 2019.

Maintain the state employee turnover rate at or below the annual regional average of surrounding states every year through 2019.

This statistic depicts the average annual employee turn over rate in the United States in 2016 and 2017, as reported by human resources (HR) professionals. During the 2017 survey, respondents reported an average annual turnover rate of ** percent.

A curated dataset of the most current U.S. employee turnover statistics for 2024–2025, including voluntary and total turnover rates, monthly quit rates, sector-level comparisons, job-level differences, reasons for leaving, preventability, and cost impacts. Compiled by HIGH5 from sources including Mercer, the U.S. Bureau of Labor Statistics (JOLTS), Gallup, Work Institute, and others.

Turnover rates for State of Oklahoma classified employees beginning in fiscal year 2000.

Turnover data by fiscal year for the City of Tempe compared to the seven market cities, which include Chandler, Gilbert, Glendale, Mesa, Phoenix, Peoria, and Scottsdale. There are two totals, one with and one without retirees.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.Data DictionaryAdditional InformationSource: Department ReportsContact: Lawrence La VictoireContact E-Mail: lawrence_lavictoire@tempe.govData Source Type: ExcelPreparation Method: Extracted from PeopleSoft, and requested data from other cities is entered manually into a spreadsheet, and calculations are conducted to determine the percent of turnover per fiscal yearPublish Frequency: AnnuallyPublish Method: Manual

In 2023, employee attrition rates decreased in the Americas and EMEA regions, however increased in the ACAP region. The Americas showed a decrease of 1.2 percent, with the ACAP region demonstrating a 3.3 percent increase. Relatively, however, these percentages were some of the best recorded between 2015 and 2023.

Overall and voluntary turnover data for State of Oklahoma classified employees beginning in fiscal year 2007.

Overall and voluntary turnover data for State of Oklahoma classified employees beginning in fiscal year 2007.

The employee attrition rate of professional services organizations worldwide ********* overall between 2013 and 2023, despite some fluctuations. During the 2023 survey, respondents reported an average employee attrition rate of **** percent.

This dataset provides employee attrition or turnover rate. This data is reported to provide city leaders with a measure that enables them make decisions about their workforce needs. This dataset will show the rate of turnover by department. The provided here can be used to view specific departmental attrition rates. Data Source: Banner This is a data report that did not require a calculation.

A time series of staff turnover rates, broken down by provider type. Staff turnover rates are the number of staff who left employment during the period expressed as a percentage of the total number of staff employed at the start of the period.

Voluntary employee turnover in business service centers in Poland in 2024 was nearly *** percent. The highest turnover was recorded in 2022.

Formal Employment: Turnover Rate: Service: Metropolitan: São Paulo data was reported at 4.350 % in Apr 2019. This records an increase from the previous number of 4.100 % for Mar 2019. Formal Employment: Turnover Rate: Service: Metropolitan: São Paulo data is updated monthly, averaging 3.700 % from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 4.560 % in May 2011 and a record low of 2.140 % in Dec 2003. Formal Employment: Turnover Rate: Service: Metropolitan: São Paulo data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB100: Formal Employment: Turnover Rate: by Region and State: Service.

Number of recruited and departed staff members per month.Disclaimer : the statistics in this dataset are presented on a monthly basis, but are updated every morning following internal recalculations. It is therefore possible that the figures may vary slightly, both for the current period - if this is given - and for previous periods.

Graph and download economic data for Labor Turnover, Gross Accession Rate, Manufacturing for United States (M0855BUSM497NNBR) from Jan 1930 to Oct 1968 about hires, gross, labor, manufacturing, rate, and USA.

The turnover rate of female employees at Golden Agri-Resources Ltd. (GAR) in Indonesia was at **** percent, while that for male employees was *** percent. GAR is listed on the Singapore stock exchange, and in 2021, it was one of the world's largest palm oil companies based on market capitalization.

In 2024, the average staff turnover rate of hospitals in the U.S. stood at **** percent. The percentage of employees leaving hospitals has decreased since the peak of ** percent in 2021. A closer look at turnover reveals that most was among less tenured staff, with the highest rates among certified nursing assistants.

Ever wondered what REALLY drives employee turnover, performance, and retention? This power-packed dataset of 50,000 records uncovers the hidden patterns behind workforce dynamics, helping you decode the true story of hiring, leadership influence, and workplace engagement.

🔍 What’s Inside? 📅 Time-Based Analysis: Track hiring, promotions, and attrition over time. 👥 Leadership Influence: Identify which Senior Leaders drive success or struggle with retention. 📊 Performance & Productivity: Measure engagement, stress levels, job satisfaction, and training effectiveness. 💰 Hiring & Cost Efficiency: Evaluate recruitment costs, time to fill positions, and internal promotions. 🏡 Work-Life Balance: Analyze work-from-home trends, overtime, and stress levels across departments. 🎯 Retention & Risk Factors: Discover who is most at risk of leaving and why with retention risk analytics.

🔥 What Can You Do With It? ✅ Build Stunning Power BI Dashboards – Transform raw data into interactive insights. ✅ Solve Real-World HR Challenges – Use analytics to predict attrition, optimize hiring, and improve retention. ✅ Uncover Leadership Trends – Identify which leaders foster growth vs. those driving attrition. ✅ Analyze Workplace Culture – Understand how job satisfaction, training, and diversity impact engagement.

🔹 Problem 1: Attrition Analysis - Who is Leaving and Why? Scenario: Your company is experiencing a high turnover rate, and leadership wants to understand who is leaving and why.

Problem 2: Leadership Impact - Who is Retaining vs. Losing Talent? Scenario: Your company’s leadership wants to assess the effectiveness of senior leaders in retaining talent and managing high-performing teams.

Problem 3: Hiring Effectiveness - Which Sources Work Best? Scenario: HR wants to optimize the hiring process by identifying the most effective recruitment sources.

Problem 4: Workforce Diversity - Is the Organization Inclusive? Scenario: The leadership wants to understand diversity trends and whether they need to improve inclusivity in hiring.

Problem 5: Work-Life Balance - Who is Overworked? Scenario: There are concerns that some employees are working too many hours, leading to burnout and lower engagement.

Problem 6: Performance & Compensation - Are High Performers Paid Well? Scenario: The HR department suspects that high performers are not being fairly compensated.

Problem 7: Training Effectiveness - Does Training Improve Performance? Scenario: HR wants to assess whether training programs are improving employee performance and retention.

Aggregated information about employee turnover in the City of Mesa. The term "Termination" used in this dataset also includes employees who left voluntarily.