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.

In 2021, the attrition rate was the highest among non-managerial women employees in the IT/ITes sector across India. Furthermore, the attrition rate among women in that sector had decreased from 2020 to 2021.

Number of persons in the labour force (employment and unemployment) and unemployment rate, by North American Industry Classification System (NAICS), gender and age group.

In the third quarter of 2025, approximately 210,000 job resignations took place in the United Kingdom, compared with 263,000 in the previous quarter. The number of resignations in Q2 2022 was the highest number taking place in a single quarter during this provided time period, reaching 446,000. In most years, there is a noticeable trend of resignations peaking in the fourth quarter of the year and being at their lowest in the first quarter. There is also a significant fall in people resigning from their jobs after the 2008 financial crisis and after the COVID-19 pandemic in 2020. The Great Resignation The high number of resignations that took place after COVID-19 hit also occurred in the United States. Throughout 2022, approximately 50 million American workers quit their jobs in a trend dubbed 'The Great Resignation' In both the UK and U.S. the trend corresponded with a very tight labor market. After emerging from the initial COVID-19 lockdowns, UK unemployment declined from 2021 onwards, falling to a low of just 3.6 percent in August 2022. There were also numerous job vacancies, which peaked in May 2024 at 1.3 million, though by the end of 2024, both indicators have returned to more typical levels. Labor market concerns for 2025 One of the main concerns of the UK government regarding the labor market is economic inactivity, in particular the reason for this inactivity, Since the COVID-19 pandemic, the number of people on long-term sick-leave, has increased substantially. At the start of 2020, there were approximately 2.12 million people economically inactive for this reason, with this increasing to almost 2.84 million by the end of 2023, with this declining only slightly to 2.77 million by the end of 2024. It is unclear if there is one overriding factor driving this surge, with possible causes including the prevalence of Long COVID, or the ongoing NHS crisis.

In recent years, the temporary employment sector in Germany has played a crucial role in the integration of refugees and foreign workers. These contributions underline its importance for the German labour market. Since 2023, however, the deteriorating economic situation has put considerable pressure on the industry. The economic slowdown led to a noticeable decline in industrial orders, which also reduced the demand for temporary workers. Between 2020 and 2025, industry turnover fell by an average of 0.5% per year. It is expected to fall by 1.9% to 28.4 billion euros in 2025. Profitability in the temporary staffing industry varies considerably depending on the business model. Companies such as Hays, which place specialised professionals such as engineers and IT experts, achieve higher profit margins as these professionals are in high demand and can achieve high hourly rates. The industry is currently facing the challenge of numerous companies having to cut staff in order to save costs. These job cuts are curbing demand for temporary staff and increasing the need for the industry to fundamentally rethink its business models. Even if the reformed Immigration Act could theoretically open up new prospects by facilitating access to international skilled labour, the actual benefit of these measures remains questionable given the general economic situation. In addition, although investments in digital solutions and process optimisation are urgently needed, they place a financial burden on companies and are associated with considerable uncertainty.The industry will face major challenges in the future, while the general economic outlook remains uncertain. Although the ongoing shortage of skilled labour could potentially offer opportunities, economic conditions are currently not ideal for expansion. An average annual decline in turnover of 0.2% is therefore expected over the next five years, meaning that industry turnover in 2030 is likely to amount to 28.2 billion euros. It is uncertain whether the measures taken to date, such as increased international recruitment and improved qualification programmes, will be sufficient to overcome the structural challenges. However, given these uncertainties, the temporary staffing industry should be able to maintain its position as a key provider of flexible labour solutions in Germany by adapting to new market conditions and developing innovative strategies.

China Industrial Enterprise: Product Inventory Turnover Days data was reported at 20.400 Day in Oct 2025. This records an increase from the previous number of 20.200 Day for Sep 2025. China Industrial Enterprise: Product Inventory Turnover Days data is updated monthly, averaging 17.300 Day from Jan 2014 (Median) to Oct 2025, with 142 observations. The data reached an all-time high of 26.100 Day in Feb 2020 and a record low of 13.300 Day in Dec 2014. China Industrial Enterprise: Product Inventory Turnover Days data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data.

Turnover is a particular problem among childcare workers and less so among preschool and kindergarten teachers. In 2022, turnover in childcare work was about 65 percent higher than in a typical job, while attrition among preschool and kindergarten teachers was on par with the typical occupation.

Robotic Warehouse Tote Swarm Market Outlook

Based on our latest research, the global robotic warehouse tote swarm market size reached USD 2.14 billion in 2024, driven by the rapid adoption of automation in supply chain and warehouse operations. The market has been expanding at a robust pace, with a recorded CAGR of 19.7% from 2020 to 2024. Looking ahead, the market is projected to reach USD 10.98 billion by 2033, as calculated by the prevailing CAGR. This impressive growth is primarily fueled by the escalating demand for efficient order fulfillment, labor cost reduction, and scalable logistics operations across diverse industry verticals, according to our latest research findings.

