The Secrétariat du Travail du Québec systematically monitors the wage clauses contained in collective agreements where the minimum size of the bargaining unit is 50 employees in the case of white collar workers and 100 employees in the case of blue collar workers. The rate of wage growth is measured for the modal employment of each collective agreement, that is, the job where the largest proportion of the target workforce is found. Purpose: To monitor the evolution of wage growth in organizations with collective agreements

Graph and download economic data for Average Hourly Earnings of Production and Nonsupervisory Employees, Total Private (AHETPI) from Jan 1964 to May 2025 about nonsupervisory, headline figure, earnings, average, establishment survey, hours, wages, production, private, employment, and USA.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jun 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

The Secrétariat du Travail du Québec systematically monitors the wage clauses contained in collective agreements where the minimum size of the bargaining unit is 50 employees in the case of white collar workers and 100 employees in the case of blue collar workers. The rate of wage growth is measured for the modal employment of each collective agreement, that is, the job where the largest proportion of the target workforce is found. Purpose: To monitor the evolution of wage growth in organizations with collective agreements

Projected salary increases for 2023 are expected to be slightly lower than 2022 across mentioned job categories in India. Salary growth for the blue-collar workforce is expected to decrease from *** percent in 2022 to * percent in 2023. Overall, the salaries were expected to grow from **** percent in 2022 to **** percent.

Graph and download economic data for Employment Cost Index: Wages and Salaries: Private Industry Workers (ECIWAG) from Q1 2001 to Q1 2025 about cost, ECI, salaries, workers, private industries, wages, private, employment, industry, inflation, indexes, and USA.

In 2024, men holding top management positions earned roughly ******* euros annually in Italy. In the same year, the annual gross salary of female top managers amounted to approximately ****** euros, ***** euros less than their male colleagues. This represents the largest grade wage gap recorded by JobPricing. Specifically, at the middle management level, the pay difference was ***** euros. Even though, the pay gap has decreased between 2016 and 2024, it was still ten percent in favor of men for white-collar workers. Unemployment rate by gender The pay gap is one of the major indicators to determine the level of gender inequality in a given society. Another relevant indicator is the unemployment rate. In 2024, the unemployment rate was higher among females than among males across all Italian regions. The southern regions Campania, Sicily, and Calabria recorded the highest levels of unemployment for both men and women. Basilicata, another southern region, registered the greatest gap between genders in terms of unemployment. Females in management positions The presence of women in top management positions is a key indicator for grasping the level of gender inequality in the working environment. In 2023, women represented **** percent of the members of the boards of companies listed on the stock exchange in Italy, recording an ongoing growth since 2013, when only **** percent of the members of the boards were women.

The Data-compilation is a selection of time-series on wage- and salary development as well as on the development of the national income in Germany from 1850 to 1985. The following studies has been included: - Walther G. Hoffmann (1965): Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts.- Rüdiger Hohls (1991): Arbeit und Verdienst. Entwicklung und Struktur der Arbeitseinkommen im Deutschen Reich und in der Bundesrepublik.- Pierenkemper, Toni (1987): Arbeitsmarkt und Angestellte im deutschen Kaiserreich 1880-1913. Interessen und Strategien als Elemente der Integration eines segmentierten Arbeitsmarktes.- Wiegand, Erich/Zapf, Wolfgang (1982): Wandel der Lebensbedingungen in Deutschland. Wohlfahrtsentwicklung seit der Industrialisierung. Tables in ZA-Online-Database HISTAT: A. Hoffmann, Walther G.: The Growth of the German Economy since the mid of the 19th centuryA.1 The average earned income per annum by industrial sector (1850-1959)A.2 The average earned income per annum in mining and saline (1850-1959)A.3 The average earned income per annum in industry and craft (1850-1959)A.4 The average earned income per annum in transport (1850-1959)A.5 The average earned income per annum in other services (1850-1959)A.6 Net national product (NNP) in factor costs in current prices and national income per capita according to Hoffmann (1850-1959)A.7 Gross value added and real national income per capita in prices of 1913 according to Hoffmann (1850-1959)A.8 The development of average earned income of employees in industry and craft, Index 1913 = 100 (1850-1959) B. Hohls, Rüdiger: The Sectoral Structure of Earnings in GermanyB.1 Nominal annual earnings of employees by industrial sector in Germany in Mark, 1885-1985B.2 Nominal earnings of white collar workers and blue collar workers in Germany, 1890-1940 C. Living costs, prices and earnings, consumer price indexC.1 Development of living costs (index) of medium employees’ households (1924-1978)C.2 Preices and earnings, index 1962 = 100 (1820-2001)C.3 Living costs, consumer price index (1820-2001) D. Pierenkemper, Toni: Employment market and employees in the German ‘Reich’ 1880-1913.D.1 Income of selected white collar categories in Mark (1880-1913)D.2 Real income of selected white collar categories (1880-1913) E. Wiegand, E.: Historical Development of Wages and Living Costs in Germany.E.1 Development of real gross income of blue collar workers in industry, index 1970 = 100 (1925-1978)E.2 Development of real gross income of blue collar workers in industry (1925-1978)E.3 Development of nominal and real national income per capita (1950-1978) E.4 Development of nominal and real national income per capita (1925-1939)E.5 National income: monthly income from dependent personal services per employee (1925-1971)E.6 Overlook: Development of wages, employed workers and gross income from dependent personal services in Germany (1810-1989)

