Panama Labour Force: Female: Hotels & Restaurants data was reported at 68,394.000 Person in 2017. This records an increase from the previous number of 66,437.000 Person for 2016. Panama Labour Force: Female: Hotels & Restaurants data is updated yearly, averaging 51,051.000 Person from Aug 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 68,394.000 Person in 2017 and a record low of 37,344.000 Person in 2003. Panama Labour Force: Female: Hotels & Restaurants data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Panama – Table PA.G004: Labour Force.

Recent graduates of tourism, hospitality, personal services, sport and recreation studies from Australian universities had a labor force participation rate of over ** percent in 2023. The labor force participation rate is calculated as the labor force divided by the total working-age population.

Panama Labour Force: Male: Hotels & Restaurants data was reported at 45,035.000 Person in 2017. This records an increase from the previous number of 41,682.000 Person for 2016. Panama Labour Force: Male: Hotels & Restaurants data is updated yearly, averaging 30,582.000 Person from Aug 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 45,035.000 Person in 2017 and a record low of 26,897.000 Person in 2006. Panama Labour Force: Male: Hotels & Restaurants data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Panama – Table PA.G004: Labour Force.

Graph and download economic data for All Employees: Leisure and Hospitality in Florida (FLLEIH) from Jan 1990 to Aug 2025 about leisure, hospitality, FL, employment, and USA.

This dataset presents the number of employees working in Qatar’s hotels and restaurants sector for establishments with 10 or more employees. It is disaggregated by gender and occupation, allowing detailed labor force analysis within the hospitality industry.

As of the second quarter of 2025, there were approximately *****million people employed in the accommodation and food services sector in the UK, compared with ****million in the first quarter of 2000.

A Workforce Strategy for Alberta's Tourism and Hospitality Industry was developed collaboratively with government and stakeholders from the tourism and hospitality industry. The strategy provides a profile of Alberta's tourism and hospitality industry and the labour force challenges and issues facing the industry. It also identifies priority actions for the industry to attract, develop and retain a high performance workforce in the sector.

Qatar Labour Force: FT: Female: Hotels and Restaurants data was reported at 17,274.000 Person in 2016. This records an increase from the previous number of 13,180.000 Person for 2015. Qatar Labour Force: FT: Female: Hotels and Restaurants data is updated yearly, averaging 3,848.500 Person from Dec 2006 (Median) to 2016, with 10 observations. The data reached an all-time high of 17,274.000 Person in 2016 and a record low of 1,146.000 Person in 2006. Qatar Labour Force: FT: Female: Hotels and Restaurants data remains active status in CEIC and is reported by Ministry of Development Planning and Statistics . The data is categorized under Global Database’s Qatar – Table QA.G003: Labour Force and Labour Force Participation Rate.

Temporary Labor Market Outlook

The global temporary labor market size was valued at approximately $500 billion in 2023 and is projected to reach around $780 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.1%. This growth is attributed to an increasing demand for flexible workforce solutions across various industry verticals and the rising need for cost-effective labor options amidst fluctuating economic conditions. The expanding gig economy and advancements in technology that facilitate remote work and temporary hiring processes are also significant contributing factors.

One of the primary growth drivers in the temporary labor market is the increasing preference for flexible work arrangements. Modern businesses are continuously seeking ways to adapt to market demands without the long-term commitment of permanent hires. Temporary labor allows companies to scale their workforce up or down based on project needs, seasonal demands, or economic conditions. This flexibility is particularly crucial in industries with high variability in workload, such as retail and hospitality, where demand can surge during certain periods and wane during others.

Another critical factor contributing to the growth of the temporary labor market is the rising trend of specialization within the workforce. As industries evolve, the demand for highly specialized skills has increased. Temporary labor provides a solution for companies needing niche expertise for specific projects or limited durations. For instance, in the IT and telecommunications sector, temporary professionals with specialized skills can be brought in to manage projects such as software development or network upgrades, ensuring that the company remains competitive without the need for permanent hires.

Technological advancements have also played a pivotal role in the expansion of the temporary labor market. Platforms and online marketplaces have emerged, making it easier for employers to connect with temporary workers and for workers to find short-term employment opportunities. These technologies streamline the hiring process, reduce overhead costs, and ensure a better match between employers' needs and workers' skills. Additionally, the growth of remote work enables businesses to hire temporary labor from a global talent pool, further enhancing their operational flexibility.