The growth trajectory of the robotic warehouse tote swarm market is strongly influenced by the increasing need for operational efficiency and accuracy in warehouse management. As e-commerce giants and retail enterprises face mounting pressure to deliver faster and error-free order fulfillment, the implementation of swarm robotics for tote handling has become a strategic necessity. These intelligent robotic swarms enhance throughput by autonomously coordinating the movement of totes, minimizing human intervention, and optimizing space utilization. Furthermore, the integration of advanced AI algorithms and real-time data analytics enables seamless adaptation to fluctuating demand and inventory levels, which is vital in today’s dynamic market environment. The ability of robotic tote swarms to scale operations quickly and efficiently is becoming a significant competitive advantage for businesses looking to strengthen their supply chain resilience.

Another critical growth factor is the rising labor shortages and increasing wage costs in key logistics hubs around the world. Warehousing and logistics industries have been grappling with high employee turnover rates and challenges in recruiting skilled labor, particularly for repetitive and physically demanding tasks. Robotic warehouse tote swarm systems address these challenges by automating material handling processes, reducing dependency on manual labor, and ensuring consistent productivity levels. Moreover, these systems contribute to enhanced workplace safety by reducing the risk of injuries associated with manual lifting and repetitive movements. As companies seek to mitigate labor-related risks and comply with stringent safety regulations, the adoption of robotic tote swarms is expected to accelerate further.

Technological advancements in robotics, artificial intelligence, and sensor technologies are also propelling the market forward. The emergence of more affordable and adaptable robotic platforms has lowered the entry barriers for small and medium enterprises, enabling them to leverage automation for tote management. Additionally, the growing interoperability of robotic systems with existing warehouse management software (WMS) and enterprise resource planning (ERP) systems is streamlining deployment processes and maximizing return on investment. With continuous innovation in swarm intelligence, path planning, and machine learning, robotic warehouse tote swarms are becoming increasingly sophisticated, capable of handling complex tasks such as dynamic route optimization, real-time obstacle avoidance, and predictive maintenance. These technological developments are expected to further drive market growth in the coming years.

Regionally, the Asia Pacific region is emerging as a pivotal market for robotic warehouse tote swarms, fueled by the rapid expansion of e-commerce and manufacturing sectors in countries such as China, Japan, and India. North America continues to lead in terms of technology adoption and market share, supported by the presence of major robotics vendors and a strong focus on supply chain digitization. Europe is witnessing steady growth, particularly in the retail and automotive sectors, as companies invest in automation to enhance competitiveness and sustainability. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with increased investments in logistics infrastructure and automation solutions. The regional outlook for the robotic warehouse tote swarm market remains highly positive, with significant growth opportunities across both developed and emerging economies.

This study introduces a novel, replicable methodology for analyzing employment dynamics within public sector agencies, focusing on turnover and staff longevity. The methodology is designed to be generalizable and applicable to diverse national contexts where detailed administrative data is available. Using payroll data from over 325,000 Chilean civil servants (2006—2020), we apply mixed-effects Cox survival models and linear mixed models to examine patterns of employment stability across state agencies. By incorporating Propensity Score Matching, we further enhance the causal interpretation of turnover changes, especially in post-election years. Finally, we introduce two key metrics—Service Frailty and Relative Turnover Difference—to quantify long-term stability and short-term, post-electoral disruptions. Our findings highlight substantial differences in turnover patterns between regular and post-election years, as well as significant inter-agency heterogeneity in turnover and employee longevity, largely driven by latent agency characteristics. While major covariates like contract type and staff rank account for some variation, much of the disparity stems from agency-specific factors. This framework offers precise, cross-nationally comparable benchmarks for understanding public sector employment dynamics. Additionally, the methodology contributes to the literature by providing transparent and scalable tools for analyzing workforce stability across different contexts.

Bermuda BM: Stocks Traded: Turnover Ratio of Domestic Shares data was reported at 6.116 % in 2022. This records a decrease from the previous number of 9.241 % for 2021. Bermuda BM: Stocks Traded: Turnover Ratio of Domestic Shares data is updated yearly, averaging 3.459 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 11.021 % in 2020 and a record low of 0.828 % in 2015. Bermuda BM: Stocks Traded: Turnover Ratio of Domestic Shares data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bermuda – Table BM.World Bank.WDI: Financial Sector. Turnover ratio is the value of domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12.;World Federation of Exchanges database.;Weighted average;Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