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

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 Data-compilation is a selection of time-series on wage- and salary development as well as on the development of the national income in Germany from 1850 to 1985. The following studies has been included:

• Walther G. Hoffmann (1965): Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts.
• Rüdiger Hohls (1991): Arbeit und Verdienst. Entwicklung und Struktur der Arbeitseinkommen im Deutschen Reich und in der Bundesrepublik.
• Pierenkemper, Toni (1987): Arbeitsmarkt und Angestellte im deutschen Kaiserreich 1880-1913. Interessen und Strategien als Elemente der Integration eines segmentierten Arbeitsmarktes.
• Wiegand, Erich/Zapf, Wolfgang (1982): Wandel der Lebensbedingungen in Deutschland. Wohlfahrtsentwicklung seit der Industrialisierung.

Tables in ZA-Online-Database HISTAT:

A. Hoffmann, Walther G.: The Growth of the German Economy since the mid of the 19th century A.1 The average earned income per annum by industrial sector (1850-1959) A.2 The average earned income per annum in mining and saline (1850-1959) A.3 The average earned income per annum in industry and craft (1850-1959) A.4 The average earned income per annum in transport (1850-1959) A.5 The average earned income per annum in other services (1850-1959) A.6 Net national product (NNP) in factor costs in current prices and national income per capita according to Hoffmann (1850-1959) A.7 Gross value added and real national income per capita in prices of 1913 according to Hoffmann (1850-1959) A.8 The development of average earned income of employees in industry and craft, Index 1913 = 100 (1850-1959)

B. Hohls, Rüdiger: The Sectoral Structure of Earnings in Germany B.1 Nominal annual earnings of employees by industrial sector in Germany in Mark, 1885-1985 B.2 Nominal earnings of white collar workers and blue collar workers in Germany, 1890-1940

C. Living costs, prices and earnings, consumer price index C.1 Development of living costs (index) of medium employees’ households (1924-1978) C.2 Preices and earnings, index 1962 = 100 (1820-2001) C.3 Living costs, consumer price index (1820-2001)

D. Pierenkemper, Toni: Employment market and employees in the German ‘Reich’ 1880-1913. D.1 Income of selected white collar categories in Mark (1880-1913) D.2 Real income of selected white collar categories (1880-1913)

E. Wiegand, E.: Historical Development of Wages and Living Costs in Germany. E.1 Development of real gross income of blue collar workers in industry, index 1970 = 100 (1925-1978) E.2 Development of real gross income of blue collar workers in industry (1925-1978) E.3 Development of nominal and real national income per capita (1950-1978) E.4 Development of nominal and real national income per capita (1925-1939) E.5 National income: monthly income from dependent personal services per employee (1925-1971) E.6 Overlook: Development of wages, employed workers and gross income from dependent personal services in Germany (1810-1989)

Minimum Wages in Philippines remained unchanged at 645 PHP/day in 2025 from 645 PHP/day in 2024. This dataset provides - Philippines Minimum Wages- actual values, historical data, forecast, chart, statistics, economic calendar and news.