Temporary Healthcare Staffing has emerged as a critical component within the broader temporary labor market, particularly in response to the dynamic needs of the healthcare industry. The demand for temporary healthcare professionals, such as nurses, medical technicians, and administrative staff, is driven by the necessity to address staffing shortages and manage fluctuating patient care demands. This flexibility is essential for healthcare facilities to maintain high standards of care, especially during peak periods or unforeseen circumstances, such as public health emergencies. Temporary healthcare staffing not only provides a solution to immediate staffing gaps but also allows healthcare providers to access specialized skills and expertise without the long-term commitment of permanent hires.

Regionally, North America remains a significant player in the temporary labor market, driven by a well-established gig economy and a high rate of technological adoption. The Asia Pacific region is expected to experience the fastest growth, with countries like India and China leading the way due to their large labor force and rapidly expanding industries. Europe also shows robust demand for temporary labor, especially in sectors like manufacturing and healthcare. The Middle East & Africa and Latin America, while smaller in market size, are gradually catching up as businesses in these regions recognize the benefits of flexible labor solutions.

Employment Type Analysis

When segmented by employment type, the temporary labor market can be broadly categorized into skilled labor, unskilled labor, and professional services. Skilled labor includes workers who have specific skills or training, such as electricians, plumbers, and machine operators. This segment is crucial for industries that require precision and expertise, like construction and manufacturing. The demand for skilled labor is robust, driven by ongoing infrastructure projects and the need for specialized trades that cannot be easily automated.

Unskilled labor, on the other hand, comprises workers who pe

This dataset presents the number of employees working in hotels and restaurants in Qatar, specifically for establishments with fewer than 10 employees. The data is broken down by gender and occupation, including proprietors, managers, technicians, and other roles. This information is essential for analyzing labor force structure in small-scale establishments within the hospitality sector.

Qatar Labour Force: FT: Hotels and Restaurants data was reported at 72,682.000 Person in 2016. This records an increase from the previous number of 51,028.000 Person for 2015. Qatar Labour Force: FT: Hotels and Restaurants data is updated yearly, averaging 31,287.000 Person from Dec 2006 (Median) to 2016, with 10 observations. The data reached an all-time high of 72,682.000 Person in 2016 and a record low of 15,010.000 Person in 2006. Qatar Labour Force: FT: Hotels and Restaurants data remains active status in CEIC and is reported by Ministry of Development Planning and Statistics . The data is categorized under Global Database’s Qatar – Table QA.G003: Labour Force and Labour Force Participation Rate.

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

Qatar Labour Force: FT: Male: Hotels and Restaurants data was reported at 55,408.000 Person in 2016. This records an increase from the previous number of 37,848.000 Person for 2015. Qatar Labour Force: FT: Male: Hotels and Restaurants data is updated yearly, averaging 28,510.000 Person from Dec 2006 (Median) to 2016, with 10 observations. The data reached an all-time high of 55,408.000 Person in 2016 and a record low of 13,864.000 Person in 2006. Qatar Labour Force: FT: Male: Hotels and Restaurants data remains active status in CEIC and is reported by Ministry of Development Planning and Statistics . The data is categorized under Global Database’s Qatar – Table QA.G003: Labour Force and Labour Force Participation Rate.

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

The global hospitality staff scheduling software market is experiencing robust growth, driven by the increasing need for efficient workforce management within the hospitality sector. The market is projected to be valued at $1.5 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness compared to traditional on-premise systems. Secondly, the increasing pressure on hospitality businesses to optimize labor costs and improve operational efficiency is driving demand for sophisticated scheduling tools that minimize labor overheads while ensuring adequate staffing levels. The integration of these systems with other hospitality management tools, such as point-of-sale and payroll systems, further enhances their value proposition. Finally, the growing emphasis on employee engagement and satisfaction within the industry is leading to the adoption of user-friendly scheduling software that allows for greater employee autonomy and control over their work schedules. The market is segmented by application (SMEs and Large Enterprises) and type (Cloud-based and Web-based). Cloud-based solutions are expected to dominate the market due to their inherent flexibility and accessibility. Large enterprises are likely to adopt the software more readily due to the potential for significant cost savings and improved operational efficiency. Regionally, North America and Europe currently hold a significant market share, driven by high adoption rates and advanced technological infrastructure. However, the Asia-Pacific region is expected to witness significant growth in the coming years, fueled by rapid economic expansion and a burgeoning hospitality sector. The competitive landscape is characterized by a mix of established players and emerging startups, constantly innovating to cater to evolving customer needs. Factors such as the high initial investment cost for implementation and the need for continuous training can act as restraints on market growth, particularly for smaller businesses.