The construction industry in Germany has seen an overall downward trend since the end of the low-interest phase that prevailed between 2016 and 2022. Pandemic-related supply chain bottlenecks in 2020 and 2021, together with the economic upheaval caused by the Russian invasion of Ukraine, caused prices for building materials and energy to rise sharply. Together with the European Central Bank's turnaround in interest rates in 2022 in response to the very dynamic inflation in the European Economic Area, this led to a noticeable decline in demand for construction work due to higher construction and financing costs. On average, turnover in the construction industry fell by 1.9% per year between 2020 and 2025. As capacity utilisation fell from 2022, the earnings situation of many construction companies increasingly deteriorated, which also manifested itself in a rising number of company insolvencies.IBISWorld expects the sector to generate a turnover of 413 billion euros in the current year, which corresponds to a decline of 1% compared to the previous year. Due to the further decline in the number of building permits in 2024 compared to 2023 and falling producer prices for building materials, a decline in turnover is also expected for 2025. Since late 2024, however, there have been signs of a stabilisation in incoming orders in the sector's leading area, residential construction.For the next five years, IBISWorld expects growth in the construction industry, albeit at a slower pace. Although the level of annual growth in residential construction is likely to increase in the medium term due to the current fall in effective interest rates for construction loans, activity in commercial construction is likely to remain impaired in the short term due to the sluggish development of the economy as a whole. Demand from state actors is likely to become more important for the construction industry overall in the near future. In view of this, IBISWorld is forecasting an average annual increase in turnover of 1.3% for the period from 2025 to 2030. In 2030, turnover in the construction industry is therefore likely to amount to 440.8 billion euros.

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors).

SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.

SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Iceland, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) :

• Annex I - Services,
• Annex II - Industry,
• Annex III - Trade, and
• Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation.

The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J).

Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced.

Main characteristics (variables) of the SBS data category:

• Business Demographic variables (e.g. Number of enterprises),
• "Output related" variables (e.g. Turnover, Value added),
• "Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments).

All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:

• Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.
• Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.
• Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section).

More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003.

Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

The United States' Bureau of Labor Statistics accounted for ***** million people working in the hospitality and leisure industry in the U.S. as of December 2024. This figure shows an increase over the previous year's figure of ***** million.

Although the results were close, the industry in the United States where customers were most likely to leave their current provider due to poor customer service appears to be cable television, with a 25 percent churn rate in 2020.

Churn rate

Churn rate, sometimes also called attrition rate, is the percentage of customers that stop utilizing a service within a time given period. It is often used to measure businesses which have a contractual customer base, especially subscriber-based service models.

The call centre industry in Germany has recorded average revenue growth of 2.8% per year since 2020. The market expansion was characterised in particular by the increased use of fraud prevention and data security technologies. The significant increase in fraud cases to almost 90,000 cases in 2023 has prompted companies to invest more in artificial intelligence and zero-trust security models to ensure the protection of sensitive customer data. At the same time, the structural change in the working model accelerated. In the wake of the coronavirus pandemic, many call centre agents were forced to work from home, which was made possible by cloud-based systems. Geographical decoupling resulted in more efficient use of office space and easier recruitment of specialised professionals. As digitalisation progressed, communication increasingly shifted from telephone calls to digital channels such as chats, emails and messaging services. Call centre software solutions increasingly integrated chatbots and voicebots, which noticeably reduced operating costs and increased the efficiency of processing routine enquiries. In this year, industry turnover is expected to increase by 2% to 7.6 billion euros. This growth will be driven primarily by advancing automation and the intensive use of artificial intelligence, which will further reduce the proportion of standardised tasks. Companies are investing in cloud-based systems in order to permanently establish home office structures and fulfil high IT security requirements at the same time. AI agents support employees in real time when processing complex enquiries, reducing processing times and increasing service quality. Despite increased personnel costs, these technologies have a cost-reducing effect as they lower the average operating costs per customer interaction. The profit margin remains stable as a result, although clients continue to exert high price pressure. Companies are increasingly adapting their pricing structures to success-based models in order to secure their profitability.In the next five years, the industry is expected to increase its turnover by an average of 2.6% annually and reach a turnover of 8.6 billion euros by 2030. This growth is due to the greater integration of personalised, AI-supported customer experiences and the transition to data-based and proactive services. Call centres are increasingly developing into analytical and strategic partners that offer consulting and optimisation expertise over and above pure services. The use of omnichannel technologies ensures seamless, cross-channel communication and is becoming a key competitive factor. At the same time, however, self-service portals and intelligent bots are increasingly replacing simple routine contacts with the client, which reduces the proportion of human interaction and increases its complexity and qualification requirements. Greater automation, falling infrastructure costs and optimised capacity management will further consolidate the industry's profit margin and strengthen its competitive position.

In financial year 2020, only about *** percent of the total workers were gig workers with Usual Principal and Subsidiary Status. There was an increase in the share of gig workers since financial year 2012.

The gig economy The gig economy essentially is a free market structure in which people are hired temporarily by a company for short-term commitments. This, however, is not a new concept to India. India has a large share of informal and casual workers participating in gig work for decades. Gig work comprises earning income that lies outside of the conventional long-term employer-employee relationship. This has gained traction since the economic downturn of the pandemic. Projections of gig work point to an increment of over **** percent to the country’s GDP.

Employee turnover and job opportunities The great reshuffle refers to the masses of people quitting their jobs primarily precipitated by the pandemic. Consequently, employee attrition and turnover rates across the country were higher than ever before. In addition, hiring processes are also being executed at unprecedented levels, particularly in the IT and tech industry. With job opportunities inundating the market, employees preferred job roles that aligned with their ambitions and prioritized work-life balance. Studies indicated that men and women wanted more flexibility in their jobs, tipping the scales in favor of hybrid and remote work environments.