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

Nepal Labour Force Survey: Employment: Hotels & Restaurants data was reported at 197.000 Person th in 2008. Nepal Labour Force Survey: Employment: Hotels & Restaurants data is updated yearly, averaging 197.000 Person th from Dec 2008 (Median) to 2008, with 1 observations. Nepal Labour Force Survey: Employment: Hotels & Restaurants data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Nepal – Table NP.G009: Labour Force Survey: Employment: Age 15 & Over: By Industry.

The global hospitality staff scheduling software market is experiencing robust growth, driven by the increasing need for efficient workforce management within the hospitality sector. The industry is witnessing a significant shift towards digital solutions, as hotels, restaurants, and other hospitality businesses strive to optimize labor costs, improve employee satisfaction, and enhance operational efficiency. This trend is further fueled by the rising adoption of cloud-based and web-based scheduling software, offering scalability and accessibility across various devices. While precise market size figures are not provided, based on industry reports and the listed companies' market presence, we can estimate the 2025 market size to be approximately $1.5 billion. Considering a reasonable CAGR of 15% (a figure reflective of SaaS growth in related sectors), the market is projected to reach approximately $3.8 billion by 2033. Key growth drivers include the increasing adoption of mobile technologies, the integration of advanced features such as time and attendance tracking, and the demand for improved forecasting capabilities to better manage staffing levels. However, the market also faces challenges such as initial implementation costs and the need for ongoing training and support for employees. The segmentation by deployment type (cloud-based, web-based) and user type (SMEs, large enterprises) reflects the diverse needs of the hospitality industry, with large enterprises often opting for more comprehensive and customizable solutions. The competitive landscape is fragmented, with numerous vendors offering a range of features and pricing models. The listed companies represent a diverse mix of established players and emerging startups, each targeting specific niches within the market. The success of these vendors is contingent on factors such as the user-friendliness of their platforms, the breadth of their functionalities, the effectiveness of their customer support, and their ability to adapt to the ever-evolving needs of the hospitality industry. Future growth will be significantly influenced by technological advancements, such as AI-powered scheduling optimization and the integration of real-time data analytics to improve forecasting accuracy. The geographic distribution of the market is relatively widespread, with North America and Europe currently holding significant market shares, followed by the Asia-Pacific region. However, emerging economies are also presenting lucrative opportunities for expansion.

United States Hospitality Industry Market size was valued at USD 15.31 Billion in 2024 and is projected to reach USD 23.68 Billion by 2031, growing at a CAGR of 5.6% from 2024 to 2031.United States Hospitality Market DriversThe market drivers for the United States Hospitality Market can be influenced by various factors. These may include:Economic conditions: The hotel industry is strongly impacted by the nation's overall economic health, which includes variables like GDP growth, employment rates, and consumer spending. People travel more and spend more on hotel services when the economy is doing well.Travel trends: The demand for hospitality services in particular locations may be influenced by shifting travel patterns, such as an increase in domestic or international travel, the rise of bleisure travel (combining business and leisure vacations), and the popularity of particular places.Technological developments: To improve customer experiences, increase operational efficiency, and customize services, the hotel sector is progressively implementing technology. Keyless entrance, personalized recommendations, and smartphone check-in are examples of trends that can affect customer preferences.Regulatory environment: The hospitality sector may be impacted by government laws and regulations, such as those pertaining to taxes, labor laws, and visa requirements. The competitiveness of the market and operating expenses might be impacted by regulatory changes.Consumer preferences: Shifts in the hospitality business can be driven by changes in consumer preferences, which can affect the kinds of services and amenities that are in demand. Examples of these shifts include a growing interest in wellness tourism, sustainable travel, or unique experiences.Rivalry: Pricing strategies and client loyalty may be impacted by the degree of rivalry in the hospitality industry, which includes the existence of well-known brands, fresh competitors, and alternative accommodation options like Airbnb.Global crises and events: The hospitality industry may be significantly impacted by events like health pandemics, natural disasters, geopolitical unrest, or economic downturns, which can alter demand and travel patterns.